From 06fe2750b216389e5fa9c167f8480ffb5ad4f14d Mon Sep 17 00:00:00 2001 From: Jack Simonson Date: Wed, 3 Jul 2019 15:45:38 -0700 Subject: [PATCH 001/215] Delete Strategy per Request --- .../01 Introduction.html | 22 ---- .../02 Strategy Theory.html | 3 - .../03 Method.html | 103 ------------------ .../04 Conclusion.html | 7 -- .../05 Algorithm.html | 29 ----- .../06 References.html | 8 -- 6 files changed, 172 deletions(-) delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html deleted file mode 100644 index ec204bf..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html +++ /dev/null @@ -1,22 +0,0 @@ -

- In this research, we investigate the Seasonality and Market Session effect in the E-Mini S&P 500 futures. A recent article, Market Seasonality Study, argues that there is a seasonality effect in the S&P 500. The study argues that it's trading strategy implemented in the period from November to May lends itself to a bull market (Bullish Season), while the trading days between June and October (Bearish Season) hold far less profit. -

- -

- As for Session effect, another article, Seasonality S&P Market Session, argues that a trading day can be broken up into eight distinct sessions: -

- - - -

- One can exploit these sessions by making a round-trip trade on a single E-Mini S&P500 futures contract by entering at the beginning of a session and exiting at the end of a session. The article argues that, in a Bullish Season (November to May, as defined in the first article), the Night Session (18:00 - 05:30) provides the most significant opportunity for profit, followed by the Afternoon Session and the Close Session. However, in a Bearish Season, only the Night Session and the Afternoon Session proved consistently profitable. -

\ No newline at end of file diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html deleted file mode 100644 index f96c2e3..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html +++ /dev/null @@ -1,3 +0,0 @@ -

- The underlying logic of this strategy is to buy at the open of a session and close our position at the conclusion of the session. Given that there are multiple profitable sessions, it is natural to come up with a combined strategy which aggregates the top three profitable bullish and the top two profitable bearish strategies to see if we can maximize the returns. In this tutorial, we will show the implementation of the algorithm on QuantConnect, and study the performance of Combined Strategy, and the performance of Night Session Strategy on Bearish Season and Bullish Season. -

\ No newline at end of file diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html deleted file mode 100644 index defd407..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html +++ /dev/null @@ -1,103 +0,0 @@ -

Step 1: Initialization

- -

- We set up an 8-year long backtest period from 2008-01-01 to 2015-12-31. Then, we choose the E-mini S&P 500 futures, sort them in ascending order according to their time to expiration and pick the nearest expiration contract. After that, we set the Bullish Season and the Bearish Season based on months and set the Night Session based on time of a day. We trigger the open and the close of trades with self.Schedule.On() function. -

- -
- -
-    
-    def Initialize(self):
-        self.SetStartDate(2008, 1, 1)  # Set Start Date
-        self.SetEndDate(2015, 12, 31) # Set End Date
-        self.SetCash(100000)  # Set Strategy Cash
-        
-        future = self.AddFuture(Futures.Indices.SP500EMini) # S&P E-mini future contracts
-        future.SetFilter(timedelta(0), timedelta(180)) # Filter according to Expiry date
-        
-        # Set benchmark
-        benchmark = self.AddEquity("SPY")
-        self.SetBenchmark(benchmark.Symbol)
-        
-        self.bullSeason = {11, 12, 1, 2, 3, 4, 5}
-        
-        self.bearSeason = {6, 7, 8, 9, 10}
-        
-        # Night session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(18, 0), Action(self.Enter))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(5, 30), Action(self.Exit))
-        
-        # Afternoon session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 15), Action(self.Enter))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.ExitException))
-        
-        # Close session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.EnterException))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(15, 15), Action(self.Exit))
-
-
- -
- -

Step 2: Set up the Trading Signals

- -

- For Combined Strategy, in Bullish Season, we buy the contract at 13:15, sell at 15:15 (Afternoon Session + Close Session) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session); in Bearish Season, we buy the contract at 13:15, sell at 14:00 (Afternoon Session only) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session). So there are two trades for every trading day. To realize this, we just need to check the month of the trading time and judge when to close the first trade (at 15:15 or at 14:00). -

- -

- For Night Session Strategy in Bullish Season or Bearish Season, we only buy at 18:00 and sell at 05:30 the next day, so there is only one trade for every trading day. This can also be realized by similar ideas (See code snippet for details). -

- -
- -
-    def OnData(self, data):
-        self.data = data
-    
-    def Enter(self):
-        if not self.Portfolio.Invested:
-            
-            for chain in self.data.FutureChains:
-                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
-                
-                if len(contracts) == 0:
-                    continue
-                
-                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
-
-                # long
-                self.MarketOrder(front.Symbol, 1)
-    
-    def Exit(self):
-        if self.Portfolio.Invested:
-            # liquidate
-            self.Liquidate()
-            
-    def EnterException(self):
-        isBear = self.Time.date().month in self.bearSeason
-        
-        if (not self.Portfolio.Invested) and isBear:
-            
-            for chain in self.data.FutureChains:
-                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
-
-                if len(contracts) == 0:
-                    continue
-                
-                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
-
-                # long
-                self.MarketOrder(front.Symbol, 1)
-    
-    def ExitException(self):
-        isBear = self.Time.date().month in self.bearSeason
-        
-        if self.Portfolio.Invested and isBear:
-            # liquidate
-            self.Liquidate()	   
-
- -
- diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html deleted file mode 100644 index b3499c3..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html +++ /dev/null @@ -1,7 +0,0 @@ -

- As can be seen in the backtest results, trading just during the Night Session, in both market seasons, performs well over an extended period. This strong performance is particularly true during a Bearish Season, where we can achieve 13% returns with a small drawdown of only 5%. - - Contrary our initial intuition, the combined strategy performs poorly after running a backtest. Despite trading during the top-performing sessions of both seasons, we don't see nearly the level of performance we would have expected, and it underperforms trading just the Night Session. The likely reason is that the Afternoon Session and Close Session underperform their theoretical results and thus drag down the combined performance. - - For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day and consider implementing other risk-management strategies. -

\ No newline at end of file diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html deleted file mode 100644 index 10d9687..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html +++ /dev/null @@ -1,29 +0,0 @@ -

- Backtest for Combined Strategy -

-
-
-
- -
-
- -

- Backtest for Night Session Strategy in Bearish Season -

-
-
-
- -
-
- -

- Backtest for Night Session Strategy in Bullish Season -

-
-
-
- -
-
\ No newline at end of file diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html deleted file mode 100644 index 06b4640..0000000 --- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html +++ /dev/null @@ -1,8 +0,0 @@ -
    -
  1. - Market Seasonality Study -
    2. -
  2. - Seasonality S&P Market Session -
    3. -
From 7b0092aa78bcc3dc9095c0d16eaa6c10137e59f1 Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 7 Jul 2019 10:38:14 -0700 Subject: [PATCH 002/215] Update 00.html --- .../01 Creating BootCamp Tutorials/00.html | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html index 28f560b..37470cf 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/00.html +++ b/08 Meta/01 Creating BootCamp Tutorials/00.html @@ -1 +1,32 @@ + + +
+ Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress. +
From 472aee4f24bfbb8ed65493919cb1e778f117aa7d Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 7 Jul 2019 10:43:40 -0700 Subject: [PATCH 003/215] Update 00.html --- 08 Meta/01 Creating BootCamp Tutorials/00.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html index 37470cf..7d60d9d 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/00.html +++ b/08 Meta/01 Creating BootCamp Tutorials/00.html @@ -28,5 +28,5 @@ .tip p { display: inline; }
- Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress. + Updated July 7th, 2019: The create BootCamp tutorial is a work in progress.
From 128d01d738e7518ce6b3589a4bbc3ccef409b131 Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 7 Jul 2019 10:44:02 -0700 Subject: [PATCH 004/215] Update 00.html --- 08 Meta/01 Creating BootCamp Tutorials/00.html | 1 + 1 file changed, 1 insertion(+) diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html index 7d60d9d..403498a 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/00.html +++ b/08 Meta/01 Creating BootCamp Tutorials/00.html @@ -27,6 +27,7 @@ } .tip p { display: inline; } +
Updated July 7th, 2019: The create BootCamp tutorial is a work in progress.
From a4a4a59daebad163f3e6c79fc3d34881e0763049 Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 7 Jul 2019 18:52:54 +0000 Subject: [PATCH 005/215] Automated push from server made by Jared Broad --- 08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html index 15ff07b..f7f62c6 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html +++ b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html @@ -7,5 +7,5 @@
  • Principles of Algorithmic Trading: Domain expertise in finance is important to be able to build thoughtful and educational content.

    • -We highly recommended working closely with the QuantConnect team as you design and code the BootCamp lessons to get continuous feedback to ensure its deployed. +The combination of all these talents is fairly rare so we're reaching out to the community to help us create this content. If you can cover all three of these categories let us know and we'll enable BootCamp editing permissions on your account. In exchange for a completed BootCamp lesson we're offering compensation which we'll cover in the section below.

    From 90d3c470cb2285e3ad30364b27ef5e91989def9f Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 7 Jul 2019 20:55:52 +0000 Subject: [PATCH 006/215] Automated push from server made by Jared Broad --- .../04 Content Structure.html | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html index ba95097..dc4c918 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html +++ b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html @@ -1,5 +1,5 @@

    -BootCamp is divided into Courses, Lessons, and Tasks. A Course groups together a super-category and is currently organized by asset-class. A Lesson focuses on the implementation of a single algorithm. A Task breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design. +BootCamp is divided into Lessons, and Tasks. A Lesson focuses on the implementation of a single algorithm. A Task breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design.

    @@ -10,17 +10,15 @@

    Core Principle

      -
    • Course: Algorithmic Trading with Equities -
      • Lesson: Buy and Hold Algorithm +
      • Lesson: Buy and Hold Algorithm
        • Task: Initializing Your Algorithm -> Set Cash
      -
    -

    Course

    +

    Lessons

    @@ -29,5 +27,5 @@

    Lessons

    Tasks

    -A Task is the atomic unit of the Boot Camp system. It is a single step required to be completed in the pathway to an algorithm. Tasks are grouped with a subheading according to algorithm implementation concept. +A Task is the atomic unit of the Boot Camp system. It is a single step required to be completed in the pathway to an algorithm. Tasks are grouped with a subheading according to algorithm implementation concept. A task should aim to implement just few lines of code so that each task can be easily achieved.

    \ No newline at end of file From d9ae9e3c44013438e00d6d5a44ce96a7608b09be Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 7 Jul 2019 21:18:07 +0000 Subject: [PATCH 007/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 29 +++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index ad0dc1e..9790f06 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -2,13 +2,38 @@ Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step.

    -

    1. Strategy Selection and Implementation

    +

    1. Strategy Selection

    Every BootCamp lesson is focused on an algorithmic strategy's implementation. The first step to planning a lesson is choosing a strategy which does not overlap with any of the existing BootCamp topics. This can be incrementally more difficult but should introduce new concepts.

    +

    +QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level. +

    + + + + + + + + + + + + + + + + + + + + +
    BootCamp LessonDifficultyStatus

    Buy and Hold (Equities/Forex)
    Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.

    Beginner

    Completed

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Beginner

    Assigned

    +

    2. Strategy Implementation

    After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.

    -

    In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson "Buy and Hold, with Trailing Stop" you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price.

    \ No newline at end of file +

    In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson "Buy and Hold, with Trailing Stop" you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.

    \ No newline at end of file From 6005ba5e24bd3b0188f76206d6ee5c908f575d95 Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 7 Jul 2019 21:43:14 +0000 Subject: [PATCH 008/215] Automated push from server made by Jared Broad --- .../01 Creating BootCamp Tutorials/05 Planning Your Lesson.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 9790f06..5324e9e 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -1,5 +1,5 @@

    -Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. +Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing.

    1. Strategy Selection

    From 7b2fd02a9927882853e24cd794fdc7fd8b2147e7 Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 7 Jul 2019 23:57:12 +0000 Subject: [PATCH 009/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 42 +++++++++++++++---- 1 file changed, 35 insertions(+), 7 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 5324e9e..0fe3f5c 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -1,5 +1,5 @@

    -Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing. +Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing.

    1. Strategy Selection

    @@ -9,24 +9,52 @@

    1. Strategy Selection

    QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level.

    + + - - + + - - + + - - + + + + + + + + + + + + + + + + + + + + + + + + + +
    BootCamp LessonDifficultyBeginner BootCamp Lessons Status

    Buy and Hold (Equities/Forex)
    Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.

    Beginner

    Completed

    Completed

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Beginner

    Assigned

    Assigned

    Momentum-Based Tactical Allocation
    Using a momentum indicator to shift investment between the S&P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.

    Available

    Liquid Universe Selection
    Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.

    Available

    200-50 EMA Momentum Universe
    Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.

    Available

    Fading The Gap
    Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.

    Available

    Open Range Breakout
    Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.

    Available
    From f2f24ce5618928b6da32e9a8cc7d9a1c2a401b8c Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 7 Jul 2019 16:58:02 -0700 Subject: [PATCH 010/215] Update 05 Planning Your Lesson.html --- .../05 Planning Your Lesson.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 0fe3f5c..3548fcd 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -17,7 +17,7 @@

    1. Strategy Selection

    Beginner BootCamp Lessons -Status +Status @@ -64,4 +64,4 @@

    2. Strategy Implementation

    After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.

    -

    In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson "Buy and Hold, with Trailing Stop" you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.

    \ No newline at end of file +

    In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson "Buy and Hold, with Trailing Stop" you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.

    From a97390cd0b4b860dcccdf9364ba7e4f5042772d6 Mon Sep 17 00:00:00 2001 From: www-data Date: Mon, 8 Jul 2019 00:56:41 +0000 Subject: [PATCH 011/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 40 +++++++++++++------ 1 file changed, 28 insertions(+), 12 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 3548fcd..5f2ce66 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -11,9 +11,14 @@

    1. Strategy Selection

    - +
    @@ -23,39 +28,50 @@

    1. Strategy Selection

    - + - + - + - - + + - - + + - - + + - - + + + +
    Beginner BootCamp Lessons

    Buy and Hold (Equities/Forex)
    Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.

    Buy and Hold (Equities/Forex)
    Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.

    +Accessing DataInitializing Algorithms +

    Completed

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    +Order ManagementBasic Charting

    Assigned

    Momentum-Based Tactical Allocation
    Using a momentum indicator to shift investment between the S&P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.

    Momentum-Based Tactical Allocation
    Using a momentum indicator to shift investment between the S&P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.

    +Multi-Asset PortfolioUsing Indicators +

    Available

    Liquid Universe Selection
    Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.

    Available

    Open Range Breakout
    Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.

    +Consolidators +

    Available

    200-50 EMA Momentum Universe
    Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.

    Available

    Liquid Universe Selection
    Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.

    +UniversesPrice Volume Filtering

    Available

    Fading The Gap
    Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.

    Available

    200-50 EMA Momentum Universe
    Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.

    UniversesSymbolData PatternHistory

    Available

    Open Range Breakout
    Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.

    Available

    Fading The Gap
    Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.

    Scheduled EventsSTD IndicatorParameter Minimization

    Available

    +
    From 2dd5d01463b911728fe8c4b78c05aebba20bd983 Mon Sep 17 00:00:00 2001 From: www-data Date: Mon, 8 Jul 2019 14:45:53 +0000 Subject: [PATCH 012/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 76 ++++++++++++++++++- 1 file changed, 72 insertions(+), 4 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 5f2ce66..d18140c 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -42,7 +42,9 @@

    1. Strategy Selection

    Momentum-Based Tactical Allocation
    Using a momentum indicator to shift investment between the S&P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.

    -Multi-Asset PortfolioUsing Indicators +Multi-Asset Portfolio +Using Indicators +Equities

    Available

    @@ -50,31 +52,97 @@

    1. Strategy Selection

    Open Range Breakout
    Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.

    Consolidators +Equities

    Available

    Liquid Universe Selection
    Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.

    -UniversesPrice Volume Filtering +Universes +Price Volume Filtering +Equities +

    Available

    -

    200-50 EMA Momentum Universe
    Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.

    UniversesSymbolData PatternHistory +

    200-50 EMA Momentum Universe
    Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.

    +Universes +SymbolData Pattern +History +Equities

    Available

    -

    Fading The Gap
    Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.

    Scheduled EventsSTD IndicatorParameter Minimization +

    Fading The Gap
    Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.

    +Scheduled Events +STD Indicator +Parameter Minimization +Equities

    Available

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Intermediate BootCamp LessonsStatus

    Pairs Trading with SMA
    Simple pairs trading strategy monitoring for divergence in correlation of two hand picked assets. Invests in a market neutral manner, using position sizing to calculate the right holding of each asset.

    +Pairs Trading +Market Neutrality +Position Sizing +Equities +

    Available

    +

    Pairs Trading with Cointegration Test
    Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test, and

    +Pairs Trading +Cointegration Test +Scheduled Events +Equities +

    Available

    +

    Liquid Value Stocks
    Selecting a universe of the 100 most liquid assets and rank by their PE-Ratio to get the best value stocks. Each month buy the 10 best value stocks, and short the worst value stocks.

    +Universe Selection +Fine Universe Selection +Long-Short Hedge +Equities +

    Available

    +

    Sector Balanced Universe Selection
    Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.

    +Universe Selection +Advanced Universe Selection +Sector Exposure +Equities +

    Available

    +
    +

    2. Strategy Implementation

    After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple. From 6ff0132a26037610361944c3e461f0bb804c0595 Mon Sep 17 00:00:00 2001 From: www-data Date: Mon, 8 Jul 2019 16:06:34 +0000 Subject: [PATCH 013/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 37 +++++++++++++++++-- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index d18140c..8a20177 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -125,7 +125,6 @@

    1. Strategy Selection

    Available

    -

    Sector Balanced Universe Selection
    Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.

    Universe Selection @@ -135,10 +134,42 @@

    1. Strategy Selection

    Available

    + +

    Hedging FX Books with Interest Rate
    Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.

    +Trading Economics +Alternative Data +Global Macro +Forex + +

    Available

    + + +

    Sentiment Analysis on Stocks
    Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most postive sentiment.

    +Psychsignal +NLP +Alternative Data +Sentiment Analysis +Equities + +

    Available

    + + + - - + + + + + + + + + + +
    Advanced BootCamp LessonsStatus

    Coming Soon

    +

    +
    From 141352655919f75223bb737d2fbe967e1427361d Mon Sep 17 00:00:00 2001 From: www-data Date: Mon, 8 Jul 2019 16:24:16 +0000 Subject: [PATCH 014/215] Automated push from server made by Jared Broad --- .../05 Planning Your Lesson.html | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 8a20177..a839fca 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -6,9 +6,18 @@

    1. Strategy Selection

    Every BootCamp lesson is focused on an algorithmic strategy's implementation. The first step to planning a lesson is choosing a strategy which does not overlap with any of the existing BootCamp topics. This can be incrementally more difficult but should introduce new concepts.

    + +

    2. Strategy Implementation

    +

    +After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple. +

    + +

    In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson "Buy and Hold, with Trailing Stop" you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.

    +

    QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level.

    + +
    +
    Lesson Algorithm
    +
    Task Guides
    +
    Validators
    +
    Hints, Solutions
    +
    Submit
    +
    + +

    1. Write Lesson Algorithm

    +

    +Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy. This should be drafted as simply as possible to ensure each task the student needs to complete will only be 2-5 lines of code. +

    +Readability is critical and the code should be well commented with descriptive variable names. Depending on the complexity of the algorithm sometimes its more readable to use string tickers instead of class variables. +

    +

    +Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in tasks by the student. +

    + + + + + + + + +
    x dx
    x
    x
    x
    + +
      +
    • Be Concise.
      • +
    • Ensure Highly Readable.
      • +
    • Keep Task Divisions in Mind.
      • +
    From 777914be38b00ef24ab94dfa08a06afb392909a2 Mon Sep 17 00:00:00 2001 From: www-data Date: Sun, 14 Jul 2019 23:30:16 +0000 Subject: [PATCH 021/215] Automated push from server made by Jared Broad --- .../06 Writing a Lesson.html | 31 ++++++++++++------- 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html index a919041..a968212 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html @@ -68,20 +68,27 @@

    1. Write Lesson Algorithm

    Readability is critical and the code should be well commented with descriptive variable names. Depending on the complexity of the algorithm sometimes its more readable to use string tickers instead of class variables.

    -Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in tasks by the student. +Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in separate tasks by the student.

    - - +
    + Action: +

    Write highly readable, concise C# and Python versions of the algorithm and request review by QuantConnect Education team. Plan ahead for divisions of the code into tasks.

    +
    + +

    +2. Write Task Guides +

    +

    +Each task has a short write up to explain the features need to complete next step of the BootCamp task. This guide should assume the student has no prior knowledge and include representitive code snippets demonstrating the key API code needed. +

    +
    - + - + + + + -
    x dx
    StyleCode Tag
    x
    x
    x

    Headings

    <h4>Initializaing Algorithms</h4>
    - -
      -
    • Be Concise.
      • -
    • Ensure Highly Readable.
      • -
    • Keep Task Divisions in Mind.
      • -
    + \ No newline at end of file From 072e3ce64ac96de19a2c29b60ec1892cff4bf03f Mon Sep 17 00:00:00 2001 From: www-data Date: Mon, 15 Jul 2019 01:03:19 +0000 Subject: [PATCH 022/215] Automated push from server made by Jared Broad --- .../06 Writing a Lesson.html | 35 ++++++++++++++++--- 1 file changed, 30 insertions(+), 5 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html index a968212..b56511a 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html @@ -1,5 +1,5 @@

    -Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy, after this you can break it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task. +Writing a BootCamp lesson starts by carefully writing out the complete code for the strategy, breaking it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task.


    - Updated July 7th, 2019: The create BootCamp tutorial is a work in progress. + Updated July 14th, 2019: The create BootCamp tutorial is a work in progress.
    From 4652d20f2c93868f209b9ec03a4b05f8bc5fe03d Mon Sep 17 00:00:00 2001 From: Rohan Talip Date: Sun, 21 Jul 2019 18:26:01 -0700 Subject: [PATCH 025/215] Updated some links in README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 371ab1a..da878ac 100644 --- a/README.md +++ b/README.md @@ -6,12 +6,12 @@ This repository is a collection of WordPress and Jupyter notebook tutorials for LEAN demonstrating financial concepts and LEAN. Tutorials are categorized into folders with each Category and Tutorial Series. The Jupyter notebook and the associated HTML webpage must use matching file names. The HTML is generated and displayed with wordpress. -Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean Engine Repository][4]. +Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean][4] Engine repository. ## New Tutorial Requests and Edits ## -Please submit new tutorial requests as an issue to the [Tutorial Repository][5]. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed! +Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed! ## Mailing List ## @@ -24,7 +24,7 @@ Contributions are warmly very welcomed but we ask you read the existing code to [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer" [2]: https://www.quantconnect.com/lean/docs "Lean Documentation" [3]: https://github.com/QuantConnect/Lean/archive/master.zip -[4]: https://github.com/QuantConnect/Lean/archive/master.zip +[4]: https://github.com/QuantConnect/Lean [5]: https://github.com/QuantConnect/Tutorials/issues [6]: https://groups.google.com/forum/#!forum/lean-engine [7]: https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md From 8e6ce223f903417e419272322de929ed714e6c89 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Mon, 22 Jul 2019 22:13:18 +0100 Subject: [PATCH 026/215] Fixes Small Capitalization Stocks Premium Anomaly Algorithm Fixes the issue on the Universe Selection functions returning empty list of Symbol that unsubscribes them. Refactors the algorithm to use a pattern that does not depend on Schedule Events and rely on OnSecuritiesChanged to liquidate securities. --- .../02 Method.html | 27 ++++++++++--------- .../03 Algorithm.html | 2 +- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html index 148d56e..f8b5aba 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html @@ -2,25 +2,26 @@ The first step is coarse universe selection. We create an investment universe with stocks that have fundmental data and with a price greater than $5.

    -
    self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.AdjustedPrice) > 5)]
+
    return [x.Symbol for x in coarse if x.HasFundamentalData and x.AdjustedPrice > 5]

    In fine universe selection, we sort the stocks in the universe by the market capitalization and choose 10 stocks with the lowest market cap. 

    def FineSelectionFunction(self, fine):
-    if self.yearly_rebalance:
-        fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)
-                                and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
-                                and (x.EarningReports.BasicEPS.TwelveMonths > 0)]
-        for i in fine:
-            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
-        sorted_market_cap = sorted(fine, key=lambda x: x.MarketCap)
-        self.filtered_fine = [i.Symbol for i in sorted_market_cap[:20]]
-        self.yearly_rebalance = False
-        return self.filtered_fine
-    else:
-        return []
+    if self.year == self.Time.year:
+        return self.symbols
+
+    # Calculate the market cap and add the "MarketCap" property to fine universe object
+    for i in fine:
+        i.MarketCap = (i.EarningReports.BasicAverageShares.ThreeMonths * 
+                       i.EarningReports.BasicEPS.TwelveMonths *
+                       i.ValuationRatios.PERatio)
+
+    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0], key=lambda x: x.MarketCap)
+
+    self.symbols = [i.Symbol for i in sorted_market_cap[:10]]
+    return self.symbols

    diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html index 5be3efc..8bab69d 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html @@ -1,6 +1,6 @@ div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">

    - +
    From 5eb304bdfb725391331db0582a2fe04771f8c208 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Mon, 22 Jul 2019 23:19:48 +0100 Subject: [PATCH 027/215] Use Price instead of AdjustedPrice to Filter Coarse Data --- .../02 Method.html | 2 +- .../03 Algorithm.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html index f8b5aba..2894c75 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html @@ -2,7 +2,7 @@ The first step is coarse universe selection. We create an investment universe with stocks that have fundmental data and with a price greater than $5.

    -
    return [x.Symbol for x in coarse if x.HasFundamentalData and x.AdjustedPrice > 5]
+
    return [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 5]

    In fine universe selection, we sort the stocks in the universe by the market capitalization and choose 10 stocks with the lowest market cap. diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html index 8bab69d..f14f61e 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html @@ -1,6 +1,6 @@ div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">

    - +
    From ca37faaeac4db7f49f7abcea089ad86f470aa7fe Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Tue, 30 Jul 2019 18:54:25 +0100 Subject: [PATCH 028/215] Fixes Monthly Universe Selection Update - Fixes the issue on the Universe Selection function returning different lists of symbols. We need to keep a single list that reflects the final selection at Fine Fundamental. - Refactors the algorithm to use a dictionary of `Momentum` instead of a `SymbolData` and use an integer to track the month change instead of Schedule Events. - Fixes the securities selection based on momentum: instead of buying those with the hightest momentum, the algorithm was buying those with the lowest. --- .../02 Method.html | 23 +++---------------- .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 5 insertions(+), 22 deletions(-) diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html index 2f6518a..1253e08 100644 --- a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html +++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html @@ -6,25 +6,9 @@

    Momentum Calculation

    Momentum is the absolute difference in stocks. \[Momentum = Close_{today}-Close_{N-days-ago}\] - LEAN has the Momentum indicator. We create a class to save the momentum value and warm up the indictor for each symbol. -

    - 
    -class SymbolData:
-    def __init__(self, symbol, lookback):
-        self.symbol = symbol
-        self.MOM = Momentum(lookback)
-
-    def WarmUpIndicator(self, history):
-        # warm up the Momentum indicator with the history request
-        for tuple in history.itertuples():
-            item = IndicatorDataPoint(self.symbol, tuple.Index, float(tuple.close))
-            self.MOM.Update(item)
-
    -

    - Dictionary self.symbolDataDict is used to save the momentum class instance SymbolData for each symbol. - In OnSecuritiesChanged event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary. Each day in OnData, the - Momentum indicator for all symbols in the dictionary will be updated with the latest closing price. + Dictionary self.mom is used to save the LEAN Momentum class instance Momentum for each symbol. + In OnSecuritiesChanged event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary and liquidate its positions. Each day in OnData, the Momentum indicator for all symbols in the dictionary will be updated with the latest closing price.

    We choose a period of 12 months for the momentum indicator. Stocks with the best 12-month momentum (12-month performance) are then added to our portfolio and are weighted equally. @@ -32,6 +16,5 @@

    Momentum Calculation

    Monthly Rebalance

    - The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the bool variable - self.monthly_rebalance to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator. + The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable self.month that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols self.symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator.

    diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html index a8ca0da..f57e9ce 100644 --- a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html +++ b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html @@ -1,6 +1,6 @@
    - +
    diff --git a/quantpedia.json b/quantpedia.json index cd0aa80..ee208ec 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -8,7 +8,7 @@ 8 : "336bbb8e9e4a55a6adfa00c0d9f904bb", 12: "72532ccafeaa844c81aa75c5696b4b24", 13: "05d0edd633b1438852d1d641af0224ae", - 14: "ec30318cc245fe200ca1261b7f26f17b", + 14: "6e3182577984f2c6950b7aacfc17301d", 15: "bd83aa417032f8407382a1c065aa7511", 16: "afd2c9d67fd51d602bf8eac2ef28d712", 18: "ba1bb35b26896e2e710a510f62230a24", From 0ff978f6cdb10271ce1b0279079abb9b43c0d7e5 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Thu, 1 Aug 2019 17:31:26 +0100 Subject: [PATCH 029/215] Addresses Peer-Review Updates the algorithm and the text to mention `Universe.Unchanged` usage. --- 04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html | 2 +- .../21 Momentum Effect in Stocks/03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html index 1253e08..2dc76cf 100644 --- a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html +++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html @@ -16,5 +16,5 @@

    Momentum Calculation

    Monthly Rebalance

    - The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable self.month that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols self.symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator. + The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable self.month that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return Universe.Unchanged. In contrast to returning an empty list or a list of previously selected symbols, returning Universe.Unchanged is the best way for monthly rebalance universe selection. If there are no open positions for certain symbol, returning an empty list will stop the data subscription of that symbol and halt updates of the indicator.

    diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html index f57e9ce..b92aa3f 100644 --- a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html +++ b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html @@ -1,6 +1,6 @@
    - +
    diff --git a/quantpedia.json b/quantpedia.json index ee208ec..567f8b4 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -8,7 +8,7 @@ 8 : "336bbb8e9e4a55a6adfa00c0d9f904bb", 12: "72532ccafeaa844c81aa75c5696b4b24", 13: "05d0edd633b1438852d1d641af0224ae", - 14: "6e3182577984f2c6950b7aacfc17301d", + 14: "e7b55d46fa8677ed8ec91dd1a0159a6e", 15: "bd83aa417032f8407382a1c065aa7511", 16: "afd2c9d67fd51d602bf8eac2ef28d712", 18: "ba1bb35b26896e2e710a510f62230a24", From b15b0b08b1223eed7d9016d75d0769e548f4ec97 Mon Sep 17 00:00:00 2001 From: Jack Simonson Date: Tue, 6 Aug 2019 17:36:12 -0700 Subject: [PATCH 030/215] Update 05 Planning Your Lesson.html Marking as assigned since they're being done internally --- .../05 Planning Your Lesson.html | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index d44db62..caeec73 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -46,7 +46,7 @@

    2. Strategy Implementation

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Order ManagementBasic Charting -

    Assigned

    +

    Completed

    @@ -55,7 +55,7 @@

    2. Strategy Implementation

    Using Indicators Equities -

    Available

    +

    Completed

    @@ -63,7 +63,7 @@

    2. Strategy Implementation

    Consolidators Equities -

    Available

    +

    Assigned

    @@ -72,7 +72,7 @@

    2. Strategy Implementation

    Price Volume Filtering Equities -

    Available

    +

    Assigned

    @@ -81,7 +81,7 @@

    2. Strategy Implementation

    SymbolData Pattern History Equities -

    Available

    +

    Assigned

    @@ -90,7 +90,7 @@

    2. Strategy Implementation

    STD Indicator Parameter Minimization Equities -

    Available

    +

    Assigned

    @@ -112,7 +112,7 @@

    2. Strategy Implementation

    Portfolio Model Universe Selection Model -

    Available

    +

    Assigned

    @@ -122,7 +122,7 @@

    2. Strategy Implementation

    Position Sizing Equities -

    Available

    +

    Assigned

    @@ -132,7 +132,7 @@

    2. Strategy Implementation

    Scheduled Events Equities -

    Available

    +

    Assigned

    @@ -142,7 +142,7 @@

    2. Strategy Implementation

    Long-Short Hedge Equities -

    Available

    +

    Assigned

    Sector Balanced Universe Selection
    Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.

    @@ -151,7 +151,7 @@

    2. Strategy Implementation

    Sector Exposure Equities -

    Available

    +

    Assigned

    Hedging FX Books with Interest Rate
    Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.

    From 560f40ecac5887a85edde121bc0cd3aad433c49d Mon Sep 17 00:00:00 2001 From: Jared Date: Tue, 6 Aug 2019 17:45:50 -0700 Subject: [PATCH 031/215] Revert "Update 05 Planning Your Lesson.html" --- .../05 Planning Your Lesson.html | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index caeec73..d44db62 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -46,7 +46,7 @@

    2. Strategy Implementation

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Order ManagementBasic Charting -

    Completed

    +

    Assigned

    @@ -55,7 +55,7 @@

    2. Strategy Implementation

    Using Indicators Equities -

    Completed

    +

    Available

    @@ -63,7 +63,7 @@

    2. Strategy Implementation

    Consolidators Equities -

    Assigned

    +

    Available

    @@ -72,7 +72,7 @@

    2. Strategy Implementation

    Price Volume Filtering Equities -

    Assigned

    +

    Available

    @@ -81,7 +81,7 @@

    2. Strategy Implementation

    SymbolData Pattern History Equities -

    Assigned

    +

    Available

    @@ -90,7 +90,7 @@

    2. Strategy Implementation

    STD Indicator Parameter Minimization Equities -

    Assigned

    +

    Available

    @@ -112,7 +112,7 @@

    2. Strategy Implementation

    Portfolio Model Universe Selection Model -

    Assigned

    +

    Available

    @@ -122,7 +122,7 @@

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    Position Sizing Equities -

    Assigned

    +

    Available

    @@ -132,7 +132,7 @@

    2. Strategy Implementation

    Scheduled Events Equities -

    Assigned

    +

    Available

    @@ -142,7 +142,7 @@

    2. Strategy Implementation

    Long-Short Hedge Equities -

    Assigned

    +

    Available

    Sector Balanced Universe Selection
    Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.

    @@ -151,7 +151,7 @@

    2. Strategy Implementation

    Sector Exposure Equities -

    Assigned

    +

    Available

    Hedging FX Books with Interest Rate
    Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.

    From c85d30a36439dad79ae5dec269fd8ad6c81145db Mon Sep 17 00:00:00 2001 From: Jared Date: Tue, 6 Aug 2019 17:53:18 -0700 Subject: [PATCH 032/215] Update 05 Planning Your Lesson.html --- .../05 Planning Your Lesson.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index d44db62..daea9e9 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -46,7 +46,7 @@

    2. Strategy Implementation

    Buy and Hold with Trailing Stop
    Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.

    Order ManagementBasic Charting -

    Assigned

    +

    Completed

    @@ -55,7 +55,7 @@

    2. Strategy Implementation

    Using Indicators Equities -

    Available

    +

    Completed

    @@ -63,7 +63,7 @@

    2. Strategy Implementation

    Consolidators Equities -

    Available

    +

    Assigned

    @@ -72,7 +72,7 @@

    2. Strategy Implementation

    Price Volume Filtering Equities -

    Available

    +

    Assigned

    @@ -81,7 +81,7 @@

    2. Strategy Implementation

    SymbolData Pattern History Equities -

    Available

    +

    Assigned

    @@ -90,7 +90,7 @@

    2. Strategy Implementation

    STD Indicator Parameter Minimization Equities -

    Available

    +

    Assigned

    @@ -190,4 +190,4 @@

    2. Strategy Implementation

    - \ No newline at end of file + From e913b2837fbc4024a95c6cb2be61ad0abb5228dd Mon Sep 17 00:00:00 2001 From: Jack Simonson Date: Tue, 6 Aug 2019 17:57:12 -0700 Subject: [PATCH 033/215] Update 05 Planning Your Lesson.html --- .../05 Planning Your Lesson.html | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index daea9e9..672b9be 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -112,7 +112,7 @@

    2. Strategy Implementation

    Portfolio Model Universe Selection Model -

    Available

    +

    Assigned

    @@ -122,7 +122,7 @@

    2. Strategy Implementation

    Position Sizing Equities -

    Available

    +

    Assigned

    @@ -132,7 +132,7 @@

    2. Strategy Implementation

    Scheduled Events Equities -

    Available

    +

    Assigned

    @@ -142,7 +142,7 @@

    2. Strategy Implementation

    Long-Short Hedge Equities -

    Available

    +

    Assigned

    Sector Balanced Universe Selection
    Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.

    @@ -151,7 +151,7 @@

    2. Strategy Implementation

    Sector Exposure Equities -

    Available

    +

    Assigned

    Hedging FX Books with Interest Rate
    Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.

    From 1f345566bb6f03af4b502d57128aaf5cf0f6f9a8 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Wed, 14 Aug 2019 21:32:19 +0100 Subject: [PATCH 034/215] Removes Tutorial on Deprecated Desktop Charting --- .../07 Desktop Charting with LEAN/00.html | 43 ------------------- .../01 Desktop Charting with LEAN.html | 27 ------------ 2 files changed, 70 deletions(-) delete mode 100644 03 Open Source/07 Desktop Charting with LEAN/00.html delete mode 100755 03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html diff --git a/03 Open Source/07 Desktop Charting with LEAN/00.html b/03 Open Source/07 Desktop Charting with LEAN/00.html deleted file mode 100644 index 9a35478..0000000 --- a/03 Open Source/07 Desktop Charting with LEAN/00.html +++ /dev/null @@ -1,43 +0,0 @@ - -
    - Note: 2019-01-20: Desktop Charting v1 works in Windows but it is no longer supported. Version 2 (Electron) is a work in progress and will be published in 2019. -
    diff --git a/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html deleted file mode 100755 index 91f56e4..0000000 --- a/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html +++ /dev/null @@ -1,27 +0,0 @@ -

    -With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN. -

    -

    - Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our API. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation. -

    -

    - Two configuration changes are required for desktop charting to work: -

    -
      -
    • Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file. -
        "environment": "backtesting-desktop",
      -
      • -
    • Insert your API token and user id into configuration file. This is required for chart streaming. -
        // To get your api access token go to quantconnect.com/account
-  "job-user-id": "....",
-  "api-access-token": "...........",
      -
      • -
    -

    - With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment. - If you get the run-time exception "The port configured in config.json is either being used or blocked by a firewall"- This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the "desktop-http-port" setting. -

    -

    - In the tutorial video below we demonstrate this feature on LEAN: -

    - From b7d105a2bc22eabd0006105a8209ac00f03371ff Mon Sep 17 00:00:00 2001 From: Martin Molinero Date: Wed, 14 Aug 2019 22:08:13 -0300 Subject: [PATCH 035/215] Update python debugging tutorial --- .../01 Introduction.html | 13 ---------- .../02 Prerequisites.html | 3 --- .../05 Limitations.html | 3 --- .../00.html | 0 .../01 Debugging Python/01 Introduction.html | 7 ++++++ .../02 Installing LEAN with Python.html} | 0 .../03 Method 1 - PTVSD.html} | 5 ++++ .../04 Method 2 - PDB.html | 24 ++++++++++++++++++ .../05 Method 3 - VisualStudio Debugger.html | 25 +++++++++++++++++++ .../01 Debugging Python/06 Limitations.html | 3 +++ .../07 Summary.html} | 0 11 files changed, 64 insertions(+), 19 deletions(-) delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html rename 03 Open Source/{01 Debugging Python in Visual Studio => 01 Debugging Python}/00.html (100%) create mode 100644 03 Open Source/01 Debugging Python/01 Introduction.html rename 03 Open Source/{01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html => 01 Debugging Python/02 Installing LEAN with Python.html} (100%) rename 03 Open Source/{01 Debugging Python in Visual Studio/04 Attaching the Debugger.html => 01 Debugging Python/03 Method 1 - PTVSD.html} (88%) create mode 100644 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html create mode 100644 03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html create mode 100644 03 Open Source/01 Debugging Python/06 Limitations.html rename 03 Open Source/{01 Debugging Python in Visual Studio/06 Summary.html => 01 Debugging Python/07 Summary.html} (100%) diff --git a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html deleted file mode 100644 index c854c52..0000000 --- a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

    - We often want to use an IDE for algorithm development because it provides comprehensive facilities such as a source code editor and a debugger. QuantConnect delivers a robust online source code editor, but not a debugger. For some algorithm developers, a debugger is an essential tool; therefore they opt to work offline with full-featured IDE. -

    -

    - Visual Studio is a full-featured IDE that makes debugging easy. Unlike other solutions that only allows debugging a single process/language, we can use it to debug a python algorithm in Lean (C# engine). -

    -

    - In this tutorial, we'll show you how to debug your algorithm in LEAN from Visual Studio. -

    diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html deleted file mode 100644 index 5f525e7..0000000 --- a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html +++ /dev/null @@ -1,3 +0,0 @@ -

    Python Tools for Visual Studio debug (ptvsd) server python library. It can be easily installed using pip:

    -
    pip install ptvsd
    - diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html deleted file mode 100644 index 566ac60..0000000 --- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html +++ /dev/null @@ -1,3 +0,0 @@ -

    Unfortunately, this debugging scenario entails some limitations.

    -

    It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).

    -

    The algorithm will always stop after ptvsd.break_into_debugger() call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks. In order to resume the algorithm execution without breakpoints, we need to detach the process (Debug -> Detach All).

    diff --git a/03 Open Source/01 Debugging Python in Visual Studio/00.html b/03 Open Source/01 Debugging Python/00.html similarity index 100% rename from 03 Open Source/01 Debugging Python in Visual Studio/00.html rename to 03 Open Source/01 Debugging Python/00.html diff --git a/03 Open Source/01 Debugging Python/01 Introduction.html b/03 Open Source/01 Debugging Python/01 Introduction.html new file mode 100644 index 0000000..ea57f4a --- /dev/null +++ b/03 Open Source/01 Debugging Python/01 Introduction.html @@ -0,0 +1,7 @@ + + +

    + In this tutorial, for those algorithm developers who opt to work offline, we will explore the different methods to debug a python algorithm. +

    \ No newline at end of file diff --git a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html b/03 Open Source/01 Debugging Python/02 Installing LEAN with Python.html similarity index 100% rename from 03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html rename to 03 Open Source/01 Debugging Python/02 Installing LEAN with Python.html diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html similarity index 88% rename from 03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html rename to 03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html index aaf8b00..4407bf1 100644 --- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html +++ b/03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html @@ -1,3 +1,8 @@ +

    Prerequisites

    +

    Python Tools for Visual Studio debug (ptvsd) server python library. It can be easily installed using pip:

    +
    pip install ptvsd
    + +

    Attaching the Debugger

    The process that we will use to attach the python debugger requires that we run Lean without debugging and attach the process.

    First, we will add the ptvsd library, and the following statements:

    diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html new file mode 100644 index 0000000..026b73a --- /dev/null +++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html @@ -0,0 +1,24 @@ +

    This method uses the built in, cross-platform, command line python debugger (pdb). + +

    +

      +
    • Edit config.json with the following paramenters:
      • +
      + 
      +      "debugging": true,
+
      +
      +
    • Run Lean without debugging by clicking on Debug -> Start Without Debugging
      • +
    • Lean will hit a first break outside algorithm code
      • +
    • Start debugging! Example commands:
      • +
      + 
      +      break add ../../../Algorithm.Python/BasicTemplateAlgorithm.py:37
+      continue
+      print(self.Portfolio.Invested)
+
      +
      +
    +

    + + \ No newline at end of file diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html new file mode 100644 index 0000000..16bec79 --- /dev/null +++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html @@ -0,0 +1,25 @@ +

    This method uses the (VisualStudio python debugger). + +

    Prerequisites

    +

    Visual Studio Python development feature. It can be easily installed at Tools -> Get Tools and Features...

    + +

    +

      +
    • Edit config.json with the following paramenters:
      • +
      + 
      +      "debugging": true,
+      "debugging-method": "VisualStudio",
+
      +
      +
    • Run Lean without debugging by clicking on Debug -> Start Without Debugging
      • +
    • Lean will stop and wait for the debugger to attach
      • +
    • Set breakpoints
      • +
    • In Visual Studio, select Debug -> Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.
      • +
    • For Connection type, select Default
      • +
    • For Attach to, select Python
      • +
    • Search for QuantConnect.Lean.Launcher.exe process and attach
      • +
    +

    + + \ No newline at end of file diff --git a/03 Open Source/01 Debugging Python/06 Limitations.html b/03 Open Source/01 Debugging Python/06 Limitations.html new file mode 100644 index 0000000..f126eb5 --- /dev/null +++ b/03 Open Source/01 Debugging Python/06 Limitations.html @@ -0,0 +1,3 @@ +

    Unfortunately, this debugging scenario entails some limitations.

    +

    It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).

    +

    Method 1 ptvsd will always stop after ptvsd.break_into_debugger() call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks. In order to resume the algorithm execution without breakpoints, we need to detach the process (Debug -> Detach All).

    diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python/07 Summary.html similarity index 100% rename from 03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html rename to 03 Open Source/01 Debugging Python/07 Summary.html From 827e272da339991879efbea5d7254fdc74580135 Mon Sep 17 00:00:00 2001 From: Martin Molinero Date: Wed, 14 Aug 2019 22:15:46 -0300 Subject: [PATCH 036/215] Small fixes --- 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html | 3 ++- .../05 Method 3 - VisualStudio Debugger.html | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html index 026b73a..93c5b88 100644 --- a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html +++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html @@ -1,4 +1,4 @@ -

    This method uses the built in, cross-platform, command line python debugger (pdb). +

    This method uses the built in, cross-platform, command line python debugger pdb.

      @@ -6,6 +6,7 @@ 
             "debugging": true,
+      "debugging-method": "CommandLine",
    • Run Lean without debugging by clicking on Debug -> Start Without Debugging
      • diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html index 16bec79..6f744e0 100644 --- a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html +++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html @@ -1,4 +1,4 @@ -

      This method uses the (VisualStudio python debugger). +

      This method uses the Visual Studio Python Development Tools.

      Prerequisites

      Visual Studio Python development feature. It can be easily installed at Tools -> Get Tools and Features...

      From 58fdb388df72157c8cafb5e6a5246122abd3f0d9 Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Wed, 21 Aug 2019 15:01:22 -0700 Subject: [PATCH 037/215] Fama-French Five-Factor Strategy --- .../01 Introduction.html | 3 + .../02 Fama French Five-Factor Model.html | 17 +++++ .../03 Algorithm.html | 66 +++++++++++++++++++ .../04 References.html | 5 ++ 4 files changed, 91 insertions(+) create mode 100644 04 Strategy Library/230 Fama French Five Factors/01 Introduction.html create mode 100644 04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html create mode 100644 04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html create mode 100644 04 Strategy Library/230 Fama French Five Factors/04 References.html diff --git a/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html new file mode 100644 index 0000000..12ef392 --- /dev/null +++ b/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html @@ -0,0 +1,3 @@ +

      + At QuantConnect, we seek to make financial models ever more accessible to our community. This article will walk through the implementation of a stock selection strategy based on the popular Fama French five-factor financial model. +

      \ No newline at end of file diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html new file mode 100644 index 0000000..fd20400 --- /dev/null +++ b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html @@ -0,0 +1,17 @@ +

      + The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends. + Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return. + The model is as follows +

      +\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\] + +

      +where +

      +
        +
      • MKT is the excess return of the market. It is the return on the value-weight market portfolio.
        • +
      • SMB is the return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stocks.
        • +
      • HML is the difference between the returns on diversified portfolios of high and low B/M stocks.
        • +
      • RMW is the difference between the returns on diversified portfolios of stocks with robust and weak profitability.
        • +
      • CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive.
        • +
      \ No newline at end of file diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html new file mode 100644 index 0000000..f552038 --- /dev/null +++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html @@ -0,0 +1,66 @@ +

      + Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. +

      +

      + In the following backtest, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will long the five stocks with the highest scores and short the five stocks with the lowest scores. +

      +
      +
      +def FineSelectionFunction(self, fine):
+    '''Select securities with highest score on Fama French 5 factors'''
+    
+    # select stocks with these 5 factors
+    
+    # Operation profit margin: Quality
+    # Book value per share: Value
+    # ROE: Profitability
+    # TotalEquity: Size
+    # TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.OperationRatios.OperationMargin.Value
+                                and x.ValuationRatios.BookValuePerShare
+                                and x.OperationRatios.ROE
+                                and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.TotalAssetsGrowth]
+                                
+    
+    # sort by factors
+    sortedByFactor1 = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByFactor2 = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedByFactor3 = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByFactor4 = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByFactor5 = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+    
+    stockBySymbol = {}
+    
+    # get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByFactor1):
+        rank1 = index
+        rank2 = sortedByFactor2.index(stock)
+        rank3 = sortedByFactor3.index(stock)
+        rank4 = sortedByFactor4.index(stock)
+        rank5 = sortedByFactor5.index(stock)
+        avgRank = np.mean([rank1,rank2,rank3,rank4,rank5])
+        stockBySymbol[stock.Symbol] = avgRank
+        
+    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
+    symbols = [x[0] for x in sorted_dict]
+    
+    # pick the stocks with the highest scores to long
+    self.longSymbols= symbols[:self.num_long]
+    # pick the stocks with the lowest scores to short
+    self.shortSymbols = symbols[-self.num_short:]
+    
+    return self.longSymbols + self.shortSymbols   
+
      +
      + +

      + In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency. +

      + +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/230 Fama French Five Factors/04 References.html b/04 Strategy Library/230 Fama French Five Factors/04 References.html new file mode 100644 index 0000000..a660292 --- /dev/null +++ b/04 Strategy Library/230 Fama French Five Factors/04 References.html @@ -0,0 +1,5 @@ + \ No newline at end of file From fcbfcea5a6613e5249d258dc53bcdf0b9d74ebf7 Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Wed, 21 Aug 2019 17:26:38 -0700 Subject: [PATCH 038/215] Requested Changes --- .../02 Fama French Five-Factor Model.html | 10 +-- .../03 Algorithm.html | 75 ++++++++++--------- 2 files changed, 44 insertions(+), 41 deletions(-) diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html index fd20400..0cd9ad3 100644 --- a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html +++ b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html @@ -9,9 +9,9 @@ where

        -
      • MKT is the excess return of the market. It is the return on the value-weight market portfolio.
        • -
      • SMB is the return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stocks.
        • -
      • HML is the difference between the returns on diversified portfolios of high and low B/M stocks.
        • -
      • RMW is the difference between the returns on diversified portfolios of stocks with robust and weak profitability.
        • -
      • CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive.
        • +
      • MKT is the excess return of the market. It is the return on the value-weighted market portfolio.
        • +
      • SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.
        • +
      • HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.
        • +
      • RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.
        • +
      • CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.
      \ No newline at end of file diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html index f552038..7f2f419 100644 --- a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html +++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html @@ -2,54 +2,52 @@ Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern.

      - In the following backtest, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will long the five stocks with the highest scores and short the five stocks with the lowest scores. + In the following backtest, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores. 

       def FineSelectionFunction(self, fine):
     '''Select securities with highest score on Fama French 5 factors'''
-    
-    # select stocks with these 5 factors
-    
-    # Operation profit margin: Quality
-    # Book value per share: Value
-    # ROE: Profitability
-    # TotalEquity: Size
-    # TotalAssetsGrowth: Investment Pattern
-    filtered = [x for x in fine if x.OperationRatios.OperationMargin.Value
-                                and x.ValuationRatios.BookValuePerShare
-                                and x.OperationRatios.ROE
+
+    # Select stocks with these 5 factors:
+    # MKT -- Book value per share: Value
+    # SMB -- TotalEquity: Size
+    # HML -- Operation profit margin: Quality
+    # RMW -- ROE: Profitability
+    # CMA -- TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
                                 and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.OperationMargin.Value
+                                and x.OperationRatios.ROE
                                 and x.OperationRatios.TotalAssetsGrowth]
-                                
-    
-    # sort by factors
-    sortedByFactor1 = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-    sortedByFactor2 = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-    sortedByFactor3 = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
-    sortedByFactor4 = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
-    sortedByFactor5 = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
-    
+
+    # Sort by factors
+    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+
     stockBySymbol = {}
-    
-    # get the rank based on 5 factors for every stock
-    for index, stock in enumerate(sortedByFactor1):
-        rank1 = index
-        rank2 = sortedByFactor2.index(stock)
-        rank3 = sortedByFactor3.index(stock)
-        rank4 = sortedByFactor4.index(stock)
-        rank5 = sortedByFactor5.index(stock)
-        avgRank = np.mean([rank1,rank2,rank3,rank4,rank5])
+
+    # Get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByMkt):
+        mktRank = self.beta_m * index
+        smbRank = self.beta_s * sortedBySmb.index(stock)
+        hmlRank = self.beta_h * sortedByHml.index(stock)
+        rmwRank = self.beta_r * sortedByRmw.index(stock)
+        cmaRank = self.beta_c * sortedByCma.index(stock)
+        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
         stockBySymbol[stock.Symbol] = avgRank
-        
+
     sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
     symbols = [x[0] for x in sorted_dict]
-    
-    # pick the stocks with the highest scores to long
+
+    # Pick the stocks with the highest scores to long
     self.longSymbols= symbols[:self.num_long]
-    # pick the stocks with the lowest scores to short
+    # Pick the stocks with the lowest scores to short
     self.shortSymbols = symbols[-self.num_short:]
-    
+
     return self.longSymbols + self.shortSymbols
      @@ -61,6 +59,11 @@
      - +
      + +

      + The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest and it performed well. + However, there are still many aspects need to be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial! +

      \ No newline at end of file From ee6ca904f2c343dc6055b406eeb41e609dcbeb23 Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Wed, 21 Aug 2019 18:04:36 -0700 Subject: [PATCH 039/215] requested changes --- .../230 Fama French Five Factors/03 Algorithm.html | 4 ++-- quantpedia.json | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html index 7f2f419..7f04f12 100644 --- a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html +++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html @@ -64,6 +64,6 @@

      - The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest and it performed well. - However, there are still many aspects need to be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial! + The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years. + These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!

      \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index cd0aa80..807c061 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -47,4 +47,5 @@ 199: "2deff750ba4eff5bf2f2138ecffb4a7c", 207: "9bcf7ac117397af393ca59f795c4abdd", 229: "5544552803512ca667342d5011dedd1d", + 230: "78601a5bd785ca32803c13525c688046", } From 2a0f7c1308930ecc90e89c86d34b7cef4f902d3b Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Thu, 22 Aug 2019 08:42:43 -0700 Subject: [PATCH 040/215] requested changes --- .../01 Introduction.html | 0 .../02 Fama French Five-Factor Model.html | 0 .../03 Algorithm.html | 0 .../04 References.html | 0 quantpedia.json | 1 - 5 files changed, 1 deletion(-) rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/01 Introduction.html (100%) rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/02 Fama French Five-Factor Model.html (100%) rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/03 Algorithm.html (100%) rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/04 References.html (100%) diff --git a/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/Fama French Five Factors/01 Introduction.html similarity index 100% rename from 04 Strategy Library/230 Fama French Five Factors/01 Introduction.html rename to 04 Strategy Library/Fama French Five Factors/01 Introduction.html diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html similarity index 100% rename from 04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html rename to 04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/Fama French Five Factors/03 Algorithm.html similarity index 100% rename from 04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html rename to 04 Strategy Library/Fama French Five Factors/03 Algorithm.html diff --git a/04 Strategy Library/230 Fama French Five Factors/04 References.html b/04 Strategy Library/Fama French Five Factors/04 References.html similarity index 100% rename from 04 Strategy Library/230 Fama French Five Factors/04 References.html rename to 04 Strategy Library/Fama French Five Factors/04 References.html diff --git a/quantpedia.json b/quantpedia.json index 807c061..cd0aa80 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -47,5 +47,4 @@ 199: "2deff750ba4eff5bf2f2138ecffb4a7c", 207: "9bcf7ac117397af393ca59f795c4abdd", 229: "5544552803512ca667342d5011dedd1d", - 230: "78601a5bd785ca32803c13525c688046", } From 3675f27e197eccdd51b7ab770a0f594573e88842 Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Thu, 22 Aug 2019 13:33:06 -0700 Subject: [PATCH 041/215] requested changes --- .../01 Introduction.html | 3 + .../02 Method.html | 93 +++++++++++++++++++ .../03 Results.html | 8 ++ .../04 Algorithm.html | 6 ++ .../05 References.html | 5 + .../01 Introduction.html | 3 - .../02 Fama French Five-Factor Model.html | 17 ---- .../03 Algorithm.html | 69 -------------- .../04 References.html | 5 - 9 files changed, 115 insertions(+), 94 deletions(-) create mode 100644 04 Strategy Library/353 Fama French Five Factors/01 Introduction.html create mode 100644 04 Strategy Library/353 Fama French Five Factors/02 Method.html create mode 100644 04 Strategy Library/353 Fama French Five Factors/03 Results.html create mode 100644 04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html create mode 100644 04 Strategy Library/353 Fama French Five Factors/05 References.html delete mode 100644 04 Strategy Library/Fama French Five Factors/01 Introduction.html delete mode 100644 04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html delete mode 100644 04 Strategy Library/Fama French Five Factors/03 Algorithm.html delete mode 100644 04 Strategy Library/Fama French Five Factors/04 References.html diff --git a/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html new file mode 100644 index 0000000..c5889c0 --- /dev/null +++ b/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html @@ -0,0 +1,3 @@ +

      + The relationship between return and risk has long been a popular topic for research. Investors have been seeking financial models that quantify risk and use it to estimate the expected return on equity. The Fama French five-factor model, improved from the Fama French three-factor model, is one of the most classic models (Fama and French, 2015). In this post, we will discuss this model and develop a stock-picking strategy based on it. +

      \ No newline at end of file diff --git a/04 Strategy Library/353 Fama French Five Factors/02 Method.html b/04 Strategy Library/353 Fama French Five Factors/02 Method.html new file mode 100644 index 0000000..1fd2f52 --- /dev/null +++ b/04 Strategy Library/353 Fama French Five Factors/02 Method.html @@ -0,0 +1,93 @@ +

      + The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends. + Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return. + The model is as follows +

      +\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\] + +

      +where +

      +
        +
      • MKT is the excess return of the market. It is the return on the value-weighted market portfolio.
        • +
      • SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.
        • +
      • HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.
        • +
      • RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.
        • +
      • CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.
        • +
      + +

      + Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. +

      + +

      + First, we run a Coarse Selection to drop equities which have no fundamental data or have too low prices. Then we select those with the highest dollar volume. + Note that a useful technique is used here: we can use Universe.Unchanged to remain the same universe when there is no necessary change, which greatly speeds up the backtest. +

      + +
      +
      +def CoarseSelectionFunction(self, coarse):
+    '''Drop securities which have no fundamental data or have too low prices.
+    Select those with highest by dollar volume'''
+
+    if self.Time < self.nextLiquidate:
+        return Universe.Unchanged
+
+    selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5],
+                        key=lambda x: x.DollarVolume, reverse=True)
+
+    return [x.Symbol for x in selected[:self.num_coarse]]   
+
      +
      + +

      + Secondly, in Fine Selection, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores. +

      +
      +
      +def FineSelectionFunction(self, fine):
+    '''Select securities with highest score on Fama French 5 factors'''
+
+    # Select stocks with these 5 factors:
+    # MKT -- Book value per share: Value
+    # SMB -- TotalEquity: Size
+    # HML -- Operation profit margin: Quality
+    # RMW -- ROE: Profitability
+    # CMA -- TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
+                                and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.OperationMargin.Value
+                                and x.OperationRatios.ROE
+                                and x.OperationRatios.TotalAssetsGrowth]
+
+    # Sort by factors
+    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+
+    stockBySymbol = {}
+
+    # Get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByMkt):
+        mktRank = self.beta_m * index
+        smbRank = self.beta_s * sortedBySmb.index(stock)
+        hmlRank = self.beta_h * sortedByHml.index(stock)
+        rmwRank = self.beta_r * sortedByRmw.index(stock)
+        cmaRank = self.beta_c * sortedByCma.index(stock)
+        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
+        stockBySymbol[stock.Symbol] = avgRank
+
+    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
+    symbols = [x[0] for x in sorted_dict]
+
+    # Pick the stocks with the highest scores to long
+    self.longSymbols= symbols[:self.num_long]
+    # Pick the stocks with the lowest scores to short
+    self.shortSymbols = symbols[-self.num_short:]
+
+    return self.longSymbols + self.shortSymbols   
+
      +
      diff --git a/04 Strategy Library/353 Fama French Five Factors/03 Results.html b/04 Strategy Library/353 Fama French Five Factors/03 Results.html new file mode 100644 index 0000000..4d05305 --- /dev/null +++ b/04 Strategy Library/353 Fama French Five Factors/03 Results.html @@ -0,0 +1,8 @@ +

      + In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency. +

      + +

      + The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years. + These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial! +

      \ No newline at end of file diff --git a/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html b/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html new file mode 100644 index 0000000..f3cdbee --- /dev/null +++ b/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/353 Fama French Five Factors/05 References.html b/04 Strategy Library/353 Fama French Five Factors/05 References.html new file mode 100644 index 0000000..4a09697 --- /dev/null +++ b/04 Strategy Library/353 Fama French Five Factors/05 References.html @@ -0,0 +1,5 @@ + \ No newline at end of file diff --git a/04 Strategy Library/Fama French Five Factors/01 Introduction.html b/04 Strategy Library/Fama French Five Factors/01 Introduction.html deleted file mode 100644 index 12ef392..0000000 --- a/04 Strategy Library/Fama French Five Factors/01 Introduction.html +++ /dev/null @@ -1,3 +0,0 @@ -

      - At QuantConnect, we seek to make financial models ever more accessible to our community. This article will walk through the implementation of a stock selection strategy based on the popular Fama French five-factor financial model. -

      \ No newline at end of file diff --git a/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html deleted file mode 100644 index 0cd9ad3..0000000 --- a/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html +++ /dev/null @@ -1,17 +0,0 @@ -

      - The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends. - Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return. - The model is as follows -

      -\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\] - -

      -where -

      -
        -
      • MKT is the excess return of the market. It is the return on the value-weighted market portfolio.
        • -
      • SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.
        • -
      • HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.
        • -
      • RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.
        • -
      • CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.
        • -
      \ No newline at end of file diff --git a/04 Strategy Library/Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/Fama French Five Factors/03 Algorithm.html deleted file mode 100644 index 7f04f12..0000000 --- a/04 Strategy Library/Fama French Five Factors/03 Algorithm.html +++ /dev/null @@ -1,69 +0,0 @@ -

      - Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. -

      -

      - In the following backtest, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores. -

      -
      -
      -def FineSelectionFunction(self, fine):
-    '''Select securities with highest score on Fama French 5 factors'''
-
-    # Select stocks with these 5 factors:
-    # MKT -- Book value per share: Value
-    # SMB -- TotalEquity: Size
-    # HML -- Operation profit margin: Quality
-    # RMW -- ROE: Profitability
-    # CMA -- TotalAssetsGrowth: Investment Pattern
-    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
-                                and x.FinancialStatements.BalanceSheet.TotalEquity
-                                and x.OperationRatios.OperationMargin.Value
-                                and x.OperationRatios.ROE
-                                and x.OperationRatios.TotalAssetsGrowth]
-
-    # Sort by factors
-    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
-    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
-    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
-
-    stockBySymbol = {}
-
-    # Get the rank based on 5 factors for every stock
-    for index, stock in enumerate(sortedByMkt):
-        mktRank = self.beta_m * index
-        smbRank = self.beta_s * sortedBySmb.index(stock)
-        hmlRank = self.beta_h * sortedByHml.index(stock)
-        rmwRank = self.beta_r * sortedByRmw.index(stock)
-        cmaRank = self.beta_c * sortedByCma.index(stock)
-        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
-        stockBySymbol[stock.Symbol] = avgRank
-
-    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
-    symbols = [x[0] for x in sorted_dict]
-
-    # Pick the stocks with the highest scores to long
-    self.longSymbols= symbols[:self.num_long]
-    # Pick the stocks with the lowest scores to short
-    self.shortSymbols = symbols[-self.num_short:]
-
-    return self.longSymbols + self.shortSymbols   
-
      -
      - -

      - In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency. -

      - -
      -
      -
      - -
      -
      - -

      - The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years. - These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial! -

      \ No newline at end of file diff --git a/04 Strategy Library/Fama French Five Factors/04 References.html b/04 Strategy Library/Fama French Five Factors/04 References.html deleted file mode 100644 index a660292..0000000 --- a/04 Strategy Library/Fama French Five Factors/04 References.html +++ /dev/null @@ -1,5 +0,0 @@ - \ No newline at end of file From 4af0db1856b075832c5487e363167cd247de6942 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Thu, 22 Aug 2019 21:46:56 +0100 Subject: [PATCH 042/215] Fixes Logic in Momentum and Style Rotation Effect Instead of the algorithm goes long the top performing ETF and short the ETF at the bottom as desired, the algorithm was doing the opposite. Refactors and adds more comments. --- .../02 Method.html | 47 ++++++++++--------- .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 28 insertions(+), 23 deletions(-) diff --git a/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html index 2146e81..2efbe7b 100644 --- a/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html +++ b/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html @@ -6,23 +6,25 @@ 
       def Initialize(self):
+
     self.SetStartDate(2001, 1, 1)
     self.SetEndDate(2018, 8, 1)
     self.SetCash(100000)
-    self.tickers = [
-                    "IJJ", # iShares S&P MidCap 400 Value Index ETF
-                    "IJS", # iShares S&P SmallCap 600 Value ETF
-                    "IVE", # iShares S&P 500 Value Index ETF
-                    "IVW", # iShares S&P 500 Growth ETF
-                    "IJK", # iShares S&P Mid-Cap 400 Growth ETF
-                    "IJT", # iShares S&P Small-Cap 600 Growth ETF
-                    ]
-    self.symbols = []
-    for ticker in self.tickers:
-        self.symbols.append(self.AddEquity(ticker, Resolution.Daily).Symbol)
-    self.SetWarmUp(timedelta(days=12*20))
-    # save all momentum indicator in the dictionary
-    self.mom = {i:self.MOM(i, 12*20, Resolution.Daily) for i in self.symbols}
+
+    tickers = ["IJJ",   # iShares S&P Mid-Cap 400 Value Index ETF
+               "IJK",   # iShares S&P Mid-Cap 400 Growth ETF
+               "IJS",   # iShares S&P Small-Cap 600 Value ETF
+               "IJT",   # iShares S&P Small-Cap 600 Growth ETF
+               "IVE",   # iShares S&P 500 Value Index ETF
+               "IVW"]   # iShares S&P 500 Growth ETF
+
+    lookback = 12*20
+
+    # Save all momentum indicator into the dictionary
+    self.mom = dict()
+    for ticker in tickers:
+        symbol = self.AddEquity(ticker, Resolution.Daily).Symbol
+        self.mom[symbol] = self.MOM(symbol, lookback)

      @@ -33,12 +35,15 @@ 

       def Rebalance(self):
+    # Order the MOM dictionary by value
     sorted_mom = sorted(self.mom, key = lambda x: self.mom[x].Current.Value)
-    invested = [x.Key for x in self.Portfolio if x.Value.Invested]
-    for i in invested:
-        if i not in [sorted_mom[0], sorted_mom[1]]:
-            self.Liquidate(i)
-    self.SetHoldings(sorted_mom[0], -0.5)
-    self.SetHoldings(sorted_mom[-1], 0.5)
+
+    # Liquidate the ETFs that are no longer selected
+    for symbol in sorted_mom[1:-1]:
+        if self.Portfolio[symbol].Invested:
+            self.Liquidate(symbol, 'No longer selected')
+
+    self.SetHoldings(sorted_mom[-1], -0.5)   # Short the ETF with lowest MOM
+    self.SetHoldings(sorted_mom[0], 0.5)     # Long the ETF with highest MOM
      -
      + \ No newline at end of file diff --git a/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html index d0bf935..ae7ab5e 100644 --- a/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html +++ b/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html @@ -1,6 +1,6 @@
      - +
      diff --git a/quantpedia.json b/quantpedia.json index 567f8b4..e92eac3 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,7 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", - 91: "d0b1d400ad21477d83ae2b4f85616318", + 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", 113: "9b1291a5f08dcc07df86363e46144084", From 9bbbe164cd72c202e05e2f5b3d1d3e8d5d0b2fc0 Mon Sep 17 00:00:00 2001 From: Daniel Chen <44457690+QilongChan@users.noreply.github.com> Date: Mon, 26 Aug 2019 13:44:06 -0700 Subject: [PATCH 043/215] Add Statistical Arbitrage Strategy of Avellaneda & Lee, 2008 --- .../01 Introduction.html | 3 + .../02 Method.html | 62 +++++++++++++++++++ .../03 Results.html | 8 +++ .../04 Algorithm.html | 6 ++ .../05 Reference.html | 5 ++ 5 files changed, 84 insertions(+) create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html new file mode 100644 index 0000000..490405c --- /dev/null +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html @@ -0,0 +1,3 @@ +

      + In this tutorial, we will study the model-driven statistical arbitrage strategies in U.S. stocks market. We employ a combination of Principal Components Analysis (PCA) and Linear Regression to implement this strategy. First, by applying PCA, we get a generalized statistical arbitrage strategy that minimizes exposure to market factors and the asset universe is projected onto its first n (n=3 in our algorithm) orthogonal principal components. Secondly, we model the mean-reverting residuals of the cluster of assets and get their weights based on the level of deviation using linear regression. +

      diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html new file mode 100644 index 0000000..759a3ca --- /dev/null +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html @@ -0,0 +1,62 @@ +

      + There are three elements of the strategy that will promote mean-reversion and the opportunity for statistical arbitrage: (1) Utilize Coarse selection to obtain the initial universe, (2) Employ PCA to historical prices to get the first 3 principal components for dimension reduction, (3) Apply Linear Regression to get the residuals for measuring the price deviation of each stock in the universe. + We will show how we apply Coarse selection first. +

      + +
      +
      +  # Sort the equities in DollarVolume decendingly
+  selected = sorted([x for x in coarse if x.Price > 5],
+                    key=lambda x: x.DollarVolume, reverse=True)
+  symbols = [x.Symbol for x in selected[:self.num_equities]]
+
      +
      + +

      + We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume. + Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us + reduce the dimension of the feature space and exclude the noise at the same time. +

      + +
      + 
      +  # Sample data for PCA (smooth it using np.log function)
+  sample = np.log(history.dropna(axis=1))
+  sample -= sample.mean() # Center it column-wise
+
+  # Fit the PCA model for sample data
+  model = PCA().fit(sample)
+
+  # Get the first n_components factors
+  factors = np.dot(sample, model.components_.T)[:,:self.num_components]
+
      +
      + +

      + Finally, we get to the linear regression part. This part helps us get the weight of each stock in the portfolio based on its price deviation measured by the residual. + If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small, + it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, + based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. + So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. + All details can be found in the following code snippet. +

      + +
      + 
      +    # Train Ordinary Least Squares linear model for each stock
+    OLSmodels = {ticker: sm.OLS(sample[ticker], factors).fit() for ticker in sample.columns}
+
+    # Get the residuals from the linear regression after PCA for each stock
+    resids = pd.DataFrame({ticker: model.resid for ticker, model in OLSmodels.items()})
+
+    # Get the Z scores by standarize the given pandas dataframe X
+    zscores = ((resids - resids.mean()) / resids.std()).iloc[-1] # residuals of the most recent day
+
+    # Get the stocks far from mean (for mean reversion)
+    selected = zscores[zscores < -1.5]
+
+    # Return the weights for each selected stock
+    weights = selected * (1 / selected.abs().sum())
+
      +
      + diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html new file mode 100644 index 0000000..ceddace --- /dev/null +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html @@ -0,0 +1,8 @@ +

      + In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years. + The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is reasonable. However, there are still many aspects can be improved. + For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the algorithm only find one or even no candidate, which might be not enough. + You might increase the number of universe. Besides, we can develop this strategy to a long & short one (now it is only a long strategy). + You could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more amazing algorithms upon this tutorial. +

      + diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html new file mode 100644 index 0000000..78e62f1 --- /dev/null +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html new file mode 100644 index 0000000..218aff7 --- /dev/null +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html @@ -0,0 +1,5 @@ + From 046e6301923ab0d9013dad6636fb332e2bde1383 Mon Sep 17 00:00:00 2001 From: Sherry Yang Date: Wed, 28 Aug 2019 12:16:31 -0700 Subject: [PATCH 044/215] Update 01 Introduction.html Made initial edits. --- .../01 Introduction.html | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html index 490405c..c3e8b0a 100644 --- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html @@ -1,3 +1,11 @@

      - In this tutorial, we will study the model-driven statistical arbitrage strategies in U.S. stocks market. We employ a combination of Principal Components Analysis (PCA) and Linear Regression to implement this strategy. First, by applying PCA, we get a generalized statistical arbitrage strategy that minimizes exposure to market factors and the asset universe is projected onto its first n (n=3 in our algorithm) orthogonal principal components. Secondly, we model the mean-reverting residuals of the cluster of assets and get their weights based on the level of deviation using linear regression. + In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy + based on the paper + Statistical Arbitrage in teh U.S. Equities Market. +

      +

      + First, we will apply PCA to minimizes our algorithm's exposure to market factors and project the first n (n=3 in our algorithm) + orthogonal principal components on our asset universe. Then we will model the mean-reverting residuals of our assets. + Next we will create our model using linear regression. Each factor will have a weight or coefficient equal to the residuals' level + of deviation from the mean.

      From e32237d82353d929f93e115fab5cde854660201d Mon Sep 17 00:00:00 2001 From: Sherry Yang Date: Wed, 28 Aug 2019 13:35:31 -0700 Subject: [PATCH 045/215] Update 02 Method.html Submit initial edits. --- .../02 Method.html | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html index 759a3ca..7118a9f 100644 --- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html +++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html @@ -1,6 +1,8 @@ + +

      Method

      +

      Step 1: Select our universe

      - There are three elements of the strategy that will promote mean-reversion and the opportunity for statistical arbitrage: (1) Utilize Coarse selection to obtain the initial universe, (2) Employ PCA to historical prices to get the first 3 principal components for dimension reduction, (3) Apply Linear Regression to get the residuals for measuring the price deviation of each stock in the universe. - We will show how we apply Coarse selection first. + ...mean-reversion and opportunity for statistical arbitrage...

      @@ -12,6 +14,7 @@
      +

      Step 2: Reduce dimensions to three principal components

      We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume. Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us @@ -32,14 +35,15 @@ +

      Step 3: Measure price deviation

      - Finally, we get to the linear regression part. This part helps us get the weight of each stock in the portfolio based on its price deviation measured by the residual. + We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small, it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. All details can be found in the following code snippet. -

      +

      
From 5d9d3a96d14563fda3b56f4b61c460111b1b48c2 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 13:36:18 -0700
Subject: [PATCH 046/215] Update 01 Introduction.html

Submit word change.
---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index c3e8b0a..0021b43 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -1,6 +1,6 @@
 
      
   In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
-  based on the paper 
+  derived from the paper 
   Statistical Arbitrage in teh U.S. Equities Market.
 
      
 
       

From 520a8fcb48c8266e2212b171f7796f9c7349c97f Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 13:40:56 -0700
Subject: [PATCH 047/215] Update 03 Results.html

Submit initial changes.
---
 .../03 Results.html                             | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index ceddace..8734b4f 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,8 +1,15 @@
+
      Results
      
 
      
-    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is reasonable. However, there are still many aspects can be improved.
-    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the algorithm only find one or even no candidate, which might be not enough.
-    You might increase the number of universe. Besides, we can develop this strategy to a long & short one (now it is only a long strategy). 
-    You could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more amazing algorithms upon this tutorial.  
+    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
+    We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
+    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is 
+    reasonable. However, there are still many aspects can be improved.
+
      
+
      
+    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the 
+    algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
+    develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
+    level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 
+    amazing algorithms upon this tutorial.  
 
      
 

From 91d15af3f084a0e19821f8a5e13741097dcb4a6e Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 13:41:58 -0700
Subject: [PATCH 048/215] Update 04 Algorithm.html

Submit initial edits.
---
 .../04 Algorithm.html                                            | 1 +
 1 file changed, 1 insertion(+)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
index 78e62f1..f404e0b 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
@@ -1,3 +1,4 @@
+
      Algorithm
      
 
      
     
      
     
      

From 360338b448a4f877bf731e572a5832f62a9585b2 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 15:56:41 -0700
Subject: [PATCH 049/215] Update 01 Introduction.html

Add background to strategy.
---
 .../01 Introduction.html                      | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 0021b43..626bd4a 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -1,11 +1,14 @@
 
      
-  In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
+  In this tutorial we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
   derived from the paper 
-  Statistical Arbitrage in teh U.S. Equities Market.
+  Statistical Arbitrage in the U.S. Equities Market.
 
      
-
       
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project the first n (n=3 in our algorithm) 
-  orthogonal principal components on our asset universe. Then we will model the mean-reverting residuals of our assets. 
-  Next we will create our model using linear regression. Each factor will have a weight or coefficient equal to the residuals' level 
-  of deviation from the mean. 
-
      
+
      
+  Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
+  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often referenced with 
+  Pairs Trading. 
+  In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
+  Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 
+  from ETF-based strategies. 
+
      
+

From cf44cdbf7972d0b7fcb2350b72dccd7df1b0f15a Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 15:57:35 -0700
Subject: [PATCH 050/215] Update 01 Introduction.html

Add grammatical change.
---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 626bd4a..4b469ed 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -5,7 +5,7 @@
 
      
 
      
   Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
-  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often referenced with 
+  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often discussed in conjunction with 
   Pairs Trading. 
   In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
   Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 

From 353d0d2b839e5083386902fbfc459f8595d6f935 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 15:59:38 -0700
Subject: [PATCH 051/215] Update 02 Method.html

Update steps.
---
 .../02 Method.html                                           | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 7118a9f..29c85cf 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,8 +1,7 @@
-
-
      Method
      
 
      Step 1: Select our universe
       
 
      
-  ...mean-reversion and opportunity for statistical arbitrage... 
+  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project some orthogonal principal components on 
+  our asset universe. Then we will model the mean-reverting residuals of our assets from our regression line.   
 
      
 
 
      

From 29f49177d586526762b81b178c9a2e3df68afbcc Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 16:01:20 -0700
Subject: [PATCH 052/215] Update 03 Results.html

Remove redundancies.
---
 .../03 Results.html                                      | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index 8734b4f..d556c32 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,12 +1,11 @@
-
      Results
      
 
      
-    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
+    In our alorithm, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
     We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is 
-    reasonable. However, there are still many aspects can be improved.
+    The performance is generally good, which indicates using PCA combined with linear regression to measure the deviation level is 
+    reasonable. 
 
      
 
      
-    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the 
+    To tune the model, we could expand the original coarse-selected universe. The current universe contains 20 equities, and sometimes the 
     algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
     develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
     level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 

From d8ab3e770a796e91f67647f54c7c86238dcad0d0 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 16:01:39 -0700
Subject: [PATCH 053/215] Update 04 Algorithm.html

Remove redundancies.
---
 .../04 Algorithm.html                                            | 1 -
 1 file changed, 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
index f404e0b..78e62f1 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
@@ -1,4 +1,3 @@
-
      Algorithm
      
 
      
     
      
     
      

From 94e32534461ee079fdd319b5e87e13d0d19aefe3 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 16:08:53 -0700
Subject: [PATCH 054/215] Update 02 Method.html

Add description changes.
---
 .../02 Method.html                                    | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 29c85cf..d0295b8 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,7 +1,6 @@
 
      Step 1: Select our universe
       
 
      
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project some orthogonal principal components on 
-  our asset universe. Then we will model the mean-reverting residuals of our assets from our regression line.   
+  We will select our universe of stocks by dropping those with prices lower than $5 and pick the ones with the highest dollar volume.   
 
      
 
 
      
@@ -15,9 +14,9 @@ 
      Step 1: Select our universe
      
 
 
      Step 2: Reduce dimensions to three principal components
      
 
      
-  We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume.
-  Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us
-  reduce the dimension of the feature space and exclude the noise at the same time.
+  First, we will apply PCA to minimizes our algorithm's exposure to market factors and select k principal components on 
+  our asset universe. Based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the 
+  historical close values). This helps us reduce the dimension of the feature space and exclude the noise at the same time.
 
      
 
 
      
@@ -36,7 +35,7 @@ 
      Step 2: Reduce dimensions to three principal components
      
 
 
      Step 3: Measure price deviation
        
 
      
-  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  Then we will model the mean-reverting residuals of our assets from our regression line. We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
   If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
   it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
   based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 

From 301888ca3c6ff637aae934b45143ead18ea0d84e Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 16:23:48 -0700
Subject: [PATCH 055/215] Update 03 Results.html

Add changes.
---
 .../03 Results.html                                 | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index d556c32..681b990 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,14 +1,11 @@
 
      
     In our alorithm, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
-    We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with linear regression to measure the deviation level is 
-    reasonable. 
+    Our result is an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years. Our performance indicates 
+    using PCA combined with linear regression to measure the deviation level is reasonable. 
 
      
 
      
-    To tune the model, we could expand the original coarse-selected universe. The current universe contains 20 equities, and sometimes the 
-    algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
-    develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
-    level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 
-    amazing algorithms upon this tutorial.  
+    To tune the model, we could expand our universe of stocks beyond the current 20 equities. 
+    We could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm 
+    (30 days in this example). 
 
      
 

From 448faa9aacb9de14feda2d74b9d01051ac04d2dc Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 16:30:53 -0700
Subject: [PATCH 056/215] Update 02 Method.html

Add changes.
---
 .../02 Method.html                             | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index d0295b8..f3a7449 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -14,9 +14,9 @@ 
      Step 1: Select our universe
      
 
 
      Step 2: Reduce dimensions to three principal components
      
 
      
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and select k principal components on 
-  our asset universe. Based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the 
-  historical close values). This helps us reduce the dimension of the feature space and exclude the noise at the same time.
+  We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  The number of principal components or eigenvectors we use is variable. In our algorithm use 3 principal components of the feature space formed by the 
+  historical close values. This helps us reduce the dimension of the feature space and exclude the noise at the same time.
 
      
 
 
      
@@ -35,12 +35,14 @@ 
      Step 2: Reduce dimensions to three principal components
      
 
 
      Step 3: Measure price deviation
        
 
      
-  Then we will model the mean-reverting residuals of our assets from our regression line. We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
-  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
-  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
-  based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
+  We will model the mean-reverting residuals of our assets from our regression line. 
+  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it 
+  more weight in the portfolio. Similarly, if the absolute value of the residual is small,
+  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get 
+  their z scores. Then, based on the z scores, it is easy to detect the level of price deviation. 
+  Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
   So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
-  All details can be found in the following code snippet.
 
      
 
 
      

From 071658ed3251d858cba65d574509cfa542157203 Mon Sep 17 00:00:00 2001
From: Sherry Yang 
Date: Wed, 28 Aug 2019 17:10:10 -0700
Subject: [PATCH 057/215] Update 02 Method.html

Add changes.
---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index f3a7449..01ba081 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -15,8 +15,8 @@ 
      Step 1: Select our universe
      
 
      Step 2: Reduce dimensions to three principal components
      
 
      
   We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
-  The number of principal components or eigenvectors we use is variable. In our algorithm use 3 principal components of the feature space formed by the 
-  historical close values. This helps us reduce the dimension of the feature space and exclude the noise at the same time.
+  The number of principal components or eigenvectors we use is variable. For the sake of demonstration we chose 3 components to account for the bulk of the variance.
+  In our algorithm use 3 principal components of the feature space formed by the historical close values. 
 
      
 
 
      

From 687fcfd7a4add970af24c42a4e9d9ba8bf1edade Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:26:59 -0700
Subject: [PATCH 058/215] Update 01 Introduction.html

---
 .../01 Introduction.html                                    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 4b469ed..42d452d 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -4,11 +4,11 @@
   Statistical Arbitrage in the U.S. Equities Market.
 
      
 
      
-  Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
-  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often discussed in conjunction with 
+  Statistical arbitrage strategies uses mean-reversion models to take advantage of pricing inefficiencies between groups of correlated 
+  securities. This class of short-term financial trading strategies produce moves that can contrarian to the broader market movement and are often discussed in conjunction with 
   Pairs Trading. 
   In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
-  Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 
+  Backtests from the period 1997-2007 support our strategy by showing that PCA-based strategies have Sharpe ratios that outperform Sharpe ratios 
   from ETF-based strategies. 
 
      
 

From d89d6c2b8e173efb40b80e5d72a790a7da54f5f3 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:35:53 -0700
Subject: [PATCH 059/215] Update 02 Method.html

---
 .../02 Method.html                            | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 01ba081..535a282 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,6 +1,6 @@
 
      Step 1: Select our universe
       
 
      
-  We will select our universe of stocks by dropping those with prices lower than $5 and pick the ones with the highest dollar volume.   
+  We will select our universe of stocks by dropping securities with prices lower than $5 and pick the ones with the highest dollar traded volume.   
 
      
 
 
      
@@ -14,9 +14,9 @@ 
      Step 1: Select our universe
      
 
 
      Step 2: Reduce dimensions to three principal components
      
 
      
-  We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
-  The number of principal components or eigenvectors we use is variable. For the sake of demonstration we chose 3 components to account for the bulk of the variance.
-  In our algorithm use 3 principal components of the feature space formed by the historical close values. 
+  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  Based on the results found in the cited paper, and for the sake of demonstration, we chose 3 components to account for the bulk of the variance.
+  In our algorithm, the 3 principal components of the feature space are formed by the historical close values. 
 
      
 
 
      
@@ -35,14 +35,14 @@ 
      Step 2: Reduce dimensions to three principal components
      
 
 
      Step 3: Measure price deviation
        
 
      
-  We will model the mean-reverting residuals of our assets from our regression line. 
-  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
-  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it 
+  We will model the mean-reverting residuals of our assets from a regression line. 
+  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation, which is measured by the residual. 
+  If the absolute value of a stock's residual is large, it means that the level of price deviation is high and we should give it 
   more weight in the portfolio. Similarly, if the absolute value of the residual is small,
-  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get 
-  their z scores. Then, based on the z scores, it is easy to detect the level of price deviation. 
-  Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
-  So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
+  it is reasonable to give the stock less weight in the portfolio. To facilitate this, we can first standardize the residuals to get 
+  their z-scores. Then, based on the z-scores, it is easy to detect the level of price deviation. 
+  Specifically, the level of deviation is higher when the absolute values of the z-scores are large. 
+  From this it is natural to use the inverse of the absolute values of the z-scores as a measurement of the weights of the portfolio. 
 
      
 
 
      

From f80a4677ec1fa6eb04aa515f293c351115298a85 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:37:01 -0700
Subject: [PATCH 060/215] Update 03 Results.html

---
 .../03 Results.html                                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index 681b990..5c3f0f7 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -4,7 +4,7 @@
     using PCA combined with linear regression to measure the deviation level is reasonable. 
 
      
 
      
-    To tune the model, we could expand our universe of stocks beyond the current 20 equities. 
+    To tune the model, we could expand our universe of stocks beyond the current 20 equities or incorporate more PCA components.
     We could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm 
     (30 days in this example). 
 
      

From 901bf8cdc0f4ed4ca5bdbff7cace5c489be8a759 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:38:00 -0700
Subject: [PATCH 061/215] Update 02 Method.html

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 535a282..bdbfa32 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -14,7 +14,7 @@ 
      Step 1: Select our universe
      
 
 
      Step 2: Reduce dimensions to three principal components
      
 
      
-  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations as a whole. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
   Based on the results found in the cited paper, and for the sake of demonstration, we chose 3 components to account for the bulk of the variance.
   In our algorithm, the 3 principal components of the feature space are formed by the historical close values. 
 
      

From 5842ba6028be9ce82b3dacf8e7f180d40cf60f52 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Thu, 29 Aug 2019 15:18:34 -0700
Subject: [PATCH 062/215] Adds Fama & French Factor File to Upcoming Tutorial

---
 .../F-F_Research_Data_Factors_daily.CSV       | 24522 ++++++++++++++++
 1 file changed, 24522 insertions(+)
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV b/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV
new file mode 100644
index 0000000..ac04c74
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV	
@@ -0,0 +1,24522 @@
+This file was created by CMPT_ME_BEME_RETS_DAILY using the 201906 CRSP database.
+The Tbill return is the simple daily rate that, over the number of trading days
+in the month, compounds to 1-month TBill rate from Ibbotson and Associates Inc.
+
+,Mkt-RF,SMB,HML,RF
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+19260821,    0.32,   -0.06,    0.49,   0.010
+19260823,    0.49,   -0.48,    0.62,   0.010
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+19260830,    0.30,    0.00,    0.42,   0.010
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+19260901,    0.55,    0.52,   -0.08,   0.009
+19260902,   -0.06,   -0.07,    0.21,   0.009
+19260903,    0.50,   -0.30,    0.03,   0.009
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+19260914,    0.90,   -0.62,    0.09,   0.009
+19260915,   -0.21,    0.40,   -0.19,   0.009
+19260916,   -0.18,   -0.33,    0.10,   0.009
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+19260918,    0.70,   -0.28,   -0.11,   0.009
+19260920,   -0.64,    0.03,   -0.01,   0.009
+19260921,    0.30,   -0.42,    0.19,   0.009
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+19260925,    0.21,    0.02,   -0.56,   0.009
+19260927,   -0.12,    0.14,    0.39,   0.009
+19260928,    0.00,   -0.02,   -0.35,   0.009
+19260929,   -0.30,    0.22,   -0.19,   0.009
+19260930,    0.30,   -0.22,    0.06,   0.009
+19261001,    0.73,   -0.27,   -0.23,   0.013
+19261002,   -0.10,   -0.02,   -0.08,   0.013
+19261004,   -1.40,   -0.10,   -0.10,   0.013
+19261005,   -1.10,    0.44,   -0.72,   0.013
+19261006,   -0.56,   -0.69,   -0.28,   0.013
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+
+Copyright 2019 Kenneth R. French

From 5a40511ba4be28644fff102c34991a5e6ba5ab73 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Thu, 29 Aug 2019 15:59:06 -0700
Subject: [PATCH 063/215] Changed to a shorter folder name

test if the long folder name caused error in downloading
---
 .../F-F_Research_Data_Factors_daily.CSV                           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{354 Expected Idiosyncratic Skewness Using Fama and French Factors => 354 Expected Idiosyncratic Skewness}/F-F_Research_Data_Factors_daily.CSV (100%)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV b/04 Strategy Library/354 Expected Idiosyncratic Skewness/F-F_Research_Data_Factors_daily.CSV
similarity index 100%
rename from 04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV
rename to 04 Strategy Library/354 Expected Idiosyncratic Skewness/F-F_Research_Data_Factors_daily.CSV

From f3e34d1878c8f3fc98fc7da36bf6b48d4969afe9 Mon Sep 17 00:00:00 2001
From: Ethan Lee 
Date: Fri, 30 Aug 2019 13:33:20 -0700
Subject: [PATCH 064/215] Update introduction link for the new section

---
 03 Open Source/00 Introduction/Introduction.html | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index d895560..2fef38d 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -6,10 +6,10 @@
 i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
 
 
-
+
   
          
-    
       100 Desktop Charting with LEAN
          
-    
      Guide to using the desktop charting environment that comes with LEAN (UX v1.0).
      
+    
       Debugging Python
      
+    
      Explore different methods to debug python algorithms.
      
     
   
      
       
@@ -17,7 +17,7 @@ 
       100 Desktop Charting with LEAN
      
 
   
          
     
       101 Backtesting from Visual Studio
          
-    
      VIsual Studio plugin integrated with the QuantConnect API.
      
+    
      Visual Studio plugin integrated with the QuantConnect API.
      
     
   
      
       

From 257541687fca48242aadbac5886065e915c4b15d Mon Sep 17 00:00:00 2001
From: Ethan Lee 
Date: Fri, 30 Aug 2019 13:43:11 -0700
Subject: [PATCH 065/215] Add course code

---
 03 Open Source/00 Introduction/Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 2fef38d..744480f 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -8,7 +8,7 @@
 
 
   
          
-    
       Debugging Python
      
+    
       100 Debugging Python
      
     
      Explore different methods to debug python algorithms.
      
     
   
      

From 735009dd82bce5d69ef84d023d7cdaf9afa08d56 Mon Sep 17 00:00:00 2001
From: Ethan Lee 
Date: Fri, 6 Sep 2019 11:13:46 -0700
Subject: [PATCH 066/215] Add new strategy mapping for search table

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e855425..6f45a59 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -513,6 +513,15 @@
         'description' => "Invests in stocks with low P/E ratio.",
         'tags'=>'Beginner,Fundamental Factors,Equities'
     ],
+    [
+        'name' => 'Mean-Reversion Statistical Arbitrage Strategy in Stocks',
+        'link' => 'strategy-library/mean-reversion-statistical-arbitrage-strategy-in-stocks',
+        'sources' => [
+            'NYU' => 'https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf'
+        ],
+        'description' => "Apply statistical arbitrage to take advantage of pricing inefficiencies in stocks.",
+        'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
+    ],
 ];
 
 ?>

From e461046b7fc884b5a7afad293388c79761fdcecb Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 6 Sep 2019 11:41:05 -0700
Subject: [PATCH 067/215] Add startegy mapping for search table

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e855425..0e5cf5a 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -513,6 +513,15 @@
         'description' => "Invests in stocks with low P/E ratio.",
         'tags'=>'Beginner,Fundamental Factors,Equities'
     ],
+    [
+        'name' => 'Fama French Five Factors',
+        'link' => 'strategy-library/fama-french-five-factors',
+        'sources' => [
+            'NYU' => 'https://www.quantpedia.com/Screener/Details/78'
+        ],
+        'description' => "Stock selecting strategy based on Fama-French Five Factors Model.",
+        'tags'=>'Long Short,Fundamental Factors,Equities'
+    ],
 ];
 
 ?>

From f4a3ee8a75773789aadc3c9740479ce53ec3b79b Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 6 Sep 2019 11:42:26 -0700
Subject: [PATCH 068/215] Add strategy mapping for search table

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 0e5cf5a..bbce99e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -517,7 +517,7 @@
         'name' => 'Fama French Five Factors',
         'link' => 'strategy-library/fama-french-five-factors',
         'sources' => [
-            'NYU' => 'https://www.quantpedia.com/Screener/Details/78'
+            'NYU' => 'https://www.sciencedirect.com/science/article/pii/S0304405X14002323'
         ],
         'description' => "Stock selecting strategy based on Fama-French Five Factors Model.",
         'tags'=>'Long Short,Fundamental Factors,Equities'

From e0d30dbd929b2e47444f86e86b7eb9535aa4ea85 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Fri, 6 Sep 2019 17:16:24 -0700
Subject: [PATCH 069/215] Tutorial - Expected Idiosyncratic Skewness

NYU Strategy -- Expected Idiosyncratic Skewness
---
 .../01 Abstract.html                          |  3 ++
 .../02 Theory.html                            |  7 +++
 .../03 Data Description.html                  |  3 ++
 .../04 Method.html                            | 50 +++++++++++++++++++
 .../05 Conclusion and Future Work.html        | 13 +++++
 .../06 Algorithm.html                         |  6 +++
 .../07 References.html                        | 11 ++++
 7 files changed, 93 insertions(+)
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
new file mode 100644
index 0000000..9b7d744
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -0,0 +1,3 @@
+
      
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The annual return during the period of July 1, 2009 to July 1, 2019 is 21.0%.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
new file mode 100644
index 0000000..b1ae556
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
@@ -0,0 +1,7 @@
+
      
+	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors (“lotto investors”) have a preference for positive skewness while others (“traditional investors”) are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
+
      
+
+
      
+	Despite the theoretical basis for the pricing effects of skewness preference, empirically testing the relation is not straightforward as expected skewness is difficult to measure. BMV accounts for the phenomenon that lagged skewness alone does not adequately forecast skewness by presenting a cross-sectional model of expected skewness using additional predictive variables. Using their model, they reaffirm the existing theory that expected idiosyncratic skewness and returns are negatively correlated. Notably, they find the Fama-French alpha of a low-expected-skewness quintile exceeds the alpha of a high-expected-skewness quintile by 1.00% per month. Furthermore, the Fama-MacBeth cross-sectional regressions have statistically significant, negative coefficients. Besides, BMV finds that the expected skewness helps explain how stocks with low idiosyncratic volatility have high expected returns.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
new file mode 100644
index 0000000..3aa2885
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -0,0 +1,3 @@
+
      
+	To execute our algorithm, we will use daily data from Kenneth French’s Data Library that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to our Github cloud. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect’s Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this online technical appendix for descriptions of these variables.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
new file mode 100644
index 0000000..b864049
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
@@ -0,0 +1,50 @@
+
      
+	We can develop a model of estimated expected idiosyncratic skewness using Fama-French three factors. Lower expected idiosyncratic skewness will predict a higher alpha. We will let the investment horizon over which investors are hoping to experience an extreme positive outcome be 1 month. And, let S(t) denote the set of trading days in the current month, and let N(t) denote the number of days in this set.
+
      
+
+
      Step 1: Getting Fama-French three-factor regression residuals
      
+
+
      
+	Let \(\epsilon_{i,d}\) be the regression residual using the Fama and French (1993) three-factor model on day d for firm i, where the regression coefficients that define this residual are estimated using daily data for days in S(t) as the time-series regression below.
+
      
+
+\[R_{i,d} - R_{f,d} = \alpha_i + \beta_i [R_{M,d} - R_{f,d}] + s_i SMB_{d} + h_i HML_{d} + \epsilon_{i,d}\]
+
+
      
+	for all day \(d \in S(t)\) and each \(i = 1,2,\dots,N\).
+
      
+
+
      Step 2: Estimating historical idiosyncratic moments
      
+
+
      
+	Let \(iv_{i,t}\) and \(is_{i,t}\) denote historical estimates of idiosyncratic volatility and skewness (respectively) for firm i using daily data for all days in S(t). We can then define \(iv_{i,t}\) and \(is_{i,t}\) as:
+
      
+
+\[iv_{i,t} = \left( \frac{1}{N(t) - 1} \sum_{d\in S(t)} \epsilon_{i,d}^2 \right)^{1/2}\]
+
+\[is_{i,t} = \frac{1}{N(t) - 2} \frac{ \sum_{d\in S(t)} \epsilon_{i,d}^3 } { iv_{i,t}^{3/2} }\]
+
+
      Step 3: Estimating expected idiosyncratic skewness
      
+
+
      
+	We need measures of expected skewness over a horizon of 1 months for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
+
      
+
+\[is_{i,t} = \beta_0^t + \beta_1^t is_{i,t-1} + \beta_2^t iv_{i,t-1} + \varepsilon_{i,t}\]
+
+
      
+	Superscripts on regression parameters are included to emphasize that we estimate these parameters using information observable at the end of month t. We then use the regression parameters from equation above, along with information observable at the end of each month t, to estimate expected skewness for each firm,
+
      
+
+\[ E_t[is_{i,t+1}] = \beta_0^t + \beta_1^t is_{i,t} + \beta_2^t iv_{i,t} \]
+
+
      
+	This approach provides feasible estimates of each month’s expected skewness and accounts for variation between historical moments and expected skewness across time.
+
      
+
+
      Step 4: Generating trading signals
      
+
+
      
+	At the end of each month, we use the results of equation above to sort stocks by expected idiosyncratic skewness. We construct our universe using the lowest 5% of expected skewness, and long our assets to construct a value-weighted portfolio.
+
      
+
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html
new file mode 100644
index 0000000..0252ef9
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html	
@@ -0,0 +1,13 @@
+
      
+	Before the BMV paper was published in 2009, a number of theories on the pricing premium for stocks with idiosyncratic skewness existed, but lacked supporting empirical evidence of the relationship between idiosyncratic skewness and returns. BMV fills this void by estimating a model of predicted skewness and using predicted skewness to explain the cross-section of returns. The paper finds that lagged idiosyncratic volatility is a stronger predictor of skewness than lagged idiosyncratic skewness.
+
      
+
+
      
+	In this implementation, we rely on idiosyncratic volatility and skewness to predict idiosyncratic skewness. Interested users can build from this implementation by trying the following extensions:
+
      
+
+
        
+	
      1.  Including a number of firm-specific variables to improve predictive power for expected idiosyncratic skewness;
+	
      2.  Using different investment horizons such as 3 months, 6 months, 1 year;
+	
      3.  Adding more lags in the time-series regression for both expected and historical idiosyncratic skewness.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
new file mode 100644
index 0000000..af8d286
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
@@ -0,0 +1,6 @@
+
      
+    
      
+    
      
+        
+    
      
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html
new file mode 100644
index 0000000..337cf0a
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html	
@@ -0,0 +1,11 @@
+
        
+  
      1. 
+    Boyer B, Mitton T, Vorkink K. Expected idiosyncratic skewness. The Review of Financial Studies. 2009 Jun 3;23(1):169-202. Online Copy
+  
        2. 
+  
      2. 
+	Fama EF, French KR. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 1993 Feb 1;33(1):3-56. Online Copy
+  
        3. 
+  
      3. 
+	Mitton T, Vorkink K. Equilibrium underdiversification and the preference for skewness. The Review of Financial Studies. 2007 Jan 29;20(4):1255-88. Online Copy
+  
        4. 
+
          

From 5b318eb5751eb70778b82ae4be6795d1359c025d Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Mon, 9 Sep 2019 15:53:54 -0700
Subject: [PATCH 070/215] Add Strategy of Keloharju, T. Linnainmaa & Nyberg,
 2014

---
 .../01 Strategy Library.php                   | 10 +++
 .../01 Introduction.html                      | 19 +++++
 .../02 Method.html                            | 70 +++++++++++++++++++
 .../03 Results.html                           |  9 +++
 .../04 Algorithm.html                         |  6 ++
 .../05 Reference.html                         |  5 ++
 6 files changed, 119 insertions(+)
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..eca1bd5 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,16 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+    [
+        'name' => 'Seasonality Effect based on Same-Calendar Month Returns',
+        'link' => 'strategy-library/seasonality-effect-based-on-same-calendar-month-returns',
+        'sources' => [
+            'NYU' => 'https://www.nber.org/papers/w20815.pdf'
+        ],
+        'description' => "A strategy that takes long and short positions based on historical same-calendar month returns",
+        'tags'=>'Seasonality,Stocks,Universe Selection'
+
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html
new file mode 100644
index 0000000..17958c4
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html	
@@ -0,0 +1,19 @@
+
          
+  This tutorial implements a seasonality strategy that trades based on historical same-calendar-month returns. The strategy is
+  derived from the paper Common Factors in Return Seasonalities. 
+
          
+
          
+  A great deal of research on seasonality effects in algorithmic trading exists. Seasonality patterns are well documented in stock returns across numerous
+  countries and in commodity and country portfolios. The phenomenon’s occurrence is not isolated to specific stocks or monthly time intervals, for example, seasonality is observed at the daily frequency as well. Our implementation reflects the existing research. 
+
          
+
          
+  In our algorithm, we will first use a coarse selection filter function to narrow down our universe to the top 100 liquid securities with a price greater than $5.
+
          
+
          
+  Next, for each security in the universe, we will calculate the monthly return for the same-calendar month of the previous year. For example, if we implement this strategy on a backtest for the period of August 2019, we would base our long and short positions on monthly returns from August 2018. We will long the securities with top monthly returns and short those with the bottom monthly returns. 
+
          
+
          
+  At the end of each month we will rebalance and repeat the strategy. The following section offers further explanation of how to implement each step of the strategy.
+
          
+  
+  
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
new file mode 100644
index 0000000..4bcd1ee
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
@@ -0,0 +1,70 @@
+
          Step 1: Select our universe
          
+
          
+    We first select liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from “Common Factors” suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the library do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
+
            
+
+
          
+
          +    # Sort the securities with prices > 5 in DollarVolume decendingly
+    selected = sorted([x for x in coarse if x.Price > 5],
+                        key=lambda x: x.DollarVolume, reverse=True)
+
+    # Get securities after coarse selection
+    symbols = [x.Symbol for x in selected[:self.num_coarse]]
+
          
+
          
+
+
          Step 2: Calculate the same-calendar month returns of the previous year
          
+
          
+    “Common Factors” indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
+
          
+
+
          
+  
          +    # Get historical close data for coarse-selected symbols of the same calendar month
+    start = self.Time.replace(day = 1, year = self.Time.year-1)
+    end = Expiry.EndOfMonth(start) - timedelta(1)
+    history = self.History(symbols, start, end, Resolution.Daily).close.unstack(level=0)
+
+    # Get the same calendar month returns for the symbols
+    MonthlyReturn = {ticker: prices.iloc[-1]/prices.iloc[0] for ticker, prices in history.iteritems()}
+
+    # Sorted the values of monthly return
+    sortedReturn = sorted(MonthlyReturn.items(), key=lambda x:x[1], reverse=True)
+
+    # Get the symbols to long / short
+    self.longSymbols = [x[0] for x in sortedReturn[:self.num_long]]
+    self.shortSymbols = [x[0] for x in sortedReturn[-self.num_short:]]
+
+    # Note that self.longSymbols/self.shortSymbols contains strings instead of symbols
+    return [x for x in symbols if str(x) in self.longSymbols + self.shortSymbols]
+  
          
+
          
+
+
          Step 3: Rebalance monthly
          
+
          
+  At the end of each month, we rebalance our portfolio, liquidate the securities that are not part of the new month’s universe, and repeat step 1 and 2. Keep in mind we use equal weights for the long and short positions of securities in our portfolio.
+
          
+
+
          
+  
          +    '''
+    Rebalance every month based on same-calendar month returns effect
+    '''
+    # Before next rebalance, do nothing
+    if self.Time < self.nextRebalance:
+        return
+
+    count = len(self.longSymbols + self.shortSymbols)
+    # Open long positions
+    for symbol in self.longSymbols:
+        self.SetHoldings(symbol, 1/count)
+
+    # Open short positions
+    for symbol in self.shortSymbols:
+        self.SetHoldings(symbol, -1/count)
+
+    # Rebalance at the end of every month
+    self.nextRebalance = Expiry.EndOfMonth(self.Time) - timedelta(1)
+  
          
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
new file mode 100644
index 0000000..d502249
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
@@ -0,0 +1,9 @@
+
          
+    In backtesting our algorithm achieves an annual rate of return over 5% with a max drawdown of approximately 30% for the past 10 years.
+    The performance indicates using the idea of same-calendar month returns makes sense. Interested users can build upon this implementation by trying the following extensions: 
+
          
+
            
+    
          1. Using the same-calendar months of multiple years (e.g. the last 5 years), instead of using the previous year as we did in this tutorial, to get more stable monthly returns.
            2. 
+    
          2. Using discounting to capture time effects in the returns.
            3. 
+    
          3. Creating the initial universe using different criteria such as quarterly, rather than monthly, returns.
            4. 
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html
new file mode 100644
index 0000000..03c24b0
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
          
+    
          
+    
          
+        
+    
          
+
          
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html
new file mode 100644
index 0000000..a859e6e
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html	
@@ -0,0 +1,5 @@
+

From d0fa07ef18618b07f0d18b7f6028825eca53d114 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Tue, 10 Sep 2019 10:53:32 -0700
Subject: [PATCH 071/215] Add Strategy of Lemperiere, Deremble & Nguyen, 2015

---
 .../01 Strategy Library.php                   | 10 +++
 .../01 Introduction.html                      |  7 ++
 .../02 Method.html                            | 73 +++++++++++++++++++
 .../03 Results.html                           |  9 +++
 .../04 Algorithm.html                         |  6 ++
 .../05 Reference.html                         |  5 ++
 6 files changed, 110 insertions(+)
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..3ca35d0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,16 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+    [
+        'name' => 'Risk Premia in Forex Markets',
+        'link' => 'strategy-library/risk-premia-in-forex-markets',
+        'sources' => [
+            'NYU' => 'https://arxiv.org/pdf/1409.7720.pdf'
+        ],
+        'description' => "A strategy based on asymmetric tail risks and excess returns in forex markets.",
+        'tags'=>'Forex,Skewness,Risk Premium'
+
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html b/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html
new file mode 100644
index 0000000..2c85d7e
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html	
@@ -0,0 +1,7 @@
+
          
+  This tutorial implements a risk premia strategy that enters long-short positions in the forex market based on signals from a skewness indicator. The strategy is derived from the paper  “Risk Premia: Asymmetric Tail Risks and Excess Returns” by Lemperiere, Deremble, Nguyen, Seager, Potters, and Bouchaud.
+
          
+
+
          
+  One of the pillars in modern finance theory is the concept of risk premium, which states the riskier an investment is today the more profitable it should be in the long run. Risk premia strategies aim to profit from risk premiums. Lemperiere et al. describe a positive linear relationship between the Sharpe ratio of risk premia strategies and their negative skewness. It provides extensive evidence that risk premium is indeed strongly correlated with the skewness of a strategy, not only in the equity world but also in currencies, options, credit, etc.
+
          
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html b/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html
new file mode 100644
index 0000000..fd1d64a
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html	
@@ -0,0 +1,73 @@
+
          Step 1: Select our forex universe
           
+
          
+  In this algorithm, we use a fixed forex universe which contains four symbols: "EURUSD", "AUDUSD", "USDCAD" and "USDJPY". By using self.AddForex(), we add the requested forex data into the data feed. 
+
          
+
+
          
+
          +  # Add forex data of the following symbols
+  for pair in ['EURUSD', 'AUDUSD', 'USDCAD', 'USDJPY']:
+      self.AddForex(pair, Resolution.Hour, Market.FXCM)    
+
          
+
          
+
+
          Step 2: Calculate the skewness indicator
          
+
          
+  In statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Lemperiere et al. suggest there is a positive relationship between risk premia strategies and their negative skewness. We will use this relationship in our trading logic. Our implementation goes long for a forex pair when the skewness indicator is lower than a minimum threshold (-0.6) and short the pair when the indicator exceeds a maximum threshold (0.6). For each forex pair in the universe, we will calculate the skewness indicator with historical close prices and select the symbols as follows:
+
          
+
+
          
+  
          +    ### In OnData()
+    # Get historical close data for the symbols
+    history = self.History(self.Securities.Keys, self.lookback, Resolution.Daily)
+    history = history.drop_duplicates().close.unstack(level=0)
+
+    # Get the skewness of the historical data
+    skewness = self.GetSkewness(history)
+
+    longSymbols = [k for k,v in skewness.items() if v < self.longSkewLevel]
+    shortSymbols = [k for k,v in skewness.items() if v > self.shortSkewLevel]
+
+  def GetSkewness(self, values):
+    '''
+    Get the skewness for all forex symbols based on its historical data
+    Ref: https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
+    '''
+    # Get the numerator of the skewness
+    numer = ((values - values.mean()) ** 3).sum()
+
+    # Get the denominator of the skewness
+    denom = self.lookback * values.std() ** 3
+
+    # Return the skewness
+    return (numer/denom).to_dict()
+  
          
+
          
+
+
          Step 3: Rebalance weekly
            
+
          
+  We rebalance every week, liquidate the forex pairs not on the trading list, then repeat steps 1-2. We use equal weights for the long and short positions of securities in our portfolio.
+
          
+
+
          
+  
          +  # Liquidate the holdings for pairs that will not trade
+  for holding in self.Portfolio.Values:
+      symbol = holding.Symbol
+      if holding.Invested and symbol.Value not in longSymbols + shortSymbols:
+          self.Liquidate(symbol, 'Not selected pair')
+
+  # Open positions for the symbols with equal weights
+  count = len(longSymbols) + len(shortSymbols)
+
+  for pair in longSymbols:
+      self.SetHoldings(pair, 1/count)
+
+  for pair in shortSymbols:
+      self.SetHoldings(pair, -1/count)
+
+  # Set next rebalance time
+  self.nextRebalance += timedelta(self.rebalanceDays)    
+  
          
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html b/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html
new file mode 100644
index 0000000..3e38c14
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html	
@@ -0,0 +1,9 @@
+
          
+    In this case our backtest results in a low annual return of approximately -0.7% over a decade. The poor performance may be due to several reasons:
+    
            
+        
          1. The fixed forex universe chosen is not large enough to properly diversify market risk
            2. 
+        
          2. The thresholds for entering long and short positions (0.6, -0.6) may need adjustment
            3. 
+        
          3. The length of historical data might be not large enough for this weekly-rebalanced strategy.
            4. 
+    
          
+    We encourage the community to further develop this strategy by testing out different symbols, thresholds, and historical data lengths.
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html b/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html
new file mode 100644
index 0000000..f3764f8
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
          
+    
          
+    
          
+        
+    
          
+
          
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html b/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html
new file mode 100644
index 0000000..dfffb15
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html	
@@ -0,0 +1,5 @@
+

From 332860875f137d6c2cf3d085e0145fce2421ee7b Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Tue, 10 Sep 2019 11:27:32 -0700
Subject: [PATCH 072/215] Add mapping of strategy 354 for search table

Strategy 354 PR: https://github.com/QuantConnect/Tutorials/pull/235
---
 .../00 Strategy Library/01 Strategy Library.php        | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..f65e379 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -530,8 +530,16 @@
         ],
         'description' => "Apply statistical arbitrage to take advantage of pricing inefficiencies in stocks.",
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
-
     ],
+	[
+		'name' => 'Expected Idiosyncratic Skewness',
+		'link' => 'strategy-library/expected-idiosyncratic-skewness',
+		'sources' => [
+			'NYU' => 'http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.825.592&rep=rep1&type=pdf'
+		],
+		'description' => "Stock selection strategy that calculates expected idiosyncratic skewness using Fama-French three-factor model, sorts stocks based on the calculated skewness, and longs the bottom 5%.",
+		'tags' => 'Equities, Skewness, Fundamental Factors, Statistical Arbitrage'
+	],
 ];
 
 ?>

From d1dca0ae71b352ec9484ae3d849429ec81cb6007 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Tue, 10 Sep 2019 14:31:02 -0700
Subject: [PATCH 073/215] Add Strategy Standardized Unexpected Earnings

Based on paper: https://www.jstor.org/stable/pdf/247321.pdf
---
 .../01 Strategy Library.php                   |   9 ++
 .../01 Abstract.html                          |   3 +
 .../02 Theory.html                            |   3 +
 .../03 Method.html                            | 124 ++++++++++++++++++
 .../04 Conclusion and Future Work.html        |  10 ++
 .../05 Algorithm.html                         |   6 +
 .../06 References.html                        |   8 ++
 7 files changed, 163 insertions(+)
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..6c8af87 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,15 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+	[
+		'name' => 'Standardized Unexpected Earnings',
+		'link' => 'strategy-library/standardized-unexpected-earnings',
+		'sources' => [
+			'NYU' => 'https://www.jstor.org/stable/pdf/247321.pdf'
+		],
+		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly."
+		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
+	]
 ];
 
 ?>
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
new file mode 100644
index 0000000..07de981
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
@@ -0,0 +1,3 @@
+
          
+	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed an 11.8% annual return using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html b/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html
new file mode 100644
index 0000000..0179e8b
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html	
@@ -0,0 +1,3 @@
+
          
+	In market efficiency literature, one frequently discussed topic is the anomalous behavior of stock returns following earnings announcements. The market does not adjust to news from earning announcements instantaneously. Instead, many studies report evidence that the direction and magnitude of returns in the post-earnings announcement period are positively correlated with the direction and magnitude of the unexpected component in the earnings releases. This observed phenomenon is consistent with suggestions that the capital market is inefficient.
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
new file mode 100644
index 0000000..130dcfd
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
@@ -0,0 +1,124 @@
+
          
+	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE’s numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
+
          
+
+\[ 
+	SUE_q = \frac{ EPS_q - EPS_{q-4} }{ \sigma( EPS_q - EPS_{q-4} ) }
+\]
+
+
          
+	where \(\sigma(X)\) is the standard deviation of X, EPS a firm’s quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE’s stock ranking changes month to month because each company’s earnings announcement release date for the quarter differs (i.e., firm A’s Q3 announcement may come out in August while firm B’s Q3 announcement comes out in September).
+
          
+
+
+
          Step 1: Narrow down the universe with a coarse selection filter function
          
+
+
          
+	We use a coarse selection filter to narrow down the universe to 1000 stocks at the beginning of each month according to dollar volume, price and whether the stock has fundamental data in our data library. 
+
          
+
+
          
+
+
          def CoarseSelectionFunction(self, coarse):
+    '''Get dynamic coarse universe to be further selected in fine selection
+    '''
+    # Before next rebalance time, just remain the current universe
+    if self.Time < self.next_rebalance:
+        return Universe.Unchanged
+        
+    ### Run the coarse selection to narrow down the universe
+    # filter by fundamental data and price & Sort descendingly by daily dollar volume
+    sorted_by_volume = sorted([ x for x in coarse if x.HasFundamentalData and x.Price > 5 ],
+                                key = lambda x: x.DollarVolume, reverse = True)
+    self.new_fine = [ x.Symbol for x in sorted_by_volume[:self.num_coarse] ]
+        
+    # Return all symbols that have appeared in Coarse Selection
+    return list( set(self.new_fine).union( set(self.eps_by_symbol.keys()) ) )
+	
          
+	
          
+
+
          Step 2: Sort the universe by SUE and select the top 5%
          
+
+
          
+	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don’t trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
+
          
+
+
+
          
+
+
          def FineSelectionAndSueSorting(self, fine):
+	'''Select symbols to trade based on sorting of SUE'''
+	
+	sue_by_symbol = dict()
+	
+	for stock in fine:
+		
+		### Save (symbol, rolling window of EPS) pair in dictionary
+		if not stock.Symbol in self.eps_by_symbol:
+			self.eps_by_symbol[stock.Symbol] = RollingWindow[float](self.months_count)
+		# update rolling window for each stock
+		self.eps_by_symbol[stock.Symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths)
+	
+		### Calculate SUE
+	
+		if stock.Symbol in self.new_fine and self.eps_by_symbol[stock.Symbol].IsReady:
+	
+			# Calculate the EPS change from four quarters ago
+			rw = self.eps_by_symbol[stock.Symbol]
+			eps_change = rw[0] - rw[self.months_eps_change]
+			
+			# Calculate the st dev of EPS change for the prior eight quarters
+			new_eps_list = list(rw)[:self.months_count - self.months_eps_change:3]
+			old_eps_list = list(rw)[self.months_eps_change::3]
+			eps_std = np.std( [ new_eps - old_eps for new_eps, old_eps in 
+								zip( new_eps_list, old_eps_list )
+							] )
+			
+			# Get Standardized Unexpected Earnings (SUE)
+			sue_by_symbol[stock.Symbol] = eps_change / eps_std
+	
+	# Sort and return the top quantile
+	sorted_dict = sorted(sue_by_symbol.items(), key = lambda x: x[1], reverse = True)
+	
+	self.long = [ x[0] for x in sorted_dict[:math.ceil( self.top_percent * len(sorted_dict) )] ]
+	# If universe is empty, OnData will not be triggered, then update next rebalance time here
+	if not self.long:
+		self.next_rebalance = Expiry.EndOfMonth(self.Time)
+	
+	return self.long
+	
          
+	
          
+
+
          Step 3: Form an equal-weighted portfolio and place orders
          
+
+
          
+	Once the symbols are selected, we form an equal-weighted portfolio and place orders. Finally, we update the next rebalance time to the beginning of the next calendar month. The portfolio will be held until liquidated at next rebalance time.
+
          
+
+
+
          
+
+
          def OnSecuritiesChanged(self, changes):
+	'''Liquidate symbols that are removed from the dynamic universe
+	'''
+	for security in changes.RemovedSecurities:
+		if security.Invested:
+			self.Liquidate(security.Symbol, 'Removed from universe')        
+	
+
+def OnData(self, data):
+	'''Monthly rebalance at the beginning of each month. Form portfolio with equal weights.
+	'''
+	# Before next rebalance, do nothing
+	if self.Time < self.next_rebalance or not self.long:
+		return
+	
+	# Placing orders (with equal weights)
+	equal_weight = 1 / len(self.long)
+	for stock in self.long:
+		self.SetHoldings(stock, equal_weight)
+	
+	# Rebalance at the beginning of every month
+	self.next_rebalance = Expiry.EndOfMonth(self.Time)
+	
          
+	
          
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
new file mode 100644
index 0000000..3ac670b
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
@@ -0,0 +1,10 @@
+
          
+	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generations a non-trivial 11.8% annual return. Interested users can build from this implementation by trying the following extensions:
+
          
+
+
            
+	
          1.  Using a more complicated measure for expected earnings to replace the historical EPS from four quarters ago.
+	
          2.  Using different investment horizons such as 3 months, 6 months, 1 year. In a longer investment horizon of n months, each month’s decile will have n subdeciles, each of which is initiated in a different month in the prior n-month period. An example is a horizon of 6 months with each month having 6 subdeciles, each initiated in a different month in the prior 6-month period.
+	
          3.  Importing custom data of analysts’ forecasts of firms’ earnings to replace the expected earnings based on historical EPS.
+	
          4.  Selecting small-size companies and then trade based on SUE ranking, since studies suggest that post-earnings announcement is more significant for small-size companies than larger ones.
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html b/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html
new file mode 100644
index 0000000..26f8e7e
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html	
@@ -0,0 +1,6 @@
+
          
+    
          
+    
          
+        
+    
          
+
          
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html b/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html
new file mode 100644
index 0000000..b138986
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html	
@@ -0,0 +1,8 @@
+
            
+  
          1. 
+    Foster G, Olsen C, Shevlin T. Earnings releases, anomalies, and the behavior of security returns. Accounting Review. 1984 Oct 1:574-603 Online Copy
+  
            2. 
+  
          2. 
+	Hou K, Xue C, Zhang L. Replicating Anomalies. The Review of Financial Studies Online Copy
+  
            3. 
+
              

From 59bcdf0671fb43027f399c6f6094a6b717080fee Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Tue, 10 Sep 2019 17:20:31 -0700
Subject: [PATCH 074/215] Requested Changes to Strategy 354

---
 .../354 Expected Idiosyncratic Skewness/02 Theory.html        | 2 +-
 .../03 Data Description.html                                  | 2 +-
 .../354 Expected Idiosyncratic Skewness/04 Method.html        | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
index b1ae556..b49f79e 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
@@ -1,5 +1,5 @@
 
              
-	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors (“lotto investors”) have a preference for positive skewness while others (“traditional investors”) are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
+	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors ("lotto investors") have a preference for positive skewness while others ("traditional investors") are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
 
              
 
 
              
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
index 3aa2885..cfe92cd 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -1,3 +1,3 @@
 
              
-	To execute our algorithm, we will use daily data from Kenneth French’s Data Library that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to our Github cloud. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect’s Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this online technical appendix for descriptions of these variables.
+	To execute our algorithm, we will use daily data from Kenneth French's Data Library that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this online technical appendix for descriptions of these variables.
 
              
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
index b864049..fa80957 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
@@ -27,7 +27,7 @@ 
              Step 2: Estimating historical idiosyncratic moments
              
 
              Step 3: Estimating expected idiosyncratic skewness
              
 
 
              
-	We need measures of expected skewness over a horizon of 1 months for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
+	We need measures of expected skewness over a horizon of 1 month for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
 
              
 
 \[is_{i,t} = \beta_0^t + \beta_1^t is_{i,t-1} + \beta_2^t iv_{i,t-1} + \varepsilon_{i,t}\]
@@ -39,7 +39,7 @@ 
              Step 3: Estimating expected idiosyncratic skewness
              
 \[ E_t[is_{i,t+1}] = \beta_0^t + \beta_1^t is_{i,t} + \beta_2^t iv_{i,t} \]
 
 
              
-	This approach provides feasible estimates of each month’s expected skewness and accounts for variation between historical moments and expected skewness across time.
+	This approach provides feasible estimates of each month's expected skewness and accounts for variation between historical moments and expected skewness across time.
 
              
 
 
              Step 4: Generating trading signals
              

From fc331e2a2b9c1303f9a1a1039aff4c25729c832d Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Tue, 10 Sep 2019 18:07:29 -0700
Subject: [PATCH 075/215] Requested Changes to Strategy 355

---
 .../01 Abstract.html                                  |  2 +-
 .../03 Method.html                                    | 11 ++++++-----
 .../04 Conclusion and Future Work.html                |  2 +-
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
index 07de981..9972cb0 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
@@ -1,3 +1,3 @@
 
              
-	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed an 11.8% annual return using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
+	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed a Sharpe ratio of 0.83 relative to SPY Sharpe of 0.88 using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
 
              
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
index 130dcfd..a1b3ba6 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
@@ -1,5 +1,5 @@
 
              
-	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE’s numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
+	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE's numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
 
              
 
 \[ 
@@ -7,7 +7,7 @@
 \]
 
 
              
-	where \(\sigma(X)\) is the standard deviation of X, EPS a firm’s quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE’s stock ranking changes month to month because each company’s earnings announcement release date for the quarter differs (i.e., firm A’s Q3 announcement may come out in August while firm B’s Q3 announcement comes out in September).
+	where \(\sigma(X)\) is the standard deviation of X, EPS a firm's quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE's stock ranking changes month to month because each company's earnings announcement release date for the quarter differs (i.e., firm A's Q3 announcement may come out in August while firm B's Q3 announcement comes out in September).
 
              
 
 
@@ -22,12 +22,13 @@ 
              Step 1: Narrow down the universe with a coarse selection filter function
              def CoarseSelectionFunction(self, coarse):
     '''Get dynamic coarse universe to be further selected in fine selection
     '''
-    # Before next rebalance time, just remain the current universe
+    # Before next rebalance time, keep the current universe unchanged
     if self.Time < self.next_rebalance:
         return Universe.Unchanged
         
     ### Run the coarse selection to narrow down the universe
-    # filter by fundamental data and price & Sort descendingly by daily dollar volume
+    # Filter stocks by price and whether they have fundamental data
+	# Then, sort descendingly by daily dollar volume
     sorted_by_volume = sorted([ x for x in coarse if x.HasFundamentalData and x.Price > 5 ],
                                 key = lambda x: x.DollarVolume, reverse = True)
     self.new_fine = [ x.Symbol for x in sorted_by_volume[:self.num_coarse] ]
@@ -40,7 +41,7 @@ 
              Step 1: Narrow down the universe with a coarse selection filter function
              Step 2: Sort the universe by SUE and select the top 5%
 
 
              
-	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don’t trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
+	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don't trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
 
              
 
 
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
index 3ac670b..291c81a 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
@@ -1,5 +1,5 @@
 
              
-	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generations a non-trivial 11.8% annual return. Interested users can build from this implementation by trying the following extensions:
+	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generates a Sharpe ratio of 0.83 relative to SPY Sharpe ratio of 0.88. Interested users can build from this implementation by trying the following extensions:
 
              
 
 
                

From 2335b97261680ce702d701beb6789421cdeced23 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Tue, 10 Sep 2019 18:14:19 -0700
Subject: [PATCH 076/215] Replaced Annual Return with Sharpe

---
 .../354 Expected Idiosyncratic Skewness/01 Abstract.html        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
index 9b7d744..5d4ea33 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -1,3 +1,3 @@
 
                
-	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The annual return during the period of July 1, 2009 to July 1, 2019 is 21.0%.
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.1 relative to SPY Sharpe ratio of 0.99 during the period of July 1, 2009 to July 1, 2019.
 
                
\ No newline at end of file

From 0d5b1ad63d0492e5ee235b99351e789faac07f30 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Wed, 11 Sep 2019 16:22:32 -0700
Subject: [PATCH 077/215] Resolve Conflict & A Few Extra Minor Changes

Minor changes include:
(1) Fixed a bug related to month-end rebalance in code.
(2) Changed texts accordingly.
---
 .../354 Expected Idiosyncratic Skewness/01 Abstract.html        | 2 +-
 .../03 Data Description.html                                    | 2 +-
 .../354 Expected Idiosyncratic Skewness/06 Algorithm.html       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
index 5d4ea33..ffa6192 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -1,3 +1,3 @@
 
                
-	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.1 relative to SPY Sharpe ratio of 0.99 during the period of July 1, 2009 to July 1, 2019.
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.03 relative to S&P 500 (SPY) Sharpe ratio of 1.00 during the period of July 1, 2009 to July 30, 2019.
 
                
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
index cfe92cd..31483b2 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -1,3 +1,3 @@
 
                
-	To execute our algorithm, we will use daily data from Kenneth French's Data Library that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this online technical appendix for descriptions of these variables.
+	To execute our algorithm, we will use daily data from Kenneth French's Data Library that captures the Fama-French three factors for the period July 1, 2009 to June 30, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this online technical appendix for descriptions of these variables.
 
                
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
index af8d286..20f54ed 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
@@ -1,6 +1,6 @@
 
                
     
                
     
                
-        
+        
     
                
 
                
\ No newline at end of file

From 7e101aff6883324720fec86c87942fb0cd23a781 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles 
Date: Thu, 12 Sep 2019 10:28:11 -0700
Subject: [PATCH 078/215] Fix strategy library map

Address missing comma, that was preventing the tutorials repository from being generated.
---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 8316643..c2eb29a 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -556,7 +556,7 @@
 		'sources' => [
 			'NYU' => 'https://www.jstor.org/stable/pdf/247321.pdf'
 		],
-		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly."
+		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
 	]
 ];

From 410079a33dff785310f3b1bd46ce2f4a55439063 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 12 Sep 2019 16:51:57 -0700
Subject: [PATCH 079/215] Changes requested

---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
index 4bcd1ee..5b5da95 100644
--- a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
@@ -1,6 +1,6 @@
 
                Step 1: Select our universe
                
 
                
-    We first select liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from “Common Factors” suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the library do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
+    We first select the top 100 liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from "Common Factors" suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the library do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
 
                  
 
 
                
@@ -16,7 +16,7 @@ 
                Step 1: Select our universe
                
 
 
                Step 2: Calculate the same-calendar month returns of the previous year
                
 
                
-    “Common Factors” indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
+    "Common Factors" indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
 
                
 
 
                

From aaf91ce77c44446bee6912867d049d958d9397e9 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 12 Sep 2019 18:03:05 -0700
Subject: [PATCH 080/215] Update 03 Results

---
 .../03 Results.html                                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
index d502249..e4e319f 100644
--- a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
@@ -1,5 +1,5 @@
 
                
-    In backtesting our algorithm achieves an annual rate of return over 5% with a max drawdown of approximately 30% for the past 10 years.
+    In backtesting our algorithm achieves a Sharpe ratio of 0.332 relative to S&P 500 (SPY) Sharpe ratio of 0.893 for the past 10 years.
     The performance indicates using the idea of same-calendar month returns makes sense. Interested users can build upon this implementation by trying the following extensions: 
 
                
 
                  

From 2d92e4e421644e93c0c187d2d84bb5758b638569 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Tue, 17 Sep 2019 10:44:40 -0700
Subject: [PATCH 081/215] Add Improved Momentum Strategy on Commodities Futures

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   3 +
 .../02 Introduction.html                      |  16 ++
 .../03 TSMOM-CF Theory.html                   |  99 +++++++++
 .../04 Method.html                            | 207 ++++++++++++++++++
 .../05 Summary.html                           |   3 +
 .../06 Algorithm.html                         |   6 +
 .../07 References.html                        |   8 +
 8 files changed, 351 insertions(+)
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1aef751..76fe5bf 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -568,6 +568,15 @@
 		],
 		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
+	],
+	[
+		'name' => 'Improved Momentum Strategy on Commodities Futures',
+		'link' => 'strategy-library/improved-momentum-strategy-on-commodities-futures',
+		'sources' => [
+			'NYU' => 'https://pdfs.semanticscholar.org/a2e9/df201d4b4774fda84a961cc804f2450988c5.pdf'
+		],
+		'description' => "An advanced momentum strategy that modifies the basic momentum strategies by introducing Baltas and Kosowski weights and rebalances the portfolio monthly. The new weighing scheme incorporates trend strength into the trading signal, uses an efficient volatility estimator, and adds a dynamic leverage mechanism.",
+		'tags' => 'Momentum, Futures, Commodities'
 	]
 ];
 
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
new file mode 100644
index 0000000..2c4d2da
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
@@ -0,0 +1,3 @@
+
                  
+	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper “Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations” by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - Momentum Effect in Commodities Futures.
+
                  
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
new file mode 100644
index 0000000..7dae98b
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
@@ -0,0 +1,16 @@
+
                  
+	Baltas and Kosowski modify the basic momentum strategy by incorporating trend strength into the trading signal, using an efficient volatility estimator, and adding a dynamic leverage mechanism. The modifications overcome these three weaknesses:
+
                  
+
+
                    
+	
                  1. 
+	An Oversimplified Trading Signal: The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy’s extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
+	
                    2. 
+	
                  2. 
+	An Inefficient Volatility Estimator: The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy’s volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
+	
                    3. 
+	
                  3. 
+	A Fixed Portfolio Allocation Mechanism: The TSMOM does not consider the correlation between assets during portfolio construction. It simply allocates funds to each asset based on the properties of the individual assets. Strategies based on TSMOM significantly underperform in the post-2008 global financial crisis (GFC) period due to the increased level of asset co-movement at the time. As a remedy, Baltas and Kosowski introduce a dynamic leverage adjustment for the overall portfolio by adding a correlation factor to the weighting scheme.
+	
                    4. 
+
                  
+
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
new file mode 100644
index 0000000..2c51e91
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -0,0 +1,99 @@
+
                  
+Baltas and Kosowski’s modifications to the basic time-series momentum strategy can be summarized in the formula below:
+
                  
+
+\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
+
+
                  
+	where:
+
                  
+\[r_{t,t+1}^{TSMOM-CF} = \text{TSMOM-CF portfolio return from time t to time t+1}\]
+\[N_t = \text{Number of portfolio constituents at time t}\]
+\[X_t^i = \text{Trading signal value of asset i at time t}\]
+\[\sigma_{P,tgt} = \text{Target level of volatility for the overall portfolio}\]
+\[\sigma_t^i = \text{Estimated volatility of asset i at time t}\]
+\[CF(\bar{\rho}_t) = \text{Correlation factor that adjusts the level of leverage applied to each portfolio constituents at time t}\] 
+\[r_{t,t+1}^i = \text{return of asset i from time t to time t+1}\]
+
+
                  
+	The formula shows that the weights for each portfolio constituent are dependent on three parts:
+
                  
+
+
+
                  Part I: Trading Rule Adjustment (\(X_t^i\))
                  
+
+
                  
+The TREND trading rule determines the trading signal based on the statistical strength of the realized return:
+
                  
+
+
+\[
+\text{TREND}_i^{12M} \quad
+\begin{cases} +1, \text{   if } t(r_{t-12,t})>+1 \\
+t(r_{t-12,t}), \text{   otherwise} \\
+-1, \text{   if } t(r_{t-12,t})<-1 \\
+\end{cases}
+\]
+
+
                  
+where t() is the t-statistic of the daily futures log-returns over the past 12 months to scale the gross exposure to each portfolio constituents.
+
                  
+
+
                  
+When the absolute value of our t-statistic is greater than 1, the trend is highly statistically significant, so the strategy puts 100% exposure to the asset. When the t-statistic is between -1 and 1, the strength of the trend is not as significant, so the strategy scales its exposure to less than 100%.
+
                  
+
+
+
                  Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))
                  
+
                  
+	Instead of estimating each asset’s volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
+
                  
+
+\[\sigma_{YZ}^2(t) = \sigma_{OJ}^2(t) + k \sigma_{SD}^2(t) + (1-k) \sigma_{RS}^2(t)\]
+
+
                  
+	where:
+
                  
+
+\[\sigma_{OJ} = \text{Overnight jump estimator (standard deviation of close-to-open daily logarithmic returns)}\]
+\[\sigma_{SD} = \text{Standard volatility estimator (standard deviation of close-to-close daily logarithmic returns)}\]
+\[\sigma_{RS} = \text{Rogers and Satchell (1991) range estimator}\]
+\[k = \text{parameter that minimizes YZ estimator variance, which is a function of the numbers of days in the estimation}\]
+
+
                  
+The formula for parameter k is below:
+
                  
+\[k = \frac{0.34}{1.34+\frac{N_D+1}{N_D-1}}\]
+
+
                  
+The Rogers and Satchell range estimator calculation is based on the following formula:
+
                  
+
+\[\sigma_{RS}^2(\tau) = h(\tau)[h(\tau)-c(\tau)]+l(\tau)[l(\tau)-c(\tau)]\]
+
+
                  
+	where \(h(\tau)\), \(l(\tau)\) and \(c(\tau)\) denote the logarithmic difference between the high, low and closing prices respectively with the opening price. The RS volatility of an asset at the end of month t, assuming a certain estimation period, is equal to the average daily RS volatility over this period.
+
                  
+
+
                  
+The estimation period is chosen to be 1 month, or 21 trading days, based on Baltas and Kosowski's suggestions.
+
                  
+
+
+
                  Part III: Correlation Factor (CF)
                  
+Baltas and Kosowski’s correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
+
+\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
+\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
+
+
                  
+	where:
+
                  
+
+\[N = \text{number of assets in the portfolio}\]
+\[\rho_{i,j} = \text{correlation between asset i, j}\]
+\[X_i = \text{trade signal of asset i}\]
+\[\bar{\rho} = \text{average pairwise signed correlation for the entire portfolio}\]
+
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html
new file mode 100644
index 0000000..2014718
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html	
@@ -0,0 +1,207 @@
+
                  
+	The strategy requires the continuous futures contract, so we import the custom data from Quandl. We manually create a universe of tradable commodity futures from all available commodity futures traded on CME and ICE. They are all liquid and active continuous contracts #1. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations. The data resolution is daily.
+
                  
+
+
+
                  Step 1: Import the data
                  
+
+
                  
+
                  +from QuantConnect.Python import PythonQuandl
+class ImprovedCommodityMomentumTrading(QCAlgorithm):
+	def Initialize(self):
+		for ticker in tickers:
+			data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+			data.SetLeverage(3) # Leverage was set to 3 for each of the futures contract
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "Settle"
+
                  
+
                  
+
+
+
+
                  Step 2: Set the portfolio target volatility and decide rebalance schedule
                  
+
                  
+
                  +def Initialize(self):
+	# Last trading date tracker to achieve rebalancing the portfolio every month
+    self.nextRebalance = self.Time
+
+	# Set portfolio target level of volatility, set to 12% 
+	self.portfolio_target_sigma = 0.12
+
                  
+
                  
+
+
                  Step 3: Implement functions to calculate the three components of Baltas and Kosowski weights
                  
+
                  1. TREND Trade Signal
                  
+
                  
+
                  +def GetTradingSignal(self, history):
+	'''
+	TREND Trading Signal
+	- Uses the t-statistics of historical daily log-returns to reflect the strength of price movement trend
+	- TREND Signal Conditions:
+		t-stat > 1 => TREND Signal = 1
+		t-stat < 1 => TREND Signal = -1
+		-1 < t-stat < 1 => TREND Signal = t-stat
+	'''
+	settle = history.settle.unstack(level = 0)
+
+	# daily futures log-returns based on close-to-close
+	log_returns = np.log(settle/settle.shift(1)).dropna()
+
+	# Calculate the t-statistics as
+	# (mean-0)/(stdev/sqrt(n)), where n is sample size
+	mean = np.mean(log_returns)
+	std = np.std(log_returns)
+	n = len(log_returns)
+	t_stat = mean/(std/np.sqrt(n))
+
+	# cap holding at 1 and -1
+	return np.clip(t_stat, a_max=1, a_min=-1)
+
                  
+
                  
+
+
                  2. Yang and Zhang Volatility Estimator
                  
+
                  
+
                  +def GetYZVolatility(self, history, available_symbols):
+	'''
+	Yang and Zhang 'Drift-Independent Volatility Estimation'
+	
+	Formula: sigma_YZ^2 = sigma_OJ^2 + self.k * sigma_SD^2 + (1-self.k)*sigma_RS^2 (Equation 20 in [1])
+		where,  sigma_OJ - (Overnight Jump Volitility estimator)
+				sigma_SD - (Standard Volitility estimator)
+				sigma_RS - (Rogers and Satchell Range Volatility estimator)'''
+	YZ_volatility = []
+
+	time_index = history.loc[available_symbols[0]].index
+	today = time_index[-1]
+
+	#Calculate YZ volatility for each security and append to list
+	for ticker in available_symbols:
+	    past_month_ohlc = history.loc[ticker].loc[today-timedelta(self.OneMonth):today]
+	    open, high, low, close = past_month_ohlc.open, past_month_ohlc.high, past_month_ohlc.low, past_month_ohlc.settle
+	    estimation_period = past_month_ohlc.shape[0]
+
+	    # Calculate constant parameter k for Yang and Zhang volatility estimator
+	    # using the formula found in Yang and Zhang (2000)
+	    k = 0.34 / (1.34 + (estimation_period + 1) / (estimation_period - 1))
+
+	    # sigma_OJ (overnight jump => stdev of close-to-open log returns)
+	    open_to_close_log_returns = np.log(open/close.shift(1))
+	    open_to_close_log_returns = open_to_close_log_returns[np.isfinite(open_to_close_log_returns)] 
+	    sigma_OJ = np.std(open_to_close_log_returns) 
+
+	    # sigma_SD (standard deviation of close-to-close log returns)
+	    close_to_close_log_returns = np.log(close/close.shift(1))
+	    close_to_close_log_returns = close_to_close_log_returns[np.isfinite(close_to_close_log_returns)]
+	    sigma_SD = np.std(close_to_close_log_returns) 
+
+	    # sigma_RS (Rogers and Satchell (1991))
+	    h = np.log(high/open)
+	    l = np.log(low/open)
+	    c = np.log(close/open)
+	    sigma_RS_daily = (h * (h - c) + l * (l - c))**0.5
+	    sigma_RS_daily = sigma_RS_daily[np.isfinite(sigma_RS_daily)] 
+	    sigma_RS = np.mean(sigma_RS_daily) 
+		
+	    # daily Yang and Zhang volatility
+	    sigma_YZ = np.sqrt(sigma_OJ**2 + k * sigma_SD**2 + (1 - k) * sigma_RS**2) 
+
+	    # append annualized volatility to the list
+	    YZ_volatility.append(sigma_YZ*np.sqrt(252)) 
+
+	return YZ_volatility
+
                  
+
                  
+
+
                  3. Correlation Factor (CF)
                  
+
                  
+
                  +def GetCorrelationFactor(self, history, trade_signals, available_symbols):
+	'''
+	Calculate the Correlation Factor, which is a function of the average pairwise correlation of all portfolio contituents
+	- the calculation is based on past three month pairwise correlation
+	- Notations:
+	    rho_bar - average pairwise correlation of all portfolio constituents
+	    CF_rho_bar - the correlation factor as a function of rho_bar'''
+
+	# Get the past three month simple daily returns for all securities
+	settle = history.settle.unstack(level = 0)
+	past_three_month_returns = settle.pct_change().loc[settle.index[-1]-timedelta(self.ThreeMonths):]
+
+	# Get number of assets 
+	N_assets = len(available_symbols)
+	
+	# Get the pairwise signed correlation matrix for all assets
+	correlation_matrix = past_three_month_returns.corr() 
+
+	# Calculate rho_bar
+	summation = 0
+	for i in range(N_assets-1):
+	    for temp in range(N_assets - 1 - i):
+		    j = i + temp + 1
+		    x_i = trade_signals[i]
+		    x_j = trade_signals[j]
+		    rho_i_j = correlation_matrix.iloc[i,j]
+		    summation += x_i * x_j * rho_i_j
+			
+	# Equation 14 in [1]
+	rho_bar = (2 * summation) / (N_assets * (N_assets - 1)) 
+
+	# Calculate the correlation factor (CF_rho_bar)
+	# Equation 18 in [1]
+	return np.sqrt(N_assets / (1 + (N_assets - 1) * rho_bar)) 
+
                  
+
                  
+
+
                  Step 4: Construct/Rebalance the Portfolio
                  
+
                  
+For efficiency purposes, a History() request is called once on each rebalance date to get all the data from the past year for all securities. We retrieve our trade signal, Yang and Zhang volatility, and correlation factor by passing the history data frame to each respective function. 
+
                  
+
+
                  
+
                  +def OnData(self, data):
+	'''
+	Monthly rebalance at the beginning of each month.
+	Portfolio weights for each constituents are calculated based on Baltas and Kosowski weights.
+	'''
+
+	# skip if less than 30 days passed since the last trading date
+	if self.Time < self.nextRebalance:
+		return
+
+	'''Monthly Rebalance Execution'''
+	# dataframe that contains the historical data for all securities
+	history = self.History(self.Securities.Keys, self.OneYear, Resolution.Daily)
+	history.replace(0, np.nan, inplace = True)
+
+	# Get the security symbols are are in the history dataframe
+	available_symbols = list(set(history.index.get_level_values(level = 0)))
+
+	# Liquidate symbols that are not in the history dataframe anymore
+	for security in self.Securities.Keys:
+		if security.Value not in available_symbols:
+			self.Liquidate(security, 'Not found in history request')
+
+	# Get the trade signals and YZ volatility for all securities
+	trade_signals = self.GetTradingSignal(history) 
+	volatility = self.GetYZVolatility(history, available_symbols) 
+	
+	# Get the correlation factor
+	CF_rho_bar = self.GetCorrelationFactor(history, trade_signals, available_symbols)
+
+	#Rebalance the portfolio according to Baltas and Kosowski suggested weights
+	N_assets = len(available_symbols)
+	for symbol, signal, vol in zip(available_symbols, trade_signals, volatility):
+		# Baltas and Kosowski weights (Equation 19 in [1])
+		weight = (signal*self.portfolio_target_sigma*CF_rho_bar)/(N_assets*vol)
+		self.SetHoldings(symbol, weight)
+
+	# Set next rebalance time
+	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+
                  
+
                  
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
new file mode 100644
index 0000000..80f9ba3
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
@@ -0,0 +1,3 @@
+
                  
+The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM’s Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the strategy library. 
+
                  
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html
new file mode 100644
index 0000000..6eebbb0
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html	
@@ -0,0 +1,6 @@
+
                  
+    
                  
+    
                  
+        
+    
                  
+
                  
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html
new file mode 100644
index 0000000..5a494dd
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html	
@@ -0,0 +1,8 @@
+
                    
+  
                  1. 		
+	Baltas, Nick & Kosowski, Robert. (2017). Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations. SSRN Electronic Journal. 10.2139/ssrn.2140091. Online Copy
+  
                    2. 
+  
                  2. 
+	Yang, Dennis & Zhang, Qiang. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. The Journal of Business, 73(3), 477-492. doi:10.1086/209650. Online Copy
+  
                    3. 
+ 
                      
\ No newline at end of file

From 409eeea47e4aacc4cb9f6cf848569ac3ad4de932 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Tue, 17 Sep 2019 10:55:32 -0700
Subject: [PATCH 082/215] Apostrophes edited

---
 .../01 Abstract.html                                        | 2 +-
 .../02 Introduction.html                                    | 4 ++--
 .../03 TSMOM-CF Theory.html                                 | 6 +++---
 .../05 Summary.html                                         | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
index 2c4d2da..a095d84 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
@@ -1,3 +1,3 @@
 
                      
-	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper “Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations” by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - Momentum Effect in Commodities Futures.
+	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations" by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - Momentum Effect in Commodities Futures.
 
                      
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
index 7dae98b..2130e92 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
@@ -4,10 +4,10 @@
 
 
                        
 	
                      1. 
-	An Oversimplified Trading Signal: The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy’s extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
+	An Oversimplified Trading Signal: The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy's extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
 	
                        2. 
 	
                      2. 
-	An Inefficient Volatility Estimator: The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy’s volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
+	An Inefficient Volatility Estimator: The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy's volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
 	
                        3. 
 	
                      3. 
 	A Fixed Portfolio Allocation Mechanism: The TSMOM does not consider the correlation between assets during portfolio construction. It simply allocates funds to each asset based on the properties of the individual assets. Strategies based on TSMOM significantly underperform in the post-2008 global financial crisis (GFC) period due to the increased level of asset co-movement at the time. As a remedy, Baltas and Kosowski introduce a dynamic leverage adjustment for the overall portfolio by adding a correlation factor to the weighting scheme.
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 2c51e91..8057d58 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -1,5 +1,5 @@
 
                        
-Baltas and Kosowski’s modifications to the basic time-series momentum strategy can be summarized in the formula below:
+Baltas and Kosowski's modifications to the basic time-series momentum strategy can be summarized in the formula below:
 
                        
 
 \[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
@@ -48,7 +48,7 @@ 
                        Part I: Trading Rule Adjustment (\(X_t^i\))
                        
 
 
                        Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))
                        
 
                        
-	Instead of estimating each asset’s volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
+	Instead of estimating each asset's volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
 
                        
 
 \[\sigma_{YZ}^2(t) = \sigma_{OJ}^2(t) + k \sigma_{SD}^2(t) + (1-k) \sigma_{RS}^2(t)\]
@@ -83,7 +83,7 @@ 
                        Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))
                        
 
 
 
                        Part III: Correlation Factor (CF)
                        
-Baltas and Kosowski’s correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
+Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
 \[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
 \[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
index 80f9ba3..572eb95 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
@@ -1,3 +1,3 @@
 
                        
-The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM’s Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the strategy library. 
+The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM's Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the strategy library. 
 
                        
\ No newline at end of file

From 8e513ede895cab4cff44a6f29725a2aa296c61cc Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Wed, 18 Sep 2019 17:58:28 -0700
Subject: [PATCH 083/215] Fixes Formula

---
 .../03 TSMOM-CF Theory.html                                   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 8057d58..9792a24 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -2,7 +2,7 @@
 Baltas and Kosowski's modifications to the basic time-series momentum strategy can be summarized in the formula below:
 
                        
 
-\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
+\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
 
 
                        
 	where:
@@ -86,7 +86,7 @@ 
                        Part III: Correlation Factor (CF)
                        
 Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
 \[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
-\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
+\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}}{N(N-1)}\]
 
 
                        
 	where:

From 0c788deb2d33f1f9182f65ff1ecb79edb4caeff6 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Thu, 19 Sep 2019 08:39:27 -0700
Subject: [PATCH 084/215] Update 03 TSMOM-CF Theory.html

---
 .../03 TSMOM-CF Theory.html                                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 9792a24..cff2f81 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -85,7 +85,7 @@ 
                        Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))
                        
 
                        Part III: Correlation Factor (CF)
                        
 Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
-\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
+\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}}\]
 \[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}}{N(N-1)}\]
 
 
                        

From 363b28750eaf96910b5464ef8342f5e767f2eb7b Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 20 Sep 2019 15:12:54 -0700
Subject: [PATCH 085/215] Add Strategy of Price and Earnings Momentum

---
 .../01 Strategy Library.php                   |  12 +-
 .../01 Introduction.html                      |  11 ++
 .../02 Method.html                            | 166 ++++++++++++++++++
 .../03 Results.html                           |   9 +
 .../04 Algorithm.html                         |   6 +
 .../05 Reference.html                         |   5 +
 6 files changed, 208 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/02 Method.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/03 Results.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1aef751..22cad2e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -568,7 +568,17 @@
 		],
 		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
-	]
+	],
+    [
+        'name' => 'Price and Earnings Momentum',
+        'link' => 'strategy-library/price-and-earnings-momentum',
+        'sources' => [
+            'NYU' => 'http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107'
+        ],
+        'description' => "A momentum strategy based on quarterly returns and earnings growth",
+        'tags'=>'Momentum, Stocks, Universe Selection, Historical Data, Rolling Window'
+
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html b/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html
new file mode 100644
index 0000000..aa3ce63
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html	
@@ -0,0 +1,11 @@
+
                        
+  In this tutorial, we will develop a strategy based on the price and earnings momentum effect of stocks. This strategy is derived from the paper  "Momentum" by N.Jegadeesh and S.Titman.
+
                        
+
+
                        
+  N.Jegadeesh et al. describe price/return momentum as a tendency for stocks that perform well over a three to twelve month period to continue to perform well over a subsequent three to twelve month period. Similarly, stocks that perform poorly over a three to twelve month period have a tendency to continue to perform poorly. They describe earnings momentum as the tendency for stocks with high earnings per share (EPS) to continue to outperform stocks with low EPS.
+
                        
+
+
                        
+  Below, we will implement a quarterly-rebalanced stock strategy based on the price and earnings momentum.
+
                        
diff --git a/04 Strategy Library/271 Price and Earning Momentum/02 Method.html b/04 Strategy Library/271 Price and Earning Momentum/02 Method.html
new file mode 100644
index 0000000..755688e
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/02 Method.html	
@@ -0,0 +1,166 @@
+
                        Step 1: Select the coarse universe
                         
+
                        
+  We will use both a coarse selection filter and in a later step, a fine universe filter, to narrow down our universe of assets. Our coarse universe filter creates a set of stocks based on volume, price, and whether fundamental data on the stock exists. In this step we filter for the top 100 liquid equities with prices greater than $5. We also exclude the equities missing fundamental data because EPS is needed in the fine selection step.
+
                        
+
+
                        
+
                        +def CoarseSelection(self, coarse):
+  '''
+  Pick the top 100 liquid equities as the coarse-selected universe
+  '''
+  # Before next rebalance time, just remain the current universe
+  if self.Time < self.nextRebalance:
+      return Universe.Unchanged
+
+  # Sort the equities (prices > 5) by Dollar Volume descendingly
+  selectedByDollarVolume = sorted([x for x in coarse if x.Price > 5 and x.HasFundamentalData], 
+                                  key = lambda x: x.DollarVolume, reverse = True)
+
+  # Pick the top 100 liquid equities as the coarse-selected universe
+  return [x.Symbol for x in selectedByDollarVolume[:self.numOfCoarse]]  
+
                        
+
                        
+
+
                        Step 2: Calculate quarterly return and earnings growth
                        
+
                        
+  N.Jegadeesh et al. state price momentum and earnings momentum can be used as two indicators for trading. We will calculate for price momentum with the "GetQuarterlyReturn" method and for earnings momentum with the "GetEarningGrowth" method.
+
                        
+
                        
+  "GetQuarterlyReturn" calculates price momentum for each symbol in our coarse universe and ranks each stock based on its quarterly return. First we request last quarter’s close price for all stocks. Then we calculate quarterly return by taking the delta of the first day’s close price and the last day’s close price. Finally, we store the symbols and their corresponding rankings by quarterly return in a dictionary in preparation for fine selection.
+
                        
+
+
                        
+  
                        +    def GetQuarterlyReturn(self, history):
+        '''
+        Get the rank of securities based on their quarterly return from historical close prices
+        Return: dictionary
+        '''
+        # Get quarterly returns for all symbols
+        # (The first row divided by the last row)
+        returns = history.iloc[0] / history.iloc[-1]
+
+        # Transform them to dictionary structure
+        returns = returns.to_dict()
+
+        # Get the rank of the returns (key: symbol; value: rank)
+        # (The symbol with the 1st quarterly return ranks the 1st, etc.)
+        ranked = sorted(returns, key = returns.get, reverse = True)
+        return {symbol: rank for rank, symbol in enumerate(ranked, 1)}
+  
                        
+
                        
+
                        
+  "GetEarningGrowth" calculates earnings momentum for each symbol in our coarse universe and ranks each stock based on its earnings growth. First we use a RollingWindow to store and update the BasicEPS to reflect quarterly earnings reports. A RollingWindow holds a set of the most recent entries of data. As we move from time t=0 forward, our rolling window will shuffle data further along to a different index until it leaves the window completely. The object in the window with index[0] refers to the most recent item. The length-1 in the window is the oldest object.
+
                        
+
+
                        
+  Our rolling window has a length of 2 so index[0] is the current EPS and index[1] is last quarter's EPS. We calculate earnings growth for each stock by taking the delta of this quarter’s EPS and last quarter’s EPS divided by last quarter’s EPS. Finally we rank each asset based on earnings growth.
+
                        
+
                        
+  
                        +    def GetEarningGrowth(self, fine):
+        '''
+        Get the rank of securities based on their EPS growth
+        Return: dictionary
+        '''
+
+        # Earning Growth by symbol
+        egBySymbol = {}
+        for stock in fine:
+
+            # Select the securities with EPS (> 0)
+            if stock.EarningReports.BasicEPS.ThreeMonths == 0:
+                continue
+
+            # Add the symbol in the dict if not exist
+            if not stock.Symbol in self.epsBySymbol:
+                self.epsBySymbol[stock.Symbol] = RollingWindow[float](2)
+
+            # Update the rolling window for each stock
+            self.epsBySymbol[stock.Symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths)
+
+            # If the rolling window is ready
+            if self.epsBySymbol[stock.Symbol].IsReady:
+                rw = self.epsBySymbol[stock.Symbol]
+                # Caculate the Earning Growth
+                egBySymbol[stock.Symbol] = (rw[0] - rw[1]) / rw[1]
+
+        # Get the rank of the Earning Growth
+        ranked = sorted(egBySymbol, key = egBySymbol.get, reverse = True)
+        return {symbol: rank for rank, symbol in enumerate(ranked, 1)}
+  
                        
+
                        
+
+
                        Step 3: Select the fine universe
                          
+
                        
+  We use a fine selection filter in addition to a coarse selection filter to refine our asset selection based on corporate fundamental data. We can use both quarterly return and earnings growth from our two indicators to generate an average rank for each stock. Then we can go long on the top 10 and short the bottom 10.
+
                        
+
+
                        
+  
                        +    def FineSelection(self, fine):
+        '''
+        Select securities based on their quarterly return and their earnings growth 
+        '''
+        symbols = [x.Symbol for x in fine]
+
+        # Get the quarterly returns for each symbol
+        history = self.History(symbols, self.rebalanceDays, Resolution.Daily)
+        history = history.drop_duplicates().close.unstack(level = 0)
+        rankByQuarterReturn = self.GetQuarterlyReturn(history)
+
+        # Get the earning growth for each symbol
+        rankByEarningGrowth = self.GetEarningGrowth(fine) 
+
+        # Get the sum of rank for each symbol and pick the top ones to long and the bottom ones to short
+        rankSumBySymbol = {key: rankByQuarterReturn.get(key, 0) + rankByEarningGrowth.get(key, 0) 
+                                for key in set(rankByQuarterReturn) | set(rankByEarningGrowth)}
+
+        # Get 10 symbols to long and short respectively
+        sortedDict = sorted(rankSumBySymbol.items(), key = lambda x: x[1], reverse = True)
+        self.longSymbols = [x[0] for x in sortedDict[:10]]
+        self.shortSymbols = [x[0] for x in sortedDict[-10:]]
+
+        return [x for x in symbols if str(x) in self.longSymbols + self.shortSymbols]    
+  
                        
+
                        
+
+
                        Step 4: Rebalance quarterly
                          
+
                        
+  We choose to rebalance every quarter and use equal weights for the long and short positions of securities in our portfolio.
+
                        
+
+
                        
+  
                        +    def OnData(self, data):
+        '''
+        Rebalance quarterly
+        '''
+        # Do nothing until next rebalance
+        if self.Time < self.nextRebalance:
+            return
+
+        # Liquidate the holdings if necessary
+        for holding in self.Portfolio.Values:
+            symbol = holding.Symbol
+            if holding.Invested and symbol.Value not in self.longSymbols + self.shortSymbols:
+                self.Liquidate(symbol, "Not Selected")
+
+        # Open positions for the symbols with equal weights
+        count = len(self.longSymbols + self.shortSymbols)
+        if count == 0:
+            return
+
+        # Enter long positions
+        for symbol in self.longSymbols:
+            self.SetHoldings(symbol, 1 / count)
+
+        # Enter short positions
+        for symbol in self.shortSymbols:
+            self.SetHoldings(symbol, -1 / count)
+
+        # Set next rebalance time
+        self.nextRebalance += timedelta(self.rebalanceDays)   
+  
                        
+
                        
\ No newline at end of file
diff --git a/04 Strategy Library/271 Price and Earning Momentum/03 Results.html b/04 Strategy Library/271 Price and Earning Momentum/03 Results.html
new file mode 100644
index 0000000..e0cab48
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/03 Results.html	
@@ -0,0 +1,9 @@
+
                        
+    Our backtest results in a Sharpe ratio of 0.59 while the SP500 Sharpe ratio is 0.8 during the same decade. This performance may be due to several factors:
+    
                          
+        
                        1. The number of stocks in our portfolio, 100, could be too low.
                          2. 
+        
                        2. Equal weighting for all stocks may not fully capture the strength of higher ranking stocks.
                          3. 
+        
                        3. Rebalancing quarterly may be too frequent for a momentum strategy in equities.
                          4. 
+    
                        
+    This tutorial shows us how to take advantage of the techniques of requesting historical data and using a RollingWindow. We hope the community can further develop strategies based on these techniques.
+
                        
\ No newline at end of file
diff --git a/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html b/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html
new file mode 100644
index 0000000..65639b2
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
                        
+    
                        
+    
                        
+        
+    
                        
+
                        
diff --git a/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html b/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html
new file mode 100644
index 0000000..8ce9247
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html	
@@ -0,0 +1,5 @@
+

From dd315de127685c40d99555d8c59f05e3c746c445 Mon Sep 17 00:00:00 2001
From: Xin Wei 
Date: Fri, 20 Sep 2019 17:12:18 -0700
Subject: [PATCH 086/215] Fixed bug in copula pairs trading

03 - Part I - Copula Method
06 - Algorithm
05 - Summary (need to be changed after cointegration code is updated)
---
 .../03 Part I - Copula Method.html            | 275 +++++++++++-------
 .../06 Algorithm.html                         |   2 +-
 2 files changed, 166 insertions(+), 111 deletions(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
index 9c26993..a16ea55 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
@@ -3,7 +3,7 @@
 
                        
 
 
                        
-  We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
+  We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of more than 9 years from January 2010 to September 2019 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
 
                        
 
 
                        Step 1: Selecting the Paired Stocks
                        
@@ -59,28 +59,42 @@ 
                        2) Filter the trading pair with statistical correlation
                        
 
-
                        def _pair_selection(self):
-
-    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
-                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
-    logreturn={}
-    for i in range(2):
-        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
-        history = self.History(syl, self.lookbackdays,Resolution.Daily)
-        # generate the log return series of paired stocks
-        close = history['close'].unstack(level=0)
-        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-        for j in tick_syl[i]:
-            logreturn[j] = df_logreturn[j]
-    # estimate coefficients of different correlation measures
-    tau_coef,pr_coef,sr_coef= [],[],[]
-    for i in range(len(tick_syl[i])):
-        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
-        tau_coef.append(kendalltau(tik_x, tik_y)[0])
-        pr_coef.append(pearsonr(tik_x, tik_y)[0])
-        sr_coef.append(spearmanr(tik_x, tik_y)[0])
-    index_max = tau_coef.index(max(tau_coef))
-    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+
                        def PairSelection(self, date):
+	'''Selects the pair of stocks with the maximum Kendall tau value.
+	It's called on first day of each month'''
+	
+	if date.month == self.month:
+		return Universe.Unchanged
+	
+	symbols = [ Symbol.Create(x, SecurityType.Equity, Market.USA) 
+				for x in [  
+							"QQQ", "XLK",
+							"XME", "EWG", 
+							"TNA", "TLT",
+							"FAS", "FAZ",
+							"XLF", "XLU",
+							"EWC", "EWA",
+							"QLD", "QID"
+						] ]
+
+	logreturns = self._get_historical_returns(symbols, self.lookbackdays)
+	
+	tau = 0
+	for i in range(0, len(symbols), 2):
+		
+		x = logreturns[str(symbols[i])]
+		y = logreturns[str(symbols[i+1])]
+		
+		# Estimate Kendall rank correlation for each pair
+		tau_ = kendalltau(x, y)[0]
+		
+		if tau > tau_:
+			continue
+
+		tau = tau_
+		self.pair = symbols[i:i+2]
+	
+	return [x.Value for x in self.pair]
 
                        
 
                
 
@@ -126,17 +140,29 @@ 
                Step 3: Estimating Copula Parameters
                
 
                
 
 
                def _parameter(self, family, tau):
-    if  family == 'clayton':
-        return 2*tau/(1-tau)
-    elif family == 'frank':
-        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
-    	# frank_fun is the squared difference
-    	# Minimize the frank_fun would give the parameter theta for the frank copula
-      integrand = lambda t: t/(np.exp(t)-1)
-    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
-      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
-    elif family == 'gumbel':
-        return 1/(1-tau)
                
+	''' Estimate the parameters for three kinds of Archimedean copulas
+	according to association between Archimedean copulas and the Kendall rank correlation measure
+	'''
+	
+	if  family == 'clayton':
+		return 2 * tau / (1 - tau)
+	
+	elif family == 'frank':
+		
+		'''
+		debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
+		frank_fun is the squared difference
+		Minimize the frank_fun would give the parameter theta for the frank copula 
+		''' 
+		
+		integrand = lambda t: t / (np.exp(t) - 1)  # generate the integrand
+		frank_fun = lambda theta: ((tau - 1) / 4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0] / theta - 1) / theta) ** 2
+		
+		return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x 
+	
+	elif family == 'gumbel':
+		return 1 / (1 - tau)
+

      Step 4: Selecting the Best Fitting Copula

      @@ -183,18 +209,23 @@

      Step 4: Selecting the Best Fitting Copula

      def _lpdf_copula(self, family, theta, u, v):
-    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
-    if  family == 'clayton':
-        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
-    elif family == 'frank':
-        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
-        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
-        pdf = num/denom
-    elif family == 'gumbel':
-        A = (-np.log(u))**theta + (-np.log(v))**theta
-        c = np.exp(-A**(1/theta))
-        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
-    return np.log(pdf)
+	'''Estimate the log probability density function of three kinds of Archimedean copulas
+	'''
+	
+	if  family == 'clayton':
+		pdf = (theta + 1) * ((u ** (-theta) + v ** (-theta) - 1) ** (-2 - 1 / theta)) * (u ** (-theta - 1) * v ** (-theta - 1))
+		
+	elif family == 'frank':
+		num = -theta * (np.exp(-theta) - 1) * (np.exp(-theta * (u + v)))
+		denom = ((np.exp(-theta * u) - 1) * (np.exp(-theta * v) - 1) + (np.exp(-theta) - 1)) ** 2
+		pdf = num / denom
+		
+	elif family == 'gumbel':
+		A = (-np.log(u)) ** theta + (-np.log(v)) ** theta
+		c = np.exp(-A ** (1 / theta))
+		pdf = c * (u * v) ** (-1) * (A ** (-2 + 2 / theta)) * ((np.log(u) * np.log(v)) ** (theta - 1)) * (1 + (theta - 1) * A ** (-1 / theta))
+		
+	return np.log(pdf)
      @@ -202,22 +233,6 @@

      Step 4: Selecting the Best Fitting Copula

      The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" & "XLK".

      -
      - -
      self.family = ['clayton', 'frank', 'gumbel']
-tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
-AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
-for i in self.family:
-    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
-    # Replace nan with zero and inf with finite numbers in lpdf list
-    lpdf = np.nan_to_num(lpdf)
-    loglikelihood = sum(lpdf)
-    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
-    # choose the copula with the minimum AIC
-    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
-
      -
      -

      Step 5: Generating the Trading Signals

      @@ -271,20 +286,57 @@

      Step 5: Generating the Trading Signals

      -
      def _set_signal(self):
-    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
-    # generate the log return series of paired stocks
-    close = history['close'].unstack(level=0)
-    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
-    # estimate Kendall'rank correlation each trading day
-    tau = kendalltau(x, y)[0]
-    # etstimate the copula parameter: theta
-    self.theta = self._parameter(self.copula, tau)
-    # simulate the empirical distribution function for returns of two paired stocks
-    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
-    # run linear regression over the two history return series
-    self.coef = stats.linregress(x,y).slope
+
      def SetSignal(self, slice):
+	'''Computes the mispricing indices to generate the trading signals.
+	It's called on first day of each month'''
+
+	if self.Time.month == self.month:
+		return
+	
+	## Compute the best copula
+	
+	# Pull historical log returns used to determine copula
+	logreturns = self._get_historical_returns(self.pair, self.numdays)
+	x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]
+
+	# Convert the two returns series to two uniform values u and v using the empirical distribution functions
+	ecdf_x, ecdf_y  = ECDF(x), ECDF(y)
+	u, v = [ecdf_x(a) for a in x], [ecdf_y(a) for a in y]
+	
+	# Compute the Akaike Information Criterion (AIC) for different copulas and choose copula with minimum AIC
+	tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
+	AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
+	
+	for i in ['clayton', 'frank', 'gumbel']:
+		param = self._parameter(i, tau)
+		lpdf = [self._lpdf_copula(i, param, x, y) for (x, y) in zip(u, v)]
+		# Replace nan with zero and inf with finite numbers in lpdf list
+		lpdf = np.nan_to_num(lpdf) 
+		loglikelihood = sum(lpdf)
+		AIC[i] = [param, -2 * loglikelihood + 2]
+		
+	# Choose the copula with the minimum AIC
+	self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+	
+	## Compute the signals
+	
+	# Generate the log return series of the selected trading pair
+	logreturns = logreturns.tail(self.lookbackdays)
+	x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]
+	
+	# Estimate Kendall'rank correlation
+	tau = kendalltau(x, y)[0] 
+	
+	# Estimate the copula parameter: theta
+	self.theta = self._parameter(self.copula, tau)
+	
+	# Simulate the empirical distribution function for returns of selected trading pair
+	self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y) 
+	
+	# Run linear regression over the two history return series and return the desired trading size ratio
+	self.coef = stats.linregress(x,y).slope
+	
+	self.month = self.Time.month
      @@ -294,39 +346,42 @@

      Step 5: Generating the Trading Signals

      -
      def OnData(self,data):
-    for i in self.syl:
-        self.price_list[i].append(self.Portfolio[i].Price)
-    # compute today's log return of 2 stocks
-    if len(self.price_list[self.syl[0]]) < 2 or len(self.price_list[self.syl[1]]) < 2: return
-    else:
-        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
-        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
-    # Convert the two returns to uniform values u and v using the empirical distribution functions
-    u_value = self.ecdf_x(return_x)
-    v_value = self.ecdf_y(return_y)
-    # Compute the mispricing indices for u and v by using estimated copula
-    self._misprice_index(self.copula, self.theta, u_value, v_value)
-    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-    if self.MI_u_v < self.floor_CL and self.MI_v_u > self.cap_CL:
-        if self.Portfolio[self.syl[0]].Quantity < 0 and self.Portfolio[self.syl[1]].Quantity > 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-        else:
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-    elif self.MI_u_v > self.cap_CL and self.MI_v_u < self.floor_CL:
-        if self.Portfolio[self.syl[0]].Quantity > 0 and self.Portfolio[self.syl[1]].Quantity < 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
-        else:
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
+
      def OnData(self, slice):
+	'''Main event handler. Implement trading logic.'''
+
+	self.SetSignal(slice)     # only executed at first day of each month
+
+	# Daily rebalance
+	if self.Time.day == self.day:
+		return
+	
+	long, short = self.pair[0], self.pair[1]
+
+	# Update current price to trading pair's historical price series
+	for kvp in self.Securities:
+		symbol = kvp.Key
+		if symbol in self.pair:
+			price = kvp.Value.Price
+			self.window[symbol].append(price)
+
+	if len(self.window[long]) < 2 or len(self.window[short]) < 2:
+		return
+	
+	# Compute the mispricing indices for u and v by using estimated copula
+	MI_u_v, MI_v_u = self._misprice_index()
+
+	# Placing orders: if long is relatively underpriced, buy the pair
+	if MI_u_v < self.floor_CL and MI_v_u > self.cap_CL:
+		
+		self.SetHoldings(short, -self.weight_v, False, f'Coef: {self.coef}')
+		self.SetHoldings(long, self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)
+
+	# Placing orders: if short is relatively underpriced, sell the pair
+	elif MI_u_v > self.cap_CL and MI_v_u < self.floor_CL:
+
+		self.SetHoldings(short, self.weight_v, False, f'Coef: {self.coef}')
+		self.SetHoldings(long, -self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)
+	
+	self.day = self.Time.day
      diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html index 038e110..21099b5 100755 --- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html +++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html @@ -4,7 +4,7 @@
      - +
      From d8340fa8d6308be805b02b7715c8f1eb584dbb33 Mon Sep 17 00:00:00 2001 From: wyiyun95 Date: Wed, 25 Sep 2019 17:27:12 -0700 Subject: [PATCH 087/215] Mandarin Translation (Strategy Library) 03, 100 full 07 partial section 01-03 --- .../01 \346\221\230\350\246\201.cn.html" | 7 + ...7\263\273\346\241\206\346\236\266.cn.html" | 71 ++++ ...0\201\224\347\263\273\346\263\225.cn.html" | 332 ++++++++++++++++++ ...5\215\217\346\225\264\346\263\225.cn.html" | 47 +++ .../05 \346\200\273\347\273\223.cn.html" | 35 ++ .../06 \347\256\227\346\263\225.cn.html" | 19 + ...0\200\203\346\226\207\347\214\256.cn.html" | 32 ++ .../01 \346\221\230\350\246\201.cn.html" | 11 + .../02 \347\256\200\344\273\213.cn.html" | 115 ++++++ .../03 \346\226\271\346\263\225.cn.html" | 252 +++++++++++++ .../01 \347\256\200\344\273\213.cn.html" | 3 + .../02 \346\226\271\346\263\225.cn.html" | 117 ++++++ .../03 \347\256\227\346\263\225.cn.html" | 6 + .../04 \346\235\245\346\272\220.cn.html" | 5 + 14 files changed, 1052 insertions(+) create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html" diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html" new file mode 100644 index 0000000..e06b3ac --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html" @@ -0,0 +1,7 @@ +

      + 在本研究中，我们研究了两种配对交易方法，并对结果进行了比较。配对交易涉及到对两种高度相关资产之间依附结构的研究。在假定会出现平均值回复的情况下，当出现价格差异时，多头或空头头寸将反向入市。一般情况下，资产价格分布是采用收益率序列高斯分布来建模的，但联合正态分布可能无法捕捉到股票对价格依赖性的一些关键特征（如尾部相关性）。我们的研究使用了联系理论来识别这些交易机会。 +

      + +

      + 我们将从数学的角度讨论联系的基本框架，并解释如何在配对交易中应用这一方法。算法的实现基于Stander Y, Marais D, BothaI(2013)的论文带有联系函数的交易策略。根据Hanson T A，Hall J R.(2012)的论文统计套利交易策略和高频交易，我们将联系配对交易策略的性能与协整配对交易方法进行了比较。协整法假设配对股票之间存在协整关系，可以识别有利可图的交易机会。实证结果表明，与传统的配对交易策略相比，基于联系的策略更容易获益。 +

      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html" new file mode 100644 index 0000000..5fdb07a --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html" @@ -0,0 +1,71 @@ +

      1.定义

      +

      + 给定一个随机向量\(X_1,X_2,...,X_p\)，其边际累积分布函数(CDF)为\(F_i(x) = P[X_i \leq x]\)。通过对每个分量进行概率积分变换，\((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\)的边际分布是一致的(from Wikipedia)。然后将\(X_1,X_2,...,X_p\)的联系定义为\(U_1,U_2,...,U_p\)的联合累积分布函数，其中每个变量U的边际分布均为\(U(0,1)\)。 +

      + +\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\] + +

      + 联系函数包含了所有边际分布的依赖性特征，能够更好地利用概率描述变量之间的线性和非线性关系。它们使边际分布能够彼此独立地建模，并且不需要对边际分布的联合行为进行假设。 +

      + +

      2.二元联系

      +

      + 由于本研究的重点是二元联系 (对于配对交易，我们有2个随机变量)，因此指定了一些概率性质。 + 设X和Y为两个随机变量，累积概率函数为\(F_1(X)\)和\(F_2(Y)\)。\(U=F_1(X), V=F_2(Y)\)是均匀分布的。则联系函数为\(C(u,v)=P(U\leq u,V\leq v)\)。取U和V联系函数的偏导数，所得到条件分布函数如下： +

      + +\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\] +\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\] + +

      3.阿基米德联系

      +

      + 除了高斯假设之外，还有许多联系函数可以用来描述变量之间的依附结构。这里我们将重点讨论其中的三种，它们分别是来自阿基米德分类中的Clayton，Gumbel和Frank。 + 阿基米德联系是基于单变量分布函数的Laplace变换参数φ。它们是由特定的生成器函数\(\phi\)创建的。 +

      + +\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\] + +

      + 概率密度函数为： +

      + +\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\] + +

      + 其中，\(\phi_{(2)}^{-1}\)是生成器函数二次导数的倒数。 +

      + + + + + + + + + + + + + + + + + + + + + + +
      联系联系函数C(u,v;θ)
      Clayton联系公式\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]
      Gumbel联系公式\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]
      Frank联系公式\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]
      + + +

      + Genest和MacKay证明了在双变量情况下联系生成器函数与Kendall秩相关tau之间的关系为： +

      + +\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\] + +

      + 因此，只要知道Kendall的tau秩测量值和生成器函数，就可以很容易地估算出阿基米德联系函数中的参数。请参照步骤3查看公式。 +

      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html" new file mode 100644 index 0000000..920e147 --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html" @@ -0,0 +1,332 @@ +

      + 交易所交易基金（ETF）有许多不同的股票板块和资产类别，为我们提供了各种不同的配对交易选择。我们的数据集包括在纳斯达克或纽约证券交易所进行基金买卖交易的每日数据。 +

      + +

      + 我们使用前3年的数据来选择联系和资产配对的最佳组合（“训练形成期”）。接下来，我们使用2011年至2017年的5年时间（“交易期”）来执行策略。在交易期间，我们使用12个月的滚动窗口数据来获取联系参数（“滚动形成期”）。 +

      + +

      步骤1：选择配对股票

      +

      + 配对选择的一般方法基于基础分析和统计分析。 +

      + +

      1)收集可能相关的配对

      +

      + 任何随机对都可以相互关联。这些变量之间可能并没有因果关系，而是由于巧合或存在某种第三种不可见因素而产生了虚假关系。因此，对于我们来说重要的是从一些有共同点的证券开始。为了说明这一点，我们选择了一些在纳斯达克或纽交所交易中流动性最强的基金。这些潜在相关配对之间的关系可能是由于指数、行业或资产类别重叠所造成的。例如，QQQ和XLK就是市场领先指数的两支基金。 +

      + +

      2)采用统计相关性来筛选交易配对

      + +

      + 为了确定分析中所包含的股票配对，将要对预先选择基金对之间的相关性进行分析。以下是我们在统计中常用的三种相关指标： +

      + + + + + + + + + + + + + + + + + + + + + + + + +
      相关测量技术
      Pearson相关系数\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]
      Kendall秩相关系数\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]
      Spearman秩相关系数\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]
       \(n\) = 每个数据集中的值数 +\(n_c\) = 一致数 +\(n_d\) = 不一致数 +\(d_i\) = \(x_i\)和\(y_i\)对应值秩之间的差异。
      + + +

      + 我们可以在Python中使用SciPy统计值程序库中的函数来获得这些系数。利用训练形成期间的每日股价收益率来计算相关系数。我们可以发现这三种相关性技术对配对基金给出了相同的相关系数排名。Pearson相关性假设两个变量都是正态分布的。因此，本文采用Kendall秩作为相关性测量法，并选择具有最高Kendall秩相关性的配对来进行配对交易。通过使用历史函数并将价格转换为对数收益序列，我们可以得出基金对的每日历史收盘价。设Px和Py为股票x和股票y的历史股价序列，则基金配对的对数收益为： +

      + +\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n，其中n是价格数据的数量 + +
      + +
      def _pair_selection(self):
+
+    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
+                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
+    logreturn={}
+    for i in range(2):
+        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
+        history = self.History(syl, self.lookbackdays,Resolution.Daily)
+        # 生成配对股票的对数收益序列
+        close = history['close'].unstack(level=0)
+        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+        for j in tick_syl[i]:
+            logreturn[j] = df_logreturn[j]
+    # 不同相关性测量法的估计系数
+    tau_coef,pr_coef,sr_coef= [],[],[]
+    for i in range(len(tick_syl[i])):
+        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
+        tau_coef.append(kendalltau(tik_x, tik_y)[0])
+        pr_coef.append(pearsonr(tik_x, tik_y)[0])
+        sr_coef.append(spearmanr(tik_x, tik_y)[0])
+    index_max = tau_coef.index(max(tau_coef))
+    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+
      +
      + +

      步骤2：估算对数收益的边缘分布

      +

      + 为了构造联系，我们需要将对数收益序列Rx和Ry转化为两个均匀分布的值u和v。这可以通过估计Rx和Ry的边际分布函数，并将回归值代入一个分布函数来实现。由于我们对两个对数收益序列的分布没有做任何假设，所以在这里我们会使用经验分布函数来接近边际分布F1(Rx)F1(Rx)和F2(Ry)F2(Ry)。统计模型库中的Python 经验累积分布（ECDF）函数将经验性的累积分布函数（CDF）作为阶梯函数提供给我们。 +

      +

      步骤3：估算联系参数

      +

      + 正如上文所讨论的，对于每个阿基米德联系来说，我们通过联系与依附测度Kendall tau之间的关系来估算联系参数。 +

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      联系Kendall's tau参数θ
      Clayton联系\[\frac{\theta}{\theta +2}\]\[\theta=2\tau(1-\tau)^{-1}\]
      Gumbel联系\[1-\theta^{-1}\]\[\theta=(1-\tau)^{-1}\]
      Frank联系\[1+4[D_1(\theta)-1]/\theta\]\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]
      \[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]
      + +
      + +
      def _parameter(self, family, tau):
+    if  family == 'clayton':
+        return 2*tau/(1-tau)
+    elif family == 'frank':
+        # debye = quad(被积函数，sys.float_info.epsilon, theta)[0]/theta是一阶Debye函数
+    	# frank_fun是平方差
+    	# 将frank_fun最小化可以得出直接联系的参数theta
+      integrand = lambda t: t/(np.exp(t)-1)
+    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
+      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
+    elif family == 'gumbel':
+        return 1/(1-tau)
      +
      + +

      步骤4：选择最适合的联系

      +

      + 得到联系函数的参数估计值后，我们使用AIC准则来选择最适合算法初始化的联系。 +

      + +\[AIC=-2L(\theta)+2k\] + +

      + 其中\(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\)是对数似然函数，k是参数的数量，在这里k=1。 +

      + +

      + 各联系函数的密度函数如下： +

      + + + + + + + + + + + + + + + + + + + + + + + + + +
      联系密度函数c(u,v;θ)
      Clayton联系\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]
      Gumbel联系\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]
      Frank联系\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]
      \[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]
      + +
      + +
      def _lpdf_copula(self, family, theta, u, v):
+    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
+    if  family == 'clayton':
+        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
+    elif family == 'frank':
+        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
+        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
+        pdf = num/denom
+    elif family == 'gumbel':
+        A = (-np.log(u))**theta + (-np.log(v))**theta
+        c = np.exp(-A**(1/theta))
+        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
+    return np.log(pdf)
+
      +
      + +

      + 所提供最适合的联系应对应于AIC准则的最低值。所选择的配对是“QQQ”和“XLK”。 +

      + +
      + +
      self.family = ['clayton', 'frank', 'gumbel']
+tau = kendalltau(x, y)[0]  # 估算Kendall秩相关
+AIC ={}  # 生成一个关键字为联系系列的字典，值 = [theta, AIC]
+for i in self.family:
+    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
+    # 在lpdf列表中用零替换非数字，用有限数目替换无穷大
+    lpdf = np.nan_to_num(lpdf)
+    loglikelihood = sum(lpdf)
+    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
+    # 选择AIC最小的联系
+    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+
      +
      + + +

      步骤5：生成交易信号

      +

      + 联系函数中包含了两个收益序列依附结构的所有信息。根据Stander Y、Marais D和Botha I，在有联系的交易策略中，使用适合联系推导出\(C(v\mid u)\)和\(C(u\mid v)\)条件边际分布函数的置信区间，即错误定价指数。当市场观察值处于置信区间之外时，表明配对交易机会是可用的。在这里，我们选取95%作为置信区间上边际，5%作为置信区间下边际。置信水平的选择是基于本文的反测试分析，该分析表明，使用95%的置信水平应该可以确定适当的交易机会。 +

      + +

      + 将股票X、股票Y的当前收益设为\(R_x, R_y\)，我们可以将“错误定价指数”定义为： +

      + +\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\] + +\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\] + +

      + 关于进一步的数学证明，请参考Xie W和Wu Y的《以联系为基础的配对交易策略》。通过表1中联系函数的偏导数，可以推导出二元联系的条件概率公式。结果如下： +

      +

      +Gumbel联系 +

      +\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\] + +\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\] + +

      + Clayton联系 +

      + +\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\] + +\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\] + +

      + Frank联系 +

      + +\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \] + +\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \] + +

      + 在选择交易配对和最合适的联系后，我们采取下列步骤进行交易。请注意，我们在每个月的第一天使用过去12个月的每日数据来实施步骤1、2、3、4，这意味着我们的经验分布函数和联系参数theta估值会每月更新一次。在每月总结中： +

      + +
        +
      • 在12个月的滚动形成期内，使用日收盘价计算这对基金的每日对数收益，然后计算Kendall秩的相关性。
        • +
      • 估算X和Y对数收益的边际分布函数，分别为ecdf_x和ecdf_y。
        • +
      • 将Kendall tau代入联系参数估计函数，得到theta的值。
        • +
      • 对两个价格系列进行线性回归。这个系数用来决定股票X和股票Y的买卖数量。例如，如果系数是2，每买入或卖出一股X，就买入或卖出2股Y。
        • +
      + +
      + +
      def _set_signal(self):
+    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
+    # 生成配对股票的对数收益序列
+    close = history['close'].unstack(level=0)
+    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
+    # 估算每个交易日的Kendall秩相关
+    tau = kendalltau(x, y)[0]
+    # 估算联系参数：theta
+    self.theta = self._parameter(self.copula, tau)
+    # 模拟两对股票收益的经验分布函数
+    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
+    # 对两个历史收益序列进行线性回归
+    self.coef = stats.linregress(x,y).slope
+
      +
      + +

      + 最后，在交易期间，我们每天使用经验分布函数ecdf_x和ecdf_y将当天的收益转换为u和v。然后，利用估算的联系C，在每个交易日计算两个错误定价指标。\(MI_{Y|X}<0.05\)和\(MI_{X|Y}>0.95\)的日子，算法构建了X的空头头寸和Y的多头头寸。\(MI_{Y|X}>0.95\)且\(MI_{X|Y}<0.05\)时，构建Y的空头头寸和X的多头头寸。 +

      + +
      + +
      def OnData(self,data):
+    for i in self.syl:
+        self.price_list[i].append(self.Portfolio[i].Price)
+    # 计算两支股票的当天对数收益
+    if len(self.price_list[self.syl[0]]) < 2 or len(self.price_list[self.syl[1]]) < 2: return
+    else:
+        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
+        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
+    # 使用经验分布函数将两种收益转换为统一值u和v
+    u_value = self.ecdf_x(return_x)
+    v_value = self.ecdf_y(return_y)
+    # 用估算联系来计算u和v的错误定价指数
+    self._misprice_index(self.copula, self.theta, u_value, v_value)
+    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+    if self.MI_u_v < self.floor_CL and self.MI_v_u > self.cap_CL:
+        if self.Portfolio[self.syl[0]].Quantity < 0 and self.Portfolio[self.syl[1]].Quantity > 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+        else:
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+    elif self.MI_u_v > self.cap_CL and self.MI_v_u < self.floor_CL:
+        if self.Portfolio[self.syl[0]].Quantity > 0 and self.Portfolio[self.syl[1]].Quantity < 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+        else:
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+
      +
      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html" new file mode 100644 index 0000000..93cb5d9 --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html" @@ -0,0 +1,47 @@ +

      + 对于协整配对交易法，我们选择了相同的基金对“GLD”和“DGL”。不需要选择联系函数，因此只有12个月的滚动形成周期。交易期为5年，从2011年1月到2017年5月。 +

      + +

      步骤1：生成价差序列

      +

      + 在每个月月初，我们使用每日收盘价生成两支基金的对数价格序列。然后基于对数价格序列数据，采用回归分析法对价差序列进行估算。对于股票X和Y，我们对对数价格序列进行线性回归，得到系数β。 +

      + +\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\] + +

      步骤2：计算阈值

      + +

      + 在本文中，利用滚动形成期价差的标准差，为交易策略设定了两个标准差的阈值。 +

      + +
      + +
      price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
+                     index = [i.Time for i in self.symbols[0].hist_window])
+
+price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
+                     index = [i.Time for i in self.symbols[1].hist_window])
+if len(price_x) < 250: return
+spread = self.regr(np.log(price_x), np.log(price_y))
+mean = np.mean(spread)
+std = np.std(spread)
+ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
+if spread[-1] > mean + self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
+        self.Sell(self.symbols[1], 100)
+        self.Buy(self.symbols[0],  ratio * 100)
+
+elif spread[-1] < mean - self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
+        self.Sell(self.symbols[0], 100)
+        self.Buy(self.symbols[1], ratio * 100)
+else:
+    self.Liquidate()
+
      +
      + +

      步骤3：设置交易信号

      +

      + 在每个交易日，只要价差偏离平均值超过两个标准差，我们就进入交易。换句话说，我们在价差平均值+2*std的当天构建X的空多头寸和Y的多头头寸。当价差<平均值-2*std时，我们在当天构建Y的空多头寸和X的多头头寸。如果价差恢复到均衡水平(定义为偏离零价差小于标准差的一半)，就会退出交易。平均值和标准偏差的值从滚动形成周期开始计算，每月更新一次。 +

      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html" new file mode 100644 index 0000000..27fcc84 --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html" @@ -0,0 +1,35 @@ +

      + 配对交易的最终目的是通过平均值回归来捕捉两种相关资产的价格差异。我们的结果表明，配对交易的联系方法要优于传统的协整方法，因为联系法基于依附结构的概率，而协整法则依赖于正常定价的简单线性回归方差。通过对联系法性能的测试，发现联系法对启动参数的敏感性较低。由于协整方法依赖于标准分布，基金对的波动性较小，交易机会也较少。 +

      + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      方法交易利润夏普指数动用资金
      联系法346274.293%1.02219.4%
      协整法9126.358%0.29823.7%
      + +

      + 一般情况下，基金的波动性并不大，因此均值回归并不会提供太多交易机会。在5年的时间里，只有39笔交易采用了协整法。根据Liew R Q和Wu Y的《配对交易联系法》，我们发现在配对交易中使用联系法可以提供更多的交易机会，因为它不需要任何严格的假设。 +

      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..932854a --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,19 @@ +

      + 联系法的回溯测试 +

      +
      +
      +
      + +
      +
      + +

      + 协整法的回溯测试 +

      +
      +
      +
      + +
      +
      diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" new file mode 100644 index 0000000..1d7ae89 --- /dev/null +++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" @@ -0,0 +1,32 @@ +
        +
      1. + Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. Online Copy +
        2. +
      2. + Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012. Online Copy +
        3. +
      3. + Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  Online Copy +
        4. +
      4. + Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.online copy +
        5. +
      5. + Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  Online Copy +
        6. +
      6. + LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016. +
        7. +
      7. + Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283 +
        8. +
      8. + Jean Folger, Pairs Trading Example Online Copy +
        9. +
      9. + Xie W, Wu Y. Copula-based pairs trading strategy[C] Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10. +
        10. +
      10. + Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives & Hedge Funds, 2013, 19(1): 12-30. +
        11. +
      diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html" new file mode 100644 index 0000000..9aa9136 --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html" @@ -0,0 +1,11 @@ +

      + 在本教程中，我们使用两阶段关联法和协整法，基于市场中性统计套利策略实施了高频动态配对交易策略。这种策略基于George J. Miao的研究。我们将这一交易策略应用于美国银行板块股票，使用2012年至2013年的10分钟股票数据对这一策略进行了回溯测试。我们交易策略所产生的复合年回报率高达29.4%，夏普指数为0.968。 +

      + +

      + 当市场表现不佳时，这种策略尤其有利可图。利润来自于错误定价，当市场下跌或波动性增加时，很可能会发生错误定价。 +

      + +

      + 为了进一步探讨这种策略，我们将策略设计得更为灵活。我们可以通过简单地更改参数，将数据分辨率更改为5分钟、10分钟甚至30分钟。选择最优的进入、结束和止损阈值也是非常重要的。每个人都可以有自己的策略。 +

      diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..f0b6f74 --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,115 @@ +

      + 高频交易（HFT）是一种定量交易，其特点是持有期短，利用复杂的计算机方法迅速交易证券。它的目标是在每笔短期交易中都获得小额利润(Cartea & Penalva, 2012)。 + 统计套利是根据一项或多项资产的预期价值对该资产进行统计错误定价的情况。当证券之间因定价效率低下而出现盈利情况时，交易者可以通过数学模型识别统计套利情况。统计套利在很大程度上取决于市场价格回归历史或预测均值的能力。单一价格法则（LOP）为这一假设奠定了基础。LOP指出，如果两支股票在各种自然状态下都具有相同的收益，则它们必定具有相同的现值(Gatev, Goetzmann, & Rouwenhorst, 2006)。因此，两支股票近似替代资产之间的价差应在一段时间内具有稳定的长期均衡价格。 +

      + +

      数据描述

      +

      + 为了获得更多高度相关的配对，我们选择了特定行业的股票。从经济角度看，我们更喜欢传统行业，因为这些行业的公司更有可能成为近似替代品。。如果我们选择N只股票，可以使用\(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\)来计算配对的数目。在演示的策略中，我们使用了80只股票，所以总共得到了3160对。我们使用分钟数据并将其聚合为较低的分辨率，因此1分钟是该策略的最高分辨率。 +

      + +

      关联法

      +

      + 关联性衡量的是具有价格趋势两只股票之间的关系。由于这两支股票相互关联，因此倾向于一起移动。关联性过滤器是对备选配对进行筛选的第一步。以A股票和B股票为例，股票之间的关联系数是一个统计数值，用于衡量股票A和股票B之间的关联程度。股票A和股票B的相关系数\(\rho\)可以通过下式获得： +

      + +\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}\] + +

      + 其中\(\bar{A}\)和\(\bar{B}\)分别是股票A和股票B的平均价格，N表示交易数据范围。\(\rho\)在[-1,1]范围内。正值的\(\rho\)越多，股票A和股票B之间的关联性就越积极。 +

      + +

      + 然而，随着时间的推移，仅仅基于关联法的配对交易将存在不稳定的缺点。关联系数并不一定意味着两支配对股票价格之间的均值回归。为了克服上述问题，将进一步采用协整法作为配对选择过程的第二步。 +

      + +

      协整法

      +

      + 协整概念是由诺贝尔奖得主Engle和Granger开发的一种创新经济学数学模型。协整法指出，在某些情况下，尽管给定两个非平稳时间序列，但两个时间序列的特定线性组合实际上是平稳的。换句话说，这两个时间序列以步调一致的模式一起移动。 +

      + +

      + 协整的定义如下：假设\(x_t\)和\(y_t\)是两个非平稳的时间序列。如果存在参数\(\gamma\)，则可以得出以下方程： +

      + +\[z_t = y_t - \gamma x_t\] + +

      + 这是一个平稳的过程，然后xt和yt将会进行协整。这一过程是研究多元时间序列中共同资产趋势的强大工具。 +

      +

      + 在我们的例子中，\(p_t^A\)和\(p_t^B\)分别为A股和B股的价格。如果假设{\({p_t^A, p_t^B}\)}都分别不平稳，则存在参数\(\gamma\)，以下方程是一个平稳过程： +

      + +\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\] + +

      + 其中\(\mu\)是协整模型的平均值。\(\epsilon_t\)是平稳的均值回复过程，被称为协整残差。参数\(\gamma\)被称为协整系数。上面的公式表示股票A和股票B的协整对模型。 +

      + +

      + 了解协整残差和协整系数如何决定交易方向是非常重要的。如果\(\epsilon\)为正值，处于给定的置信区间内，这是股票 A定价相对较高而股票B定价相对较低的信号，我们将买入股票B，并抛出股票A。如果\(\epsilon\)为负值，则买入股票A并抛出股票B。 +

      + +

      协整验证（可选阅读部分）

      +

      + 在Engle-Granger法中(Engle & Granger, 1987)，如上文方程式所述，我们首先在股票A和股票B之间建立协整回归，然后使用普通最小二乘法（OLS）估算回归参数\(\mu\)和\(\gamma\)。我们对回归残差\(epsilon_t\)进行了测试，以确定其是否平稳。 +

      + +

      + 在协整领域中，最受欢迎的平稳性测试是Augmented Dickey Fuller （ADF）测试，此测试用于确定回归残差\(\epsilon\)是否具有单位根。 +

      +

      + 利用ADF测试来检验回归残差是否存在单位根： +

      + +\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \Delta Z_{t-i} + \mu_t\] + +

      + 其中\(\alpha\)是常数，\(\beta\)是时间趋势系数，p是自回归过程的滞后阶，\(\mu_t\)是误差项，与连续性无关。 +

      + +

      + 方程中滞后阶p的数量通常是未知的，因此需要进行估算。为了确定滞后阶p的数量，采用了滞后阶选择的信息准则。这里我们选择贝叶斯信息准则(BIC)。 +

      + +\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\] + +

      + 其中T为样本量。 +

      +

      + 然后在零假设\(H_0 : \gamma = 0\)与替代假设\(H_1 : \gamma < 0\)的情况下，使用ADF测试对回归残差\(\epsilon\)进行单位根检验。通过下式得出ADF测试的统计值 +

      + +\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\] + +

      + 将上述方程的测试结果与ADF测试的临界值进行比较。如果测试结果小于临界值，则否定零假设。这意味着回归残差\(\epsilon\)是平稳的。因此，两支股票价格{\({p_t^A, p_t^B}\)}是协整的。 +

      +

      配对交易策略

      +

      + 配对交易策略使用基于回归残差\(\epsilon\)的交易信号，并被建模为均值回复过程。 +

      + +

      + 为了选择合适的股票进行配对交易，采用了两阶段的关联协整法。第一步是识别来自同一行业的潜在股票配对，使用关联方法选择关联系数至少达到0.9的股票配对。第二步是检验通过关联性测试股票配对的协整性。如果协整性的测试值等于或小于-3.34（这也是95%置信水平的阈值），则否定零假设\(H_0 : \gamma = 0\)，因此残差\(\epsilon\)是平稳的，股票对通过了协整测试。第三步是根据协整测试值对所有通过两阶段测试的股票对进行排序。协整测试值越小，股票对的排名越高。排名靠前的金融股票配对将用于配对交易。 +

      +

      + 策略的最后一步是定义交易规则。打开一个配对交易，回归残差\(\epsilon_t\)必须超过高于标准值的正\(\sigma\)标准差，或是低于标准值的负\(\sigma\)标准差。若残差为正，则抛出股票B，买入股票A；如果残差为负，则抛出股票A，买入股票B。当回归残差(\epsilon_t\)返回到一定水平时，配对交易结束。此外，为了防止在单一配对交易中损失过多，当残差达到\(4\epsilon\)正负标准差时，采用止损关闭配对交易。 +

      + +

      + 在训练期间，每个培训数据包含3个月的周期，这是一个动态滚动窗口规模。在训练期结束后，我们立即开始为期一个月的交易周期，动态滚动窗口自动向前移动，记录每对股票的新价格。在第一个交易期之后，我们使用更新后的股票价格再次选择交易配对，并开始另一个交易周期。 +

      + +

      参数调整

      +

      + 该策略的性能对参数非常敏感。主要有四个参数需要调整：开始阀值、结束阀值、止损阀值和数据分辨率。 +

      +

      + 开始阈值代表有多少残差\(\epsilon\)超过标准差，可通过\(\frac{\epsilon - \bar{\epsilon}}{\sigma}\)进行计算。默认情况下，我们将其设置为2.32和-2.32，如果假设残差服从正态分布，则这是99%置信区间的阈值。结束阈值的计算方法与开始阈值相同，我们将其默认设置为0.5，可以尽早结束以防止进一步的发散。 +

      +

      + 止损阀值设置为4.5。这取决于我们能承受的错误定价水平。我们对风险的容忍度越高，我们对这一参数的设置就可以越高。但是，如果我们将这个数字设置得太低，我们可能会在止损反转前结束过多的配对。 +

      diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..2be6e77 --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,252 @@ +

      + 在此类交易策略中，我们将定义一个名为“配对”的类。我们不直接对股票进行管理，而是对股票配对进行管理，这可以使我们更方便地计算关联性和协整性，更新配对股票的价格，并交易选定的配对。 +

      + +

      步骤1：配对分类定义

      +

      + 配对由两支股票组成，即股票A和股票B。这一分类有几种属性。基本属性包括股票A和股票B的代码、包含这两支股票时间和价格的数据分析框架、当前误差、最后一个数据点的误差，以及记录股票价格以便进行更新的列表。我们不会每5分钟更新一次数据框架，而是将价格记录在列表中，每月更新一次数据框架。由于操作数据框架非常耗时，这可以使算法的速度至少提高10倍。每个月使用cor_update方法更新配对股票中两支股票之间的关联性。采用cointegration_test每月进行最小二乘法（OLS）回归，进行ADF测试，计算残差的均值和标准差。这种方法还会将这些计算值作为属性分配给配对对象。 +

      + +
      + +
      class pairs(object):
+    def __init__(self, a, b):
+        self.a = a
+        self.b = b
+        self.name = str(a) + ':' + str(b)
+        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
+    # The number of bars in the rolling window would be determined by the resolution, so we get this
+      information from the shape of the DataFrame here.
+        self.num_bar = self.df.shape[0]
+        self.cor = self.df.corr().ix[0][1]
+    # Set the initial signals to be 0
+        self.error = 0
+        self.last_error = 0
+        self.a_price = []
+        self.a_date = []
+        self.b_price = []
+        self.b_date = []
+
+    def cor_update(self):
+        self.cor = self.df.corr().ix[0][1]
+
+    def cointegration_test(self):
+        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
+    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
+      the tuple is the test value.
+        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
+        self.mean_error = np.mean(self.model.resid)
+        self.sd = np.std(self.model.resid)
+
+    def price_record(self,data_a,data_b):
+        self.a_price.append(float(data_a.Close))
+        self.a_date.append(data_a.EndTime)
+        self.b_price.append(float(data_b.Close))
+        self.b_date.append(data_b.EndTime)
+
+    def df_update(self):
+        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
+                 [self.a_date]).dropna()
+        self.df = pd.concat([self.df,new_df])
+        self.df = self.df.tail(self.num_bar)
+    # after updating the DataFrame, we empty the lists for the incoming data
+        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
+            list = []
+
      +
      +

      Step 2: Generate and Clean Pairs

      +

      + The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming. + The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio. +

      + +
      + +
      def generate_pairs(self):
+    for i in range(len(self.symbols)):
+        for j in range(i+1,len(self.symbols)):
+            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
+
+    self.pair_list = [x for x in self.pair_list if x.cor > self.pair_threshold]
+
+    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
+
+    if len(self.pair_list) > self.pair_num:
+        	self.pair_list = self.pair_list[:self.pair_num]
+
+def pair_clean(self,list):
+    l = []
+    l.append(list[0])
+    for i in list:
+        symbols = [x.a for x in l] + [x.b for x in l]
+        if i.a not in symbols and i.b not in symbols:
+            l.append(i)
+        else:
+            pass
+    return l
+
      +
      + +

      Step 3: Warming up Period

      +

      + This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data. +

      + +
      + +
      if len(self.symbols[0].prices) < self.num_bar:
+    for symbol in self.symbols:
+        if data.ContainsKey(i) is True:
+    	    symbol.prices.append(float(data[symbol].Close))
+            symbol.dates.append(data[symbol].EndTime)
+        else:
+            self.Log('%s is missing'%str(symbol))
+            self.symbols.remove(symbol)
+    self.data_count = 0
+    return
      +
      +

      Step 4: Pairs Selection

      +

      + This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0. +

      + +
      + +
      if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
+    if self.generate_count == 0:
+        for symbol in self.symbols:
+        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
+
+        self.generate_pairs()
+        self.generate_count +=1
+        self.Log('pair list length:'+str(len(self.pair_list)))
+
+        for pair in self.pair_list:
+            pair.cor_update()
+    # Update the DataFrame and correlation selection
+    if len(self.pair_list[0].a_price) != 0:
+        for pair in self.pair_list:
+    	    pair.df_update()
+            pair.cor_update()
+
+    self.selected_pair = [x for x in self.pair_list if x.cor > 0.9]
+    # Cointegration test
+    for pair in self.selected_pair:
+        pair.cointegration_test()
+
+    self.selected_pair = [x for x in self.selected_pair if x.adf < self.BIC]
+    self.selected_pair.sort(key = lambda x: x.adf)
+    # If no pair passed the two-stage test, return.
+    if len(self.selected_pair) == 0:
+        self.Log('no selected pair')
+        self.count += 1
+        return
+    # clean the pair to avoid overlapping stocks.
+    self.selected_pair = self.pair_clean(self.selected_pair)
+    # assign a property to the selected pair, this is a signal that would be used for trading.
+    for pair in self.selected_pair:
+        pair.touch = 0
+        self.Log(str(pair.adf) + pair.name)
+    # limit the number of selected pairs.
+    if len(self.selected_pair) > self.selected_num:
+        self.selected_pair = self.selected_pair[:self.selected_num]
+
+    self.count +=1
+    self.data_count = 0
+    return
+
      +
      + +

      Step 5: Trade Period

      +

      + It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count < self.one_month. This means it's in the trading period. +

      + +

      Updating Pairs

      +

      + This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals. +

      + +
      + +
      num_select = len(self.selected_pair)
+for pair in self.pair_list:
+    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
+        i.price_record(data[i.a],data[i.b])
+    else:
+        self.Log('%s has no data'%str(pair.name))
+        self.pair_list.remove(pair)
+
+for pair in self.selected_pair:
+    pair.last_error = pair.error
+
+for pair in self.trading_pairs:
+    pair.last_error = pair.error
      +
      +

      Opening Pairs Trading

      +

      + For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B. + When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use. +

      + +
      + +
      for i in self.selected_pair:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
+    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
+    self.trading_pairs:
+        if i.touch == 0:
+            if i.error < i.mean_error - self.open_size*i.sd and i.last_error > i.mean_error -
+            self.open_size*i.sd:
+                i.touch += -1
+            elif i.error > i.mean_error + self.open_size*i.sd and i.last_error < i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error > i.mean_error - self.open_size*i.sd and i.last_error < i.mean_error -
+            self.open_size*i.sd:
+                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
+                i.record_model = i.model
+                i.record_mean_error = i.mean_error
+                i.record_sd = i.sd
+                self.trading_pairs.append(i)
+                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
+                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
+                i.touch = 0
+         elif i.touch == 1:
+             if i.error < i.mean_error + self.open_size*i.sd and i.last_error > i.mean_error +
+             self.open_size*i.sd:
+             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
+             i.record_model = i.model
+             i.record_mean_error = i.mean_error
+             i.record_sd = i.sd
+             self.trading_pairs.append(i)
+             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
+             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
+             i.touch = 0
+         else:
+             pass
+    else:
+        pass
+
      +
      + +

      Closing Pairs Trading

      +

      + This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs. +

      + +
      + +
      for i in self.trading_pairs:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
+    if ((i.error < i.record_mean_error + self.close_size*i.record_sd and i.last_error >i.record_mean_error + self.close_size*i.record_sd) or (i.error > i.record_mean_error -
+    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
+    self.stop_loss*i.record_sd:
+        self.Log('close %s to stop loss'%str(i.name))
+        self.Liquidate(i.a)
+        self.Liquidate(i.b)
+        self.trading_pairs.remove(i)
+    else:
+        pass
      +
      diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..1645559 --- /dev/null +++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,3 @@ +

      + WTI-Brent价差指的是两种原油价格之间的差异：即多头的西德克萨斯中质原油(WTI)和空头布伦特原油(Brent)。多年来，两者之间的价格差距平均只有几美元。由于这两种原油非常相似，它们的价差显示出很强的可预测性，通常在某个平均值附近波动。因此，有可能使用偏离公允价差的值来押注回复到公允价值。本文提出了一种基于价差价格差异的交易策略。 +

      diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..979fd31 --- /dev/null +++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,117 @@ +

      + 我们从Macrotrends下载WTI和Brent原油价格，并使用自定义数据下载器导入csv文件。单位为美元/桶。 +

      +
      +
      +  class WTI(PythonData):
+      "Class to import WTI Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/jpie3z6j0stp97d/wti-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = WTI()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+
+  class BRENT(PythonData):
+      "Class to import BRENT Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/w380c4n7xjmdqxl/brent-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = BRENT()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+
      +
      +

      + 价差定义为WTI价格与Brent价格之间的差。接下来，我们需要使用指标SimpleMovingAverage来计算价差系列的移动平均值。由于指标使用的是价格差异而不是价格系列，因此需要使用历史请求手动初始化指标。 +

      +
      +
      +self.SpreadSMA = SimpleMovingAverage(20)
+hist = self.History(["WTI", "BRENT"], 400, Resolution.Daily)["value"].unstack(level=0).dropna()
+hist_20days = hist[-20:]
+spread = (hist_20days["WTI"] - hist_20days["BRENT"]).dropna()
+for index, value in spread.items():
+    self.SpreadSMA.Update(index, value)
+
      +
      +

      + 为了得到价差的公允价值，我们对WTI和Brent价格在过去一年的历史价格进行了线性回归。 +

      + \[P_{Brent}=\beta \cdot P_{WTI}+\alpha\] +

      + 那么差价的公允价值是 +

      +\[Fair \ Spread =(1-\beta)\cdot CurrentPrice_{WTI}-\alpha\] +

      +
      +
      +hist_one_year = hist[-252:]
+X = hist_one_year["WTI"][:, np.newaxis]
+y = hist_one_year["BRENT"]
+self.regr = linear_model.LinearRegression()
+self.regr.fit(X, y)
+
      +
      +

      + 公允价值每天计算一次。如果当前价差值高于SMA 20，那么我们将在收盘时进入价差空头头寸(押注价差将降至SMA 20所代表的公允价值)。当价差低于公允价值时，交易在交易日收盘时结束。如果当前价差低于SMA 20，那么我们就进入多头头寸，押注价差将会增加，当价差超过公允价值时，交易将在交易日收盘时结束。 +

      + +
      +
      +def OnData(self, data):
+    if not (data.ContainsKey("WTI") and data.ContainsKey("BRENT")): return
+    self.Plot("Spread Plot", "Spread", data["WTI"].Price - data["BRENT"].Price)
+
+    self.SpreadSMA.Update(self.Time, data["WTI"].Price - data["BRENT"].Price)
+    if not self.SpreadSMA.IsReady: return
+    spread = self.Securities["WTI"].Price - self.Securities["BRENT"].Price
+    fair_value =self.Securities["WTI"].Price - Decimal(self.regr.predict([[self.Securities["WTI"].Price]])[0])
+
+    if spread > self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong):
+        self.SetHoldings("WTI", -0.5)
+        self.SetHoldings("BRENT", 0.5) 
+        self.Plot("Spread Plot", "Long Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    elif spread < self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort):
+        self.SetHoldings("WTI", 0.5)
+        self.SetHoldings("BRENT", -0.5)
+        self.Plot("Spread Plot", "Short Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    if self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong and spread < fair_value:
+        self.Liquidate()
+
+    if self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort and spread > fair_value:
+        self.Liquidate()
+
      +
      +

      + 为了显示价差序列的趋势，我们添加了价差图，并在价差曲线上标记了价差的多头/空头点。 +

      +
      +
      +spreadPlot = Chart("Spread Plot")
+spreadPlot.AddSeries(Series("Spread", SeriesType.Line, 0))
+spreadPlot.AddSeries(Series("Long Spread Trade", SeriesType.Scatter, 0))
+spreadPlot.AddSeries(Series("Short Spread Trade", SeriesType.Scatter, 0))
+self.AddChart(spreadPlot)
+
      +
      diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..15151d8 --- /dev/null +++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..5cbb24e --- /dev/null +++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + From 4cd938580117501807f3d659da69146a81321000 Mon Sep 17 00:00:00 2001 From: Alethea <31491434+AlinXlin@users.noreply.github.com> Date: Thu, 26 Sep 2019 10:36:54 -0700 Subject: [PATCH 088/215] Add Commodities Futures Trend Following --- .../01 Abstract.html | 3 + .../02 Introduction.html | 21 +++ .../03 Method.html | 126 ++++++++++++++++++ .../04 Summary.html | 7 + .../05 Algorithm.html | 6 + .../06 References.html | 5 + 6 files changed, 168 insertions(+) create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/06 References.html diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html b/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html new file mode 100644 index 0000000..46860f0 --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html @@ -0,0 +1,3 @@ +

      + In this tutorial we implement a trend following strategy on commodities futures based on a 2014 paper "Two Centuries Of Trend Following" by Y. Lempérière, C. Deremble, P. Seager, M. Potters, and J. P. Bouchaud. +

      \ No newline at end of file diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html b/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html new file mode 100644 index 0000000..21ba8d1 --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html @@ -0,0 +1,21 @@ +

      +The paper highlights the existence of trends as an anomaly that contradicts the efficient market hypothesis. If financial markets are completely efficient as the hypothesis suggests, then asset price changes should be totally unpredictable. In other words, no systematic excess return based on public information should exist since asset prices ought to reflect all public information available. However observationally, trend existence in the market do exist. They make it possible to use the simple trend following strategy which states, buy when prices goes up and sell when prices goes down. Numerous academic studies have demonstrated that trend following strategies generate persistent returns over long periods of time. +

      + +

      +The paper extends the backtest period of trend following strategies to two centuries and demonstrates statistically significant systematic excess returns on four asset classes (commodities, currencies, stock indices, and bonds). It implements a risk managed strategy that buys or sells a quantity of \(\sigma_n^{-1}\) of the underlying contract depending on the sign of \(s_n\). +

      + +

      +The signal \(s_n(t)\) at the beginning of month t is: +

      + +\[s_n(t) = \frac{p(t-1)-\text{<}p\text{>}_{n,t-1}}{\sigma_n(t-1)}\] + +

      +where \(\text{<}p\text{>}_{n,t-1}\) is last month's exponential moving average of past prices with a decay rate equal to n months,\(p(t-1)\) is the price of last month, and \(\sigma_n(t-1)\) is last month's volatility, estimated as the exponential moving average of the absolute monthly price changes, with a decay rate equal to n months. The decay rate was set to 5 months. +

      + +

      +Below, we will implement the above monthly-rebalanced trend following strategy on commodities futures. +

      \ No newline at end of file diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html new file mode 100644 index 0000000..783e8bf --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html @@ -0,0 +1,126 @@ +

      + The strategy requires the continuous futures contract, so we import the custom data from Quandl. We manually create a universe of tradable commodity futures. They are all liquid and active continuous contracts #1. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations. The data resolution is daily. +

      + + +

      Step 1: Import futures data from Quandl

      + +

      +The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper. We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY. +

      + +
      +
      +from QuantConnect.Python import PythonQuandl
+class ImprovedCommodityMomentumTrading(QCAlgorithm):
+	def Initialize(self):
+		tickers = ["CHRIS/CME_W1",  # Wheat Futures, Continuous Contract #1
+                   "CHRIS/CME_C1",  # Corn Futures, Continuous Contract #1
+                   "CHRIS/CME_LC1", # Live Cattle Futures, Continuous Contract #1 
+                   "CHRIS/CME_CL1",  # Crude Oil Futures, Continuous Contract #1
+                   "CHRIS/CME_NG1",  # Natural Gas (Henry Hub) Physical Futures, Continuous Contract #1
+                   "CHRIS/LIFFE_W1", # White Sugar Future, Continuous Contract #1
+                   "CHRIS/CME_HG1"] # Copper Futures, Continuous Contract #1
+		for ticker in tickers:
+			data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+			data.SetLeverage(3) 
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "Settle"
+
      +
      + + + +

      Step 2: Create a SymbolData class to store and update the number of contracts to trade for each security

      + +

      +In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a MonthlyConsolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly produced monthly data. +

      +
      +
      +def Initialize(self):                   
+	# Container to store the SymbolData object for each security
+	self.Data = {}
+	
+	for ticker in tickers:
+		# Add Quandl data and set desired leverage
+		data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+		data.SetLeverage(3) 
+		
+		# Create a monthly consolidator for each security
+		MonthlyConsolidator = self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
+		
+		# Create a SymbolData object for each security to store relevant indicators and calculated quantity of contracts to Buy/Sell
+		self.Data[data.Symbol] = SymbolData()
+def CalendarHandler(self, bar):
+	'''
+	Event Handler that updates the SymbolData object for each security when a new monthly bar becomes available
+	'''
+	self.Data[bar.Symbol].Update(bar)
+
      +
      + +

      +The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum. +

      + +
      +
      +class SymbolData:
+    '''
+    Contains the relevant indicators used to calculate number of contracts to Buy/Sell
+    '''
+    def __init__(self):
+        self.ema = ExponentialMovingAverage("MonthEMA", 5)
+		
+		# Volatility estimation is defined as the EMA of absolute monthly price changes
+		# Use Momentum indicator to get absolute monthly price changes. Then use the IndicatorExtensions.Of and pass the momentum indicator values to get the volatility
+        self.mom = Momentum("MonthMOM", 1) 
+        self.vol = IndicatorExtensions.Of(ExponentialMovingAverage("Vol", 5), self.mom)
+        self.Quantity = 0
+        
+    def Update(self, bar):
+        self.ema.Update(bar.Time, bar.Value)
+        self.mom.Update(bar.Time, bar.Value)
+        self.vol.Update(bar.Time, self.mom.Current.Value)
+        if self.ema.IsReady and self.vol.IsReady:
+            # Equation 1 in [1]
+            signal = ( bar.Value - self.ema.Current.Value )/ self.vol.Current.Value
+            # Equation 2 in [1]
+            self.Quantity = np.sign(signal)/abs(self.vol.Current.Value)
+        
+        return self.Quantity != 0
+
      +
      + +

      Step 3: Buy and Sell at the beginning of each month

      +

      +Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we have set a warm up period of 5 months which prepares data to allow the algorithm to execute trades on the start date. +

      + +
      +
      +
+def Initialize(self):
+	# Set decay rate equal to 5 months and warm up period
+	period = 150
+	self.SetWarmUp(period)
+	
+	# Set monthly rebalance
+	self.nextRebalance = self.Time
+def OnData(self, data):
+	'''
+	Buy/Sell security every month
+	'''
+	if self.IsWarmingUp: return
+	if self.Time < self.nextRebalance: return
+	
+	for symbol in data.Keys:
+		symbolData = self.Data[symbol]
+		if symbolData.Quantity != 0:
+			self.MarketOrder(symbol, symbolData.Quantity)
+	
+	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+
      +
      \ No newline at end of file diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html new file mode 100644 index 0000000..db5ce31 --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html @@ -0,0 +1,7 @@ +

      +For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.273, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds). +

      + +

      +This tutorial demonstrates trends as one of the most powerful sources of anomalous excess returns in financial markets. We hope to inspire the community to develop more trend-based strategies and encourage you to test out this strategy on other asset classes from the original paper. +

      \ No newline at end of file diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html new file mode 100644 index 0000000..d21ff42 --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html b/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html new file mode 100644 index 0000000..cb5636b --- /dev/null +++ b/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html @@ -0,0 +1,5 @@ +
        +
      1. + Y. Lempérière, C. Deremble, P. Seager, M. Potters, J. P. Bouchaud (2014). Two centuries of trend following. Online Copy +
        2. +
          \ No newline at end of file From bbf9e5e7f5a0a8ec8deaf53cfcc78fca3a8c332c Mon Sep 17 00:00:00 2001 From: Alethea <31491434+AlinXlin@users.noreply.github.com> Date: Thu, 26 Sep 2019 10:43:18 -0700 Subject: [PATCH 089/215] Add Commodities Futures Trend Following --- .../00 Strategy Library/01 Strategy Library.php | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 76fe5bf..ec88ded 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -577,6 +577,15 @@ ], 'description' => "An advanced momentum strategy that modifies the basic momentum strategies by introducing Baltas and Kosowski weights and rebalances the portfolio monthly. The new weighing scheme incorporates trend strength into the trading signal, uses an efficient volatility estimator, and adds a dynamic leverage mechanism.", 'tags' => 'Momentum, Futures, Commodities' + ], + [ + 'name' => 'Commodities Futures Trend Following', + 'link' => 'strategy-library/commodities-futures-trend-following', + 'sources' => [ + 'NYU' => 'https://arxiv.org/pdf/1404.3274.pdf' + ], + 'description' => "A simple trend following strategy on commodities futures.", + 'tags' => 'Momentum, Futures, Commodities' ] ]; From 499d462b0fee3348c5344efd40bf0742763f003c Mon Sep 17 00:00:00 2001 From: Alethea <31491434+AlinXlin@users.noreply.github.com> Date: Thu, 26 Sep 2019 14:11:05 -0700 Subject: [PATCH 090/215] Edit: Add Commodities Futures Trend Following Edited text and attached new edited algorithm --- .../03 Method.html | 67 +++++++++---------- .../04 Summary.html | 2 +- .../05 Algorithm.html | 2 +- 3 files changed, 34 insertions(+), 37 deletions(-) diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html index 783e8bf..6e8e094 100644 --- a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html +++ b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html @@ -6,7 +6,7 @@

          Step 1: Import futures data from Quandl

          -The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper. We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY. +The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper. We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY.

          @@ -35,7 +35,7 @@

          Step 1: Import futures data from Quandl

          Step 2: Create a SymbolData class to store and update the number of contracts to trade for each security

          -In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a MonthlyConsolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly produced monthly data. +In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a monthly consolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly received monthly data. 

          @@ -49,9 +49,10 @@ 
          Step 2: Create a SymbolData class to store and update the number of contract
 		data.SetLeverage(3) 
 		
 		# Create a monthly consolidator for each security
-		MonthlyConsolidator = self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
+		self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
 		
-		# Create a SymbolData object for each security to store relevant indicators and calculated quantity of contracts to Buy/Sell
+		# Create a SymbolData object for each security to store relevant indicators 
+		# and calculate quantity of contracts to Buy/Sell
 		self.Data[data.Symbol] = SymbolData()
 def CalendarHandler(self, bar):
 	'''
@@ -62,7 +63,7 @@ 
          Step 2: Create a SymbolData class to store and update the number of contract

          -The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum. +The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum indicators. The Introduction section above detailed the formula for calculating the number of contracts to Buy/Sell. We implement the formula in the Update function.

          @@ -73,54 +74,50 @@

          Step 2: Create a SymbolData class to store and update the number of contract ''' def __init__(self): self.ema = ExponentialMovingAverage("MonthEMA", 5) - - # Volatility estimation is defined as the EMA of absolute monthly price changes - # Use Momentum indicator to get absolute monthly price changes. Then use the IndicatorExtensions.Of and pass the momentum indicator values to get the volatility - self.mom = Momentum("MonthMOM", 1) - self.vol = IndicatorExtensions.Of(ExponentialMovingAverage("Vol", 5), self.mom) + + # Volatility estimation is defined as the EMA of absolute monthly price changes + # Use Momentum indicator to get absolute monthly price changes. + # Then use the IndicatorExtensions.EMA and pass the momentum indicator values to get the volatility + self.mom = Momentum("MonthMOM", 1) + # Note: self.vol will automatically be updated with self.mom + self.vol = IndicatorExtensions.EMA(self.mom, 5) + self.Quantity = 0 - + + def Update(self, bar): self.ema.Update(bar.Time, bar.Value) self.mom.Update(bar.Time, bar.Value) - self.vol.Update(bar.Time, self.mom.Current.Value) + if self.ema.IsReady and self.vol.IsReady: # Equation 1 in [1] - signal = ( bar.Value - self.ema.Current.Value )/ self.vol.Current.Value + signal = (bar.Value - self.ema.Current.Value) / self.vol.Current.Value # Equation 2 in [1] self.Quantity = np.sign(signal)/abs(self.vol.Current.Value) - + return self.Quantity != 0

          Step 3: Buy and Sell at the beginning of each month

          -Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we have set a warm up period of 5 months which prepares data to allow the algorithm to execute trades on the start date. +Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we warm up the algorithm with 150 days of data to allow the algorithm to execute trades on the start date. 

          -
-def Initialize(self):
-	# Set decay rate equal to 5 months and warm up period
-	period = 150
-	self.SetWarmUp(period)
-	
-	# Set monthly rebalance
-	self.nextRebalance = self.Time
 def OnData(self, data):
-	'''
-	Buy/Sell security every month
-	'''
-	if self.IsWarmingUp: return
-	if self.Time < self.nextRebalance: return
-	
-	for symbol in data.Keys:
-		symbolData = self.Data[symbol]
-		if symbolData.Quantity != 0:
-			self.MarketOrder(symbol, symbolData.Quantity)
-	
-	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+        '''
+        Buy/Sell security every month
+        '''
+        if self.Time < self.nextRebalance or self.IsWarmingUp:
+            return
+
+        for symbol in data.Keys:
+            symbolData = self.Data[symbol]
+            if symbolData.Quantity != 0:
+                self.MarketOrder(symbol, symbolData.Quantity)
+
+        self.nextRebalance = Expiry.EndOfMonth(self.Time)
          \ No newline at end of file diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html index db5ce31..363d402 100644 --- a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html +++ b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html @@ -1,5 +1,5 @@

          -For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.273, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds). +For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.266, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds).

          diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html index d21ff42..c56a62c 100644 --- a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html +++ b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html @@ -1,6 +1,6 @@

          - +
          From e4ecb10f76813386b6582543e8a0decdcec6df1c Mon Sep 17 00:00:00 2001 From: wyiyun95 Date: Thu, 26 Sep 2019 17:36:56 -0700 Subject: [PATCH 091/215] Mandarin Translation (Strategy Library) 01, 07, 09, 162, 20, 22, 36, 39 full --- .../01 \347\256\200\344\273\213.cn.html" | 14 ++ ...7\274\211\347\220\206\350\256\272.cn.html" | 44 ++++ .../03 \346\226\271\346\263\225.cn.html" | 95 +++++++ .../04 \346\200\273\347\273\223.cn.html" | 14 ++ .../05 \347\256\227\346\263\225.cn.html" | 9 + ...0\200\203\346\226\207\347\214\256.cn.html" | 8 + .../03 \346\226\271\346\263\225.cn.html" | 52 ++-- .../04 \347\273\223\346\236\234.cn.html" | 20 ++ .../05 \346\200\273\347\273\223.cn.html" | 3 + .../06 \347\256\227\346\263\225.cn.html" | 6 + ...0\200\203\346\226\207\347\214\256.cn.html" | 16 ++ .../01 \346\221\230\350\246\201.cn.html" | 3 + .../02 Factor Selection.html | 2 +- ...7\264\240\351\200\211\346\213\251.cn.html" | 236 ++++++++++++++++++ ...7\245\250\351\200\211\346\213\251.cn.html" | 17 ++ .../04 \347\256\227\346\263\225.cn.html" | 6 + ...0\200\203\346\226\207\347\214\256.cn.html" | 5 + .../01 \347\256\200\344\273\213.cn.html" | 3 + .../02 \346\226\271\346\263\225.cn.html" | 63 +++++ .../03 \347\256\227\346\263\225.cn.html" | 6 + .../04 \346\235\245\346\272\220.cn.html" | 5 + .../01 \347\256\200\344\273\213.cn.html" | 3 + .../02 \346\226\271\346\263\225.cn.html" | 19 ++ .../03 \347\256\227\346\263\225.cn.html" | 6 + .../04 \346\235\245\346\272\220.cn.html" | 5 + .../01 \347\256\200\344\273\213.cn.html" | 3 + .../02 \346\226\271\346\263\225.cn.html" | 6 + .../03 \347\273\223\350\256\272.cn.html" | 3 + .../04 \347\256\227\346\263\225.cn.html" | 14 ++ .../05 \346\235\245\346\272\220.cn.html" | 5 + .../01 \347\256\200\344\273\213.cn.html" | 3 + .../02 \346\226\271\346\263\225.cn.html" | 120 +++++++++ .../03 \347\256\227\346\263\225.cn.html" | 6 + .../04 \346\235\245\346\272\220.cn.html" | 5 + .../01 \347\256\200\344\273\213.cn.html" | 6 + .../02 \346\226\271\346\263\225.cn.html" | 57 +++++ .../03 \347\256\227\346\263\225.cn.html" | 6 + .../04 \346\235\245\346\272\220.cn.html" | 5 + 38 files changed, 870 insertions(+), 29 deletions(-) create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html" create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html" create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" create mode 100644 "04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html" create mode 100644 "04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html" create mode 100644 "04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html" create mode 100644 "04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" create mode 100644 "04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html" create mode 100644 "04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html" create mode 100644 "04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html" create mode 100644 "04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html" create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html" create mode 100644 "04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html" create mode 100644 "04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html" create mode 100644 "04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html" create mode 100644 "04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html" diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..9ccb0d0 --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,14 @@ +

          + 本教程执行一个简单的线性回归来构建资本资产定价模型(CAPM)，这是由William F. Sharpe和Harry Markowitz开发的一个经典模型。该模型对各资产都会产生alpha和beta值，并通过做多alpha值最高的股票进行交易。本教程将演示以下内容： +

          + +
            +
          • 如何使用历史数据
            • +
          • 设置事件处理程序
            • +
          • 进行线性回归
            • +
          • 在QuantConnect算法实验室中构建自己的函数
            • +
          + +

          + 该策略的实施表明，在上个月跑赢大盘的股票很可能在接下来的一个月里再次跑赢大盘。该算法在市场平稳时性能良好。然而，当市场波动性增加时，模型未能捕捉到alpha值，且表现不佳。我们从中了解到，市场波动降低了线性回归系数的显著性水平，特别是当我们使用日收益率来拟合模型时。 +

          diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html" new file mode 100644 index 0000000..7add139 --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html" @@ -0,0 +1,44 @@ +

          + 资本资产定价模型(CAPM)描述了系统性风险与资产(通常是股票)预期收益之间的关系。给定风险的资产预期收益计算公式如下： +

          + +\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \] + +

          + 其中： +

          + +\[r_f = Risk Free Rate\] +\[\beta = Beta of the security\] + +\[r_m = Expected market return\] + +\[\epsilon = Tracking error\] + +

          + 将公式重构如下，可以更好地理解这个公式： +

          + +\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \] + +

          + 方程的左边给出了资产收益和无风险利率之间的差额，即"超额收益"。如果我们将市场超额收益与资产超额收益进行对比，斜率代表资产的"beta"。因此，beta也可以通过公式计算： +

          + +\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\] + +

          + 因此beta可以描述为： +

          + +\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\] + +

          + 由上式可知，beta可以解释为“关联相对波动”。为了更加简化，可以通过简单的线性回归来计算beta，线性回归可以看作是解释收僧的一个因素，跟踪误差可以表示alpha。为了使这个理论对我们的算法更加便利，我们将上面的公式改为如下形式： +

          + +\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\] + +

          + 等式右边的r*(1-β) 是一个非常小的项目，在道琼斯前30强企业的背景下可以忽略不计。如果我们使用基准收益对股票收益进行回归，斜率和截距将分别为beta和alpha。 +

          diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..b64df7a --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,95 @@ +

          + 我们的投资逻辑简单明了。我们认为上个月跑赢大盘的股票将继续跑赢大盘。我们根据alpha值对股票进行排名，每个月我们都会对排名前两支的股票“做多”。要使这一战略发挥作用，我们需要在每月月初做下列工作： +

          + +
            +
          • 计算道琼斯前30强股票在过去21个交易日的历史价格，并计算它们的日收益。
            • +
          • 根据基准(S&P 500指数，SPY)对每支股票的收益进行简单的线性回归。
            • +
          • 根据截距对股票进行排名。
            • +
          • 平掉所有仓位，并购买排序列表中的排名前两支股票。
            • +
          +

          + 道琼斯指数成份股很少变动，最近一次变动发生在2015年3月19日。为了使实施更加容易，我们在这个算法中简单地列出了当前的道琼期成份股。这意味着该算法最早的开始日期是2015年3月19日。 +

          + +

          步骤1：设置事件处理程序

          +

          + 在初始化方法中，我们定义了日程事件来触发投资组合的每月重新平衡。有关如何使用日程事件的更多细节，可以阅读Documentation或查看示例ScheduledEventsAlgorithm。 +

          +
          + +
          def Initialize(self):
+    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
+
          +
          + +

          步骤2：线性回归函数

          +

          + 为了进行线性回归，我们需要编写一个函数来获取价格数据并输出回归结果。函数获得“资产价格”列表(x)和一个“基准价格”列表(y)，然后计算变化百分比并进行线性回归。输出是一个包含截距和斜率的元组。 +

          + +
          + +
          def regression(self,x,y):
+    x = np.array(x)
+    x = np.diff(x)/x[:-1]
+    y = np.array(y)
+    y = np.diff(y)/y[:-1]
+    A = np.vstack([x, np.ones(len(x))]).T
+    result = np.linalg.lstsq(A, y)[0]
+    beta = result[0]
+    alpha = result[1]
+    return(alpha,beta)
+
          +
          +

          步骤3：历史功能

          +

          + 每个月我们都会使用History API获得道琼斯30强成份股的历史价格。数据作为复杂的Slice对象从API返回。为了使其在算法中可以使用，我们将资产价格和基准价格提取到一个列表中。 +

          +
          + +
          def get_regression_data(self,symbol,history):
+    symbol_price = []
+    benchmark_price = []
+    for i in history:
+        bar = i[symbol]
+        benchmark = i[self.benchmark]
+        symbol_price.append(bar.Close)
+        benchmark_price.append(benchmark.Close)
+
+    result = self.regression(symbol_price,benchmark_price)
+    return result
+
          +
          +

          步骤4：再平衡功能

          +

          + 此功能是所有动作发生的地方，将作为预定事件在每个月的第一个交易日执行。SetHoldings的第二个参数是小数，将其设置为“1”表示算法将投资组合设置为“100%多头”而不使用杠杆。有关这一功能的更多信息可以在链接SetHoldings上阅读。 +

          + +
          + +
          def rebalance(self):
+    # 获得历史股票代码和价格，然后放入元组中
+    history = self.History(self.regression_dates, Resolution.Daily)
+    filter = []
+    for i in self.symbols:
+        filter.append((i,self.get_regression_data(i, history)[0]))
+    # 根据alpha排序筛选
+    filter.sort(key = lambda x : x[1],reverse = True)
+    sorted_symbols = []
+    for i in range(2):
+    	sorted_symbols.append(filter[i][0])
+    # 获得所持有股票的代码
+    holding_list = []
+    for i in self.Portfolio:
+    	if i.Value.Invested:
+    		holding_list.append(i.Value.Symbol)
+    # 如果我们不打算继续持有现有的股份，则将其出售
+    if holding_list:
+    	for i in holding_list:
+    		if i not in sorted_symbols:
+    			self.Liquidate(i)
+    # 做多列表中的两支股票
+    for i in sorted_symbols:
+    	self.SetHoldings(i,1)
+
          diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html" new file mode 100644 index 0000000..c68387d --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html" @@ -0,0 +1,14 @@ +

          + 我们已经证明，在一个平稳的市场中，上个月跑赢大盘的股票很可能在接下来的一个月里再次跑赢大盘。当市场波动时，线性回归的显著性水平降低，模型性能下降。我们可以通过观察资产(x)和基准(y)的协方差来理解这一点。当协方差减小到零时，beta将会减小。 +

          + +\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \beta{x})^2}\] + +

          + 作为实验，我们根据2015年的市场数据对算法进行了测试。对市场来说，这是一个极不稳定的时期，波动回到了接近于零的平均值，并在当年8月18日至8月25日期间下跌了近10%。该算法在今年的表现很差，收益率为-11.58%。与这一策略相关的风险包括大幅削减、缺乏对冲和止损。由于我们使用杠杆，风险增加了，因此在1月份有追加保证金的通知。我们可以通过应用以下技术来提高性能： +

          + +
            +
          • 进行优化：我们可以实施均值方差分析来确定每月的资产配置，选择更多的股票进行交易。这将降低我们的风险，更科学地管理投资组合。
            • +
          • 考虑beta：如果我们想要更加积极，我们可以结合alpha和beta来选择目标。这意味着我们选择的股票具有比市场波动更大的高alpha值。然而，如果我们是保守的投资者，我们可以使策略保持市场中立，这意味着投资组合不会受到市场表现的影响。例如，如果我们做多两只beta值为1和-1的股票，并分别持有相同的头寸大小，那么我们的投资组合就会变得市场中性。
            • +
          diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..1f296ed --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,9 @@ +

          + 使用OptionChainProvider进行回溯测试。 +

          +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" new file mode 100644 index 0000000..f6f75d0 --- /dev/null +++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" @@ -0,0 +1,8 @@ +
            +
          1. + https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average +
            2. +
          2. + Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf +
            3. +
          diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" index 2be6e77..a69dc45 100644 --- "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" @@ -15,11 +15,10 @@

          步骤1：配对分类定义

          self.b = b self.name = str(a) + ':' + str(b) self.df = pd.concat([a.df,b.df],axis = 1).dropna() - # The number of bars in the rolling window would be determined by the resolution, so we get this - information from the shape of the DataFrame here. + # 滚动窗口中的条数应由分辨率确定，因此我们可以得到 self.num_bar = self.df.shape[0] self.cor = self.df.corr().ix[0][1] - # Set the initial signals to be 0 + # 将初始信号设置为0 self.error = 0 self.last_error = 0 self.a_price = [] @@ -32,8 +31,7 @@

          步骤1：配对分类定义

          def cointegration_test(self): self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit() - # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in - the tuple is the test value. + # 此行对残差进行ADF测试，ts.adfuller() 返回元组，第一元素为 self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0] self.mean_error = np.mean(self.model.resid) self.sd = np.std(self.model.resid) @@ -49,15 +47,14 @@

          步骤1：配对分类定义

          [self.a_date]).dropna() self.df = pd.concat([self.df,new_df]) self.df = self.df.tail(self.num_bar) - # after updating the DataFrame, we empty the lists for the incoming data + # 更新数据框架后，我们清空输入数据列表 for list in [self.a_price,self.a_date,self.b_price,self.b_date]: list = []
          -

          Step 2: Generate and Clean Pairs

          +

          步骤2：生成并整理配对

          - The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming. - The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio. + 函数generate_pair使用股票代码生成配对。self.pair_threshold和self.pair_num是预先确定的，用于控制候选配对的数量。在我们的回溯测试期间，self.pair_list中的配对将保留并更新。我们将self.pair_threshold设置为0.88，将self.pair_num设置为120，以限制列表中配对的数量。如果我们列表中的配对过多，那么回溯测试将会非常耗时。在两阶段筛选后会调用函数pair_clean。如果第一对包含股票A和股票B，第二对包含股票B和股票C，我们将删除第二对，因为重叠的信号会干扰投资组合的平衡。

          @@ -87,9 +84,9 @@

          Step 2: Generate and Clean Pairs

          -

          Step 3: Warming up Period

          +

          步骤3：预热期

          - This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data. + 此部分属于OnData步骤。我们将self.num_bar设置为三个月内TradeBar的数量，这将由决议决定。在此期间，我们将股票价格填入列表，并将每支股票的价格列表作为属性分配给代码。如果代码列表中没有数据，我们还将从代码列表中删除此代码。

          @@ -105,9 +102,9 @@

          Step 3: Warming up Period

          self.data_count = 0 return
          -

          Step 4: Pairs Selection

          +

          步骤4：配对选择

          - This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0. + 这一过程同样属于OnData步骤。如果这是此算法的第一个交易周期，则此步骤将生成配对。如果不是，它将更新self.pair_list中每对股票的数据框架和关联系数。然后将关联系数大于0.9的配对选择到self.selected_pair中。然后对self.selected_pair中的所有配对进行协整，测试值小于-3.34的配对将被选择到最终列表中。此步骤还会限制最终列表中的股票数量，在默认情况下我们将self.selected_num设置为10。self.count是一个标志，用于计算我们所接收到数据点的数量。一旦达到1个月的数量，这意味着一个交易周期已经过去，它将被设置为0。

          @@ -123,31 +120,31 @@

          Step 4: Pairs Selection

          for pair in self.pair_list: pair.cor_update() - # Update the DataFrame and correlation selection + # 更新数据框架和关联性选择 if len(self.pair_list[0].a_price) != 0: for pair in self.pair_list: pair.df_update() pair.cor_update() self.selected_pair = [x for x in self.pair_list if x.cor > 0.9] - # Cointegration test + # 协整测试 for pair in self.selected_pair: pair.cointegration_test() self.selected_pair = [x for x in self.selected_pair if x.adf < self.BIC] self.selected_pair.sort(key = lambda x: x.adf) - # If no pair passed the two-stage test, return. + # 如果没有配对通过两阶段测试，则返回 if len(self.selected_pair) == 0: self.Log('no selected pair') self.count += 1 return - # clean the pair to avoid overlapping stocks. + # 整理配对避免出现重叠股票 self.selected_pair = self.pair_clean(self.selected_pair) - # assign a property to the selected pair, this is a signal that would be used for trading. + # 为所选配对分析属性，这是用于交易的信号 for pair in self.selected_pair: pair.touch = 0 self.Log(str(pair.adf) + pair.name) - # limit the number of selected pairs. + # 限制选择配对的数量 if len(self.selected_pair) > self.selected_num: self.selected_pair = self.selected_pair[:self.selected_num] @@ -157,14 +154,14 @@

          Step 4: Pairs Selection

          -

          Step 5: Trade Period

          +

          步骤5：交易期

          - It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count < self.one_month. This means it's in the trading period. + 如果我们把交易期间的所有代码都粘贴在一起，清单将会过长。因此，我们将代码分为三个部分： 更新配对、开始配对交易和结束配对交易。 但是所有这些行都属于OnData步骤，并且都在此条件下：即self.count != 0且self.count < self.one_month。这意味着它在交易期内。

          -

          Updating Pairs

          +

          更新配对

          - This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals. + 这一步将更新每对股票的价格。它还会更新名为“last_error”的信号，在此之后，配对会立即接收到新的信号。

          @@ -183,10 +180,9 @@

          Updating Pairs

          for pair in self.trading_pairs: pair.last_error = pair.error
          -

          Opening Pairs Trading

          +

          开始配对交易

          - For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B. - When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use. + 对于self.selected_pair中的每对股票，我们接受当前的股票价格，然后使用协整模型计算残差\(\epsilon\)，这是分配给配对的属性，命名为“误差”。 self.trading.pairs是存储交易配对的列表。一旦配对交易开始，配对就会被添加到列表中，当交易结束时将被删除。属性“touch”是一个信号。如果残差\(\epsilon\)超出正阈值标准偏差（我们在此设置为\(\ 2.23*sigma\))，信号会+ 1；如果低于负阈值偏差(\(\ -2.23*sigma\)，则信号会-1。对于那些获得+1信号的配对，如果误差超过正阈值，则会有开始交易的信号。我们买入股票B并抛出股票A。对于那些获得-1信号的配对，如果误差低于负阈值，我们买入股票A并抛出股票B。这是必要的，当我们开始交易时，我们需要记录残差的当前模型、当前平均值和标准。因为如果我们进入一个新的交易周期，而交易尚未结束，配对的协整模型、平均值和标准差将发生变化。我们需要使用最初的阈值来结束交易。同时把配对添加到self.trading_pairs中。我们还需要将信号“touch”设置为0，以便进一步使用。

          @@ -229,9 +225,9 @@

          Opening Pairs Trading

          -

          Closing Pairs Trading

          +

          结束配对交易

          - This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs. + 此部分控制退出交易。它的工作原理与开始部分相似。它使用记录原始模型和阈值来决定我们是否应该结束仓位。如果残差\(\epsilon\)达到结束阈值，我们将平仓股票A和股票B结束交易。如果残差继续偏离平均值，并且偏离太远，我们也会平仓止损。当我们结束配对交易时，我们也从self.trading_pair中删除了这些配对。

          diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html" new file mode 100644 index 0000000..9361eda --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html" @@ -0,0 +1,20 @@ +

          + 我们使用了2013年1月至2016年12月的10分钟分辨率数据对该策略进行了回溯测试。为了证明样本的培训结果，我们随机选取了2016年9月7日至2013年11月30日的训练期。 +

          +

          + 下表展示了上述训练期间所选择的前10组配对。可以看出，关联系数最高的配对不一定具有最好的ADF测试值。我们用ADF测试值来进行排序，因为它更加稳健。 +

          + +Tutorial07-pairs-trading-1 + +

          + 下表的上半部分是ING和TCB配对的股票价格。下半部分表示残差偏离平均值的次数。如果我们将开始阈值设为2.32，则有5个交易机会。 +

          + +Tutorial07-pairs-trading-2 + +

          + 下图是残差的密度图。从图中可以看出，误差近似正常分布。 +

          + +Tutorial07-pairs-trading-3 diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html" new file mode 100644 index 0000000..fa65597 --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html" @@ -0,0 +1,3 @@ +

          + 该策略被认为是市场中性策略，因为它是一种对价格趋同进行押注的多/空策略。回溯测试beta为-0.112，在我们的预期范围内。从理论上讲，我们使用的分辨率越高，获胜的几率就越高，因为一方面，更高的分辨率会增加我们训练期间的数据点数量，这将使我们更难通过两阶段测试；另一方面，高分辨率的数据可以让我们更准确地捕捉微小的利润。然而，在性能和回测时间之间存在权衡。较高的分辨率将导致回溯测试时间急剧增加。初始化步骤中股票的数量也会影响我们的性能。理论上，我们拥有的股票越多，我们可能选择的股票配对就越好。但过多的股票也会耗费时间，值得一提的是，每个行业的优化参数是不同的。这取决于特定行业价格模式的特点。绘制出配对价格和观察残差是调整阈值的较好选择。 +

          diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..69b6365 --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" new file mode 100644 index 0000000..b50463a --- /dev/null +++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" @@ -0,0 +1,16 @@ +
            + +
          1. + George J. Miao High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach Online Copy +
            2. +
          2. + Cartea & Penalva, 2012, Where is the value in high frequency trading? Online Copy +
            3. +
          3. + Gatev, Goetzmann, & Rouwenhorst, 2006, Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. Online Copy +
            4. +
          4. + Engle and Granger, Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. Online Copy +
            5. + +
          diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html" new file mode 100644 index 0000000..64620eb --- /dev/null +++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html" @@ -0,0 +1,3 @@ +

          + 近年来，投资要素在全球机构投资者中非常受欢迎。在本教程中，我们首先开发了一个要素选择模型，用于测试要素是否具有区分股市中潜在赢家和输家的能力。然后，根据2015年基于要素的土耳其股票选择模型，我们利用这些预先选择要素，实现了要素排序选股算法，Ayhan Yüksel。 +

          diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html index eb8ac07..663d5e1 100755 --- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html +++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html @@ -51,7 +51,7 @@

          Step 1: Ranking the stocks by factor values

          2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths as an example. - It is the sum of Tier 1 and Tier 2 Capital. x.Symbol.Value + It is the sum of Tier 1 and Tier 2 Capital. x.Symbol.Value can give the string symbol of selected stock x. Then we save those sorted symbols as self.symbol.

          diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html" new file mode 100644 index 0000000..b8cefb8 --- /dev/null +++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html" @@ -0,0 +1,236 @@ +

          + QuantConnect为美国股市提供了晨星基本面数据。估值比率是每日数据。对于其他项目，如营业比率和财务报表，可以根据不同的属性获得不同时期的数据。详细的可用要素请参阅Data Library。 +

          +

          + 该算法的设计是为了每次测试一个要素的重要性。 +

          + +
          + +
          def Initialize(self):
+	self.SetStartDate(2005,01,01)  # 设置开始日期
+	self.SetEndDate(2012,03,01)    # 设置结束日期
+	self.SetCash(50000)            # 设置策略现金
+	self.UniverseSettings.Resolution = Resolution.Daily
+	self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
+	self.AddEquity("SPY") # add benchmark
+	self.numOfCourseSymbols = 200
+	self.numOfPortfolio = 5
+	self._changes = None
+	self.flag1 = 1  # 控制粗选和精选函数每月重新平衡的变量
+	self.flag2 = 0  # 控制OnData函数每月重新平衡的变量
+	self.flag3 = 0  # 记录重新平衡次数的变量
+        # 将不同投资组合的月收益存储在一个数据框架内
+	self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1))
+        # 预定事件在SPY的第一个交易日解除
+	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))
+
          +
          +

          步骤1：按要素值对股票进行排序

          +

          + 1. 首先，我们按每日美元交易量对股票进行排序，并将美元交易量最高的股票作为候选股票。有一种方便的方法使用我们的集合选择API。在默认情况下，集合每天都会刷新，但也可以根据需要经常刷新。这是由可变UniverseSettings.Resolution控制的。您可以参考documentation了解更多详细信息。在这里，我们使用Scheduled events API来触发在每个月第一个交易日运行的代码，并使用三个标志变量来控制CoarseSelection，FineSelection和Ondata函数的重新平衡。 +

          +

          + 粗略集合选择是由QuantConnect提供的内置集合数据，它允许你对超过16,000个符号的集合进行筛选，在你的算法之前进行粗略的筛选。由于粗选功能考虑了包括基金在内的所有无基础数据股票，所以我们需要属性x.HasFundamentalData将这些股票排除在我们的候选股票池之外。 +

          +
          + +
          # 按照每日美元交易量对数据进行排序，并取排名最上面的条目
+def CoarseSelectionFunction(self, coarse):
+    if self.flag1:
+        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
+        sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
+        top = sortedByVolume[:self.numOfCourseSymbols]
+				return [i.Symbol for i in top]
+    else:
+        return []
+
          +
          + +

          + 2. 我们在每个月初提取候选股票的要素值，并按其要素值的升序对股票进行排序。例如，这里我们使用了12个月的总风险基础资本数据x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths。它是一级资本和二级资本的总和。x.Symbol.Value可以给出所选股票x的串符号，然后将这些排序后的符号保存为self.symbol。 +

          + +
          + +
          def FineSelectionFunction(self, fine):
+	if self.flag1:
+		self.flag1 = 0
+		self.flag2 = 1
+		# 通过删除要素值为零的股票来进行精细筛选
+		filtered_fine = [x for x in fine if x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths != 0 ]
+		# 按照要素值倒序排序
+		sorted_fine = sorted(filtered_fine, key=lambda x: x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths, reverse=True)
+		self.symbol = [str(x.Symbol.Value) for x in sorted_fine]
+		# 要素值 = [x.ValuationRatios.PERatio for x in sorted_fine]
+		self.flag3 = self.flag3 + 1
+ 	 	return []
+	else:
+		return []
+
          +
          + +

          步骤2：计算投资组合的月收益

          +

          + 1. 在每个月底，我们提取每支股票的一个月历史收盘价，并计算月收益。 +

          + +
          + +
          sorted_symbol = self.symbol
+self.AddEquity("SPY") # 添加基准
+for x in sorted_symbol:
+	self.AddEquity(x)
+history = self.History(20,Resolution.Daily)
+monthly_return =[]
+new_symbol_list =[]
+for j in range(len(sorted_symbol)):
+	try:
+		daily_price = []
+		for slice in history:
+			bar = slice[sorted_symbol[j]]
+			daily_price.append(float(bar.Close))
+		new_symbol_list.append(sorted_symbol[j])
+ 		monthly_return.append(daily_price[-1] / daily_price[0] - 1)
+	except:
+		self.Log("No history data for " + str(sorted_symbol[j]))
+		del daily_price
+# 月收益列表的长度应能够被投资组合的数量整除
+monthly_return = monthly_return[:int(math.floor(len(monthly_return) / self.numOfPortfolio) * self.numOfPortfolio)]
+
          +
          + + +

          + 2. 我们将股票分为5个投资组合，计算每个投资组合的平均月收益。然后在数据框架df_return的最后一行添加基准“SPY”的月收益。 +

          + +
          + +
          reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
+# 计算不同投资组合的平均收益
+port_avg_return = np.mean(reshape_return,axis=1).tolist()
+# 将"SPY"的收益作为基准添加到收益列表的末尾
+benchmark_syl = self.AddEquity("SPY").Symbol
+history_benchmark = self.History(20,Resolution.Daily)
+benchmark_daily_price = [float(slice[benchmark_syl].Close) for slice in history_benchmark]
+benchmark_monthly_return = (benchmark_daily_price[-1]/benchmark_daily_price[0]) - 1
+port_avg_return.append(benchmark_monthly_return)
+self.df_return[str(self.flag3)] = port_avg_return
+
          +
          +

          步骤3：生成度量来测试要素重要性

          +

          + 在得到投资组合的月收益和基准后，我们计算了整个回溯测试期间各投资组合的年平均收益和基准之上的超额收益。然后我们生成三个度量来判断每个因素的重要性。 +

          + +
            +
          • 第一个度量是投资组合收益与其排名之间的相关性。关联系数绝对值应大于0.8。
            • +
          • 如果排名第一投资组合的收益大于排名垫底的投资组合，我们将其定义为盈利投资组合和亏损投资组合，反之亦然。盈利概率是盈利投资组合收益超过基准收益的概率。亏损概率是指亏损组合收益低于基准的概率。如果要素显著，则损益概率均应大于0.4。
            • +
          • 盈利投资组合的超额收益应大于0.25，亏损投资组合的超额收益应小于0.05。
            • +
          + +
          + +
          def calculate_criteria(self,df_port_return):
+	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
+	annual_return = (total_return+1)**(1./6)-1
+	excess_return = annual_return - np.array(annual_return)[-1]
+	correlation = annual_return[0:5].corr(pd.Series([5,4,3,2,1],index = annual_return[0:5].index))
+	# 高收益的高要素
+	if np.array(total_return)[0] > np.array(total_return)[-2]:
+		loss_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess<0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess>0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[0]
+		loss_port_excess_return = np.array(excess_return)[-2]
+	# 低收益的高要素
+	else:
+		loss_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess<0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess>0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[-2]
+		loss_port_excess_return = np.array(excess_return)[0]
+	test_result = {}
+	test_result["correelation"]=correlation
+	test_result["win probality"]=win_prob
+	test_result["loss probality"]=loss_prob
+	test_result["win portfolio excess return"]=win_port_excess_return
+	test_result["loss portfolio excess return"]=loss_port_excess_return
+
+	return test_result
          +
          + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
          要素显著性测试结果
          要素 FCFYield BuyBackYield PriceChange1MTrailingDividendYield EVToEBITDA RevenueGrowthBookValuePerShare
          关联性 -0.936 -0.987 0.918 -0.981 0.9390.89-0.92
          盈利概率0.6300.639 1 0.667 0.722 0.690.69
          亏损概率 0.4260.472 1 0.518 0.472 0.420.40
          超额收益（盈利） 0.324 0.2120.303 0.225 0.414 0.23 0.27
          超额收益（亏损） 0.060 0.037 -1.67 0.043 0.042 0.07 0.06
          + +

          + 我们选择了4个要素：FCFYield、PriceChange1M、BookValuePerShare和RevenueGrowth。 +

          diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html" new file mode 100644 index 0000000..00bbed2 --- /dev/null +++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html" @@ -0,0 +1,17 @@ +接下来我们将选择股票。 +

          步骤1：按照要素值对股票排序

          +

          + 首先，我们删除没有基础数据或要素值为零的股票。对于每种预先选择的因素，我们根据这些因素值对股票进行排序。当要素相关性为负时，顺序递减；要素相关性为正时，顺序递增。 +

          + +

          步骤2：计算平均加权综合要素得分

          +

          + 第二步是使用不同的选择要素变量来计算每支股票的平均加权综合要素得分。 +

          + +
            +
          • 首先，根据要素顺序，我们将股票集合分成不同的五分位投资组合，分别为P1、P2、P3、P4和P5。投资组合的排序列出了要素模型的偏好，即第一投资组合（P1）对应“最优”的股票，第五投资组合（P5）对应“最不喜爱”的股票。假设总共有n支股票。然后进入第一投资组合的股票得分为p，进入第二级投资组合的股票得分为p-1，以此类推。然后我们可以得到每支股票的分数。我们对每个要素都进行了相同的计算。
            • +
          • 其次，我们将6个因素的得分结合起来，使用平均加权方案来计算“综合要素得分”。然后可以得到每支股票的综合要素得分。
            • +
          • 第三，然后我们根据综合要素得分对我们集合中的股票进行排名，并在每个月初选择排名最高的20支股票来构建我们的投资组合。
            • +
          • 每个月月底，我们都会重复上述步骤来构建新的投资组合，调整持有的股票。
            • +
          diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..bcf0bc2 --- /dev/null +++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" new file mode 100644 index 0000000..145c7bf --- /dev/null +++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" @@ -0,0 +1,5 @@ +
            +
          1. + Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy +
            2. +
          diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..e81a4a4 --- /dev/null +++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,3 @@ +

          + 动量异常的主要原因是投资者的行为偏差，例如反应不足和确认偏差。动量策略通常会使用由成千上万只股票组成的投资组合来计算动量要素收益。这对于持有小型投资组合的小型散户投资者来说是不可能的。与大型对冲基金相比，它们受到限制，无法进行多样化投资。在本教程中，我们将构建一个由50支股票组成的小型投资组合，以检查动量所产生影响。 +

          diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..61298fc --- /dev/null +++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,63 @@ +

          + 投资集合包括所有美国上市公司。没有基本数据的股票被排除在集合之外。 +

          +
          +
          +def CoarseSelectionFunction(self, coarse):
+    if self.yearly_rebalance:
+        # 放弃没有基本数据的股票
+        self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
+        return self.filtered_coarse
+    else:
+        return []
+
          +
          +

          + 在FineSelectionFunction中，由于流动性较低，市值最低的股票（占集合的25%）被排除在外。动量被定义为过去12个月的股市收益。动量利润是根据排名公司的年度收益进行计算的。排名周期为一年。 +

          +
          +
          +  def FineSelectionFunction(self, fine):
+      if self.yearly_rebalance:
+          # 计算年收益和市值
+          for i in fine:
+              i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+          top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len(fine)*0.75)]
+          has_return = []
+          for i in top_market_cap:
+              history = self.History([i.Symbol], timedelta(days=365), Resolution.Daily)
+              if not history.empty:
+                  close = history.loc[str(i.Symbol)]['close']
+                  i.returns = (close[0]-close[-1])/close[-1]
+                  has_return.append(i)
+          sorted_by_return = sorted(has_return, key = lambda x: x.returns)
+          self.long = [i.Symbol for i in sorted_by_return[-10:]]
+          self.short = [i.Symbol for i in sorted_by_return[:10]]
+
+          return self.long+self.short
+      else:
+          return []
+
          +
          +

          + 投资者做多表现最好的10支股票，做空表现最差的10支股票。投资组合每年都进行平均加权和重新平衡。 +

          +
          +
          +  def OnData(self, data):
+      if not self.yearly_rebalance: return
+      if self.long and self.short:
+          stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+          # 平仓未进入交易清单的股票
+          for i in stocks_invested:
+              if i not in self.long+self.short:
+                  self.Liquidate(i)
+          for i in self.short:
+              self.SetHoldings(i, -0.5/len(self.short))
+          for i in self.long:
+              self.SetHoldings(i, 0.5/len(self.long))
+          self.long = None
+          self.short = None
+          self.yearly_rebalance = False
+
          +
          diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..6bbcd8e --- /dev/null +++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..0cfa5d6 --- /dev/null +++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + diff --git "a/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..f770bb0 --- /dev/null +++ "b/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,3 @@ +

          + 套利交易在外汇市场上很常见。此策略系统性地出售低利率货币，买入高利率货币。资产的“套利”是持有该资产的机会成本。套利交易策略持有相对于其他货币的某一种货币，以获取利率之间的价差。我们可以把这种策略看作是从一个利率较低的国家借钱，然后将钱投资到另一个利率较高的国家。 +

          diff --git "a/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..2fc26c1 --- /dev/null +++ "b/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,19 @@ +

          导入自定义数据

          +

          + 央行利率数据来自Quandl。对于交易集合来说，我们选择了央行利率数据在Quandl中可用的9种货币。导入自定义数据的方法是AddData(type, symbol, resoltuion, timeZone, fillDataForward)。由于自定义文件具有唯一的colume名称，因此我们需要创建一个类别来指定利率的colume名称。 +

          +
          +
          +from QuantConnect.Python import PythonQuandl
+class QuandlRate(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = 'Value'
+
          +
          +

          + 我们将利率符号和对应的外汇资产符号保存到字典中。 +

          +

          每月调整交易

          +

          + 下一步，我们根据利率的值对外汇符号进行排序。这种算法会做多利率最高的货币，做空利率最低的货币。该战略每月都会重新调整。采用日程事件法在每个月的第一个交易日触发重新调整事件。 +

          diff --git "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..1bb774b --- /dev/null +++ "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..18f54dd --- /dev/null +++ "b/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..6b02628 --- /dev/null +++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,3 @@ +

          + 此算法检验了国家指数交易所交易基金的动量效应。 +

          diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..7b69538 --- /dev/null +++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,6 @@ +

          +此算法选取35个国家的指数基金作为交易集合。由于集合中的符号不会随时间发生变化，我们使用动量指示器辅助方法self.MOM(symbol, period, resolution)。这种辅助方法会创建一个新的动量指示器，并计算证券在绝对n周期中的变化。与指示器构造函数Momentum(period)相反，辅助方法指示器将根据给定的分辨率自动更新。 +

          +

          + 在Initialize()中，我们将预热周期设置为动量周期并创建字典self.data，以保存每个符号的指示符。每个月，将选择6个月势头最好的前5支指数基金进行多头仓位。没有列在榜首的基金将被斩仓。采用日程事件API制定投资组合，在每个月月初时重新平衡。 +

          diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html" new file mode 100644 index 0000000..b3aecfd --- /dev/null +++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html" @@ -0,0 +1,3 @@ +

          + 算法结果表明，国家指数存在动量效应。在2002年至2018年的16年时间里，持有一个月的策略（即在过去6个月中表现最好的5个国家指数基金投资组合）的表现每年要超出平均加权投资组合约40%。 +

          diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..cd34f0c --- /dev/null +++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,14 @@ +

          动量效应

          +
          +
          +
          + +
          +
          +

          平均加权基准

          +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..2334ff4 --- /dev/null +++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..c26b591 --- /dev/null +++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,3 @@ +

          + 成长型股票指的是高质量、成功的公司，它们的盈利预期将继续以高于市场平均水平的速度增长。成长型股票通常具有高市盈率(P/E)和高市净率(P/B)。有时，成长型股票被认为价格过高。价值型股票是指股息率高或财务比率（如市盈率和市净率）低的股票。价值型股票通常会被市场低估。此算法将基于投资者情绪和价值型股票相对于成长型股票表现之间的关系来建立多空头寸。 +

          diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..3104b1b --- /dev/null +++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,120 @@ +

          投资情绪的测量

          +

          + 要衡量投资者的情绪，我们使用的指标是：芝加哥期权交易所(CBOE)的股票看跌/看涨比率和市场波动率(VIX)指数。VIX指数是利用标准普尔500指数期权的隐含波动率构建的，它显示了市场对30天波动率的预期。芝加哥期权交易所(CBOE) 股票看跌/看涨比率的计算方法是将芝加哥期权交易所股票看跌期权的交易量除以芝加哥期权交易所股票看涨期权的交易量。看跌/看涨期权比率上升，意味着股票交易员买入的看跌期权多于看涨期权，表明市场人气看跌，而看跌/看涨比率下降则被视为市场人气上涨。 +

          +

          + 我们从Quandl导入每日波动率数据。CBOE提供了从2006年11月1日到现在的成交量看跌/看涨比率数据，因此我们从CBOE导入自定义数据。 +

          +
          +
          +class SentimentAndStyleRotationAlgorithm(QCAlgorithm):
+  def Initialize(self):
+      self.SetStartDate(2010, 1, 1)
+      self.SetEndDate(2018, 7, 1)
+      self.SetCash(100000)
+      self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily)
+      self.AddData(CBOE, "PutCallRatio", Resolution.Daily)
+
+class QuandlVix(PythonQuandl):
+    '''Quandl VIX data class'''
+    def __init__(self):
+        self.ValueColumnName = "VIX Close"
+
+class CBOE(PythonData):
+    '''Cboe Equity Volume Put/Call Ratios (11-01-2006 to present) Custom Data Class'''
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/equitypc.csv", SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        if not (line.strip() and line[0].isdigit()): return None
+        index = CBOE()
+        index.Symbol = config.Symbol
+
+        try:
+            # 示例文件格式：
+            # 日期        看涨      看跌       合计      看跌/看涨比率
+            # 11/1/06    976510    623929    1600439    0.64
+            data = line.split(',')
+            index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d")
+            index.Value = Decimal(data[4])
+
+        except ValueError:
+                return None
+
+        return index
+
          +
          +

          股票增长和价值的衡量

          +

          + 纽交所和纳斯达克的所有股票都被用作投资集合。在CoarseSelectionFunction中，我们剔除了没有基本数据的基金。在FineSelectionFunction中，股票根据市值大小被分成十等分。我们的算法只使用前百分之三十来避免小型非流动性股票的潜在问题。 +

          +
          +
          +def FineSelectionFunction(self, fine):
+    if self.month_start:
+        self.selection = True
+
+        fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
+                                and i.EarningReports.BasicEPS.TwelveMonths>0
+                                and i.ValuationRatios.PERatio>0
+                                and i.ValuationRatios.PBRatio>0]
+        # 计算市场价值并将“市值”属性添加到精细集合对象中
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        # 根据市值对精细对象分类
+        sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)]
+        decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)]
+        decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
+
          +
          +

          + 在接下来的步骤中，我们根据市净率将十等分中的每一份再细分为五个投资组合。对于前百分之三十的股票，价值型投资组合由市净率最低的五分之一公司组成，而成长型投资组合则由市净率最高的股票组成。 +

          +
          +
          +sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio)
+# 价值型投资组合由市净率最低的五分之一公司组成
+PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)]
+PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)]
+PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)]
+self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3]
+# 成长型投资组合由市净率最高的五分之一公司组成
+PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):]
+PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):]
+PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):]
+self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3]
+
          +
          + +

          投资者情绪和股票风格之间的关系

          +

          + 根据Lee和Song的研究论文《When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies》，当芝加哥期权交易所股票看跌/看涨比率相对较低，并且波动率指数相对较高时，价值型股票的表现往往优于成长型股票。当看跌/看涨比率和波动率指数均较高时，价值组合的表现明显要逊于成长型投资组合。要将每日看跌/看涨比率和波动率数据转换为月度数值，我们取最近一个月和前六个月的平均值。 +

          +

          + 如果最近芝加哥期权交易所的月平均看跌/看涨比率低于6个月平均水平，而波动率指数(VIX)的一个月平均水平高于6个月平均水平，那么该算法就会做多由排名前百分之三十价值股(市净率最低的五分之一)组成的平均加权投资组合。如果最近芝加哥期权交易所的月平均看跌/看涨比率和VIX指数均高于6个月平均水平，该算法就会做空价值股。否则，该算法既做多价值型股票，也做多成长型股票。持仓期为三个月，投资组合每三个月重新平衡一次。 +

          +
          +
          +stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+for i in stocks_invested:
+    if i not in self.value_portfolio+self.growth_portfolio:
+        self.Liquidate(i)
+
+if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value:
+    if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value:
+        long_weight = 1/len(self.value_portfolio)
+        for long in self.value_portfolio:
+            self.SetHoldings(long, long_weight)
+    elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value:
+        short_weight = 1/len(self.value_portfolio)
+        for short in self.value_portfolio:
+            self.SetHoldings(short, -short_weight)
+else:
+    long_weight = 1/len(self.value_portfolio+self.growth_portfolio)
+    for long in self.value_portfolio+self.growth_portfolio:
+        self.SetHoldings(long, long_weight)
+
          +
          diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..9af8464 --- /dev/null +++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..d965226 --- /dev/null +++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + diff --git "a/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html" new file mode 100644 index 0000000..69d32c7 --- /dev/null +++ "b/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html" @@ -0,0 +1,6 @@ +

          + 资产增长效基于高资产增长股票的表现弱于低资产增长股票。一些研究认为，低资产成长型股票的收益溢价与风险补偿是一致的。有两种流行的观点支持这种回报溢价。一种观点是，随着公司的成长，公司的资产组合风险会降低，因为现有资产取代了公司未来预期投资的资产价值。第二种观点是，由于将过去的收益推断为高资产成长型企业的增长，导致了对成长型企业的系统性市场定价错误。 +

          +

          + 这一策略将在低资产增长型公司做多，同时在高资产增长的公司做空。值得注意的是，这一策略需要根据当前和过去一年的基本数据来进行分析。因此，这种策略需要一年的实时交易时间来确定信号。 +

          \ No newline at end of file diff --git "a/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html" new file mode 100644 index 0000000..983e337 --- /dev/null +++ "b/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html" @@ -0,0 +1,57 @@ +

          + 第一步是粗略和精细集合选择。通过粗选，我们创建了一个包含所有在纽交所、美国证券交易所和纳斯达克上市的非金融类美国股票的投资领域，这些股票都包含基本数据。这一集合将被保存下来，这样我们就可以对下一年中总资产的年度变化进行分析。 +

          +
          +
          +def CoarseSelectionFunction(self, coarse):
+        if self.yearly_rebalance:
+            filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)
+                                                            and (x.Market == "usa")]
+            return filtered_coarse
+        else: 
+            return []
+
+def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        fine = [x for x in fine if x.FinancialStatements.BalanceSheet.TotalAssets.Value > 0
+                and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))
+                and (x.CompanyReference.IndustryTemplateCode!="B")
+                and (x.CompanyReference.IndustryTemplateCode!="I")]
+        if not self.previous_fine:
+            self.previous_fine = fine
+            self.yearly_rebalance = False
+            return []
+        else:
+            self.filtered_fine = self.Calculate(fine,self.previous_fine)
+            sorted_filter = sorted(self.filtered_fine, key=lambda x: x.delta_assets)
+            self.filtered_fine = [i.Symbol for i in sorted_filter]
+            self.previous_fine = fine
+            return self.filtered_fine
+    else:
+        return []
+
          +
          +

          + 在精细集合选择中，我们从上一年和当年的股票中计算出它们的总资产增长。然后根据采用的计算按照升序对股票进行排序。要注意的是，我们通过去年总资产的增长比例来计算样本公司之间的规模差异。 +

          +
          +
          +def Calculate(self, current, previous):
+        growth = []
+        for stock_data in current:
+            try:
+                prev_data = None
+                for x in previous:
+                    if x.Symbol == stock_data.Symbol:
+                        prev_data = x
+                        break
+                stock_data.delta_assets = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value) 
+                growth.append(stock_data)
+            except:
+                pass
+        return growth
+
          +
          +

          + 在OnData()中，我们做空排序列表中前百分之十的股票，做多后百分之十的股票。投资组合每年会在6月底重新调整。 +

          diff --git "a/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html" new file mode 100644 index 0000000..04cf1c1 --- /dev/null +++ "b/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html" @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          diff --git "a/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html" new file mode 100644 index 0000000..a9d5834 --- /dev/null +++ "b/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html" @@ -0,0 +1,5 @@ + \ No newline at end of file From 9af6160d72f423673858345735b2e212bb7c28a5 Mon Sep 17 00:00:00 2001 From: Kamuela Franco Date: Wed, 2 Oct 2019 00:09:50 +0100 Subject: [PATCH 092/215] Fix typographical error --- .../02 Introduction to Options/01 What Will I learn %3F.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html index 2e6c07b..f05701e 100644 --- a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html +++ b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html @@ -1,7 +1,7 @@
          General Features of Options
          QuantConnect Options API
          -
          Options Pricing: Black-Sholes-Merton Model
          +
          Options Pricing: Black-Scholes-Merton Model
          Stochastic Process
          Monte Carlo Method
          The Greek Letters
          From 5b30b123ecf13e09e05e6be8ac1b5a2f7a9db2a7 Mon Sep 17 00:00:00 2001 From: Jared Date: Tue, 8 Oct 2019 15:04:29 -0700 Subject: [PATCH 093/215] Update 05 Planning Your Lesson.html --- .../05 Planning Your Lesson.html | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 672b9be..6174895 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -63,7 +63,7 @@

          2. Strategy Implementation

          Consolidators Equities -

          Assigned

          +

          Completed

          @@ -72,7 +72,7 @@

          2. Strategy Implementation

          Price Volume Filtering Equities -

          Assigned

          +

          Completed

          @@ -81,7 +81,7 @@

          2. Strategy Implementation

          SymbolData Pattern History Equities -

          Assigned

          +

          Completed

          @@ -90,7 +90,7 @@

          2. Strategy Implementation

          STD Indicator Parameter Minimization Equities -

          Assigned

          +

          Completed

          From 9e1c8a80dc1cc064b037d0abdeae5864f4098b63 Mon Sep 17 00:00:00 2001 From: Sherry Yang Date: Tue, 12 Nov 2019 09:11:03 -0800 Subject: [PATCH 094/215] Update 05 Planning Your Lesson.html Fixed some typos. --- .../05 Planning Your Lesson.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html index 6174895..67986a8 100644 --- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html +++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html @@ -106,7 +106,7 @@

          2. Strategy Implementation

          -

          Separation of Concerns with the Algorithm Framework
          A simple strategy to buy SPY each morning on market open using the algorithm framework - a scaffolding for powerful strategy design.

          +

          The Algorithm Framework
          A simple strategy to buy SPY each morning on market open using the algorithm framework - a scaffolding for powerful strategy design.

          Algorithm Framework Execution Model Portfolio Model @@ -126,7 +126,7 @@

          2. Strategy Implementation

          -

          Pairs Trading with Cointegration Test
          Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test, and

          +

          Pairs Trading with Cointegration Test
          Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test.

          Pairs Trading Cointegration Test Scheduled Events @@ -163,7 +163,7 @@

          2. Strategy Implementation

          Available

          -

          Sentiment Analysis on Stocks
          Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most postive sentiment.

          +

          Sentiment Analysis on Stocks
          Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most positive sentiment.

          Psychsignal NLP Alternative Data From 2f00ffcbf4662b62cd27ffe61ce83134c94f8e4c Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Sat, 23 Nov 2019 01:02:24 +0000 Subject: [PATCH 095/215] Refactors Strategy 01 --- .../03 Method.html | 114 ++++++++---------- .../05 Algorithm.html | 7 +- 2 files changed, 54 insertions(+), 67 deletions(-) diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html index b6d9f96..3675eee 100755 --- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html +++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html @@ -1,7 +1,6 @@

          Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:

          -
          • Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.
          • Conduct simple linear regression on the return of each stock against a benchmark (S&P 500 index, SPY).
            • @@ -11,85 +10,76 @@

            Dow Jones components change very infrequently, with the last change being on March 19th, 2015. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.

            -

            Step 1: Setup Event Handler

            In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the Documentation or see the example ScheduledEventsAlgorithm.

            - 
            def Initialize(self):
-    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
-
            -
            - -

            Step 2: Linear Regression Function

            + self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), + self.TimeRules.AfterMarketOpen(self.symbols[0]), + self.Rebalance) +
          +

          Step 2: History Function

          - In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope. + Each month we get the historical prices of the DOW30 components using the History API. The data is returned from the API as a pandas.DataFrame indexed by Symbol objects. The close data is selected and the data frame is unstack to create columns of Symbol objects.

          -
          - -
          def regression(self,x,y):
-    x = np.array(x)
-    x = np.diff(x)/x[:-1]
-    y = np.array(y)
-    y = np.diff(y)/y[:-1]
-    A = np.vstack([x, np.ones(len(x))]).T
-    result = np.linalg.lstsq(A, y)[0]
-    beta = result[0]
-    alpha = result[1]
-    return(alpha,beta)
-
          +
          # Fetch the historical data to perform the linear regression
+history = self.History(
+    self.symbols + [self.benchmark], 
+    self.lookback,
+    Resolution.Daily).close.unstack(level=0)
          -

          Step 3: History Function

          +

          Step 3: Symbol Selection Function

          - Each month we get the historical prices of the DOW30 components using the History API. The data is returned from the API as complex Slice objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list. + We aim to trade the two assets with the highest alpha to the benchmark. In order to conduct linear regression to find the alpha (linear regression intercept), we need to compute returns (percentage change of closing price) benchmark and the asset then conduct a linear regression.

          +
          def SelectSymbols(self, history):
+    '''Select symbols with the highest intercept/alpha to the benchmark
+    '''
+    alphas = dict()
 
-
          def get_regression_data(self,symbol,history):
-    symbol_price = []
-    benchmark_price = []
-    for i in history:
-        bar = i[symbol]
-        benchmark = i[self.benchmark]
-        symbol_price.append(bar.Close)
-        benchmark_price.append(benchmark.Close)
+    # Get the benchmark returns
+    benchmark = history[self.benchmark].pct_change().dropna()
 
-    result = self.regression(symbol_price,benchmark_price)
-    return result
-
          
+    # Conducts linear regression for each symbol and save the intercept/alpha
+    for symbol in self.symbols:
+
+        # Get the security returns
+        returns = history[symbol].pct_change().dropna()
+        returns = np.vstack([returns, np.ones(len(returns))]).T
+
+        # Simple linear regression function in Numpy
+        result = np.linalg.lstsq(returns, benchmark)
+        alphas[symbol] = result[0][1]
+
+    # Select symbols with the highest intercept/alpha to the benchmark
+    selected = sorted(alphas.items(), key=lambda x: x[1], reverse=True)[:2]
+    return [x[0] for x in selected]

          Step 4: Rebalance Function:

          - This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: SetHoldings. + This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The algorithm closes all positions of securities that were not selected using Liquidate and go 100% long for both of the selected symbols using SetHoldings.

          -
          +
          def Rebalance(self):
+
+    # Fetch the historical data to perform the linear regression
+    history = self.History(
+        self.symbols + [self.benchmark], 
+        self.lookback,
+        Resolution.Daily).close.unstack(level=0)
+
+    symbols = self.SelectSymbols(history)
+
+    # Liquidate positions that are not held by selected symbols
+    for holdings in self.Portfolio.Values:
+        symbol = holdings.Symbol
+        if symbol not in symbols and holdings.Invested:
+            self.Liquidate(symbol)
 
-
          def rebalance(self):
-    # get historical stock symbols and prices, then put them in tuples
-    history = self.History(self.regression_dates, Resolution.Daily)
-    filter = []
-    for i in self.symbols:
-        filter.append((i,self.get_regression_data(i, history)[0]))
-    # sort the filter by alpha
-    filter.sort(key = lambda x : x[1],reverse = True)
-    sorted_symbols = []
-    for i in range(2):
-    	sorted_symbols.append(filter[i][0])
-    # get the symbols of our holding stocks
-    holding_list = []
-    for i in self.Portfolio:
-    	if i.Value.Invested:
-    		holding_list.append(i.Value.Symbol)
-    # if we have holdings and we are not going to hold them anymore, sell them
-    if holding_list:
-    	for i in holding_list:
-    		if i not in sorted_symbols:
-    			self.Liquidate(i)
-    # Long the 2 stock in our list.
-    for i in sorted_symbols:
-    	self.SetHoldings(i,1)
-		
          
+    # Invest 100% in the each of the selected symbols
+    for symbol in symbols:
+        self.SetHoldings(symbol, 1)
          \ No newline at end of file diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html index 3d63cc8..f47a119 100755 --- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html +++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html @@ -1,9 +1,6 @@ -

          - Backtest using OptionChainProvider -

          - +
          -
          +
          \ No newline at end of file From ead032b94e57fc7c5c6cc0de7086d0800d8b6076 Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Mon, 25 Nov 2019 15:43:58 -0800 Subject: [PATCH 096/215] Fix bug in the strategy listing. Missing comma to separate two elements. --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index b3ff4a6..5909ba0 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -578,7 +578,7 @@ 'description' => "A momentum strategy based on quarterly returns and earnings growth", 'tags'=>'Momentum, Stocks, Universe Selection, Historical Data, Rolling Window' - ] + ], [ 'name' => 'Improved Momentum Strategy on Commodities Futures', 'link' => 'strategy-library/improved-momentum-strategy-on-commodities-futures', From 9e319ea54ff013ae6d365f3d6b5eef65c178f3ba Mon Sep 17 00:00:00 2001 From: marii Date: Tue, 21 Jan 2020 17:21:31 -0800 Subject: [PATCH 097/215] initial commit --- 00 Home/01 Home/01 Home.html | 170 ++++++++++++++++++++++------------- 1 file changed, 110 insertions(+), 60 deletions(-) diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html index 170ca8b..a5161a8 100644 --- a/00 Home/01 Home/01 Home.html +++ b/00 Home/01 Home/01 Home.html @@ -7,72 +7,122 @@

          Learn to use QuantCo

          -
          -
          -
          - API Tutorials - - Tutorial Series - + + +
          +
          +
          +
          + +
          +
          + +

          + API Tutorials +

          +           + Learn how to build algorithms in QuantConnect +
          +
          -
          - Open Source - - Strategy Library - +
          +
          +
          + +
          +
          + +

          + Tutorial Series +

          +           + Step by step introductions to the basics of python and finance +
          +
          +
          +
          +
          +
          +
          +
          + +
          +
          + +

          + Open Source +

          +           + Deploy and harness the LEAN algorithmic trading engine +
          +
          +
          +
          +
          +
          + +
          +
          + +

          + Strategy Library +

          +           + Hundreds of academic papers implemented as LEAN trading strategies +
          +
          - From 248eb8f2d3f6fb918186a7e7768d830e036c87c2 Mon Sep 17 00:00:00 2001 From: marii Date: Tue, 21 Jan 2020 23:54:27 -0800 Subject: [PATCH 098/215] fix floating issue --- 00 Home/01 Home/01 Home.html | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html index a5161a8..df7c9f0 100644 --- a/00 Home/01 Home/01 Home.html +++ b/00 Home/01 Home/01 Home.html @@ -1,5 +1,5 @@ -
          -
          +
          +

          Tutorials

          @@ -116,13 +116,9 @@

          font-size: 18px; line-height: 1.4rem; } - - .documentation #doc-content h2.page-sub-heading { - display:none !important; + .documentation #doc-content .article-body { + flex: 0 10000px; } - h1.automated { - display: none; - } - + \ No newline at end of file From 9ef74f26206826486122d3f170d971e4d5f27716 Mon Sep 17 00:00:00 2001 From: marii Date: Tue, 21 Jan 2020 23:58:53 -0800 Subject: [PATCH 099/215] fix floating issue --- 00 Home/01 Home/01 Home.html | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html index df7c9f0..753cefd 100644 --- a/00 Home/01 Home/01 Home.html +++ b/00 Home/01 Home/01 Home.html @@ -120,5 +120,13 @@

          flex: 0 10000px; } + .documentation #doc-content h2.page-sub-heading { + display:none !important; + } + + h1.automated { + display: none; + } + \ No newline at end of file From f2125016c2b2c29c730a7d9063c6363db4e0d5ea Mon Sep 17 00:00:00 2001 From: marii Date: Wed, 22 Jan 2020 13:42:11 -0800 Subject: [PATCH 100/215] Change some fonts and change container width --- 00 Home/01 Home/01 Home.html | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html index 753cefd..d28f89b 100644 --- a/00 Home/01 Home/01 Home.html +++ b/00 Home/01 Home/01 Home.html @@ -1,4 +1,4 @@ -
          +
          @@ -9,7 +9,7 @@

          Learn to use QuantCo

          -
          +
          @@ -108,6 +108,7 @@

          vertical-align:middle; padding-left: 25px; line-height: 1.8rem; + font-weight: bold; } .content h4 { margin: 2px 0; @@ -116,9 +117,7 @@

          font-size: 18px; line-height: 1.4rem; } - .documentation #doc-content .article-body { - flex: 0 10000px; - } + .documentation #doc-content h2.page-sub-heading { display:none !important; From 8a210cb193d36625ed25f19c4ca0adad37b55ddc Mon Sep 17 00:00:00 2001 From: Mariia Date: Sun, 26 Jan 2020 12:23:27 -0800 Subject: [PATCH 101/215] Add margin && made links secure --- 00 Home/01 Home/01 Home.html | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html index d28f89b..ff41ca3 100644 --- a/00 Home/01 Home/01 Home.html +++ b/00 Home/01 Home/01 Home.html @@ -9,11 +9,11 @@

          Learn to use QuantCo

          -
          -
          +
          +
          - +
          @@ -25,10 +25,10 @@

          -
          +
          - +
          @@ -42,10 +42,10 @@

          -
          +
          - +
          @@ -57,10 +57,10 @@

          -
          +
          - +
          @@ -79,11 +79,6 @@

          * { box-sizing: border-box; } - .column { - float: left; - width: 50%; - padding: 10px; - } .row:after { content: ""; display: table; From 74ecdf2f441c6f54d40c1a34de11a793a1a512af Mon Sep 17 00:00:00 2001 From: Jared Date: Tue, 28 Jan 2020 08:55:00 -0800 Subject: [PATCH 102/215] Update 02 Rate of Return.html --- .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 9204847..5ae0cc5 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -75,7 +75,7 @@

          Logarithm Return

          aapl = aapl_table.loc['2017-3',['Open','Close']] #take log return aapl['log_price'] = np.log(aapl.Close) -aapl['log_return'] = np.log_price.diff() +aapl['log_return'] = aapl['log_price'].diff() print aapl

          From 0f739251155332300e5dc0af0bb730b97f3cd6d6 Mon Sep 17 00:00:00 2001 From: Gerardo Salazar Date: Mon, 10 Feb 2020 17:43:26 -0800 Subject: [PATCH 103/215] Updates Lean Report Creator tutorial to use new Report creator --- 03 Open Source/04 Lean Report Creator/00.html | 2 +- .../01 Introduction.html | 12 +++-- .../02 Creating a Report.html | 28 +++++++--- .../03 Source Backtest.html | 31 +++++------ .../04 Customizing Report Contents.html | 28 +++++++--- .../05 Interpreting Report Charts.html | 54 ++++++------------- 6 files changed, 78 insertions(+), 77 deletions(-) diff --git a/03 Open Source/04 Lean Report Creator/00.html b/03 Open Source/04 Lean Report Creator/00.html index f5722bc..02cabcd 100644 --- a/03 Open Source/04 Lean Report Creator/00.html +++ b/03 Open Source/04 Lean Report Creator/00.html @@ -1 +1 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/03 Open Source/04 Lean Report Creator/01 Introduction.html b/03 Open Source/04 Lean Report Creator/01 Introduction.html index 74cffbc..f5f8f98 100644 --- a/03 Open Source/04 Lean Report Creator/01 Introduction.html +++ b/03 Open Source/04 Lean Report Creator/01 Introduction.html @@ -1,8 +1,14 @@

          -The LEAN Report Creator is a report generated from backtesting-result objects and allows you to quickly create polished, professional-grade reports for each backtest (see a full example report generated by LRC). Our hope is that you can use these reports to share your strategy performance with prospective investors. +The LEAN Report Creator (LRC) is a report generated from backtesting-result objects and allows you to quickly create polished, professional-grade reports for each backtest (see a full example report generated by LRC). We hope that you can use these reports to share your strategy performance with prospective investors. +The report was generated using the examples provided in this tutorial.

          -Users working through the QuantConnect.com IDE can have these reports generated automatically at the end of a backtest. To generate one, look at the “Report” tab below your backtest result charts. +The algorithm used to generate the example report is provided below.

          - + + +

          +Users working through the QuantConnect.com IDE can have these reports generated automatically at the end of a backtest. To generate one, look at the “Report” tab below your backtest result charts and click "Request Report". +

          + diff --git a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html index d34f496..a2ea27a 100644 --- a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html +++ b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html @@ -1,13 +1,14 @@

          -The LEAN Report Creator is a python script located in the PythonToolbox. It is a command line tool which takes the json backtest result object as its input. You can run it like this: +The Lean Report Creator is a project located in the Report folder. It is a command line tool that takes the JSON backtest result object as its input. +You can run it using command line arguments, or by filling in the config.json file. 

          -python CreateLeanReport.py--backtest=./sample.json --output=./report.html --user=data.json
+./QuantConnect.Report.exe --backtest-data-source-file sample.json --report-destination Example.html

          -The program has three key options you should configure to your requirements: +The program has a few options you should configure to your requirements:

          @@ -19,16 +20,27 @@ - - + + + + + - + - - + + + + + + + + + +
          --backtestLocation of the source backtest json file.--backtest-data-source-fileLocation of the source backtest JSON file.
          --live-data-source-fileLocation of the source live JSON file.
          --output--report-destination Resulting output HTML file location.
          --userJson data object for the user data of the report (see example).--strategy-nameName of the strategy. This will appear at the top-right corner of each page in the report.
          --strategy-versionVersion number of the strategy. This will appear at the top-right corner next to the strategy name.
          --strategy-descriptionDescription that describes what the strategy does. This will appear under the "Strategy Description" section of the report.
          diff --git a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html index 8a4bc3a..f7fa836 100644 --- a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html +++ b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html @@ -1,32 +1,21 @@

          -The --backtest switch refers to a JSON file created by the LEAN Engine. This holds all of the backtest data which is required for generating the report html. This data can be sourced for desktop or cloud backtests. +The --backtest-data-source-file switch refers to a JSON file created by the LEAN Engine. This holds all of the backtest data which is required for generating the report HTML. This data can be sourced from desktop or cloud backtests.

          Desktop Backtesting Results

          -By default LEAN saves the backtest result data to disk inside of your LEAN launcher folder: Lean/Launcher/bin/Debug/BasicTemplateAlgorithm.json. It is saved there by the BaseResultHandler.SaveResult method. You can access it like this from the Python Toolbox: +By default, LEAN saves the backtest result data to disk inside of your LEAN launcher folder: Lean/Launcher/bin/Debug/BasicTemplateAlgorithm.json. You can access it like this from the Report executable:

          -
          python CreateLeanReport.py --backtest=../Launcher/bin/Debug/BasicTemplateAlgorithm.json ....
          +
          ./QuantConnect.Report.exe --backtest-data-source-file ../../../Launcher/bin/Debug/BasicTemplateAlgorithm.json ...

          Cloud Backtesting Results

          -Backtest processed reports can also be downloaded via API. The following example imports the QuantConnect API class; and uses it to download the final HTML report. To access the API you will need to know your API User Id and Token, which can be located on your Account page. +Backtest processed reports can be downloaded via API. The following example imports the QuantConnect API class and uses it to download the final HTML report. To access the API you will need to know your API User ID and Token, which can be obtained in the Account page.

          -Option 1: Easy: Downloading Cloud Generated Reports: -
          -
          ->>> from IPython.core.display import display, HTML
->>> from quantconnect.api import Api
->>> api = Api(your-user-id, your-token)
->>> lean_report = api.read_backtest_report(project-id, backtest-id)
->>> display(HTML(lean_report['report']))
-
          -
          - -Option 2: Harder: Downloading Cloud Generated Backtest Json Files: +Downloading Cloud Generated Backtest JSON Files: 
           >>> from quantconnect.api import Api
@@ -38,7 +27,13 @@ 
          Cloud Backtesting Results
          +Downloading Cloud Generated Backtest JSON Files (Manually): +

          +You can manually download the backtest result file and place the file in the location configured in the config.json file. +

          + +

          -You can update your report description by clicking on the Project Description button in your project panel. The report automatically uses your profile picture for the report image. +You can update your report description by clicking on the Project Description button in your project panel.

          - \ No newline at end of file + \ No newline at end of file diff --git a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html index c9a997f..7c86acc 100644 --- a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html +++ b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html @@ -1,12 +1,24 @@

          -The report imports a profile image and description of the strategy. This is stored inside a JSON file user_data.json. You can customize the contents of this file to automatically generate future reports with the new data: +The report can contain a description of the strategy. This is set either in the config.json file or via the command line argument --strategy-description. +An example config.json configuration is provided below. 

          {
-    "authorName": "Joe Blogs",
-    "authorPicture": "AuthorProfile.png",
-    "authorBiography": "Put your biography here.",
-    "projectName": " -- Page title / project name --",
-    "projectDescription": " -- Longer description text area -- "
-}
          -
          \ No newline at end of file + "data-folder": "../../../Data", + "strategy-name": "Example Algo (FX + Equity) Strategy", + "strategy-version": "v1.2.0", + "strategy-description": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.", + "live-data-source-file": "", + "backtest-data-source-file": "example_algo_fx.json", + "report-destination": "Report.html", + + ... +} + +
          + +

          +We recommend that you use the existing config.example.json +and rename it to config.json once you have finished editing the file. Please rebuild the project on rename or if you make any changes to the configuration +so that the file is copied into the binary output path. +

          \ No newline at end of file diff --git a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html index a5d8f47..e7a0510 100644 --- a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html +++ b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html @@ -1,63 +1,39 @@

          Cumulative Return

          - +

          -This chart shows the cumulative returns for your strategy in orange and the benchmark in gray. +This chart shows the cumulative returns for your strategy in blue for backtesting, orange for live trading, and the benchmark in gray.

          -

          Daily Return

          - +

          Returns per Trade

          +

          -This chart shows the daily returns for your strategy. When the return is positive, a orange bar will show above the horizontal line; when the return is negative, a gray bar will show below the horizontal line. +This chart shows a histogram showing the distribution of returns per trade over the algorithm's backtesting or live trading period.

          Top 5 Drawdown Periods

          - +

          -This chart shows the drawdown of each day. A certain day's drawdown is defined as the percentage of loss compared to the maximum value prior to this day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors. +This chart shows the drawdown of each day. A certain day's drawdown is defined as the percentage of loss compared to the maximum value before this day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors.

          Monthly Returns

          - +

          -This chart shows the return of each month. We convert original price series into monthly series, and calculate the returns of each month. The green color indicates positive return, the red color indicates negative return, and the greater the loss is, the darker the color is; the yellow color means the gain or loss is rather small; the white color means the month is not included in the backtest period. The values in the cells are in percentage. +This chart shows the return of each month. We convert original price series into monthly series and calculate the returns of each month. The green color indicates positive return, the red color indicates negative return, and the greater the loss is, the darker the color is; the yellow color means the gain or loss is rather small; the white color means the month is not included in the backtest period. The values in the cells are in percentage.

          -

          Annual Returns

          - -

          This chart shows the return of each year. We calculate the total return within each year, shown by the blue bars. The red dotted line represents the average of the annual returns. If the backtest covers less than 12 months of a certain year, then the value in the chart is the actual return which is not annualized. + +

          This chart shows the return of each year. We calculate the total return within each year, shown by the blue bars. The red dotted line represents the average of the annual returns.

          Crisis Events

          - -

          This group of charts shows the behaviors of both your strategy and the benchmark during a certain historical period. We set the value of your strategy the same as the benchmark at the beginning of each crisis event, and the lines represent the cumulative returns of your strategy and benchmark from the beginning of this crisis event. The report only draws the crisis event charts whose time periods are covered by your strategy. -

          - -

          Rolling Portfolio Beta to Equity

          - -

          -This chart shows the rolling portfolio beta to the benchmark. This chart is drawn based on daily data. Every day, we calculate the beta of your portfolio to the benchmark over the past 6 months (gray line) or 12 months (blue line).

          -

          -A beta close to 1 means the strategy has a risk exposure similar to the benchmark. A beta higher than 1 means the strategy has more risk than the benchmark, a beta close to 0 means the strategy is "market neutral", which isn't much affected by market situation. Beta can also be negative - in this situation the the strategy has opposite risk exposure to the benchmark. We won't draw this chart when your backtest period is less than 12 months. -

          - -

          Rolling Sharpe Ratio

          - -

          This chart shows the rolling sharpe ratio of your strategy. The rolling sharpe ratio is calculated on daily data, and annualized. Every day, we calculate the sharpe ratio of your portfolio over the past 6 months, and connect the sharpe ratios into a line. The red dotted line represents the mean value of the total sharpe ratios. We won't draw this chart when your backtest period is less than 6 months. -

          - -

          Net Holdings

          - -

          This chart shows the net holdings of your portfolio. The net holding is the aggregated weight of risky assets in your portfolio. It could be either positive (when your total position is long), negative (when your total position is short) or 0 (when you only hold cash). The net holding changes only if new order is fired. The chart is drawn based on minute data, which means we aggregate all the positions of a minute together. -

          - -

          Leverage

          - -

          This chart shows the leverage of your portfolio. The value of the leverage is always non-negative. When you only hold cash, the leverage is 0; a leverage smaller than 1 means you either long assets with money less than your portfolio value or short assets with total value less than your portfolio value; a leverage larger than 1 means you either borrow money to buy assets or short assets whose value is larger than your portfolio value. The leverage changes only if new order is fired. + +

          This group of charts shows the behaviors of both your strategy and the benchmark during a certain historical period. We set the value of your strategy the same as the benchmark at the beginning of each crisis event, and the lines represent the cumulative returns of your strategy and benchmark from the beginning of this crisis event. The report only draws the crisis event charts whose periods are covered by your strategy.

          Asset Allocations

          - +

          -The asset allocation charts show a time-weighted average of each class of asset to your portfolio. When a certain asset has very small percentage and is too small to be shown in the pie chart, it will be incorporated into an "Others" category. The value of the percentage could be either positive or negative. +The asset allocation charts show a time-weighted average of each asset to your portfolio. When a certain asset has a very small percentage and is too small to be shown in the pie chart, it will be incorporated into an "Others" category. The value of the percentage could be either positive or negative.

          From 1f8ffff6cc34a4ce1ad1e6b4e48feaf3bc91d50e Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Sat, 22 Feb 2020 00:04:30 +0000 Subject: [PATCH 104/215] Refactors Small Capitalization Stocks Premium Uses FineFundamental.MarketCap. Current version raises exception. --- .../02 Method.html | 16 ++++------------ .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 6 insertions(+), 14 deletions(-) diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html index 2894c75..98c8577 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html @@ -9,19 +9,11 @@ 

          def FineSelectionFunction(self, fine):
-    if self.year == self.Time.year:
-        return self.symbols
+    ''' Selects the stocks by lowest market cap '''
+    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0],
+        key=lambda x: x.MarketCap)
 
-    # Calculate the market cap and add the "MarketCap" property to fine universe object
-    for i in fine:
-        i.MarketCap = (i.EarningReports.BasicAverageShares.ThreeMonths * 
-                       i.EarningReports.BasicEPS.TwelveMonths *
-                       i.ValuationRatios.PERatio)
-
-    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0], key=lambda x: x.MarketCap)
-
-    self.symbols = [i.Symbol for i in sorted_market_cap[:10]]
-    return self.symbols
+    return [x.Symbol for x in sorted_market_cap[:self.count]]

          diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html index f14f61e..cc3a665 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html @@ -1,6 +1,6 @@ div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">

          - +
          diff --git a/quantpedia.json b/quantpedia.json index e92eac3..0d3bd2a 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -16,7 +16,7 @@ 21: "203a6729604c80a71b5c3b2baa2b3f69", 22: "d7285b5353d51cd5bd033f205e5faf44", 23: "12a417567b5706fdff6cf6265506b98c", - 25: "da3bf5ee608fb8b7dc952372dd925542", + 25: "ae6b2ce97f01f10af90c8823828ae5c9", 26: "d8cd05d85b47eea4381d2ca20abf2f74", 38: "55081a6fa81c88fd75579c2f838a1180", 41: "ac6336b5911ac0d4848bae6546ea1dde", From d5f9742be6c3fb98570fc10678d807dc020c3ca8 Mon Sep 17 00:00:00 2001 From: AlexCatarino Date: Sat, 22 Feb 2020 00:53:27 +0000 Subject: [PATCH 105/215] Refactors Turn of the Month in Equity Indexes Uses Portfolio.Invested to control the liquidation logic. Remove unnecessary schedules event. --- .../02 Method.html | 35 +++++++++---------- .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 19 insertions(+), 20 deletions(-) diff --git a/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html index a055225..2f57e97 100644 --- a/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html +++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html @@ -1,29 +1,28 @@

          - We start by creating scheduled events. The event at MonthEnd() will trigger the algorithm to buy SPY and the event at MonthStart() will start the process to sell SPY. + We start by creating a scheduled event, MonthEnd(), that will trigger the algorithm to buy SPY.

          -
          -self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.rebalance)
-self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.purchase)
+
          self.Schedule.On(
+    self.DateRules.MonthEnd(self.spy),
+    self.TimeRules.AfterMarketOpen(self.spy, 1),
+    self.Purchase)

          - We will purchase the SPY immediately, and we will wait 3 trading days, as suggested, before liquidating our portfolio. Assigning self.sell_flag in the scheduled event handler will help us wait 3 days before executing the liquidate order in OnData(). The equity index is bought and sold every month. + We will purchase the SPY immediately, and we will wait 3 trading days, as suggested, before liquidating our portfolio. The boolean self.Portfolio.Invested will help us wait 3 days before executing the liquidate order in OnData(). The equity index is bought and sold every month.

          -
          -def purchase(self):
-        self.SetHoldings("SPY", 1)
-    
-def rebalance(self):
-    self.sell_flag = True
-    
+
          def Purchase(self):
+    ''' Immediately purchases the ETF at market opening '''
+    self.SetHoldings(self.spy, 1)
+    self.days = 0
+
 def OnData(self, data):
-    if self.sell_flag:
+    if self.Portfolio.Invested:
         self.days += 1
-        if self.days == 3:
-            self.Liquidate()
-            self.sell_flag = False
-            self.days = 0
+
+        # Liquidates after 3 days
+        if self.days > 3:
+            self.Liquidate(self.spy, 'Liquidate after 3 days')
 
          
-
          +
          \ No newline at end of file diff --git a/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html index a56b8e9..ddaf1fb 100644 --- a/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html +++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html @@ -1,6 +1,6 @@
          - +
          diff --git a/quantpedia.json b/quantpedia.json index e92eac3..a73d0e5 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -19,7 +19,7 @@ 25: "da3bf5ee608fb8b7dc952372dd925542", 26: "d8cd05d85b47eea4381d2ca20abf2f74", 38: "55081a6fa81c88fd75579c2f838a1180", - 41: "ac6336b5911ac0d4848bae6546ea1dde", + 41: "d91ba9f0676ddc619a573b4d9135d988", 43: "707c5bb33fede32269a986370bf8a0a9", 44: "6fe04ea9049f2cf36d3c64ee672254ea", 51: "b6d06b2074e4efe807c29135507fa2ab", From 05c657672b02c10ce5458b4006eec717ac188bf5 Mon Sep 17 00:00:00 2001 From: Ayushman Chhabra Date: Wed, 11 Mar 2020 02:56:34 -0400 Subject: [PATCH 106/215] Fix typographical error --- .../04 Select Contracts.html | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html index 8f31d28..6a16b78 100755 --- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html +++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html @@ -160,18 +160,18 @@ 
          for i in slice.OptionChains:
     if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike > 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike < 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    optionchain = i.Value
+    # differentiate the call and put options
+    call = [x for x in optionchain if x.Right == 0]
+    put = [x for x in optionchain if x.Right == 1]
+    # choose ITM contracts
+    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike > 0]
+    # or choose ATM contracts
+    contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+    # or choose OTM contracts
+    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike < 0]
+    # sort the contracts by their expiration dates
+    contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)

          From 5b97f854d81bee500e268464788542de5aefdbee Mon Sep 17 00:00:00 2001 From: Ayushman Chhabra Date: Wed, 11 Mar 2020 06:33:43 -0400 Subject: [PATCH 107/215] Fix code indentation --- .../04 Select Contracts.html | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html index 6a16b78..fa6bde3 100755 --- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html +++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html @@ -65,11 +65,11 @@ 

          def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+	      optionchain = i.Value
+	      self.Log("underlying price:" + str(optionchain.Underlying.Price))
+        df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+        index=[x.Symbol.Value for x in optionchain],
+        columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
         self.Log(str(df))
          From f08072dd121f4ffb9364da98dbe74949eba617dd Mon Sep 17 00:00:00 2001 From: Ayushman Chhabra Date: Fri, 13 Mar 2020 23:09:09 -0400 Subject: [PATCH 108/215] Made the requested changes and then another --- .../04 Select Contracts.html | 30 ++++++++++--------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html index fa6bde3..99d89ed 100755 --- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html +++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html @@ -63,14 +63,14 @@ 
          def OnData(self,slice):
-    for i in slice.OptionChains:
-        if i.Key != self.symbol: continue
-	      optionchain = i.Value
-	      self.Log("underlying price:" + str(optionchain.Underlying.Price))
+    for kvp in slice.OptionChains:
+        if kvp.Key != self.symbol:
+            continue
+	      optionchain = kvp.Value
         df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
         index=[x.Symbol.Value for x in optionchain],
         columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
+        self.Log(f"Underlying price: {optionchain.Underlying.Price}\n{df}")
          @@ -158,20 +158,22 @@
          -
          for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    optionchain = i.Value
+
          for kvp in slice.OptionChains:
+    if kvp.Key != self.symbol:
+        continue
+    optionchain = kvp.Value
     # differentiate the call and put options
-    call = [x for x in optionchain if x.Right == 0]
-    put = [x for x in optionchain if x.Right == 1]
+    call = [x for x in optionchain if x.Right == OptionRight.Call]
+    put = [x for x in optionchain if x.Right == OptionRight.Put]
+    price = optionchain.Underlying.Price
     # choose ITM contracts
-    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike > 0]
+    contracts = [x for x in call if price - x.Strike > 0]
     # or choose ATM contracts
-    contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+    contracts = [x for x in call if price - x.Strike == 0]
     # or choose OTM contracts
-    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike < 0]
+    contracts = [x for x in call if price - x.Strike < 0]
     # sort the contracts by their expiration dates
-    contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    contracts = sorted(contracts, key = lambda x: x.Expiry, reverse = True)

          From 7f31149a37fdbed4fbee691ee32b392bad6ad2e3 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 10:25:24 -0700 Subject: [PATCH 109/215] Distinguished notations for interest rates Nominal and effective interest rates were denoted by the same notation r. The change is to clearly distinguish them. --- .../02 Rate of Return.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 5ae0cc5..4ad9640 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -55,11 +55,11 @@

          Logarithm Return

          From the above limitation equation, we know that if we assume continuous compounding:

          -\[e^r = 1 + r = \frac{p_t}{p_0}\] +\[e^{r_{nominal}} = 1 + r_{effective} = \frac{p_t}{p_0}\]

          Then we take \(ln\) on both side of the equation:

          -\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\] +\[r_{nominal} = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]

          Here we got the logarithmic return, or continuously compounded return. This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:

          @@ -127,7 +127,7 @@

          Logarithm Return

          It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:

          -\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\] +\[1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]

          According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.

          From 9c4c3ffbeaa1528aced846eb2f56a861761354fc Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 11:17:53 -0700 Subject: [PATCH 110/215] Explained why effective rate is close to nominal Explained why effective rate is close to nominal rate with compounding every millisecond. --- .../02 Rate of Return.html | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 4ad9640..04f1154 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -61,7 +61,12 @@

          Logarithm Return

          \[r_{nominal} = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]

          - Here we got the logarithmic return, or continuously compounded return. This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example: + Here we got the logarithmic return, or continuously compounded return. This return is the nominal return with the interest compounding every millisecond. To see how it is close to effective interest rate, recall the equation above: +

          +\[e^{r_{nominal}} = 1 + r_{effective}\] +

          then we have

          +\[r_{effective} = e^{r_{nominal}} - 1 \approx r_{nominal}\] +

          where the second equality holds due to Taylor Expansion and the interest rate being small. This is frequently used when calculating returns, because once we take the logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as an example:

          From afebd8582225d6c851f139b2ef72c0dee724e285 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 11:24:13 -0700 Subject: [PATCH 111/215] Adjusted the goal being finding effective rate --- .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 04f1154..aab985e 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -132,7 +132,7 @@

          Logarithm Return

          It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:

          -\[1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\] +\[1 + r_{effective} \approx 1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]

          According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.

          From a9346ad8c406cbe336e87465f07ec16adee7b734 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:06:45 -0700 Subject: [PATCH 112/215] Fixed a few typos --- .../06 Rate of Return, Mean and Variance/03 Mean.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html index 9d51b9c..72b9534 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html @@ -24,12 +24,12 @@

          Geometric Mean

          Let's calculate the geometric mean of a series of single-period return:

          -\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\] -\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\] +\[1+\bar{r} = \sqrt[t]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}*\frac{p_1}{p_0}}\] +\[(1+\bar{r}) = \sqrt[t]{\frac{p_t}{p_0}}\]

          Now the equation becomes the form which we are familiar with:

          -\[(1+\bar{r})^n = \frac{p_t}{p_0}\] +\[(1+\bar{r})^t = \frac{p_t}{p_0}\]

          This is why we said it make sense when applied to growth rates.

          From 3cb69147929d476cf585fca14d8122ae49b17516 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:09:17 -0700 Subject: [PATCH 113/215] Fixed the symbol for variance --- .../04 Variance and Standard Deviation.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html index 5161451..f11f818 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html @@ -1,6 +1,6 @@

          Variance

          - Variance is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by + Variance is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma^2\), and it's calculated by

          \[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]

          From 8cc8293cbb9a9b4c9d67071eabe5a4701bc01b37 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:17:14 -0700 Subject: [PATCH 114/215] Typo fixed --- .../02 Random Variables.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html index 30c1882..82718e8 100755 --- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html +++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html @@ -1,3 +1,3 @@

          - First let's start with the concept of random variable. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called discrete random variable. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}. The other kind of variable is continuous random variable. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\). + First let's start with the concept of random variable. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imagine rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called discrete random variable. A discrete variable can take on a finite number of values. For our example, we can only take numbers from {1,2,3,4,5,6}. The other kind of variable is continuous random variable. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).

          From 6e028a2f72be076f20c74fd1d2011a547cebac9a Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:34:39 -0700 Subject: [PATCH 115/215] Fixed multiple typos --- .../03 Distributions.html | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html index d80c6dc..933b38c 100755 --- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html +++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html @@ -1,10 +1,10 @@

          - Each random variable follows a probability distribution, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\). However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use probability density function(PDF) function to describe the probability that a value is in a specific range. We we cover this later. For each probability distribution function, we have a cumulative distribution function(CDF). It defines as \(P(X<x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations. + Each random variable follows a probability distribution, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represent a probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\). However, we can't use this for a continuous distribution, because the probability of drawing a specific number from a continuous variable is 0, due to the infinite possible outcomes we have. Instead, we use probability density function(PDF) function to describe the probability that a value is in a specific range. We cover this later. For each probability distribution function, we have a cumulative distribution function(CDF). It is defined as \(P(X\leq x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.

          Uniform Distribution

          - Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions. Here we use python to simulate rolling a dice 10000 times. + Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and tossing a fare coin are classical uniform distributions. Here we use python to simulate rolling a dice 10000 times.

          @@ -24,7 +24,7 @@

          Uniform Distribution

          - We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution: + We create a series of random variables here. We can plot the values on the x-axis and put their numbers of occurrences on the y-axis to have a direct view of the distribution:

          @@ -39,7 +39,7 @@

          Uniform Distribution

          plot1

          - Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X<3)\). + Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X\leq 3)\).

          @@ -50,7 +50,7 @@

          Uniform Distribution

          - \(P(X<3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as + \(P(X\leq 3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations are less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate its mean: it's the center of the distribution because every outcome is of equal weight. For our dice example, we can think of it as

          \[\mu = (1+2+3+4+5+6)/6 = 3.5\]

          From b481cf1c10e048bafcb95129dc9cf443cc4e9835 Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:21:59 -0700 Subject: [PATCH 116/215] Updates SPY data source from Quandl to QC --- .../02 Confidence Interval.html | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html index 82a7170..f38faaf 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html @@ -8,13 +8,18 @@

          Sample Error

          import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
+
+qb = QuantBook()
+spy = qb.AddEquity("SPY").Symbol
+
+#get SPY data from August 2010 to the present
+start_date = datetime(2010, 8, 1, 0, 0, 0)
+end_date = qb.Time
+spy_table = qb.History(spy, start_date, end_date, Resolution.Daily)
+
+spy_total = spy_table[['open','close']]
 #calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
+spy_log_return = np.log(spy_total.close).diff().dropna()
 print 'Population mean:', np.mean(spy_log_return)
 [out]: Population mean: 0.000443353825615
 print 'Population standard deviation:',np.std(spy_log_return)

From 798365481ed6cbaef79960301363c0bacf0a5004 Mon Sep 17 00:00:00 2001
From: Rahul 
Date: Mon, 4 May 2020 10:28:53 -0700
Subject: [PATCH 117/215] Updates print syntax

---
 .../02 Confidence Interval.html                  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
index f38faaf..2d4404b 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -20,9 +20,9 @@ 
          Sample Error
          
 spy_total = spy_table[['open','close']]
 #calculate log returns
 spy_log_return = np.log(spy_total.close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
+print('Population mean:', np.mean(spy_log_return))
 [out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
+print('Population standard deviation:',np.std(spy_log_return))
 [out]: Population standard deviation: 0.00784267293815
          @@ -34,13 +34,13 @@

          Sample Error

          -print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+print('10 days sample returns:', np.mean(spy_log_return.tail(10)))
 [out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+print('10 days sample standard deviation:', np.std(spy_log_return.tail(10)))
 [out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+print('1000 days sample returns:', np.mean(spy_log_return.tail(1000)))
 [out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+print('1000 days sample standard deviation:', np.std(spy_log_return.tail(1000)))
 [out]: 1000 days sample standard deviation: 0.00766589174299
          @@ -75,9 +75,9 @@

          Confidence Interval

          bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000))))) upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000))))) #print the outcomes -print '10 days 95% confidence inverval:', (bottom_1,upper_1) +print('10 days 95% confidence inverval:', (bottom_1,upper_1)) [out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079) -print '1000 days 95% confidence inverval:', (bottom_2,upper_2) +print('1000 days 95% confidence inverval:', (bottom_2,upper_2)) [out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957) From f2152f9f12c76f5fb536ccddafac8c5fc990df6c Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:34:56 -0700 Subject: [PATCH 118/215] Update to print syntax --- .../03 Hypothesis testing.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html index acd6912..e25e886 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html @@ -9,7 +9,7 @@ mean_10 = np.mean(spy_log_return.tail(10)) std_10 = np.std(spy_log_return.tail(10)) s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000']) -print s +print(s) [out]: mean_10 0.000845 std_10 0.003136 @@ -25,7 +25,7 @@ 
          bottom = 0 - 1.64*std_1000/np.sqrt(1000)
 upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00039756352254768874, 0.00039756352254768874)
          @@ -37,7 +37,7 @@ 
          bottom = 0 - 1.96*std_1000/np.sqrt(1000)
 upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00047513689280089639, 0.00047513689280089639)
          @@ -55,7 +55,7 @@

          -
          print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+
          print(np.sqrt(1000)*(mean_1000 - 0)/std_1000)
 [out]: 1.90922032428
          @@ -66,7 +66,7 @@ 
          import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
+print((1 - st.norm.cdf(1.9488)))
 [out]: 0.025659656888
          @@ -82,10 +82,10 @@ 
          mean_1200 = np.mean(spy_log_return.tail(1200))
 std_1200 = np.std(spy_log_return.tail(1200))
 z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
+print('z-score = ',z_score)
 [out]: z-score =  2.19793023185
 p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
+print('p_value = ',p_value)
 [out]: p_value =  0.0139770390655
          From d5513f57c7dfdebcf6fd97709b501911ae18ab26 Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:38:06 -0700 Subject: [PATCH 119/215] Removed duplicate of tutorial --- .../04 Summary.html | 213 ------------------ 1 file changed, 213 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html index 3cc20c3..724eb85 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html @@ -1,216 +1,3 @@ -

          Introduction

          -

          - In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a sample to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis. -

          -

          - As we mentioned before, both mean and standard deviation are point estimation, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is. -

          - -

          Confidence Interval

          -

          Sample Error

          -

          - Let's use the daily return on S&P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000? -

          - -
          - -
          import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
-#calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
-[out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
-[out]: Population standard deviation: 0.00784267293815
-
          -
          - -

          - Now let's check the recent 10 days sample and recent 1000 days sample: -

          - -
          - -
          -print '10 days sample returns:', np.mean(spy_log_return.tail(10))
-[out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
-[out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
-[out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
-[out]: 1000 days sample standard deviation: 0.00766589174299
-
          -
          -

          - As we expected, the two samples has different means and variances. -

          - -

          Confidence Interval

          -

          - In order to estimate the range of population mean, we define standard error of the mean as follows: -

          -\[SE = \frac{\sigma}{\sqrt{n}}\] -

          - Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size. -

          -

          - Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as: -

          -\[(\mu - 1.96*SE, \mu + 1.96*SE)\] -

          - Where \(\mu\) is the sample mean and SE is the standard error. -

          -

          - This interval is called confidence interval. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above: -

          -
          - -
          -#apply the formula above to calculate confidence interval
-bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-#print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
-[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
-[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
-
          -
          - -

          - As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean! -

          - -

          Confidence Interval of Normal Distribution

          -

          - Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words: -

          -\[\%90 upperabnd = \mu + 1.64*SE\] -\[\%90 lowerband = \mu + 1.64*SE\] -

          - The same for other confidence intervals. It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically: -

          -\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\] -\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\] -\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\] -

          - This can also be remembered by using the chart: -

          -empirical rule -

          Central Limit Theory

          -

          - As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is: -

          -\[(\mu - 1.96*SE, \mu + 1.96*SE)\] -

          - Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution? The answer is yes. This assumption is supported by central limit theorem. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed. This is the foundation of population mean confidence interval estimation. -

          - -

          Hypothesis testing

          -

          - Now we can talk about hypothesis testing. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample. Let's start from observing our sample: -

          - -
          - -
          mean_1000 = np.mean(spy_log_return.tail(1000))
-std_1000 = np.std(spy_log_return.tail(1000))
-mean_10 = np.mean(spy_log_return.tail(10))
-std_10 = np.std(spy_log_return.tail(10))
-s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
-
-[out]: mean_10      0.000845
-       std_10       0.003136
-       mean_1000    0.000463
-       std_1000     0.007666
-
          -
          -

          - We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be: -

          - -
          - -
          bottom = 0 - 1.64*std_1000/np.sqrt(1000)
-upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00039756352254768874, 0.00039756352254768874)
-
          -
          -

          - Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we rejected the hypothesis that the daily return on S&P500 from aug 2010 is zero. Can we claim that with 95% confidence level? -

          - -
          - -
          bottom = 0 - 1.96*std_1000/np.sqrt(1000)
-upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00047513689280089639, 0.00047513689280089639)
-
          -
          -

          - This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level. We have actually already finished a hypothesis testing above! In general, we have null hypothesis \(H_0\) and alternative hypothesis. They are usually in the following forms: -

          -\[H_0:\bar{\mu} = 0\] -\[H_0:\bar{\mu} \neq 0\] -

          - If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis. Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or Z-score. Z-score is defined as: -

          -\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\] -

          - Let's calculate the Z score from our sample: -

          -
          - -
          print np.sqrt(1000)*(mean_1000 - 0)/std_1000
-[out]: 1.90922032428
-
          -
          -

          - We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis. One reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python: -

          - -
          - -
          import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
-[out]: 0.025659656888
-
          -
          -

          - It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called p-value. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called two-tail test. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart: -

          -confidence interval -

          - If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations: -

          -
          - -
          mean_1200 = np.mean(spy_log_return.tail(1200))
-std_1200 = np.std(spy_log_return.tail(1200))
-z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
-[out]: z-score =  2.19793023185
-p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
-[out]: p_value =  0.0139770390655
-
          -
          -

          - Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up. -

          -

          Summary

          In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.

          From 72b385ec06e54ce397a76ddfd156fb6114e64682 Mon Sep 17 00:00:00 2001 From: Weiqi Zhang Date: Mon, 4 May 2020 23:42:12 -0700 Subject: [PATCH 120/215] Update 05 Options Moneyness.html Fix typo. --- .../01 General Features of Options/05 Options Moneyness.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html index 5f7a43a..ae5cbc4 100755 --- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html +++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html @@ -26,5 +26,5 @@ But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.

          - Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration. + Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.

          From 02ca9566222cde5c4d25f3bd333bdc79661525ce Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:23:50 -0600 Subject: [PATCH 121/215] Add residual momentum strategy writeup --- .../01 Introduction.html | 3 ++ .../136 Residual Momentum/02 Method.html | 31 +++++++++++++++++++ .../136 Residual Momentum/03 Algorithm.html | 6 ++++ .../136 Residual Momentum/04 References.html | 8 +++++ 4 files changed, 48 insertions(+) create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html new file mode 100644 index 0000000..28518a1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html @@ -0,0 +1,3 @@ +

          + Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. +

          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html new file mode 100644 index 0000000..5b927d1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -0,0 +1,31 @@ +

          Importing Custom Data

          +

          + The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the Fama-French data source URL from our Tutorials repo. We save the loaded data into a DataFrame in the alpha model. +

          + +

          Universe Selection

          +

          + In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices. +

          + +

          Calculate Residual Momentum Score

          +

          + During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable. +

          + +\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] + +\begin{p} + where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. +\end{p} + +\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] + +

          + If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance. +

          + +

          Rebalancing

          +

          + We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance. +

          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html new file mode 100644 index 0000000..90f2cf2 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html new file mode 100644 index 0000000..bd3b043 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/04 References.html @@ -0,0 +1,8 @@ +
            +
          1. + Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009) +
            2. +
          2. + Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017) +
            3. +
          \ No newline at end of file From 66a5e71f586cd9cf6d5ddfadd8bbf5263c0b00c8 Mon Sep 17 00:00:00 2001 From: Jared Date: Fri, 15 May 2020 10:35:20 -0700 Subject: [PATCH 122/215] Update 05 Options Moneyness.html --- .../01 General Features of Options/05 Options Moneyness.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html index ae5cbc4..a089ece 100755 --- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html +++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html @@ -26,5 +26,5 @@ But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.

          - Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration. + Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=-$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.

          From dac2cfe5f606a9f82e1d7ec0f790479deb8aafa1 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:23:50 -0600 Subject: [PATCH 123/215] Add residual momentum strategy writeup --- .../01 Introduction.html | 3 ++ .../136 Residual Momentum/02 Method.html | 31 +++++++++++++++++++ .../136 Residual Momentum/03 Algorithm.html | 6 ++++ .../136 Residual Momentum/04 References.html | 8 +++++ 4 files changed, 48 insertions(+) create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html new file mode 100644 index 0000000..28518a1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html @@ -0,0 +1,3 @@ +

          + Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. +

          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html new file mode 100644 index 0000000..5b927d1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -0,0 +1,31 @@ +

          Importing Custom Data

          +

          + The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the Fama-French data source URL from our Tutorials repo. We save the loaded data into a DataFrame in the alpha model. +

          + +

          Universe Selection

          +

          + In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices. +

          + +

          Calculate Residual Momentum Score

          +

          + During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable. +

          + +\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] + +\begin{p} + where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. +\end{p} + +\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] + +

          + If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance. +

          + +

          Rebalancing

          +

          + We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance. +

          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html new file mode 100644 index 0000000..90f2cf2 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -0,0 +1,6 @@ +
          +
          +
          + +
          +
          \ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html new file mode 100644 index 0000000..bd3b043 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/04 References.html @@ -0,0 +1,8 @@ +
            +
          1. + Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009) +
            2. +
          2. + Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017) +
            3. +
          \ No newline at end of file From 642a1e337b988ae6bff49802f84ed774986d01f7 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:41:30 -0600 Subject: [PATCH 124/215] Add strategy to quantpedia json and strategy library files --- .../00 Strategy Library/01 Strategy Library.php | 10 ++++++++++ quantpedia.json | 1 + 2 files changed, 11 insertions(+) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5909ba0..6470de6 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -91,6 +91,16 @@ 'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.', 'tags'=>'Beginner,Momentum,ETFs' + ], + [ + 'name' => 'Residual Momentum', + 'link' => 'strategy-library/residual-momentum', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/136' + ], + 'description' => 'Constructs a long/short portfolio based on trailing residual momentum normalized by its standard deviation', + 'tags'=>'Intermediate,Equities,Momentum' + ], [ 'name' => 'Sector Momentum', diff --git a/quantpedia.json b/quantpedia.json index 13a6d8d..ffdd5f6 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -40,6 +40,7 @@ 113: "9b1291a5f08dcc07df86363e46144084", 114: "7a404f43e9191ac5bce9a9446572cb98", 125: "1f9f254c932d8e7ae85e803affd52a7b", + 136: "10e626ccfe29e78f0eaead76158e788c", 152: "b35034509d95c173fed1c0adcecc5be0", 155: "886a9f73bc29142f14fcb2c2d01404a9", 162: "5761c1817a9dcb805be487f9e32adfa1", From 6fe5dc6fd34f3068cb82c7433af8233cc5ac8a42 Mon Sep 17 00:00:00 2001 From: dhantisha <44590120+dhantisha@users.noreply.github.com> Date: Sat, 23 May 2020 08:14:52 +0200 Subject: [PATCH 125/215] added var ticket = --- .../03 Tracking and Managing Orders/01 Overview.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html index ca80763..445f481 100755 --- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html +++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html @@ -16,7 +16,7 @@           - + From 105b688a4d46a16b46c25485b0121fe750114376 Mon Sep 17 00:00:00 2001 From: metricquant <44590120+metricquant@users.noreply.github.com> Date: Sat, 23 May 2020 22:29:07 +0200 Subject: [PATCH 126/215] removed reference to a C# example The example includes both C# and Python code, yet the text refers to only C#. --- .../03 Tracking and Managing Orders/02 Updating Orders.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html index a923569..668d4a1 100755 --- a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html +++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html @@ -1,5 +1,5 @@

          -Once you have an order ticket you can use it to get order fields. In C# it looks like: +Once you have an order ticket you can use it to get order fields. 

          var currentStopPrice = _ticket.Get(OrderField.StopPrice);

From 0afa0cefd413c0f27187685e5399277994fceba0 Mon Sep 17 00:00:00 2001
From: Jared 
Date: Sun, 24 May 2020 07:47:18 -0700
Subject: [PATCH 127/215] Update 01 Overview.html

---
 .../03 Tracking and Managing Orders/01 Overview.html | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
index 445f481..9c9b242 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -16,27 +16,27 @@
          - + - + - + - + - + - +
          Market OrderMarketOrder("SPY", 100);var ticket = MarketOrder("SPY", 100);
          Limit Order
          Market Ordervar ticket = MarketOrder("SPY", 100);var ticket = MarketOrder("SPY", 100);self.ticket = self.MarketOrder("SPY", 100)
          Limit Ordervar ticket = LimitOrder("SPY", 100, 100.10m);var ticket = LimitOrder("SPY", 100, 100.10m);self.ticket = self.LimitOrder("SPY", 100, 100.1)
          Stop Market Ordervar ticket = StopMarketOrder("SPY", 100, 100.10m);var ticket = StopMarketOrder("SPY", 100, 100.10m);self.ticket = self.StopMarketOrder("SPY", 100, 100.1)
          Stop Limit Ordervar ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);self.ticket = self.StopLimitOrder("SPY", 100, 100.12, 99.5)
          Market On Open Ordervar ticket = MarketOnOpen("SPY", 100);var ticket = MarketOnOpen("SPY", 100);self.ticket = self.MarketOnOpen("SPY", 100)
          Market On Close Ordervar ticket = MarketOnClose("SPY", 100);var ticket = MarketOnClose("SPY", 100);self.ticket = self.MarketOnClose("SPY", 100)
          From cbab941fe0af715e9596e33b4ae28779c04b0199 Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 24 May 2020 08:19:13 -0700 Subject: [PATCH 128/215] Update 01 Introduction.html --- .../136 Residual Momentum/01 Introduction.html | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html index 28518a1..57d67e1 100644 --- a/04 Strategy Library/136 Residual Momentum/01 Introduction.html +++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html @@ -1,3 +1,6 @@

          - Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. -

          \ No newline at end of file + Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the + residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic + Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. + This strategy is claimed to be more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. +

          From 87290fff62a1af50edee0ed334a91ef12e9940a1 Mon Sep 17 00:00:00 2001 From: David Jacobson Date: Tue, 26 May 2020 04:25:57 -0400 Subject: [PATCH 129/215] Update 04 Select Contracts.html Fix documentation to be consistent about 0 1 being call / put --- .../02 QuantConnect Options API/04 Select Contracts.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index cbf5dbc..54084fb 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -42,8 +42,8 @@ x.Right Get the right being purchased -x.Right ＝ 1  call option[right to buy] -x.Right ＝ 0  put option[right to sell] +x.Right ＝ 0  call option[right to buy] +x.Right ＝ 1  put option[right to sell] x.UnderlyingLastPrice From a6514b4ea1285e994f88aa7d31309f3aa55bd3d2 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 26 May 2020 15:18:27 -0600 Subject: [PATCH 130/215] Replace residual momentum strategy backtest with code reviewed one --- 04 Strategy Library/136 Residual Momentum/02 Method.html | 6 +++--- 04 Strategy Library/136 Residual Momentum/03 Algorithm.html | 2 +- quantpedia.json | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html index 5b927d1..7ed4ada 100644 --- a/04 Strategy Library/136 Residual Momentum/02 Method.html +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -15,9 +15,9 @@

          Calculate Residual Momentum Score

          \[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] -\begin{p} - where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. -\end{p} +

          + Where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. +

          \[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html index 90f2cf2..a989254 100644 --- a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -1,6 +1,6 @@
          - +
          \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index ffdd5f6..87ee747 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -40,7 +40,7 @@ 113: "9b1291a5f08dcc07df86363e46144084", 114: "7a404f43e9191ac5bce9a9446572cb98", 125: "1f9f254c932d8e7ae85e803affd52a7b", - 136: "10e626ccfe29e78f0eaead76158e788c", + 136: "d8932a9697f7e98e9a00761d37e627dd", 152: "b35034509d95c173fed1c0adcecc5be0", 155: "886a9f73bc29142f14fcb2c2d01404a9", 162: "5761c1817a9dcb805be487f9e32adfa1", From 96fd9d1760026754d7c3ab397f581bafc0a5c7cb Mon Sep 17 00:00:00 2001 From: Jared Broad Date: Sun, 7 Jun 2020 08:27:04 -0700 Subject: [PATCH 131/215] Delete visual studio tutorial --- .../02 Backtesting from Visual Studio/00.html | 1 - .../01 Introduction.html | 13 ------ .../02 Installation.html | 33 ------------- .../03 Plugin Features.html | 46 ------------------- .../04 Backtest Tool Window.html | 31 ------------- .../05 Troubleshooting.html | 7 --- 6 files changed, 131 deletions(-) delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/00.html delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/02 Installation.html delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html diff --git a/03 Open Source/02 Backtesting from Visual Studio/00.html b/03 Open Source/02 Backtesting from Visual Studio/00.html deleted file mode 100644 index 4b86dc6..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/00.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html deleted file mode 100644 index 4005af9..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

          -The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports Visual Studio 2015, 2017 and 2019. -

          - -

          - You can download the plugin here: - QuantConnect.VisualStudioPlugin_20190711.vsix *
          -* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing. -

          diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html deleted file mode 100644 index e97282f..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html +++ /dev/null @@ -1,33 +0,0 @@ - -

          -To install the plugin simply execute ("double click") the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below. -

          - -

          Build

          -
            -
          1. Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.
            2. -
          2. Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.
            3. -
          3. Verify in the Visual Studio ‘Output’ panel the compilation succeeded with no errors.
            4. -
          - -

          Installation

          -

          -If you have intstalled a previous version of the plugin you first need to remove this first. You do this from the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and click uninstall. Once uninstalled; remember to restart Visual Studio. -

          -

          -After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. If you built sucessfully you should be able to execute ‘QuantConnect.VisualStudioPlugin.vsix’. -

          - -

          Setting Up Your Plugin

          -
            -
          1. Login to QuantConnect and navigate to your My Account page.
            2. -
          2. In the API panel, you will find your a ‘unique access token’ and your ‘user id’ which are required to connect to the API. Copy these to your clipboard.
            3. -
          3. From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your API credentials and click Login.
            4. -
          -
          - -
          - -

          -Manually logging in is only required the first time. After the first login, the plugin will automatically log in using previously saved credentials. -

          \ No newline at end of file diff --git a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html deleted file mode 100644 index 8a4607c..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html +++ /dev/null @@ -1,46 +0,0 @@ -

          -The Visual Studio plugin can currently save files to a project, compile the project, and backtest it in the cloud. Through the accompanying "tool window" it can also rename, open or add a note to a backtest, and create a new project. -

          - -

          Saving Files to QuantConnect

          -

          -Save files from your local project to a QuantConnect project. You can save many files at a time. -

          -
            -
          1. Select the desired files with your mouse from the Solution Explorer tool window.
            2. -
          2. Right-click selection → ‘Save to QuantConnect’.
            3. -
          3. Select target project name: -
            -
            4. -
          4. - Result: -
              -
            • If the operation succeeds you will be informed through the VS Status Bar.
              • -
            • If any error occurs you will be informed through VS Status Bar and with a popup box.
              • -
            -
            -
            5. -
          - -

          Send for Backtesting

          -

          -This feature allows you to upload one or more files to a target project, compile it and backtest it in the QuantConnect cloud. -

          -
            -
          1. Select the desired files you'd like to backtest.
            2. -
          2. Right-click → ‘Send for Backtesting’
            3. -
          3. Select the target project with the project list view.
            4. -
          4. Result: -
              -
            • If the operation succeeds you will be informed through the VS Status Bar and the target QC project will be opened using the default browser pointing at your new backtest.
              • -
            • If any error occurs you will be informed through VS Status Bar and with a popup box.
              • -
            -
            -
            -If the error is due to a compilation error, the pop-up box will contain the first compilation error. -

            -
            -
            -If the error is due to a run-time issue during the backtesting process, the popup box will contain error details and the corresponding stack trace. - -
            5. \ No newline at end of file diff --git a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html deleted file mode 100644 index 8066c56..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html +++ /dev/null @@ -1,31 +0,0 @@ -

            -The backtesting tool window utility allows you to monitor and control ongoing backtests, along with editing various properties of existing completed backtests. -

            -
            - -
            -

            -If you select a project ‘BuyTheDip_007’ using the tool windows combo box and launch a backtest using ‘Send For Backtesting’ for ‘BuyTheDip_007’ project, it will display the backtests progress in the tool window. -

            - -

            How do I get the Backtest Window?

            -

            -From the Visual Studio IDE go to ‘View’ menu → ‘Other Windows’ → ‘QuantConnect’. If there are previous valid credentials, the tool window will auto login when open or when the user performs an action. -

            - -

            Available Backtest Window Features

            -
              -
            • Open Backtest: Open backtest viewer on QuantConnect.
              • -
            • Refresh/Reload Project List
              • -
            • Delete Backtest
              • -
            • Edit Backtest: Rename or add notes to backtest results. -
              - -
              -
              • -
            • Create a New Project -
              - -
              -
              • -
            \ No newline at end of file diff --git a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html deleted file mode 100644 index 9312fcf..0000000 --- a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html +++ /dev/null @@ -1,7 +0,0 @@ -

            -VisualStudio plugin can write log data to the VisualStudio activity log, but only if VisualStudio is started with the /log parameter switch. To debug the QuantConnect plugin start VisualStudio with the following command: -

            -
            devenv /log <path-to-log>
            -

            -See Visual Studio Documentation for more information. -

            \ No newline at end of file From 58429f39bf70e37c5815e195397fd7239b5f6379 Mon Sep 17 00:00:00 2001 From: Jared Date: Sun, 7 Jun 2020 08:33:38 -0700 Subject: [PATCH 132/215] Update Introduction.html --- 03 Open Source/00 Introduction/Introduction.html | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html index 744480f..dff5ed2 100644 --- a/03 Open Source/00 Introduction/Introduction.html +++ b/03 Open Source/00 Introduction/Introduction.html @@ -14,14 +14,6 @@

            100 Debugging Python

          - -
          -

          101 Backtesting from Visual Studio

          -

          Visual Studio plugin integrated with the QuantConnect API.

          - -
          -           -

          102 Using the API File Provider

          @@ -52,4 +44,4 @@

          301 Brokerage Development Guide

          Guide to implementing your own brokerage in LEAN.

          -           \ No newline at end of file + From 0afb0eaab71b9c54a55fcc6f1e7d76d1a5c02c0e Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Fri, 12 Jun 2020 09:38:50 -0700 Subject: [PATCH 133/215] Update image protocol --- 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html index 93c5b88..a0d48d6 100644 --- a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html +++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html @@ -22,4 +22,4 @@

    - \ No newline at end of file + From 1d76f6ce967fd92e68435b9b4374e33a84962aac Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Fri, 12 Jun 2020 09:39:27 -0700 Subject: [PATCH 134/215] Update Gif protocol. --- .../05 Method 3 - VisualStudio Debugger.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html index 6f744e0..7767d5e 100644 --- a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html +++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html @@ -22,4 +22,4 @@

    Prerequisites

    - \ No newline at end of file + From 291389c69ca15cc7094b3c84476731a0232dde7f Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 15 Jun 2020 18:37:05 -0600 Subject: [PATCH 135/215] Adds strategy 85 - Momentum in Mutual Fund Returns --- .../01 Strategy Library.php | 9 + .../01 Introduction.html | 8 + .../02 Method.html | 179 ++++++++++++++++++ .../03 Algorithm.html | 6 + .../04 References.html | 5 + quantpedia.json | 1 + 6 files changed, 208 insertions(+) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6470de6..0f120a9 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -337,6 +337,15 @@ 'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.', 'tags'=>'Intermediate,Long Short,Equities' ], + [ + 'name' => 'Momentum in Mutual Fund Returns', + 'link' => 'strategy-library/momentum-in-mutual-fund-returns', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85' + ], + 'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.', + 'tags'=>'Intermediate,Long Short,Equities' + ], [ 'name' => 'Momentum and Style Rotation Effect', 'link' => 'strategy-library/momentum-and-style-rotation-effect', diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html new file mode 100644 index 0000000..3b57a7e --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -0,0 +1,8 @@ +

    + Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide + significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the + persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds + with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in + individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use + their respective share price as a proxy for fund performance and NAV. +

    diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html new file mode 100644 index 0000000..119b3e7 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -0,0 +1,179 @@ +

    Universe Selection

    +

    + In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. +

    +
    +
    +def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+
    +
    + +

    + In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry. +

    +
    +
    +def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+
    +
    + +

    Alpha Construction

    +

    + When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to + long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to + calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs. +

    +
    +
    +def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+
    +
    + +

    + For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers + a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator. +

    +
    +
    +def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
    +
    + +

    Alpha Update

    +

    + On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model. +

    +
    +
    +def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+
    +
    + +

    Alpha Ranking

    +

    + We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with + the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. + The symbols with a larger final sum have a greater index in the list we return. +

    +
    +
    +@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+
    +
    + +

    + We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and + then computing the respective values. +

    +
    +
    +@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+
    +
    + +

    Alpha Insights

    +

    + We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the + universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest + rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months. +

    +
    +
    +def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+
    +
    + +

    Portfolio Construction

    +

    + We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of + insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol + with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate + the net direction of the symbols with the following helper method. +

    +
    +
    +def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+
    +
    + + diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html new file mode 100644 index 0000000..646988c --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -0,0 +1,6 @@ +
    +
    +
    + +
    +
    \ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html new file mode 100644 index 0000000..29e36f1 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html @@ -0,0 +1,5 @@ +
      +
    1. + Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011. +
      2. +
    \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index 87ee747..255ed4f 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,6 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", + 85: "cdcfb760e59da292a56e08c04a219179", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From a4ead7960f6c38dcaa90cba9548822fbd7621125 Mon Sep 17 00:00:00 2001 From: hsm207 Date: Sun, 28 Jun 2020 15:44:12 +0800 Subject: [PATCH 136/215] Fix typo --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6470de6..e5ab9e0 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -50,7 +50,7 @@ 'tags'=>'Expert,Pairs Trading,Equities' ], [ - 'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market', + 'name' => 'The Momentum Strategy Based on the Low Frequency Component of Forex Market', 'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market', 'description' => 'Applies high frequency filter to the momentum strategy.', 'tags'=>'Intermediate,Momentum,Forex' From 0316975d2d33489b9da8852a23265032a787b52f Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 13 Jul 2020 09:31:45 -0600 Subject: [PATCH 137/215] Add strategy library workflow --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/README.md b/README.md index da878ac..5e2fd23 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,22 @@ The mailing list for the project can be found on [Google Groups][6] Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7]. +## Strategy Library Development Workflow ## +To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps: +1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process). +2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png). +3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py). +4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)). +5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project. +6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples). +7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)). +8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)): + - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload). + - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023. + - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json). + - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID). +9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)). + [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer" [2]: https://www.quantconnect.com/lean/docs "Lean Documentation" [3]: https://github.com/QuantConnect/Lean/archive/master.zip From a927c4c081002b6031009e6d8edbc4a03f7a2a79 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 15 Jun 2020 18:37:05 -0600 Subject: [PATCH 138/215] Adds strategy 85 - Momentum in Mutual Fund Returns --- .../01 Strategy Library.php | 9 + .../01 Introduction.html | 8 + .../02 Method.html | 179 ++++++++++++++++++ .../03 Algorithm.html | 6 + .../04 References.html | 5 + quantpedia.json | 1 + 6 files changed, 208 insertions(+) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index e5ab9e0..28ae3dc 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -337,6 +337,15 @@ 'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.', 'tags'=>'Intermediate,Long Short,Equities' ], + [ + 'name' => 'Momentum in Mutual Fund Returns', + 'link' => 'strategy-library/momentum-in-mutual-fund-returns', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85' + ], + 'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.', + 'tags'=>'Intermediate,Long Short,Equities' + ], [ 'name' => 'Momentum and Style Rotation Effect', 'link' => 'strategy-library/momentum-and-style-rotation-effect', diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html new file mode 100644 index 0000000..3b57a7e --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -0,0 +1,8 @@ +

    + Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide + significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the + persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds + with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in + individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use + their respective share price as a proxy for fund performance and NAV. +

    diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html new file mode 100644 index 0000000..119b3e7 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -0,0 +1,179 @@ +

    Universe Selection

    +

    + In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. +

    +
    +
    +def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+
    +
    + +

    + In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry. +

    +
    +
    +def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+
    +
    + +

    Alpha Construction

    +

    + When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to + long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to + calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs. +

    +
    +
    +def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+
    +
    + +

    + For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers + a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator. +

    +
    +
    +def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
    +
    + +

    Alpha Update

    +

    + On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model. +

    +
    +
    +def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+
    +
    + +

    Alpha Ranking

    +

    + We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with + the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. + The symbols with a larger final sum have a greater index in the list we return. +

    +
    +
    +@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+
    +
    + +

    + We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and + then computing the respective values. +

    +
    +
    +@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+
    +
    + +

    Alpha Insights

    +

    + We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the + universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest + rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months. +

    +
    +
    +def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+
    +
    + +

    Portfolio Construction

    +

    + We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of + insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol + with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate + the net direction of the symbols with the following helper method. +

    +
    +
    +def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+
    +
    + + diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html new file mode 100644 index 0000000..646988c --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -0,0 +1,6 @@ +
    +
    +
    + +
    +
    \ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html new file mode 100644 index 0000000..29e36f1 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html @@ -0,0 +1,5 @@ +
      +
    1. + Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011. +
      2. +
    \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index 87ee747..255ed4f 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,6 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", + 85: "cdcfb760e59da292a56e08c04a219179", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From bfc770811be63b9c81ef66222702a165fe907672 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 17 Jul 2020 13:42:10 -0600 Subject: [PATCH 139/215] define NAV, refactor code, fix lookback bug --- .../01 Introduction.html | 13 ++-- .../02 Method.html | 65 ++++++++++--------- .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 4 files changed, 45 insertions(+), 37 deletions(-) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html index 3b57a7e..ec8c6d8 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -1,8 +1,9 @@

    - Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide - significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the - persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds - with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in - individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use - their respective share price as a proxy for fund performance and NAV. + Researchers have shown that the historical returns of a mutual fund and the nearness of its net asset value (NAV) to + a previous high can provide significant predictive power about the fund's future returns. In respect to the + historical returns, some have attributed the persistence to investor herding and macroeconomic variables. When it + comes to the NAV, some suggest the outperformance of funds with a NAV near its trailing high is a result of + anchoring bias in investors' psychology. As we do not have access to invest in individual mutual funds on the QC + platform or access to NAV metrics, in this tutorial, we trade asset management firms and use their respective share + price as a proxy for fund performance and NAV.

    diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html index 119b3e7..cd4436e 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -1,14 +1,14 @@

    Universe Selection

    - In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. + In coarse universe selection, we return symbols that have fundamental data. 

     def SelectCoarse(self, algorithm, coarse):
     if self.month == algorithm.Time.month:
-        return Universe.Unchanged
-    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
-    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+            return Universe.Unchanged
+            
+    return [x.Symbol for x in coarse if x.HasFundamentalData]
    @@ -19,8 +19,8 @@

    Universe Selection

     def SelectFine(self, algorithm, fine):
     self.month = algorithm.Time.month
-    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
-    return [ x.Symbol for x in filtered[:self.fine_size] ]
+        
+    return [f.Symbol for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
    @@ -34,14 +34,10 @@

    Alpha Construction

     def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
     if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
-        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
-        algorithm.Quit()
-        return
-
+        algorithm.Quit(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        
     if pct_long_short <= 0 or pct_long_short > 50:
-        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
-        algorithm.Quit()
-        return
+        algorithm.Quit(f"Requirement violated: 0 < pct_long_short <= 50")
 
     self.roc_lookback_months = roc_lookback_months
     self.nearness_lookback_months = nearness_lookback_months
@@ -70,7 +66,8 @@ 
    Alpha Construction
    
 
 
    Alpha Update
    
 
    
-  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model.
+  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction 
+  model. We instruct the alpha model to emit insights on a monthly basis by adding the following guard to the Update method. 
 
    
 
    
 
    @@ -81,34 +78,45 @@ 
    Alpha Update
    
         return []
     self.month = time.month
 
-    return self.generate_insights(self.ranked_symbols, time)
+    ...
 
    
 
    
 
 
    Alpha Ranking
    
 
    
-  We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with 
-  the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. 
-  The symbols with a larger final sum have a greater index in the list we return.
+  We only rank symbols that have enough history to fill the rate of change lookback window. Therefore, we define the 
+  IsReady method of the ROCAndNearness as
 
    
 
    
 
     @property
-def ranked_symbols(self):
-    ranking_df = pd.DataFrame()
+def IsReady(self):
+    return self.get_lookback(self.roc_lookback_months).shape[0] > 1
+
    
+
    
 
+
    
+  To rank the symbols, we start by filling a DataFrame with the rate of change and nearness to trailing high values for each 
+  symbol. When the DataFrame is full, we rank the symbols by both metrics and sum the ranks. The symbols with a larger final 
+  sum have a greater index in the `ranked_symbols` list.
+
    
+
    
+
    +def Update(self, algorithm, data):
+    ...
+    ranking_df = pd.DataFrame()
     for symbol, symbol_data in self.symbol_data_by_symbol.items():
         if symbol_data.IsReady:
             row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
             ranking_df = ranking_df.append(row)
-
-    return ranking_df.rank().sum(axis=1).sort_values().index
+    ranked_symbols =  ranking_df.rank().sum(axis=1).sort_values().index
+   ...
 
    
 
    
 
 
    
-  We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and 
-  then computing the respective values.
+  Calculating the rate of change and nearness factors is done by slicing the historical data into the approriate lookback window 
+  size, and then computing the respective values.
 
    
 
    
 
    @@ -129,14 +137,14 @@ 
    Alpha Ranking
    
 
    Alpha Insights
    
 
    
   We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the 
-  universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest 
-  rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
+  universe we long and short is customizable in the alpha model constructor. Here, we long the 25% of symbols with the highest 
+  rank, short the 25% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
 
    
 
    
 
    -def generate_insights(self, ranked_symbols, time):
+def Update(self, algorithm, data):
+    ...
     insights = []
-
     num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
     if num_long_short > 0:
         hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
@@ -144,7 +152,6 @@ 
    Alpha Insights
    
             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
         for symbol in ranked_symbols[:num_long_short]:
             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
-
     return insights
 
    
 
    
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
index 646988c..ade7d3b 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 255ed4f..cbf4970 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,7 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
-     85: "cdcfb760e59da292a56e08c04a219179",
+     85: "ba030edb022016f967a6296a556d717e",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From ccbf44f6741687e50985494f4a6baa3cb3d90ef0 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Wed, 29 Jul 2020 18:07:56 -0600
Subject: [PATCH 140/215] Adds intraday etf momentum strategy

---
 .../01 Strategy Library.php                   |  11 +-
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  19 +++
 .../1026 Intraday ETF Momentum/03 Method.html | 143 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Conclusion.html                        |  23 +++
 .../06 References.html                        |   6 +
 7 files changed, 214 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..30af0f7 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+    [
+        'name' => 'Intraday ETF Momentum',
+        'link' => 'strategy-library/intraday-etf-momentum',
+        'sources' => [
+            'NYU' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2440866'
+        ],
+        'description' => "A momentum strategy based on returns of the market open",
+        'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
new file mode 100644
index 0000000..c9aef2c
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html	
@@ -0,0 +1,7 @@
+
    
+  In this tutorial, we implement an intraday momentum strategy that trades some of the most actively traded ETFs. 
+  Specifically, we observe the return generated from the first half-hour of the trading day to predict the sign of 
+  the trading day's last half-hour return. Researchers have shown that this momentum pattern is statistically and 
+  economically significant, even after accounting for trading fees. The algorithm we design here is a recreation of 
+  the research completed by Gao, Han, Li, and Zhou (2017).
+
    
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
new file mode 100644
index 0000000..c06af0a
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html	
@@ -0,0 +1,19 @@
+
    
+  News items are usually released before the opening bell. As it takes time for traders to digest and interpret the 
+  news, the first half-hour of trading typically has relatively higher levels of volume and volatility. Additionally, 
+  as traders attempt to mitigate overnight risk by unloading positions near the close, the last half-hour of trading 
+  also sees these higher levels of volume and volatility. These characteristics can be observed from the image below, 
+  which is reproducible in the attached research notebook.
+
    
+
+Tutorial1026-intraday-etf-momentum-1 
+
+
    
+  Bogousslavsky (2016) points out that some investors are late-informed or simply prefer to delay their trading until 
+  the market close. As a result, a positive correlation exists between the direction of the opening and closing 
+  periods. Gao et al (2017) find that when trading this momentum strategy, the average annual return over their 
+  sample period was 6.67% for SPY, 11.72% for IWM, and 24.22% for IYR. Equal-weighting these returns leads to a 
+  combined average annual return of 14.2%.
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
new file mode 100644
index 0000000..29481ab
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html	
@@ -0,0 +1,143 @@
+
    Universe Selection
    
+
    
+  We implement a manual universe selection model that supplies a subset of the proposed ETFs in the attached research 
+  paper. Gao et al (2017) select the following tickers: DIA, QQQ, IWM, EEM, FXI, EFA, VWO, XLF, IYR, and TLT. In an 
+  effort to increase the backtest performance, we narrow our universe to SPY, IWM, and IYR.
+
    
+
    
+
    +tickers = ['SPY',  # S&P 500
+           'IWM',  # Russell 2000
+           'IYR'   # Real Estate ETF
+]
+symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+
    
+
    
+
+
    Alpha Construction
    
+
    
+  The IntradayMomentumAlphaModel emits insights to take positions for the last `return_bar_count` minutes of the day 
+  in the direction of the return for the first `return_bar_count` minutes of the day. During construction, we create 
+  a dictionary to store IntradayMomentum data for each symbol, define a method to determine the sign of returns, and 
+  specify the value of `return_bar_count`. In this tutorial, we follow Gao et al (2017) in setting `return_bar_count`
+  to 30 by default.
+
    
+
    
+
    +class IntradayMomentumAlphaModel(AlphaModel):
+    intraday_momentum_by_symbol = {}
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+
+    def __init__(self, algorithm, return_bar_count = 30):
+        self.return_bar_count = return_bar_count
+
    
+
    
+
+
    Alpha Securities Management
    
+
    
+  When a new security is added to the universe, we create an IntradayMomentum object for it to store information 
+  needed to calculate morning returns. The management of the IntradayMomentum objects occurs in the alpha model's 
+  OnSecuritiesChanged method.
+
    
+
    
+
    +def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.intraday_momentum_by_symbol[security.Symbol] = IntradayMomentum(security, algorithm)
+
+    for security in changes.RemovedSecurities:
+        self.intraday_momentum_by_symbol.pop(security.Symbol, None)
+
    
+
    
+
+
    
+  The definition of the IntradayMomentum class is shown below. We save a reference to the security's exchange so we 
+  can access the market hours of the exchange when generating insights.
+
    
+
    
+
    +class IntradayMomentum:
+    def __init__(self, security, algorithm):
+        self.symbol = security.Symbol
+        self.exchange = security.Exchange
+
+        self.bars_seen_today = 0
+        self.yesterdays_close = algorithm.History(self.symbol, 1, Resolution.Daily).loc[self.symbol].close[0]
+        self.morning_return = 0
+
    
+
    
+
+
    Alpha Update
    
+
    
+  With each call to the alpha model's Update method, we count the number of bars the algorithm has received for each 
+  symbol. If we've reached the end of the morning window, we calculate the morning return. If we are at the 
+  beginning of the close window, we emit an insight in the direction of the morning window's return. If we are at 
+  the end of the day, we save the closing price and reset the counter for the number of bars seen today.
+
    
+
    
+
    +def Update(self, algorithm, slice):
+    insights = []
+
+    for symbol, intraday_momentum in self.intraday_momentum_by_symbol.items():
+        if slice.ContainsKey(symbol) and slice[symbol] is not None:
+            intraday_momentum.bars_seen_today += 1
+            
+            # End of the morning return
+            if intraday_momentum.bars_seen_today == self.return_bar_count:
+                intraday_momentum.morning_return = (slice[symbol].Close - intraday_momentum.yesterdays_close) / intraday_momentum.yesterdays_close
+            
+            ## Beginning of the close
+            next_close_time = intraday_momentum.exchange.Hours.GetNextMarketClose(slice.Time, False)
+            mins_to_close = int((next_close_time - slice.Time).total_seconds() / 60)
+            
+            if mins_to_close == self.return_bar_count + 1:
+                insight = Insight.Price(intraday_momentum.symbol, 
+                                        next_close_time, 
+                                        self.sign(intraday_momentum.morning_return))
+                insights.append(insight)
+                continue
+            
+            # End of the day
+            if not intraday_momentum.exchange.DateTimeIsOpen(slice.Time):
+                intraday_momentum.yesterdays_close = slice[symbol].Close
+                intraday_momentum.bars_seen_today = 0
+            
+    return insights
+
    
+
    
+
+
    Trade Execution
    
+
    
+  The attached research paper holds positions for the last 30 minutes of the trading day, exiting at the market close.
+  In order to accomplish this, we create a custom execution model. The model defined below submits a market order for 
+  the entry while also submitting a market on close order in the same time step.
+
    
+
    
+
    +class CloseOnCloseExecutionModel(ExecutionModel):
+    def __init__(self):
+        self.targetsCollection = PortfolioTargetCollection()
+        self.invested_symbols = []
+
+    def Execute(self, algorithm, targets):
+        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        self.targetsCollection.AddRange(targets)
+        if self.targetsCollection.Count > 0:
+            for target in self.targetsCollection.OrderByMarginImpact(algorithm):
+                # calculate remaining quantity to be ordered
+                quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
+                if quantity == 0:
+                    continue
+
+                algorithm.MarketOrder(target.Symbol, quantity)
+                algorithm.MarketOnCloseOrder(target.Symbol, -quantity)
+
+            self.targetsCollection.ClearFulfilled(algorithm)
+
    
+
    
+
+
+
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
new file mode 100644
index 0000000..c7304ae
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
    
+    
    
+    
    
+        
+    
    
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
new file mode 100644
index 0000000..7623dda
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -0,0 +1,23 @@
+
    
+  We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period.
+  In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the 
+  inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their 
+  research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum 
+  pattern. These areas of future research include:
+
    
+
+
      
+  
    • 
+    Trading only on days with economic news events by utilizing the 
+    TradingEconomics data set. 
+    Gae et al (2017) suggest using the Michigan Consumer Sentiment Index, and news released on gross domestic 
+    product or the consumer price index.
+  
      • 
+  
    • Restricting trading to times of greater volatility or during financial crises.
      • 
+  
    • Incorporating a volume threshold the morning session must pass to signal a trade for the close.
      • 
+  
    • Increasing diversification by extending the universe to include ETFs from other sectors.
      • 
+  
    • 
+    Considering the return from multiple n-minute periods throughout the day to predict the return of the closing 
+    period.
+  
      • 
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html
new file mode 100644
index 0000000..a3127a1
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html	
@@ -0,0 +1,6 @@
+
      
+  
    1. 
+    Gao, Lei and Han, Yufeng and Li, Sophia Zhengzi and Zhou, Guofu, Market Intraday Momentum (June 19, 2017). 
+    Online copy
+  
      2. 
+
    
\ No newline at end of file

From 75a44e5d0877fd4c51bcafa9b911aaab1727879f Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 3 Aug 2020 08:48:05 -0700
Subject: [PATCH 141/215] Added 279 temporal cnn strategy

---
 .../01 Abstract.html                          |   6 +
 .../02 Introduction.html                      |   8 +
 .../03 Method.html                            | 280 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 References.html                        |   7 +
 5 files changed, 307 insertions(+)
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
new file mode 100644
index 0000000..a6cf634
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html	
@@ -0,0 +1,6 @@
+
    
+    In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices.
+
    
+
    
+    Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
new file mode 100644
index 0000000..1fa0f4e
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html	
@@ -0,0 +1,8 @@
+
    
+    Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements.
+    More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from
+    medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price
+    movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in
+    image classification, and now, researchers are applying CNNs to extract patterns, also known as features, from times-series
+    data to forecast future stock prices.
+
    
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
new file mode 100644
index 0000000..1a4765a
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html	
@@ -0,0 +1,280 @@
+
    Overview
    
+
    
+    Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data
+    to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and
+    bet on the direction of the price movement based on our model prediction. We will walk through the code required
+    for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand.
+
    
+
+
    Inputs/Outputs
    
+
+
    
+    Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model.
+    The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a
+    direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps).
+    The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will
+    form the labels for which our model will try to classify, thus we have a classification problem.
+
    
+
+
+
    Neural Network Model Architecture
    
+
+
    
+    Now, we will need to build our Neural Network Architecture, which we will build using Keras,
+    a high-level Python Deep Learning API. To begin, we will need a few import statements:
+
    
+
+
    
+
    +    import tensorflow as tf
+    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
+    from tensorflow.keras import Model
+    from tensorflow.keras import metrics
+    from tensorflow.keras.losses import CategoricalCrossentropy
+    from tensorflow.keras import utils
+    from sklearn.preprocessing import StandardScaler
+    import numpy as np
+    import math
+
    
+
    
+
+
+
    
+    We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input
+    variables (OHLCV), our input shape will be 15 x 5.
+
    
+
+
    
+
    +    inputs = Input(shape=(15, 5))
+
    
+
    
+
+
+
    
+    We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as
+    the Neural Network's method of extracting patterns from the time-series data.
+
    
+
+
    
+
    +    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
+
    
+
    
+
+
+
    
+
    +    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
+    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
+    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
+
+    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
+    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
+    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
+
    
+
    
+
+
+
    
+    These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten
+    our layer.
+
    
+
+
    
+
    +    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
+    flattened = Flatten()(combined)
+
    
+
    
+
+
+
    
+    Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down),
+    this layer will have three nodes.
+
    
+
+
    
+
    +    outputs = Dense(3, activation='softmax')(flattened)
+
    
+
    
+
+
    
+    The resulting Neural Network Architecture is shown in the following:
+
    
+
+temporal cnn model architecture
+
+
+
    Preparing the Data for Our Model
    
+
+
    
+    First, we need to define a class and a few variables:
+
    
+
+
    
+
    +    input_vars = ['open', 'high', 'low', 'close', 'volume']
+
+    class Direction:
+        UP = 0
+        DOWN = 1
+        STATIONARY = 2
+
+    rolling_avg_window_size = 5
+
+    shift = -(rolling_avg_window_size-1)
+
+    stationary_threshold = .0001
+
+    scaler = StandardScaler()
+
    
+
    
+
+
    
+    input_vars define the variables we want to use to make our predictions. The class Direction
+    defines a few integers that we will label our data with (labels are needed for classification problems). The reason
+    we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on,
+    rolling_avg_window_size is the number of time steps used for the calculate the average of future closing prices,
+    described earlier in
+    Inputs/Outputs (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5).
+    The constant stationary_threshold defines the threshold for a change in price to be considered
+    stationary, and this change also described in Inputs/Outputs. The shift is the shift needed to align
+    the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces
+    manageable for our Neural Network model. The scaler object will be used later to scale our data.
+    The purpose of the variables will become clearer in use.
+
    
+    Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate
+    the percent change from the close at t, we use the following lines of code:
+
    
+
+
    
+
    +    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
+    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
+
    
+
    
+
+
    
+    The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize
+    this is a more advanced resource).
+    Here, .shift(shift) aligns the five time step rolling average 'close_avg' column to the end of the last
+    time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input
+    and labeled data for our model much easier.
+
    
+
+
    
+    To label our data, we need to first define a function that we will use with the DataFrame's apply() method.
+    Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda.
+
    
+
+
    
+
    +    def label_data(row):
+        if row['close_avg_change_pct'] > stationary_threshold:
+            return Direction.UP
+        elif row['close_avg_change_pct'] < -stationary_threshold:
+            return Direction.DOWN
+        else:
+            return Direction.STATIONARY
+
    
+
    
+
+
    
+    Now, we apply the above function to our DataFrame to get a column of labels:
+
    
+
+
    
+
    +    df['movement_labels'] = df.apply(label_data, axis=1)
+
    
+
    
+
+
    
+    With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into
+    lists:
+
    
+
+
    
+
    +    data = []
+    labels = []
+
+    for i in range(len(df)-self.n_tsteps+1+shift):
+        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
+        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
+        labels.append(label)
+
+    data = np.array(data)
+
    
+
    
+
+
    
+    Here, we iterate numerically through the DataFrame, with a carefully calculated value in our range()
+    function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because
+    Keras works best with numpy arrays.
+
    
+
+
    
+    Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the
+    range of values is normalized across the features.
+
    
+
+
    
+
    +    dim1, dim2, dim3 = data.shape
+    data = data.reshape(dim1*dim2, dim3)
+    data = scaler.fit_transform(data)
+    data = data.reshape(dim1, dim2, dim3)
+
    
+
    
+
+
    
+    The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after,
+    we can return the data to the original shape with another reshaping.
+
    
+
+
    
+    Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of
+    1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following:
+
    
+
+
    
+
    +    labels = utils.to_categorical(labels, num_classes=3)
+
    
+
    
+
+
    
+    Specifying num_classes to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary).
+
    
+
+
    
+    We have now finished the walk through of the difficult parts of the code.
+
    
+
+
    Trading
    
+
+
    
+    After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be
+    found in Algorithm) we can
+    use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make
+    a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit
+    an Price Insight with direction InsightDirection.Up (resp. InsightDirection.Down). Since we are
+    betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights
+    in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight
+    with a timedelta with a random integer between one and five to constrain the number of insights we emit.
+
    
+
+
+
    The Rest
    
+
+
    
+    We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the
+    necessary code to execute the strategy can be found in Algorithm.
+
    
+
+
+
+
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
new file mode 100644
index 0000000..9e98ce9
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
    
+    
    
+    
    
+        
+    
    
+
    
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html
new file mode 100644
index 0000000..dedbeb6
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html	
@@ -0,0 +1,7 @@
+
      
+  
    1. 
+      Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis:
+      "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019;
+      https://arxiv.org/pdf/1901.08280.pdf.
+  
      2. 
+
    
\ No newline at end of file

From a7c9fbec4ab53b8a8334d2acde9d6b87dec13848 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 3 Aug 2020 08:50:08 -0700
Subject: [PATCH 142/215] 279 temporal cnn update 00 SL

---
 .../00 Strategy Library/01 Strategy Library.php       | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..2efe9eb 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+	[
+        'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model',
+        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn',
+        'sources' => [
+            'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf'
+        ],
+        'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.",
+        'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning'
+    ]
 ];
 
 ?>

From cb872175e7804070859b0b63661189572f8ad161 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Mon, 3 Aug 2020 17:45:37 -0600
Subject: [PATCH 143/215] Fix missing div tags

---
 .../03 Method.html                                            | 3 ++-
 .../03 Algorithm.html                                         | 2 +-
 .../03 Algorithm.html                                         | 2 +-
 .../29 Term Structure Effect in Commodities/03 Algorithm.html | 4 ++--
 .../92 Price Earnings Anomaly/03 Algorithm.html               | 2 +-
 5 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index 3675eee..ffff064 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -82,4 +82,5 @@ 
    Step 4: Rebalance Function:
    
 
     # Invest 100% in the each of the selected symbols
     for symbol in symbols:
-        self.SetHoldings(symbol, 1)
    
\ No newline at end of file
+        self.SetHoldings(symbol, 1)
    + \ No newline at end of file diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html index 635c7b2..ad5e9f8 100644 --- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html +++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
    diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html index cc3a665..b50f912 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
    diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html index 1707718..ada8627 100644 --- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html +++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html @@ -1,6 +1,6 @@

    Algorithm

    -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
    @@ -8,7 +8,7 @@
    -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
    diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html index dce77dd..35a5232 100644 --- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html +++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
    From 53e3114c169f284aba74960ff1b61d9ad3837982 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 11 Aug 2020 17:03:22 -0600 Subject: [PATCH 144/215] Adds strategy 1028 - Ichimoku Clouds in the Energy Sector --- .../01 Strategy Library.php | 11 +- .../01 Abstract.html | 8 + .../02 Introduction.html | 35 ++++ .../03 Method.html | 150 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Benchmark Performance.html | 10 ++ ...06 Market & Competition Qualification.html | 13 ++ .../07 Conclusion.html | 21 +++ .../08 References.html | 6 + 9 files changed, 259 insertions(+), 1 deletion(-) create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index e5ab9e0..e4eb7df 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -606,7 +606,16 @@ ], 'description' => "A simple trend following strategy on commodities futures.", 'tags' => 'Momentum, Futures, Commodities' - ] + ], + [ + 'name' => 'Ichimoku Clouds in the Energy Sector', + 'link' => 'strategy-library/ichimoku-clouds-in-the-energy-sector', + 'sources' => [ + 'SSRN' => 'https://ssrn.com/abstract=3520582' + ], + 'description' => 'A techincal indicator crossover strategy trading the largest energy companies.', + 'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities' + ], ]; ?> diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html new file mode 100644 index 0000000..3dd4983 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html @@ -0,0 +1,8 @@ +

    + Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the + largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the + effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an + impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib + (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular + energy sector ETF. +

    diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html new file mode 100644 index 0000000..c5efef1 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html @@ -0,0 +1,35 @@ +

    + A vast amount of research studies have been published which document mixed results when utilizing technical + analysis to forecast future prices of securities. The Ichimoku Cloud, one of the most widely-used technical + indicators in Japan, was first publicized by Goichi Hosoda. In 1996, Hidenobu Sasaki reworked the framework to + form the current charting analysis tool. This indicator is composed of 5 lines in a time series, each of which + are described mathematically in + online resources. +

    + +

    + Gurrib (2020) finds that applying a simple trading strategy using the time series of the Ichimoku Cloud can + increase the mean return of a basket containing the top energy stocks from 21.5% (buy-and-hold) to 194% over a 7 + year period. The components of the Ichimoku Cloud that Gurrib (2020) utilizes in this strategy are the Chikou + Span, Senkou Span A, and Senkou Span B. The Senkou Span lines form the top and bottom of the Ichimoku Cloud. The + strategy that we trade off of these lines is defined as follows: +

    + +
      +
    • Long when the Chikou line crosses the top of the cloud from below.
      • +
    • Short when the Chikou line crosses the bottom of the cloud from above.
      • +
    + +

    + For better understanding, here is a visualization of the price of XOM, it's Ichimoku Cloud time series, and the + resulting buy/sell signals. +

    + +Tutorial1028-ichimoku-cloud-1 + +

    + Note: all of the plots throughout this tutorial are reproducible in the attached research notebook, along with + some descriptive statistics for securities in the universe. +

    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html new file mode 100644 index 0000000..8669ce3 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html @@ -0,0 +1,150 @@ +

    Universe Selection

    +

    + Gurrib (2020) selects a universe of the 10 largest-weighed constituents of the S&P Composite 1500 Energy Index + over the testing period. This inherently incorporates lookahead-bias into the study as the security weights are + sourced over the period the trading simulation occurs. Furthermore, since the publication of Gurrib (2020), some + of the securities have even been delisted. Thus, to eliminate lookahead-bias and avoid delistings, we implement a + universe selection model that provides the trading system with the 10 largest companies in the energy sector as of + the current date in the backtest. Since the largest companies change infrequently, we only refresh the universe on + a monthly basis. +

    + +
    +
    +def SelectCoarse(self, algorithm, coarse):
+    if algorithm.Time.month == self.month:
+        return Universe.Unchanged
+    return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    
+    energy_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy ]
+    sorted_by_market_cap = sorted(energy_stocks, key=lambda x: x.MarketCap, reverse=True)
+    return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+
    +
    + + +

    Alpha Construction

    +

    + The IchimokuCloudCrossOverAlphaModel emits insights to hold a long position after the Chikou Span crosses over the + top of the cloud for a given security. Additionally, the strategy is made symmetrical by entering short positions + after the Chikou Span crosses below the bottom of the cloud. During construction of this alpha model, we simply + set up a dictionary to hold a SymbolData object for each symbol in the universe. +

    + +
    +
    +class IchimokuCloudCrossOverAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+
    +
    + +

    + The SymbolData class constructor is shown below. We first set up two class variables, `previous_location` and + `direction`. The former enables the algorithm to signal when the Chikou Span crosses over the boundaries of the + Ichimoku Cloud. The latter is added to ensure we continue to emit daily insights in the proper direction. Inside + the `__init__` method is where we create the + IchimokuKinkoHyo indicator and warm + it up. +

    + +
    +
    +class SymbolData:
+    previous_location = None
+    direction = None
+    
+    def __init__(self, symbol, algorithm):
+        # Create Ichimoku indicator
+        self.ichimoku = IchimokuKinkoHyo()
+        
+        # Warm up indicator
+        history = algorithm.History(symbol, self.ichimoku.WarmUpPeriod + 1, Resolution.Daily).loc[symbol]
+        for idx, row in history.iterrows():
+            if self.ichimoku.IsReady:
+                self.previous_location = self.get_location()
+            
+            tradebar = TradeBar(idx, symbol, row.open, row.high, row.low, row.close, row.volume)
+            self.ichimoku.Update(tradebar)
+
    +
    + + +

    Determining the Indicator Location

    +

    + We define the following helper method to return the location of the Chikou Span with respect to the cloud. The + alpha model utilizes this helper method to determine when the Chikou Span is exiting the Ichimoku Cloud. +

    + +
    +
    +def get_location(self):
+    chikou = self.ichimoku.Chikou.Current.Value
+    
+    senkou_span_a = self.ichimoku.SenkouA.Current.Value
+    senkou_span_b = self.ichimoku.SenkouB.Current.Value
+    cloud_top = max(senkou_span_a, senkou_span_b)
+    cloud_bottom = min(senkou_span_a, senkou_span_b)
+    
+    if chikou > cloud_top:
+        return 1    # Above cloud
+    if chikou < cloud_bottom:
+        return -1   # Below cloud
+        
+    return 0        # Inside cloud
+
    +
    + + +

    Alpha Update

    +

    + As new TradeBars are provided to the + alpha model's Update method, we update the Ichimoku indicator of each symbol. We then emit insights for the + symbols that have their Chikou Span breaking out of their respective Ichimoku Cloud in a new direction. To + maintain positions while we wait for another crossover in the Ichimoku Cloud, we emit insights on a daily basis + with 1-day duration. +

    + +
    +
    +def Update(self, algorithm, data):
+    insights = []
+    
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if not data.ContainsKey(symbol) or data[symbol] is None:
+            continue
+        
+        # Update indicator with the latest TradeBar
+        symbol_data.ichimoku.Update(data[symbol])
+        
+        # Determine insight direction
+        current_location = symbol_data.get_location()
+        if symbol_data.previous_location is not None: # Indicator is ready
+            if symbol_data.previous_location != 1 and current_location == 1:
+                symbol_data.direction = InsightDirection.Up
+            if symbol_data.previous_location != -1 and current_location == -1:
+                symbol_data.direction = InsightDirection.Down
+        
+        symbol_data.previous_location = current_location
+        
+        # Emit insight
+        if symbol_data.direction:
+            insight = Insight.Price(symbol, timedelta(days=1), symbol_data.direction)
+            insights.append(insight)
+    
+    return insights
+
    +
    + + +

    Portfolio Construction & Trade Execution

    +

    + Following the guidelines of Alpha Streams + and the Quant League competition, we + utilize the + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

    diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html new file mode 100644 index 0000000..487c85d --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html @@ -0,0 +1,6 @@ +
    +
    +
    + +
    +
    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html new file mode 100644 index 0000000..f3592aa --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html @@ -0,0 +1,10 @@ +

    + To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a + popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the + benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over + the backtest period. +

    + +Tutorial1028-ichimoku-cloud-2 \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html new file mode 100644 index 0000000..e63b0d3 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html @@ -0,0 +1,13 @@ +

    + Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

    + +
      +
    • Profitable
      • +
    • PSR >= 80%
      • +
    • Max drawdown duration <= 6 months
      • +
    • Insights contain the following properties: Symbol, Duration, Direction, and Weight
      • +
    • Minute or second data resolution
      • +
    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html new file mode 100644 index 0000000..4631b74 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html @@ -0,0 +1,21 @@ +

    + While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash, + we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The + strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of + 0.92. To continue the development of this strategy, future areas of research include: +

    + +
      +
    • + Only trading when a bullish or bearish trend are confirmed. Gurrib (2020) provides conditions for evaluating + the trend. +
      • +
    • Adjusting the data resolution used within the indicator.
      • +
    • Considering fundamental or alternative data points before placing trades.
      • +
    • Adjusting the parameters of the Ichimoku indicator.
      • +
    • + Replacing the portfolio construction model with one that only allocates 10% of the capital base to each security. + This will violate the requirements of the Alpha Streams and Quant League competition, but it will reduce the + transaction costs and provide a more accurate equity curve of the underlying strategies performance. +
      • +
    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html new file mode 100644 index 0000000..37c3e11 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html @@ -0,0 +1,6 @@ +
      +
    1. + Gurrib, Ikhlaas, Can the Leading Us Energy Stock Prices Be Predicted Using Ichimoku Clouds? (January 16, 2020). + Online Copy +
      2. +
    \ No newline at end of file From 6142dbf3ae57c8d8500e5722e8ff4b84d1106e97 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 09:29:42 -0700 Subject: [PATCH 145/215] fixed 01 strategy library information --- .../00 Strategy Library/01 Strategy Library.php | 4 ++-- .../01 Abstract.html | 0 .../02 Introduction.html | 0 .../03 Method.html | 0 .../04 Algorithm.html | 0 .../05 References.html | 0 6 files changed, 2 insertions(+), 2 deletions(-) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/01 Abstract.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/02 Introduction.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/03 Method.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/04 Algorithm.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/05 References.html (100%) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 2efe9eb..1c02f03 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -608,8 +608,8 @@ 'tags' => 'Momentum, Futures, Commodities' ], [ - 'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model', - 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn', + 'name' => 'Forecasting Stock Prices using a Temporal CNN Model', + 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn-model', 'sources' => [ 'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf' ], diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html From 0328c543a7d0cf791ffa8d7e561ca875029ae620 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 09:35:47 -0700 Subject: [PATCH 146/215] Adds 292 Leveraged ETFS --- .../00 Strategy Library/01 Strategy Library.php | 9 +++++++++ .../01 Abstract.html | 3 +++ .../02 Introduction.html | 9 +++++++++ .../03 Method.html | 12 ++++++++++++ .../04 Algorithm.html | 6 ++++++ .../05 Results.html | 5 +++++ .../06 References.html | 7 +++++++ 7 files changed, 51 insertions(+) create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 2efe9eb..4ddbbdf 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -615,6 +615,15 @@ ], 'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.", 'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning' + ], + [ + 'name' => 'Leveraged ETFs with Systematic Risk Management', + 'link' => 'leveraged-etfs-with-systematic-risk-management', + 'sources' => [ + 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' + ], + 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500', + 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html new file mode 100644 index 0000000..6e0ee07 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html @@ -0,0 +1,3 @@ +

    + In this tutorial, we attempt to beat the returns of the S&P500 Index using leverage and systematic risk management. +

    diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html new file mode 100644 index 0000000..0b1b370 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html @@ -0,0 +1,9 @@ +

    + When measuring the profitability of a strategy, it is usually not enough to be profitable, as it should also beat + the benchmark, for which the SP500 Index is most commonly used. If not, what would be the reason to not just invest + in a low-cost SP500 index instead (not accounting for risk)? However, what if we could beat the SP500 with the SP500? + This is what Gayed et al. proposed with their strategy of using Leveraged SP500 indices with Systematic Risk Management. + They propose that Moving Averages are a good method to assess volatility in the market, and they use it to manage risk, + which is especially important since the effects of price swings in a leveraged ETF are magnified due to the leverage. + With this method, we hope to outperform the SP500 while at the same time, attempt to reduce drawdown. +

    diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html new file mode 100644 index 0000000..a43a70e --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html @@ -0,0 +1,12 @@ +

    + We develop hold/liquidate positions based on the 200-day Simple Moving Average (SMA) of our ETF, for which we use SSO, + a 2x leveraged SP500 index ETF. With 200 days instead of using fewer days, say 50, we reduce the number of trades per year, + thereby reducing transaction costs and the effects of slippage. Moving on, if the current price of SOO is above the 200-day SMA, + we hold SSO, and if SSO dips below our 200-day SMA, we sell liquidate our position and rotate our position into short-term + treasuries, which is done through SHY, an ETF that tracks 1-3 year U.S. Treasury Bonds. If we are holding SHY, and the + current price of SSO moves above the 200-day SMA, then we rotate back into SSO and liquidate SHY. +

    + + + + diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html new file mode 100644 index 0000000..3b0ead5 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html @@ -0,0 +1,6 @@ +
    +
    +
    + +
    +
    diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html new file mode 100644 index 0000000..dfa86cf --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -0,0 +1,5 @@ +

    + We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years, + our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY + in final ROI. The algorithm produced a CAGR of 11.49% during the five years. +

    \ No newline at end of file diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html new file mode 100644 index 0000000..c1ce885 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html @@ -0,0 +1,7 @@ +
      +
    1. + Gayed, Michael and Bilello, Charles, Leverage for the Long Run - A Systematic Approach to Managing Risk and + Magnifying Returns in Stocks (March 3, 2016). 2016 Charles H. Dow Award. + Online Copy. +
      2. +
    \ No newline at end of file From ba096a3db4291a475c71e7356b4c4b0f24aff9f6 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 12:03:40 -0700 Subject: [PATCH 147/215] updated algorithm --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index 9e98ce9..ef8f26c 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    From ac9c4113615c14c7feb66311d29081f9cdbf3b47 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 13:14:33 -0700 Subject: [PATCH 148/215] removed feature-292 from PR --- .../01 Strategy Library.php | 9 - .../01 Abstract.html | 6 - .../02 Introduction.html | 8 - .../03 Method.html | 280 ------------------ .../04 Algorithm.html | 6 - .../05 References.html | 7 - 6 files changed, 316 deletions(-) delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 4ddbbdf..cb9f4b9 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -607,15 +607,6 @@ 'description' => "A simple trend following strategy on commodities futures.", 'tags' => 'Momentum, Futures, Commodities' ], - [ - 'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model', - 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn', - 'sources' => [ - 'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf' - ], - 'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.", - 'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning' - ], [ 'name' => 'Leveraged ETFs with Systematic Risk Management', 'link' => 'leveraged-etfs-with-systematic-risk-management', diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html deleted file mode 100644 index a6cf634..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html +++ /dev/null @@ -1,6 +0,0 @@ -

    - In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices. -

    -

    - Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading -

    \ No newline at end of file diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html deleted file mode 100644 index 1fa0f4e..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html +++ /dev/null @@ -1,8 +0,0 @@ -

    - Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements. - More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from - medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price - movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in - image classification, and now, researchers are applying CNNs to extract patterns, also known as features, from times-series - data to forecast future stock prices. -

    diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html deleted file mode 100644 index 1a4765a..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html +++ /dev/null @@ -1,280 +0,0 @@ -

    Overview

    -

    - Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data - to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and - bet on the direction of the price movement based on our model prediction. We will walk through the code required - for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand. -

    - -

    Inputs/Outputs

    - -

    - Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model. - The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a - direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps). - The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will - form the labels for which our model will try to classify, thus we have a classification problem. -

    - - -

    Neural Network Model Architecture

    - -

    - Now, we will need to build our Neural Network Architecture, which we will build using Keras, - a high-level Python Deep Learning API. To begin, we will need a few import statements: -

    - -
    -
    -    import tensorflow as tf
-    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
-    from tensorflow.keras import Model
-    from tensorflow.keras import metrics
-    from tensorflow.keras.losses import CategoricalCrossentropy
-    from tensorflow.keras import utils
-    from sklearn.preprocessing import StandardScaler
-    import numpy as np
-    import math
-
    -
    - - -

    - We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input - variables (OHLCV), our input shape will be 15 x 5. -

    - -
    -
    -    inputs = Input(shape=(15, 5))
-
    -
    - - -

    - We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as - the Neural Network's method of extracting patterns from the time-series data. -

    - -
    -
    -    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
-
    -
    - - -
    -
    -    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
-    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
-    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
-
-    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
-    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
-    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
-
    -
    - - -

    - These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten - our layer. -

    - -
    -
    -    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
-    flattened = Flatten()(combined)
-
    -
    - - -

    - Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down), - this layer will have three nodes. -

    - -
    -
    -    outputs = Dense(3, activation='softmax')(flattened)
-
    -
    - -

    - The resulting Neural Network Architecture is shown in the following: -

    - -temporal cnn model architecture - - -

    Preparing the Data for Our Model

    - -

    - First, we need to define a class and a few variables: -

    - -
    -
    -    input_vars = ['open', 'high', 'low', 'close', 'volume']
-
-    class Direction:
-        UP = 0
-        DOWN = 1
-        STATIONARY = 2
-
-    rolling_avg_window_size = 5
-
-    shift = -(rolling_avg_window_size-1)
-
-    stationary_threshold = .0001
-
-    scaler = StandardScaler()
-
    -
    - -

    - input_vars define the variables we want to use to make our predictions. The class Direction - defines a few integers that we will label our data with (labels are needed for classification problems). The reason - we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on, - rolling_avg_window_size is the number of time steps used for the calculate the average of future closing prices, - described earlier in - Inputs/Outputs (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5). - The constant stationary_threshold defines the threshold for a change in price to be considered - stationary, and this change also described in Inputs/Outputs. The shift is the shift needed to align - the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces - manageable for our Neural Network model. The scaler object will be used later to scale our data. - The purpose of the variables will become clearer in use. -

    - Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate - the percent change from the close at t, we use the following lines of code: -

    - -
    -
    -    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
-    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
-
    -
    - -

    - The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize - this is a more advanced resource). - Here, .shift(shift) aligns the five time step rolling average 'close_avg' column to the end of the last - time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input - and labeled data for our model much easier. -

    - -

    - To label our data, we need to first define a function that we will use with the DataFrame's apply() method. - Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda. -

    - -
    -
    -    def label_data(row):
-        if row['close_avg_change_pct'] > stationary_threshold:
-            return Direction.UP
-        elif row['close_avg_change_pct'] < -stationary_threshold:
-            return Direction.DOWN
-        else:
-            return Direction.STATIONARY
-
    -
    - -

    - Now, we apply the above function to our DataFrame to get a column of labels: -

    - -
    -
    -    df['movement_labels'] = df.apply(label_data, axis=1)
-
    -
    - -

    - With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into - lists: -

    - -
    -
    -    data = []
-    labels = []
-
-    for i in range(len(df)-self.n_tsteps+1+shift):
-        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
-        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
-        labels.append(label)
-
-    data = np.array(data)
-
    -
    - -

    - Here, we iterate numerically through the DataFrame, with a carefully calculated value in our range() - function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because - Keras works best with numpy arrays. -

    - -

    - Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the - range of values is normalized across the features. -

    - -
    -
    -    dim1, dim2, dim3 = data.shape
-    data = data.reshape(dim1*dim2, dim3)
-    data = scaler.fit_transform(data)
-    data = data.reshape(dim1, dim2, dim3)
-
    -
    - -

    - The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after, - we can return the data to the original shape with another reshaping. -

    - -

    - Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of - 1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following: -

    - -
    -
    -    labels = utils.to_categorical(labels, num_classes=3)
-
    -
    - -

    - Specifying num_classes to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary). -

    - -

    - We have now finished the walk through of the difficult parts of the code. -

    - -

    Trading

    - -

    - After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be - found in Algorithm) we can - use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make - a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit - an Price Insight with direction InsightDirection.Up (resp. InsightDirection.Down). Since we are - betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights - in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight - with a timedelta with a random integer between one and five to constrain the number of insights we emit. -

    - - -

    The Rest

    - -

    - We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the - necessary code to execute the strategy can be found in Algorithm. -

    - - - - diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html deleted file mode 100644 index 9e98ce9..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html +++ /dev/null @@ -1,6 +0,0 @@ -
    -
    -
    - -
    -
    diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html deleted file mode 100644 index dedbeb6..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html +++ /dev/null @@ -1,7 +0,0 @@ -
      -
    1. - Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis: - "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019; - https://arxiv.org/pdf/1901.08280.pdf. -
      2. -
    \ No newline at end of file From b922d8c1ac92684470ce8020782b9d8f1260a1de Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 14:00:50 -0700 Subject: [PATCH 149/215] updated algorithm and methodology --- .../03 Method.html | 3 +++ .../04 Algorithm.html | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html index 1a4765a..e56515f 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html @@ -265,6 +265,9 @@

    Trading

    betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight with a timedelta with a random integer between one and five to constrain the number of insights we emit. +
    + As an additional note, we decided to set the trading fees to zero (the default fee + is one dollar per trade), or else our algorithm will only lose money due to the frequency at which we trade.

    diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index ef8f26c..bf5b897 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    From 6a8345a2d3720170ea4bc4eb4e8fe075c04e82fa Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 14:30:29 -0700 Subject: [PATCH 150/215] fixed error with certain starting dates --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index bf5b897..b2d6569 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    From 8c7f56c4223d4545afb61b56b92b6193240c2d55 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 16:53:08 -0700 Subject: [PATCH 151/215] Leveraged ETFs with Systematic Risk Management --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index b2d6569..f3cdced 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    From be1fa00392688a3af30a3d05c04aa74e8ac0b662 Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Thu, 13 Aug 2020 10:02:50 -0700 Subject: [PATCH 152/215] Fix missing opening bracket. --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 1 + 1 file changed, 1 insertion(+) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index c1c2ad7..ce7332d 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -634,6 +634,7 @@ 'description' => 'A techincal indicator crossover strategy trading the largest energy companies.', 'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities' ], + [ 'name' => 'Intraday ETF Momentum', 'link' => 'strategy-library/intraday-etf-momentum', 'sources' => [ From d5da7a245726b7c2e51f83141f1c215ae6bca507 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 13:20:33 -0700 Subject: [PATCH 153/215] Leveraged ETFs with Systematic Risk Management --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index f3cdced..ec0cfdf 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    From 72f735277380dcb79f6a046b06a48a108583132b Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:25:28 -0700 Subject: [PATCH 154/215] fixed typo and link --- .../00 Strategy Library/01 Strategy Library.php | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index cb9f4b9..5516ba3 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -609,11 +609,11 @@ ], [ 'name' => 'Leveraged ETFs with Systematic Risk Management', - 'link' => 'leveraged-etfs-with-systematic-risk-management', + 'link' => 'strategy-library/leveraged-etfs-with-systematic-risk-management', 'sources' => [ 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' ], - 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500', + 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500', 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; From c5c4e8c8ce2dc46d6282824a07549dd836dd2373 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:34:10 -0700 Subject: [PATCH 155/215] updated Results section --- .../05 Results.html | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html index dfa86cf..34fa59b 100644 --- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -1,5 +1,4 @@

    - We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years, - our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY - in final ROI. The algorithm produced a CAGR of 11.49% during the five years. + Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period + yielded a Sharpe Ratio of .572.

    \ No newline at end of file From dd18c61a059301e86e84cfb07c0f49b99ce9ed87 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:36:32 -0700 Subject: [PATCH 156/215] improved wording of description --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5516ba3..1f9fe06 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -613,7 +613,7 @@ 'sources' => [ 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' ], - 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500', + 'description' => 'We apply Simple Moving Averages to manage the risk of holding leveraged ETFs in an attempt to beat the S&P500', 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; From b7865614fd803ca14d376453528125c21caca440 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Mon, 17 Aug 2020 11:10:56 -0700 Subject: [PATCH 157/215] updated description --- .../05 Results.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html index 34fa59b..ade26d7 100644 --- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -1,4 +1,4 @@

    - Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period - yielded a Sharpe Ratio of .572. + We use the S&P 500 as our benchmark, which we track by using the SPY ETF. Our strategy yielded a Sharpe Ratio of .732 + over the past five years, while buying and holding SPY for the same period yielded a Sharpe Ratio of .572.

    \ No newline at end of file From 3aa608104eae8d3c059c8ecbcc753ecd201e21b3 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 19:14:13 -0600 Subject: [PATCH 158/215] Add relative metrics to strategy 1026 --- .../04 Algorithm.html | 2 +- .../05 Conclusion.html | 89 ++++++++++++++++++- 2 files changed, 86 insertions(+), 5 deletions(-) diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html index c7304ae..1379e93 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    \ No newline at end of file diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html index 7623dda..c03c6ca 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html @@ -1,9 +1,90 @@

    We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period. - In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the - inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their - research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum - pattern. These areas of future research include: + Comparing the strategy to the S&P 500 benchmark, the strategy has a lower Sharpe ratio during the backtesting + period and during the recovery from the 2020 stock market crash. However, the strategy greatly outperforms the + benchmark during the downfall of the 2020 crash, achieving a 4.8 Sharpe ratio. Throughout all of the time periods + we tested, the strategy had a lower annual standard deviation than the benchmark, meaning more consistent returns. + A breakdown of the results from all of the testing periods can be seen in the table below. +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Period NameStart DateEnd DateStrategySharpeASD
    Backtest1/1/20158/16/2020Strategy-0.7640.05
    Benchmark0.7090.185
    Fall 20158/10/201510/10/2015Strategy-0.6960.058
    Benchmark-1.2430.793
    2020 Crash2/19/20203/23/2020Strategy4.8180.266
    Benchmark-1.2430.793
    2020 Recovery3/23/20206/8/2020Strategy0.6020.103
    Benchmark13.7610.386
    + + +

    + We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs + in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs, + and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the + original research paper. Refer to the backtest results. +

    + +

    + Throughout their research paper, Gao et al (2017) provide several suggestions to increase the return generated by + this momentum pattern. These areas of future research include:

      From 5611f91976a151d6c1d0f46a3470516433701a57 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 19:58:35 -0600 Subject: [PATCH 159/215] Fix typo --- .../1026 Intraday ETF Momentum/05 Conclusion.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html index c03c6ca..ff087ea 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html @@ -78,7 +78,7 @@

      We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs, - and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the + and slippage, the strategy still has a lower Sharpe ratio than the S&P 500 and doesn't match the results found in the original research paper. Refer to the backtest results.

      From fd71669aff0599ec339f3b3f28aae2b789f65815 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 20:15:37 -0600 Subject: [PATCH 160/215] Add relative metrics to strat 1028 --- .../01 Abstract.html | 6 +- .../04 Algorithm.html | 2 +- .../05 Benchmark Performance.html | 10 -- .../05 Relative Performance.html | 92 +++++++++++++++++++ .../07 Conclusion.html | 8 +- 5 files changed, 100 insertions(+), 18 deletions(-) delete mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html index 3dd4983..587ce33 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html @@ -2,7 +2,7 @@ Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an - impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib - (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular - energy sector ETF. + impressive 176 Sharpe ratio during the downfall of the 2020 oil price war, the strategy has worse performance than + found by Gurrib (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of + a popular energy sector ETF.

      diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html index 487c85d..56ab2db 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html @@ -1,6 +1,6 @@
      - +
      \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html deleted file mode 100644 index f3592aa..0000000 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html +++ /dev/null @@ -1,10 +0,0 @@ -

      - To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a - popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the - benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over - the backtest period. -

      - -Tutorial1028-ichimoku-cloud-2 \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html new file mode 100644 index 0000000..a1d1610 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html @@ -0,0 +1,92 @@ +

      + To analyze the value of this trading strategy, we compare its performance to buying and holding a popular ETF + tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the benchmark. We + can see from the plot below how the portfolio would have performed just had we had just invested in the benchmark. +

      + +Tutorial1028-ichimoku-cloud-2 + +

      + We now analyze the Sharpe ratio and annual standard deviation of returns for both the strategy and the benchmark. + From the table below, we can see the results of the strategy and the benchmark over the entire backtest period, the + Fall 2015 crisis, and the 2020 oil price war. The strategy has a lower Sharpe ratio than the benchmark across all + of the time periods we tested, except for the crash during the 2020 oil price war, where it generated an impressive + 176 Sharpe ratio. We can also see the strategy has a lower annual standard deviation accross all of the time + frames, implying that the strategy has more consistent returns than the benchmark. +

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Period NameStart DateEnd DateStrategySharpeASD
      Backtest1/1/20158/16/2020Strategy-0.310.223
      Benchmark-0.0830.312
      Fall 20158/10/201510/10/2015Strategy-0.310.294
      Benchmark0.2420.351
      2020 Crash2/19/20203/23/2020Strategy176.5240.949
      Benchmark-0.9021.108
      2020 Recovery3/23/20206/8/2020Strategy-1.5560.447
      Benchmark46.0680.703
      + +

      + We find the lack of performance for this strategy is not largely attributed to the transaction costs. After + ignoring the transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the + benchmark and doesn't match the results found in the original research paper. See the backtest results + here. +

      \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html index 4631b74..452f0d8 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html @@ -1,8 +1,8 @@

      - While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash, - we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The - strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of - 0.92. To continue the development of this strategy, future areas of research include: + While the strategy examined herein produces a 176 Sharpe ratio throughout the downfall of the 2020 oil price war + and stock market crash, we conclude the strategy does not currently provide as profitable of results as documented + by Gurrib (2020). The strategy experiences a -0.234 Sharpe ratio over the entire backtest period. To continue the + development of this strategy, future areas of research include:

        From 273015c388b224938520ba2f6957dd0f7852c4d1 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Tue, 18 Aug 2020 13:56:53 -0700 Subject: [PATCH 161/215] updated algorithm --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index ec0cfdf..9d83532 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
        - +
        From 5e2cdad4412f2a862cd2d4829a4dd6b98525e51a Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 18 Aug 2020 15:37:07 -0600 Subject: [PATCH 162/215] Add relative metrics to strat 85 and fix PCM bug --- .../02 Method.html | 2 +- .../04 Relative Performance.html | 81 +++++++++++++++++++ ...05 Market & Competition Qualification.html | 15 ++++ .../06 Conclusion.html | 17 ++++ ...{04 References.html => 07 References.html} | 0 5 files changed, 114 insertions(+), 1 deletion(-) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html rename 04 Strategy Library/85 Momentum in Mutual Fund Returns/{04 References.html => 07 References.html} (100%) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html index cd4436e..a43b163 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -106,7 +106,7 @@

        Alpha Ranking

        ... ranking_df = pd.DataFrame() for symbol, symbol_data in self.symbol_data_by_symbol.items(): - if symbol_data.IsReady: + if data.ContainsKey(symbol) and symbol_data.IsReady: row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol]) ranking_df = ranking_df.append(row) ranked_symbols = ranking_df.rank().sum(axis=1).sort_values().index diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html new file mode 100644 index 0000000..26cb3ef --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html @@ -0,0 +1,81 @@ +

        + To analyze the value of this trading strategy, we compare its performance to buying-and-holding the S&P 500 index + ETF, SPY. We can see the results from the table below. The strategy has a lower Sharpe ratio than the SPY for all of + the time frames we tested, except for the downfall of the 2020 stock market crash. During this time it greatly + outperformed the SPY, achieving a 10.4 Sharpe ratio. We also notice that the strategy generates more consistent + returns than the benchmark, documented by the lower annual standard deviation of returns throughout all the testing + periods. +

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Period NameStart DateEnd DateStrategySharpeASD
        Backtest1/1/20158/16/2020Strategy0.1920.046
        Benchmark0.7090.186
        Fall 20158/10/201510/10/2015Strategy-1.4480.052
        Benchmark-0.7240.251
        2020 Crash2/19/20203/23/2020Strategy10.3860.104
        Benchmark-1.2430.793
        2020 Recovery3/23/20206/8/2020Strategy-2.9420.177
        Benchmark13.7610.386
        + +

        + We find the lack of performance for this strategy is not largely attributed to the trading fees. After ignoring the + transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the benchmark. See + the backtest results here. +

        diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html new file mode 100644 index 0000000..fbc487d --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html @@ -0,0 +1,15 @@ +

        + Although this strategy passes several of the + metrics required for Alpha Streams and + the Quant League competition, it requires further work to meet the following requirements: +

        + + +
          +
        • Profitable
          • +
        • PSR >= 80%
          • +
        • Max drawdown duration <= 6 months
          • +
        • Insights contain the following properties: Symbol, Duration, Direction, and Weight
          • +
        • Minute or second data resolution
          • +
        • Insight Weighting or Equal Weighting Portfolio Construction model
          • +
        \ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html new file mode 100644 index 0000000..763902d --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html @@ -0,0 +1,17 @@ +

        + The momentum pattern examined throughout this tutorial has a greater Sharpe ratio than the SPY during the downfall + of the 2020 stock market crash and has more a lower annual standard deviation of returns than the SPY over all the + periods we tested. However, we conclude the strategy, which is loosely based on the research of Sapp (2010), does + not consistently outperform our benchmark. To continue the development of this strategy, future areas of research + include: +

        + + +
          +
        • + Incorporating historical returns and NAV of mutual funds to better-reflect the strategy documented by Saap (2010). +
          • +
        • Adjusting the parameters in the ROCAndNearnessAlphaModel.
          • +
        • Performing more filtering and sorting in the AssetManagementUniverseSelection model.
          • +
        • Testing other portfolio construction techniques.
          • +
        diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html similarity index 100% rename from 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html rename to 04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html From a398733997925cfd76a15c44332d3176dfc56dc2 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 18 Aug 2020 16:05:58 -0600 Subject: [PATCH 163/215] Update backtest with PCM fix --- .../85 Momentum in Mutual Fund Returns/03 Algorithm.html | 2 +- quantpedia.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html index ade7d3b..93109c0 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -1,6 +1,6 @@
        - +
        \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index cbf4970..22f8d06 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,7 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", - 85: "ba030edb022016f967a6296a556d717e", + 85: "a86b19f12b40d8a676bf7f885742631d", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From 53c3c878864a735c9e826d4208cd63e733a7ff52 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Wed, 19 Aug 2020 13:09:15 -0600 Subject: [PATCH 164/215] Add relative metrics & fix execution model bug --- .../01 Strategy Library.php | 9 ++ .../01 Abstract.html | 7 + .../02 Background.html | 9 ++ .../03 Method.html | 129 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Relative Performance.html | 91 ++++++++++++ ...06 Market & Competition Qualification.html | 19 +++ .../07 Conclusion.html | 10 ++ .../08 References.html | 8 ++ 9 files changed, 288 insertions(+) create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index ce7332d..426d089 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -642,6 +642,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Intraday Arbitrage Between Index ETFs', + 'link' => 'strategy-library/intraday-arbitrage-between-index-etfs', + 'sources' => [ + 'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1709599' + ], + 'description' => "A strategy that tracks the price paths of two correlated ETFs and takes advantage of mis-pricings that arise when the price paths diverge", + 'tags'=>'Intermediate, Equities, Arbitrage, Intraday' ] ]; diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html new file mode 100644 index 0000000..7d69c19 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html @@ -0,0 +1,7 @@ +

        + In this tutorial, we implement an intraday arbitrage strategy that capitalizes on deviations between two closely correlated + index ETFs. Even though at times both ETFs may hold different constituents and different weights of securities while tracking + the index, they are both highly correlated and extremely liquid. Researchers have shown these two properties are essential to + an arbitrage system's success. The algorithm we implement here is inspired by the work of Kakushadze and Serur (2018) and + Marshall, Nguyen, and Visaltanachoti (2010). +

        diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html new file mode 100644 index 0000000..8c03fe6 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html @@ -0,0 +1,9 @@ +

        + Marshall et al (2010) define an arbitrage opportunity as when the bid price of ETF A (B) diverts high enough away from the ask + price of ETF B (A) such that their quotient reaches a threshold. In their paper, an arbitrage opportunity is only acted upon + when the threshold is satisfied for 15 seconds. When these criteria are met, the algorithm enters the arbitrage trade by going + long ETF B (A) and short ETF A (B). When the spread reverts back to where the bid of ETF B (A) >= the ask of ETF A (B) for 15 + seconds, the positions are liquidated. An overview of the trade process is illustrated in the image below. +

        + +Tutorial1023-intraday-arbitrage-1 \ No newline at end of file diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html new file mode 100644 index 0000000..ecac338 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html @@ -0,0 +1,129 @@ +

        Universe Selection

        +

        + We implement a manual universe selection model that includes our two ETFs, SPY and IVV. The attached research notebook finds + the correlation of daily returns to be >0.99. +

        +
        +
        +tickers = ['IVV', 'SPY']
+symbols = [ Symbol.Create(t, SecurityType.Equity, Market.USA) for t in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+
        +
        + +

        Spread Adjustments

        +

        + Plotting the ratio of the security prices shows its trending behavior. +

        +Tutorial1023-intraday-arbitrage-2 +

        + Without adjusting this ratio over time, an arbitrage system would be stuck in a single trade for majority of the backtest. To + resolve this, we subtract a trailing mean from each data point. +

        +Tutorial1023-intraday-arbitrage-3 +

        + Both of the above plots can be reproduced in the attached research notebook. During backtesting, this adjustment is done + during trading by setting up a + QuoteBarConsolidator for each security in our + universe. On each new consolidated QuoteBar, we + update the trailing window of L1 data, then calculate the latest spread adjustment values. +

        +
        +
        +# In OnSecuritiesChanged
+for symbol in self.symbols:
+    self.consolidators[symbol] = QuoteBarConsolidator(1)
+    self.consolidators[symbol].DataConsolidated += self.CustomDailyHandler
+    algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidators[symbol])
+
+def CustomDailyHandler(self, sender, consolidated):
+    # Add new data point to history while removing expired history
+    self.history[consolidated.Symbol]['bids'] = np.append(self.history[consolidated.Symbol]['bids'][1:], consolidated.Bid.Close)
+    self.history[consolidated.Symbol]['asks'] = np.append(self.history[consolidated.Symbol]['asks'][1:], consolidated.Ask.Close)
+    
+    self.update_spread_adjusters()
+
+def update_spread_adjusters(self):
+    for i in range(2):
+        numerator_history = self.history[self.symbols[i]]['bids']
+        denominator_history = self.history[self.symbols[abs(i-1)]]['asks']
+        self.spread_adjusters[i] = (numerator_history / denominator_history).mean()
+
        +
        + + +

        Alpha Construction

        +

        + The ArbitrageAlphaModel monitors the intraday bid and ask prices of the securities in the universe. In the constructor, we + can specify the model parameters. In this tutorial, we select a shorter window an arbitrage opportunity must be active before + we act on it by setting `order_delay` to 3. +

        +
        +
        +class ArbitrageAlphaModel(AlphaModel):
+    symbols = [] # IVV, SPY
+    entry_timer = [0, 0]
+    exit_timer = [0, 0]
+    spread_adjusters = [0, 0]
+    long_side = -1
+    consolidators = {}
+    history = {}
+
+    def __init__(self, order_delay = 3, profit_pct_threshold = 0.02, window_size = 400):
+        self.order_delay = order_delay
+        self.pct_threshold = profit_pct_threshold / 100
+        self.window_size = window_size
+
        +
        + + +

        Trade Signals

        +

        + To emit insights, we check if either side of the arbitrage strategy warrants an entry. If no new entries are to be made, the + algorithm then looks to exit any current positions. With this design, we can flip our long/short bias without first + flattening our position. We use a practically-infinite insight durations as we do not know how long the algorithm will be in + an arbitrage trade. +

        +
        +
        +# Search for entries
+for i in range(2):
+    if quotebars[abs(i-1)].Bid.Close / quotebars[i].Ask.Close - self.spread_adjusters[abs(i-1)] >= self.pct_threshold:
+        self.entry_timer[i] += 1
+        if self.entry_timer[i] == self.order_delay:
+            self.exit_timer = [0, 0]
+            if self.long_side == i:
+                return []
+            self.long_side = i
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Up),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Down)]
+        else:
+            return []
+    self.entry_timer[i] = 0
+
+# Search for an exit
+if self.long_side >= 0: # In a position
+    if quotebars[self.long_side].Bid.Close / quotebars[abs(self.long_side-1)].Ask.Close - self.spread_adjusters[self.long_side] >= 0: # Exit signal
+        self.exit_timer[self.long_side] += 1
+        if self.exit_timer[self.long_side] == self.order_delay: # Exit signal lasted long enough
+            self.exit_timer[self.long_side] = 0
+            i = self.long_side
+            self.long_side = -1
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Flat),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Flat)]
+        else:
+            return []
+return []
+
        +
        + + +

        Portfolio Construction & Trade Execution

        +

        + Following the guidelines of Alpha Streams + and the Quant League competition, we + utilize the + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

        diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html new file mode 100644 index 0000000..c59088b --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        \ No newline at end of file diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html new file mode 100644 index 0000000..fd71509 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html @@ -0,0 +1,91 @@ +

        + We analyze the performance of this strategy by comparing it to the S&P 500 ETF benchmark, SPY. We notice that the + strategy has a lower Sharpe ratio over all of our testing periods than the benchmark, except for the Fall 2015 + crisis where it achieved a 2.8 Sharpe ratio. The strategy also has a lower annual standard deviation of returns + when compared to the SPY, implying more consistent returns over time. A breakdown of the strategy's performance + across all our testing periods is displayed in the table below. +

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Period NameStart DateEnd DateStrategySharpeASD
        Backtest8/11/20158/11/2020Strategy-0.4470.053
        Benchmark0.7320.192
        Fall 20158/10/201510/10/2015Strategy2.8370.225
        Benchmark-0.7240.251
        2020 Crash2/19/20203/23/2020Strategy-4.1960.209
        Benchmark-1.2430.793
        2020 Recovery3/23/20206/8/2020Strategy-3.4430.013
        Benchmark13.7610.386
        + + +

        + The lack of performance for this arbitrage strategy is mostly attributed to the fees it incurs while trading. This + is common for an intraday arbitrage strategy, but we discuss ways to reduces these fees in the conclusion of this + tutorial. After removing the costs of commissions, crossing the spread, and slippage, the strategy outperforms the + SPY over the entire backtesting period. Without these costs, the strategy generates a 1.09 Share ratio while the + SPY generates a 0.732 Sharpe ratio. See the backtest performance without fees below. +

        + + +
        +
        +
        + +
        +
        diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html new file mode 100644 index 0000000..c76ecb6 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html @@ -0,0 +1,19 @@ +

        + Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

        + + +
          +
        • Profitable
          • +
        • PSR >= 80%
          • +
        • Max drawdown duration <= 6 months
          • +
        • Insights contain the following properties: Symbol, Duration, Direction, and Weight
          • +
        • Alphas need to place at least 10 trades per month for the majority of the backtest
          • +
        + +

        + The algorithm currently places trades during 12 unique months throughout the backtest. Since the backtest spans + across 61 months, it places trades through a minority of the backtest. +

        diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html new file mode 100644 index 0000000..3df342d --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html @@ -0,0 +1,10 @@ +

        + The intraday arbitrage strategy we built and tested throughout this tutorial underperforms the SPY benchmark in + terms of Sharpe ratio when including trading costs. Without these costs, we found the strategy outperforms the SPY + in terms of Sharpe ratio. In our implementation, we specified the alpha model to initiate trading when atleast a + 0.02% profit threshold is reached for 3 seconds. Both of these parameters are set lower than the strategy examined + in Marshall et al (2010) for demonstration purposes. Increasing the profit threshold will lead to more profitable, + but fewer, trades that may overcome the cost of trading. We leave this area of study for future research. + Additional areas of future research include increasing the resolution of data from second to tick and incorportating + an execution model that utilizes limit orders to reduce fees. +

        diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html new file mode 100644 index 0000000..a684227 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html @@ -0,0 +1,8 @@ +
          +
        1. + Marshall, Ben R. and Nguyen, Nhut (Nick) Hoang and Visaltanachoti, Nuttawat, ETF Arbitrage: Intraday Evidence (November 16, 2010). Online copy +
          2. +
        2. + Kakushadze, Zura and Serur, Juan Andrés, 151 Trading Strategies (August 17, 2018). Z. Kakushadze and J.A. Serur. 151 Trading Strategies. Cham, Switzerland: Palgrave Macmillan, an imprint of Springer Nature, 1st Edition (2018), XX, 480 pp; ISBN 978-3-030-02791-9. Online copy +
          3. +
        \ No newline at end of file From 85f17f582596286a29138f65bd752b32d942b9d3 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 10:39:19 -0700 Subject: [PATCH 165/215] updated algorithm --- .../05 Results.html | 5 +++++ .../{05 References.html => 06 References.html} | 0 2 files changed, 5 insertions(+) create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html rename 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/{05 References.html => 06 References.html} (100%) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html new file mode 100644 index 0000000..2b6a817 --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html @@ -0,0 +1,5 @@ +

        + Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over + two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the + algorithm is non-deterministic, users may see different results from repeated backtests. +

        \ No newline at end of file diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html From 5f6d72305f567da1d1544b3ed45ebb4798514d65 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 12:02:33 -0700 Subject: [PATCH 166/215] updated algorithm --- .../04 Algorithm.html | 2 +- .../05 Results.html | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index 9d83532..0a1b0bd 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
        - +
        diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html index 2b6a817..a9d5b42 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html @@ -1,5 +1,6 @@

        - Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over - two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the - algorithm is non-deterministic, users may see different results from repeated backtests. + Since our algorithm is non-deterministic, users should expect to see different results in repeated backtests. From + running our algorithm ten times, we achieved Sharpe Ratios with an average of .211, a maximum of 0.917, and minimum + of -0.312, and a standard deviation of 0.327. As we traded three technology stocks, we compare our results to QQQ. + Comparing our algorithm to QQQ, our average Sharpe of .211 is significantly lower than the 0.967 Sharpe of QQQ.

        \ No newline at end of file From 2b686253c3d088fb9bf3008bbc2b36a16cc11d08 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:32:57 -0700 Subject: [PATCH 167/215] updated algorithm --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 4 ++ .../02 Introduction.html | 9 ++++ .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Video Walkthrough.html | 5 ++ .../06 Results.html | 8 +++ .../07 References.html | 7 +++ 8 files changed, 98 insertions(+) create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index ce7332d..e4d357f 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -642,6 +642,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Optimal Pairs Trading', + 'link' => 'strategy-library/optimal-pairs-trading', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf' + ], + 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", + 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' ] ]; diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html new file mode 100644 index 0000000..f913c9e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html @@ -0,0 +1,4 @@ +

        + In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation + levels. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html new file mode 100644 index 0000000..d0a7cd5 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html @@ -0,0 +1,9 @@ +

        + Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit + from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter, + which we have implemented in this post. + However, today, we will model the portfolio values of holding positions in + a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate + the position in the pair of stocks. Let (number) refer to the corresponding equation in the given paper, which can + be found under the References section. For example, (2.1) refers to equation 2.1 in the paper. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html new file mode 100644 index 0000000..594383e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -0,0 +1,50 @@ +

        Computing the OU Coefficients

        +

        Say there are two arbitrary stocks A and B. For each value β in .01, .02, .03, …, 1.00:

        +
          +
        1. We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
          2. +
        2. Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
          3. +
        +$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +

        where

        +

        xj is the value at the jth index of the portfolio values

        +

        and 

        +$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +

        and n = the number of portfolio values

        +

        and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

        +

        We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

        + + +

        Deriving the Optimal Entry and Liquidation Levels

        +

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        +$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ +$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ +$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +

        Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

        +

        With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

        +

        As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

        +$f'(x) = \frac{f(x+h)-f(x)}{h}$ +

        where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

        +

        To find b*, we solve for b in the following equation:

        +$F(b)=(b-c)F'(b)\quad(4.3)$ +

        We solve this equation by getting all terms to one side:

        +$F(b)-(b-c)F'(b)=0$ +

        Finding the root of the above equation results in b*. 

        +

        Now to derive d*, we solve for d in the following equation:

        +$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +

        Again, we will move all terms to one side:

        +$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +

        And finding the root of this equation results in d*.

        +


        The full method of finding b* and d* can be found in the OptimalStopping.py + file in the Algorithm section.

        + +

        Trading

        +

        + Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices + for stocks A and B. We then train our model on these data points, and we get the + b*, d*, and β* values. We then keep track of a hypothetical portfolio of holding $1 + of A and -$β* of B. Once the value of our hypothetical portfolio is less than or equal to b*, + we allocate 1.0 of our capital to long of A and short B using (β* x capital used for A) worth of stock. + Once the hypothetical portfolio value we tracking reaches d*, we liquidate our positions. We repeat these + trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to + update our b*, d*, and β* values. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html new file mode 100644 index 0000000..40bda21 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html new file mode 100644 index 0000000..1e7e3ae --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html @@ -0,0 +1,5 @@ + \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html new file mode 100644 index 0000000..3eb8a0f --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html @@ -0,0 +1,8 @@ +

        + Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period + yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods + of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the + entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage + our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters + of the algorithm. +

        \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html new file mode 100644 index 0000000..87de37d --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -0,0 +1,7 @@ +
          +
        1. + Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). + International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. + Online Copy. +
          2. +
        \ No newline at end of file From f02f7eaccecf43df2079c10bb6b97791ff50bf63 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:43:40 -0700 Subject: [PATCH 168/215] updated algorithm --- .../1029 Optimal Pairs Trading/04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html index 40bda21..04066ff 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -1,6 +1,6 @@
        - +
        From fc66d22377675ad67b0fa1090611938fce46f0b2 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:32:57 -0700 Subject: [PATCH 169/215] updated algorithm --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 4 ++ .../02 Introduction.html | 9 ++++ .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Video Walkthrough.html | 5 ++ .../06 Results.html | 8 +++ .../07 References.html | 7 +++ 8 files changed, 98 insertions(+) create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5671080..c65c7b8 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -651,6 +651,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Optimal Pairs Trading', + 'link' => 'strategy-library/optimal-pairs-trading', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf' + ], + 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", + 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' ] ]; diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html new file mode 100644 index 0000000..f913c9e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html @@ -0,0 +1,4 @@ +

        + In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation + levels. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html new file mode 100644 index 0000000..d0a7cd5 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html @@ -0,0 +1,9 @@ +

        + Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit + from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter, + which we have implemented in this post. + However, today, we will model the portfolio values of holding positions in + a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate + the position in the pair of stocks. Let (number) refer to the corresponding equation in the given paper, which can + be found under the References section. For example, (2.1) refers to equation 2.1 in the paper. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html new file mode 100644 index 0000000..594383e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -0,0 +1,50 @@ +

        Computing the OU Coefficients

        +

        Say there are two arbitrary stocks A and B. For each value β in .01, .02, .03, …, 1.00:

        +
          +
        1. We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
          2. +
        2. Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
          3. +
        +$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +

        where

        +

        xj is the value at the jth index of the portfolio values

        +

        and 

        +$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +

        and n = the number of portfolio values

        +

        and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

        +

        We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

        + + +

        Deriving the Optimal Entry and Liquidation Levels

        +

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        +$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ +$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ +$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +

        Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

        +

        With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

        +

        As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

        +$f'(x) = \frac{f(x+h)-f(x)}{h}$ +

        where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

        +

        To find b*, we solve for b in the following equation:

        +$F(b)=(b-c)F'(b)\quad(4.3)$ +

        We solve this equation by getting all terms to one side:

        +$F(b)-(b-c)F'(b)=0$ +

        Finding the root of the above equation results in b*. 

        +

        Now to derive d*, we solve for d in the following equation:

        +$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +

        Again, we will move all terms to one side:

        +$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +

        And finding the root of this equation results in d*.

        +


        The full method of finding b* and d* can be found in the OptimalStopping.py + file in the Algorithm section.

        + +

        Trading

        +

        + Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices + for stocks A and B. We then train our model on these data points, and we get the + b*, d*, and β* values. We then keep track of a hypothetical portfolio of holding $1 + of A and -$β* of B. Once the value of our hypothetical portfolio is less than or equal to b*, + we allocate 1.0 of our capital to long of A and short B using (β* x capital used for A) worth of stock. + Once the hypothetical portfolio value we tracking reaches d*, we liquidate our positions. We repeat these + trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to + update our b*, d*, and β* values. +

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html new file mode 100644 index 0000000..40bda21 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html new file mode 100644 index 0000000..1e7e3ae --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html @@ -0,0 +1,5 @@ + \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html new file mode 100644 index 0000000..3eb8a0f --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html @@ -0,0 +1,8 @@ +

        + Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period + yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods + of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the + entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage + our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters + of the algorithm. +

        \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html new file mode 100644 index 0000000..87de37d --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -0,0 +1,7 @@ +
          +
        1. + Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). + International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. + Online Copy. +
          2. +
        \ No newline at end of file From 81c8ab32405ffd6cf6acf51635fe97dd8a05791f Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:43:40 -0700 Subject: [PATCH 170/215] updated algorithm --- .../1029 Optimal Pairs Trading/04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html index 40bda21..04066ff 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -1,6 +1,6 @@
        - +
        From bc5492740e6f0ebfa6df6dbb095de46839466c9f Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 16:34:22 -0700 Subject: [PATCH 171/215] fixed typos --- 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index 594383e..fd3f849 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -15,7 +15,7 @@

        Computing the OU Coefficients

        Deriving the Optimal Entry and Liquidation Levels

        -

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        +

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ From 700abdbfd2115364f0d091bfbb4edfe69e5bd0f9 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 16:37:47 -0700 Subject: [PATCH 172/215] fixed typos --- 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 4 ---- 1 file changed, 4 deletions(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index 8ab9f29..fd3f849 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -15,11 +15,7 @@

        Computing the OU Coefficients

        Deriving the Optimal Entry and Liquidation Levels

        -<<<<<<< HEAD

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        -======= -

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        ->>>>>>> origin/feature-310-optimal-pairs-trading $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ From fdf740bdf7815509d61ac17e968b7d10ef1155dd Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 18 Sep 2020 10:25:18 -0700 Subject: [PATCH 173/215] changed LaTex format as $$ don't render properly --- .../1029 Optimal Pairs Trading/03 Method.html | 20 +++++++++---------- .../07 References.html | 2 +- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index fd3f849..83986fa 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -4,11 +4,11 @@

        Computing the OU Coefficients

      • We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
      • Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
        • -$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +\[-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)\]

        where

        xj is the value at the jth index of the portfolio values

        and 

        -$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +\[\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}\]

        and n = the number of portfolio values

        and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

        We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

        @@ -16,23 +16,23 @@

        Computing the OU Coefficients

        Deriving the Optimal Entry and Liquidation Levels

        With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

        -$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ -$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ -$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +\[F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)\] +\[G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)\] +\[V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)\]

        Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

        With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

        As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

        -$f'(x) = \frac{f(x+h)-f(x)}{h}$ +\[f'(x) = \frac{f(x+h)-f(x)}{h}\]

        where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

        To find b*, we solve for b in the following equation:

        -$F(b)=(b-c)F'(b)\quad(4.3)$ +\[F(b)=(b-c)F'(b)\quad(4.3)\]

        We solve this equation by getting all terms to one side:

        -$F(b)-(b-c)F'(b)=0$ +\[F(b)-(b-c)F'(b)=0\]

        Finding the root of the above equation results in b*. 

        Now to derive d*, we solve for d in the following equation:

        -$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +\[G(d)(V'(d)-1)=G'(d)(V(d)-d-c)\]

        Again, we will move all terms to one side:

        -$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +\[G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0\]

        And finding the root of this equation results in d*.


        The full method of finding b* and d* can be found in the OptimalStopping.py file in the Algorithm section.

        diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html index 87de37d..10b2dac 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -2,6 +2,6 @@
      • Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. - Online Copy. + Online Copy.
        • \ No newline at end of file From 7fb1a0c3b7c573ca18d19ef20beb614a34219a5f Mon Sep 17 00:00:00 2001 From: Aaron Janeiro Stone <60862650+aarjaneiro@users.noreply.github.com> Date: Fri, 18 Sep 2020 14:26:12 -0400 Subject: [PATCH 174/215] Correction of a typo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) →\text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}) --- .../12 Modern Portfolio Theory/04 Mean-Variance Analysis.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html index 51a1bf9..e60dcab 100755 --- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html +++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html @@ -26,7 +26,7 @@

        Capital Market Line

        Since there is only n = 1 risky asset, the variance of the CML portfolio return is

        -\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \] +\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}) \]

        Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight: From 0fe294f7414b458dded00ddcad919e1a7269dd54 Mon Sep 17 00:00:00 2001 From: Alexandre Catarino Date: Fri, 18 Sep 2020 19:34:28 +0100 Subject: [PATCH 175/215] Add Data Directory and Fama-French Data Factors Latest files from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html --- Data/F-F_Research_Data_5_Factors_2x3.CSV | 748 + .../F-F_Research_Data_5_Factors_2x3_daily.CSV | 14374 +++++++++ Data/F-F_Research_Data_Factors.CSV | 1231 + Data/F-F_Research_Data_Factors_daily.CSV | 24797 ++++++++++++++++ Data/F-F_Research_Data_Factors_weekly.CSV | 4916 +++ 5 files changed, 46066 insertions(+) create mode 100644 Data/F-F_Research_Data_5_Factors_2x3.CSV create mode 100644 Data/F-F_Research_Data_5_Factors_2x3_daily.CSV create mode 100644 Data/F-F_Research_Data_Factors.CSV create mode 100644 Data/F-F_Research_Data_Factors_daily.CSV create mode 100644 Data/F-F_Research_Data_Factors_weekly.CSV diff --git a/Data/F-F_Research_Data_5_Factors_2x3.CSV b/Data/F-F_Research_Data_5_Factors_2x3.CSV new file mode 100644 index 0000000..8adb14e --- /dev/null +++ b/Data/F-F_Research_Data_5_Factors_2x3.CSV @@ -0,0 +1,748 @@ +This file was created by CMPT_ME_BEME_OP_INV_RETS using the 202007 CRSP database. +The 1-month TBill return is from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RMW,CMA,RF +196307, -0.39, -0.47, -0.83, 0.66, -1.15, 0.27 +196308, 5.07, -0.79, 1.67, 0.40, -0.40, 0.25 +196309, -1.57, -0.48, 0.18, -0.76, 0.24, 0.27 +196310, 2.53, -1.29, -0.10, 2.75, -2.24, 0.29 +196311, -0.85, -0.84, 1.71, -0.45, 2.22, 0.27 +196312, 1.83, -1.89, -0.12, 0.07, -0.30, 0.29 +196401, 2.24, 0.08, 1.59, 0.22, 1.50, 0.30 +196402, 1.54, 0.32, 2.83, 0.06, 0.85, 0.26 +196403, 1.41, 1.41, 3.32, -2.01, 2.93, 0.31 +196404, 0.10, -1.52, -0.55, -1.35, -1.08, 0.29 +196405, 1.42, -0.68, 1.98, -0.26, 0.24, 0.26 +196406, 1.27, 0.09, 0.68, -0.42, 0.14, 0.30 +196407, 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-8.44, 4.68 + 2000, -17.60, 4.21, 39.69, 26.67, 32.74, 5.89 + 2001, -15.20, 23.68, 19.52, 19.84, 11.79, 3.83 + 2002, -22.76, 5.82, 7.47, 20.39, 14.47, 1.65 + 2003, 30.75, 24.49, 5.40, -20.46, 17.26, 1.02 + 2004, 10.72, 7.34, 8.08, 8.34, -7.86, 1.20 + 2005, 3.09, -0.75, 8.33, 1.71, -5.03, 2.98 + 2006, 10.60, 1.53, 14.11, 2.08, 8.19, 4.80 + 2007, 1.04, -7.94, -14.65, 4.37, -7.80, 4.66 + 2008, -38.34, 3.34, 0.82, 15.06, 4.16, 1.60 + 2009, 28.26, 7.99, -9.17, 2.78, -2.67, 0.10 + 2010, 17.37, 13.28, -5.31, -1.48, 9.93, 0.12 + 2011, 0.44, -5.78, -8.35, 12.60, -0.88, 0.04 + 2012, 16.28, -0.08, 9.68, -4.71, 9.41, 0.06 + 2013, 35.20, 7.68, 1.33, -4.44, 1.31, 0.02 + 2014, 11.70, -8.10, -1.74, 1.04, -1.70, 0.02 + 2015, 0.07, -5.85, -9.65, 0.89, -8.49, 0.02 + 2016, 13.30, 9.20, 23.02, 4.82, 9.77, 0.20 + 2017, 21.50, -5.84, -13.98, 6.81, -11.80, 0.80 + 2018, -6.93, -5.35, -9.16, -1.37, 0.21, 1.81 + 2019, 28.28, -6.22, -11.67, 4.99, -2.81, 2.14 diff --git a/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV new file mode 100644 index 0000000..de66995 --- /dev/null +++ b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV @@ -0,0 +1,14374 @@ +This file was created by CMPT_ME_BEME_OP_INV_RETS_DAILY using the 202007 CRSP database. +The 1-month TBill return is from Ibbotson and Associates, Inc. + +,Mkt-RF,SMB,HML,RMW,CMA,RF +19630701, -0.67, 0.00, -0.32, -0.01, 0.15, 0.012 +19630702, 0.79, -0.27, 0.27, -0.07, -0.19, 0.012 +19630703, 0.63, -0.17, -0.09, 0.17, -0.33, 0.012 +19630705, 0.40, 0.08, -0.28, 0.08, -0.33, 0.012 +19630708, -0.63, 0.04, -0.18, -0.29, 0.13, 0.012 +19630709, 0.45, 0.00, 0.10, 0.14, -0.04, 0.012 +19630710, -0.18, 0.21, 0.01, 0.06, -0.07, 0.012 +19630711, -0.16, 0.14, -0.30, -0.06, 0.05, 0.012 +19630712, -0.12, 0.02, -0.11, 0.12, 0.04, 0.012 +19630715, -0.62, 0.07, -0.03, 0.17, -0.06, 0.012 +19630716, -0.07, -0.08, 0.14, -0.06, -0.07, 0.012 +19630717, -0.33, -0.07, 0.15, 0.04, 0.10, 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5.65, 1.85, 0.55, 0.00 +201308, -2.71, 0.30, -2.76, 0.00 +201309, 3.77, 2.92, -1.20, 0.00 +201310, 4.18, -1.49, 1.10, 0.00 +201311, 3.12, 1.25, 0.27, 0.00 +201312, 2.81, -0.51, -0.30, 0.00 +201401, -3.32, 0.86, -2.10, 0.00 +201402, 4.65, 0.31, -0.46, 0.00 +201403, 0.43, -1.88, 5.06, 0.00 +201404, -0.19, -4.26, 1.07, 0.00 +201405, 2.06, -1.84, -0.19, 0.00 +201406, 2.61, 3.07, -0.76, 0.00 +201407, -2.04, -4.24, 0.01, 0.00 +201408, 4.24, 0.35, -0.58, 0.00 +201409, -1.97, -3.83, -1.23, 0.00 +201410, 2.52, 4.21, -1.68, 0.00 +201411, 2.55, -2.08, -2.99, 0.00 +201412, -0.06, 2.54, 2.06, 0.00 +201501, -3.11, -0.58, -3.47, 0.00 +201502, 6.13, 0.51, -1.83, 0.00 +201503, -1.12, 3.03, -0.46, 0.00 +201504, 0.59, -2.97, 1.85, 0.00 +201505, 1.36, 0.91, -1.36, 0.00 +201506, -1.53, 2.81, -0.79, 0.00 +201507, 1.54, -4.15, -4.14, 0.00 +201508, -6.04, 0.50, 2.66, 0.00 +201509, -3.08, -2.65, 0.53, 0.00 +201510, 7.75, -1.97, -0.06, 0.00 +201511, 0.56, 3.66, -0.50, 0.00 +201512, -2.17, -2.83, -2.60, 0.01 +201601, -5.77, -3.35, 2.08, 0.01 +201602, -0.07, 0.80, -0.49, 0.02 +201603, 6.96, 0.86, 1.15, 0.02 +201604, 0.92, 0.69, 3.26, 0.01 +201605, 1.78, -0.28, -1.82, 0.01 +201606, -0.05, 0.65, -1.46, 0.02 +201607, 3.95, 2.63, -1.09, 0.02 +201608, 0.50, 1.17, 3.37, 0.02 +201609, 0.25, 2.04, -1.47, 0.02 +201610, -2.02, -4.40, 4.14, 0.02 +201611, 4.86, 5.46, 8.32, 0.01 +201612, 1.81, 0.12, 3.60, 0.03 +201701, 1.94, -1.02, -2.77, 0.04 +201702, 3.57, -2.04, -1.82, 0.04 +201703, 0.17, 1.19, -3.17, 0.03 +201704, 1.09, 0.71, -1.87, 0.05 +201705, 1.06, -2.56, -3.81, 0.06 +201706, 0.78, 2.16, 1.35, 0.06 +201707, 1.87, -1.38, -0.26, 0.07 +201708, 0.16, -1.61, -2.22, 0.09 +201709, 2.51, 4.52, 3.02, 0.09 +201710, 2.25, -1.83, -0.08, 0.09 +201711, 3.12, -0.67, -0.04, 0.08 +201712, 1.06, -1.28, 0.11, 0.09 +201801, 5.58, -2.92, -1.55, 0.11 +201802, -3.65, 0.28, -1.09, 0.11 +201803, -2.35, 3.88, 0.00, 0.12 +201804, 0.29, 1.13, 0.56, 0.14 +201805, 2.65, 5.35, -3.21, 0.14 +201806, 0.48, 1.23, -2.40, 0.14 +201807, 3.19, -2.20, 0.47, 0.16 +201808, 3.44, 1.14, -4.13, 0.16 +201809, 0.06, -2.37, -1.32, 0.15 +201810, -7.68, -4.78, 3.47, 0.19 +201811, 1.69, -0.81, 0.27, 0.18 +201812, -9.55, -2.63, -1.49, 0.19 +201901, 8.41, 3.00, -0.60, 0.21 +201902, 3.40, 2.07, -2.83, 0.18 +201903, 1.10, -3.16, -4.04, 0.19 +201904, 3.96, -1.70, 1.96, 0.21 +201905, -6.94, -1.23, -2.35, 0.21 +201906, 6.93, 0.33, -1.08, 0.18 +201907, 1.19, -2.05, 0.17, 0.19 +201908, -2.58, -2.42, -5.05, 0.16 +201909, 1.43, -0.96, 6.81, 0.18 +201910, 2.06, 0.26, -2.04, 0.15 +201911, 3.87, 0.87, -1.80, 0.12 +201912, 2.77, 0.68, 1.93, 0.14 +202001, -0.11, -3.10, -6.37, 0.13 +202002, -8.13, 0.98, -4.05, 0.12 +202003, -13.39, -5.15, -14.17, 0.12 +202004, 13.65, 2.76, -1.18, 0.00 +202005, 5.58, 2.46, -4.92, 0.01 +202006, 2.45, 2.68, -2.19, 0.01 +202007, 5.77, -2.31, -1.27, 0.01 + + Annual Factors: January-December +,Mkt-RF,SMB,HML,RF + 1927, 29.47, -2.46, -3.75, 3.12 + 1928, 35.39, 4.41, -5.83, 3.56 + 1929, -19.54, -30.78, 11.96, 4.75 + 1930, -31.23, -5.19, -12.29, 2.41 + 1931, -45.11, 3.51, -14.32, 1.07 + 1932, -9.39, 4.91, 10.49, 0.96 + 1933, 57.05, 48.86, 28.15, 0.30 + 1934, 3.02, 25.43, -27.38, 0.16 + 1935, 44.96, 9.99, 9.78, 0.17 + 1936, 32.07, 17.89, 35.86, 0.18 + 1937, -34.96, -14.00, -3.97, 0.31 + 1938, 28.48, 9.35, -12.18, -0.02 + 1939, 2.70, 5.85, -19.17, 0.02 + 1940, -7.14, 0.79, -0.82, 0.00 + 1941, -10.53, -4.04, 11.13, 0.06 + 1942, 16.20, 5.05, 19.87, 0.27 + 1943, 27.96, 33.35, 38.97, 0.35 + 1944, 20.97, 17.98, 15.61, 0.33 + 1945, 38.38, 25.56, 11.40, 0.33 + 1946, -6.73, -3.79, 2.98, 0.35 + 1947, 2.95, -7.08, 9.76, 0.50 + 1948, 1.07, -9.14, 3.52, 0.81 + 1949, 19.12, 3.93, -4.55, 1.10 + 1950, 28.82, 0.93, 27.01, 1.20 + 1951, 19.22, -4.93, -5.93, 1.49 + 1952, 11.80, -6.66, 3.34, 1.66 + 1953, -1.05, -1.16, -7.71, 1.82 + 1954, 49.35, -2.18, 26.21, 0.86 + 1955, 23.75, -6.71, 5.77, 1.57 + 1956, 5.90, -1.17, -1.60, 2.46 + 1957, -13.16, -2.72, -6.39, 3.14 + 1958, 43.45, 14.81, 13.16, 1.54 + 1959, 9.76, 5.43, 1.86, 2.95 + 1960, -1.46, -2.76, -4.87, 2.66 + 1961, 24.81, 1.49, 5.19, 2.13 + 1962, -12.90, -8.25, 8.80, 2.73 + 1963, 17.84, -5.88, 15.66, 3.12 + 1964, 12.54, -0.99, 9.86, 3.54 + 1965, 10.52, 21.80, 7.36, 3.93 + 1966, -13.51, 2.59, -0.68, 4.76 + 1967, 24.49, 50.69, -8.58, 4.21 + 1968, 8.79, 24.50, 18.49, 5.21 + 1969, -17.54, -13.98, -9.81, 6.58 + 1970, -6.49, -11.79, 22.34, 6.52 + 1971, 11.78, 5.62, -11.29, 4.39 + 1972, 13.05, -11.95, 1.75, 3.84 + 1973, -26.19, -23.44, 18.08, 6.93 + 1974, -35.75, -0.60, 9.67, 8.00 + 1975, 32.44, 15.28, 9.49, 5.80 + 1976, 21.91, 14.69, 24.50, 5.08 + 1977, -8.26, 22.95, 7.51, 5.12 + 1978, 1.03, 14.38, 0.37, 7.18 + 1979, 13.09, 21.12, -2.12, 10.38 + 1980, 22.13, 5.57, -25.06, 11.24 + 1981, -18.13, 7.23, 25.01, 14.71 + 1982, 10.66, 8.89, 13.59, 10.54 + 1983, 13.74, 13.67, 20.85, 8.80 + 1984, -6.05, -8.31, 19.63, 9.85 + 1985, 24.91, 0.12, 1.35, 7.72 + 1986, 10.12, -9.60, 9.58, 6.16 + 1987, -3.87, -11.00, -1.64, 5.47 + 1988, 11.55, 5.90, 14.77, 6.35 + 1989, 20.49, -12.72, -4.29, 8.37 + 1990, -13.95, -14.18, -9.72, 7.81 + 1991, 29.18, 16.13, -14.41, 5.60 + 1992, 6.23, 7.58, 24.28, 3.51 + 1993, 8.21, 5.80, 18.91, 2.90 + 1994, -4.10, -1.06, -0.69, 3.90 + 1995, 31.22, -9.09, 5.30, 5.60 + 1996, 15.96, -3.71, 6.16, 5.21 + 1997, 25.96, -6.76, 17.46, 5.26 + 1998, 19.46, -26.02, -8.89, 4.86 + 1999, 20.57, 14.85, -31.77, 4.68 + 2000, -17.60, -1.51, 39.69, 5.89 + 2001, -15.20, 18.09, 19.52, 3.83 + 2002, -22.76, 4.72, 7.47, 1.65 + 2003, 30.75, 26.18, 5.40, 1.02 + 2004, 10.72, 4.85, 8.08, 1.20 + 2005, 3.09, -1.93, 8.33, 2.98 + 2006, 10.60, 0.24, 14.11, 4.80 + 2007, 1.04, -7.14, -14.65, 4.66 + 2008, -38.34, 3.18, 0.82, 1.60 + 2009, 28.26, 9.39, -9.17, 0.10 + 2010, 17.37, 13.77, -5.31, 0.12 + 2011, 0.44, -6.04, -8.35, 0.04 + 2012, 16.28, -1.28, 9.68, 0.06 + 2013, 35.20, 7.18, 1.33, 0.02 + 2014, 11.70, -8.11, -1.74, 0.02 + 2015, 0.07, -4.03, -9.65, 0.02 + 2016, 13.30, 6.65, 23.02, 0.20 + 2017, 21.50, -4.68, -13.98, 0.80 + 2018, -6.93, -3.40, -9.16, 1.81 + 2019, 28.28, -6.09, -11.67, 2.14 + +Copyright 2020 Kenneth R. French diff --git a/Data/F-F_Research_Data_Factors_daily.CSV b/Data/F-F_Research_Data_Factors_daily.CSV new file mode 100644 index 0000000..f502001 --- /dev/null +++ b/Data/F-F_Research_Data_Factors_daily.CSV @@ -0,0 +1,24797 @@ +This file was created by CMPT_ME_BEME_RETS_DAILY using the 202007 CRSP database. +The Tbill return is the simple daily rate that, over the number of trading days +in the month, compounds to 1-month TBill rate from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RF +19260701, 0.10, -0.24, -0.28, 0.009 +19260702, 0.45, -0.32, -0.08, 0.009 +19260706, 0.17, 0.27, -0.35, 0.009 +19260707, 0.09, -0.59, 0.03, 0.009 +19260708, 0.21, -0.36, 0.15, 0.009 +19260709, -0.71, 0.44, 0.56, 0.009 +19260710, 0.62, -0.50, -0.15, 0.009 +19260712, 0.04, 0.03, 0.54, 0.009 +19260713, 0.48, -0.26, -0.23, 0.009 +19260714, 0.04, 0.09, -0.48, 0.009 +19260715, -0.43, 0.54, -0.30, 0.009 +19260716, 0.53, 0.01, -0.57, 0.009 +19260717, 0.34, 0.43, -0.63, 0.009 +19260719, -0.01, 0.01, -0.49, 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French diff --git a/Data/F-F_Research_Data_Factors_weekly.CSV b/Data/F-F_Research_Data_Factors_weekly.CSV new file mode 100644 index 0000000..a6741c9 --- /dev/null +++ b/Data/F-F_Research_Data_Factors_weekly.CSV @@ -0,0 +1,4916 @@ +This file was created by CMPT_ME_BEME_RETS_WEEKLY using the 202007 CRSP database. +The Tbill return is the weekly rate that, over four weeks, +compounds to 1-month TBill rate from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RF +19260702, 1.60, -0.57, -0.90, 0.056 +19260710, 0.36, -0.86, 0.27, 0.056 +19260717, 1.01, 0.83, -1.84, 0.056 +19260724, -2.05, 0.15, -0.25, 0.056 +19260731, 3.04, -1.86, -0.85, 0.056 +19260807, 2.01, 0.08, 0.53, 0.063 +19260814, 0.33, -0.66, 0.76, 0.063 +19260821, -1.11, 0.26, 1.95, 0.063 +19260828, 0.53, 0.07, 0.84, 0.063 +19260903, 1.87, -0.41, 0.55, 0.057 +19260911, -1.27, 0.16, -0.01, 0.057 +19260918, 0.16, -1.10, 0.32, 0.057 +19260925, 0.62, -0.80, -0.39, 0.057 +19261002, 0.48, -0.08, -0.48, 0.080 +19261009, -4.14, -0.31, 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+20190913, 1.33, 2.86, 5.05, 0.044 +20190920, -0.64, -0.55, -1.00, 0.044 +20190927, -1.36, -1.86, 2.00, 0.044 +20191004, -0.40, -0.46, -2.46, 0.038 +20191011, 0.69, -0.43, 0.22, 0.038 +20191018, 0.47, 0.76, 0.55, 0.038 +20191025, 1.38, 0.34, 0.70, 0.038 +20191101, 1.47, 0.38, -0.60, 0.031 +20191108, 1.01, -0.41, 2.31, 0.031 +20191115, 0.78, -0.68, -1.76, 0.031 +20191122, -0.19, -0.06, -1.56, 0.031 +20191129, 1.13, 1.53, -1.59, 0.031 +20191206, 0.10, 0.47, 0.85, 0.035 +20191213, 0.74, 0.16, 0.77, 0.035 +20191220, 1.71, 0.36, -0.02, 0.035 +20191227, 0.49, -0.49, -0.40, 0.035 +20200103, -0.12, -0.29, 0.33, 0.032 +20200110, 0.94, -0.92, -2.20, 0.032 +20200117, 2.00, 0.64, -1.34, 0.032 +20200124, -1.14, -1.23, -1.12, 0.032 +20200131, -2.05, -1.13, -1.53, 0.032 +20200207, 3.13, -0.37, -0.77, 0.030 +20200214, 1.73, -0.02, -0.93, 0.030 +20200221, -1.06, 0.74, -0.04, 0.030 +20200228, -11.50, 0.63, -2.40, 0.030 +20200306, 0.10, -1.66, -4.86, 0.031 +20200313, -9.71, -5.94, -3.56, 0.031 +20200320, -14.57, 1.63, -5.82, 0.031 +20200327, 10.32, -0.97, 1.01, 0.031 +20200403, -2.49, -2.20, -5.46, 0.001 +20200409, 12.35, 2.84, 7.88, 0.001 +20200417, 3.24, -1.67, -8.58, 0.001 +20200424, -0.85, 2.54, -2.01, 0.001 +20200501, 0.01, 2.31, 4.85, 0.002 +20200508, 4.24, 2.26, -3.52, 0.002 +20200515, -2.20, -1.25, -7.30, 0.002 +20200522, 3.59, 3.29, 4.01, 0.002 +20200529, 2.98, -1.12, 3.73, 0.002 +20200605, 4.97, 0.59, 9.93, 0.003 +20200612, -4.73, -1.27, -5.23, 0.003 +20200619, 2.29, 1.27, -3.21, 0.003 +20200626, -2.83, 0.84, -4.13, 0.003 +20200702, 4.04, 0.67, -1.66, 0.003 +20200710, 2.10, -1.86, -1.38, 0.003 +20200717, 1.20, 1.76, 2.38, 0.003 +20200724, -0.31, -0.23, 2.91, 0.003 +20200731, 1.73, -0.51, -2.53, 0.003 + +Copyright 2020 Kenneth R. French From 3e73e48d51e188552783db87513255e5741c26ac Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 18 Sep 2020 14:15:52 -0700 Subject: [PATCH 176/215] Adds 315 G-Score Investing --- .../01 Strategy Library.php | 9 +++ .../1030 G-Score Investing/01 Abstract.html | 3 + .../02 Introduction.html | 10 ++++ .../1030 G-Score Investing/03 Method.html | 57 +++++++++++++++++++ .../1030 G-Score Investing/04 Algorithm.html | 6 ++ .../1030 G-Score Investing/05 Results.html | 5 ++ .../1030 G-Score Investing/06 References.html | 6 ++ 7 files changed, 96 insertions(+) create mode 100644 04 Strategy Library/1030 G-Score Investing/01 Abstract.html create mode 100644 04 Strategy Library/1030 G-Score Investing/02 Introduction.html create mode 100644 04 Strategy Library/1030 G-Score Investing/03 Method.html create mode 100644 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html create mode 100644 04 Strategy Library/1030 G-Score Investing/05 Results.html create mode 100644 04 Strategy Library/1030 G-Score Investing/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..edba6dd 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'G-Score Investing', + 'link' => 'strategy-library/g-score-investing', + 'sources' => [ + 'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=403180' + ], + 'description' => "Applying G-Score Investing to Invest in a Portfolio of Technology Stocks", + 'tags'=>'Fundamentals, Factor Investing, G-Score Investing, MorningStar data, equities' ] ]; diff --git a/04 Strategy Library/1030 G-Score Investing/01 Abstract.html b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html new file mode 100644 index 0000000..248865d --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html @@ -0,0 +1,3 @@ +

        + In this tutorial, we apply G-Score Investing to choose a Universe of stocks to invest in. +

        diff --git a/04 Strategy Library/1030 G-Score Investing/02 Introduction.html b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html new file mode 100644 index 0000000..b26b9f7 --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html @@ -0,0 +1,10 @@ +

        + Analyzing a company’s fundamentals is a method of trading that doesn’t + rely purely on price and volume data. We will apply the use of computers to automate + the analysis of this data, and we will do so using a method of + Factor Investing, + the process of using different attributes, in this case, fundamental data, to choose + stocks to purchase. More specifically, we will use G-Score investing, and evaluate companies + on seven factors that we will detail later. We specifically choose companies with Book-to-Market + due to abnormal returns as a result of the Risk Premium Effect. +

        diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html new file mode 100644 index 0000000..b6ef87b --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html @@ -0,0 +1,57 @@ +

        + We first sort all companies that have fundamental data by their Book-to-Market ratio, and narrow our universe to the + bottom quartile. We measure the Book-to-Market ratio using + fine.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths divided by + fine.MarketCap. In this strategy, we will use Technology as the industry of choice, thus, we further + narrow this universe to Technology stocks only. +

        + +

        + For each of the conditions that are described below, if met, one point will be added to the G-Score. + Thus, with seven factors, our G-Score can range from 0 to 7. We evaluate a company based on the following: +

        + + +
          +
        • + The Return on Assets (ROA) is greater than the contemporaneous industry median. In other words, the ROA for + the analyzed company is greater than the median of the ROAs of all companies in the same industry
          + We measure this value using fine.OperationRatios.ROA.OneYear +
          • +
        • + The Cash Flow Return on Assets (CFROA) is greater than the contemporaneous industry median
          + We measure this value using fine.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths + divided by fine.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths +
          • +
        • + The CFROA is greater than the ROA +
          • +
        • + The Variance of the ROA (VARROA) is lower than the contemporaneous industry median
          + We measure this by storing the past twelve values of fine.OperationRatios.ROA.ThreeMonths in a + RollingWindow and computing the variance of the values in the RollingWindow +
          • +
        • + The Research and Development Expenditure (R&D) is higher than the contemporaneous industry median
          + We measure this using fine.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths +
          • +
        • + The Capital Expenditure (CapEx) is higher than the contemporaneous industry median
          + We measure this using fine.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths +
          • +
        • + The Advertisement Expenditure (Ad) is higher than the contemporaneous industry median
          + We measure this using fine.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths +
          • +
        + +

        + The fundamental data used in our algorithms is sourced from MorningStar, and to read more about our fundamental data, + please visit the Fundamentals section of our + documentation. +

        + +

        + Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher +

        + diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html new file mode 100644 index 0000000..108b8ca --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html new file mode 100644 index 0000000..c8570c2 --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html @@ -0,0 +1,5 @@ +

        + Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track + using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is + outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period. +

        \ No newline at end of file diff --git a/04 Strategy Library/1030 G-Score Investing/06 References.html b/04 Strategy Library/1030 G-Score Investing/06 References.html new file mode 100644 index 0000000..f21473a --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/06 References.html @@ -0,0 +1,6 @@ +
          +
        1. + Mohanram, Partha S., Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement + nalysis (April 2004). Online Copy. +
          2. +
        \ No newline at end of file From 140b9612c017265b3e8d4720eff49b69e69e9ad0 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 18 Sep 2020 15:22:35 -0600 Subject: [PATCH 177/215] Fix strategy library map file --- .../01 Strategy Library.php | 9 + .../01 Abstract.html | 8 + .../02 Background.html | 14 ++ .../03 Method.html | 183 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Conclusion.html | 26 +++ .../06 References.html | 11 ++ 7 files changed, 257 insertions(+) create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..384bc60 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'Using News Sentiment to Predict Price Direction of Drug Manufacturers', + 'link' => 'strategy-library/using-news-sentiment-to-predict-price-direction-of-drug-manufacturers', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/abs/1812.04199' + ], + 'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.", + 'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday' ] ]; diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html new file mode 100644 index 0000000..ab35081 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html @@ -0,0 +1,8 @@ +

        + Several studies have found that press releases and other media can impact the perspective of investors. In this + tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug + manufacturer following positive news releases. Our findings show that when combining the effect with the + day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the + trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is + inspired by the work of Isah, Shah, & Zulkernine (2018). +

        diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html new file mode 100644 index 0000000..60fe4eb --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html @@ -0,0 +1,14 @@ +

        + The use of alternative data sets to forecast stock prices has increased in recent years as the fundamental and + technical analysis spaces increase in competition. Utilizing Natural Language Processing (NLP) techniques to + analyze the sentiment of news releases and other text related to publicly traded companies has caught the interest + of many quant researchers. Such online information is frequently released and can be interpreted in a virtually + unlimited number of ways, leading to a novel approach to determining the "societal mood" (Isah et al, 2018, p. 2) + towards a company. +

        + +

        + There are several ways to implement a NLP system. In this tutorial, we utilize a dictionary to quantify the + sentiment of news releases. The dictionary provided herein was sourced from Isah et al (2018), where it's use + achieved a 70% accuracy when targeting several hand-picked stocks in India's pharmaceutical industry. +

        \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html new file mode 100644 index 0000000..ac080d3 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html @@ -0,0 +1,183 @@ +

        Universe Selection

        +

        + We implement a universe selection model that provides the trading system with companies classified by + MorningStar as being in the drug + manufacturing industry group. We narrow our universe to include only the drug manufacturers with the greatest PE + ratios and dollar volume. +

        +
        +
        +def SelectCoarse(self, algorithm, coarse):
+    has_fundamentals = [c for c in coarse if c.HasFundamentalData]
+    sorted_by_dollar_volume = sorted(has_fundamentals, key=lambda c: c.DollarVolume, reverse=True)
+    return [ x.Symbol for x in sorted_by_dollar_volume[:self.coarse_size] ]
+
+def SelectFine(self, algorithm, fine):
+    drug_manufacturers = [f for f in fine if f.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.DrugManufacturers]
+    sorted_by_pe = sorted(drug_manufacturers, key=lambda f: f.ValuationRatios.PERatio, reverse=True)
+    return [ x.Symbol for x in sorted_by_pe[:self.fine_size] ]
+
        +
        + + +

        Alpha Construction

        +

        + The DrugNewsSentimentAlphaModel emits insights to take long intraday positions for securities that have positive + news sentiment. During construction of the model, we: +

        + +
          +
        • Create a dictionary to store SymbolData for each symbol
          • +
        • Gather the sentiment dictionary provided by Isah et al (2018)
          • +
        • Determine the maximum number of grams we need to analyze news articles
          • +
        • Define a method to determine the sign of sentiment
          • +
        • Specify the value of `bars_before_insight`
          • +
        + +

        + The `bars_before_insight` parameter determines how many bars the alpha model should observe after the market opens + before emitting insights. Isah et al (2018) batch the news released by each company into 30-minute intervals + before analyzing the sentiment of the batch. In this tutorial, we follow a similar procedure by setting + `bars_before_insight` to 30. +

        + +
        +
        +class DrugNewsSentimentAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    sentiment_by_phrase = SentimentByPhrase.dictionary
+    max_phrase_words = max([len(phrase.split()) for phrase in sentiment_by_phrase.keys()])
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+    
+    def __init__(self, bars_before_insight=30):
+        self.bars_before_insight = bars_before_insight
+
        +
        + +

        Alpha Securities Management

        +

        + When a new security is added to the universe, we create a SymbolData object for it to store information unique to + each security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method. +

        + +
        +
        +def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+    
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            algorithm.RemoveSecurity(symbol_data.tiingo_symbol)
+
        +
        + +

        + The definition of the SymbolData class is shown below. We add properties to it to track the cumulative sentiment + of news releases over time and the number of bars the alpha model has received for each security since the market + open. In the constructor, we save a reference to the security's exchange so we can access the market hours of the + exchange when generating insights. This is also where we initialize the + Tiingo news feed for each security. +

        + +
        +
        +class SymbolData:
+    cumulative_sentiment = 0
+    bars_seen_today = 0
+    
+    def __init__(self, security, algorithm):
+        self.exchange = security.Exchange
+        self.tiingo_symbol = algorithm.AddData(TiingoNews, security.Symbol).Symbol
+
        +
        + + +

        Alpha Update

        +

        + As new Tiingo objects are provided to the alpha model's Update method, we update the cumulative sentiment for each + security. The cumulative sentiment counter is reset at each market close. Therefore, when we emit insights + 30-minutes after the open, we are considering the sentiment of the news articles released from the previous close + to the current time. We employ the findings of Berument & Kiymaz (2001), restricting the alpha model's trading to + Wednesday, the most profitable day of the week. Positions are entered 30-minutes after the open and exited at the + close. +

        + +
        +
        +def Update(self, algorithm, data):
+    insights = []
+        
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+    
+        # If it's after-hours or within 30-minutes of the open, update
+        # cumulative sentiment for each symbol    
+        if symbol_data.bars_seen_today < self.bars_before_insight:
+            tiingo_symbol = symbol_data.tiingo_symbol
+            if data.ContainsKey(tiingo_symbol) and data[tiingo_symbol] is not None:
+                article = data[tiingo_symbol]
+                symbol_data.cumulative_sentiment += self.CalculateSentiment(article)
+    
+        if data.ContainsKey(symbol) and data[symbol] is not None:
+            symbol_data.bars_seen_today += 1
+
+            # 30-mintes after the open, emit insights in the direction of the cumulative sentiment.
+            # Only emit insights on Wednesdays to capture the analomaly documented by Berument and 
+            # Kiymaz (2001).
+            if symbol_data.bars_seen_today == self.bars_before_insight and data.Time.weekday() == 2:
+                    next_close_time = symbol_data.exchange.Hours.GetNextMarketClose(data.Time, False)
+                    direction = self.sign(symbol_data.cumulative_sentiment)
+                    if direction == 0:
+                        continue
+                    insight = Insight.Price(symbol, 
+                                            next_close_time - timedelta(minutes=2),
+                                            direction)
+                    insights.append(insight)
+    
+            # At the close, reset the cumulative sentiment
+            if not symbol_data.exchange.DateTimeIsOpen(data.Time):
+                symbol_data.cumulative_sentiment = 0
+                symbol_data.bars_seen_today = 0
+    
+    return insights
+
        +
        + + +

        Calculating Sentiment

        +

        + We define the following helper method to return the sentiment of a Tiingo news article by analyzing the article's + title and description. The `sentiment_by_phrase` dictionary was retrieved from queensbamlab's + NewsSentiment GitHub repository. Although we have + adjusted the dictionary to lowercase and removed some redundancies, this is the same dictionary used by Isah et + al (2018). "The dictionary was created by leveraging author's domain expertise and thorough analysis of news + articles over the years" (p. 3). +

        + +
        +
        +def CalculateSentiment(self, article):
+    sentiment = 0
+    for content in (article.Title, article.Description):
+        words = content.lower().split()
+        for num_words in range(1, self.max_phrase_words + 1):
+            for gram in ngrams(words, num_words):
+                phrase = ' '.join(gram)
+                if phrase in self.sentiment_by_phrase.keys():
+                    sentiment += self.sentiment_by_phrase[phrase]
+    return sentiment
+
        +
        + + +

        Portfolio Construction & Trade Execution

        +

        + Following the guidelines of Alpha Streams and the + Quant League competition, we utilize the + + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

        + diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html new file mode 100644 index 0000000..43dfb6f --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html new file mode 100644 index 0000000..553c5a6 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html @@ -0,0 +1,26 @@ +

        + We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide + as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with + a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting + trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability + over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated + by LEAN. +

        + +

        +To continue the development of this strategy, future areas of research include: +

        + +
          +
        • Adding a threshold parameter the cumulative sentiment counter must pass to signal trades
          • +
        • + Only analyzing the sentiment of articles which contain keywords like "US, "USA", 'Q1', and others in their + titles +
          • +
        • + Stemming and removing + punctuation from articles before calculating their sentiment +
          • +
        • Adding phrases to the sentiment dictionary or adjust the sentiment values
          • +
        • Adding other data feeds beside just Tiingo
          • +
        \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html new file mode 100644 index 0000000..8f8f421 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html @@ -0,0 +1,11 @@ +
          +
        1. + Shah, Dev, Haruna Isah, and Farhana Zulkernine. “Predicting the Effects of News Sentiments on the Stock Market.” + 2018 IEEE International Conference on Big Data (Big Data) (2018). + Online copy +
          2. +
        2. + Berument, Hakan and Kiymaz, Halil, The Day of the Week Effect on Stock Market Volatility (2001). Journal of + Economics and Finance, Vol.25, No.2, pp. 181-193. Online copy +
          3. +
        \ No newline at end of file From 0f88f5770ebc6f2b0f26aac80b0a1c6acd244c4c Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 18 Sep 2020 15:23:55 -0600 Subject: [PATCH 178/215] Fix typo --- .../01 Abstract.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html index ab35081..387cf48 100644 --- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html @@ -1,7 +1,7 @@

        Several studies have found that press releases and other media can impact the perspective of investors. In this tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug - manufacturer following positive news releases. Our findings show that when combining the effect with the + manufacturers following positive news releases. Our findings show that when combining the effect with the day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is inspired by the work of Isah, Shah, & Zulkernine (2018). From 6235e6b7a9661b0ee4c9435b785f69fb1c34764b Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 25 Sep 2020 12:54:30 -0700 Subject: [PATCH 179/215] Adds 321 SVM Wavelet Forecasting --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 3 ++ .../02 Introduction.html | 10 ++++ .../03 Method.html | 52 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Results.html | 8 +++ .../06 References.html | 5 ++ 7 files changed, 93 insertions(+) create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..e979478 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'SVM Wavelet Forecasting', + 'link' => 'strategy-library/svm-wavelet-forecasting', + 'sources' => [ + 'Academia' => 'https://www.academia.edu/37180223/SVR_Wavelet_Adaptive_Model_for_Forecasting_Financial_Time_Series' + ], + 'description' => "Forecasting EURJPY prices with an SVM Wavelet model", + 'tags'=>'Support Vector Machines (SVM), Forex, Forecasting, Wavelet, Discrete Wavelet Transform' ] ]; diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html new file mode 100644 index 0000000..fb88b27 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html @@ -0,0 +1,3 @@ +

        + In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices. +

        diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html new file mode 100644 index 0000000..168b024 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html @@ -0,0 +1,10 @@ +

        + Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we + combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often + used for classification problems, such as classifying proteins, they can also be applied in regression problems, and + are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be + used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so + when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to + forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate + forecast, which we detail in the Method section. +

        diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html new file mode 100644 index 0000000..1a923a5 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html @@ -0,0 +1,52 @@ +

        + Given EURJPY data, our first step is to decompose our data into multiple resolutions. + We work with wavelets using the pywt package. For denoising, Daubechies and + Symlets are good choices for Wavelets, and we use Symlets 10 in our strategy. We create a Symlets 10 Wavelet + using the following:  +

        + +
        +
        +    w = pywt.Wavelet('sym10')
+
        +
        +

        To determine the length of the data we’d need for a certain number of levels after decomposition, we can solve for:

        +\[log_{2}(\frac{len(data)}{wavelength-1})=levels\] +

        + Given the length of a Symlet 10 wavelet is 20, if we want three levels, we + solve for len(data) to get len(data) = 152, which means data would need to have at least + 152 values. Since we will denoise our components using thresholding, + we specify threshold = 0.5 to indicate the strength of the thresholding. + This threshold value can be any number between 0 and 1. +

        +

        To decompose our data, we use: 

        + +
        +
        +    coeffs = pywt.wavedec(data, w)
+
        +
        + +

        For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

        + +
        +
        +    for i in range(1, len(coeffs)):
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+        forecasted = __svm_forecast(coeffs[i])
+        coeffs[i] = np.roll(coeffs[i], -1)
+        coeffs[i][-1] = forecasted
+
        +
        + +

        The __svm_forecast method fits partitioned data to an SVM model then predicts one value into the + future, and can be found under SVMWavelet.py file under Algorithm 

        +

        Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:

        +
        +
        +    datarec = pywt.waverec(coeffs, w)
+
        +
        +

        Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or datarec[-1].

        + +

        Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.

        diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html new file mode 100644 index 0000000..fc4ed47 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html @@ -0,0 +1,6 @@ +
        +
        +
        + +
        +
        diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html new file mode 100644 index 0000000..d3cea27 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html @@ -0,0 +1,8 @@ +

        The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163, + while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:

        +
          +
        • Testing different Wavelet types (e.g. Daubechies)
          • +
        • Trying out other time resolutions (e.g. Minute)
          • +
        • Using alternative Decomposition methods (e.g. Stationary Wavelet Transform) 
          • +
        +

        If a user comes across any interesting results with modifications of this algorithm, we’d love to hear about it in the Community Forum.

        \ No newline at end of file diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html new file mode 100644 index 0000000..1cc8866 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html @@ -0,0 +1,5 @@ +
          +
        1. + M. S. Raimundo and J. Okamoto, "SVR-wavelet adaptive model for forecasting financial time series," 2018 International Conference on Information and Computer Technologies (ICICT), DeKalb, IL, 2018, pp. 111-114, doi: 10.1109/INFOCT.2018.8356851. Online Copy. +
          2. +
        \ No newline at end of file From e66d96aed7e3ee445d5c95e339392aa1883afbc5 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Mon, 28 Sep 2020 08:29:12 -0700 Subject: [PATCH 180/215] Adds 321 SVM Wavelet Forecasting --- .../1031 SVM Wavelet Forecasting/03 Method.html | 10 +++++++--- .../1031 SVM Wavelet Forecasting/04 Algorithm.html | 2 +- .../1031 SVM Wavelet Forecasting/05 Results.html | 2 +- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html index 1a923a5..6651dd7 100644 --- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html @@ -27,12 +27,16 @@
    -

    For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

    +

    For each component, we threshold/denoise the component (except for the first component, the approximation coefficients), + roll the values of the component one spot to the left, + and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

    -    for i in range(1, len(coeffs)):
-        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    for i in range(len(coeffs)):
+        if i > 0:
+            # we don't want to threshold the approximation coefficients
+            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
         forecasted = __svm_forecast(coeffs[i])
         coeffs[i] = np.roll(coeffs[i], -1)
         coeffs[i][-1] = forecasted
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index fc4ed47..868c5d9 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index d3cea27..c80ae5c 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-
    The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163,
+
    The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:
    
 
      
     
    • Testing different Wavelet types (e.g. Daubechies)
      • 

From 920bf30f1ad9e98a66db45d643fdcfceb72ad8db Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 2 Oct 2020 19:06:33 -0600
Subject: [PATCH 181/215] Update cointegration algorithm and write-up

---
 .../04 Part II - Cointegration Method.html    | 53 ++++++++++---------
 .../05 Summary.html                           | 18 +++----
 .../06 Algorithm.html                         |  4 +-
 3 files changed, 40 insertions(+), 35 deletions(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 7011faf..76bf605 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -9,39 +9,44 @@ 
      Step 1: Generate the Spread Series
      
 
 \[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
 
-
      Step 2: Compute the Threshold
      
+
      Step 2: Compute the Signals
      
 
 
      
-  Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
+  Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
+  is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  sizes are scaled by the coefficient β.
 
      
 
 
      
 
-
      price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
-                     index = [i.Time for i in self.symbols[0].hist_window])
+
      log_close_x = np.log(self.closes_by_symbol[self.x_symbol])
+log_close_y = np.log(self.closes_by_symbol[self.y_symbol])
+
+spread, beta = self.regr(log_close_x, log_close_y)
 
-price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
-                     index = [i.Time for i in self.symbols[1].hist_window])
-if len(price_x) < 250: return
-spread = self.regr(np.log(price_x), np.log(price_y))
 mean = np.mean(spread)
 std = np.std(spread)
-ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
-if spread[-1] > mean + self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
-        self.Sell(self.symbols[1], 100)
-        self.Buy(self.symbols[0],  ratio * 100)
-
-elif spread[-1] < mean - self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
-        self.Sell(self.symbols[0], 100)
-        self.Buy(self.symbols[1], ratio * 100)
+
+x_holdings = self.Portfolio[self.x_symbol]
+
+if x_holdings.Invested:
+    if x_holdings.IsShort and spread[-1] <= mean or \
+        x_holdings.IsLong and spread[-1] >= mean:
+        self.Liquidate()
 else:
-    self.Liquidate()
+    if beta < 1:
+        x_weight = 0.5
+        y_weight = 0.5 / beta
+    else:
+        x_weight = 0.5 / beta
+        y_weight = 0.5
+    
+    if spread[-1] < mean - self.threshold * std:
+        self.SetHoldings(self.y_symbol, -y_weight) 
+        self.SetHoldings(self.x_symbol, x_weight)
+    if spread[-1] > mean + self.threshold * std:
+        self.SetHoldings(self.x_symbol, -x_weight)
+        self.SetHoldings(self.y_symbol, y_weight)
 
      
 
      
-
-
      Step 3: Set up the Trading Signals
      
-
      
-  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread mean+2*std. We construct short positions in Y and long positions in X on the day that spread<mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
-
      
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
index cd9ff46..44ab024 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -15,21 +15,21 @@
 
 
 Copula
-346
-274.293%
-1.022
-19.4%
+493
+8.884%
+0.12
+26.1%
 
 
 Cointegration
-91
-26.358%
-0.298
-23.7%
+126
+4.517%
+0.196
+3.9%
 
 
 
 
 
      
-  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
+  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 91 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
 
      
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 21099b5..228f289 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 
      
     
      
     
      
-        
+        
     
      
 
      
 
@@ -14,6 +14,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      

From d831869a62cc3858617fa9a275d6ab6492eee23d Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 2 Oct 2020 19:12:06 -0600
Subject: [PATCH 182/215] Fix typo

---
 .../04 Part II - Cointegration Method.html                      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 76bf605..d36c895 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -14,7 +14,7 @@ 
      Step 2: Compute the Signals
      
 
      
   Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
   is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
-  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing spread value. The position
   sizes are scaled by the coefficient β.
 
      
 

From adb03722c992d5267f8ecef3cc275e3b45c2c999 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 5 Oct 2020 16:56:27 -0700
Subject: [PATCH 183/215] update backtest

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 108b8ca..52a4b06 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index c8570c2..cefd93b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
      
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
      
\ No newline at end of file

From a11df07a89239053b1e37e3247ad47fe11101275 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 16 Oct 2020 15:54:15 -0600
Subject: [PATCH 184/215] Adds Gradient Boosting Strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  24 +++
 .../03 Method.html                            | 163 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  53 ++++++
 ...06 Market & Competition Qualification.html |  13 ++
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |   5 +
 9 files changed, 297 insertions(+)
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..7e5dd34 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'Gradient Boosting Model',
+     'link' => 'strategy-library/gradient-boosting-model',
+     'sources' => [
+       'arXiv' => 'https://ssrn.com/abstract=2323899'
+     ],
+     'description' => "Forecasts future intraday returns with a gradient boosting model trained on technical indicators",
+     'tags'=>'Gradient Boost, Regression Trees, Equities, Machine Learning'
   ]
 ];
 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
new file mode 100644
index 0000000..1d309c1
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+
      
+  In this tutorial, we train a Gradient Boosting Model (GBM) to forecast the intraday price movements of the SPY ETF using a 
+  collection of technical indicators. The implementation is based on the research produced by Zhou et al (2013), where a GBM 
+  was found to produce an annualized Sharpe ratio greater than 20. Our research shows that throughout a 5 year backtest, the 
+  model underperforms the SPY with its current parameter set. However, we finish the tutorial with highlighting potential 
+  areas of further research to improve the model’s performance.
+
      
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
new file mode 100644
index 0000000..49f45d3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -0,0 +1,24 @@
+
      
+  A GBM is trained by setting the initial model prediction to the mean target value in the training set. The model then 
+  iteratively builds regression trees to predict the model’s pseudo-residuals on the training set to tighten the fit. The 
+  pseudo-residuals are the differences between the target value and the model’s prediction on the current training iteration 
+  for each sample. The model’s predictions are made by summing the mean target value and the products of the learning rate 
+  and the regression tree outputs. The full algorithm is shown here.
+
      
+
+
      
+  Tutorial1033-gradient-boost-1
+
      
+
+
      
+  We provide technical indicator values as inputs to the GBM. The model is trained to predict the security’s return over the 
+  next 10 minutes and the performance of the model’s predictions are assessed using the mean squared error loss function.
+
      
+
+\[ MSE = \frac{\Sigma_{i=1}^n(y_i - \hat{y}_i)^2}{n}  \]
+
+
      
+  Zhou et al (2013) utilize custom loss functions to fit their GBM in a manner that aims to maximize the profit-and-loss or 
+  Sharpe ratio over the training data set. The attached notebook shows training the GBM with these custom loss functions 
+  leads to poor model predictions.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
new file mode 100644
index 0000000..7e88ae2
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html	
@@ -0,0 +1,163 @@
+
      Universe Selection
      
+
      
+  We use a ManualUniverseSelectionModel to subscribe to the SPY ETF. The algorithm is designed to work with minute and
+  second data resolutions. In our implementation, we use data on a minute resolution.
+
      
+
      
+
      +symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+
      
+
      
+
+
+
      Alpha Construction
      
+
      
+  The GradientBoostingAlphaModel predicts the direction of the SPY at each timestep. Each position taken is held for 
+  10 minutes, although this duration is customizable in the constructor. During construction of this alpha model, we 
+  simply set up a dictionary to hold a SymbolData object for each symbol in the universe. In the case where the 
+  universe consists of multiple securities, the alpha model holds each with equal weighting.
+
      
+
      
+
      +class GradientBoostingAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    
+    def __init__(self, hold_duration = 10):
+        self.hold_duration = hold_duration
+        self.weight = 1
+
      
+
      
+
+
+
      Alpha Securities Management
      
+
      
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method.
+
      
+
      
+
      +def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        symbol = security.Symbol
+        self.symbol_data_by_symbol[symbol] = SymbolData(symbol, algorithm, self.hold_duration)
+            
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
+    self.weight = 1 / len(self.symbol_data_by_symbol)
+
+
      
+
      
+
+
+
      SymbolData Class
      
+
      
+  The SymbolData class is used in this algorithm to manage indicators, train the GBM, and produce trading predictions. 
+  The constructor definition is shown below. The class is designed to train at the end of each month, using the 
+  previous 4 weeks of data to fit the GBM that consists of 20 stumps (regression trees with 2 leaves). To ensure 
+  overnight holds are avoided, the class uses 
+  Scheduled Events to stop trading
+  near the market close.
+
      
+
      
+
      +class SymbolData:    
+    def __init__(self, symbol, algorithm, hold_duration, k_start=0.5, k_end=5,
+                    k_step=0.25, training_weeks=4, max_depth=1, num_leaves=2, num_trees=20,
+                    commission=0.02, spread_cost=0.03):
+        self.symbol = symbol
+        self.algorithm = algorithm
+        self.hold_duration = hold_duration
+        self.resolution = algorithm.UniverseSettings.Resolution
+        self.training_length = int(training_weeks * 5 * 6.5 * 60) # training_weeks in minutes
+        self.max_depth = max_depth
+        self.num_leaves = num_leaves
+        self.num_trees = num_trees
+        self.cost = commission + spread_cost
+
+        self.indicator_consolidators = []
+
+        # Train a model at the end of each month
+        self.model = None
+        algorithm.Train(algorithm.DateRules.MonthEnd(symbol),
+                        algorithm.TimeRules.BeforeMarketClose(symbol),
+                          self.train)
+
+        # Avoid overnight holds
+        self.allow_predictions = False
+        self.events = [
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.AfterMarketOpen(symbol, 0),
+                                  self.start_predicting),
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.BeforeMarketClose(symbol, hold_duration + 1),
+                                  self.stop_predicting)
+        ]
+
+        self.setup_indicators(k_start, k_end, k_step)
+        self.train()
+
      
+
      
+
+
+
      GBM Predictions
      
+
      
+  For brevity, we omit the model training logic. Although, the code can be seen in the attached backtest. To make 
+  predictions, we define the following method inside the SymbolData class. A position is held in the predicted 
+  direction only if the predicted return in that direction exceeds the cost of the trade.
+
      
+
      
+
      +def predict_direction(self):
+    if self.model is None or not self.allow_predictions:
+        return 0
+
+    input_data = [[]]
+    for _, indicators in self.indicators_by_indicator_type.items():
+        for indicator in indicators:
+            input_data[0].append(indicator.Current.Value)
+                
+    return_prediction = self.model.predict(input_data)
+    if return_prediction > self.cost:
+        return 1
+    if return_prediction < -self.cost:
+        return -1
+    return 0
+
      
+
      
+
+
+
      Alpha Update
      
+
      
+  As new TradeBars are provided to the alpha model's Update method, each SymbolData object makes a directional 
+  prediction for its security. If the prediction is not flat, the alpha model emits an insight in that direction with 
+  a duration of 10 minutes.
+
      
+
      
+
      +def Update(self, algorithm, data):
+    insights = []
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        direction = symbol_data.predict_direction()
+        if direction:
+            hold_duration = timedelta(minutes=self.hold_duration) # Should match universe resolution
+            insights.append(Insight.Price(symbol, hold_duration, direction, None, None, None, self.weight))
+
+    return insights
+
      
+
      
+
+
+
      Portfolio Construction & Trade Execution
      
+
      
+  Following the guidelines of Alpha Streams 
+  and the Quant League competition, we 
+  utilize the 
+  InsightWeightingPortfolioConstructionModel and the 
+  
+  ImmediateExecutionModel.
+
      
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
new file mode 100644
index 0000000..223e7f3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
      
+    
      
+    
      
+        
+    
      
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
new file mode 100644
index 0000000..88d08bf
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -0,0 +1,53 @@
+
+    
+        
+            
+            
+            
+            
+            
+            
+        
+    
+    
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+    
+
      Period NameStart DateEnd DateStrategySharpeVariance
      5 Year Backtest9/1/20159/17/2020Strategy-0.7160.006
      Benchmark0.8450.036
      2020 Crash2/19/20203/23/2020Strategy-2.8790.101
      Benchmark-1.2430.628
      2020 Recovery3/23/20206/8/2020Strategy-2.3290.027
      Benchmark13.7610.149
      
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d2ac60d
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,13 @@
+
      
+  Although this strategy passes several of the 
+  metrics required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+
      
+
+
+
        
+  
      • Profitable
        • 
+  
      • PSR >= 80%
        • 
+  
      • Max drawdown duration <= 6 months
        • 
+  
      • Handles dividends and splits
        • 
+
      
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
new file mode 100644
index 0000000..10701d6
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+
      
+  The GBM implemented in this tutorial has a lower Sharpe ratio than the S&P 500 index ETF benchmark over the periods 
+  we tested. However, the strategy generates a lower annual variance over all the testing period, implying more 
+  consistent returns than buy-and-hold. To continue the development of this strategy, future areas of research 
+  include:
+
      
+
+
        
+  
      • Adjusting parameters in the GradientBoostingAlphaModel and SymbolData classes
        • 
+  
      • Testing other custom loss functions
        • 
+  
      • Changing the data resolution from minutes to seconds
        • 
+  
      • Using more/other technical indicators
        • 
+  
      • 
+    Adding a model to predict the cost of trading (slippage, commissions, market impact) for each security instead of 
+    prescribing a fixed amount
+  
        • 
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/08 References.html b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html
new file mode 100644
index 0000000..385172c
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html	
@@ -0,0 +1,5 @@
+
        
+  
      1. 
+    Zhou, Nan and Cheng, Wen and Qin, Yichen and Yin, Zongcheng, Evolution of High Frequency Systematic Trading: A Performance-Driven Gradient Boosting Model (September 10, 2013). Online copy
+  
        2. 
+
      
\ No newline at end of file

From 79fc67e91a61a0b9fc43f2859bb4ca797db73379 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 16 Oct 2020 16:41:39 -0700
Subject: [PATCH 185/215] fixed typos

---
 .../01 Abstract.html                          |  2 +-
 .../02 Introduction.html                      | 14 +++++------
 .../03 Method.html                            | 24 +++++++++----------
 3 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html
index fb88b27..a93480f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
@@ -1,3 +1,3 @@
 
      
-    In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices.
+    In this tutorial, we apply an SVM Wavelet model in an attempt to forecast EURJPY prices.
 
      
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 168b024..986ebed 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -1,10 +1,10 @@
 
      
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
-    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often
-    used for classification problems, such as classifying proteins, they can also be applied in regression problems, and
-    are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be
-    used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so
-    when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to
-    forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate
-    forecast, which we detail in the Method section.
+    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
+    used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
+    leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
+    decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
+    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 
      
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
index 6651dd7..5cd8e93 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
@@ -7,7 +7,7 @@
 
 
      
 
      -    w = pywt.Wavelet('sym10')
+w = pywt.Wavelet('sym10')
 
      
 
      
 
      To determine the length of the data we’d need for a certain number of levels after decomposition, we can solve for:
      
@@ -23,7 +23,7 @@
 
 
      
 
      -    coeffs = pywt.wavedec(data, w)
+coeffs = pywt.wavedec(data, w)
 
      
 
      
 
@@ -33,24 +33,24 @@
 
 
      
 
      -    for i in range(len(coeffs)):
-        if i > 0:
-            # we don't want to threshold the approximation coefficients
-            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
-        forecasted = __svm_forecast(coeffs[i])
-        coeffs[i] = np.roll(coeffs[i], -1)
-        coeffs[i][-1] = forecasted
+for i in range(len(coeffs)):
+    if i > 0:
+        # we don't want to threshold the approximation coefficients
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    forecasted = __svm_forecast(coeffs[i])
+    coeffs[i] = np.roll(coeffs[i], -1)
+    coeffs[i][-1] = forecasted
 
      
 
      
 
 
      The __svm_forecast method fits partitioned data to an SVM model then predicts one value into the
-    future, and can be found under SVMWavelet.py file under Algorithm 
      
+    future, and can be found under SVMWavelet.py file under the Algorithm section
      
 
      Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:
      
 
      
 
      -    datarec = pywt.waverec(coeffs, w)
+datarec = pywt.waverec(coeffs, w)
 
      
 
      
 
      Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or datarec[-1].
      
 
-
      Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.
      
+
      Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have a larger allocation.
      

From 09e9a54af61023974d291b481fa7cce0cfdeb8b9 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 16 Oct 2020 16:43:02 -0700
Subject: [PATCH 186/215] fixed typos

---
 .../1031 SVM Wavelet Forecasting/02 Introduction.html         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 986ebed..6fe7274 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -2,9 +2,9 @@
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
     combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
     used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
-    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allow us
     to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
     leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
     decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
-    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
+    ahead of each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 
      

From f1cfa4561d7fcba2cdd7d2a88794e1140eab2a69 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 16:29:26 -0600
Subject: [PATCH 187/215] Add relative metrics to news sentiment strategy

---
 .../01 Abstract.html                                |  3 ++-
 .../05 Conclusion.html                              | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
index 387cf48..d02e534 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
@@ -3,6 +3,7 @@
   tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug 
   manufacturers following positive news releases. Our findings show that when combining the effect with the 
   day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the 
-  trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is 
+  trading system to remain profitable throughout the 2020 stock market crash. However, the algorithm 
+  underperforms the S&P 500 market index ETF, SPY, over the same time period. The algorithm we design here is 
   inspired by the work of Isah, Shah, & Zulkernine (2018).
 
      
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html
index 553c5a6..8f0a19e 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
@@ -1,10 +1,11 @@
 
      
-  We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide 
-  as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with 
-  a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting 
-  trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability 
-  over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated 
-  by LEAN.
+  We conclude that deploying the sentiment analysis strategy on the US drug manufacturing industry does not provide 
+  as accurate of results as found by Isah et al (2018). Only after restricting trading to the most profitable day of 
+  the week (Berument & Kiymaz, 2001) does the strategy achieve profitability over our testing period. Overall, the 
+  strategy produces a Sharpe ratio of 0.116, while the 
+  SPY benchmark
+  produces a 0.971 Sharpe ratio during the same period. We attribute the decrease in performance to the commissions 
+  and spread costs simulated by LEAN.
 
      
 
 
      

From b606a4f8f03eba2060a611875de59a9f1ab12de2 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 16:45:26 -0600
Subject: [PATCH 188/215] Add missing bracket to gradient boosting algo image

---
 .../1033 Gradient Boosting Model/02 Background.html             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
index 49f45d3..597b32d 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -7,7 +7,7 @@
 
      
 
 
      
-  Tutorial1033-gradient-boost-1
+  Tutorial1033-gradient-boost-1
 
      
 
 
      

From 95e76f2ef3d3722d5e05de72f769bda4b8a5921f Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 15:52:54 -0700
Subject: [PATCH 189/215] updated algo

---
 .../1031 SVM Wavelet Forecasting/04 Algorithm.html              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index 868c5d9..935af6f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      

From acb4f1ffd84421887cfceeafd8ad4d7ae2e738d4 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 15:53:37 -0700
Subject: [PATCH 190/215] updated resuls

---
 .../1031 SVM Wavelet Forecasting/05 Results.html                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index c80ae5c..96be5b5 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-
      The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
+
      The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.252,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:
      
 
        
     
      • Testing different Wavelet types (e.g. Daubechies)
        • 

From 37d0c973209b93184a39b7d0c4f04b2e89b1257c Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 16:21:44 -0700
Subject: [PATCH 191/215] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 52a4b06..11f46e3 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
        
     
        
     
        
-        
+        
     
        
 
        
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index cefd93b..f9af05b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
        
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
        
\ No newline at end of file

From fc5ef6d18ae739434b1f8cf9056d93879e759f71 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 16:22:54 -0700
Subject: [PATCH 192/215] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/03 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html
index b6ef87b..3a493ab 100644
--- a/04 Strategy Library/1030 G-Score Investing/03 Method.html	
+++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html	
@@ -52,6 +52,6 @@
 
        
 
 
        
-    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher
+    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher.
 
        
 

From ce40497bbf02d5587fe1229e825e93752c43adeb Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 18:15:01 -0600
Subject: [PATCH 193/215] Make relative tables mobile friendly

---
 .../05 Relative Performance.html                               | 2 ++
 .../1026 Intraday ETF Momentum/05 Conclusion.html              | 2 ++
 .../05 Relative Performance.html                               | 2 ++
 .../1033 Gradient Boosting Model/05 Relative Performance.html  | 3 +++
 4 files changed, 9 insertions(+)

diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html
index fd71509..2d3e6c9 100644
--- a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
@@ -6,6 +6,7 @@
   across all our testing periods is displayed in the table below.
 
        
 
+
        
 
@@ -72,6 +73,7 @@
         
        
     
         
        
     
 
        
+
        
 
 
 
        
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
index ff087ea..bad583b 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -7,6 +7,7 @@
   A breakdown of the results from all of the testing periods can be seen in the table below.
 
        
 
+
        
 
@@ -73,6 +74,7 @@
         
        
     
         
        
     
 
          
+
        
 
 
 
        
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html
index a1d1610..07a20e4 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
@@ -17,6 +17,7 @@
   frames, implying that the strategy has more consistent returns than the benchmark.
 
        
 
+
        
 
@@ -83,6 +84,7 @@
         
        
     
         
        
     
 
        
+
        
 
 
        
   We find the lack of performance for this strategy is not largely attributed to the transaction costs. After 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
index 88d08bf..415eea9 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -1,3 +1,4 @@
+
        
 
@@ -51,3 +52,5 @@
         
        
     
         
        
     
 
        
+
        
+

From dd941cd87883482bba20bc09a2fca642bfd8c263 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Wed, 28 Oct 2020 16:08:48 -0700
Subject: [PATCH 194/215] fixed algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 11f46e3..2c62d81 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
        
     
        
     
        
-        
+        
     
        
 
        
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index f9af05b..5f327af 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
        
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.778 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
        
\ No newline at end of file

From 9f30601e58cecd646ecd46f69cce5a57b8750c3a Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Thu, 5 Nov 2020 17:36:42 -0800
Subject: [PATCH 195/215] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index bcf0bc2..60c8e6c 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
        
     
        
     
        
-        
+        
     
        
 
        

From 9ef83b5ff7ad231b106bff7e7c0130719d0e3e94 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 6 Nov 2020 15:42:22 -0800
Subject: [PATCH 196/215] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index 60c8e6c..8586edb 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
        
     
        
     
        
-        
+        
     
        
 
        

From 65f9e86bf866688998d76599ce3b80df25d45fc9 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Wed, 11 Nov 2020 17:23:37 -0800
Subject: [PATCH 197/215] fixed algorithm

---
 .../05 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index f47a119..645e3da 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
        
     
        
     
        
-        
+        
     
        
 
        
\ No newline at end of file

From 0f13ec60484bfa85c7097f358515b5717152dbf2 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 18:37:06 -0700
Subject: [PATCH 198/215] Adds gaussian naive bayes model strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  44 ++++
 .../03 Method.html                            | 223 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  56 +++++
 ...06 Market & Competition Qualification.html |  12 +
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |  12 +
 9 files changed, 386 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2edbaff..9fdb414 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -714,6 +714,15 @@
      ],
      'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
      'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday'
+  ],
+  [
+     'name' => 'Gaussian Naive Bayes Model',
+     'link' => 'strategy-library/gaussian-naive-bayes-model',
+     'sources' => [
+         'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
+     ],
+     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];
 
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
new file mode 100644
index 0000000..9421bb8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+
        
+  Naïve Bayes models have become popular for their success in spam email filtering. In this tutorial, we train 
+  Gaussian Naïve Bayes (GNB) classifiers to forecast the daily returns of stocks in the technology sector given the 
+  historical returns of the sector. Our implementation shows the strategy has a greater Sharpe and lower variance 
+  than the SPY ETF over a 5 year backtest and during the 2020 stock market crash. The algorithm we build here follows 
+  the research done by Lu (2016) and Imandoust & Bolandraftar (2014).
+
        
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
new file mode 100644
index 0000000..2643372
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -0,0 +1,44 @@
+
        
+  Naïve Bayes models classify observations into a set of classes by utilizing 
+  Bayes’ Theorem
+
        
+
+\[\text{posterior} = \frac{ \text{prior } * \text{ likelihood} } {\text{evidence}}\]
+
+
        
+  In symbols, this translates to
+
        
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
+where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+
        
+  The Naïve Bayes model assumes the features are independent, so that 
+
        
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)\prod_{j=1}^{n} P(x_j | c_i)}{P(x_1, ..., x_n)} \propto P(c_i)\prod_{j=1}^{n} P(x_j|c_i)\]
+
+
        
+  The class that is most probable given the observation is then determined by solving
+
        
+
+\[\hat{c} = \arg\max_{i \in \{1, ..., m\}} P(c_i) \prod_{j=1}^{n} P(x_j | c_i)\]
+
+
+
        
+  In our use case, the classes in the model are: positive, negative, or flat future return for a security. The features
+  are the last 4 daily returns of the universe constituents. Since we are dealing with continuous data, we extend the 
+  model to a GNB model by replacing \(P(x_j|c_i)\) in the equation above. First, we find the mean \(\mu_j\) and standard 
+  deviation \(\sigma_j^2\) of the \(x_j\) feature vector in the training set labeled class \(c_i\). A normal distribution 
+  parameterized by \(\mu_j\) and \(\sigma_j^2\) is then used to determine the likelihood of the observations. If \(o\) is the 
+  observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
+
        
+
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+
+
        
+  The mechanics of the GNB model can be seen visually in 
+  this video. Note that the GNB model has 2 underlying 
+  assumptions: the feature vectors are independent and normally distributed. We do not test for these properties, but 
+  rather leave it as an area of future research.
+
        
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
new file mode 100644
index 0000000..8ef11c2
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html	
@@ -0,0 +1,223 @@
+
        Universe Selection
        
+
        
+  Following Lu (2016), we implement a custom universe selection model to select the largest stocks from the technology 
+  sector. We restrict our universe to have a size of 10, but this can be easily customized via the `fine_size` 
+  parameter in the constructor.
+
        
+
        
+
        +class BigTechUniverseSelectionModel(FundamentalUniverseSelectionModel):
+    def __init__(self, fine_size=10):
+        self.fine_size = fine_size
+        self.month = -1
+        super().__init__(True)
+
+    def SelectCoarse(self, algorithm, coarse):
+        if algorithm.Time.month == self.month:
+            return Universe.Unchanged
+        return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+    
+    def SelectFine(self, algorithm, fine):
+        self.month = algorithm.Time.month
+        
+        tech_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology ]
+        sorted_by_market_cap = sorted(tech_stocks, key=lambda x: x.MarketCap, reverse=True)
+        return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+
        
+
        
+
+
+
        Alpha Construction
        
+
        
+  The GaussianNaiveBayesAlphaModel predicts the direction each security will move from a given day’s open to the next 
+  day’s open. When constructing this alpha model, we set up a dictionary to hold a SymbolData object for each symbol 
+  in the universe and a flag to show the universe has changed. 
+
        
+
        
+
        +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    new_securities = False
+
        
+
        
+
+
+
        Alpha Securities Management
        
+
        
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method. In 
+  this algorithm, since we train the Gaussian Naive Bayes classifier using the historical returns of the securities 
+  in the universe, we flag to train the model every time the universe changes.
+
        
+
        
+
        +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def OnSecuritiesChanged(self, algorithm, changes):
+        for security in changes.AddedSecurities:
+            self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+            
+        for security in changes.RemovedSecurities:
+            symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+            if symbol_data:
+                symbol_data.dispose()
+        
+        self.new_securities = True
+
        
+
        
+
+
+
        SymbolData Class
        
+
        
+  The SymbolData class is used to store training data for the GaussianNaiveBayesAlphaModel and manage a consolidator 
+  subscription. In the constructor, we specify the training parameters, setup the consolidator, and warm up the 
+  training data.
+
        
+
        
+
        +class SymbolData:
+    def __init__(self, security, algorithm, num_days_per_sample=4, num_samples=100):
+        self.exchange = security.Exchange
+        self.symbol = security.Symbol
+        self.algorithm = algorithm
+        self.num_days_per_sample = num_days_per_sample
+        self.num_samples = num_samples
+        self.previous_open = 0
+        self.model = None
+        
+        # Setup consolidators
+        self.consolidator = TradeBarConsolidator(timedelta(days=1))
+        self.consolidator.DataConsolidated += self.CustomDailyHandler
+        algorithm.SubscriptionManager.AddConsolidator(self.symbol, self.consolidator)
+        
+        # Warm up ROC lookback
+        self.roc_window = np.array([])
+        self.labels_by_day = pd.Series()
+        
+        data = {f'{self.symbol.ID}_(t-{i})' : [] for i in range(1, num_days_per_sample + 1)}
+        self.features_by_day = pd.DataFrame(data)
+        
+        lookback = num_days_per_sample + num_samples + 1
+        history = algorithm.History(self.symbol, lookback, Resolution.Daily)
+        if history.empty or 'close' not in history:
+            algorithm.Log(f"Not enough history for {self.symbol} yet")    
+            return
+        
+        history = history.loc[self.symbol]
+        history['open_close_return'] = (history.close - history.open) / history.open
+        
+        start = history.shift(-1).open
+        end = history.shift(-2).open
+        history['future_return'] = (end - start) / start
+        
+        for day, row in history.iterrows():
+            self.previous_open = row.open
+            if self.update_features(day, row.open_close_return) and not pd.isnull(row.future_return):
+                row = pd.Series([np.sign(row.future_return)], index=[day])
+                self.labels_by_day = self.labels_by_day.append(row)[-self.num_samples:]
+
        
+
        
+
+
        
+  The update_features method is called to update our training features with the latest data passed to the algorithm. 
+  It returns True/False, representing if the features are in place to start updating the training labels.
+
        
+
+
        
+
        +class SymbolData:
+    ...
+
+    def update_features(self, day, open_close_return):
+        self.roc_window = np.append(open_close_return, self.roc_window)[:self.num_days_per_sample]
+        
+        if len(self.roc_window) < self.num_days_per_sample:
+            return False
+            
+        self.features_by_day.loc[day] = self.roc_window
+        self.features_by_day = self.features_by_day[-(self.num_samples+2):]
+        return True
+
        
+
        
+
+
+
+
+
        Model Training
        
+
        
+  The GNB model is trained each day the universe has changed. By default, it uses 100 samples to train. The features 
+  are the historical open-to-close returns of the universe constituents. The labels are the returns from the open at 
+  T+1 to the open at T+2 at each time step for each security.
+
        
+
        
+
        +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def train(self):
+        features = pd.DataFrame()
+        labels_by_symbol = {}
+        
+        # Gather training data
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                features = pd.concat([features, symbol_data.features_by_day], axis=1)
+                labels_by_symbol[symbol] = symbol_data.labels_by_day
+        
+        # Train the GNB model
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                symbol_data.model = GaussianNB().fit(features.iloc[:-2], labels_by_symbol[symbol])
+
        
+
        
+
+
+
        Alpha Update
        
+
        
+  As new TradeBars are provided to the alpha model's Update method, we collect the latest TradeBar’s open-to-close 
+  return for each security in the universe. We then predict the direction of each security using the security’s 
+  corresponding GNB model, and return insights accordingly.
+
        
+
        
+
        +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def Update(self, algorithm, data):
+        if self.new_securities:
+            self.train()
+            self.new_securities = False
+        
+        tradable_symbols = {}
+        features = [[]]
+        
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if data.ContainsKey(symbol) and data[symbol] is not None and symbol_data.IsReady:
+                tradable_symbols[symbol] = symbol_data
+                features[0].extend(symbol_data.features_by_day.iloc[-1].values)
+
+        insights = []
+        if len(tradable_symbols) == 0:
+            return []
+        weight = 1 / len(tradable_symbols)
+        for symbol, symbol_data in tradable_symbols.items():
+            direction = symbol_data.model.predict(features)
+            if direction:
+                insights.append(Insight.Price(symbol, data.Time + timedelta(days=1, seconds=-1), 
+                                              direction, None, None, None, weight))
+
+        return insights
+
        
+
        
+
+
+
        Portfolio Construction & Trade Execution
        
+
        
+  Following the guidelines of Alpha Streams 
+  and the Quant League competition, we 
+  utilize the 
+  InsightWeightingPortfolioConstructionModel and the 
+  
+  ImmediateExecutionModel.
+
        
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
new file mode 100644
index 0000000..7f48965
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
        
+    
        
+    
        
+        
+    
        
+
        
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
new file mode 100644
index 0000000..3ddfffe
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html	
@@ -0,0 +1,56 @@
+
        
+
+    
+        
+            
+            
+            
+            
+            
+            
+        
+    
+    
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+    
+
        Period NameStart DateEnd DateStrategySharpeVariance
        5 Year Backtest10/1/201510/13/2020Strategy0.970.016
        Benchmark0.8050.029
        2020 Crash2/19/20203/23/2020Strategy-0.9810.353
        Benchmark-1.40.474
        2020 Recovery3/23/20206/8/2020Strategy-2.0110.035
        Benchmark8.7650.103
        
+
        
+
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d16c9d8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,12 @@
+
        
+  Although this strategy passes several of the 
+  metrics required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+
        
+
+
          
+  
        • PSR >= 80%
          • 
+  
        • Max drawdown duration <= 6 months
          • 
+  
        • Handles dividends and splits
          • 
+  
        • Minute or second data resolution
          • 
+
        
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
new file mode 100644
index 0000000..99f052c
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+
        
+  The GNB model strategy implemented in this tutorial produced a greater Sharpe ratio and lower annual variance than 
+  buying and holding the S&P 500 index ETF benchmark over the backtesting period. In addition to outperforming during 
+  the entire backtest, the strategy also outperformed during the 2020 stock market crash. 
+
        
+
+
        
+  To continue the development of this strategy, future areas of research include:
+
        
+
+
          
+  
        • Adjusting parameters in the SymbolData class
          • 
+  
        • Trying other features and labels for the GNB model
          • 
+  
        • Adjusting the universe parameters and targeted sector
          • 
+  
        • Adding handlers for corporate actions
          • 
+  
        • Filter for stocks with independent and normal returns
          • 
+
        
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
new file mode 100644
index 0000000..0320284
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html	
@@ -0,0 +1,12 @@
+
          
+  
        1. 
+    Imandoust, S. B., & Mohammad, B. (2014). Forecasting the direction of stock market index movement using three 
+    data mining techniques: the case of Tehran Stock Exchange. Journal of Engineering Research and Applications, 
+    6(2), 106-117. 
+    Online copy
+  
          2. 
+  
        2. 
+    Lu, N. (2016). A Machine Learning Approach to Automated Trading.  
+    Online copy
+  
          3. 
+
        
\ No newline at end of file

From 93cfc1f62b1904001eb57a767e167956284b646b Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 18:39:43 -0700
Subject: [PATCH 199/215] Fix description

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9fdb414..9fc944f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -721,7 +721,7 @@
      'sources' => [
          'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
      ],
-     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'description' => "Forecasts the next day's return of technology stocks by fitting a gaussian naive bayes model to the historical returns of the technology sector constituents.",
      'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];

From 28e73de28c4e10b564278a805f5b34dc8ec725c8 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 20:56:41 -0700
Subject: [PATCH 200/215] Fix latex error

---
 .../1036 Gaussian Naive Bayes Model/02 Background.html     | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
index 2643372..f576900 100644
--- a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -10,7 +10,10 @@
 
        
 
 \[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
-where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+
        
+  where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
        
 
 
        
   The Naïve Bayes model assumes the features are independent, so that 
@@ -34,7 +37,7 @@
   observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
 
        
 
-\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2}} \]
 
 
        
   The mechanics of the GNB model can be seen visually in 

From a8b4090d6a95dd4a58b59c3a55551e730a6c9590 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 20 Nov 2020 19:13:33 -0700
Subject: [PATCH 201/215] Add video walkthrough

---
 .../03 Video Walkthrough.html                                | 5 +++++
 .../{03 Method.html => 04 Method.html}                       | 0
 .../{04 Algorithm.html => 05 Algorithm.html}                 | 0
 ...elative Performance.html => 06 Relative Performance.html} | 0
 ...ation.html => 07 Market & Competition Qualification.html} | 0
 .../{07 Conclusion.html => 08 Conclusion.html}               | 0
 .../{08 References.html => 09 References.html}               | 0
 7 files changed, 5 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{03 Method.html => 04 Method.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{04 Algorithm.html => 05 Algorithm.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{05 Relative Performance.html => 06 Relative Performance.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{06 Market & Competition Qualification.html => 07 Market & Competition Qualification.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{07 Conclusion.html => 08 Conclusion.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{08 References.html => 09 References.html} (100%)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
new file mode 100644
index 0000000..3a3ef40
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html	
@@ -0,0 +1,5 @@
+
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html

From 227973336f5633a840abd9953f1ae0734f471070 Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Tue, 8 Dec 2020 21:47:33 +0000
Subject: [PATCH 202/215] Update Link to The Refernce

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 .../05 References.html                                          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html
index 145c7bf..4d6f9c3 100644
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
@@ -1,5 +1,5 @@
 
          
   
        1. 
-    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
+    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
   
          2. 
 
        

From d204c4e0e21e02048a61ae7b4363b4c1234641bb Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Tue, 8 Dec 2020 21:49:01 +0000
Subject: [PATCH 203/215] Update Reference in Chinese Version

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 ...05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
index 145c7bf..4d6f9c3 100644
--- "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -1,5 +1,5 @@
 
          
   
        1. 
-    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
+    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
   
          2. 
 
        

From 55297e639875aa0a4ccf7f107e9c7b062355b099 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Wed, 10 Feb 2021 16:32:44 -0700
Subject: [PATCH 204/215] Update Short Term Reversal strategy tutorial and
 algorithm

---
 .../01 Abstract.html                          |  12 +-
 .../02 Method.html                            | 182 +++++++++++-------
 .../03 Summary.html                           |   5 -
 .../04 Algorithm.html                         |   2 +-
 .../05 References.html                        |   5 -
 .../05 Relative Performance.html              |  56 ++++++
 ...07 Market & Competition Qualification.html |  13 ++
 .../08 Conclusion.html                        |  16 ++
 .../09 References.html                        |   6 +
 9 files changed, 208 insertions(+), 89 deletions(-)
 delete mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
 delete mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
index eae0057..e1419ee 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -1,8 +1,8 @@
 
        
-  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "Another look at trading costs and short-term reversal profits". The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
-
        
-
        
-  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
-  In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
-  In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
+  In this tutorial, we implement a version of the short-term reversal strategy published by De Groot, Huij, & Zhou 
+  (2012). The strategy works by observing the returns of each security in the universe over the previous month. Every 
+  week, the algorithm longs the worst performers and shorts the top performers. The original strategy outlined in the 
+  literature considers the entire universe of stocks when trading. To reduce trading costs, we limit our universe to 
+  the most liquid large cap stocks. Our analysis shows the strategy underperforms the S&P 500 index during all our 
+  backtest periods except the 2020 market crash.
 
        
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index a666b03..8b9f021 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -1,103 +1,141 @@
 
        
-  The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
+  The strategy code mainly consists of four parts: Initialization, Universe Selection, OnData, and OnSecuritiesChanged.
 
        
 
-
        Step 1: Initialization
        
+
        Algorithm Initialization
        
 
        
-  In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use self._numOfWeeks to count?the number of weeks that have passed since the start date, and self._LastDay to indicate whether it is a new week. self._ifWarmUp is true when the self._numOfWeeks is 3, which means as long as next week's data come, we can make our investment decisions.??self._stocks is a list containing all the symbols of the 100 stocks that are taken into consideration. self._values is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
+  When initializing the algorithm, we add a coarse universe selection method and specify several parameters to use 
+  when selecting securities.
 
        
-
 
        
-
-
            def Initialize(self):
-        self.SetStartDate(2005, 1, 1)
-        self.SetEndDate(2017, 5, 10)
-        self.SetCash(1000000)
-        
+
        +class ShortTimeReversal(QCAlgorithm):
+    def Initialize(self):
+        # ...
+      
         self.UniverseSettings.Resolution = Resolution.Daily
-        self.AddUniverse(self.CoarseSelectionFunction)
-        self._numberOfSymbols = 100
-        self._numberOfTradings = int(0.1 * self._numberOfSymbols)
-        
-        self._numOfWeeks = 0
-        self._LastDay = -1
-        self._ifWarmUp = False
-        
-        self._stocks = []
-        self._values = {}
+        self.AddUniverse(self.SelectCoarse)
+      
+        self.dollar_volume_selection_size = 100
+        self.roc_selection_size = int(0.1 * self.dollar_volume_selection_size)
+      
+        self.lookback = 22
+        self.roc_by_symbol = {}
+        self.week = 0
+
 
        
 
        
-
        
-  Also, we need to use?CoarseSelectionFunction to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
-
        
 
-
        
 
-
        -def CoarseSelectionFunction(self, coarse):
-     sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-     top100 = sortedByDollarVolume[:self._numberOfSymbols]
-     return [i.Symbol for i in top100]
-
        
-
        
-
        Step 2:Warm Up
        
+
+
        Universe Selection
        
 
        
-  Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
+  The coarse universe selection method creates a RateOfChange 
+  indicator for each of the top 100 
+  most liquid securities in the market. Upon creation, the indicator is manually warmed-up with historical closing 
+  prices. After the indicators are ready, the universe selects the securities with the 10 best and 10 worst 
+  RateOfChange values.
 
        
 
        
+
        +class ShortTimeReversal(QCAlgorithm):
+    # ...
+
+    def SelectCoarse(self, coarse):
+        
+        # We should keep a dictionary for all securities that have been selected
+        for cf in coarse:
+            symbol = cf.Symbol
+            if symbol in self.roc_by_symbol:
+                self.roc_by_symbol[symbol].Update(cf.EndTime, cf.AdjustedPrice)
+
+        # Refresh universe each week
+        week_number = self.Time.date().isocalendar()[1]
+        if week_number == self.week:
+            return Universe.Unchanged
+        self.week = week_number
+
+        # sort and select by dollar volume
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+        selected = {cf.Symbol: cf for cf in sortedByDollarVolume[:self.dollar_volume_selection_size]} 
+        
+        # New selections need a history request to warm up the indicator
+        symbols = [k for k in selected.keys()
+            if k not in self.roc_by_symbol or not self.roc_by_symbol[k].IsReady]
+
+        if symbols:
+            history = self.History(symbols, self.lookback, Resolution.Daily)
+            if history.empty:
+                self.Log(f'No history for {", ".join([x.Value for x in symbols])}')
+            history = history.close.unstack(0)
+
+            for symbol in symbols:
 
-
        self._stocks = []
-self.uni_symbol = None
-symbols = self.UniverseManager.Keys
-for i in symbols:
-        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
-                self.uni_symbol = i
-        for i in self.UniverseManager[self.uni_symbol].Members:
-                self._stocks.append(i.Value.Symbol)
-                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
+                if symbol not in history:
+                    continue
 
+                # Create and warm-up the RateOfChange indicator
+                roc = RateOfChange(self.lookback)
+                for time, price in history[symbol].dropna().iteritems():
+                    roc.Update(time, price)
+                
+                if roc.IsReady:
+                    self.roc_by_symbol[symbol] = roc
+        
+        # Sort the symbols by their ROC values
+        selectedRateOfChange = {}
+        for symbol in selected.keys():
+            if symbol in self.roc_by_symbol:
+                selectedRateOfChange[symbol] = self.roc_by_symbol[symbol]
+        sortedByRateOfChange = sorted(selectedRateOfChange.items(), key=lambda kv: kv[1], reverse=True)
+        
+        # Define the top and the bottom to buy and sell
+        self.rocTop = [x[0] for x in sortedByRateOfChange[:self.roc_selection_size]]
+        self.rocBottom = [x[0] for x in sortedByRateOfChange[-self.roc_selection_size:]]
+        
+        return self.rocTop + self.rocBottom
 
        
 
        
-
        
-  We get all the symbols of qualified stocks from UniverseManager and keep them in self._stocks which is a list. Then we create for each key in the dictionary self._values a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
-
        
 
-
        
 
-
        for stock in self._stocks:
-        self._values[stock].append(self.Securities[stock].Price)
        
-
        
-
        Step 3:Weekly Rebalancing
        
+
        The OnData Method
        
 
        
-  After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
+  As new data is passed to the OnData method, we issue orders to form a long-short portfolio. We long the securities 
+  with the lowest RateOfChange values and short those with the largest values. After rebalancing, we clear the 
+  `rocTop` and `rocBottom` lists to ensure we don’t trade again until the universe is refreshed.
 
        
-
 
        
+
        +class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-
        returns = {}
-for stock in self._stocks:
-        newPrice = self.Securities[stock].Price
-        oldPrice = self._values[stock].pop(0)
-        self._values[stock].append(newPrice)
-        returns[stock] = newPrice/oldPrice
+    def OnData(self, data):
+        # Rebalance
+        for symbol in self.rocTop:
+            self.SetHoldings(symbol, -0.5/len(self.rocTop))
+        for symbol in self.rocBottom:
+            self.SetHoldings(symbol, 0.5/len(self.rocBottom))
+        
+        # Clear the list of securities we have placed orders for
+        # to avoid new trades before the next universe selection
+        self.rocTop.clear() 
+        self.rocBottom.clear()
 
        
 
        
+
+
        The OnSecuritiesChanged Method
        
 
        
-  Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
+  We are rebalancing the portfolio on a weekly basis, but securities can leave our defined universe between rebalance 
+  days. To accommodate this, we liquidate any securities that are removed from the universe in the 
+  OnSecuritiesChanged method.
 
        
-
 
        
+
        +class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-
        newArr = [(v,k) for k,v in returns.items()]
-newArr.sort()
-for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
-        self.SetHoldings(stock, 0)
-for ret, stock in newArr[0:self._numberOfTradings]:
-        self.SetHoldings(stock, 0.5/self._numberOfTradings)
-for ret, stock in newArr[-self._numberOfTradings:]:
-        self.SetHoldings(stock, -0.5/self._numberOfTradings)
+    def OnSecuritiesChanged(self, changes):
+        for security in changes.RemovedSecurities:
+            self.Liquidate(security.Symbol, 'Removed from Universe')
 
        
-
        
-
        
-  Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
-
        
+
    \ No newline at end of file diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html deleted file mode 100755 index 0c2b46e..0000000 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html +++ /dev/null @@ -1,5 +0,0 @@ -

    - In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead. - If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&P 500 benchmark. - Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc. -

    diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html index 009e778..c2e37d5 100755 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html deleted file mode 100644 index 6c1e761..0000000 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html +++ /dev/null @@ -1,5 +0,0 @@ -
      -
    1. - Groot, Wilma (2011). Another look at trading costs and short-term reversal profit, page 1,? Online Copy -
      2. -
    diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html new file mode 100644 index 0000000..3d18c34 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html @@ -0,0 +1,56 @@ +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Period NameStart DateEnd DateStrategySharpeVariance
    5 Year Backtest1/1/20161/1/2021Strategy0.240.058
    Benchmark0.8250.028
    2020 Crash2/19/20203/23/2020Strategy-1.0250.917
    Benchmark-1.40.474
    2020 Recovery3/23/20206/8/2020Strategy1.6880.16
    Benchmark8.7650.103
    +
    + diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html new file mode 100644 index 0000000..1c0cad5 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html @@ -0,0 +1,13 @@ +

    + Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

    + +
      +
    • PSR >= 80%
      • +
    • Emits insights
      • +
    • Max drawdown <= 10%
      • +
    • Handles dividends and splits
      • +
    • Minute or second data resolution
      • +
    diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html new file mode 100644 index 0000000..ae81428 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html @@ -0,0 +1,16 @@ +

    + The short-term reversal strategy implemented in this tutorial produced a lower Sharpe ratio than the S&P + 500 index ETF benchmark over all our testing periods except during the 2020 market crash. To continue the + development of this strategy, future areas of research include: +

    + +

    + To continue the development of this strategy, future areas of research include: +

    + +
      +
    • Increasing the data resolution and adding risk management logic.
      • +
    • Applying the strategy to a different universe of securities.
      • +
    • Testing other lookback periods for the RateOfChange indicator. Some researchers exclude the most-recent month’s price action from the indicator’s calculation.
      • +
    • Adjusting the weight for each insight emitted from the alpha model. Perhaps the most extreme performer of all the securities the alpha model is about to long (short) should be given the largest weight of all the securities the model is about to long (short).
      • +
    \ No newline at end of file diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html new file mode 100644 index 0000000..2a52089 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html @@ -0,0 +1,6 @@ +
      +
    1. + de Groot, Wilma and Huij, Joop and Zhou, Weili, Another Look at Trading Costs and Short-Term Reversal + Profits (July 1, 2011). Online copy +
      2. +
    \ No newline at end of file From 5aaf69e507ff2f368f5986d9a19257da90160f20 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 5 Mar 2021 11:43:23 -0700 Subject: [PATCH 205/215] Updating fama french tutorial --- .../02 Fama-French Three-Factor Model.html | 7 ++--- .../05 Other Factors.html | 12 -------- .../{06 Summary.html => 05 Summary.html} | 6 ++-- .../{07 Algorithm.html => 06 Algorithm.html} | 30 +++++++++---------- ...{08 References.html => 07 References.html} | 3 ++ 5 files changed, 23 insertions(+), 35 deletions(-) delete mode 100755 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{06 Summary.html => 05 Summary.html} (98%) mode change 100755 => 100644 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{07 Algorithm.html => 06 Algorithm.html} (78%) mode change 100755 => 100644 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{08 References.html => 07 References.html} (81%) diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html index 21d0568..1f83f6d 100755 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html @@ -1,8 +1,5 @@ 

    - This model was proposed in 1993 by Eugene Fama and Kenneth French to describe stock returns.[ref] Fama, E F; French, K R (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref] -

    -

    - The 3-factor model is + This model was proposed in 1993 by Eugene Fama and Kenneth French to describe stock returns. The 3-factor model is

    \[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \] @@ -12,7 +9,7 @@
    • MKT is the excess return of the market. It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.
    • SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.
      • -
    • HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.
      • +
    • HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have a higher book to price ratio (B/P) than growth stocks.

    diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html deleted file mode 100755 index 510c436..0000000 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html +++ /dev/null @@ -1,12 +0,0 @@ -

    - The Fama-French 5-Factor model comprises two more factors: -

    -
      -
    • RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.
      • -
    • CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.
      • -
    - -

    - RMW was proposed by Novy-Marx (2013) who wrote that: - "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014) who pointed out that: A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns. Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns -

    diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html old mode 100755 new mode 100644 similarity index 98% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html index 549200f..d2b7e7a --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html @@ -1,3 +1,3 @@ -

    - In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice. -

    +

    + In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice. +

    diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html old mode 100755 new mode 100644 similarity index 78% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html index e3c69c8..2fd2fa2 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html @@ -1,15 +1,15 @@ -

    - Multi-factor strategies are stock picking strategies. Here we try to implement a 2013 paper published by AQR Capital Management. - The paper recommends picking stocks by their value, quality (profitability) and momentum. - The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value. - The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices. -

    -

    - In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. -

    -
    -
    -
    - -
    -
    +

    + Multi-factor strategies are stock picking strategies. Here we try to implement a 2013 paper published by AQR Capital Management. + The paper recommends picking stocks by their value, quality (profitability) and momentum. + The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value. + The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices. +

    +

    + In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. +

    +
    +
    +
    + +
    +
    diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html similarity index 81% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html index 79b3bcb..eb7216e 100644 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html @@ -14,4 +14,7 @@
  • Robert Novy-Marx (2013). The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from rnm.simon.rochester.edu/research/OSoV.pdf
    • +
  • + Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56. doi: 10.1016/0304-405X(93)90023-5. Retrieved from rady.ucsd.edu. +
    • From 304f01fe6fa9e01d15c3155cab4f3b03d5e251fc Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 5 Mar 2021 11:44:46 -0700 Subject: [PATCH 206/215] Update value factor definition to match algo --- .../14 Fama-French Multi-Factor Models/06 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html index 2fd2fa2..3eb4334 100644 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html @@ -5,7 +5,7 @@ The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.

    - In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. + In our backtested strategy, we used operating profit margin to measure quality, book value per share to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.

    From b1fc9c2bb99abb67fdf008e118597f55bab491ba Mon Sep 17 00:00:00 2001 From: Jared Date: Wed, 17 Mar 2021 10:17:08 -0700 Subject: [PATCH 207/215] Update 01 Definition.html --- 07 Applied Options[]/02 Bull Call Spread/01 Definition.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html index 0418abb..c361521 100755 --- a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html +++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html @@ -5,7 +5,7 @@ This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss resulting from a drop in the price of the underlying stock but still creates a ceiling to the profit while the underlying price is increasing.

    - Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows: + Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 50 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:

    From 6750665e5c3f051e4ab37bbafb7835d1bed848b3 Mon Sep 17 00:00:00 2001 From: Web Editor Date: Mon, 11 Oct 2021 16:34:56 -0400 Subject: [PATCH 208/215] Automated push from server made by Alexandre Catarino --- .../02 Fetching Data.html | 196 +++++++++--------- 1 file changed, 98 insertions(+), 98 deletions(-) diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html index 325ecd6..181f0fc 100755 --- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html +++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html @@ -1,98 +1,98 @@ -

    - Here we use the Quandl API to retrieve data. -

    -
    - -
    import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
-
    -
    -

    - We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates: -

    -
    - -
    aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table['Adj. Close']['2017']
-print aapl
-
    -
    - -

    - Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm']. -

    -
    - -
    print aapl['2017-3']
-Date
-2017-03-01    138.657681
-2017-03-02    137.834404
-2017-03-03    138.647762
-2017-03-06    138.211326
-2017-03-07    138.389868
-2017-03-08    137.874080
-2017-03-09    137.556672
-2017-03-10    138.012946
-2017-03-13    138.072460
-2017-03-14    137.864161
-2017-03-15    139.322254
-2017-03-16    139.550391
-2017-03-17    138.856061
-2017-03-20    140.314154
-2017-03-21    138.707276
-2017-03-22    140.274478
-2017-03-23    139.778528
-2017-03-24    139.500796
-2017-03-27    139.738852
-2017-03-28    142.635200
-2017-03-29    142.952608
-2017-03-30    142.764147
-2017-03-31    142.496334
-
    -
    - -

    - Or in several consecutive months: -

    -
    - -
    aapl['2017-2':'2017-4']
-
    -
    - -

    - .head(N) and .tail(N) are methods for quickly accessing the first or last N elements. -

    -
    - -
    print aapl.head()
-print aapl.tail(10)
-
    -
    -

    - The output: -

    -
    - -
    -Date
-2017-01-03    114.715378
-2017-01-04    114.586983
-2017-01-05    115.169696
-2017-01-06    116.453639
-2017-01-09    117.520300
-Name: Adj. Close, dtype: float64
-Date
-2017-08-08    159.433108
-2017-08-09    160.409148
-2017-08-10    155.270000
-2017-08-11    157.480000
-2017-08-14    159.850000
-2017-08-15    161.600000
-2017-08-16    160.950000
-2017-08-17    157.870000
-2017-08-18    157.500000
-2017-08-21    157.210000
-Name: Adj. Close, dtype: float64
-
    -
    +

    + Here we use the Quandl API to retrieve data... +

    +
    + +
    import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
+
    +
    +

    + We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates: +

    +
    + +
    aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table['Adj. Close']['2017']
+print aapl
+
    +
    + +

    + Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm']. +

    +
    + +
    print aapl['2017-3']
+Date
+2017-03-01    138.657681
+2017-03-02    137.834404
+2017-03-03    138.647762
+2017-03-06    138.211326
+2017-03-07    138.389868
+2017-03-08    137.874080
+2017-03-09    137.556672
+2017-03-10    138.012946
+2017-03-13    138.072460
+2017-03-14    137.864161
+2017-03-15    139.322254
+2017-03-16    139.550391
+2017-03-17    138.856061
+2017-03-20    140.314154
+2017-03-21    138.707276
+2017-03-22    140.274478
+2017-03-23    139.778528
+2017-03-24    139.500796
+2017-03-27    139.738852
+2017-03-28    142.635200
+2017-03-29    142.952608
+2017-03-30    142.764147
+2017-03-31    142.496334
+
    +
    + +

    + Or in several consecutive months: +

    +
    + +
    aapl['2017-2':'2017-4']
+
    +
    + +

    + .head(N) and .tail(N) are methods for quickly accessing the first or last N elements. +

    +
    + +
    print aapl.head()
+print aapl.tail(10)
+
    +
    +

    + The output: +

    +
    + +
    +Date
+2017-01-03    114.715378
+2017-01-04    114.586983
+2017-01-05    115.169696
+2017-01-06    116.453639
+2017-01-09    117.520300
+Name: Adj. Close, dtype: float64
+Date
+2017-08-08    159.433108
+2017-08-09    160.409148
+2017-08-10    155.270000
+2017-08-11    157.480000
+2017-08-14    159.850000
+2017-08-15    161.600000
+2017-08-16    160.950000
+2017-08-17    157.870000
+2017-08-18    157.500000
+2017-08-21    157.210000
+Name: Adj. Close, dtype: float64
+
    +
    From eb15f94de0868141eb5bb55909c4e7ead648f79f Mon Sep 17 00:00:00 2001 From: Web Editor Date: Fri, 11 Mar 2022 01:59:09 -0500 Subject: [PATCH 209/215] Automated push from server made by Louis Szeto --- .../02 QuantConnect Options API/04 Select Contracts.html | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index 54084fb..e9ca2d1 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -65,12 +65,12 @@ 
    def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+    optionchain = i.Value
+    self.Log("underlying price:" + str(optionchain.Underlying.Price))
+    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
 			   index=[x.Symbol.Value for x in optionchain],
 			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
+    self.Log(str(df))
    From 7f51fe51a3dcbd4c46ac07aeefe2d678ed94bdde Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:01:25 +0800 Subject: [PATCH 210/215] Update 04 Select Contracts.html --- .../04 Select Contracts.html | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index e9ca2d1..7e92e12 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -65,12 +65,12 @@ 
    def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-    optionchain = i.Value
-    self.Log("underlying price:" + str(optionchain.Underlying.Price))
-    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-    self.Log(str(df))
+        optionchain = i.Value
+        self.Log("underlying price:" + str(optionchain.Underlying.Price))
+        df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+                           index=[x.Symbol.Value for x in optionchain],
+                           columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
    From 406089ace2b2521c0bd698e0120196c776346094 Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:13:16 +0800 Subject: [PATCH 211/215] Update 04 Select Contracts.html Correct indentation and variable names --- .../04 Select Contracts.html | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index 7e92e12..07ea589 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -160,18 +160,18 @@ 
    for i in slice.OptionChains:
     if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike > 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike < 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    optionchain = i.Value
+    # differentiate the call and put options
+    call = [x for x in optionchain if x.Right == 0]
+    put = [x for x in optionchain if x.Right == 1]
+    # choose ITM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike > 0]
+    # or choose ATM contracts
+    contracts = sorted(optionchain, key = lambda x: abs(x.UnderlyingLastPrice - x.Strike))[0]
+    # or choose OTM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike < 0]
+    # sort the contracts by their expiration dates
+    contracts = sorted(contracts, key = lambda x: x.Expiry, reverse = True)

    From 86fba1494c5934965d6d08be3dfc2e7e77ebd1f0 Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:14:13 +0800 Subject: [PATCH 212/215] Update 02 Add Options.html Correct formatting --- .../02 QuantConnect Options API/02 Add Options.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html index a1d5a37..44c5959 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html @@ -22,8 +22,8 @@ 

    def Initialize(self):
-    self.SetStartDate(2017, 01, 01)  #Set Start Date
-    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetStartDate(2017, 1, 1)  #Set Start Date
+    self.SetEndDate(2017, 6, 30)  #Set End Date
     self.SetCash(50000)  #Set Strategy Cash
     equity = self.AddEquity("GOOG", Resolution.Minute) # Add the underlying stock: Google
     option = self.AddOption("GOOG", Resolution.Minute) # Add the option corresponding to underlying stock

From 25963e427c4cee06d7778fecca635126745242cf Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Thu, 19 May 2022 23:54:45 +0100
Subject: [PATCH 213/215] Move Workflow to Documentation Page

---
 README.md | 30 ++++++------------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 5e2fd23..c9846d7 100644
--- a/README.md
+++ b/README.md
@@ -8,40 +8,22 @@ This repository is a collection of WordPress and Jupyter notebook tutorials for
 
 Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean][4] Engine repository.
 
-
-## New Tutorial Requests and Edits ##
-
-Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
-
-## Mailing List ##
-
-The mailing list for the project can be found on [Google Groups][6]
-
 ## Contributors and Pull Requests ##
 
 Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7].
 
 ## Strategy Library Development Workflow ##
-To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps:
-1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process).
-2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png).
-3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py).
-4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)).
-5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project.
-6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples).
-7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)).
-8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)):
-    - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload).
-    - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023.
-    - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json).
-    - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID).
-9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)).
+
+To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow the steps on the [documentation page](https://www.quantconnect.com/docs/v2/writing-algorithms/strategy-library#03-Contribute-Tutorials)
+
+## New Tutorial Requests and Edits ##
+
+Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
 
 [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer"
 [2]: https://www.quantconnect.com/lean/docs "Lean Documentation"
 [3]: https://github.com/QuantConnect/Lean/archive/master.zip
 [4]: https://github.com/QuantConnect/Lean
 [5]: https://github.com/QuantConnect/Tutorials/issues
-[6]: https://groups.google.com/forum/#!forum/lean-engine
 [7]: https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md
 [8]: https://www.quantconnect.com/slack

From 67580984b715b02d829840831b8bc487f29c7213 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles 
Date: Wed, 15 Jun 2022 17:35:12 -0700
Subject: [PATCH 214/215] Update backtest embed url.

---
 .../05 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../04 Algorithm.html                         |  4 +--
 .../05 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../04 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../113 January Barometer/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../13 Asset Class Momentum/03 Algorithm.html |  2 +-
 .../14 Sector Momentum/03 Algorithm.html      |  2 +-
 .../15 Short Term Reversal/03 Algorithm.html  |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../16 Overnight Anomaly/03 Algorithm.html    |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../17 Forex Momentum/03 Algorithm.html       |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../20 Forex Carry Trade/03 Algorithm.html    |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  4 +--
 .../04 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../32 Gold Market Timing/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../83 Pre-Holiday Effect/03 Algorithm.html   |  2 +-
 .../06 Algorithm.html                         | 18 ++++++-------
 .../13 Market Risk/06 Algorithm.html          | 26 +++++++++----------
 .../05 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../01 Covered Call/04 Algorithm.html         |  4 +--
 .../02 Bull Call Spread/04 Algorithm.html     |  4 +--
 .../03 Long Straddle/04 Algorithm.html        |  4 +--
 .../04 Long Strangle/04 Algorithm.html        |  4 +--
 .../05 Butterfly Spread/04 Algorithm.html     |  4 +--
 .../06 Iron Condor/04 Algorithm.html          |  4 +--
 .../07 Iron Butterfly/04 Algorithm.html       |  4 +--
 .../08 Protective Collar/04 Algorithm.html    |  4 +--
 65 files changed, 98 insertions(+), 98 deletions(-)

diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"
index 1f296ed..4a79e84 100644
--- "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
index 5c357f4..9717146 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"
index 932854a..c5d9f52 100644
--- "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
index 58173b8..3358861 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
index 01b99d6..0115518 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
index 69b6365..3d55776 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"
index 69b6365..3d55776 100644
--- "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
index 03b3ba2..f9505bd 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"
index bcf0bc2..d1a72fa 100644
--- "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html
index 15151d8..ccc8b22 100644
--- a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"
index 15151d8..ccc8b22 100644
--- "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html
index 516d50a..7ff9460 100644
--- a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
+++ b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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+        
     
    
 
    
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
index 53170f5..b8d9c7d 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/113 January Barometer/03 Algorithm.html b/04 Strategy Library/113 January Barometer/03 Algorithm.html
index be69ba3..e85f53d 100644
--- a/04 Strategy Library/113 January Barometer/03 Algorithm.html	
+++ b/04 Strategy Library/113 January Barometer/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html
index f94dcba..6dc92eb 100644
--- a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html
index ca04542..77b7892 100644
--- a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
+++ b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html
index 6189f9c..e7aa2b2 100644
--- a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html	
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html
index 0e5258c..d001aad 100644
--- a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html
index 0cb1b39..80765bc 100644
--- a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
index 09e4c6f..1970b93 100644
--- a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
+++ b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html
index e3a87f0..41faf63 100644
--- a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html
index e4a3020..cb7b3a9 100644
--- a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html
index 6bbcd8e..219c782 100644
--- a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html	
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"
index 6bbcd8e..219c782 100644
--- "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html
index b7fd60e..f8b0c4d 100644
--- a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html
index 0a0907d..97890a9 100644
--- a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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+        
     
    
 
    
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html
index 17a97c9..4b088a3 100644
--- a/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html
index c75531f..66e4458 100644
--- a/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html
index 1bb774b..d433b13 100644
--- a/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html	
+++ b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"
index 1bb774b..d433b13 100644
--- "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html
index b581b49..f2180a0 100644
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+++ b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html
index 37631d8..1f5050c 100644
--- a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
@@ -2,13 +2,13 @@ 
    The Momentum Effect
    
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    Equal Weighted Benchmark
    
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"
index cd34f0c..7078bbf 100644
--- "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"	
@@ -2,13 +2,13 @@ 
    动量效应
    
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    平均加权基准
    
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html
index 3bc488d..bdafd79 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html
index 547579a..2b216a7 100644
--- a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html	
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html b/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html
index 5041f10..15edb83 100644
--- a/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html b/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html
index a0bce57..0733d81 100644
--- a/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html	
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
index ada8627..f820ca9 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -3,7 +3,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -11,7 +11,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html
index 91052ac..b781228 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html
index e0ed93e..9029e2c 100644
--- a/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html
index 414a43d..c74f50c 100644
--- a/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html	
+++ b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html
index a38d444..b3b0694 100644
--- a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html	
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
index 9af8464..cff2414 100644
--- a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"
index 9af8464..cff2414 100644
--- "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html
index c07b1c7..926770b 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html
index 7cf2e86..36343ed 100644
--- a/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html	
+++ b/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html
index f8ca3b6..529a2d1 100644
--- a/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html	
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html
index 55f69ca..8caed63 100644
--- a/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html	
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html
index 3956745..57f98de 100644
--- a/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html	
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html
index 430008f..d3f5c48 100644
--- a/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html	
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html
index c9839cd..91f1e5a 100644
--- a/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html
index 36e3092..0447f1a 100644
--- a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html	
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
index b59109e..64580ab 100644
--- a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
+++ b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
index 8d6082c..309e3c2 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -1,9 +1,9 @@
-
    
-  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
-
    
-
    
-    
    
-    
    
-        
-    
    
-
    
+
    
+  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
+
    
+
    
+    
    
+    
    
+        
+    
    
+
    
diff --git a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html
index 3069322..64474c1 100755
--- a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
@@ -1,13 +1,13 @@
-
    
-    
    
-    
    
-        
-    
    
-
    
-
-
    
-    
    
-    
    
-        
-    
    
-
    
+
    
+    
    
+    
    
+        
+    
    
+
    
+
+
    
+    
    
+    
    
+        
+    
    
+
    
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 64b1d35..e485feb 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index 12d6ce9..b196a80 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index 9fa4e5e..6858e6e 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html
index 0e1c81b..ef1b62b 100755
--- a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
index 3e98c40..89e3d28 100755
--- a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html
index 78ab066..09a8a85 100755
--- a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
+++ b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html
index 134f5b1..beb7ac0 100755
--- a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
index cf2d154..7a08824 100755
--- a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html
index cd42f7d..05d0e03 100755
--- a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html
index 245885d..77dc3ce 100755
--- a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
+++ b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    

From 144ea92bd184b1cd91f0af35c8aa288e97d1ed5f Mon Sep 17 00:00:00 2001
From: avorobiev 
Date: Mon, 28 Jul 2025 17:58:07 +0300
Subject: [PATCH 215/215] Correct intrinsic and time value calculation in AAPL
 example

---
 .../01 General Features of Options/04 The Value of Options.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html
index f0877cc..b392bbd 100755
--- a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
@@ -12,5 +12,5 @@
 \[Time Value= Premium-Intrinsic Value\]

    -For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values. +For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $150. The intrinsic value of this contract is 150-143=$7, the time value is 10-7=$3. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.