-MarketOrder("SPY", 100);var ticket = MarketOrder("SPY", 100);self.ticket = self.MarketOrder("SPY", 100)var ticket = LimitOrder("SPY", 100, 100.10m);var ticket = LimitOrder("SPY", 100, 100.10m);self.ticket = self.LimitOrder("SPY", 100, 100.1)var ticket = StopMarketOrder("SPY", 100, 100.10m);var ticket = StopMarketOrder("SPY", 100, 100.10m);self.ticket = self.StopMarketOrder("SPY", 100, 100.1)var 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)var ticket = MarketOnOpen("SPY", 100);var ticket = MarketOnOpen("SPY", 100);self.ticket = self.MarketOnOpen("SPY", 100)var ticket = MarketOnClose("SPY", 100);var ticket = MarketOnClose("SPY", 100);self.ticket = self.MarketOnClose("SPY", 100)-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); 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
Visual Studio plugin integrated with the QuantConnect API.
- -Guide to implementing your own brokerage in LEAN.
\ 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
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 @@ 
\ No newline at end of file
+
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.
-
-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. -
- --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’. -
- -
--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. -
- --Save files from your local project to a QuantConnect project. You can save many files at a time. -
-
-This feature allows you to upload one or more files to a target project, compile it and backtest it in the QuantConnect cloud. -
-
-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. -
- --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. -
- -
-
--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 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..9fc944f 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' @@ -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', @@ -327,6 +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', @@ -596,7 +615,115 @@ ], 'description' => "A simple trend following strategy on commodities futures.", 'tags' => 'Momentum, Futures, Commodities' - ] + ], + [ + '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' + ], + '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' => '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 the risk of holding leveraged ETFs in an attempt to beat the S&P500', + 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' + ], + [ + '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' => "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' + ] ]; ?> 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 @@- 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 spread 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)
- 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 @@- 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 @@@@ -13,6 +13,6 @@
- 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.
-- 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] --
- 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)-
- 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()
- 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 @@| Period Name | +Start Date | +End Date | +Strategy | +Sharpe | +Variance | +
|---|---|---|---|---|---|
| 5 Year Backtest | +1/1/2016 | +1/1/2021 | +Strategy | +0.24 | +0.058 | +
| Benchmark | +0.825 | +0.028 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +-1.025 | +0.917 | +
| Benchmark | +-1.4 | +0.474 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +1.688 | +0.16 | +
| Benchmark | +8.765 | +0.103 | +
+ 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: +
+ ++ 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: +
+ ++ 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. +
+ +
\ 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 @@
++ 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) ) ++
+ Plotting the ratio of the security prices shows its trending behavior. +
+
++ 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. +
+
++ 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() ++
+ 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
+
++ 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 [] ++
+ 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 @@ ++ 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 Name | +Start Date | +End Date | +Strategy | +Sharpe | +ASD | +
|---|---|---|---|---|---|
| Backtest | +8/11/2015 | +8/11/2020 | +Strategy | +-0.447 | +0.053 | +
| Benchmark | +0.732 | +0.192 | +|||
| Fall 2015 | +8/10/2015 | +10/10/2015 | +Strategy | +2.837 | +0.225 | +
| Benchmark | +-0.724 | +0.251 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +-4.196 | +0.209 | +
| Benchmark | +-1.243 | +0.793 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +-3.443 | +0.013 | +
| Benchmark | +13.761 | +0.386 | +
+ 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. +
+ + ++ 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: +
+ + ++ 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 @@ ++ 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 Model/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html new file mode 100644 index 0000000..1fa0f4e --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/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 Model/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html new file mode 100644 index 0000000..e56515f --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html @@ -0,0 +1,283 @@ ++ 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. +
+ ++ 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. +
+ + ++ 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: +
+ +
+
+
++ 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. +
+ +
+ 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.
+
+ 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.
+
+ 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 Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html new file mode 100644 index 0000000..0a1b0bd --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -0,0 +1,6 @@ ++ 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 diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html new file mode 100644 index 0000000..dedbeb6 --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html @@ -0,0 +1,7 @@ ++ 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 @@ ++ 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 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 @@ ++ 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. +
+ +
+
++ 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 @@ ++ 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 ++
+ 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
+
++ 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 ++
+ 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 ++
+ 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) ++
+ We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period. + 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 Name | +Start Date | +End Date | +Strategy | +Sharpe | +ASD | +
|---|---|---|---|---|---|
| Backtest | +1/1/2015 | +8/16/2020 | +Strategy | +-0.764 | +0.05 | +
| Benchmark | +0.709 | +0.185 | +|||
| Fall 2015 | +8/10/2015 | +10/10/2015 | +Strategy | +-0.696 | +0.058 | +
| Benchmark | +-1.243 | +0.793 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +4.818 | +0.266 | +
| Benchmark | +-1.243 | +0.793 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +0.602 | +0.103 | +
| Benchmark | +13.761 | +0.386 | +
+ 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 than 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: +
+ ++ 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 + 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. 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/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 @@ ++ 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] ] ++
+ The DrugNewsSentimentAlphaModel emits insights to take long intraday positions for securities that have positive + news sentiment. During construction of the model, we: +
+ ++ 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
+
++ 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 ++
+ 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 ++
+ 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 ++
+ 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 @@ ++ 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. +
+ ++To continue the development of this strategy, future areas of research include: +
+ ++ 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 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/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: +
+ ++ For better understanding, here is a visualization of the price of XOM, it's Ichimoku Cloud time series, and the + resulting buy/sell signals. +
+ +
+
++ 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 @@ ++ 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] ] ++
+ 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) ++
+ 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 ++
+ 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 ++
+ 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..56ab2db --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html @@ -0,0 +1,6 @@ ++ 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. +
+ +
+
++ 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 Name | +Start Date | +End Date | +Strategy | +Sharpe | +ASD | +
|---|---|---|---|---|---|
| Backtest | +1/1/2015 | +8/16/2020 | +Strategy | +-0.31 | +0.223 | +
| Benchmark | +-0.083 | +0.312 | +|||
| Fall 2015 | +8/10/2015 | +10/10/2015 | +Strategy | +-0.31 | +0.294 | +
| Benchmark | +0.242 | +0.351 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +176.524 | +0.949 | +
| Benchmark | +-0.902 | +1.108 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +-1.556 | +0.447 | +
| Benchmark | +46.068 | +0.703 | +
+ 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/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: +
+ ++ 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: +
+ ++ 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..83986fa --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -0,0 +1,50 @@ +Say there are two arbitrary stocks A and B. For each value β in .01, .02, .03, …, 1.00:
+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.
+ + +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:
+\[F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)\] +\[G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)\] +\[V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)\] +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.
+ 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..04066ff --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -0,0 +1,6 @@ ++ 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..10b2dac --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -0,0 +1,7 @@ ++ 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..3a493ab --- /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 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..2c62d81 --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html @@ -0,0 +1,6 @@ ++ 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 0.778 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 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 @@ ++ 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 new file mode 100644 index 0000000..6fe7274 --- /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 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 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 of 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 new file mode 100644 index 0000000..5cd8e93 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html @@ -0,0 +1,56 @@ ++ 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, 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(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 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) ++
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 a 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..935af6f --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html @@ -0,0 +1,6 @@ +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:
+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 @@ ++ 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..597b32d --- /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. +
+ +
++ 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 @@ ++ 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
+
++ 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
+
++ 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) + ++
+ 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() ++
+ 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 ++
+ 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 ++
+ 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 @@ +| Period Name | +Start Date | +End Date | +Strategy | +Sharpe | +Variance | +
|---|---|---|---|---|---|
| 5 Year Backtest | +9/1/2015 | +9/17/2020 | +Strategy | +-0.716 | +0.006 | +
| Benchmark | +0.845 | +0.036 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +-2.879 | +0.101 | +
| Benchmark | +-1.243 | +0.628 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +-2.329 | +0.027 | +
| Benchmark | +13.761 | +0.149 | +
+ 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: +
+ + ++ 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: +
+ ++ 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..f576900 --- /dev/null +++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html @@ -0,0 +1,47 @@ ++ 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 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/04 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html new file mode 100644 index 0000000..8ef11c2 --- /dev/null +++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html @@ -0,0 +1,223 @@ ++ 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] ] ++
+ 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
+
++ 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 ++
+ 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 ++
+ 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])
+
++ 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
+
++ 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/05 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html new file mode 100644 index 0000000..7f48965 --- /dev/null +++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html @@ -0,0 +1,6 @@ +| Period Name | +Start Date | +End Date | +Strategy | +Sharpe | +Variance | +
|---|---|---|---|---|---|
| 5 Year Backtest | +10/1/2015 | +10/13/2020 | +Strategy | +0.97 | +0.016 | +
| Benchmark | +0.805 | +0.029 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +-0.981 | +0.353 | +
| Benchmark | +-1.4 | +0.474 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +-2.011 | +0.035 | +
| Benchmark | +8.765 | +0.103 | +
+ 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: +
+ ++ 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: +
+ ++ 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. +
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..7ed4ada --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -0,0 +1,31 @@ ++ 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. +
+ ++ 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. +
+ ++ 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. +
+ +\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] + ++ 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. +
+ +\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] + ++ 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. +
+ ++ 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..a989254 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -0,0 +1,6 @@ +Algorithm
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> ++ 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 new file mode 100644 index 0000000..a43b163 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -0,0 +1,186 @@ ++ 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 + + return [x.Symbol for x in coarse if x.HasFundamentalData] ++
+ 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 + + return [f.Symbol for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement] ++
+ 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.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.Quit(f"Requirement violated: 0 < pct_long_short <= 50") + + 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() ++
+ 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. +
++def Update(self, algorithm, data): + # Emit insights on a monthly basis + time = algorithm.Time + if self.month == time.month: + return [] + self.month = time.month + + ... ++
+ 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 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 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
+ ...
+
++ 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. +
++@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() ++
+ 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 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 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) + 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 ++
+ 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
+
++ 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 Name | +Start Date | +End Date | +Strategy | +Sharpe | +ASD | +
|---|---|---|---|---|---|
| Backtest | +1/1/2015 | +8/16/2020 | +Strategy | +0.192 | +0.046 | +
| Benchmark | +0.709 | +0.186 | +|||
| Fall 2015 | +8/10/2015 | +10/10/2015 | +Strategy | +-1.448 | +0.052 | +
| Benchmark | +-0.724 | +0.251 | +|||
| 2020 Crash | +2/19/2020 | +3/23/2020 | +Strategy | +10.386 | +0.104 | +
| Benchmark | +-1.243 | +0.793 | +|||
| 2020 Recovery | +3/23/2020 | +6/8/2020 | +Strategy | +-2.942 | +0.177 | +
| Benchmark | +13.761 | +0.386 | +
+ 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: +
+ + ++ 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: +
+ + +- 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 ++
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.
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 @@Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight: 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: +
+- 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 @@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 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 74% 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..3eb4334 --- 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, 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. +
+-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.
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
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..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
@@ -42,8 +42,8 @@
x.RightGet 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.UnderlyingLastPricedef 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))
@@ -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)
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/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:
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 @@
-
+
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, 1.74, 0.53, 0.68, 0.14, 1.84, 0.30
+196408, -1.44, 0.30, 0.09, 0.06, 0.36, 0.28
+196409, 2.69, -0.31, 1.65, -0.48, 0.58, 0.28
+196410, 0.59, 0.88, 1.14, -0.29, 0.48, 0.29
+196411, 0.00, -0.27, -1.98, 0.60, -0.16, 0.29
+196412, 0.03, -0.58, -2.55, 1.13, -1.63, 0.31
+196501, 3.54, 2.49, 0.18, 0.84, 0.11, 0.28
+196502, 0.44, 3.31, 0.22, 0.40, -0.69, 0.30
+196503, -1.34, 2.03, 1.09, -0.30, 0.73, 0.36
+196504, 3.11, 1.18, 0.71, 0.26, -2.30, 0.31
+196505, -0.77, 0.05, -1.63, -0.38, 0.70, 0.31
+196506, -5.51, -4.30, 0.55, 0.12, 0.41, 0.35
+196507, 1.43, 1.08, 2.18, -1.41, -0.02, 0.31
+196508, 2.73, 2.72, -0.96, 2.06, -0.75, 0.33
+196509, 2.86, 0.62, -0.10, -0.82, 0.82, 0.31
+196510, 2.60, 3.42, 1.52, 1.22, -0.76, 0.31
+196511, -0.03, 5.19, 0.21, -1.06, -0.90, 0.35
+196512, 1.01, 2.64, 1.95, -1.28, -0.45, 0.33
+196601, 0.72, 4.72, 3.49, -2.83, -0.07, 0.38
+196602, -1.21, 4.86, 0.27, -0.15, -1.44, 0.35
+196603, -2.51, 0.22, -2.07, 1.30, -0.01, 0.38
+196604, 2.14, 3.36, -0.54, 0.41, -0.91, 0.34
+196605, -5.66, -5.18, -1.55, 1.66, -1.52, 0.41
+196606, -1.44, 1.34, 0.50, 0.06, 0.80, 0.38
+196607, -1.63, -0.50, 0.96, -0.45, 1.77, 0.35
+196608, -7.91, -3.03, 0.47, 0.05, 0.77, 0.41
+196609, -1.06, -1.18, 0.53, -1.65, 2.50, 0.40
+196610, 3.86, -6.53, 2.87, -3.64, 4.28, 0.45
+196611, 1.40, 3.37, -4.46, 4.17, -6.53, 0.40
+196612, 0.13, 1.91, -1.22, 0.68, -0.13, 0.40
+196701, 8.15, 9.00, 2.22, 0.62, -2.97, 0.43
+196702, 0.78, 3.03, -2.17, 1.94, -0.94, 0.36
+196703, 3.99, 1.90, 0.31, 0.90, -1.51, 0.39
+196704, 3.89, 0.46, -2.64, 2.43, -3.75, 0.32
+196705, -4.33, 2.37, 0.80, -1.75, 1.61, 0.33
+196706, 2.41, 6.46, 0.96, -0.64, -2.39, 0.27
+196707, 4.58, 3.48, 2.65, 0.51, 2.72, 0.31
+196708, -0.89, 0.76, 1.46, 0.42, 1.41, 0.31
+196709, 3.11, 2.46, -2.47, 0.22, -0.95, 0.32
+196710, -3.09, 0.47, -3.39, 0.81, -2.55, 0.39
+196711, 0.37, -0.06, -1.71, 1.29, -2.29, 0.36
+196712, 3.05, 5.75, -0.39, -0.64, 0.03, 0.33
+196801, -4.06, 4.48, 4.75, -4.58, 6.45, 0.40
+196802, -3.75, -2.91, 1.17, -0.14, 2.49, 0.39
+196803, 0.20, -1.52, -0.59, 1.17, -1.15, 0.38
+196804, 9.05, 6.25, -1.03, 2.79, -3.66, 0.43
+196805, 2.28, 7.03, 0.84, 0.39, -1.82, 0.45
+196806, 0.69, -0.26, 0.67, -1.33, 2.68, 0.43
+196807, -2.72, -1.33, 5.48, -3.00, 3.61, 0.48
+196808, 1.34, 2.29, 1.00, -0.60, 0.40, 0.42
+196809, 4.03, 2.77, 0.23, -2.03, 0.92, 0.43
+196810, 0.42, -0.43, 2.89, -1.30, 2.81, 0.44
+196811, 5.43, 2.41, -0.90, 0.46, -2.52, 0.42
+196812, -3.94, 3.60, 0.02, -1.78, 1.65, 0.43
+196901, -1.25, -0.46, 1.69, -1.54, 1.41, 0.53
+196902, -5.84, -4.12, 0.91, 2.17, 0.80, 0.46
+196903, 2.64, -0.41, -0.46, -1.34, -0.42, 0.46
+196904, 1.46, -0.82, 0.03, 0.38, 0.06, 0.53
+196905, -0.10, -0.18, 0.73, -0.97, 1.49, 0.48
+196906, -7.18, -5.49, -1.09, 4.26, -1.55, 0.51
+196907, -7.00, -3.37, 1.42, 1.40, 1.97, 0.53
+196908, 4.68, 0.71, -3.87, 1.16, -4.08, 0.50
+196909, -2.98, 1.29, -3.19, 3.42, -0.83, 0.62
+196910, 5.06, 3.89, -3.19, 0.01, -2.03, 0.60
+196911, -3.79, -2.41, -1.12, 1.44, 0.32, 0.52
+196912, -2.63, -3.70, -3.02, 2.47, -2.04, 0.64
+197001, -8.10, 3.09, 3.04, -1.77, 3.92, 0.60
+197002, 5.13, -2.58, 4.04, -2.28, 3.06, 0.62
+197003, -1.06, -2.40, 4.25, -0.87, 4.43, 0.57
+197004, -11.00, -6.30, 6.40, -0.64, 5.88, 0.50
+197005, -6.92, -4.42, 3.60, -1.30, 3.62, 0.53
+197006, -5.79, -2.17, 0.87, 0.19, 2.90, 0.58
+197007, 6.93, -0.69, 0.96, -0.07, 1.90, 0.52
+197008, 4.49, 1.54, 1.03, 0.57, -0.15, 0.53
+197009, 4.18, 8.49, -5.58, 0.24, -5.93, 0.54
+197010, -2.28, -4.50, 0.27, 1.83, 2.38, 0.46
+197011, 4.60, -4.00, 1.63, 1.75, 1.59, 0.46
+197012, 5.72, 3.02, 1.01, 0.14, 0.28, 0.42
+197101, 4.84, 7.54, 1.38, -2.19, -0.14, 0.38
+197102, 1.41, 2.05, -1.29, 0.65, -0.72, 0.33
+197103, 4.13, 2.15, -4.04, 1.97, -2.69, 0.30
+197104, 3.15, -0.37, 0.72, -1.56, 0.72, 0.28
+197105, -3.98, -1.17, -1.38, 1.46, 0.30, 0.29
+197106, -0.10, -1.45, -2.06, 1.52, -1.74, 0.37
+197107, -4.50, -1.41, 0.17, 0.48, 1.62, 0.40
+197108, 3.79, -0.18, 2.67, -0.34, 2.60, 0.47
+197109, -0.85, 0.19, -2.96, 2.59, -1.56, 0.37
+197110, -4.42, -1.65, -0.43, 1.48, -1.24, 0.37
+197111, -0.46, -2.90, -1.70, 2.45, -0.36, 0.37
+197112, 8.71, 3.27, -0.35, -0.43, -1.83, 0.37
+197201, 2.49, 6.33, 1.99, -1.48, 0.29, 0.29
+197202, 2.87, 0.95, -2.76, 1.65, -0.52, 0.25
+197203, 0.63, -0.49, -1.73, 1.58, -0.19, 0.27
+197204, 0.29, 0.15, 0.23, -0.59, -0.89, 0.29
+197205, 1.25, -3.15, -2.69, 2.33, -1.85, 0.30
+197206, -2.43, -0.35, -2.51, 1.75, -0.36, 0.29
+197207, -0.80, -2.74, 0.78, 1.08, -0.59, 0.31
+197208, 3.26, -3.53, 4.69, -2.02, 2.95, 0.29
+197209, -1.14, -2.25, 0.47, 1.67, -1.98, 0.34
+197210, 0.52, -2.53, 1.37, -0.12, -0.05, 0.40
+197211, 4.60, -0.53, 4.76, -1.98, 3.35, 0.37
+197212, 0.62, -1.82, -2.27, 2.60, -2.20, 0.37
+197301, -3.29, -2.79, 2.68, 0.40, 0.92, 0.44
+197302, -4.85, -4.01, 1.70, -0.34, -0.01, 0.41
+197303, -1.30, -2.31, 2.78, -1.07, 0.73, 0.46
+197304, -5.68, -3.00, 5.69, -1.63, 2.71, 0.52
+197305, -2.94, -5.98, 0.21, 2.02, -1.79, 0.51
+197306, -1.56, -2.54, 1.44, -0.27, 0.22, 0.51
+197307, 5.05, 7.26, -5.35, -0.10, -3.46, 0.64
+197308, -3.82, -1.75, 1.13, -1.36, 1.20, 0.70
+197309, 4.75, 3.54, 2.19, -2.30, 1.82, 0.68
+197310, -0.83, -0.27, 1.75, -1.94, 2.59, 0.65
+197311, -12.75, -7.26, 4.09, -2.71, 1.50, 0.56
+197312, 0.61, -4.60, 4.10, -2.80, 2.30, 0.64
+197401, -0.17, 10.41, 5.98, -3.01, 4.46, 0.63
+197402, -0.47, 0.16, 2.56, -1.93, 2.67, 0.58
+197403, -2.81, 2.64, -0.11, 2.82, 0.36, 0.56
+197404, -5.29, -0.63, 1.03, 2.86, 1.84, 0.75
+197405, -4.68, -3.16, -2.07, 5.08, -0.01, 0.75
+197406, -2.83, -0.02, 0.77, 0.62, 3.05, 0.60
+197407, -8.05, 1.99, 5.14, -3.26, 4.58, 0.70
+197408, -9.35, 0.30, 2.50, -0.26, 2.55, 0.60
+197409, -11.77, 1.46, 5.49, -4.28, 5.91, 0.81
+197410, 16.10, -6.85, -9.99, -0.03, -2.94, 0.51
+197411, -4.51, -1.44, -0.16, -3.39, 2.95, 0.54
+197412, -3.45, -4.36, 0.00, -0.65, 3.23, 0.70
+197501, 13.66, 12.79, 8.44, -0.89, -0.86, 0.58
+197502, 5.56, -0.65, -4.56, 1.15, -2.14, 0.43
+197503, 2.66, 3.98, 2.49, 1.23, -1.25, 0.41
+197504, 4.23, -0.59, -1.11, 1.37, -1.35, 0.44
+197505, 5.19, 2.89, -4.02, -1.05, -0.61, 0.44
+197506, 4.83, 1.32, 1.32, -2.62, 1.07, 0.41
+197507, -6.59, 3.42, 1.70, 0.43, 1.19, 0.48
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+201901, 8.41, 3.08, -0.60, -0.70, -1.36, 0.21
+201902, 3.40, 1.78, -2.83, 0.23, -1.47, 0.18
+201903, 1.10, -3.58, -4.04, 0.87, -1.03, 0.19
+201904, 3.96, -1.15, 1.96, 1.72, -2.18, 0.21
+201905, -6.94, -1.51, -2.35, -0.43, 1.78, 0.21
+201906, 6.93, 0.36, -1.08, 0.94, -0.38, 0.18
+201907, 1.19, -1.89, 0.17, -0.04, 0.34, 0.19
+201908, -2.58, -3.30, -5.05, 0.41, -0.88, 0.16
+201909, 1.43, 0.27, 6.81, 1.98, 3.50, 0.18
+201910, 2.06, 0.22, -2.04, 0.39, -0.99, 0.15
+201911, 3.87, 0.52, -1.80, -1.52, -1.24, 0.12
+201912, 2.77, 0.96, 1.93, 0.19, 1.29, 0.14
+202001, -0.11, -4.40, -6.37, -1.24, -2.34, 0.13
+202002, -8.13, -0.05, -4.05, -1.67, -2.49, 0.12
+202003, -13.39, -8.42, -14.17, -1.33, 1.21, 0.12
+202004, 13.65, 2.81, -1.18, 2.65, -1.03, 0.00
+202005, 5.58, 1.92, -4.92, 0.66, -3.28, 0.01
+202006, 2.45, 1.93, -2.19, -0.02, 0.34, 0.01
+202007, 5.77, -3.11, -1.27, 0.61, 1.07, 0.01
+
+ Annual Factors: January-December
+,Mkt-RF,SMB,HML,RMW,CMA,RF
+ 1964, 12.54, 0.33, 9.86, -2.99, 6.80, 3.54
+ 1965, 10.52, 24.41, 7.36, -0.79, -3.17, 3.93
+ 1966, -13.51, 2.15, -0.68, -0.12, -0.34, 4.76
+ 1967, 24.49, 50.40, -8.58, 7.53, -15.04, 4.21
+ 1968, 8.79, 26.32, 18.49, -12.84, 16.25, 5.21
+ 1969, -17.54, -14.06, -9.81, 11.77, -4.14, 6.58
+ 1970, -6.49, -12.36, 22.34, -2.65, 24.45, 6.52
+ 1971, 11.78, 5.58, -11.29, 10.16, -5.86, 4.39
+ 1972, 13.05, -11.43, 1.75, 7.99, -3.05, 3.84
+ 1973, -26.19, -20.00, 18.08, -9.03, 6.66, 6.93
+ 1974, -35.75, 0.67, 9.67, -4.04, 22.88, 8.00
+ 1975, 32.44, 19.05, 9.49, 0.53, 0.48, 5.80
+ 1976, 21.91, 19.90, 24.50, -6.67, 7.47, 5.08
+ 1977, -8.26, 24.64, 7.51, 2.16, -0.39, 5.12
+ 1978, 1.03, 13.68, 0.37, 5.07, 4.15, 7.18
+ 1979, 13.09, 21.05, -2.12, -2.80, -0.99, 10.38
+ 1980, 22.13, 4.91, -25.06, 14.08, -11.32, 11.24
+ 1981, -18.13, 7.48, 25.01, -1.60, 10.36, 14.71
+ 1982, 10.66, 7.42, 13.59, -4.46, 18.01, 10.54
+ 1983, 13.74, 11.84, 20.85, 0.67, 16.43, 8.80
+ 1984, -6.05, -8.45, 19.63, 14.88, 3.84, 9.85
+ 1985, 24.91, -0.62, 1.35, 11.89, -2.49, 7.72
+ 1986, 10.12, -10.37, 9.58, 7.13, 9.84, 6.16
+ 1987, -3.87, -11.28, -1.64, 6.20, 6.90, 5.47
+ 1988, 11.55, 6.46, 14.77, 3.08, 9.74, 6.35
+ 1989, 20.49, -13.02, -4.29, 2.74, 8.21, 8.37
+ 1990, -13.95, -14.30, -9.72, 7.44, 0.60, 7.81
+ 1991, 29.18, 15.77, -14.41, 12.06, -14.91, 5.60
+ 1992, 6.23, 9.07, 24.28, 7.54, 6.96, 3.51
+ 1993, 8.21, 6.01, 18.91, -8.55, 11.99, 2.90
+ 1994, -4.10, -2.51, -0.69, 6.35, 3.90, 3.90
+ 1995, 31.22, -8.17, 5.30, 1.97, 2.81, 5.60
+ 1996, 15.96, -2.80, 6.16, 15.53, 0.52, 5.21
+ 1997, 25.96, -6.17, 17.46, 10.51, 5.19, 5.26
+ 1998, 19.46, -28.67, -8.89, 0.63, -4.60, 4.86
+ 1999, 20.57, 8.56, -31.77, -27.89, -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, 0.012
+19630718, -0.54, 0.27, 0.05, -0.03, 0.32, 0.012
+19630719, -0.23, -0.06, -0.05, 0.16, -0.31, 0.012
+19630722, -0.70, -0.10, -0.31, -0.01, -0.10, 0.012
+19630723, 0.01, -0.10, -0.20, 0.04, -0.08, 0.012
+19630724, 0.46, -0.10, -0.01, -0.11, -0.31, 0.012
+19630725, -0.04, 0.14, 0.26, -0.16, 0.18, 0.012
+19630726, 0.35, -0.14, 0.07, 0.04, -0.08, 0.012
+19630729, 0.11, -0.17, 0.26, 0.07, -0.11, 0.012
+19630730, 0.84, -0.29, -0.27, 0.53, -0.40, 0.012
+19630731, -0.13, 0.11, -0.01, -0.15, 0.34, 0.012
+19630801, -0.08, -0.12, -0.12, 0.17, -0.02, 0.011
+19630802, 0.29, -0.14, 0.15, -0.05, 0.02, 0.011
+19630805, 0.57, -0.42, 0.16, 0.03, 0.11, 0.011
+19630806, 0.60, -0.34, 0.25, 0.01, -0.03, 0.011
+19630807, -0.19, 0.04, 0.17, -0.19, 0.14, 0.011
+19630808, 0.14, -0.01, -0.25, 0.29, -0.33, 0.011
+19630809, 0.60, -0.09, -0.06, 0.22, -0.17, 0.011
+19630812, 0.24, 0.20, 0.41, -0.13, 0.32, 0.011
+19630813, 0.27, -0.26, -0.03, 0.05, -0.04, 0.011
+19630814, 0.30, -0.08, 0.21, -0.19, 0.05, 0.011
+19630815, 0.43, -0.29, 0.37, -0.19, 0.00, 0.011
+19630816, 0.15, 0.07, 0.10, 0.04, -0.18, 0.011
+19630819, -0.05, -0.01, -0.10, -0.06, -0.06, 0.011
+19630820, -0.05, 0.02, -0.25, 0.02, 0.09, 0.011
+19630821, -0.11, 0.34, -0.12, -0.07, 0.09, 0.011
+19630822, 0.39, 0.07, 0.09, -0.09, -0.31, 0.011
+19630823, 0.34, 0.00, 0.10, 0.06, -0.03, 0.011
+19630826, 0.18, -0.12, 0.14, 0.11, -0.20, 0.011
+19630827, -0.44, 0.10, -0.05, -0.01, 0.14, 0.011
+19630828, 0.77, 0.08, 0.44, 0.12, 0.21, 0.011
+19630829, 0.18, 0.11, 0.02, 0.10, 0.11, 0.011
+19630830, 0.44, 0.11, -0.09, 0.13, -0.29, 0.011
+19630903, 0.23, 0.07, 0.06, 0.02, 0.20, 0.014
+19630904, -0.01, 0.06, -0.06, -0.39, -0.05, 0.014
+19630905, 0.47, -0.10, 0.11, -0.09, 0.03, 0.014
+19630906, -0.27, -0.17, -0.01, 0.13, -0.04, 0.014
+19630909, -0.36, -0.25, -0.01, 0.05, 0.13, 0.014
+19630910, 0.52, -0.17, 0.42, 0.06, -0.24, 0.014
+19630911, 0.24, -0.20, 0.20, -0.07, 0.32, 0.014
+19630912, -0.09, 0.11, -0.11, -0.20, -0.13, 0.014
+19630913, 0.02, 0.35, -0.15, -0.31, 0.12, 0.014
+19630916, -0.16, 0.15, 0.19, -0.08, -0.38, 0.014
+19630917, 0.03, -0.20, 0.17, 0.05, 0.08, 0.014
+19630918, -0.43, 0.12, -0.24, -0.15, -0.06, 0.014
+19630919, 0.52, -0.39, 0.17, 0.15, -0.02, 0.014
+19630920, 0.04, -0.07, -0.01, 0.09, -0.34, 0.014
+19630923, -0.50, -0.09, -0.21, 0.09, 0.15, 0.014
+19630924, 0.35, -0.33, 0.12, -0.07, 0.07, 0.014
+19630925, -0.58, -0.25, -0.08, -0.41, 0.15, 0.014
+19630926, -0.81, 0.47, -0.35, -0.06, 0.29, 0.014
+19630927, -0.18, 0.28, -0.22, 0.20, -0.07, 0.014
+19630930, -0.60, 0.20, 0.08, 0.21, -0.06, 0.014
+19631001, 0.65, -0.21, 0.25, -0.05, -0.13, 0.013
+19631002, 0.10, -0.13, 0.00, 0.02, -0.12, 0.013
+19631003, 0.67, -0.29, 0.85, -0.35, 0.17, 0.013
+19631004, 0.00, 0.03, 0.06, 0.00, 0.16, 0.013
+19631007, -0.23, 0.18, -0.02, -0.19, -0.03, 0.013
+19631008, -0.14, 0.19, 0.25, -0.03, -0.21, 0.013
+19631009, -0.61, 0.15, -0.05, -0.01, 0.20, 0.013
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+19631021, 0.03, -0.22, -0.30, 0.55, 0.08, 0.013
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+19631024, 0.35, -0.07, 0.47, 0.07, -0.12, 0.013
+19631025, 0.85, -0.43, -0.89, 0.63, -0.95, 0.013
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+19631104, -0.43, 0.08, 0.36, -0.22, 0.45, 0.015
+19631106, -0.77, 0.10, 0.25, -0.12, 0.51, 0.015
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+19631111, 0.20, 0.22, 0.15, 0.13, -0.08, 0.015
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+19631114, -0.41, 0.12, -0.03, -0.11, 0.35, 0.015
+19631115, -0.75, 0.35, 0.00, -0.15, 0.61, 0.015
+19631118, -0.74, 0.08, -0.23, 0.25, -0.04, 0.015
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+19631121, -1.25, -0.11, 0.03, -0.44, 0.39, 0.015
+19631122, -2.90, -0.58, 0.05, 0.62, 0.50, 0.015
+19631126, 3.94, -1.22, 0.87, -0.37, -0.36, 0.015
+19631127, -0.16, 0.35, 0.13, -0.02, -0.07, 0.015
+19631129, 1.34, 0.33, 0.31, -0.23, -0.18, 0.015
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diff --git a/Data/F-F_Research_Data_Factors.CSV b/Data/F-F_Research_Data_Factors.CSV
new file mode 100644
index 0000000..c1069e4
--- /dev/null
+++ b/Data/F-F_Research_Data_Factors.CSV
@@ -0,0 +1,1231 @@
+This file was created by CMPT_ME_BEME_RETS using the 202007 CRSP database.
+The 1-month TBill return is from Ibbotson and Associates, Inc.
+
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+199704, 4.04, -5.65, -0.06, 0.43
+199705, 6.74, 4.90, -3.89, 0.49
+199706, 4.10, 1.32, 1.23, 0.37
+199707, 7.33, -2.80, 0.82, 0.43
+199708, -4.15, 7.31, 1.38, 0.41
+199709, 5.35, 2.61, 0.01, 0.44
+199710, -3.80, -0.68, 1.95, 0.42
+199711, 2.98, -4.95, 0.78, 0.39
+199712, 1.32, -2.35, 3.45, 0.48
+199801, 0.15, -1.16, -1.45, 0.43
+199802, 7.04, 0.01, -0.13, 0.39
+199803, 4.76, -0.93, 1.05, 0.39
+199804, 0.73, 0.21, 0.73, 0.43
+199805, -3.07, -3.75, 4.16, 0.40
+199806, 3.18, -3.16, -2.33, 0.41
+199807, -2.46, -5.12, -0.96, 0.40
+199808, -16.08, -5.31, 3.40, 0.43
+199809, 6.15, -0.14, -3.31, 0.46
+199810, 7.13, -3.29, -2.21, 0.32
+199811, 6.10, 1.07, -3.15, 0.31
+199812, 6.16, -0.35, -4.46, 0.38
+199901, 3.50, 0.37, -4.03, 0.35
+199902, -4.08, -5.67, 1.40, 0.35
+199903, 3.45, -3.92, -2.65, 0.43
+199904, 4.33, 4.00, 2.53, 0.37
+199905, -2.46, 3.42, 2.40, 0.34
+199906, 4.77, 3.05, -3.60, 0.40
+199907, -3.49, 2.67, -0.76, 0.38
+199908, -1.38, -1.27, -1.31, 0.39
+199909, -2.79, 3.34, -3.39, 0.39
+199910, 6.12, -6.95, -2.89, 0.39
+199911, 3.37, 7.37, -6.51, 0.36
+199912, 7.72, 7.12, -8.73, 0.44
+200001, -4.74, 4.95, -0.28, 0.41
+200002, 2.45, 21.70, -9.93, 0.43
+200003, 5.20, -16.87, 7.37, 0.47
+200004, -6.40, -7.72, 8.61, 0.46
+200005, -4.42, -5.11, 2.57, 0.50
+200006, 4.64, 13.85, -9.86, 0.40
+200007, -2.51, -2.76, 8.06, 0.48
+200008, 7.03, -1.15, -0.66, 0.50
+200009, -5.45, -1.32, 6.13, 0.51
+200010, -2.76, -3.81, 5.64, 0.56
+200011, -10.72, -2.77, 11.26, 0.51
+200012, 1.19, 0.96, 7.38, 0.50
+200101, 3.13, 6.54, -4.86, 0.54
+200102, -10.05, -0.72, 12.87, 0.38
+200103, -7.26, 0.35, 6.46, 0.42
+200104, 7.94, 0.54, -4.72, 0.39
+200105, 0.72, 2.58, 3.17, 0.32
+200106, -1.94, 6.03, -1.03, 0.28
+200107, -2.13, -4.35, 5.57, 0.30
+200108, -6.46, 2.50, 2.50, 0.31
+200109, -9.25, -6.12, 1.60, 0.28
+200110, 2.46, 7.63, -8.10, 0.22
+200111, 7.54, -0.41, 2.01, 0.17
+200112, 1.61, 4.57, 1.10, 0.15
+200201, -1.44, 1.19, 3.33, 0.14
+200202, -2.29, -1.10, 2.50, 0.13
+200203, 4.24, 4.23, 1.10, 0.13
+200204, -5.20, 5.94, 3.93, 0.15
+200205, -1.38, -3.21, 1.68, 0.14
+200206, -7.21, 4.27, 0.12, 0.13
+200207, -8.18, -5.30, -3.44, 0.15
+200208, 0.50, -2.44, 2.53, 0.14
+200209, -10.35, 2.57, 1.32, 0.14
+200210, 7.84, -2.90, -5.45, 0.14
+200211, 5.96, 2.84, -1.12, 0.12
+200212, -5.76, 0.01, 2.23, 0.11
+200301, -2.57, 1.38, -0.94, 0.10
+200302, -1.88, -0.34, -1.45, 0.09
+200303, 1.09, 0.89, -2.07, 0.10
+200304, 8.22, 0.56, 1.03, 0.10
+200305, 6.05, 4.69, -0.29, 0.09
+200306, 1.42, 1.67, 0.69, 0.10
+200307, 2.35, 5.24, -1.14, 0.07
+200308, 2.34, 2.60, 2.03, 0.07
+200309, -1.24, 0.80, 0.01, 0.08
+200310, 6.08, 2.68, 1.77, 0.07
+200311, 1.35, 2.02, 1.85, 0.07
+200312, 4.29, -3.00, 2.41, 0.08
+200401, 2.15, 2.80, 1.97, 0.07
+200402, 1.40, -1.43, 0.50, 0.06
+200403, -1.32, 1.75, 0.22, 0.09
+200404, -1.83, -2.06, -2.62, 0.08
+200405, 1.17, -0.21, -0.39, 0.06
+200406, 1.86, 2.26, 1.39, 0.08
+200407, -4.06, -3.81, 4.11, 0.10
+200408, 0.08, -1.62, 1.03, 0.11
+200409, 1.60, 3.03, -0.25, 0.11
+200410, 1.43, 0.31, -0.63, 0.11
+200411, 4.54, 3.90, 1.79, 0.15
+200412, 3.43, 0.11, -0.08, 0.16
+200501, -2.76, -1.51, 1.96, 0.16
+200502, 1.89, -0.50, 1.65, 0.16
+200503, -1.97, -1.37, 1.61, 0.21
+200504, -2.61, -3.98, -0.35, 0.21
+200505, 3.65, 2.88, -0.81, 0.24
+200506, 0.57, 2.62, 2.63, 0.23
+200507, 3.92, 2.93, -0.52, 0.24
+200508, -1.22, -0.92, 1.27, 0.30
+200509, 0.49, -0.58, 0.77, 0.29
+200510, -2.02, -1.21, 0.22, 0.27
+200511, 3.61, 0.90, -1.19, 0.31
+200512, -0.25, -0.46, 0.44, 0.32
+200601, 3.04, 5.42, 1.13, 0.35
+200602, -0.30, -0.37, -0.25, 0.34
+200603, 1.46, 3.54, 0.60, 0.37
+200604, 0.73, -1.34, 2.61, 0.36
+200605, -3.57, -3.05, 2.54, 0.43
+200606, -0.35, -0.35, 0.87, 0.40
+200607, -0.78, -4.07, 2.94, 0.40
+200608, 2.03, 0.91, -1.71, 0.42
+200609, 1.84, -1.37, 0.04, 0.41
+200610, 3.23, 1.73, -0.03, 0.41
+200611, 1.71, 0.86, 0.07, 0.42
+200612, 0.87, -1.10, 3.16, 0.40
+200701, 1.40, 0.10, -0.11, 0.44
+200702, -1.96, 1.32, -0.09, 0.38
+200703, 0.68, -0.05, -0.22, 0.43
+200704, 3.49, -2.06, -1.15, 0.44
+200705, 3.24, 0.02, -0.04, 0.41
+200706, -1.96, 0.77, -1.12, 0.40
+200707, -3.73, -2.51, -3.33, 0.40
+200708, 0.92, -0.13, -2.24, 0.42
+200709, 3.22, -2.29, -1.86, 0.32
+200710, 1.80, 0.22, -2.59, 0.32
+200711, -4.83, -2.62, -1.18, 0.34
+200712, -0.87, 0.20, -0.51, 0.27
+200801, -6.36, -0.90, 3.65, 0.21
+200802, -3.09, -0.24, -0.94, 0.13
+200803, -0.93, 0.95, -0.14, 0.17
+200804, 4.60, -1.64, -0.95, 0.18
+200805, 1.86, 3.22, -1.38, 0.18
+200806, -8.44, 1.28, -2.41, 0.17
+200807, -0.77, 2.41, 5.93, 0.15
+200808, 1.53, 3.61, 1.55, 0.13
+200809, -9.24, -1.17, 6.32, 0.15
+200810, -17.23, -2.33, -2.88, 0.08
+200811, -7.86, -2.96, -6.03, 0.03
+200812, 1.74, 3.63, -0.28, 0.00
+200901, -8.12, 0.13, -11.23, 0.00
+200902, -10.10, 0.22, -7.30, 0.01
+200903, 8.95, -0.12, 3.64, 0.02
+200904, 10.19, 4.76, 5.53, 0.01
+200905, 5.21, -2.34, -0.21, 0.00
+200906, 0.43, 2.59, -2.66, 0.01
+200907, 7.72, 2.09, 5.31, 0.01
+200908, 3.33, -0.90, 7.76, 0.01
+200909, 4.08, 2.45, 0.91, 0.01
+200910, -2.59, -4.22, -4.17, 0.00
+200911, 5.56, -2.50, -0.19, 0.00
+200912, 2.75, 6.10, -0.01, 0.01
+201001, -3.36, 0.38, 0.31, 0.00
+201002, 3.40, 1.21, 3.13, 0.00
+201003, 6.31, 1.43, 2.13, 0.01
+201004, 2.00, 4.97, 2.82, 0.01
+201005, -7.89, 0.05, -2.39, 0.01
+201006, -5.56, -1.97, -4.48, 0.01
+201007, 6.93, 0.17, -0.26, 0.01
+201008, -4.77, -3.00, -1.96, 0.01
+201009, 9.54, 3.92, -3.13, 0.01
+201010, 3.88, 1.13, -2.61, 0.01
+201011, 0.60, 3.71, -0.90, 0.01
+201012, 6.82, 0.68, 3.81, 0.01
+201101, 1.99, -2.47, 0.81, 0.01
+201102, 3.49, 1.53, 1.09, 0.01
+201103, 0.45, 2.60, -1.58, 0.01
+201104, 2.90, -0.34, -2.52, 0.00
+201105, -1.27, -0.70, -2.07, 0.00
+201106, -1.75, -0.16, -0.31, 0.00
+201107, -2.36, -1.35, -1.21, 0.00
+201108, -5.99, -3.05, -2.45, 0.01
+201109, -7.59, -3.52, -1.39, 0.00
+201110, 11.35, 3.42, -0.18, 0.00
+201111, -0.28, -0.17, -0.34, 0.00
+201112, 0.74, -0.70, 1.78, 0.00
+201201, 5.05, 2.15, -1.14, 0.00
+201202, 4.42, -1.74, 0.09, 0.00
+201203, 3.11, -0.62, 0.91, 0.00
+201204, -0.85, -0.52, -0.49, 0.00
+201205, -6.19, 0.00, -0.58, 0.01
+201206, 3.89, 0.76, 0.44, 0.00
+201207, 0.79, -2.61, -0.26, 0.00
+201208, 2.55, 0.40, 1.28, 0.01
+201209, 2.73, 0.49, 1.52, 0.01
+201210, -1.76, -1.15, 3.79, 0.01
+201211, 0.78, 0.59, -0.98, 0.01
+201212, 1.18, 1.48, 3.58, 0.01
+201301, 5.57, 0.38, 0.91, 0.00
+201302, 1.29, -0.45, 0.01, 0.00
+201303, 4.03, 0.81, -0.28, 0.00
+201304, 1.55, -2.43, 0.59, 0.00
+201305, 2.80, 1.66, 2.56, 0.00
+201306, -1.20, 1.17, -0.17, 0.00
+201307, 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
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+20200721, 0.16, 0.86, 3.24, 0.000
+20200722, 0.49, -0.58, -0.34, 0.000
+20200723, -1.20, 0.72, 1.96, 0.000
+20200724, -0.75, -0.80, 0.47, 0.000
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+20200730, -0.29, 0.47, -1.81, 0.000
+20200731, 0.61, -1.54, -0.65, 0.000
+
+Copyright 2020 Kenneth R. 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
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+
+Copyright 2020 Kenneth R. French
diff --git a/README.md b/README.md
index da878ac..c9846d7 100644
--- a/README.md
+++ b/README.md
@@ -8,24 +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.
+## Contributors and Pull Requests ##
-## 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!
+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].
-## Mailing List ##
+## Strategy Library Development Workflow ##
-The mailing list for the project can be found on [Google Groups][6]
+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)
-## Contributors and Pull Requests ##
+## New Tutorial Requests and Edits ##
-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].
+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
diff --git a/quantpedia.json b/quantpedia.json
index 13a6d8d..22f8d06 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,12 +34,14 @@
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