From 7f31149a37fdbed4fbee691ee32b392bad6ad2e3 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 10:25:24 -0700 Subject: [PATCH 001/107] Distinguished notations for interest rates Nominal and effective interest rates were denoted by the same notation r. The change is to clearly distinguish them. --- .../02 Rate of Return.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 5ae0cc5..4ad9640 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -55,11 +55,11 @@

Logarithm Return

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

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

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

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

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

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

Logarithm Return

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

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

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

From 9c4c3ffbeaa1528aced846eb2f56a861761354fc Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 11:17:53 -0700 Subject: [PATCH 002/107] Explained why effective rate is close to nominal Explained why effective rate is close to nominal rate with compounding every millisecond. --- .../02 Rate of Return.html | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 4ad9640..04f1154 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -61,7 +61,12 @@

Logarithm Return

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

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

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

then we have

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

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

From afebd8582225d6c851f139b2ef72c0dee724e285 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 11:24:13 -0700 Subject: [PATCH 003/107] Adjusted the goal being finding effective rate --- .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html index 04f1154..aab985e 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html @@ -132,7 +132,7 @@

Logarithm Return

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

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

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

From a9346ad8c406cbe336e87465f07ec16adee7b734 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:06:45 -0700 Subject: [PATCH 004/107] Fixed a few typos --- .../06 Rate of Return, Mean and Variance/03 Mean.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html index 9d51b9c..72b9534 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html @@ -24,12 +24,12 @@

Geometric Mean

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

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

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

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

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

From 3cb69147929d476cf585fca14d8122ae49b17516 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:09:17 -0700 Subject: [PATCH 005/107] Fixed the symbol for variance --- .../04 Variance and Standard Deviation.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html index 5161451..f11f818 100755 --- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html +++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html @@ -1,6 +1,6 @@

Variance

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

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

From 8cc8293cbb9a9b4c9d67071eabe5a4701bc01b37 Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:17:14 -0700 Subject: [PATCH 006/107] Typo fixed --- .../02 Random Variables.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html index 30c1882..82718e8 100755 --- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html +++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html @@ -1,3 +1,3 @@

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

From 6e028a2f72be076f20c74fd1d2011a547cebac9a Mon Sep 17 00:00:00 2001 From: metricwang Date: Sat, 25 Apr 2020 12:34:39 -0700 Subject: [PATCH 007/107] Fixed multiple typos --- .../03 Distributions.html | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html index d80c6dc..933b38c 100755 --- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html +++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html @@ -1,10 +1,10 @@

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

Uniform Distribution

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

@@ -24,7 +24,7 @@

Uniform Distribution

- We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution: + We create a series of random variables here. We can plot the values on the x-axis and put their numbers of occurrences on the y-axis to have a direct view of the distribution:

@@ -39,7 +39,7 @@

Uniform Distribution

plot1

- Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X<3)\). + Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X\leq 3)\).

@@ -50,7 +50,7 @@

Uniform Distribution

- \(P(X<3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as + \(P(X\leq 3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations are less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate its mean: it's the center of the distribution because every outcome is of equal weight. For our dice example, we can think of it as

\[\mu = (1+2+3+4+5+6)/6 = 3.5\]

From b481cf1c10e048bafcb95129dc9cf443cc4e9835 Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:21:59 -0700 Subject: [PATCH 008/107] Updates SPY data source from Quandl to QC --- .../02 Confidence Interval.html | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html index 82a7170..f38faaf 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html @@ -8,13 +8,18 @@

Sample Error

import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
+
+qb = QuantBook()
+spy = qb.AddEquity("SPY").Symbol
+
+#get SPY data from August 2010 to the present
+start_date = datetime(2010, 8, 1, 0, 0, 0)
+end_date = qb.Time
+spy_table = qb.History(spy, start_date, end_date, Resolution.Daily)
+
+spy_total = spy_table[['open','close']]
 #calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
+spy_log_return = np.log(spy_total.close).diff().dropna()
 print 'Population mean:', np.mean(spy_log_return)
 [out]: Population mean: 0.000443353825615
 print 'Population standard deviation:',np.std(spy_log_return)

From 798365481ed6cbaef79960301363c0bacf0a5004 Mon Sep 17 00:00:00 2001
From: Rahul 
Date: Mon, 4 May 2020 10:28:53 -0700
Subject: [PATCH 009/107] Updates print syntax

---
 .../02 Confidence Interval.html                  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
index f38faaf..2d4404b 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -20,9 +20,9 @@ 
Sample Error

 spy_total = spy_table[['open','close']]
 #calculate log returns
 spy_log_return = np.log(spy_total.close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
+print('Population mean:', np.mean(spy_log_return))
 [out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
+print('Population standard deviation:',np.std(spy_log_return))
 [out]: Population standard deviation: 0.00784267293815
@@ -34,13 +34,13 @@

Sample Error

-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+print('10 days sample returns:', np.mean(spy_log_return.tail(10)))
 [out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+print('10 days sample standard deviation:', np.std(spy_log_return.tail(10)))
 [out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+print('1000 days sample returns:', np.mean(spy_log_return.tail(1000)))
 [out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+print('1000 days sample standard deviation:', np.std(spy_log_return.tail(1000)))
 [out]: 1000 days sample standard deviation: 0.00766589174299
@@ -75,9 +75,9 @@

Confidence Interval

bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000))))) upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000))))) #print the outcomes -print '10 days 95% confidence inverval:', (bottom_1,upper_1) +print('10 days 95% confidence inverval:', (bottom_1,upper_1)) [out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079) -print '1000 days 95% confidence inverval:', (bottom_2,upper_2) +print('1000 days 95% confidence inverval:', (bottom_2,upper_2)) [out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957) From f2152f9f12c76f5fb536ccddafac8c5fc990df6c Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:34:56 -0700 Subject: [PATCH 010/107] Update to print syntax --- .../03 Hypothesis testing.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html index acd6912..e25e886 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html @@ -9,7 +9,7 @@ mean_10 = np.mean(spy_log_return.tail(10)) std_10 = np.std(spy_log_return.tail(10)) s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000']) -print s +print(s) [out]: mean_10 0.000845 std_10 0.003136 @@ -25,7 +25,7 @@ 
bottom = 0 - 1.64*std_1000/np.sqrt(1000)
 upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00039756352254768874, 0.00039756352254768874)
@@ -37,7 +37,7 @@ 
bottom = 0 - 1.96*std_1000/np.sqrt(1000)
 upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00047513689280089639, 0.00047513689280089639)
@@ -55,7 +55,7 @@

-
print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+
print(np.sqrt(1000)*(mean_1000 - 0)/std_1000)
 [out]: 1.90922032428
@@ -66,7 +66,7 @@ 
import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
+print((1 - st.norm.cdf(1.9488)))
 [out]: 0.025659656888
@@ -82,10 +82,10 @@ 
mean_1200 = np.mean(spy_log_return.tail(1200))
 std_1200 = np.std(spy_log_return.tail(1200))
 z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
+print('z-score = ',z_score)
 [out]: z-score =  2.19793023185
 p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
+print('p_value = ',p_value)
 [out]: p_value =  0.0139770390655
From d5513f57c7dfdebcf6fd97709b501911ae18ab26 Mon Sep 17 00:00:00 2001 From: Rahul Date: Mon, 4 May 2020 10:38:06 -0700 Subject: [PATCH 011/107] Removed duplicate of tutorial --- .../04 Summary.html | 213 ------------------ 1 file changed, 213 deletions(-) diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html index 3cc20c3..724eb85 100755 --- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html +++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html @@ -1,216 +1,3 @@ -

Introduction

-

- In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a sample to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis. -

-

- As we mentioned before, both mean and standard deviation are point estimation, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is. -

- -

Confidence Interval

-

Sample Error

-

- Let's use the daily return on S&P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000? -

- -
- -
import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
-#calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
-[out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
-[out]: Population standard deviation: 0.00784267293815
-
-
- -

- Now let's check the recent 10 days sample and recent 1000 days sample: -

- -
- -
-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
-[out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
-[out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
-[out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
-[out]: 1000 days sample standard deviation: 0.00766589174299
-
-
-

- As we expected, the two samples has different means and variances. -

- -

Confidence Interval

-

- In order to estimate the range of population mean, we define standard error of the mean as follows: -

-\[SE = \frac{\sigma}{\sqrt{n}}\] -

- Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size. -

-

- Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as: -

-\[(\mu - 1.96*SE, \mu + 1.96*SE)\] -

- Where \(\mu\) is the sample mean and SE is the standard error. -

-

- This interval is called confidence interval. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above: -

-
- -
-#apply the formula above to calculate confidence interval
-bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-#print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
-[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
-[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
-
-
- -

- As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean! -

- -

Confidence Interval of Normal Distribution

-

- Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words: -

-\[\%90 upperabnd = \mu + 1.64*SE\] -\[\%90 lowerband = \mu + 1.64*SE\] -

- The same for other confidence intervals. It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically: -

-\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\] -\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\] -\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\] -

- This can also be remembered by using the chart: -

-empirical rule -

Central Limit Theory

-

- As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is: -

-\[(\mu - 1.96*SE, \mu + 1.96*SE)\] -

- Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution? The answer is yes. This assumption is supported by central limit theorem. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed. This is the foundation of population mean confidence interval estimation. -

- -

Hypothesis testing

-

- Now we can talk about hypothesis testing. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample. Let's start from observing our sample: -

- -
- -
mean_1000 = np.mean(spy_log_return.tail(1000))
-std_1000 = np.std(spy_log_return.tail(1000))
-mean_10 = np.mean(spy_log_return.tail(10))
-std_10 = np.std(spy_log_return.tail(10))
-s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
-
-[out]: mean_10      0.000845
-       std_10       0.003136
-       mean_1000    0.000463
-       std_1000     0.007666
-
-
-

- We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be: -

- -
- -
bottom = 0 - 1.64*std_1000/np.sqrt(1000)
-upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00039756352254768874, 0.00039756352254768874)
-
-
-

- Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we rejected the hypothesis that the daily return on S&P500 from aug 2010 is zero. Can we claim that with 95% confidence level? -

- -
- -
bottom = 0 - 1.96*std_1000/np.sqrt(1000)
-upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00047513689280089639, 0.00047513689280089639)
-
-
-

- This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level. We have actually already finished a hypothesis testing above! In general, we have null hypothesis \(H_0\) and alternative hypothesis. They are usually in the following forms: -

-\[H_0:\bar{\mu} = 0\] -\[H_0:\bar{\mu} \neq 0\] -

- If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis. Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or Z-score. Z-score is defined as: -

-\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\] -

- Let's calculate the Z score from our sample: -

-
- -
print np.sqrt(1000)*(mean_1000 - 0)/std_1000
-[out]: 1.90922032428
-
-
-

- We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis. One reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python: -

- -
- -
import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
-[out]: 0.025659656888
-
-
-

- It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called p-value. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called two-tail test. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart: -

-confidence interval -

- If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations: -

-
- -
mean_1200 = np.mean(spy_log_return.tail(1200))
-std_1200 = np.std(spy_log_return.tail(1200))
-z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
-[out]: z-score =  2.19793023185
-p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
-[out]: p_value =  0.0139770390655
-
-
-

- Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up. -

-

Summary

In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.

From 72b385ec06e54ce397a76ddfd156fb6114e64682 Mon Sep 17 00:00:00 2001 From: Weiqi Zhang Date: Mon, 4 May 2020 23:42:12 -0700 Subject: [PATCH 012/107] Update 05 Options Moneyness.html Fix typo. --- .../01 General Features of Options/05 Options Moneyness.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html index 5f7a43a..ae5cbc4 100755 --- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html +++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html @@ -26,5 +26,5 @@ But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.

- Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration. + Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.

From 02ca9566222cde5c4d25f3bd333bdc79661525ce Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:23:50 -0600 Subject: [PATCH 013/107] Add residual momentum strategy writeup --- .../01 Introduction.html | 3 ++ .../136 Residual Momentum/02 Method.html | 31 +++++++++++++++++++ .../136 Residual Momentum/03 Algorithm.html | 6 ++++ .../136 Residual Momentum/04 References.html | 8 +++++ 4 files changed, 48 insertions(+) create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html new file mode 100644 index 0000000..28518a1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html @@ -0,0 +1,3 @@ +

+ Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. +

\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html new file mode 100644 index 0000000..5b927d1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -0,0 +1,31 @@ +

Importing Custom Data

+

+ The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the Fama-French data source URL from our Tutorials repo. We save the loaded data into a DataFrame in the alpha model. +

+ +

Universe Selection

+

+ In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices. +

+ +

Calculate Residual Momentum Score

+

+ During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable. +

+ +\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] + +\begin{p} + where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. +\end{p} + +\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] + +

+ If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance. +

+ +

Rebalancing

+

+ We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance. +

\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html new file mode 100644 index 0000000..90f2cf2 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html new file mode 100644 index 0000000..bd3b043 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/04 References.html @@ -0,0 +1,8 @@ +
    +
  1. + Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009) +
    2. +
  2. + Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017) +
    3. +
\ No newline at end of file From 66a5e71f586cd9cf6d5ddfadd8bbf5263c0b00c8 Mon Sep 17 00:00:00 2001 From: Jared Date: Fri, 15 May 2020 10:35:20 -0700 Subject: [PATCH 014/107] Update 05 Options Moneyness.html --- .../01 General Features of Options/05 Options Moneyness.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html index ae5cbc4..a089ece 100755 --- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html +++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html @@ -26,5 +26,5 @@ But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.

- Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration. + Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=-$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.

From dac2cfe5f606a9f82e1d7ec0f790479deb8aafa1 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:23:50 -0600 Subject: [PATCH 015/107] Add residual momentum strategy writeup --- .../01 Introduction.html | 3 ++ .../136 Residual Momentum/02 Method.html | 31 +++++++++++++++++++ .../136 Residual Momentum/03 Algorithm.html | 6 ++++ .../136 Residual Momentum/04 References.html | 8 +++++ 4 files changed, 48 insertions(+) create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html new file mode 100644 index 0000000..28518a1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html @@ -0,0 +1,3 @@ +

+ Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling. +

\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html new file mode 100644 index 0000000..5b927d1 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/02 Method.html @@ -0,0 +1,31 @@ +

Importing Custom Data

+

+ The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the Fama-French data source URL from our Tutorials repo. We save the loaded data into a DataFrame in the alpha model. +

+ +

Universe Selection

+

+ In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices. +

+ +

Calculate Residual Momentum Score

+

+ During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable. +

+ +\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \] + +\begin{p} + where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score. +\end{p} + +\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \] + +

+ If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance. +

+ +

Rebalancing

+

+ We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance. +

\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html new file mode 100644 index 0000000..90f2cf2 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
\ No newline at end of file diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html new file mode 100644 index 0000000..bd3b043 --- /dev/null +++ b/04 Strategy Library/136 Residual Momentum/04 References.html @@ -0,0 +1,8 @@ +
    +
  1. + Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009) +
    2. +
  2. + Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017) +
    3. +
\ No newline at end of file From 642a1e337b988ae6bff49802f84ed774986d01f7 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 15 May 2020 11:41:30 -0600 Subject: [PATCH 016/107] Add strategy to quantpedia json and strategy library files --- .../00 Strategy Library/01 Strategy Library.php | 10 ++++++++++ quantpedia.json | 1 + 2 files changed, 11 insertions(+) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5909ba0..6470de6 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -91,6 +91,16 @@ 'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.', 'tags'=>'Beginner,Momentum,ETFs' + ], + [ + 'name' => 'Residual Momentum', + 'link' => 'strategy-library/residual-momentum', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/136' + ], + 'description' => 'Constructs a long/short portfolio based on trailing residual momentum normalized by its standard deviation', + 'tags'=>'Intermediate,Equities,Momentum' + ], [ 'name' => 'Sector Momentum', diff --git a/quantpedia.json b/quantpedia.json index 13a6d8d..ffdd5f6 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -40,6 +40,7 @@ 113: "9b1291a5f08dcc07df86363e46144084", 114: "7a404f43e9191ac5bce9a9446572cb98", 125: "1f9f254c932d8e7ae85e803affd52a7b", + 136: "10e626ccfe29e78f0eaead76158e788c", 152: "b35034509d95c173fed1c0adcecc5be0", 155: "886a9f73bc29142f14fcb2c2d01404a9", 162: "5761c1817a9dcb805be487f9e32adfa1", From 6fe5dc6fd34f3068cb82c7433af8233cc5ac8a42 Mon Sep 17 00:00:00 2001 From: dhantisha <44590120+dhantisha@users.noreply.github.com> Date: Sat, 23 May 2020 08:14:52 +0200 Subject: [PATCH 017/107] added var ticket = --- .../03 Tracking and Managing Orders/01 Overview.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html index ca80763..445f481 100755 --- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html +++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html @@ -16,7 +16,7 @@ Market Order -MarketOrder("SPY", 100); +var ticket = MarketOrder("SPY", 100); Limit Order From 105b688a4d46a16b46c25485b0121fe750114376 Mon Sep 17 00:00:00 2001 From: metricquant <44590120+metricquant@users.noreply.github.com> Date: Sat, 23 May 2020 22:29:07 +0200 Subject: [PATCH 018/107] removed reference to a C# example The example includes both C# and Python code, yet the text refers to only C#. --- .../03 Tracking and Managing Orders/02 Updating Orders.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html index a923569..668d4a1 100755 --- a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html +++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html @@ -1,5 +1,5 @@

-Once you have an order ticket you can use it to get order fields. In C# it looks like: +Once you have an order ticket you can use it to get order fields. 

var currentStopPrice = _ticket.Get(OrderField.StopPrice);

From 0afa0cefd413c0f27187685e5399277994fceba0 Mon Sep 17 00:00:00 2001
From: Jared 
Date: Sun, 24 May 2020 07:47:18 -0700
Subject: [PATCH 019/107] Update 01 Overview.html

---
 .../03 Tracking and Managing Orders/01 Overview.html | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
index 445f481..9c9b242 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -16,27 +16,27 @@
 
 
 Market Order
-var ticket = MarketOrder("SPY", 100);
+var ticket = MarketOrder("SPY", 100);self.ticket = self.MarketOrder("SPY", 100)
 
 
 Limit Order
-var ticket = LimitOrder("SPY", 100, 100.10m);
+var ticket = LimitOrder("SPY", 100, 100.10m);self.ticket = self.LimitOrder("SPY", 100, 100.1)
 
 
 Stop Market Order
-var ticket = StopMarketOrder("SPY", 100, 100.10m);
+var ticket = StopMarketOrder("SPY", 100, 100.10m);self.ticket = self.StopMarketOrder("SPY", 100, 100.1)
 
 
 Stop Limit Order
-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)
 
 
 Market On Open Order
-var ticket = MarketOnOpen("SPY", 100);
+var ticket = MarketOnOpen("SPY", 100);self.ticket = self.MarketOnOpen("SPY", 100)
 
 
 Market On Close Order
-var ticket = MarketOnClose("SPY", 100);
+var ticket = MarketOnClose("SPY", 100);self.ticket = self.MarketOnClose("SPY", 100)
 
 
 

From cbab941fe0af715e9596e33b4ae28779c04b0199 Mon Sep 17 00:00:00 2001
From: Jared 
Date: Sun, 24 May 2020 08:19:13 -0700
Subject: [PATCH 020/107] Update 01 Introduction.html

---
 .../136 Residual Momentum/01 Introduction.html             | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html
index 28518a1..57d67e1 100644
--- a/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
+++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
@@ -1,3 +1,6 @@
 

-  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
-

\ No newline at end of file
+  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the 
+  residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic 
+  Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. 
+  This strategy is claimed to be more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
+


From 87290fff62a1af50edee0ed334a91ef12e9940a1 Mon Sep 17 00:00:00 2001
From: David Jacobson 
Date: Tue, 26 May 2020 04:25:57 -0400
Subject: [PATCH 021/107] Update 04 Select Contracts.html

Fix documentation to be consistent about 0 1 being call / put
---
 .../02 QuantConnect Options API/04 Select Contracts.html      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index cbf5dbc..54084fb 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -42,8 +42,8 @@
 
 x.Right
 Get the right being purchased
-x.Right ＝ 1  call option[right to buy]
-x.Right ＝ 0  put option[right to sell]
+x.Right ＝ 0  call option[right to buy]
+x.Right ＝ 1  put option[right to sell]
 
 
 x.UnderlyingLastPrice

From a6514b4ea1285e994f88aa7d31309f3aa55bd3d2 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Tue, 26 May 2020 15:18:27 -0600
Subject: [PATCH 022/107] Replace residual momentum strategy backtest with code
 reviewed one

---
 04 Strategy Library/136 Residual Momentum/02 Method.html    | 6 +++---
 04 Strategy Library/136 Residual Momentum/03 Algorithm.html | 2 +-
 quantpedia.json                                             | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html
index 5b927d1..7ed4ada 100644
--- a/04 Strategy Library/136 Residual Momentum/02 Method.html	
+++ b/04 Strategy Library/136 Residual Momentum/02 Method.html	
@@ -15,9 +15,9 @@ 
Calculate Residual Momentum Score

 
 \[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \]
 
-\begin{p}
-  where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
-\end{p}
+

+  Where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
+

 
 \[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \]
 
diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html
index 90f2cf2..a989254 100644
--- a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 

     

     

-        
+        
     

 

\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index ffdd5f6..87ee747 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -40,7 +40,7 @@
      113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",
      125: "1f9f254c932d8e7ae85e803affd52a7b",
-     136: "10e626ccfe29e78f0eaead76158e788c",
+     136: "d8932a9697f7e98e9a00761d37e627dd",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      162: "5761c1817a9dcb805be487f9e32adfa1",

From 96fd9d1760026754d7c3ab397f581bafc0a5c7cb Mon Sep 17 00:00:00 2001
From: Jared Broad 
Date: Sun, 7 Jun 2020 08:27:04 -0700
Subject: [PATCH 023/107] Delete visual studio tutorial

---
 .../02 Backtesting from Visual Studio/00.html |  1 -
 .../01 Introduction.html                      | 13 ------
 .../02 Installation.html                      | 33 -------------
 .../03 Plugin Features.html                   | 46 -------------------
 .../04 Backtest Tool Window.html              | 31 -------------
 .../05 Troubleshooting.html                   |  7 ---
 6 files changed, 131 deletions(-)
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/00.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html

diff --git a/03 Open Source/02 Backtesting from Visual Studio/00.html b/03 Open Source/02 Backtesting from Visual Studio/00.html
deleted file mode 100644
index 4b86dc6..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/00.html	
+++ /dev/null
@@ -1 +0,0 @@
-
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
deleted file mode 100644
index 4005af9..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-
-

-The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports Visual Studio 2015, 2017 and 2019.
-

-
-

-   You can download the plugin here: 
-     QuantConnect.VisualStudioPlugin_20190711.vsix *

-* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.
-

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
deleted file mode 100644
index e97282f..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ /dev/null
@@ -1,33 +0,0 @@
-
-

-To install the plugin simply execute ("double click") the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
-
 
-
-
Build

-
    
-    
  1. Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.
    2. 
-    
  2. Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.
    3. 
-    
  3. Verify in the Visual Studio ‘Output’ panel the compilation succeeded with no errors.
    4. 
-

-
-
Installation

-

-If you have intstalled a previous version of the plugin you first need to remove this first. You do this from the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and click uninstall. Once uninstalled; remember to restart Visual Studio.
-

-

-After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. If you built sucessfully you should be able to execute ‘QuantConnect.VisualStudioPlugin.vsix’.
-

-
-
Setting Up Your Plugin

-
    
-    
  1. Login to QuantConnect and navigate to your My Account page.
    2. 
-    
  2. In the API panel, you will find your a ‘unique access token’ and your ‘user id’ which are required to connect to the API. Copy these to your clipboard.
    3. 
-    
  3. From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your API credentials and click Login.
    4. 
-

-

-
-

-
-

-Manually logging in is only required the first time. After the first login, the plugin will automatically log in using previously saved credentials.
-

\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
deleted file mode 100644
index 8a4607c..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html	
+++ /dev/null
@@ -1,46 +0,0 @@
-

-The Visual Studio plugin can currently save files to a project, compile the project, and backtest it in the cloud. Through the accompanying "tool window" it can also rename, open or add a note to a backtest, and create a new project.
-

-
-
Saving Files to QuantConnect

-

-Save files from your local project to a QuantConnect project. You can save many files at a time.
-

-
    
- 
  1. Select the desired files with your mouse from the Solution Explorer tool window.
    2. 
- 
  2. Right-click selection → ‘Save to QuantConnect’.
    3. 
- 
  3. Select target project name:
-   
    
-
    4. 
-
  4. 
-  Result:
-   
      
-    
    • If the operation succeeds you will be informed through the VS Status Bar.
      • 
-    
    • If any error occurs you will be informed through VS Status Bar and with a popup box.
      • 
-   
    
-   
    
-
    5. 
-

-
-
Send for Backtesting

-

-This feature allows you to upload one or more files to a target project, compile it and backtest it in the QuantConnect cloud.
-

-
    
- 
  1. Select the desired files you'd like to backtest.
    2. 
- 
  2. Right-click → ‘Send for Backtesting’
    3. 
- 
  3. Select the target project with the project list view.
    4. 
- 
  4. Result:
- 
      
-    
    • If the operation succeeds you will be informed through the VS Status Bar and the target QC project will be opened using the default browser pointing at your new backtest.
      • 
-    
    • If any error occurs you will be informed through VS Status Bar and with a popup box.
      • 
-   
    
-   
    
-
    
-If the error is due to a compilation error, the pop-up box will contain the first compilation error.
-

    
-
    
-
    
-If the error is due to a run-time issue during the backtesting process, the popup box will contain error details and the corresponding stack trace.
-
-
    5. 
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
deleted file mode 100644
index 8066c56..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html	
+++ /dev/null
@@ -1,31 +0,0 @@
-
    
-The backtesting tool window utility allows you to monitor and control ongoing backtests, along with editing various properties of existing completed backtests.
-
    
-
    
-
-
    
-
    
-If you select a project ‘BuyTheDip_007’ using the tool windows combo box and launch a backtest using ‘Send For Backtesting’ for ‘BuyTheDip_007’ project, it will display the backtests progress in the tool window.
-
    
-
-
    How do I get the Backtest Window?
    
-
    
-From the Visual Studio IDE go to ‘View’ menu → ‘Other Windows’ → ‘QuantConnect’. If there are previous valid credentials, the tool window will auto login when open or when the user performs an action.
-
    
-
-
    Available Backtest Window Features
    
-
      
- 
    •  Open Backtest: Open backtest viewer on QuantConnect.
      • 
- 
    •  Refresh/Reload Project List
      •  
- 
    •  Delete Backtest
      • 
- 
    •  Edit Backtest: Rename or add notes to backtest results.
-  
      
-
-
      
- 
      • 
- 
    •  Create a New Project
-  
      
-
-
      
- 
      • 
-
    
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html
deleted file mode 100644
index 9312fcf..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html	
+++ /dev/null
@@ -1,7 +0,0 @@
-
    
-VisualStudio plugin can write log data to the VisualStudio activity log, but only if VisualStudio is started with the /log parameter switch. To debug the QuantConnect plugin start VisualStudio with the following command:
-
    
-
    devenv /log <path-to-log>
    
-
    
-See Visual Studio Documentation for more information.
-
    
\ No newline at end of file

From 58429f39bf70e37c5815e195397fd7239b5f6379 Mon Sep 17 00:00:00 2001
From: Jared 
Date: Sun, 7 Jun 2020 08:33:38 -0700
Subject: [PATCH 024/107] Update Introduction.html

---
 03 Open Source/00 Introduction/Introduction.html | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 744480f..dff5ed2 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -14,14 +14,6 @@ 
     100 Debugging Python
- -
-

101 Backtesting from Visual Studio

-

Visual Studio plugin integrated with the QuantConnect API.

- -
- -

102 Using the API File Provider

@@ -52,4 +44,4 @@

301 Brokerage Development Guide

Guide to implementing your own brokerage in LEAN.

- \ No newline at end of file + From 0afb0eaab71b9c54a55fcc6f1e7d76d1a5c02c0e Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Fri, 12 Jun 2020 09:38:50 -0700 Subject: [PATCH 025/107] Update image protocol --- 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html index 93c5b88..a0d48d6 100644 --- a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html +++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html @@ -22,4 +22,4 @@

- \ No newline at end of file + From 1d76f6ce967fd92e68435b9b4374e33a84962aac Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Fri, 12 Jun 2020 09:39:27 -0700 Subject: [PATCH 026/107] Update Gif protocol. --- .../05 Method 3 - VisualStudio Debugger.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html index 6f744e0..7767d5e 100644 --- a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html +++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html @@ -22,4 +22,4 @@

Prerequisites

- \ No newline at end of file + From 291389c69ca15cc7094b3c84476731a0232dde7f Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 15 Jun 2020 18:37:05 -0600 Subject: [PATCH 027/107] Adds strategy 85 - Momentum in Mutual Fund Returns --- .../01 Strategy Library.php | 9 + .../01 Introduction.html | 8 + .../02 Method.html | 179 ++++++++++++++++++ .../03 Algorithm.html | 6 + .../04 References.html | 5 + quantpedia.json | 1 + 6 files changed, 208 insertions(+) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6470de6..0f120a9 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -337,6 +337,15 @@ 'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.', 'tags'=>'Intermediate,Long Short,Equities' ], + [ + 'name' => 'Momentum in Mutual Fund Returns', + 'link' => 'strategy-library/momentum-in-mutual-fund-returns', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85' + ], + 'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.', + 'tags'=>'Intermediate,Long Short,Equities' + ], [ 'name' => 'Momentum and Style Rotation Effect', 'link' => 'strategy-library/momentum-and-style-rotation-effect', diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html new file mode 100644 index 0000000..3b57a7e --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -0,0 +1,8 @@ +

+ Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide + significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the + persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds + with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in + individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use + their respective share price as a proxy for fund performance and NAV. +

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html new file mode 100644 index 0000000..119b3e7 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -0,0 +1,179 @@ +

Universe Selection

+

+ In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. +

+
+
+def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+
+
+ +

+ In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry. +

+
+
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+
+
+ +

Alpha Construction

+

+ When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to + long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to + calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs. +

+
+
+def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+
+
+ +

+ For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers + a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator. +

+
+
+def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
+
+ +

Alpha Update

+

+ On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model. +

+
+
+def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+
+
+ +

Alpha Ranking

+

+ We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with + the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. + The symbols with a larger final sum have a greater index in the list we return. +

+
+
+@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+
+
+ +

+ We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and + then computing the respective values. +

+
+
+@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+
+
+ +

Alpha Insights

+

+ We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the + universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest + rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months. +

+
+
+def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+
+
+ +

Portfolio Construction

+

+ We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of + insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol + with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate + the net direction of the symbols with the following helper method. +

+
+
+def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+
+
+ + diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html new file mode 100644 index 0000000..646988c --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
\ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html new file mode 100644 index 0000000..29e36f1 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html @@ -0,0 +1,5 @@ +
    +
  1. + Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011. +
    2. +
\ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index 87ee747..255ed4f 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,6 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", + 85: "cdcfb760e59da292a56e08c04a219179", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From a4ead7960f6c38dcaa90cba9548822fbd7621125 Mon Sep 17 00:00:00 2001 From: hsm207 Date: Sun, 28 Jun 2020 15:44:12 +0800 Subject: [PATCH 028/107] Fix typo --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6470de6..e5ab9e0 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -50,7 +50,7 @@ 'tags'=>'Expert,Pairs Trading,Equities' ], [ - 'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market', + 'name' => 'The Momentum Strategy Based on the Low Frequency Component of Forex Market', 'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market', 'description' => 'Applies high frequency filter to the momentum strategy.', 'tags'=>'Intermediate,Momentum,Forex' From 0316975d2d33489b9da8852a23265032a787b52f Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 13 Jul 2020 09:31:45 -0600 Subject: [PATCH 029/107] Add strategy library workflow --- README.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/README.md b/README.md index da878ac..5e2fd23 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,22 @@ The mailing list for the project can be found on [Google Groups][6] Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7]. +## Strategy Library Development Workflow ## +To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps: +1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process). +2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png). +3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py). +4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)). +5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project. +6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples). +7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)). +8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)): + - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload). + - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023. + - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json). + - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID). +9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)). + [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer" [2]: https://www.quantconnect.com/lean/docs "Lean Documentation" [3]: https://github.com/QuantConnect/Lean/archive/master.zip From a927c4c081002b6031009e6d8edbc4a03f7a2a79 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 15 Jun 2020 18:37:05 -0600 Subject: [PATCH 030/107] Adds strategy 85 - Momentum in Mutual Fund Returns --- .../01 Strategy Library.php | 9 + .../01 Introduction.html | 8 + .../02 Method.html | 179 ++++++++++++++++++ .../03 Algorithm.html | 6 + .../04 References.html | 5 + quantpedia.json | 1 + 6 files changed, 208 insertions(+) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index e5ab9e0..28ae3dc 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -337,6 +337,15 @@ 'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.', 'tags'=>'Intermediate,Long Short,Equities' ], + [ + 'name' => 'Momentum in Mutual Fund Returns', + 'link' => 'strategy-library/momentum-in-mutual-fund-returns', + 'sources' => [ + 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85' + ], + 'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.', + 'tags'=>'Intermediate,Long Short,Equities' + ], [ 'name' => 'Momentum and Style Rotation Effect', 'link' => 'strategy-library/momentum-and-style-rotation-effect', diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html new file mode 100644 index 0000000..3b57a7e --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -0,0 +1,8 @@ +

+ Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide + significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the + persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds + with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in + individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use + their respective share price as a proxy for fund performance and NAV. +

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html new file mode 100644 index 0000000..119b3e7 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -0,0 +1,179 @@ +

Universe Selection

+

+ In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. +

+
+
+def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+
+
+ +

+ In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry. +

+
+
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+
+
+ +

Alpha Construction

+

+ When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to + long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to + calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs. +

+
+
+def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+
+
+ +

+ For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers + a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator. +

+
+
+def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
+
+ +

Alpha Update

+

+ On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model. +

+
+
+def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+
+
+ +

Alpha Ranking

+

+ We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with + the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. + The symbols with a larger final sum have a greater index in the list we return. +

+
+
+@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+
+
+ +

+ We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and + then computing the respective values. +

+
+
+@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+
+
+ +

Alpha Insights

+

+ We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the + universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest + rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months. +

+
+
+def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+
+
+ +

Portfolio Construction

+

+ We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of + insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol + with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate + the net direction of the symbols with the following helper method. +

+
+
+def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+
+
+ + diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html new file mode 100644 index 0000000..646988c --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
\ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html new file mode 100644 index 0000000..29e36f1 --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html @@ -0,0 +1,5 @@ +
    +
  1. + Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011. +
    2. +
\ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index 87ee747..255ed4f 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,6 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", + 85: "cdcfb760e59da292a56e08c04a219179", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From bfc770811be63b9c81ef66222702a165fe907672 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 17 Jul 2020 13:42:10 -0600 Subject: [PATCH 031/107] define NAV, refactor code, fix lookback bug --- .../01 Introduction.html | 13 ++-- .../02 Method.html | 65 ++++++++++--------- .../03 Algorithm.html | 2 +- quantpedia.json | 2 +- 4 files changed, 45 insertions(+), 37 deletions(-) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html index 3b57a7e..ec8c6d8 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html @@ -1,8 +1,9 @@

- Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide - significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the - persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds - with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in - individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use - their respective share price as a proxy for fund performance and NAV. + Researchers have shown that the historical returns of a mutual fund and the nearness of its net asset value (NAV) to + a previous high can provide significant predictive power about the fund's future returns. In respect to the + historical returns, some have attributed the persistence to investor herding and macroeconomic variables. When it + comes to the NAV, some suggest the outperformance of funds with a NAV near its trailing high is a result of + anchoring bias in investors' psychology. As we do not have access to invest in individual mutual funds on the QC + platform or access to NAV metrics, in this tutorial, we trade asset management firms and use their respective share + price as a proxy for fund performance and NAV.

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html index 119b3e7..cd4436e 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -1,14 +1,14 @@

Universe Selection

- In coarse universe selection, we return symbols that have fundamental data and are traded in the US market. + In coarse universe selection, we return symbols that have fundamental data. 

 def SelectCoarse(self, algorithm, coarse):
     if self.month == algorithm.Time.month:
-        return Universe.Unchanged
-    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
-    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+            return Universe.Unchanged
+            
+    return [x.Symbol for x in coarse if x.HasFundamentalData]
@@ -19,8 +19,8 @@

Universe Selection

 def SelectFine(self, algorithm, fine):
     self.month = algorithm.Time.month
-    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
-    return [ x.Symbol for x in filtered[:self.fine_size] ]
+        
+    return [f.Symbol for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
@@ -34,14 +34,10 @@

Alpha Construction

 def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
     if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
-        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
-        algorithm.Quit()
-        return
-
+        algorithm.Quit(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        
     if pct_long_short <= 0 or pct_long_short > 50:
-        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
-        algorithm.Quit()
-        return
+        algorithm.Quit(f"Requirement violated: 0 < pct_long_short <= 50")
 
     self.roc_lookback_months = roc_lookback_months
     self.nearness_lookback_months = nearness_lookback_months
@@ -70,7 +66,8 @@ 
Alpha Construction

 
 
Alpha Update

 

-  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model.
+  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction 
+  model. We instruct the alpha model to emit insights on a monthly basis by adding the following guard to the Update method. 
 

 

 
@@ -81,34 +78,45 @@ 
Alpha Update

         return []
     self.month = time.month
 
-    return self.generate_insights(self.ranked_symbols, time)
+    ...
 

 

 
 
Alpha Ranking

 

-  We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with 
-  the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. 
-  The symbols with a larger final sum have a greater index in the list we return.
+  We only rank symbols that have enough history to fill the rate of change lookback window. Therefore, we define the 
+  IsReady method of the ROCAndNearness as
 

 

 
 @property
-def ranked_symbols(self):
-    ranking_df = pd.DataFrame()
+def IsReady(self):
+    return self.get_lookback(self.roc_lookback_months).shape[0] > 1
+

+

 
+

+  To rank the symbols, we start by filling a DataFrame with the rate of change and nearness to trailing high values for each 
+  symbol. When the DataFrame is full, we rank the symbols by both metrics and sum the ranks. The symbols with a larger final 
+  sum have a greater index in the `ranked_symbols` list.
+

+

+
+def Update(self, algorithm, data):
+    ...
+    ranking_df = pd.DataFrame()
     for symbol, symbol_data in self.symbol_data_by_symbol.items():
         if symbol_data.IsReady:
             row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
             ranking_df = ranking_df.append(row)
-
-    return ranking_df.rank().sum(axis=1).sort_values().index
+    ranked_symbols =  ranking_df.rank().sum(axis=1).sort_values().index
+   ...
 

 

 
 

-  We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and 
-  then computing the respective values.
+  Calculating the rate of change and nearness factors is done by slicing the historical data into the approriate lookback window 
+  size, and then computing the respective values.
 

 

 
@@ -129,14 +137,14 @@ 
Alpha Ranking

 
Alpha Insights

 

   We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the 
-  universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest 
-  rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
+  universe we long and short is customizable in the alpha model constructor. Here, we long the 25% of symbols with the highest 
+  rank, short the 25% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
 

 

 
-def generate_insights(self, ranked_symbols, time):
+def Update(self, algorithm, data):
+    ...
     insights = []
-
     num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
     if num_long_short > 0:
         hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
@@ -144,7 +152,6 @@ 
Alpha Insights

             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
         for symbol in ranked_symbols[:num_long_short]:
             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
-
     return insights
 

 

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
index 646988c..ade7d3b 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 

     

     

-        
+        
     

 

\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 255ed4f..cbf4970 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,7 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
-     85: "cdcfb760e59da292a56e08c04a219179",
+     85: "ba030edb022016f967a6296a556d717e",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From ccbf44f6741687e50985494f4a6baa3cb3d90ef0 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Wed, 29 Jul 2020 18:07:56 -0600
Subject: [PATCH 032/107] Adds intraday etf momentum strategy

---
 .../01 Strategy Library.php                   |  11 +-
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  19 +++
 .../1026 Intraday ETF Momentum/03 Method.html | 143 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Conclusion.html                        |  23 +++
 .../06 References.html                        |   6 +
 7 files changed, 214 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..30af0f7 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+    [
+        'name' => 'Intraday ETF Momentum',
+        'link' => 'strategy-library/intraday-etf-momentum',
+        'sources' => [
+            'NYU' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2440866'
+        ],
+        'description' => "A momentum strategy based on returns of the market open",
+        'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
new file mode 100644
index 0000000..c9aef2c
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html	
@@ -0,0 +1,7 @@
+

+  In this tutorial, we implement an intraday momentum strategy that trades some of the most actively traded ETFs. 
+  Specifically, we observe the return generated from the first half-hour of the trading day to predict the sign of 
+  the trading day's last half-hour return. Researchers have shown that this momentum pattern is statistically and 
+  economically significant, even after accounting for trading fees. The algorithm we design here is a recreation of 
+  the research completed by Gao, Han, Li, and Zhou (2017).
+

diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
new file mode 100644
index 0000000..c06af0a
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html	
@@ -0,0 +1,19 @@
+

+  News items are usually released before the opening bell. As it takes time for traders to digest and interpret the 
+  news, the first half-hour of trading typically has relatively higher levels of volume and volatility. Additionally, 
+  as traders attempt to mitigate overnight risk by unloading positions near the close, the last half-hour of trading 
+  also sees these higher levels of volume and volatility. These characteristics can be observed from the image below, 
+  which is reproducible in the attached research notebook.
+

+
+Tutorial1026-intraday-etf-momentum-1 
+
+

+  Bogousslavsky (2016) points out that some investors are late-informed or simply prefer to delay their trading until 
+  the market close. As a result, a positive correlation exists between the direction of the opening and closing 
+  periods. Gao et al (2017) find that when trading this momentum strategy, the average annual return over their 
+  sample period was 6.67% for SPY, 11.72% for IWM, and 24.22% for IYR. Equal-weighting these returns leads to a 
+  combined average annual return of 14.2%.
+

\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
new file mode 100644
index 0000000..29481ab
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html	
@@ -0,0 +1,143 @@
+
Universe Selection

+

+  We implement a manual universe selection model that supplies a subset of the proposed ETFs in the attached research 
+  paper. Gao et al (2017) select the following tickers: DIA, QQQ, IWM, EEM, FXI, EFA, VWO, XLF, IYR, and TLT. In an 
+  effort to increase the backtest performance, we narrow our universe to SPY, IWM, and IYR.
+

+

+
+tickers = ['SPY',  # S&P 500
+           'IWM',  # Russell 2000
+           'IYR'   # Real Estate ETF
+]
+symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+

+

+
+
Alpha Construction

+

+  The IntradayMomentumAlphaModel emits insights to take positions for the last `return_bar_count` minutes of the day 
+  in the direction of the return for the first `return_bar_count` minutes of the day. During construction, we create 
+  a dictionary to store IntradayMomentum data for each symbol, define a method to determine the sign of returns, and 
+  specify the value of `return_bar_count`. In this tutorial, we follow Gao et al (2017) in setting `return_bar_count`
+  to 30 by default.
+

+

+
+class IntradayMomentumAlphaModel(AlphaModel):
+    intraday_momentum_by_symbol = {}
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+
+    def __init__(self, algorithm, return_bar_count = 30):
+        self.return_bar_count = return_bar_count
+

+

+
+
Alpha Securities Management

+

+  When a new security is added to the universe, we create an IntradayMomentum object for it to store information 
+  needed to calculate morning returns. The management of the IntradayMomentum objects occurs in the alpha model's 
+  OnSecuritiesChanged method.
+

+

+
+def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.intraday_momentum_by_symbol[security.Symbol] = IntradayMomentum(security, algorithm)
+
+    for security in changes.RemovedSecurities:
+        self.intraday_momentum_by_symbol.pop(security.Symbol, None)
+

+

+
+

+  The definition of the IntradayMomentum class is shown below. We save a reference to the security's exchange so we 
+  can access the market hours of the exchange when generating insights.
+

+

+
+class IntradayMomentum:
+    def __init__(self, security, algorithm):
+        self.symbol = security.Symbol
+        self.exchange = security.Exchange
+
+        self.bars_seen_today = 0
+        self.yesterdays_close = algorithm.History(self.symbol, 1, Resolution.Daily).loc[self.symbol].close[0]
+        self.morning_return = 0
+

+

+
+
Alpha Update

+

+  With each call to the alpha model's Update method, we count the number of bars the algorithm has received for each 
+  symbol. If we've reached the end of the morning window, we calculate the morning return. If we are at the 
+  beginning of the close window, we emit an insight in the direction of the morning window's return. If we are at 
+  the end of the day, we save the closing price and reset the counter for the number of bars seen today.
+

+

+
+def Update(self, algorithm, slice):
+    insights = []
+
+    for symbol, intraday_momentum in self.intraday_momentum_by_symbol.items():
+        if slice.ContainsKey(symbol) and slice[symbol] is not None:
+            intraday_momentum.bars_seen_today += 1
+            
+            # End of the morning return
+            if intraday_momentum.bars_seen_today == self.return_bar_count:
+                intraday_momentum.morning_return = (slice[symbol].Close - intraday_momentum.yesterdays_close) / intraday_momentum.yesterdays_close
+            
+            ## Beginning of the close
+            next_close_time = intraday_momentum.exchange.Hours.GetNextMarketClose(slice.Time, False)
+            mins_to_close = int((next_close_time - slice.Time).total_seconds() / 60)
+            
+            if mins_to_close == self.return_bar_count + 1:
+                insight = Insight.Price(intraday_momentum.symbol, 
+                                        next_close_time, 
+                                        self.sign(intraday_momentum.morning_return))
+                insights.append(insight)
+                continue
+            
+            # End of the day
+            if not intraday_momentum.exchange.DateTimeIsOpen(slice.Time):
+                intraday_momentum.yesterdays_close = slice[symbol].Close
+                intraday_momentum.bars_seen_today = 0
+            
+    return insights
+

+

+
+
Trade Execution

+

+  The attached research paper holds positions for the last 30 minutes of the trading day, exiting at the market close.
+  In order to accomplish this, we create a custom execution model. The model defined below submits a market order for 
+  the entry while also submitting a market on close order in the same time step.
+

+

+
+class CloseOnCloseExecutionModel(ExecutionModel):
+    def __init__(self):
+        self.targetsCollection = PortfolioTargetCollection()
+        self.invested_symbols = []
+
+    def Execute(self, algorithm, targets):
+        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        self.targetsCollection.AddRange(targets)
+        if self.targetsCollection.Count > 0:
+            for target in self.targetsCollection.OrderByMarginImpact(algorithm):
+                # calculate remaining quantity to be ordered
+                quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
+                if quantity == 0:
+                    continue
+
+                algorithm.MarketOrder(target.Symbol, quantity)
+                algorithm.MarketOnCloseOrder(target.Symbol, -quantity)
+
+            self.targetsCollection.ClearFulfilled(algorithm)
+

+

+
+
+
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
new file mode 100644
index 0000000..c7304ae
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html	
@@ -0,0 +1,6 @@
+

+    

+    

+        
+    

+

\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
new file mode 100644
index 0000000..7623dda
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -0,0 +1,23 @@
+

+  We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period.
+  In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the 
+  inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their 
+  research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum 
+  pattern. These areas of future research include:
+

+
+
    
+  
  • 
+    Trading only on days with economic news events by utilizing the 
+    TradingEconomics data set. 
+    Gae et al (2017) suggest using the Michigan Consumer Sentiment Index, and news released on gross domestic 
+    product or the consumer price index.
+  
    • 
+  
  • Restricting trading to times of greater volatility or during financial crises.
    • 
+  
  • Incorporating a volume threshold the morning session must pass to signal a trade for the close.
    • 
+  
  • Increasing diversification by extending the universe to include ETFs from other sectors.
    • 
+  
  • 
+    Considering the return from multiple n-minute periods throughout the day to predict the return of the closing 
+    period.
+  
    • 
+

\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html
new file mode 100644
index 0000000..a3127a1
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html	
@@ -0,0 +1,6 @@
+
    
+  
  1. 
+    Gao, Lei and Han, Yufeng and Li, Sophia Zhengzi and Zhou, Guofu, Market Intraday Momentum (June 19, 2017). 
+    Online copy
+  
    2. 
+

\ No newline at end of file

From 75a44e5d0877fd4c51bcafa9b911aaab1727879f Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 3 Aug 2020 08:48:05 -0700
Subject: [PATCH 033/107] Added 279 temporal cnn strategy

---
 .../01 Abstract.html                          |   6 +
 .../02 Introduction.html                      |   8 +
 .../03 Method.html                            | 280 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 References.html                        |   7 +
 5 files changed, 307 insertions(+)
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
new file mode 100644
index 0000000..a6cf634
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html	
@@ -0,0 +1,6 @@
+

+    In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices.
+

+

+    Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading
+

\ No newline at end of file
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
new file mode 100644
index 0000000..1fa0f4e
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html	
@@ -0,0 +1,8 @@
+

+    Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements.
+    More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from
+    medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price
+    movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in
+    image classification, and now, researchers are applying CNNs to extract patterns, also known as features, from times-series
+    data to forecast future stock prices.
+

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
new file mode 100644
index 0000000..1a4765a
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html	
@@ -0,0 +1,280 @@
+
Overview

+

+    Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data
+    to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and
+    bet on the direction of the price movement based on our model prediction. We will walk through the code required
+    for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand.
+

+
+
Inputs/Outputs

+
+

+    Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model.
+    The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a
+    direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps).
+    The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will
+    form the labels for which our model will try to classify, thus we have a classification problem.
+

+
+
+
Neural Network Model Architecture

+
+

+    Now, we will need to build our Neural Network Architecture, which we will build using Keras,
+    a high-level Python Deep Learning API. To begin, we will need a few import statements:
+

+
+

+
+    import tensorflow as tf
+    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
+    from tensorflow.keras import Model
+    from tensorflow.keras import metrics
+    from tensorflow.keras.losses import CategoricalCrossentropy
+    from tensorflow.keras import utils
+    from sklearn.preprocessing import StandardScaler
+    import numpy as np
+    import math
+

+

+
+
+

+    We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input
+    variables (OHLCV), our input shape will be 15 x 5.
+

+
+

+
+    inputs = Input(shape=(15, 5))
+

+

+
+
+

+    We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as
+    the Neural Network's method of extracting patterns from the time-series data.
+

+
+

+
+    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
+

+

+
+
+

+
+    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
+    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
+    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
+
+    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
+    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
+    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
+

+

+
+
+

+    These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten
+    our layer.
+

+
+

+
+    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
+    flattened = Flatten()(combined)
+

+

+
+
+

+    Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down),
+    this layer will have three nodes.
+

+
+

+
+    outputs = Dense(3, activation='softmax')(flattened)
+

+

+
+

+    The resulting Neural Network Architecture is shown in the following:
+

+
+temporal cnn model architecture
+
+
+
Preparing the Data for Our Model

+
+

+    First, we need to define a class and a few variables:
+

+
+

+
+    input_vars = ['open', 'high', 'low', 'close', 'volume']
+
+    class Direction:
+        UP = 0
+        DOWN = 1
+        STATIONARY = 2
+
+    rolling_avg_window_size = 5
+
+    shift = -(rolling_avg_window_size-1)
+
+    stationary_threshold = .0001
+
+    scaler = StandardScaler()
+

+

+
+

+    input_vars define the variables we want to use to make our predictions. The class Direction
+    defines a few integers that we will label our data with (labels are needed for classification problems). The reason
+    we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on,
+    rolling_avg_window_size is the number of time steps used for the calculate the average of future closing prices,
+    described earlier in
+    Inputs/Outputs (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5).
+    The constant stationary_threshold defines the threshold for a change in price to be considered
+    stationary, and this change also described in Inputs/Outputs. The shift is the shift needed to align
+    the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces
+    manageable for our Neural Network model. The scaler object will be used later to scale our data.
+    The purpose of the variables will become clearer in use.
+

+    Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate
+    the percent change from the close at t, we use the following lines of code:
+

+
+

+
+    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
+    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
+

+

+
+

+    The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize
+    this is a more advanced resource).
+    Here, .shift(shift) aligns the five time step rolling average 'close_avg' column to the end of the last
+    time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input
+    and labeled data for our model much easier.
+

+
+

+    To label our data, we need to first define a function that we will use with the DataFrame's apply() method.
+    Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda.
+

+
+

+
+    def label_data(row):
+        if row['close_avg_change_pct'] > stationary_threshold:
+            return Direction.UP
+        elif row['close_avg_change_pct'] < -stationary_threshold:
+            return Direction.DOWN
+        else:
+            return Direction.STATIONARY
+

+

+
+

+    Now, we apply the above function to our DataFrame to get a column of labels:
+

+
+

+
+    df['movement_labels'] = df.apply(label_data, axis=1)
+

+

+
+

+    With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into
+    lists:
+

+
+

+
+    data = []
+    labels = []
+
+    for i in range(len(df)-self.n_tsteps+1+shift):
+        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
+        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
+        labels.append(label)
+
+    data = np.array(data)
+

+

+
+

+    Here, we iterate numerically through the DataFrame, with a carefully calculated value in our range()
+    function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because
+    Keras works best with numpy arrays.
+

+
+

+    Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the
+    range of values is normalized across the features.
+

+
+

+
+    dim1, dim2, dim3 = data.shape
+    data = data.reshape(dim1*dim2, dim3)
+    data = scaler.fit_transform(data)
+    data = data.reshape(dim1, dim2, dim3)
+

+

+
+

+    The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after,
+    we can return the data to the original shape with another reshaping.
+

+
+

+    Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of
+    1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following:
+

+
+

+
+    labels = utils.to_categorical(labels, num_classes=3)
+

+

+
+

+    Specifying num_classes to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary).
+

+
+

+    We have now finished the walk through of the difficult parts of the code.
+

+
+
Trading

+
+

+    After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be
+    found in Algorithm) we can
+    use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make
+    a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit
+    an Price Insight with direction InsightDirection.Up (resp. InsightDirection.Down). Since we are
+    betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights
+    in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight
+    with a timedelta with a random integer between one and five to constrain the number of insights we emit.
+

+
+
+
The Rest

+
+

+    We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the
+    necessary code to execute the strategy can be found in Algorithm.
+

+
+
+
+
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
new file mode 100644
index 0000000..9e98ce9
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html	
@@ -0,0 +1,6 @@
+

+    

+    

+        
+    

+

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html
new file mode 100644
index 0000000..dedbeb6
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html	
@@ -0,0 +1,7 @@
+
    
+  
  1. 
+      Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis:
+      "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019;
+      https://arxiv.org/pdf/1901.08280.pdf.
+  
    2. 
+

\ No newline at end of file

From a7c9fbec4ab53b8a8334d2acde9d6b87dec13848 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 3 Aug 2020 08:50:08 -0700
Subject: [PATCH 034/107] 279 temporal cnn update 00 SL

---
 .../00 Strategy Library/01 Strategy Library.php       | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..2efe9eb 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+	[
+        'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model',
+        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn',
+        'sources' => [
+            'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf'
+        ],
+        'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.",
+        'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning'
+    ]
 ];
 
 ?>

From cb872175e7804070859b0b63661189572f8ad161 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Mon, 3 Aug 2020 17:45:37 -0600
Subject: [PATCH 035/107] Fix missing div tags

---
 .../03 Method.html                                            | 3 ++-
 .../03 Algorithm.html                                         | 2 +-
 .../03 Algorithm.html                                         | 2 +-
 .../29 Term Structure Effect in Commodities/03 Algorithm.html | 4 ++--
 .../92 Price Earnings Anomaly/03 Algorithm.html               | 2 +-
 5 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index 3675eee..ffff064 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -82,4 +82,5 @@ 
Step 4: Rebalance Function:

 
     # Invest 100% in the each of the selected symbols
     for symbol in symbols:
-        self.SetHoldings(symbol, 1)

\ No newline at end of file
+        self.SetHoldings(symbol, 1)
+ \ No newline at end of file diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html index 635c7b2..ad5e9f8 100644 --- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html +++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html index cc3a665..b50f912 100644 --- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html +++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html index 1707718..ada8627 100644 --- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html +++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html @@ -1,6 +1,6 @@

Algorithm

-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
@@ -8,7 +8,7 @@
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html index dce77dd..35a5232 100644 --- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html +++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html @@ -1,4 +1,4 @@ -div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;"> +
From 53e3114c169f284aba74960ff1b61d9ad3837982 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 11 Aug 2020 17:03:22 -0600 Subject: [PATCH 036/107] Adds strategy 1028 - Ichimoku Clouds in the Energy Sector --- .../01 Strategy Library.php | 11 +- .../01 Abstract.html | 8 + .../02 Introduction.html | 35 ++++ .../03 Method.html | 150 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Benchmark Performance.html | 10 ++ ...06 Market & Competition Qualification.html | 13 ++ .../07 Conclusion.html | 21 +++ .../08 References.html | 6 + 9 files changed, 259 insertions(+), 1 deletion(-) create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index e5ab9e0..e4eb7df 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -606,7 +606,16 @@ ], 'description' => "A simple trend following strategy on commodities futures.", 'tags' => 'Momentum, Futures, Commodities' - ] + ], + [ + 'name' => 'Ichimoku Clouds in the Energy Sector', + 'link' => 'strategy-library/ichimoku-clouds-in-the-energy-sector', + 'sources' => [ + 'SSRN' => 'https://ssrn.com/abstract=3520582' + ], + 'description' => 'A techincal indicator crossover strategy trading the largest energy companies.', + 'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities' + ], ]; ?> diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html new file mode 100644 index 0000000..3dd4983 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html @@ -0,0 +1,8 @@ +

+ Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the + largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the + effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an + impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib + (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular + energy sector ETF. +

diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html new file mode 100644 index 0000000..c5efef1 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html @@ -0,0 +1,35 @@ +

+ A vast amount of research studies have been published which document mixed results when utilizing technical + analysis to forecast future prices of securities. The Ichimoku Cloud, one of the most widely-used technical + indicators in Japan, was first publicized by Goichi Hosoda. In 1996, Hidenobu Sasaki reworked the framework to + form the current charting analysis tool. This indicator is composed of 5 lines in a time series, each of which + are described mathematically in + online resources. +

+ +

+ Gurrib (2020) finds that applying a simple trading strategy using the time series of the Ichimoku Cloud can + increase the mean return of a basket containing the top energy stocks from 21.5% (buy-and-hold) to 194% over a 7 + year period. The components of the Ichimoku Cloud that Gurrib (2020) utilizes in this strategy are the Chikou + Span, Senkou Span A, and Senkou Span B. The Senkou Span lines form the top and bottom of the Ichimoku Cloud. The + strategy that we trade off of these lines is defined as follows: +

+ +
    +
  • Long when the Chikou line crosses the top of the cloud from below.
    • +
  • Short when the Chikou line crosses the bottom of the cloud from above.
    • +
+ +

+ For better understanding, here is a visualization of the price of XOM, it's Ichimoku Cloud time series, and the + resulting buy/sell signals. +

+ +Tutorial1028-ichimoku-cloud-1 + +

+ Note: all of the plots throughout this tutorial are reproducible in the attached research notebook, along with + some descriptive statistics for securities in the universe. +

\ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html new file mode 100644 index 0000000..8669ce3 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html @@ -0,0 +1,150 @@ +

Universe Selection

+

+ Gurrib (2020) selects a universe of the 10 largest-weighed constituents of the S&P Composite 1500 Energy Index + over the testing period. This inherently incorporates lookahead-bias into the study as the security weights are + sourced over the period the trading simulation occurs. Furthermore, since the publication of Gurrib (2020), some + of the securities have even been delisted. Thus, to eliminate lookahead-bias and avoid delistings, we implement a + universe selection model that provides the trading system with the 10 largest companies in the energy sector as of + the current date in the backtest. Since the largest companies change infrequently, we only refresh the universe on + a monthly basis. +

+ +
+
+def SelectCoarse(self, algorithm, coarse):
+    if algorithm.Time.month == self.month:
+        return Universe.Unchanged
+    return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    
+    energy_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy ]
+    sorted_by_market_cap = sorted(energy_stocks, key=lambda x: x.MarketCap, reverse=True)
+    return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+
+
+ + +

Alpha Construction

+

+ The IchimokuCloudCrossOverAlphaModel emits insights to hold a long position after the Chikou Span crosses over the + top of the cloud for a given security. Additionally, the strategy is made symmetrical by entering short positions + after the Chikou Span crosses below the bottom of the cloud. During construction of this alpha model, we simply + set up a dictionary to hold a SymbolData object for each symbol in the universe. +

+ +
+
+class IchimokuCloudCrossOverAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+
+
+ +

+ The SymbolData class constructor is shown below. We first set up two class variables, `previous_location` and + `direction`. The former enables the algorithm to signal when the Chikou Span crosses over the boundaries of the + Ichimoku Cloud. The latter is added to ensure we continue to emit daily insights in the proper direction. Inside + the `__init__` method is where we create the + IchimokuKinkoHyo indicator and warm + it up. +

+ +
+
+class SymbolData:
+    previous_location = None
+    direction = None
+    
+    def __init__(self, symbol, algorithm):
+        # Create Ichimoku indicator
+        self.ichimoku = IchimokuKinkoHyo()
+        
+        # Warm up indicator
+        history = algorithm.History(symbol, self.ichimoku.WarmUpPeriod + 1, Resolution.Daily).loc[symbol]
+        for idx, row in history.iterrows():
+            if self.ichimoku.IsReady:
+                self.previous_location = self.get_location()
+            
+            tradebar = TradeBar(idx, symbol, row.open, row.high, row.low, row.close, row.volume)
+            self.ichimoku.Update(tradebar)
+
+
+ + +

Determining the Indicator Location

+

+ We define the following helper method to return the location of the Chikou Span with respect to the cloud. The + alpha model utilizes this helper method to determine when the Chikou Span is exiting the Ichimoku Cloud. +

+ +
+
+def get_location(self):
+    chikou = self.ichimoku.Chikou.Current.Value
+    
+    senkou_span_a = self.ichimoku.SenkouA.Current.Value
+    senkou_span_b = self.ichimoku.SenkouB.Current.Value
+    cloud_top = max(senkou_span_a, senkou_span_b)
+    cloud_bottom = min(senkou_span_a, senkou_span_b)
+    
+    if chikou > cloud_top:
+        return 1    # Above cloud
+    if chikou < cloud_bottom:
+        return -1   # Below cloud
+        
+    return 0        # Inside cloud
+
+
+ + +

Alpha Update

+

+ As new TradeBars are provided to the + alpha model's Update method, we update the Ichimoku indicator of each symbol. We then emit insights for the + symbols that have their Chikou Span breaking out of their respective Ichimoku Cloud in a new direction. To + maintain positions while we wait for another crossover in the Ichimoku Cloud, we emit insights on a daily basis + with 1-day duration. +

+ +
+
+def Update(self, algorithm, data):
+    insights = []
+    
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if not data.ContainsKey(symbol) or data[symbol] is None:
+            continue
+        
+        # Update indicator with the latest TradeBar
+        symbol_data.ichimoku.Update(data[symbol])
+        
+        # Determine insight direction
+        current_location = symbol_data.get_location()
+        if symbol_data.previous_location is not None: # Indicator is ready
+            if symbol_data.previous_location != 1 and current_location == 1:
+                symbol_data.direction = InsightDirection.Up
+            if symbol_data.previous_location != -1 and current_location == -1:
+                symbol_data.direction = InsightDirection.Down
+        
+        symbol_data.previous_location = current_location
+        
+        # Emit insight
+        if symbol_data.direction:
+            insight = Insight.Price(symbol, timedelta(days=1), symbol_data.direction)
+            insights.append(insight)
+    
+    return insights
+
+
+ + +

Portfolio Construction & Trade Execution

+

+ Following the guidelines of Alpha Streams + and the Quant League competition, we + utilize the + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html new file mode 100644 index 0000000..487c85d --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
\ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html new file mode 100644 index 0000000..f3592aa --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html @@ -0,0 +1,10 @@ +

+ To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a + popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the + benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over + the backtest period. +

+ +Tutorial1028-ichimoku-cloud-2 \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html new file mode 100644 index 0000000..e63b0d3 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html @@ -0,0 +1,13 @@ +

+ Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

+ +
    +
  • Profitable
    • +
  • PSR >= 80%
    • +
  • Max drawdown duration <= 6 months
    • +
  • Insights contain the following properties: Symbol, Duration, Direction, and Weight
    • +
  • Minute or second data resolution
    • +
\ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html new file mode 100644 index 0000000..4631b74 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html @@ -0,0 +1,21 @@ +

+ While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash, + we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The + strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of + 0.92. To continue the development of this strategy, future areas of research include: +

+ +
    +
  • + Only trading when a bullish or bearish trend are confirmed. Gurrib (2020) provides conditions for evaluating + the trend. +
    • +
  • Adjusting the data resolution used within the indicator.
    • +
  • Considering fundamental or alternative data points before placing trades.
    • +
  • Adjusting the parameters of the Ichimoku indicator.
    • +
  • + Replacing the portfolio construction model with one that only allocates 10% of the capital base to each security. + This will violate the requirements of the Alpha Streams and Quant League competition, but it will reduce the + transaction costs and provide a more accurate equity curve of the underlying strategies performance. +
    • +
\ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html new file mode 100644 index 0000000..37c3e11 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html @@ -0,0 +1,6 @@ +
    +
  1. + Gurrib, Ikhlaas, Can the Leading Us Energy Stock Prices Be Predicted Using Ichimoku Clouds? (January 16, 2020). + Online Copy +
    2. +
\ No newline at end of file From 6142dbf3ae57c8d8500e5722e8ff4b84d1106e97 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 09:29:42 -0700 Subject: [PATCH 037/107] fixed 01 strategy library information --- .../00 Strategy Library/01 Strategy Library.php | 4 ++-- .../01 Abstract.html | 0 .../02 Introduction.html | 0 .../03 Method.html | 0 .../04 Algorithm.html | 0 .../05 References.html | 0 6 files changed, 2 insertions(+), 2 deletions(-) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/01 Abstract.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/02 Introduction.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/03 Method.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/04 Algorithm.html (100%) rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/05 References.html (100%) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 2efe9eb..1c02f03 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -608,8 +608,8 @@ 'tags' => 'Momentum, Futures, Commodities' ], [ - 'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model', - 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn', + 'name' => 'Forecasting Stock Prices using a Temporal CNN Model', + 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn-model', 'sources' => [ 'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf' ], diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html From 0328c543a7d0cf791ffa8d7e561ca875029ae620 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 09:35:47 -0700 Subject: [PATCH 038/107] Adds 292 Leveraged ETFS --- .../00 Strategy Library/01 Strategy Library.php | 9 +++++++++ .../01 Abstract.html | 3 +++ .../02 Introduction.html | 9 +++++++++ .../03 Method.html | 12 ++++++++++++ .../04 Algorithm.html | 6 ++++++ .../05 Results.html | 5 +++++ .../06 References.html | 7 +++++++ 7 files changed, 51 insertions(+) create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 2efe9eb..4ddbbdf 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -615,6 +615,15 @@ ], 'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.", 'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning' + ], + [ + 'name' => 'Leveraged ETFs with Systematic Risk Management', + 'link' => 'leveraged-etfs-with-systematic-risk-management', + 'sources' => [ + 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' + ], + 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500', + 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html new file mode 100644 index 0000000..6e0ee07 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html @@ -0,0 +1,3 @@ +

+ In this tutorial, we attempt to beat the returns of the S&P500 Index using leverage and systematic risk management. +

diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html new file mode 100644 index 0000000..0b1b370 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html @@ -0,0 +1,9 @@ +

+ When measuring the profitability of a strategy, it is usually not enough to be profitable, as it should also beat + the benchmark, for which the SP500 Index is most commonly used. If not, what would be the reason to not just invest + in a low-cost SP500 index instead (not accounting for risk)? However, what if we could beat the SP500 with the SP500? + This is what Gayed et al. proposed with their strategy of using Leveraged SP500 indices with Systematic Risk Management. + They propose that Moving Averages are a good method to assess volatility in the market, and they use it to manage risk, + which is especially important since the effects of price swings in a leveraged ETF are magnified due to the leverage. + With this method, we hope to outperform the SP500 while at the same time, attempt to reduce drawdown. +

diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html new file mode 100644 index 0000000..a43a70e --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html @@ -0,0 +1,12 @@ +

+ We develop hold/liquidate positions based on the 200-day Simple Moving Average (SMA) of our ETF, for which we use SSO, + a 2x leveraged SP500 index ETF. With 200 days instead of using fewer days, say 50, we reduce the number of trades per year, + thereby reducing transaction costs and the effects of slippage. Moving on, if the current price of SOO is above the 200-day SMA, + we hold SSO, and if SSO dips below our 200-day SMA, we sell liquidate our position and rotate our position into short-term + treasuries, which is done through SHY, an ETF that tracks 1-3 year U.S. Treasury Bonds. If we are holding SHY, and the + current price of SSO moves above the 200-day SMA, then we rotate back into SSO and liquidate SHY. +

+ + + + diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html new file mode 100644 index 0000000..3b0ead5 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html @@ -0,0 +1,6 @@ +
+
+
+ +
+
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html new file mode 100644 index 0000000..dfa86cf --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -0,0 +1,5 @@ +

+ We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years, + our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY + in final ROI. The algorithm produced a CAGR of 11.49% during the five years. +

\ No newline at end of file diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html new file mode 100644 index 0000000..c1ce885 --- /dev/null +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html @@ -0,0 +1,7 @@ +
    +
  1. + Gayed, Michael and Bilello, Charles, Leverage for the Long Run - A Systematic Approach to Managing Risk and + Magnifying Returns in Stocks (March 3, 2016). 2016 Charles H. Dow Award. + Online Copy. +
    2. +
\ No newline at end of file From ba096a3db4291a475c71e7356b4c4b0f24aff9f6 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 12:03:40 -0700 Subject: [PATCH 039/107] updated algorithm --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index 9e98ce9..ef8f26c 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
- +
From ac9c4113615c14c7feb66311d29081f9cdbf3b47 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 13:14:33 -0700 Subject: [PATCH 040/107] removed feature-292 from PR --- .../01 Strategy Library.php | 9 - .../01 Abstract.html | 6 - .../02 Introduction.html | 8 - .../03 Method.html | 280 ------------------ .../04 Algorithm.html | 6 - .../05 References.html | 7 - 6 files changed, 316 deletions(-) delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 4ddbbdf..cb9f4b9 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -607,15 +607,6 @@ 'description' => "A simple trend following strategy on commodities futures.", 'tags' => 'Momentum, Futures, Commodities' ], - [ - 'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model', - 'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn', - 'sources' => [ - 'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf' - ], - 'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.", - 'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning' - ], [ 'name' => 'Leveraged ETFs with Systematic Risk Management', 'link' => 'leveraged-etfs-with-systematic-risk-management', diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html deleted file mode 100644 index a6cf634..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html +++ /dev/null @@ -1,6 +0,0 @@ -

- In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices. -

-

- Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading -

\ No newline at end of file diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html deleted file mode 100644 index 1fa0f4e..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html +++ /dev/null @@ -1,8 +0,0 @@ -

- Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements. - More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from - medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price - movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in - image classification, and now, researchers are applying CNNs to extract patterns, also known as features, from times-series - data to forecast future stock prices. -

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html deleted file mode 100644 index 1a4765a..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html +++ /dev/null @@ -1,280 +0,0 @@ -

Overview

-

- Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data - to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and - bet on the direction of the price movement based on our model prediction. We will walk through the code required - for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand. -

- -

Inputs/Outputs

- -

- Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model. - The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a - direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps). - The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will - form the labels for which our model will try to classify, thus we have a classification problem. -

- - -

Neural Network Model Architecture

- -

- Now, we will need to build our Neural Network Architecture, which we will build using Keras, - a high-level Python Deep Learning API. To begin, we will need a few import statements: -

- -
-
-    import tensorflow as tf
-    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
-    from tensorflow.keras import Model
-    from tensorflow.keras import metrics
-    from tensorflow.keras.losses import CategoricalCrossentropy
-    from tensorflow.keras import utils
-    from sklearn.preprocessing import StandardScaler
-    import numpy as np
-    import math
-
-
- - -

- We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input - variables (OHLCV), our input shape will be 15 x 5. -

- -
-
-    inputs = Input(shape=(15, 5))
-
-
- - -

- We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as - the Neural Network's method of extracting patterns from the time-series data. -

- -
-
-    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
-
-
- - -
-
-    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
-    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
-    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
-
-    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
-    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
-    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
-
-
- - -

- These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten - our layer. -

- -
-
-    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
-    flattened = Flatten()(combined)
-
-
- - -

- Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down), - this layer will have three nodes. -

- -
-
-    outputs = Dense(3, activation='softmax')(flattened)
-
-
- -

- The resulting Neural Network Architecture is shown in the following: -

- -temporal cnn model architecture - - -

Preparing the Data for Our Model

- -

- First, we need to define a class and a few variables: -

- -
-
-    input_vars = ['open', 'high', 'low', 'close', 'volume']
-
-    class Direction:
-        UP = 0
-        DOWN = 1
-        STATIONARY = 2
-
-    rolling_avg_window_size = 5
-
-    shift = -(rolling_avg_window_size-1)
-
-    stationary_threshold = .0001
-
-    scaler = StandardScaler()
-
-
- -

- input_vars define the variables we want to use to make our predictions. The class Direction - defines a few integers that we will label our data with (labels are needed for classification problems). The reason - we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on, - rolling_avg_window_size is the number of time steps used for the calculate the average of future closing prices, - described earlier in - Inputs/Outputs (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5). - The constant stationary_threshold defines the threshold for a change in price to be considered - stationary, and this change also described in Inputs/Outputs. The shift is the shift needed to align - the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces - manageable for our Neural Network model. The scaler object will be used later to scale our data. - The purpose of the variables will become clearer in use. -

- Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate - the percent change from the close at t, we use the following lines of code: -

- -
-
-    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
-    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
-
-
- -

- The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize - this is a more advanced resource). - Here, .shift(shift) aligns the five time step rolling average 'close_avg' column to the end of the last - time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input - and labeled data for our model much easier. -

- -

- To label our data, we need to first define a function that we will use with the DataFrame's apply() method. - Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda. -

- -
-
-    def label_data(row):
-        if row['close_avg_change_pct'] > stationary_threshold:
-            return Direction.UP
-        elif row['close_avg_change_pct'] < -stationary_threshold:
-            return Direction.DOWN
-        else:
-            return Direction.STATIONARY
-
-
- -

- Now, we apply the above function to our DataFrame to get a column of labels: -

- -
-
-    df['movement_labels'] = df.apply(label_data, axis=1)
-
-
- -

- With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into - lists: -

- -
-
-    data = []
-    labels = []
-
-    for i in range(len(df)-self.n_tsteps+1+shift):
-        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
-        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
-        labels.append(label)
-
-    data = np.array(data)
-
-
- -

- Here, we iterate numerically through the DataFrame, with a carefully calculated value in our range() - function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because - Keras works best with numpy arrays. -

- -

- Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the - range of values is normalized across the features. -

- -
-
-    dim1, dim2, dim3 = data.shape
-    data = data.reshape(dim1*dim2, dim3)
-    data = scaler.fit_transform(data)
-    data = data.reshape(dim1, dim2, dim3)
-
-
- -

- The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after, - we can return the data to the original shape with another reshaping. -

- -

- Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of - 1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following: -

- -
-
-    labels = utils.to_categorical(labels, num_classes=3)
-
-
- -

- Specifying num_classes to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary). -

- -

- We have now finished the walk through of the difficult parts of the code. -

- -

Trading

- -

- After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be - found in Algorithm) we can - use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make - a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit - an Price Insight with direction InsightDirection.Up (resp. InsightDirection.Down). Since we are - betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights - in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight - with a timedelta with a random integer between one and five to constrain the number of insights we emit. -

- - -

The Rest

- -

- We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the - necessary code to execute the strategy can be found in Algorithm. -

- - - - diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html deleted file mode 100644 index 9e98ce9..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html +++ /dev/null @@ -1,6 +0,0 @@ -
-
-
- -
-
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html deleted file mode 100644 index dedbeb6..0000000 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html +++ /dev/null @@ -1,7 +0,0 @@ -
    -
  1. - Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis: - "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019; - https://arxiv.org/pdf/1901.08280.pdf. -
    2. -
\ No newline at end of file From b922d8c1ac92684470ce8020782b9d8f1260a1de Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 14:00:50 -0700 Subject: [PATCH 041/107] updated algorithm and methodology --- .../03 Method.html | 3 +++ .../04 Algorithm.html | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html index 1a4765a..e56515f 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html @@ -265,6 +265,9 @@

Trading

betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights in the respective direction for timedeltas of one through five. However, we choose to only emit an Insight with a timedelta with a random integer between one and five to constrain the number of insights we emit. +
+ As an additional note, we decided to set the trading fees to zero (the default fee + is one dollar per trade), or else our algorithm will only lose money due to the frequency at which we trade.

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index ef8f26c..bf5b897 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
- +
From 6a8345a2d3720170ea4bc4eb4e8fe075c04e82fa Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 14:30:29 -0700 Subject: [PATCH 042/107] fixed error with certain starting dates --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index bf5b897..b2d6569 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
- +
From 8c7f56c4223d4545afb61b56b92b6193240c2d55 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Wed, 12 Aug 2020 16:53:08 -0700 Subject: [PATCH 043/107] Leveraged ETFs with Systematic Risk Management --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index b2d6569..f3cdced 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
- +
From be1fa00392688a3af30a3d05c04aa74e8ac0b662 Mon Sep 17 00:00:00 2001 From: Gustavo Aviles Date: Thu, 13 Aug 2020 10:02:50 -0700 Subject: [PATCH 044/107] Fix missing opening bracket. --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 1 + 1 file changed, 1 insertion(+) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index c1c2ad7..ce7332d 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -634,6 +634,7 @@ 'description' => 'A techincal indicator crossover strategy trading the largest energy companies.', 'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities' ], + [ 'name' => 'Intraday ETF Momentum', 'link' => 'strategy-library/intraday-etf-momentum', 'sources' => [ From d5da7a245726b7c2e51f83141f1c215ae6bca507 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 13:20:33 -0700 Subject: [PATCH 045/107] Leveraged ETFs with Systematic Risk Management --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index f3cdced..ec0cfdf 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
- +
From 72f735277380dcb79f6a046b06a48a108583132b Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:25:28 -0700 Subject: [PATCH 046/107] fixed typo and link --- .../00 Strategy Library/01 Strategy Library.php | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index cb9f4b9..5516ba3 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -609,11 +609,11 @@ ], [ 'name' => 'Leveraged ETFs with Systematic Risk Management', - 'link' => 'leveraged-etfs-with-systematic-risk-management', + 'link' => 'strategy-library/leveraged-etfs-with-systematic-risk-management', 'sources' => [ 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' ], - 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500', + 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500', 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; From c5c4e8c8ce2dc46d6282824a07549dd836dd2373 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:34:10 -0700 Subject: [PATCH 047/107] updated Results section --- .../05 Results.html | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html index dfa86cf..34fa59b 100644 --- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -1,5 +1,4 @@

- We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years, - our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY - in final ROI. The algorithm produced a CAGR of 11.49% during the five years. + Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period + yielded a Sharpe Ratio of .572.

\ No newline at end of file From dd18c61a059301e86e84cfb07c0f49b99ce9ed87 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 13 Aug 2020 16:36:32 -0700 Subject: [PATCH 048/107] improved wording of description --- 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5516ba3..1f9fe06 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -613,7 +613,7 @@ 'sources' => [ 'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701' ], - 'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500', + 'description' => 'We apply Simple Moving Averages to manage the risk of holding leveraged ETFs in an attempt to beat the S&P500', 'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF' ] ]; From b7865614fd803ca14d376453528125c21caca440 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Mon, 17 Aug 2020 11:10:56 -0700 Subject: [PATCH 049/107] updated description --- .../05 Results.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html index 34fa59b..ade26d7 100644 --- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html +++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html @@ -1,4 +1,4 @@

- Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period - yielded a Sharpe Ratio of .572. + We use the S&P 500 as our benchmark, which we track by using the SPY ETF. Our strategy yielded a Sharpe Ratio of .732 + over the past five years, while buying and holding SPY for the same period yielded a Sharpe Ratio of .572.

\ No newline at end of file From 3aa608104eae8d3c059c8ecbcc753ecd201e21b3 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 19:14:13 -0600 Subject: [PATCH 050/107] Add relative metrics to strategy 1026 --- .../04 Algorithm.html | 2 +- .../05 Conclusion.html | 89 ++++++++++++++++++- 2 files changed, 86 insertions(+), 5 deletions(-) diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html index c7304ae..1379e93 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html @@ -1,6 +1,6 @@
- +
\ No newline at end of file diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html index 7623dda..c03c6ca 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html @@ -1,9 +1,90 @@

We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period. - In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the - inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their - research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum - pattern. These areas of future research include: + Comparing the strategy to the S&P 500 benchmark, the strategy has a lower Sharpe ratio during the backtesting + period and during the recovery from the 2020 stock market crash. However, the strategy greatly outperforms the + benchmark during the downfall of the 2020 crash, achieving a 4.8 Sharpe ratio. Throughout all of the time periods + we tested, the strategy had a lower annual standard deviation than the benchmark, meaning more consistent returns. + A breakdown of the results from all of the testing periods can be seen in the table below. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Period NameStart DateEnd DateStrategySharpeASD
Backtest1/1/20158/16/2020Strategy-0.7640.05
Benchmark0.7090.185
Fall 20158/10/201510/10/2015Strategy-0.6960.058
Benchmark-1.2430.793
2020 Crash2/19/20203/23/2020Strategy4.8180.266
Benchmark-1.2430.793
2020 Recovery3/23/20206/8/2020Strategy0.6020.103
Benchmark13.7610.386
+ + +

+ We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs + in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs, + and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the + original research paper. Refer to the backtest results. +

+ +

+ Throughout their research paper, Gao et al (2017) provide several suggestions to increase the return generated by + this momentum pattern. These areas of future research include:

    From 5611f91976a151d6c1d0f46a3470516433701a57 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 19:58:35 -0600 Subject: [PATCH 051/107] Fix typo --- .../1026 Intraday ETF Momentum/05 Conclusion.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html index c03c6ca..ff087ea 100644 --- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html +++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html @@ -78,7 +78,7 @@

    We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs, - and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the + and slippage, the strategy still has a lower Sharpe ratio than the S&P 500 and doesn't match the results found in the original research paper. Refer to the backtest results.

    From fd71669aff0599ec339f3b3f28aae2b789f65815 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Mon, 17 Aug 2020 20:15:37 -0600 Subject: [PATCH 052/107] Add relative metrics to strat 1028 --- .../01 Abstract.html | 6 +- .../04 Algorithm.html | 2 +- .../05 Benchmark Performance.html | 10 -- .../05 Relative Performance.html | 92 +++++++++++++++++++ .../07 Conclusion.html | 8 +- 5 files changed, 100 insertions(+), 18 deletions(-) delete mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html index 3dd4983..587ce33 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html @@ -2,7 +2,7 @@ Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an - impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib - (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular - energy sector ETF. + impressive 176 Sharpe ratio during the downfall of the 2020 oil price war, the strategy has worse performance than + found by Gurrib (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of + a popular energy sector ETF.

    diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html index 487c85d..56ab2db 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html @@ -1,6 +1,6 @@
    - +
    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html deleted file mode 100644 index f3592aa..0000000 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html +++ /dev/null @@ -1,10 +0,0 @@ -

    - To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a - popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the - benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over - the backtest period. -

    - -Tutorial1028-ichimoku-cloud-2 \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html new file mode 100644 index 0000000..a1d1610 --- /dev/null +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html @@ -0,0 +1,92 @@ +

    + To analyze the value of this trading strategy, we compare its performance to buying and holding a popular ETF + tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the benchmark. We + can see from the plot below how the portfolio would have performed just had we had just invested in the benchmark. +

    + +Tutorial1028-ichimoku-cloud-2 + +

    + We now analyze the Sharpe ratio and annual standard deviation of returns for both the strategy and the benchmark. + From the table below, we can see the results of the strategy and the benchmark over the entire backtest period, the + Fall 2015 crisis, and the 2020 oil price war. The strategy has a lower Sharpe ratio than the benchmark across all + of the time periods we tested, except for the crash during the 2020 oil price war, where it generated an impressive + 176 Sharpe ratio. We can also see the strategy has a lower annual standard deviation accross all of the time + frames, implying that the strategy has more consistent returns than the benchmark. +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Period NameStart DateEnd DateStrategySharpeASD
    Backtest1/1/20158/16/2020Strategy-0.310.223
    Benchmark-0.0830.312
    Fall 20158/10/201510/10/2015Strategy-0.310.294
    Benchmark0.2420.351
    2020 Crash2/19/20203/23/2020Strategy176.5240.949
    Benchmark-0.9021.108
    2020 Recovery3/23/20206/8/2020Strategy-1.5560.447
    Benchmark46.0680.703
    + +

    + We find the lack of performance for this strategy is not largely attributed to the transaction costs. After + ignoring the transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the + benchmark and doesn't match the results found in the original research paper. See the backtest results + here. +

    \ No newline at end of file diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html index 4631b74..452f0d8 100644 --- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html +++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html @@ -1,8 +1,8 @@

    - While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash, - we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The - strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of - 0.92. To continue the development of this strategy, future areas of research include: + While the strategy examined herein produces a 176 Sharpe ratio throughout the downfall of the 2020 oil price war + and stock market crash, we conclude the strategy does not currently provide as profitable of results as documented + by Gurrib (2020). The strategy experiences a -0.234 Sharpe ratio over the entire backtest period. To continue the + development of this strategy, future areas of research include:

      From 273015c388b224938520ba2f6957dd0f7852c4d1 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Tue, 18 Aug 2020 13:56:53 -0700 Subject: [PATCH 053/107] updated algorithm --- .../04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index ec0cfdf..9d83532 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
      - +
      From 5e2cdad4412f2a862cd2d4829a4dd6b98525e51a Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 18 Aug 2020 15:37:07 -0600 Subject: [PATCH 054/107] Add relative metrics to strat 85 and fix PCM bug --- .../02 Method.html | 2 +- .../04 Relative Performance.html | 81 +++++++++++++++++++ ...05 Market & Competition Qualification.html | 15 ++++ .../06 Conclusion.html | 17 ++++ ...{04 References.html => 07 References.html} | 0 5 files changed, 114 insertions(+), 1 deletion(-) create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html rename 04 Strategy Library/85 Momentum in Mutual Fund Returns/{04 References.html => 07 References.html} (100%) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html index cd4436e..a43b163 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html @@ -106,7 +106,7 @@

      Alpha Ranking

      ... ranking_df = pd.DataFrame() for symbol, symbol_data in self.symbol_data_by_symbol.items(): - if symbol_data.IsReady: + if data.ContainsKey(symbol) and symbol_data.IsReady: row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol]) ranking_df = ranking_df.append(row) ranked_symbols = ranking_df.rank().sum(axis=1).sort_values().index diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html new file mode 100644 index 0000000..26cb3ef --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html @@ -0,0 +1,81 @@ +

      + To analyze the value of this trading strategy, we compare its performance to buying-and-holding the S&P 500 index + ETF, SPY. We can see the results from the table below. The strategy has a lower Sharpe ratio than the SPY for all of + the time frames we tested, except for the downfall of the 2020 stock market crash. During this time it greatly + outperformed the SPY, achieving a 10.4 Sharpe ratio. We also notice that the strategy generates more consistent + returns than the benchmark, documented by the lower annual standard deviation of returns throughout all the testing + periods. +

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Period NameStart DateEnd DateStrategySharpeASD
      Backtest1/1/20158/16/2020Strategy0.1920.046
      Benchmark0.7090.186
      Fall 20158/10/201510/10/2015Strategy-1.4480.052
      Benchmark-0.7240.251
      2020 Crash2/19/20203/23/2020Strategy10.3860.104
      Benchmark-1.2430.793
      2020 Recovery3/23/20206/8/2020Strategy-2.9420.177
      Benchmark13.7610.386
      + +

      + We find the lack of performance for this strategy is not largely attributed to the trading fees. After ignoring the + transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the benchmark. See + the backtest results here. +

      diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html new file mode 100644 index 0000000..fbc487d --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html @@ -0,0 +1,15 @@ +

      + Although this strategy passes several of the + metrics required for Alpha Streams and + the Quant League competition, it requires further work to meet the following requirements: +

      + + +
        +
      • Profitable
        • +
      • PSR >= 80%
        • +
      • Max drawdown duration <= 6 months
        • +
      • Insights contain the following properties: Symbol, Duration, Direction, and Weight
        • +
      • Minute or second data resolution
        • +
      • Insight Weighting or Equal Weighting Portfolio Construction model
        • +
      \ No newline at end of file diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html new file mode 100644 index 0000000..763902d --- /dev/null +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html @@ -0,0 +1,17 @@ +

      + The momentum pattern examined throughout this tutorial has a greater Sharpe ratio than the SPY during the downfall + of the 2020 stock market crash and has more a lower annual standard deviation of returns than the SPY over all the + periods we tested. However, we conclude the strategy, which is loosely based on the research of Sapp (2010), does + not consistently outperform our benchmark. To continue the development of this strategy, future areas of research + include: +

      + + +
        +
      • + Incorporating historical returns and NAV of mutual funds to better-reflect the strategy documented by Saap (2010). +
        • +
      • Adjusting the parameters in the ROCAndNearnessAlphaModel.
        • +
      • Performing more filtering and sorting in the AssetManagementUniverseSelection model.
        • +
      • Testing other portfolio construction techniques.
        • +
      diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html similarity index 100% rename from 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html rename to 04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html From a398733997925cfd76a15c44332d3176dfc56dc2 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Tue, 18 Aug 2020 16:05:58 -0600 Subject: [PATCH 055/107] Update backtest with PCM fix --- .../85 Momentum in Mutual Fund Returns/03 Algorithm.html | 2 +- quantpedia.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html index ade7d3b..93109c0 100644 --- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html +++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html @@ -1,6 +1,6 @@
      - +
      \ No newline at end of file diff --git a/quantpedia.json b/quantpedia.json index cbf4970..22f8d06 100644 --- a/quantpedia.json +++ b/quantpedia.json @@ -34,7 +34,7 @@ 77: "0483e5a7094604254ab37eda8b5141b8", 78: "27fb5f05b0e48f488f0994d8d83ddc77", 83: "fdfcddd132eaf55039d867c03efe3012", - 85: "ba030edb022016f967a6296a556d717e", + 85: "a86b19f12b40d8a676bf7f885742631d", 91: "95cffbeec0d003da873b791d3a10f60f", 100: "5ee5507fef6bf190ae533ce05ccaa785", 102: "cd2d187e44a00c7b19f64aee8b0895d9", From 53c3c878864a735c9e826d4208cd63e733a7ff52 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Wed, 19 Aug 2020 13:09:15 -0600 Subject: [PATCH 056/107] Add relative metrics & fix execution model bug --- .../01 Strategy Library.php | 9 ++ .../01 Abstract.html | 7 + .../02 Background.html | 9 ++ .../03 Method.html | 129 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Relative Performance.html | 91 ++++++++++++ ...06 Market & Competition Qualification.html | 19 +++ .../07 Conclusion.html | 10 ++ .../08 References.html | 8 ++ 9 files changed, 288 insertions(+) create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index ce7332d..426d089 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -642,6 +642,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Intraday Arbitrage Between Index ETFs', + 'link' => 'strategy-library/intraday-arbitrage-between-index-etfs', + 'sources' => [ + 'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1709599' + ], + 'description' => "A strategy that tracks the price paths of two correlated ETFs and takes advantage of mis-pricings that arise when the price paths diverge", + 'tags'=>'Intermediate, Equities, Arbitrage, Intraday' ] ]; diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html new file mode 100644 index 0000000..7d69c19 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html @@ -0,0 +1,7 @@ +

      + In this tutorial, we implement an intraday arbitrage strategy that capitalizes on deviations between two closely correlated + index ETFs. Even though at times both ETFs may hold different constituents and different weights of securities while tracking + the index, they are both highly correlated and extremely liquid. Researchers have shown these two properties are essential to + an arbitrage system's success. The algorithm we implement here is inspired by the work of Kakushadze and Serur (2018) and + Marshall, Nguyen, and Visaltanachoti (2010). +

      diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html new file mode 100644 index 0000000..8c03fe6 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html @@ -0,0 +1,9 @@ +

      + Marshall et al (2010) define an arbitrage opportunity as when the bid price of ETF A (B) diverts high enough away from the ask + price of ETF B (A) such that their quotient reaches a threshold. In their paper, an arbitrage opportunity is only acted upon + when the threshold is satisfied for 15 seconds. When these criteria are met, the algorithm enters the arbitrage trade by going + long ETF B (A) and short ETF A (B). When the spread reverts back to where the bid of ETF B (A) >= the ask of ETF A (B) for 15 + seconds, the positions are liquidated. An overview of the trade process is illustrated in the image below. +

      + +Tutorial1023-intraday-arbitrage-1 \ No newline at end of file diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html new file mode 100644 index 0000000..ecac338 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html @@ -0,0 +1,129 @@ +

      Universe Selection

      +

      + We implement a manual universe selection model that includes our two ETFs, SPY and IVV. The attached research notebook finds + the correlation of daily returns to be >0.99. +

      +
      +
      +tickers = ['IVV', 'SPY']
+symbols = [ Symbol.Create(t, SecurityType.Equity, Market.USA) for t in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+
      +
      + +

      Spread Adjustments

      +

      + Plotting the ratio of the security prices shows its trending behavior. +

      +Tutorial1023-intraday-arbitrage-2 +

      + Without adjusting this ratio over time, an arbitrage system would be stuck in a single trade for majority of the backtest. To + resolve this, we subtract a trailing mean from each data point. +

      +Tutorial1023-intraday-arbitrage-3 +

      + Both of the above plots can be reproduced in the attached research notebook. During backtesting, this adjustment is done + during trading by setting up a + QuoteBarConsolidator for each security in our + universe. On each new consolidated QuoteBar, we + update the trailing window of L1 data, then calculate the latest spread adjustment values. +

      +
      +
      +# In OnSecuritiesChanged
+for symbol in self.symbols:
+    self.consolidators[symbol] = QuoteBarConsolidator(1)
+    self.consolidators[symbol].DataConsolidated += self.CustomDailyHandler
+    algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidators[symbol])
+
+def CustomDailyHandler(self, sender, consolidated):
+    # Add new data point to history while removing expired history
+    self.history[consolidated.Symbol]['bids'] = np.append(self.history[consolidated.Symbol]['bids'][1:], consolidated.Bid.Close)
+    self.history[consolidated.Symbol]['asks'] = np.append(self.history[consolidated.Symbol]['asks'][1:], consolidated.Ask.Close)
+    
+    self.update_spread_adjusters()
+
+def update_spread_adjusters(self):
+    for i in range(2):
+        numerator_history = self.history[self.symbols[i]]['bids']
+        denominator_history = self.history[self.symbols[abs(i-1)]]['asks']
+        self.spread_adjusters[i] = (numerator_history / denominator_history).mean()
+
      +
      + + +

      Alpha Construction

      +

      + The ArbitrageAlphaModel monitors the intraday bid and ask prices of the securities in the universe. In the constructor, we + can specify the model parameters. In this tutorial, we select a shorter window an arbitrage opportunity must be active before + we act on it by setting `order_delay` to 3. +

      +
      +
      +class ArbitrageAlphaModel(AlphaModel):
+    symbols = [] # IVV, SPY
+    entry_timer = [0, 0]
+    exit_timer = [0, 0]
+    spread_adjusters = [0, 0]
+    long_side = -1
+    consolidators = {}
+    history = {}
+
+    def __init__(self, order_delay = 3, profit_pct_threshold = 0.02, window_size = 400):
+        self.order_delay = order_delay
+        self.pct_threshold = profit_pct_threshold / 100
+        self.window_size = window_size
+
      +
      + + +

      Trade Signals

      +

      + To emit insights, we check if either side of the arbitrage strategy warrants an entry. If no new entries are to be made, the + algorithm then looks to exit any current positions. With this design, we can flip our long/short bias without first + flattening our position. We use a practically-infinite insight durations as we do not know how long the algorithm will be in + an arbitrage trade. +

      +
      +
      +# Search for entries
+for i in range(2):
+    if quotebars[abs(i-1)].Bid.Close / quotebars[i].Ask.Close - self.spread_adjusters[abs(i-1)] >= self.pct_threshold:
+        self.entry_timer[i] += 1
+        if self.entry_timer[i] == self.order_delay:
+            self.exit_timer = [0, 0]
+            if self.long_side == i:
+                return []
+            self.long_side = i
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Up),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Down)]
+        else:
+            return []
+    self.entry_timer[i] = 0
+
+# Search for an exit
+if self.long_side >= 0: # In a position
+    if quotebars[self.long_side].Bid.Close / quotebars[abs(self.long_side-1)].Ask.Close - self.spread_adjusters[self.long_side] >= 0: # Exit signal
+        self.exit_timer[self.long_side] += 1
+        if self.exit_timer[self.long_side] == self.order_delay: # Exit signal lasted long enough
+            self.exit_timer[self.long_side] = 0
+            i = self.long_side
+            self.long_side = -1
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Flat),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Flat)]
+        else:
+            return []
+return []
+
      +
      + + +

      Portfolio Construction & Trade Execution

      +

      + Following the guidelines of Alpha Streams + and the Quant League competition, we + utilize the + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

      diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html new file mode 100644 index 0000000..c59088b --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      \ No newline at end of file diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html new file mode 100644 index 0000000..fd71509 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html @@ -0,0 +1,91 @@ +

      + We analyze the performance of this strategy by comparing it to the S&P 500 ETF benchmark, SPY. We notice that the + strategy has a lower Sharpe ratio over all of our testing periods than the benchmark, except for the Fall 2015 + crisis where it achieved a 2.8 Sharpe ratio. The strategy also has a lower annual standard deviation of returns + when compared to the SPY, implying more consistent returns over time. A breakdown of the strategy's performance + across all our testing periods is displayed in the table below. +

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Period NameStart DateEnd DateStrategySharpeASD
      Backtest8/11/20158/11/2020Strategy-0.4470.053
      Benchmark0.7320.192
      Fall 20158/10/201510/10/2015Strategy2.8370.225
      Benchmark-0.7240.251
      2020 Crash2/19/20203/23/2020Strategy-4.1960.209
      Benchmark-1.2430.793
      2020 Recovery3/23/20206/8/2020Strategy-3.4430.013
      Benchmark13.7610.386
      + + +

      + The lack of performance for this arbitrage strategy is mostly attributed to the fees it incurs while trading. This + is common for an intraday arbitrage strategy, but we discuss ways to reduces these fees in the conclusion of this + tutorial. After removing the costs of commissions, crossing the spread, and slippage, the strategy outperforms the + SPY over the entire backtesting period. Without these costs, the strategy generates a 1.09 Share ratio while the + SPY generates a 0.732 Sharpe ratio. See the backtest performance without fees below. +

      + + +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html new file mode 100644 index 0000000..c76ecb6 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html @@ -0,0 +1,19 @@ +

      + Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

      + + +
        +
      • Profitable
        • +
      • PSR >= 80%
        • +
      • Max drawdown duration <= 6 months
        • +
      • Insights contain the following properties: Symbol, Duration, Direction, and Weight
        • +
      • Alphas need to place at least 10 trades per month for the majority of the backtest
        • +
      + +

      + The algorithm currently places trades during 12 unique months throughout the backtest. Since the backtest spans + across 61 months, it places trades through a minority of the backtest. +

      diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html new file mode 100644 index 0000000..3df342d --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html @@ -0,0 +1,10 @@ +

      + The intraday arbitrage strategy we built and tested throughout this tutorial underperforms the SPY benchmark in + terms of Sharpe ratio when including trading costs. Without these costs, we found the strategy outperforms the SPY + in terms of Sharpe ratio. In our implementation, we specified the alpha model to initiate trading when atleast a + 0.02% profit threshold is reached for 3 seconds. Both of these parameters are set lower than the strategy examined + in Marshall et al (2010) for demonstration purposes. Increasing the profit threshold will lead to more profitable, + but fewer, trades that may overcome the cost of trading. We leave this area of study for future research. + Additional areas of future research include increasing the resolution of data from second to tick and incorportating + an execution model that utilizes limit orders to reduce fees. +

      diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html new file mode 100644 index 0000000..a684227 --- /dev/null +++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html @@ -0,0 +1,8 @@ +
        +
      1. + Marshall, Ben R. and Nguyen, Nhut (Nick) Hoang and Visaltanachoti, Nuttawat, ETF Arbitrage: Intraday Evidence (November 16, 2010). Online copy +
        2. +
      2. + Kakushadze, Zura and Serur, Juan Andrés, 151 Trading Strategies (August 17, 2018). Z. Kakushadze and J.A. Serur. 151 Trading Strategies. Cham, Switzerland: Palgrave Macmillan, an imprint of Springer Nature, 1st Edition (2018), XX, 480 pp; ISBN 978-3-030-02791-9. Online copy +
        3. +
      \ No newline at end of file From 85f17f582596286a29138f65bd752b32d942b9d3 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 10:39:19 -0700 Subject: [PATCH 057/107] updated algorithm --- .../05 Results.html | 5 +++++ .../{05 References.html => 06 References.html} | 0 2 files changed, 5 insertions(+) create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html rename 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/{05 References.html => 06 References.html} (100%) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html new file mode 100644 index 0000000..2b6a817 --- /dev/null +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html @@ -0,0 +1,5 @@ +

      + Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over + two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the + algorithm is non-deterministic, users may see different results from repeated backtests. +

      \ No newline at end of file diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html similarity index 100% rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html From 5f6d72305f567da1d1544b3ed45ebb4798514d65 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 12:02:33 -0700 Subject: [PATCH 058/107] updated algorithm --- .../04 Algorithm.html | 2 +- .../05 Results.html | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html index 9d83532..0a1b0bd 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html @@ -1,6 +1,6 @@
      - +
      diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html index 2b6a817..a9d5b42 100644 --- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html +++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html @@ -1,5 +1,6 @@

      - Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over - two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the - algorithm is non-deterministic, users may see different results from repeated backtests. + Since our algorithm is non-deterministic, users should expect to see different results in repeated backtests. From + running our algorithm ten times, we achieved Sharpe Ratios with an average of .211, a maximum of 0.917, and minimum + of -0.312, and a standard deviation of 0.327. As we traded three technology stocks, we compare our results to QQQ. + Comparing our algorithm to QQQ, our average Sharpe of .211 is significantly lower than the 0.967 Sharpe of QQQ.

      \ No newline at end of file From 2b686253c3d088fb9bf3008bbc2b36a16cc11d08 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:32:57 -0700 Subject: [PATCH 059/107] updated algorithm --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 4 ++ .../02 Introduction.html | 9 ++++ .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Video Walkthrough.html | 5 ++ .../06 Results.html | 8 +++ .../07 References.html | 7 +++ 8 files changed, 98 insertions(+) create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index ce7332d..e4d357f 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -642,6 +642,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Optimal Pairs Trading', + 'link' => 'strategy-library/optimal-pairs-trading', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf' + ], + 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", + 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' ] ]; diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html new file mode 100644 index 0000000..f913c9e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html @@ -0,0 +1,4 @@ +

      + In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation + levels. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html new file mode 100644 index 0000000..d0a7cd5 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html @@ -0,0 +1,9 @@ +

      + Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit + from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter, + which we have implemented in this post. + However, today, we will model the portfolio values of holding positions in + a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate + the position in the pair of stocks. Let (number) refer to the corresponding equation in the given paper, which can + be found under the References section. For example, (2.1) refers to equation 2.1 in the paper. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html new file mode 100644 index 0000000..594383e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -0,0 +1,50 @@ +

      Computing the OU Coefficients

      +

      Say there are two arbitrary stocks A and B. For each value β in .01, .02, .03, …, 1.00:

      +
        +
      1. We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
        2. +
      2. Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
        3. +
      +$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +

      where

      +

      xj is the value at the jth index of the portfolio values

      +

      and 

      +$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +

      and n = the number of portfolio values

      +

      and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

      +

      We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

      + + +

      Deriving the Optimal Entry and Liquidation Levels

      +

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      +$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ +$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ +$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +

      Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

      +

      With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

      +

      As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

      +$f'(x) = \frac{f(x+h)-f(x)}{h}$ +

      where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

      +

      To find b*, we solve for b in the following equation:

      +$F(b)=(b-c)F'(b)\quad(4.3)$ +

      We solve this equation by getting all terms to one side:

      +$F(b)-(b-c)F'(b)=0$ +

      Finding the root of the above equation results in b*. 

      +

      Now to derive d*, we solve for d in the following equation:

      +$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +

      Again, we will move all terms to one side:

      +$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +

      And finding the root of this equation results in d*.

      +


      The full method of finding b* and d* can be found in the OptimalStopping.py + file in the Algorithm section.

      + +

      Trading

      +

      + Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices + for stocks A and B. We then train our model on these data points, and we get the + b*, d*, and β* values. We then keep track of a hypothetical portfolio of holding $1 + of A and -$β* of B. Once the value of our hypothetical portfolio is less than or equal to b*, + we allocate 1.0 of our capital to long of A and short B using (β* x capital used for A) worth of stock. + Once the hypothetical portfolio value we tracking reaches d*, we liquidate our positions. We repeat these + trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to + update our b*, d*, and β* values. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html new file mode 100644 index 0000000..40bda21 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html new file mode 100644 index 0000000..1e7e3ae --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html @@ -0,0 +1,5 @@ + \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html new file mode 100644 index 0000000..3eb8a0f --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html @@ -0,0 +1,8 @@ +

      + Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period + yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods + of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the + entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage + our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters + of the algorithm. +

      \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html new file mode 100644 index 0000000..87de37d --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -0,0 +1,7 @@ +
        +
      1. + Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). + International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. + Online Copy. +
        2. +
      \ No newline at end of file From f02f7eaccecf43df2079c10bb6b97791ff50bf63 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:43:40 -0700 Subject: [PATCH 060/107] updated algorithm --- .../1029 Optimal Pairs Trading/04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html index 40bda21..04066ff 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -1,6 +1,6 @@
      - +
      From fc66d22377675ad67b0fa1090611938fce46f0b2 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:32:57 -0700 Subject: [PATCH 061/107] updated algorithm --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 4 ++ .../02 Introduction.html | 9 ++++ .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Video Walkthrough.html | 5 ++ .../06 Results.html | 8 +++ .../07 References.html | 7 +++ 8 files changed, 98 insertions(+) create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 5671080..c65c7b8 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -651,6 +651,15 @@ ], 'description' => "A momentum strategy based on returns of the market open", 'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly' + ], + [ + 'name' => 'Optimal Pairs Trading', + 'link' => 'strategy-library/optimal-pairs-trading', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf' + ], + 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", + 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' ] ]; diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html new file mode 100644 index 0000000..f913c9e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html @@ -0,0 +1,4 @@ +

      + In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation + levels. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html new file mode 100644 index 0000000..d0a7cd5 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html @@ -0,0 +1,9 @@ +

      + Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit + from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter, + which we have implemented in this post. + However, today, we will model the portfolio values of holding positions in + a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate + the position in the pair of stocks. Let (number) refer to the corresponding equation in the given paper, which can + be found under the References section. For example, (2.1) refers to equation 2.1 in the paper. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html new file mode 100644 index 0000000..594383e --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -0,0 +1,50 @@ +

      Computing the OU Coefficients

      +

      Say there are two arbitrary stocks A and B. For each value β in .01, .02, .03, …, 1.00:

      +
        +
      1. We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
        2. +
      2. Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
        3. +
      +$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +

      where

      +

      xj is the value at the jth index of the portfolio values

      +

      and 

      +$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +

      and n = the number of portfolio values

      +

      and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

      +

      We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

      + + +

      Deriving the Optimal Entry and Liquidation Levels

      +

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      +$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ +$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ +$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +

      Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

      +

      With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

      +

      As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

      +$f'(x) = \frac{f(x+h)-f(x)}{h}$ +

      where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

      +

      To find b*, we solve for b in the following equation:

      +$F(b)=(b-c)F'(b)\quad(4.3)$ +

      We solve this equation by getting all terms to one side:

      +$F(b)-(b-c)F'(b)=0$ +

      Finding the root of the above equation results in b*. 

      +

      Now to derive d*, we solve for d in the following equation:

      +$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +

      Again, we will move all terms to one side:

      +$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +

      And finding the root of this equation results in d*.

      +


      The full method of finding b* and d* can be found in the OptimalStopping.py + file in the Algorithm section.

      + +

      Trading

      +

      + Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices + for stocks A and B. We then train our model on these data points, and we get the + b*, d*, and β* values. We then keep track of a hypothetical portfolio of holding $1 + of A and -$β* of B. Once the value of our hypothetical portfolio is less than or equal to b*, + we allocate 1.0 of our capital to long of A and short B using (β* x capital used for A) worth of stock. + Once the hypothetical portfolio value we tracking reaches d*, we liquidate our positions. We repeat these + trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to + update our b*, d*, and β* values. +

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html new file mode 100644 index 0000000..40bda21 --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html new file mode 100644 index 0000000..1e7e3ae --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html @@ -0,0 +1,5 @@ + \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html new file mode 100644 index 0000000..3eb8a0f --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html @@ -0,0 +1,8 @@ +

      + Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period + yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods + of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the + entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage + our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters + of the algorithm. +

      \ No newline at end of file diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html new file mode 100644 index 0000000..87de37d --- /dev/null +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -0,0 +1,7 @@ +
        +
      1. + Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). + International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. + Online Copy. +
        2. +
      \ No newline at end of file From 81c8ab32405ffd6cf6acf51635fe97dd8a05791f Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 14:43:40 -0700 Subject: [PATCH 062/107] updated algorithm --- .../1029 Optimal Pairs Trading/04 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html index 40bda21..04066ff 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html @@ -1,6 +1,6 @@
      - +
      From bc5492740e6f0ebfa6df6dbb095de46839466c9f Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 16:34:22 -0700 Subject: [PATCH 063/107] fixed typos --- 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index 594383e..fd3f849 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -15,7 +15,7 @@

      Computing the OU Coefficients

      Deriving the Optimal Entry and Liquidation Levels

      -

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      +

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ From 700abdbfd2115364f0d091bfbb4edfe69e5bd0f9 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Thu, 17 Sep 2020 16:37:47 -0700 Subject: [PATCH 064/107] fixed typos --- 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 4 ---- 1 file changed, 4 deletions(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index 8ab9f29..fd3f849 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -15,11 +15,7 @@

      Computing the OU Coefficients

      Deriving the Optimal Entry and Liquidation Levels

      -<<<<<<< HEAD

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      -======= -

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      ->>>>>>> origin/feature-310-optimal-pairs-trading $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ From fdf740bdf7815509d61ac17e968b7d10ef1155dd Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 18 Sep 2020 10:25:18 -0700 Subject: [PATCH 065/107] changed LaTex format as $$ don't render properly --- .../1029 Optimal Pairs Trading/03 Method.html | 20 +++++++++---------- .../07 References.html | 2 +- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html index fd3f849..83986fa 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html @@ -4,11 +4,11 @@

      Computing the OU Coefficients

    • We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $β B (we short B) on each of the 252 days 
    • Then we compute the OU coefficients (θ, µ, σ) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:
      • -$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$ +\[-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)\]

      where

      xj is the value at the jth index of the portfolio values

      and 

      -$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$ +\[\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}\]

      and n = the number of portfolio values

      and Δt = 1 ÷ (days between the start and end dates of the portfolio values), 

      We then select the β, which we differentiate as β*, that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding θ*, µ*, σ* values. The implementation of finding θ*, µ*, and σ* can be found in ou_mle.py in the Algorithm section.

      @@ -16,23 +16,23 @@

      Computing the OU Coefficients

      Deriving the Optimal Entry and Liquidation Levels

      With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don’t buy the portfolio of $1 of A and -$β* of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$β* of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:

      -$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$ -$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$ -$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$ +\[F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)\] +\[G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)\] +\[V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)\]

      Where constants c = the cost of transaction and r = investor’s subject discount rate. We choose to set c = r = .05 as these were the values given in the paper. 

      With these equations, we can now solve for the optimal liquidation level, which we denote as b*, as well as the optimal entry level, which we denote at d*. Note: the reason we derive the optimal liquidation level first is because we use b* in the computation of d*.

      As deriving b* and d* require the derivative of the functions given above, given an arbitrary function input x and an arbitrary function f(x), we approximate f’(x) with the following equation equation:

      -$f'(x) = \frac{f(x+h)-f(x)}{h}$ +\[f'(x) = \frac{f(x+h)-f(x)}{h}\]

      where h is some arbitrarily small value (we set h = 1 x 10-4 in our implementation).

      To find b*, we solve for b in the following equation:

      -$F(b)=(b-c)F'(b)\quad(4.3)$ +\[F(b)=(b-c)F'(b)\quad(4.3)\]

      We solve this equation by getting all terms to one side:

      -$F(b)-(b-c)F'(b)=0$ +\[F(b)-(b-c)F'(b)=0\]

      Finding the root of the above equation results in b*. 

      Now to derive d*, we solve for d in the following equation:

      -$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$ +\[G(d)(V'(d)-1)=G'(d)(V(d)-d-c)\]

      Again, we will move all terms to one side:

      -$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$ +\[G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0\]

      And finding the root of this equation results in d*.


      The full method of finding b* and d* can be found in the OptimalStopping.py file in the Algorithm section.

      diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html index 87de37d..10b2dac 100644 --- a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html +++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html @@ -2,6 +2,6 @@
    • Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015). International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015. - Online Copy. + Online Copy.
      • \ No newline at end of file From 7fb1a0c3b7c573ca18d19ef20beb614a34219a5f Mon Sep 17 00:00:00 2001 From: Aaron Janeiro Stone <60862650+aarjaneiro@users.noreply.github.com> Date: Fri, 18 Sep 2020 14:26:12 -0400 Subject: [PATCH 066/107] Correction of a typo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) →\text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}) --- .../12 Modern Portfolio Theory/04 Mean-Variance Analysis.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html index 51a1bf9..e60dcab 100755 --- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html +++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html @@ -26,7 +26,7 @@

      Capital Market Line

      Since there is only n = 1 risky asset, the variance of the CML portfolio return is

      -\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \] +\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}) \]

      Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight: From 0fe294f7414b458dded00ddcad919e1a7269dd54 Mon Sep 17 00:00:00 2001 From: Alexandre Catarino Date: Fri, 18 Sep 2020 19:34:28 +0100 Subject: [PATCH 067/107] Add Data Directory and Fama-French Data Factors Latest files from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html --- Data/F-F_Research_Data_5_Factors_2x3.CSV | 748 + .../F-F_Research_Data_5_Factors_2x3_daily.CSV | 14374 +++++++++ Data/F-F_Research_Data_Factors.CSV | 1231 + Data/F-F_Research_Data_Factors_daily.CSV | 24797 ++++++++++++++++ Data/F-F_Research_Data_Factors_weekly.CSV | 4916 +++ 5 files changed, 46066 insertions(+) create mode 100644 Data/F-F_Research_Data_5_Factors_2x3.CSV create mode 100644 Data/F-F_Research_Data_5_Factors_2x3_daily.CSV create mode 100644 Data/F-F_Research_Data_Factors.CSV create mode 100644 Data/F-F_Research_Data_Factors_daily.CSV create mode 100644 Data/F-F_Research_Data_Factors_weekly.CSV diff --git a/Data/F-F_Research_Data_5_Factors_2x3.CSV b/Data/F-F_Research_Data_5_Factors_2x3.CSV new file mode 100644 index 0000000..8adb14e --- /dev/null +++ b/Data/F-F_Research_Data_5_Factors_2x3.CSV @@ -0,0 +1,748 @@ +This file was created by CMPT_ME_BEME_OP_INV_RETS using the 202007 CRSP database. +The 1-month TBill return is from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RMW,CMA,RF +196307, -0.39, -0.47, -0.83, 0.66, -1.15, 0.27 +196308, 5.07, -0.79, 1.67, 0.40, -0.40, 0.25 +196309, -1.57, -0.48, 0.18, -0.76, 0.24, 0.27 +196310, 2.53, -1.29, -0.10, 2.75, -2.24, 0.29 +196311, -0.85, -0.84, 1.71, -0.45, 2.22, 0.27 +196312, 1.83, -1.89, -0.12, 0.07, -0.30, 0.29 +196401, 2.24, 0.08, 1.59, 0.22, 1.50, 0.30 +196402, 1.54, 0.32, 2.83, 0.06, 0.85, 0.26 +196403, 1.41, 1.41, 3.32, -2.01, 2.93, 0.31 +196404, 0.10, -1.52, -0.55, -1.35, -1.08, 0.29 +196405, 1.42, -0.68, 1.98, -0.26, 0.24, 0.26 +196406, 1.27, 0.09, 0.68, -0.42, 0.14, 0.30 +196407, 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-8.44, 4.68 + 2000, -17.60, 4.21, 39.69, 26.67, 32.74, 5.89 + 2001, -15.20, 23.68, 19.52, 19.84, 11.79, 3.83 + 2002, -22.76, 5.82, 7.47, 20.39, 14.47, 1.65 + 2003, 30.75, 24.49, 5.40, -20.46, 17.26, 1.02 + 2004, 10.72, 7.34, 8.08, 8.34, -7.86, 1.20 + 2005, 3.09, -0.75, 8.33, 1.71, -5.03, 2.98 + 2006, 10.60, 1.53, 14.11, 2.08, 8.19, 4.80 + 2007, 1.04, -7.94, -14.65, 4.37, -7.80, 4.66 + 2008, -38.34, 3.34, 0.82, 15.06, 4.16, 1.60 + 2009, 28.26, 7.99, -9.17, 2.78, -2.67, 0.10 + 2010, 17.37, 13.28, -5.31, -1.48, 9.93, 0.12 + 2011, 0.44, -5.78, -8.35, 12.60, -0.88, 0.04 + 2012, 16.28, -0.08, 9.68, -4.71, 9.41, 0.06 + 2013, 35.20, 7.68, 1.33, -4.44, 1.31, 0.02 + 2014, 11.70, -8.10, -1.74, 1.04, -1.70, 0.02 + 2015, 0.07, -5.85, -9.65, 0.89, -8.49, 0.02 + 2016, 13.30, 9.20, 23.02, 4.82, 9.77, 0.20 + 2017, 21.50, -5.84, -13.98, 6.81, -11.80, 0.80 + 2018, -6.93, -5.35, -9.16, -1.37, 0.21, 1.81 + 2019, 28.28, -6.22, -11.67, 4.99, -2.81, 2.14 diff --git a/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV new file mode 100644 index 0000000..de66995 --- /dev/null +++ b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV @@ -0,0 +1,14374 @@ +This file was created by CMPT_ME_BEME_OP_INV_RETS_DAILY using the 202007 CRSP database. +The 1-month TBill return is from Ibbotson and Associates, Inc. + +,Mkt-RF,SMB,HML,RMW,CMA,RF +19630701, -0.67, 0.00, -0.32, -0.01, 0.15, 0.012 +19630702, 0.79, -0.27, 0.27, -0.07, -0.19, 0.012 +19630703, 0.63, -0.17, -0.09, 0.17, -0.33, 0.012 +19630705, 0.40, 0.08, -0.28, 0.08, -0.33, 0.012 +19630708, -0.63, 0.04, -0.18, -0.29, 0.13, 0.012 +19630709, 0.45, 0.00, 0.10, 0.14, -0.04, 0.012 +19630710, -0.18, 0.21, 0.01, 0.06, -0.07, 0.012 +19630711, -0.16, 0.14, -0.30, -0.06, 0.05, 0.012 +19630712, -0.12, 0.02, -0.11, 0.12, 0.04, 0.012 +19630715, -0.62, 0.07, -0.03, 0.17, -0.06, 0.012 +19630716, -0.07, -0.08, 0.14, -0.06, -0.07, 0.012 +19630717, -0.33, -0.07, 0.15, 0.04, 0.10, 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5.65, 1.85, 0.55, 0.00 +201308, -2.71, 0.30, -2.76, 0.00 +201309, 3.77, 2.92, -1.20, 0.00 +201310, 4.18, -1.49, 1.10, 0.00 +201311, 3.12, 1.25, 0.27, 0.00 +201312, 2.81, -0.51, -0.30, 0.00 +201401, -3.32, 0.86, -2.10, 0.00 +201402, 4.65, 0.31, -0.46, 0.00 +201403, 0.43, -1.88, 5.06, 0.00 +201404, -0.19, -4.26, 1.07, 0.00 +201405, 2.06, -1.84, -0.19, 0.00 +201406, 2.61, 3.07, -0.76, 0.00 +201407, -2.04, -4.24, 0.01, 0.00 +201408, 4.24, 0.35, -0.58, 0.00 +201409, -1.97, -3.83, -1.23, 0.00 +201410, 2.52, 4.21, -1.68, 0.00 +201411, 2.55, -2.08, -2.99, 0.00 +201412, -0.06, 2.54, 2.06, 0.00 +201501, -3.11, -0.58, -3.47, 0.00 +201502, 6.13, 0.51, -1.83, 0.00 +201503, -1.12, 3.03, -0.46, 0.00 +201504, 0.59, -2.97, 1.85, 0.00 +201505, 1.36, 0.91, -1.36, 0.00 +201506, -1.53, 2.81, -0.79, 0.00 +201507, 1.54, -4.15, -4.14, 0.00 +201508, -6.04, 0.50, 2.66, 0.00 +201509, -3.08, -2.65, 0.53, 0.00 +201510, 7.75, -1.97, -0.06, 0.00 +201511, 0.56, 3.66, -0.50, 0.00 +201512, -2.17, -2.83, -2.60, 0.01 +201601, -5.77, -3.35, 2.08, 0.01 +201602, -0.07, 0.80, -0.49, 0.02 +201603, 6.96, 0.86, 1.15, 0.02 +201604, 0.92, 0.69, 3.26, 0.01 +201605, 1.78, -0.28, -1.82, 0.01 +201606, -0.05, 0.65, -1.46, 0.02 +201607, 3.95, 2.63, -1.09, 0.02 +201608, 0.50, 1.17, 3.37, 0.02 +201609, 0.25, 2.04, -1.47, 0.02 +201610, -2.02, -4.40, 4.14, 0.02 +201611, 4.86, 5.46, 8.32, 0.01 +201612, 1.81, 0.12, 3.60, 0.03 +201701, 1.94, -1.02, -2.77, 0.04 +201702, 3.57, -2.04, -1.82, 0.04 +201703, 0.17, 1.19, -3.17, 0.03 +201704, 1.09, 0.71, -1.87, 0.05 +201705, 1.06, -2.56, -3.81, 0.06 +201706, 0.78, 2.16, 1.35, 0.06 +201707, 1.87, -1.38, -0.26, 0.07 +201708, 0.16, -1.61, -2.22, 0.09 +201709, 2.51, 4.52, 3.02, 0.09 +201710, 2.25, -1.83, -0.08, 0.09 +201711, 3.12, -0.67, -0.04, 0.08 +201712, 1.06, -1.28, 0.11, 0.09 +201801, 5.58, -2.92, -1.55, 0.11 +201802, -3.65, 0.28, -1.09, 0.11 +201803, -2.35, 3.88, 0.00, 0.12 +201804, 0.29, 1.13, 0.56, 0.14 +201805, 2.65, 5.35, -3.21, 0.14 +201806, 0.48, 1.23, -2.40, 0.14 +201807, 3.19, -2.20, 0.47, 0.16 +201808, 3.44, 1.14, -4.13, 0.16 +201809, 0.06, -2.37, -1.32, 0.15 +201810, -7.68, -4.78, 3.47, 0.19 +201811, 1.69, -0.81, 0.27, 0.18 +201812, -9.55, -2.63, -1.49, 0.19 +201901, 8.41, 3.00, -0.60, 0.21 +201902, 3.40, 2.07, -2.83, 0.18 +201903, 1.10, -3.16, -4.04, 0.19 +201904, 3.96, -1.70, 1.96, 0.21 +201905, -6.94, -1.23, -2.35, 0.21 +201906, 6.93, 0.33, -1.08, 0.18 +201907, 1.19, -2.05, 0.17, 0.19 +201908, -2.58, -2.42, -5.05, 0.16 +201909, 1.43, -0.96, 6.81, 0.18 +201910, 2.06, 0.26, -2.04, 0.15 +201911, 3.87, 0.87, -1.80, 0.12 +201912, 2.77, 0.68, 1.93, 0.14 +202001, -0.11, -3.10, -6.37, 0.13 +202002, -8.13, 0.98, -4.05, 0.12 +202003, -13.39, -5.15, -14.17, 0.12 +202004, 13.65, 2.76, -1.18, 0.00 +202005, 5.58, 2.46, -4.92, 0.01 +202006, 2.45, 2.68, -2.19, 0.01 +202007, 5.77, -2.31, -1.27, 0.01 + + Annual Factors: January-December +,Mkt-RF,SMB,HML,RF + 1927, 29.47, -2.46, -3.75, 3.12 + 1928, 35.39, 4.41, -5.83, 3.56 + 1929, -19.54, -30.78, 11.96, 4.75 + 1930, -31.23, -5.19, -12.29, 2.41 + 1931, -45.11, 3.51, -14.32, 1.07 + 1932, -9.39, 4.91, 10.49, 0.96 + 1933, 57.05, 48.86, 28.15, 0.30 + 1934, 3.02, 25.43, -27.38, 0.16 + 1935, 44.96, 9.99, 9.78, 0.17 + 1936, 32.07, 17.89, 35.86, 0.18 + 1937, -34.96, -14.00, -3.97, 0.31 + 1938, 28.48, 9.35, -12.18, -0.02 + 1939, 2.70, 5.85, -19.17, 0.02 + 1940, -7.14, 0.79, -0.82, 0.00 + 1941, -10.53, -4.04, 11.13, 0.06 + 1942, 16.20, 5.05, 19.87, 0.27 + 1943, 27.96, 33.35, 38.97, 0.35 + 1944, 20.97, 17.98, 15.61, 0.33 + 1945, 38.38, 25.56, 11.40, 0.33 + 1946, -6.73, -3.79, 2.98, 0.35 + 1947, 2.95, -7.08, 9.76, 0.50 + 1948, 1.07, -9.14, 3.52, 0.81 + 1949, 19.12, 3.93, -4.55, 1.10 + 1950, 28.82, 0.93, 27.01, 1.20 + 1951, 19.22, -4.93, -5.93, 1.49 + 1952, 11.80, -6.66, 3.34, 1.66 + 1953, -1.05, -1.16, -7.71, 1.82 + 1954, 49.35, -2.18, 26.21, 0.86 + 1955, 23.75, -6.71, 5.77, 1.57 + 1956, 5.90, -1.17, -1.60, 2.46 + 1957, -13.16, -2.72, -6.39, 3.14 + 1958, 43.45, 14.81, 13.16, 1.54 + 1959, 9.76, 5.43, 1.86, 2.95 + 1960, -1.46, -2.76, -4.87, 2.66 + 1961, 24.81, 1.49, 5.19, 2.13 + 1962, -12.90, -8.25, 8.80, 2.73 + 1963, 17.84, -5.88, 15.66, 3.12 + 1964, 12.54, -0.99, 9.86, 3.54 + 1965, 10.52, 21.80, 7.36, 3.93 + 1966, -13.51, 2.59, -0.68, 4.76 + 1967, 24.49, 50.69, -8.58, 4.21 + 1968, 8.79, 24.50, 18.49, 5.21 + 1969, -17.54, -13.98, -9.81, 6.58 + 1970, -6.49, -11.79, 22.34, 6.52 + 1971, 11.78, 5.62, -11.29, 4.39 + 1972, 13.05, -11.95, 1.75, 3.84 + 1973, -26.19, -23.44, 18.08, 6.93 + 1974, -35.75, -0.60, 9.67, 8.00 + 1975, 32.44, 15.28, 9.49, 5.80 + 1976, 21.91, 14.69, 24.50, 5.08 + 1977, -8.26, 22.95, 7.51, 5.12 + 1978, 1.03, 14.38, 0.37, 7.18 + 1979, 13.09, 21.12, -2.12, 10.38 + 1980, 22.13, 5.57, -25.06, 11.24 + 1981, -18.13, 7.23, 25.01, 14.71 + 1982, 10.66, 8.89, 13.59, 10.54 + 1983, 13.74, 13.67, 20.85, 8.80 + 1984, -6.05, -8.31, 19.63, 9.85 + 1985, 24.91, 0.12, 1.35, 7.72 + 1986, 10.12, -9.60, 9.58, 6.16 + 1987, -3.87, -11.00, -1.64, 5.47 + 1988, 11.55, 5.90, 14.77, 6.35 + 1989, 20.49, -12.72, -4.29, 8.37 + 1990, -13.95, -14.18, -9.72, 7.81 + 1991, 29.18, 16.13, -14.41, 5.60 + 1992, 6.23, 7.58, 24.28, 3.51 + 1993, 8.21, 5.80, 18.91, 2.90 + 1994, -4.10, -1.06, -0.69, 3.90 + 1995, 31.22, -9.09, 5.30, 5.60 + 1996, 15.96, -3.71, 6.16, 5.21 + 1997, 25.96, -6.76, 17.46, 5.26 + 1998, 19.46, -26.02, -8.89, 4.86 + 1999, 20.57, 14.85, -31.77, 4.68 + 2000, -17.60, -1.51, 39.69, 5.89 + 2001, -15.20, 18.09, 19.52, 3.83 + 2002, -22.76, 4.72, 7.47, 1.65 + 2003, 30.75, 26.18, 5.40, 1.02 + 2004, 10.72, 4.85, 8.08, 1.20 + 2005, 3.09, -1.93, 8.33, 2.98 + 2006, 10.60, 0.24, 14.11, 4.80 + 2007, 1.04, -7.14, -14.65, 4.66 + 2008, -38.34, 3.18, 0.82, 1.60 + 2009, 28.26, 9.39, -9.17, 0.10 + 2010, 17.37, 13.77, -5.31, 0.12 + 2011, 0.44, -6.04, -8.35, 0.04 + 2012, 16.28, -1.28, 9.68, 0.06 + 2013, 35.20, 7.18, 1.33, 0.02 + 2014, 11.70, -8.11, -1.74, 0.02 + 2015, 0.07, -4.03, -9.65, 0.02 + 2016, 13.30, 6.65, 23.02, 0.20 + 2017, 21.50, -4.68, -13.98, 0.80 + 2018, -6.93, -3.40, -9.16, 1.81 + 2019, 28.28, -6.09, -11.67, 2.14 + +Copyright 2020 Kenneth R. French diff --git a/Data/F-F_Research_Data_Factors_daily.CSV b/Data/F-F_Research_Data_Factors_daily.CSV new file mode 100644 index 0000000..f502001 --- /dev/null +++ b/Data/F-F_Research_Data_Factors_daily.CSV @@ -0,0 +1,24797 @@ +This file was created by CMPT_ME_BEME_RETS_DAILY using the 202007 CRSP database. +The Tbill return is the simple daily rate that, over the number of trading days +in the month, compounds to 1-month TBill rate from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RF +19260701, 0.10, -0.24, -0.28, 0.009 +19260702, 0.45, -0.32, -0.08, 0.009 +19260706, 0.17, 0.27, -0.35, 0.009 +19260707, 0.09, -0.59, 0.03, 0.009 +19260708, 0.21, -0.36, 0.15, 0.009 +19260709, -0.71, 0.44, 0.56, 0.009 +19260710, 0.62, -0.50, -0.15, 0.009 +19260712, 0.04, 0.03, 0.54, 0.009 +19260713, 0.48, -0.26, -0.23, 0.009 +19260714, 0.04, 0.09, -0.48, 0.009 +19260715, -0.43, 0.54, -0.30, 0.009 +19260716, 0.53, 0.01, -0.57, 0.009 +19260717, 0.34, 0.43, -0.63, 0.009 +19260719, -0.01, 0.01, -0.49, 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French diff --git a/Data/F-F_Research_Data_Factors_weekly.CSV b/Data/F-F_Research_Data_Factors_weekly.CSV new file mode 100644 index 0000000..a6741c9 --- /dev/null +++ b/Data/F-F_Research_Data_Factors_weekly.CSV @@ -0,0 +1,4916 @@ +This file was created by CMPT_ME_BEME_RETS_WEEKLY using the 202007 CRSP database. +The Tbill return is the weekly rate that, over four weeks, +compounds to 1-month TBill rate from Ibbotson and Associates Inc. + +,Mkt-RF,SMB,HML,RF +19260702, 1.60, -0.57, -0.90, 0.056 +19260710, 0.36, -0.86, 0.27, 0.056 +19260717, 1.01, 0.83, -1.84, 0.056 +19260724, -2.05, 0.15, -0.25, 0.056 +19260731, 3.04, -1.86, -0.85, 0.056 +19260807, 2.01, 0.08, 0.53, 0.063 +19260814, 0.33, -0.66, 0.76, 0.063 +19260821, -1.11, 0.26, 1.95, 0.063 +19260828, 0.53, 0.07, 0.84, 0.063 +19260903, 1.87, -0.41, 0.55, 0.057 +19260911, -1.27, 0.16, -0.01, 0.057 +19260918, 0.16, -1.10, 0.32, 0.057 +19260925, 0.62, -0.80, -0.39, 0.057 +19261002, 0.48, -0.08, -0.48, 0.080 +19261009, -4.14, -0.31, 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+20190913, 1.33, 2.86, 5.05, 0.044 +20190920, -0.64, -0.55, -1.00, 0.044 +20190927, -1.36, -1.86, 2.00, 0.044 +20191004, -0.40, -0.46, -2.46, 0.038 +20191011, 0.69, -0.43, 0.22, 0.038 +20191018, 0.47, 0.76, 0.55, 0.038 +20191025, 1.38, 0.34, 0.70, 0.038 +20191101, 1.47, 0.38, -0.60, 0.031 +20191108, 1.01, -0.41, 2.31, 0.031 +20191115, 0.78, -0.68, -1.76, 0.031 +20191122, -0.19, -0.06, -1.56, 0.031 +20191129, 1.13, 1.53, -1.59, 0.031 +20191206, 0.10, 0.47, 0.85, 0.035 +20191213, 0.74, 0.16, 0.77, 0.035 +20191220, 1.71, 0.36, -0.02, 0.035 +20191227, 0.49, -0.49, -0.40, 0.035 +20200103, -0.12, -0.29, 0.33, 0.032 +20200110, 0.94, -0.92, -2.20, 0.032 +20200117, 2.00, 0.64, -1.34, 0.032 +20200124, -1.14, -1.23, -1.12, 0.032 +20200131, -2.05, -1.13, -1.53, 0.032 +20200207, 3.13, -0.37, -0.77, 0.030 +20200214, 1.73, -0.02, -0.93, 0.030 +20200221, -1.06, 0.74, -0.04, 0.030 +20200228, -11.50, 0.63, -2.40, 0.030 +20200306, 0.10, -1.66, -4.86, 0.031 +20200313, -9.71, -5.94, -3.56, 0.031 +20200320, -14.57, 1.63, -5.82, 0.031 +20200327, 10.32, -0.97, 1.01, 0.031 +20200403, -2.49, -2.20, -5.46, 0.001 +20200409, 12.35, 2.84, 7.88, 0.001 +20200417, 3.24, -1.67, -8.58, 0.001 +20200424, -0.85, 2.54, -2.01, 0.001 +20200501, 0.01, 2.31, 4.85, 0.002 +20200508, 4.24, 2.26, -3.52, 0.002 +20200515, -2.20, -1.25, -7.30, 0.002 +20200522, 3.59, 3.29, 4.01, 0.002 +20200529, 2.98, -1.12, 3.73, 0.002 +20200605, 4.97, 0.59, 9.93, 0.003 +20200612, -4.73, -1.27, -5.23, 0.003 +20200619, 2.29, 1.27, -3.21, 0.003 +20200626, -2.83, 0.84, -4.13, 0.003 +20200702, 4.04, 0.67, -1.66, 0.003 +20200710, 2.10, -1.86, -1.38, 0.003 +20200717, 1.20, 1.76, 2.38, 0.003 +20200724, -0.31, -0.23, 2.91, 0.003 +20200731, 1.73, -0.51, -2.53, 0.003 + +Copyright 2020 Kenneth R. French From 3e73e48d51e188552783db87513255e5741c26ac Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 18 Sep 2020 14:15:52 -0700 Subject: [PATCH 068/107] Adds 315 G-Score Investing --- .../01 Strategy Library.php | 9 +++ .../1030 G-Score Investing/01 Abstract.html | 3 + .../02 Introduction.html | 10 ++++ .../1030 G-Score Investing/03 Method.html | 57 +++++++++++++++++++ .../1030 G-Score Investing/04 Algorithm.html | 6 ++ .../1030 G-Score Investing/05 Results.html | 5 ++ .../1030 G-Score Investing/06 References.html | 6 ++ 7 files changed, 96 insertions(+) create mode 100644 04 Strategy Library/1030 G-Score Investing/01 Abstract.html create mode 100644 04 Strategy Library/1030 G-Score Investing/02 Introduction.html create mode 100644 04 Strategy Library/1030 G-Score Investing/03 Method.html create mode 100644 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html create mode 100644 04 Strategy Library/1030 G-Score Investing/05 Results.html create mode 100644 04 Strategy Library/1030 G-Score Investing/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..edba6dd 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'G-Score Investing', + 'link' => 'strategy-library/g-score-investing', + 'sources' => [ + 'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=403180' + ], + 'description' => "Applying G-Score Investing to Invest in a Portfolio of Technology Stocks", + 'tags'=>'Fundamentals, Factor Investing, G-Score Investing, MorningStar data, equities' ] ]; diff --git a/04 Strategy Library/1030 G-Score Investing/01 Abstract.html b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html new file mode 100644 index 0000000..248865d --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html @@ -0,0 +1,3 @@ +

      + In this tutorial, we apply G-Score Investing to choose a Universe of stocks to invest in. +

      diff --git a/04 Strategy Library/1030 G-Score Investing/02 Introduction.html b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html new file mode 100644 index 0000000..b26b9f7 --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html @@ -0,0 +1,10 @@ +

      + Analyzing a company’s fundamentals is a method of trading that doesn’t + rely purely on price and volume data. We will apply the use of computers to automate + the analysis of this data, and we will do so using a method of + Factor Investing, + the process of using different attributes, in this case, fundamental data, to choose + stocks to purchase. More specifically, we will use G-Score investing, and evaluate companies + on seven factors that we will detail later. We specifically choose companies with Book-to-Market + due to abnormal returns as a result of the Risk Premium Effect. +

      diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html new file mode 100644 index 0000000..b6ef87b --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html @@ -0,0 +1,57 @@ +

      + We first sort all companies that have fundamental data by their Book-to-Market ratio, and narrow our universe to the + bottom quartile. We measure the Book-to-Market ratio using + fine.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths divided by + fine.MarketCap. In this strategy, we will use Technology as the industry of choice, thus, we further + narrow this universe to Technology stocks only. +

      + +

      + For each of the conditions that are described below, if met, one point will be added to the G-Score. + Thus, with seven factors, our G-Score can range from 0 to 7. We evaluate a company based on the following: +

      + + +
        +
      • + The Return on Assets (ROA) is greater than the contemporaneous industry median. In other words, the ROA for + the analyzed company is greater than the median of the ROAs of all companies in the same industry
        + We measure this value using fine.OperationRatios.ROA.OneYear +
        • +
      • + The Cash Flow Return on Assets (CFROA) is greater than the contemporaneous industry median
        + We measure this value using fine.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths + divided by fine.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths +
        • +
      • + The CFROA is greater than the ROA +
        • +
      • + The Variance of the ROA (VARROA) is lower than the contemporaneous industry median
        + We measure this by storing the past twelve values of fine.OperationRatios.ROA.ThreeMonths in a + RollingWindow and computing the variance of the values in the RollingWindow +
        • +
      • + The Research and Development Expenditure (R&D) is higher than the contemporaneous industry median
        + We measure this using fine.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths +
        • +
      • + The Capital Expenditure (CapEx) is higher than the contemporaneous industry median
        + We measure this using fine.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths +
        • +
      • + The Advertisement Expenditure (Ad) is higher than the contemporaneous industry median
        + We measure this using fine.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths +
        • +
      + +

      + The fundamental data used in our algorithms is sourced from MorningStar, and to read more about our fundamental data, + please visit the Fundamentals section of our + documentation. +

      + +

      + Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher +

      + diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html new file mode 100644 index 0000000..108b8ca --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html new file mode 100644 index 0000000..c8570c2 --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html @@ -0,0 +1,5 @@ +

      + Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track + using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is + outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period. +

      \ No newline at end of file diff --git a/04 Strategy Library/1030 G-Score Investing/06 References.html b/04 Strategy Library/1030 G-Score Investing/06 References.html new file mode 100644 index 0000000..f21473a --- /dev/null +++ b/04 Strategy Library/1030 G-Score Investing/06 References.html @@ -0,0 +1,6 @@ +
        +
      1. + Mohanram, Partha S., Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement + nalysis (April 2004). Online Copy. +
        2. +
      \ No newline at end of file From 140b9612c017265b3e8d4720eff49b69e69e9ad0 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 18 Sep 2020 15:22:35 -0600 Subject: [PATCH 069/107] Fix strategy library map file --- .../01 Strategy Library.php | 9 + .../01 Abstract.html | 8 + .../02 Background.html | 14 ++ .../03 Method.html | 183 ++++++++++++++++++ .../04 Algorithm.html | 6 + .../05 Conclusion.html | 26 +++ .../06 References.html | 11 ++ 7 files changed, 257 insertions(+) create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..384bc60 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'Using News Sentiment to Predict Price Direction of Drug Manufacturers', + 'link' => 'strategy-library/using-news-sentiment-to-predict-price-direction-of-drug-manufacturers', + 'sources' => [ + 'arXiv' => 'https://arxiv.org/abs/1812.04199' + ], + 'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.", + 'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday' ] ]; diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html new file mode 100644 index 0000000..ab35081 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html @@ -0,0 +1,8 @@ +

      + Several studies have found that press releases and other media can impact the perspective of investors. In this + tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug + manufacturer following positive news releases. Our findings show that when combining the effect with the + day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the + trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is + inspired by the work of Isah, Shah, & Zulkernine (2018). +

      diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html new file mode 100644 index 0000000..60fe4eb --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html @@ -0,0 +1,14 @@ +

      + The use of alternative data sets to forecast stock prices has increased in recent years as the fundamental and + technical analysis spaces increase in competition. Utilizing Natural Language Processing (NLP) techniques to + analyze the sentiment of news releases and other text related to publicly traded companies has caught the interest + of many quant researchers. Such online information is frequently released and can be interpreted in a virtually + unlimited number of ways, leading to a novel approach to determining the "societal mood" (Isah et al, 2018, p. 2) + towards a company. +

      + +

      + There are several ways to implement a NLP system. In this tutorial, we utilize a dictionary to quantify the + sentiment of news releases. The dictionary provided herein was sourced from Isah et al (2018), where it's use + achieved a 70% accuracy when targeting several hand-picked stocks in India's pharmaceutical industry. +

      \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html new file mode 100644 index 0000000..ac080d3 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html @@ -0,0 +1,183 @@ +

      Universe Selection

      +

      + We implement a universe selection model that provides the trading system with companies classified by + MorningStar as being in the drug + manufacturing industry group. We narrow our universe to include only the drug manufacturers with the greatest PE + ratios and dollar volume. +

      +
      +
      +def SelectCoarse(self, algorithm, coarse):
+    has_fundamentals = [c for c in coarse if c.HasFundamentalData]
+    sorted_by_dollar_volume = sorted(has_fundamentals, key=lambda c: c.DollarVolume, reverse=True)
+    return [ x.Symbol for x in sorted_by_dollar_volume[:self.coarse_size] ]
+
+def SelectFine(self, algorithm, fine):
+    drug_manufacturers = [f for f in fine if f.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.DrugManufacturers]
+    sorted_by_pe = sorted(drug_manufacturers, key=lambda f: f.ValuationRatios.PERatio, reverse=True)
+    return [ x.Symbol for x in sorted_by_pe[:self.fine_size] ]
+
      +
      + + +

      Alpha Construction

      +

      + The DrugNewsSentimentAlphaModel emits insights to take long intraday positions for securities that have positive + news sentiment. During construction of the model, we: +

      + +
        +
      • Create a dictionary to store SymbolData for each symbol
        • +
      • Gather the sentiment dictionary provided by Isah et al (2018)
        • +
      • Determine the maximum number of grams we need to analyze news articles
        • +
      • Define a method to determine the sign of sentiment
        • +
      • Specify the value of `bars_before_insight`
        • +
      + +

      + The `bars_before_insight` parameter determines how many bars the alpha model should observe after the market opens + before emitting insights. Isah et al (2018) batch the news released by each company into 30-minute intervals + before analyzing the sentiment of the batch. In this tutorial, we follow a similar procedure by setting + `bars_before_insight` to 30. +

      + +
      +
      +class DrugNewsSentimentAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    sentiment_by_phrase = SentimentByPhrase.dictionary
+    max_phrase_words = max([len(phrase.split()) for phrase in sentiment_by_phrase.keys()])
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+    
+    def __init__(self, bars_before_insight=30):
+        self.bars_before_insight = bars_before_insight
+
      +
      + +

      Alpha Securities Management

      +

      + When a new security is added to the universe, we create a SymbolData object for it to store information unique to + each security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method. +

      + +
      +
      +def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+    
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            algorithm.RemoveSecurity(symbol_data.tiingo_symbol)
+
      +
      + +

      + The definition of the SymbolData class is shown below. We add properties to it to track the cumulative sentiment + of news releases over time and the number of bars the alpha model has received for each security since the market + open. In the constructor, we save a reference to the security's exchange so we can access the market hours of the + exchange when generating insights. This is also where we initialize the + Tiingo news feed for each security. +

      + +
      +
      +class SymbolData:
+    cumulative_sentiment = 0
+    bars_seen_today = 0
+    
+    def __init__(self, security, algorithm):
+        self.exchange = security.Exchange
+        self.tiingo_symbol = algorithm.AddData(TiingoNews, security.Symbol).Symbol
+
      +
      + + +

      Alpha Update

      +

      + As new Tiingo objects are provided to the alpha model's Update method, we update the cumulative sentiment for each + security. The cumulative sentiment counter is reset at each market close. Therefore, when we emit insights + 30-minutes after the open, we are considering the sentiment of the news articles released from the previous close + to the current time. We employ the findings of Berument & Kiymaz (2001), restricting the alpha model's trading to + Wednesday, the most profitable day of the week. Positions are entered 30-minutes after the open and exited at the + close. +

      + +
      +
      +def Update(self, algorithm, data):
+    insights = []
+        
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+    
+        # If it's after-hours or within 30-minutes of the open, update
+        # cumulative sentiment for each symbol    
+        if symbol_data.bars_seen_today < self.bars_before_insight:
+            tiingo_symbol = symbol_data.tiingo_symbol
+            if data.ContainsKey(tiingo_symbol) and data[tiingo_symbol] is not None:
+                article = data[tiingo_symbol]
+                symbol_data.cumulative_sentiment += self.CalculateSentiment(article)
+    
+        if data.ContainsKey(symbol) and data[symbol] is not None:
+            symbol_data.bars_seen_today += 1
+
+            # 30-mintes after the open, emit insights in the direction of the cumulative sentiment.
+            # Only emit insights on Wednesdays to capture the analomaly documented by Berument and 
+            # Kiymaz (2001).
+            if symbol_data.bars_seen_today == self.bars_before_insight and data.Time.weekday() == 2:
+                    next_close_time = symbol_data.exchange.Hours.GetNextMarketClose(data.Time, False)
+                    direction = self.sign(symbol_data.cumulative_sentiment)
+                    if direction == 0:
+                        continue
+                    insight = Insight.Price(symbol, 
+                                            next_close_time - timedelta(minutes=2),
+                                            direction)
+                    insights.append(insight)
+    
+            # At the close, reset the cumulative sentiment
+            if not symbol_data.exchange.DateTimeIsOpen(data.Time):
+                symbol_data.cumulative_sentiment = 0
+                symbol_data.bars_seen_today = 0
+    
+    return insights
+
      +
      + + +

      Calculating Sentiment

      +

      + We define the following helper method to return the sentiment of a Tiingo news article by analyzing the article's + title and description. The `sentiment_by_phrase` dictionary was retrieved from queensbamlab's + NewsSentiment GitHub repository. Although we have + adjusted the dictionary to lowercase and removed some redundancies, this is the same dictionary used by Isah et + al (2018). "The dictionary was created by leveraging author's domain expertise and thorough analysis of news + articles over the years" (p. 3). +

      + +
      +
      +def CalculateSentiment(self, article):
+    sentiment = 0
+    for content in (article.Title, article.Description):
+        words = content.lower().split()
+        for num_words in range(1, self.max_phrase_words + 1):
+            for gram in ngrams(words, num_words):
+                phrase = ' '.join(gram)
+                if phrase in self.sentiment_by_phrase.keys():
+                    sentiment += self.sentiment_by_phrase[phrase]
+    return sentiment
+
      +
      + + +

      Portfolio Construction & Trade Execution

      +

      + Following the guidelines of Alpha Streams and the + Quant League competition, we utilize the + + EqualWeightingPortfolioConstructionModel and the + + ImmediateExecutionModel. +

      + diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html new file mode 100644 index 0000000..43dfb6f --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html new file mode 100644 index 0000000..553c5a6 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html @@ -0,0 +1,26 @@ +

      + We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide + as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with + a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting + trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability + over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated + by LEAN. +

      + +

      +To continue the development of this strategy, future areas of research include: +

      + +
        +
      • Adding a threshold parameter the cumulative sentiment counter must pass to signal trades
        • +
      • + Only analyzing the sentiment of articles which contain keywords like "US, "USA", 'Q1', and others in their + titles +
        • +
      • + Stemming and removing + punctuation from articles before calculating their sentiment +
        • +
      • Adding phrases to the sentiment dictionary or adjust the sentiment values
        • +
      • Adding other data feeds beside just Tiingo
        • +
      \ No newline at end of file diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html new file mode 100644 index 0000000..8f8f421 --- /dev/null +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html @@ -0,0 +1,11 @@ +
        +
      1. + Shah, Dev, Haruna Isah, and Farhana Zulkernine. “Predicting the Effects of News Sentiments on the Stock Market.” + 2018 IEEE International Conference on Big Data (Big Data) (2018). + Online copy +
        2. +
      2. + Berument, Hakan and Kiymaz, Halil, The Day of the Week Effect on Stock Market Volatility (2001). Journal of + Economics and Finance, Vol.25, No.2, pp. 181-193. Online copy +
        3. +
      \ No newline at end of file From 0f88f5770ebc6f2b0f26aac80b0a1c6acd244c4c Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 18 Sep 2020 15:23:55 -0600 Subject: [PATCH 070/107] Fix typo --- .../01 Abstract.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html index ab35081..387cf48 100644 --- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html +++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html @@ -1,7 +1,7 @@

      Several studies have found that press releases and other media can impact the perspective of investors. In this tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug - manufacturer following positive news releases. Our findings show that when combining the effect with the + manufacturers following positive news releases. Our findings show that when combining the effect with the day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is inspired by the work of Isah, Shah, & Zulkernine (2018). From 6235e6b7a9661b0ee4c9435b785f69fb1c34764b Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Fri, 25 Sep 2020 12:54:30 -0700 Subject: [PATCH 071/107] Adds 321 SVM Wavelet Forecasting --- .../01 Strategy Library.php | 9 ++++ .../01 Abstract.html | 3 ++ .../02 Introduction.html | 10 ++++ .../03 Method.html | 52 +++++++++++++++++++ .../04 Algorithm.html | 6 +++ .../05 Results.html | 8 +++ .../06 References.html | 5 ++ 7 files changed, 93 insertions(+) create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php index 6144105..e979478 100644 --- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php +++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php @@ -678,6 +678,15 @@ ], 'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process", 'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping' + ], + [ + 'name' => 'SVM Wavelet Forecasting', + 'link' => 'strategy-library/svm-wavelet-forecasting', + 'sources' => [ + 'Academia' => 'https://www.academia.edu/37180223/SVR_Wavelet_Adaptive_Model_for_Forecasting_Financial_Time_Series' + ], + 'description' => "Forecasting EURJPY prices with an SVM Wavelet model", + 'tags'=>'Support Vector Machines (SVM), Forex, Forecasting, Wavelet, Discrete Wavelet Transform' ] ]; diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html new file mode 100644 index 0000000..fb88b27 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html @@ -0,0 +1,3 @@ +

      + In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices. +

      diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html new file mode 100644 index 0000000..168b024 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html @@ -0,0 +1,10 @@ +

      + Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we + combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often + used for classification problems, such as classifying proteins, they can also be applied in regression problems, and + are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be + used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so + when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to + forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate + forecast, which we detail in the Method section. +

      diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html new file mode 100644 index 0000000..1a923a5 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html @@ -0,0 +1,52 @@ +

      + Given EURJPY data, our first step is to decompose our data into multiple resolutions. + We work with wavelets using the pywt package. For denoising, Daubechies and + Symlets are good choices for Wavelets, and we use Symlets 10 in our strategy. We create a Symlets 10 Wavelet + using the following:  +

      + +
      +
      +    w = pywt.Wavelet('sym10')
+
      +
      +

      To determine the length of the data we’d need for a certain number of levels after decomposition, we can solve for:

      +\[log_{2}(\frac{len(data)}{wavelength-1})=levels\] +

      + Given the length of a Symlet 10 wavelet is 20, if we want three levels, we + solve for len(data) to get len(data) = 152, which means data would need to have at least + 152 values. Since we will denoise our components using thresholding, + we specify threshold = 0.5 to indicate the strength of the thresholding. + This threshold value can be any number between 0 and 1. +

      +

      To decompose our data, we use: 

      + +
      +
      +    coeffs = pywt.wavedec(data, w)
+
      +
      + +

      For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

      + +
      +
      +    for i in range(1, len(coeffs)):
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+        forecasted = __svm_forecast(coeffs[i])
+        coeffs[i] = np.roll(coeffs[i], -1)
+        coeffs[i][-1] = forecasted
+
      +
      + +

      The __svm_forecast method fits partitioned data to an SVM model then predicts one value into the + future, and can be found under SVMWavelet.py file under Algorithm 

      +

      Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:

      +
      +
      +    datarec = pywt.waverec(coeffs, w)
+
      +
      +

      Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or datarec[-1].

      + +

      Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.

      diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html new file mode 100644 index 0000000..fc4ed47 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html @@ -0,0 +1,6 @@ +
      +
      +
      + +
      +
      diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html new file mode 100644 index 0000000..d3cea27 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html @@ -0,0 +1,8 @@ +

      The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163, + while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:

      +
        +
      • Testing different Wavelet types (e.g. Daubechies)
        • +
      • Trying out other time resolutions (e.g. Minute)
        • +
      • Using alternative Decomposition methods (e.g. Stationary Wavelet Transform) 
        • +
      +

      If a user comes across any interesting results with modifications of this algorithm, we’d love to hear about it in the Community Forum.

      \ No newline at end of file diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html new file mode 100644 index 0000000..1cc8866 --- /dev/null +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html @@ -0,0 +1,5 @@ +
        +
      1. + M. S. Raimundo and J. Okamoto, "SVR-wavelet adaptive model for forecasting financial time series," 2018 International Conference on Information and Computer Technologies (ICICT), DeKalb, IL, 2018, pp. 111-114, doi: 10.1109/INFOCT.2018.8356851. Online Copy. +
        2. +
      \ No newline at end of file From e66d96aed7e3ee445d5c95e339392aa1883afbc5 Mon Sep 17 00:00:00 2001 From: Shile Wen Date: Mon, 28 Sep 2020 08:29:12 -0700 Subject: [PATCH 072/107] Adds 321 SVM Wavelet Forecasting --- .../1031 SVM Wavelet Forecasting/03 Method.html | 10 +++++++--- .../1031 SVM Wavelet Forecasting/04 Algorithm.html | 2 +- .../1031 SVM Wavelet Forecasting/05 Results.html | 2 +- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html index 1a923a5..6651dd7 100644 --- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html +++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html @@ -27,12 +27,16 @@
-

For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

+

For each component, we threshold/denoise the component (except for the first component, the approximation coefficients), + roll the values of the component one spot to the left, + and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:

-    for i in range(1, len(coeffs)):
-        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    for i in range(len(coeffs)):
+        if i > 0:
+            # we don't want to threshold the approximation coefficients
+            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
         forecasted = __svm_forecast(coeffs[i])
         coeffs[i] = np.roll(coeffs[i], -1)
         coeffs[i][-1] = forecasted
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index fc4ed47..868c5d9 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 

     

     

-        
+        
     

 

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index d3cea27..c80ae5c 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-
The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163,
+
The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:

 
    
     
  • Testing different Wavelet types (e.g. Daubechies)
    • 

From 920bf30f1ad9e98a66db45d643fdcfceb72ad8db Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 2 Oct 2020 19:06:33 -0600
Subject: [PATCH 073/107] Update cointegration algorithm and write-up

---
 .../04 Part II - Cointegration Method.html    | 53 ++++++++++---------
 .../05 Summary.html                           | 18 +++----
 .../06 Algorithm.html                         |  4 +-
 3 files changed, 40 insertions(+), 35 deletions(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 7011faf..76bf605 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -9,39 +9,44 @@ 
    Step 1: Generate the Spread Series
    
 
 \[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
 
-
    Step 2: Compute the Threshold
    
+
    Step 2: Compute the Signals
    
 
 
    
-  Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
+  Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
+  is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  sizes are scaled by the coefficient β.
 
    
 
 
    
 
-
    price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
-                     index = [i.Time for i in self.symbols[0].hist_window])
+
    log_close_x = np.log(self.closes_by_symbol[self.x_symbol])
+log_close_y = np.log(self.closes_by_symbol[self.y_symbol])
+
+spread, beta = self.regr(log_close_x, log_close_y)
 
-price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
-                     index = [i.Time for i in self.symbols[1].hist_window])
-if len(price_x) < 250: return
-spread = self.regr(np.log(price_x), np.log(price_y))
 mean = np.mean(spread)
 std = np.std(spread)
-ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
-if spread[-1] > mean + self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
-        self.Sell(self.symbols[1], 100)
-        self.Buy(self.symbols[0],  ratio * 100)
-
-elif spread[-1] < mean - self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
-        self.Sell(self.symbols[0], 100)
-        self.Buy(self.symbols[1], ratio * 100)
+
+x_holdings = self.Portfolio[self.x_symbol]
+
+if x_holdings.Invested:
+    if x_holdings.IsShort and spread[-1] <= mean or \
+        x_holdings.IsLong and spread[-1] >= mean:
+        self.Liquidate()
 else:
-    self.Liquidate()
+    if beta < 1:
+        x_weight = 0.5
+        y_weight = 0.5 / beta
+    else:
+        x_weight = 0.5 / beta
+        y_weight = 0.5
+    
+    if spread[-1] < mean - self.threshold * std:
+        self.SetHoldings(self.y_symbol, -y_weight) 
+        self.SetHoldings(self.x_symbol, x_weight)
+    if spread[-1] > mean + self.threshold * std:
+        self.SetHoldings(self.x_symbol, -x_weight)
+        self.SetHoldings(self.y_symbol, y_weight)
 
    
 
    
-
-
    Step 3: Set up the Trading Signals
    
-
    
-  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread mean+2*std. We construct short positions in Y and long positions in X on the day that spread<mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
-
    
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
index cd9ff46..44ab024 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -15,21 +15,21 @@
 
 
 Copula
-346
-274.293%
-1.022
-19.4%
+493
+8.884%
+0.12
+26.1%
 
 
 Cointegration
-91
-26.358%
-0.298
-23.7%
+126
+4.517%
+0.196
+3.9%
 
 
 
 
 
    
-  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
+  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 91 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
 
    
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 21099b5..228f289 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    

From d831869a62cc3858617fa9a275d6ab6492eee23d Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 2 Oct 2020 19:12:06 -0600
Subject: [PATCH 074/107] Fix typo

---
 .../04 Part II - Cointegration Method.html                      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 76bf605..d36c895 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -14,7 +14,7 @@ 
    Step 2: Compute the Signals
    
 
    
   Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
   is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
-  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing spread value. The position
   sizes are scaled by the coefficient β.
 
    
 

From adb03722c992d5267f8ecef3cc275e3b45c2c999 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Mon, 5 Oct 2020 16:56:27 -0700
Subject: [PATCH 075/107] update backtest

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 108b8ca..52a4b06 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index c8570c2..cefd93b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
    
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
    
\ No newline at end of file

From a11df07a89239053b1e37e3247ad47fe11101275 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 16 Oct 2020 15:54:15 -0600
Subject: [PATCH 076/107] Adds Gradient Boosting Strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  24 +++
 .../03 Method.html                            | 163 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  53 ++++++
 ...06 Market & Competition Qualification.html |  13 ++
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |   5 +
 9 files changed, 297 insertions(+)
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..7e5dd34 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'Gradient Boosting Model',
+     'link' => 'strategy-library/gradient-boosting-model',
+     'sources' => [
+       'arXiv' => 'https://ssrn.com/abstract=2323899'
+     ],
+     'description' => "Forecasts future intraday returns with a gradient boosting model trained on technical indicators",
+     'tags'=>'Gradient Boost, Regression Trees, Equities, Machine Learning'
   ]
 ];
 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
new file mode 100644
index 0000000..1d309c1
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+
    
+  In this tutorial, we train a Gradient Boosting Model (GBM) to forecast the intraday price movements of the SPY ETF using a 
+  collection of technical indicators. The implementation is based on the research produced by Zhou et al (2013), where a GBM 
+  was found to produce an annualized Sharpe ratio greater than 20. Our research shows that throughout a 5 year backtest, the 
+  model underperforms the SPY with its current parameter set. However, we finish the tutorial with highlighting potential 
+  areas of further research to improve the model’s performance.
+
    
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
new file mode 100644
index 0000000..49f45d3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -0,0 +1,24 @@
+
    
+  A GBM is trained by setting the initial model prediction to the mean target value in the training set. The model then 
+  iteratively builds regression trees to predict the model’s pseudo-residuals on the training set to tighten the fit. The 
+  pseudo-residuals are the differences between the target value and the model’s prediction on the current training iteration 
+  for each sample. The model’s predictions are made by summing the mean target value and the products of the learning rate 
+  and the regression tree outputs. The full algorithm is shown here.
+
    
+
+
    
+  Tutorial1033-gradient-boost-1
+
    
+
+
    
+  We provide technical indicator values as inputs to the GBM. The model is trained to predict the security’s return over the 
+  next 10 minutes and the performance of the model’s predictions are assessed using the mean squared error loss function.
+
    
+
+\[ MSE = \frac{\Sigma_{i=1}^n(y_i - \hat{y}_i)^2}{n}  \]
+
+
    
+  Zhou et al (2013) utilize custom loss functions to fit their GBM in a manner that aims to maximize the profit-and-loss or 
+  Sharpe ratio over the training data set. The attached notebook shows training the GBM with these custom loss functions 
+  leads to poor model predictions.
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
new file mode 100644
index 0000000..7e88ae2
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html	
@@ -0,0 +1,163 @@
+
    Universe Selection
    
+
    
+  We use a ManualUniverseSelectionModel to subscribe to the SPY ETF. The algorithm is designed to work with minute and
+  second data resolutions. In our implementation, we use data on a minute resolution.
+
    
+
    
+
    +symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+
    
+
    
+
+
+
    Alpha Construction
    
+
    
+  The GradientBoostingAlphaModel predicts the direction of the SPY at each timestep. Each position taken is held for 
+  10 minutes, although this duration is customizable in the constructor. During construction of this alpha model, we 
+  simply set up a dictionary to hold a SymbolData object for each symbol in the universe. In the case where the 
+  universe consists of multiple securities, the alpha model holds each with equal weighting.
+
    
+
    
+
    +class GradientBoostingAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    
+    def __init__(self, hold_duration = 10):
+        self.hold_duration = hold_duration
+        self.weight = 1
+
    
+
    
+
+
+
    Alpha Securities Management
    
+
    
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method.
+
    
+
    
+
    +def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        symbol = security.Symbol
+        self.symbol_data_by_symbol[symbol] = SymbolData(symbol, algorithm, self.hold_duration)
+            
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
+    self.weight = 1 / len(self.symbol_data_by_symbol)
+
+
    
+
    
+
+
+
    SymbolData Class
    
+
    
+  The SymbolData class is used in this algorithm to manage indicators, train the GBM, and produce trading predictions. 
+  The constructor definition is shown below. The class is designed to train at the end of each month, using the 
+  previous 4 weeks of data to fit the GBM that consists of 20 stumps (regression trees with 2 leaves). To ensure 
+  overnight holds are avoided, the class uses 
+  Scheduled Events to stop trading
+  near the market close.
+
    
+
    
+
    +class SymbolData:    
+    def __init__(self, symbol, algorithm, hold_duration, k_start=0.5, k_end=5,
+                    k_step=0.25, training_weeks=4, max_depth=1, num_leaves=2, num_trees=20,
+                    commission=0.02, spread_cost=0.03):
+        self.symbol = symbol
+        self.algorithm = algorithm
+        self.hold_duration = hold_duration
+        self.resolution = algorithm.UniverseSettings.Resolution
+        self.training_length = int(training_weeks * 5 * 6.5 * 60) # training_weeks in minutes
+        self.max_depth = max_depth
+        self.num_leaves = num_leaves
+        self.num_trees = num_trees
+        self.cost = commission + spread_cost
+
+        self.indicator_consolidators = []
+
+        # Train a model at the end of each month
+        self.model = None
+        algorithm.Train(algorithm.DateRules.MonthEnd(symbol),
+                        algorithm.TimeRules.BeforeMarketClose(symbol),
+                          self.train)
+
+        # Avoid overnight holds
+        self.allow_predictions = False
+        self.events = [
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.AfterMarketOpen(symbol, 0),
+                                  self.start_predicting),
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.BeforeMarketClose(symbol, hold_duration + 1),
+                                  self.stop_predicting)
+        ]
+
+        self.setup_indicators(k_start, k_end, k_step)
+        self.train()
+
    
+
    
+
+
+
    GBM Predictions
    
+
    
+  For brevity, we omit the model training logic. Although, the code can be seen in the attached backtest. To make 
+  predictions, we define the following method inside the SymbolData class. A position is held in the predicted 
+  direction only if the predicted return in that direction exceeds the cost of the trade.
+
    
+
    
+
    +def predict_direction(self):
+    if self.model is None or not self.allow_predictions:
+        return 0
+
+    input_data = [[]]
+    for _, indicators in self.indicators_by_indicator_type.items():
+        for indicator in indicators:
+            input_data[0].append(indicator.Current.Value)
+                
+    return_prediction = self.model.predict(input_data)
+    if return_prediction > self.cost:
+        return 1
+    if return_prediction < -self.cost:
+        return -1
+    return 0
+
    
+
    
+
+
+
    Alpha Update
    
+
    
+  As new TradeBars are provided to the alpha model's Update method, each SymbolData object makes a directional 
+  prediction for its security. If the prediction is not flat, the alpha model emits an insight in that direction with 
+  a duration of 10 minutes.
+
    
+
    
+
    +def Update(self, algorithm, data):
+    insights = []
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        direction = symbol_data.predict_direction()
+        if direction:
+            hold_duration = timedelta(minutes=self.hold_duration) # Should match universe resolution
+            insights.append(Insight.Price(symbol, hold_duration, direction, None, None, None, self.weight))
+
+    return insights
+
    
+
    
+
+
+
    Portfolio Construction & Trade Execution
    
+
    
+  Following the guidelines of Alpha Streams 
+  and the Quant League competition, we 
+  utilize the 
+  InsightWeightingPortfolioConstructionModel and the 
+  
+  ImmediateExecutionModel.
+
    
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
new file mode 100644
index 0000000..223e7f3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
    
+    
    
+    
    
+        
+    
    
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
new file mode 100644
index 0000000..88d08bf
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -0,0 +1,53 @@
+
+    
+        
+            
+            
+            
+            
+            
+            
+        
+    
+    
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+    
+
    Period NameStart DateEnd DateStrategySharpeVariance
    5 Year Backtest9/1/20159/17/2020Strategy-0.7160.006
    Benchmark0.8450.036
    2020 Crash2/19/20203/23/2020Strategy-2.8790.101
    Benchmark-1.2430.628
    2020 Recovery3/23/20206/8/2020Strategy-2.3290.027
    Benchmark13.7610.149
    
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d2ac60d
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,13 @@
+
    
+  Although this strategy passes several of the 
+  metrics required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+
    
+
+
+
      
+  
    • Profitable
      • 
+  
    • PSR >= 80%
      • 
+  
    • Max drawdown duration <= 6 months
      • 
+  
    • Handles dividends and splits
      • 
+
    
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
new file mode 100644
index 0000000..10701d6
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+
    
+  The GBM implemented in this tutorial has a lower Sharpe ratio than the S&P 500 index ETF benchmark over the periods 
+  we tested. However, the strategy generates a lower annual variance over all the testing period, implying more 
+  consistent returns than buy-and-hold. To continue the development of this strategy, future areas of research 
+  include:
+
    
+
+
      
+  
    • Adjusting parameters in the GradientBoostingAlphaModel and SymbolData classes
      • 
+  
    • Testing other custom loss functions
      • 
+  
    • Changing the data resolution from minutes to seconds
      • 
+  
    • Using more/other technical indicators
      • 
+  
    • 
+    Adding a model to predict the cost of trading (slippage, commissions, market impact) for each security instead of 
+    prescribing a fixed amount
+  
      • 
+
    
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/08 References.html b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html
new file mode 100644
index 0000000..385172c
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html	
@@ -0,0 +1,5 @@
+
      
+  
    1. 
+    Zhou, Nan and Cheng, Wen and Qin, Yichen and Yin, Zongcheng, Evolution of High Frequency Systematic Trading: A Performance-Driven Gradient Boosting Model (September 10, 2013). Online copy
+  
      2. 
+
    
\ No newline at end of file

From 79fc67e91a61a0b9fc43f2859bb4ca797db73379 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 16 Oct 2020 16:41:39 -0700
Subject: [PATCH 077/107] fixed typos

---
 .../01 Abstract.html                          |  2 +-
 .../02 Introduction.html                      | 14 +++++------
 .../03 Method.html                            | 24 +++++++++----------
 3 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html
index fb88b27..a93480f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
@@ -1,3 +1,3 @@
 
    
-    In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices.
+    In this tutorial, we apply an SVM Wavelet model in an attempt to forecast EURJPY prices.
 
    
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 168b024..986ebed 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -1,10 +1,10 @@
 
    
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
-    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often
-    used for classification problems, such as classifying proteins, they can also be applied in regression problems, and
-    are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be
-    used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so
-    when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to
-    forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate
-    forecast, which we detail in the Method section.
+    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
+    used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
+    leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
+    decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
+    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 
    
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
index 6651dd7..5cd8e93 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
@@ -7,7 +7,7 @@
 
 
    
 
    -    w = pywt.Wavelet('sym10')
+w = pywt.Wavelet('sym10')
 
    
 
    
 
    To determine the length of the data we’d need for a certain number of levels after decomposition, we can solve for:
    
@@ -23,7 +23,7 @@
 
 
    
 
    -    coeffs = pywt.wavedec(data, w)
+coeffs = pywt.wavedec(data, w)
 
    
 
    
 
@@ -33,24 +33,24 @@
 
 
    
 
    -    for i in range(len(coeffs)):
-        if i > 0:
-            # we don't want to threshold the approximation coefficients
-            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
-        forecasted = __svm_forecast(coeffs[i])
-        coeffs[i] = np.roll(coeffs[i], -1)
-        coeffs[i][-1] = forecasted
+for i in range(len(coeffs)):
+    if i > 0:
+        # we don't want to threshold the approximation coefficients
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    forecasted = __svm_forecast(coeffs[i])
+    coeffs[i] = np.roll(coeffs[i], -1)
+    coeffs[i][-1] = forecasted
 
    
 
    
 
 
    The __svm_forecast method fits partitioned data to an SVM model then predicts one value into the
-    future, and can be found under SVMWavelet.py file under Algorithm 
    
+    future, and can be found under SVMWavelet.py file under the Algorithm section
    
 
    Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:
    
 
    
 
    -    datarec = pywt.waverec(coeffs, w)
+datarec = pywt.waverec(coeffs, w)
 
    
 
    
 
    Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or datarec[-1].
    
 
-
    Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.
    
+
    Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the InsightWeightPortfolioConstructionModel so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have a larger allocation.
    

From 09e9a54af61023974d291b481fa7cce0cfdeb8b9 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 16 Oct 2020 16:43:02 -0700
Subject: [PATCH 078/107] fixed typos

---
 .../1031 SVM Wavelet Forecasting/02 Introduction.html         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 986ebed..6fe7274 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -2,9 +2,9 @@
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
     combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
     used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
-    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allow us
     to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
     leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
     decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
-    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
+    ahead of each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 
    

From f1cfa4561d7fcba2cdd7d2a88794e1140eab2a69 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 16:29:26 -0600
Subject: [PATCH 079/107] Add relative metrics to news sentiment strategy

---
 .../01 Abstract.html                                |  3 ++-
 .../05 Conclusion.html                              | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
index 387cf48..d02e534 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
@@ -3,6 +3,7 @@
   tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug 
   manufacturers following positive news releases. Our findings show that when combining the effect with the 
   day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the 
-  trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is 
+  trading system to remain profitable throughout the 2020 stock market crash. However, the algorithm 
+  underperforms the S&P 500 market index ETF, SPY, over the same time period. The algorithm we design here is 
   inspired by the work of Isah, Shah, & Zulkernine (2018).
 
    
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html
index 553c5a6..8f0a19e 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
@@ -1,10 +1,11 @@
 
    
-  We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide 
-  as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with 
-  a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting 
-  trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability 
-  over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated 
-  by LEAN.
+  We conclude that deploying the sentiment analysis strategy on the US drug manufacturing industry does not provide 
+  as accurate of results as found by Isah et al (2018). Only after restricting trading to the most profitable day of 
+  the week (Berument & Kiymaz, 2001) does the strategy achieve profitability over our testing period. Overall, the 
+  strategy produces a Sharpe ratio of 0.116, while the 
+  SPY benchmark
+  produces a 0.971 Sharpe ratio during the same period. We attribute the decrease in performance to the commissions 
+  and spread costs simulated by LEAN.
 
    
 
 
    

From b606a4f8f03eba2060a611875de59a9f1ab12de2 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 16:45:26 -0600
Subject: [PATCH 080/107] Add missing bracket to gradient boosting algo image

---
 .../1033 Gradient Boosting Model/02 Background.html             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
index 49f45d3..597b32d 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -7,7 +7,7 @@
 
    
 
 
    
-  Tutorial1033-gradient-boost-1
+  Tutorial1033-gradient-boost-1
 
    
 
 
    

From 95e76f2ef3d3722d5e05de72f769bda4b8a5921f Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 15:52:54 -0700
Subject: [PATCH 081/107] updated algo

---
 .../1031 SVM Wavelet Forecasting/04 Algorithm.html              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index 868c5d9..935af6f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    

From acb4f1ffd84421887cfceeafd8ad4d7ae2e738d4 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 15:53:37 -0700
Subject: [PATCH 082/107] updated resuls

---
 .../1031 SVM Wavelet Forecasting/05 Results.html                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index c80ae5c..96be5b5 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-
    The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
+
    The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.252,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:
    
 
      
     
    • Testing different Wavelet types (e.g. Daubechies)
      • 

From 37d0c973209b93184a39b7d0c4f04b2e89b1257c Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 16:21:44 -0700
Subject: [PATCH 083/107] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 52a4b06..11f46e3 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index cefd93b..f9af05b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
      
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
      
\ No newline at end of file

From fc5ef6d18ae739434b1f8cf9056d93879e759f71 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 23 Oct 2020 16:22:54 -0700
Subject: [PATCH 084/107] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/03 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html
index b6ef87b..3a493ab 100644
--- a/04 Strategy Library/1030 G-Score Investing/03 Method.html	
+++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html	
@@ -52,6 +52,6 @@
 
      
 
 
      
-    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher
+    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher.
 
      
 

From ce40497bbf02d5587fe1229e825e93752c43adeb Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 23 Oct 2020 18:15:01 -0600
Subject: [PATCH 085/107] Make relative tables mobile friendly

---
 .../05 Relative Performance.html                               | 2 ++
 .../1026 Intraday ETF Momentum/05 Conclusion.html              | 2 ++
 .../05 Relative Performance.html                               | 2 ++
 .../1033 Gradient Boosting Model/05 Relative Performance.html  | 3 +++
 4 files changed, 9 insertions(+)

diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html
index fd71509..2d3e6c9 100644
--- a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
@@ -6,6 +6,7 @@
   across all our testing periods is displayed in the table below.
 
      
 
+
      
 
@@ -72,6 +73,7 @@
         
      
     
         
      
     
 
      
+
      
 
 
 
      
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
index ff087ea..bad583b 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -7,6 +7,7 @@
   A breakdown of the results from all of the testing periods can be seen in the table below.
 
      
 
+
      
 
@@ -73,6 +74,7 @@
         
      
     
         
      
     
 
        
+
      
 
 
 
      
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html
index a1d1610..07a20e4 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
@@ -17,6 +17,7 @@
   frames, implying that the strategy has more consistent returns than the benchmark.
 
      
 
+
      
 
@@ -83,6 +84,7 @@
         
      
     
         
      
     
 
      
+
      
 
 
      
   We find the lack of performance for this strategy is not largely attributed to the transaction costs. After 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
index 88d08bf..415eea9 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -1,3 +1,4 @@
+
      
 
@@ -51,3 +52,5 @@
         
      
     
         
      
     
 
      
+
      
+

From dd941cd87883482bba20bc09a2fca642bfd8c263 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Wed, 28 Oct 2020 16:08:48 -0700
Subject: [PATCH 086/107] fixed algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 11f46e3..2c62d81 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index f9af05b..5f327af 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 
      
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.778 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 
      
\ No newline at end of file

From 9f30601e58cecd646ecd46f69cce5a57b8750c3a Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Thu, 5 Nov 2020 17:36:42 -0800
Subject: [PATCH 087/107] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index bcf0bc2..60c8e6c 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      

From 9ef83b5ff7ad231b106bff7e7c0130719d0e3e94 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Fri, 6 Nov 2020 15:42:22 -0800
Subject: [PATCH 088/107] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index 60c8e6c..8586edb 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      

From 65f9e86bf866688998d76599ce3b80df25d45fc9 Mon Sep 17 00:00:00 2001
From: Shile Wen 
Date: Wed, 11 Nov 2020 17:23:37 -0800
Subject: [PATCH 089/107] fixed algorithm

---
 .../05 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index f47a119..645e3da 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
      
     
      
     
      
-        
+        
     
      
 
      
\ No newline at end of file

From 0f13ec60484bfa85c7097f358515b5717152dbf2 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 18:37:06 -0700
Subject: [PATCH 090/107] Adds gaussian naive bayes model strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  44 ++++
 .../03 Method.html                            | 223 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  56 +++++
 ...06 Market & Competition Qualification.html |  12 +
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |  12 +
 9 files changed, 386 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2edbaff..9fdb414 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -714,6 +714,15 @@
      ],
      'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
      'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday'
+  ],
+  [
+     'name' => 'Gaussian Naive Bayes Model',
+     'link' => 'strategy-library/gaussian-naive-bayes-model',
+     'sources' => [
+         'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
+     ],
+     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];
 
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
new file mode 100644
index 0000000..9421bb8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+
      
+  Naïve Bayes models have become popular for their success in spam email filtering. In this tutorial, we train 
+  Gaussian Naïve Bayes (GNB) classifiers to forecast the daily returns of stocks in the technology sector given the 
+  historical returns of the sector. Our implementation shows the strategy has a greater Sharpe and lower variance 
+  than the SPY ETF over a 5 year backtest and during the 2020 stock market crash. The algorithm we build here follows 
+  the research done by Lu (2016) and Imandoust & Bolandraftar (2014).
+
      
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
new file mode 100644
index 0000000..2643372
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -0,0 +1,44 @@
+
      
+  Naïve Bayes models classify observations into a set of classes by utilizing 
+  Bayes’ Theorem
+
      
+
+\[\text{posterior} = \frac{ \text{prior } * \text{ likelihood} } {\text{evidence}}\]
+
+
      
+  In symbols, this translates to
+
      
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
+where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+
      
+  The Naïve Bayes model assumes the features are independent, so that 
+
      
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)\prod_{j=1}^{n} P(x_j | c_i)}{P(x_1, ..., x_n)} \propto P(c_i)\prod_{j=1}^{n} P(x_j|c_i)\]
+
+
      
+  The class that is most probable given the observation is then determined by solving
+
      
+
+\[\hat{c} = \arg\max_{i \in \{1, ..., m\}} P(c_i) \prod_{j=1}^{n} P(x_j | c_i)\]
+
+
+
      
+  In our use case, the classes in the model are: positive, negative, or flat future return for a security. The features
+  are the last 4 daily returns of the universe constituents. Since we are dealing with continuous data, we extend the 
+  model to a GNB model by replacing \(P(x_j|c_i)\) in the equation above. First, we find the mean \(\mu_j\) and standard 
+  deviation \(\sigma_j^2\) of the \(x_j\) feature vector in the training set labeled class \(c_i\). A normal distribution 
+  parameterized by \(\mu_j\) and \(\sigma_j^2\) is then used to determine the likelihood of the observations. If \(o\) is the 
+  observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
+
      
+
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+
+
      
+  The mechanics of the GNB model can be seen visually in 
+  this video. Note that the GNB model has 2 underlying 
+  assumptions: the feature vectors are independent and normally distributed. We do not test for these properties, but 
+  rather leave it as an area of future research.
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
new file mode 100644
index 0000000..8ef11c2
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html	
@@ -0,0 +1,223 @@
+
      Universe Selection
      
+
      
+  Following Lu (2016), we implement a custom universe selection model to select the largest stocks from the technology 
+  sector. We restrict our universe to have a size of 10, but this can be easily customized via the `fine_size` 
+  parameter in the constructor.
+
      
+
      
+
      +class BigTechUniverseSelectionModel(FundamentalUniverseSelectionModel):
+    def __init__(self, fine_size=10):
+        self.fine_size = fine_size
+        self.month = -1
+        super().__init__(True)
+
+    def SelectCoarse(self, algorithm, coarse):
+        if algorithm.Time.month == self.month:
+            return Universe.Unchanged
+        return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+    
+    def SelectFine(self, algorithm, fine):
+        self.month = algorithm.Time.month
+        
+        tech_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology ]
+        sorted_by_market_cap = sorted(tech_stocks, key=lambda x: x.MarketCap, reverse=True)
+        return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+
      
+
      
+
+
+
      Alpha Construction
      
+
      
+  The GaussianNaiveBayesAlphaModel predicts the direction each security will move from a given day’s open to the next 
+  day’s open. When constructing this alpha model, we set up a dictionary to hold a SymbolData object for each symbol 
+  in the universe and a flag to show the universe has changed. 
+
      
+
      
+
      +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    new_securities = False
+
      
+
      
+
+
+
      Alpha Securities Management
      
+
      
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method. In 
+  this algorithm, since we train the Gaussian Naive Bayes classifier using the historical returns of the securities 
+  in the universe, we flag to train the model every time the universe changes.
+
      
+
      
+
      +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def OnSecuritiesChanged(self, algorithm, changes):
+        for security in changes.AddedSecurities:
+            self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+            
+        for security in changes.RemovedSecurities:
+            symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+            if symbol_data:
+                symbol_data.dispose()
+        
+        self.new_securities = True
+
      
+
      
+
+
+
      SymbolData Class
      
+
      
+  The SymbolData class is used to store training data for the GaussianNaiveBayesAlphaModel and manage a consolidator 
+  subscription. In the constructor, we specify the training parameters, setup the consolidator, and warm up the 
+  training data.
+
      
+
      
+
      +class SymbolData:
+    def __init__(self, security, algorithm, num_days_per_sample=4, num_samples=100):
+        self.exchange = security.Exchange
+        self.symbol = security.Symbol
+        self.algorithm = algorithm
+        self.num_days_per_sample = num_days_per_sample
+        self.num_samples = num_samples
+        self.previous_open = 0
+        self.model = None
+        
+        # Setup consolidators
+        self.consolidator = TradeBarConsolidator(timedelta(days=1))
+        self.consolidator.DataConsolidated += self.CustomDailyHandler
+        algorithm.SubscriptionManager.AddConsolidator(self.symbol, self.consolidator)
+        
+        # Warm up ROC lookback
+        self.roc_window = np.array([])
+        self.labels_by_day = pd.Series()
+        
+        data = {f'{self.symbol.ID}_(t-{i})' : [] for i in range(1, num_days_per_sample + 1)}
+        self.features_by_day = pd.DataFrame(data)
+        
+        lookback = num_days_per_sample + num_samples + 1
+        history = algorithm.History(self.symbol, lookback, Resolution.Daily)
+        if history.empty or 'close' not in history:
+            algorithm.Log(f"Not enough history for {self.symbol} yet")    
+            return
+        
+        history = history.loc[self.symbol]
+        history['open_close_return'] = (history.close - history.open) / history.open
+        
+        start = history.shift(-1).open
+        end = history.shift(-2).open
+        history['future_return'] = (end - start) / start
+        
+        for day, row in history.iterrows():
+            self.previous_open = row.open
+            if self.update_features(day, row.open_close_return) and not pd.isnull(row.future_return):
+                row = pd.Series([np.sign(row.future_return)], index=[day])
+                self.labels_by_day = self.labels_by_day.append(row)[-self.num_samples:]
+
      
+
      
+
+
      
+  The update_features method is called to update our training features with the latest data passed to the algorithm. 
+  It returns True/False, representing if the features are in place to start updating the training labels.
+
      
+
+
      
+
      +class SymbolData:
+    ...
+
+    def update_features(self, day, open_close_return):
+        self.roc_window = np.append(open_close_return, self.roc_window)[:self.num_days_per_sample]
+        
+        if len(self.roc_window) < self.num_days_per_sample:
+            return False
+            
+        self.features_by_day.loc[day] = self.roc_window
+        self.features_by_day = self.features_by_day[-(self.num_samples+2):]
+        return True
+
      
+
      
+
+
+
+
+
      Model Training
      
+
      
+  The GNB model is trained each day the universe has changed. By default, it uses 100 samples to train. The features 
+  are the historical open-to-close returns of the universe constituents. The labels are the returns from the open at 
+  T+1 to the open at T+2 at each time step for each security.
+
      
+
      
+
      +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def train(self):
+        features = pd.DataFrame()
+        labels_by_symbol = {}
+        
+        # Gather training data
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                features = pd.concat([features, symbol_data.features_by_day], axis=1)
+                labels_by_symbol[symbol] = symbol_data.labels_by_day
+        
+        # Train the GNB model
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                symbol_data.model = GaussianNB().fit(features.iloc[:-2], labels_by_symbol[symbol])
+
      
+
      
+
+
+
      Alpha Update
      
+
      
+  As new TradeBars are provided to the alpha model's Update method, we collect the latest TradeBar’s open-to-close 
+  return for each security in the universe. We then predict the direction of each security using the security’s 
+  corresponding GNB model, and return insights accordingly.
+
      
+
      
+
      +class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def Update(self, algorithm, data):
+        if self.new_securities:
+            self.train()
+            self.new_securities = False
+        
+        tradable_symbols = {}
+        features = [[]]
+        
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if data.ContainsKey(symbol) and data[symbol] is not None and symbol_data.IsReady:
+                tradable_symbols[symbol] = symbol_data
+                features[0].extend(symbol_data.features_by_day.iloc[-1].values)
+
+        insights = []
+        if len(tradable_symbols) == 0:
+            return []
+        weight = 1 / len(tradable_symbols)
+        for symbol, symbol_data in tradable_symbols.items():
+            direction = symbol_data.model.predict(features)
+            if direction:
+                insights.append(Insight.Price(symbol, data.Time + timedelta(days=1, seconds=-1), 
+                                              direction, None, None, None, weight))
+
+        return insights
+
      
+
      
+
+
+
      Portfolio Construction & Trade Execution
      
+
      
+  Following the guidelines of Alpha Streams 
+  and the Quant League competition, we 
+  utilize the 
+  InsightWeightingPortfolioConstructionModel and the 
+  
+  ImmediateExecutionModel.
+
      
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
new file mode 100644
index 0000000..7f48965
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+
      
+    
      
+    
      
+        
+    
      
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
new file mode 100644
index 0000000..3ddfffe
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html	
@@ -0,0 +1,56 @@
+
      
+
+    
+        
+            
+            
+            
+            
+            
+            
+        
+    
+    
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+        
+            
+            
+            
+            
+            
+            
+        
+        
+            
+            
+            
+        
+    
+
      Period NameStart DateEnd DateStrategySharpeVariance
      5 Year Backtest10/1/201510/13/2020Strategy0.970.016
      Benchmark0.8050.029
      2020 Crash2/19/20203/23/2020Strategy-0.9810.353
      Benchmark-1.40.474
      2020 Recovery3/23/20206/8/2020Strategy-2.0110.035
      Benchmark8.7650.103
      
+
      
+
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d16c9d8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,12 @@
+
      
+  Although this strategy passes several of the 
+  metrics required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+
      
+
+
        
+  
      • PSR >= 80%
        • 
+  
      • Max drawdown duration <= 6 months
        • 
+  
      • Handles dividends and splits
        • 
+  
      • Minute or second data resolution
        • 
+
      
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
new file mode 100644
index 0000000..99f052c
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+
      
+  The GNB model strategy implemented in this tutorial produced a greater Sharpe ratio and lower annual variance than 
+  buying and holding the S&P 500 index ETF benchmark over the backtesting period. In addition to outperforming during 
+  the entire backtest, the strategy also outperformed during the 2020 stock market crash. 
+
      
+
+
      
+  To continue the development of this strategy, future areas of research include:
+
      
+
+
        
+  
      • Adjusting parameters in the SymbolData class
        • 
+  
      • Trying other features and labels for the GNB model
        • 
+  
      • Adjusting the universe parameters and targeted sector
        • 
+  
      • Adding handlers for corporate actions
        • 
+  
      • Filter for stocks with independent and normal returns
        • 
+
      
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
new file mode 100644
index 0000000..0320284
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html	
@@ -0,0 +1,12 @@
+
        
+  
      1. 
+    Imandoust, S. B., & Mohammad, B. (2014). Forecasting the direction of stock market index movement using three 
+    data mining techniques: the case of Tehran Stock Exchange. Journal of Engineering Research and Applications, 
+    6(2), 106-117. 
+    Online copy
+  
        2. 
+  
      2. 
+    Lu, N. (2016). A Machine Learning Approach to Automated Trading.  
+    Online copy
+  
        3. 
+
      
\ No newline at end of file

From 93cfc1f62b1904001eb57a767e167956284b646b Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 18:39:43 -0700
Subject: [PATCH 091/107] Fix description

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

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9fdb414..9fc944f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -721,7 +721,7 @@
      'sources' => [
          'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
      ],
-     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'description' => "Forecasts the next day's return of technology stocks by fitting a gaussian naive bayes model to the historical returns of the technology sector constituents.",
      'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];

From 28e73de28c4e10b564278a805f5b34dc8ec725c8 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Thu, 19 Nov 2020 20:56:41 -0700
Subject: [PATCH 092/107] Fix latex error

---
 .../1036 Gaussian Naive Bayes Model/02 Background.html     | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
index 2643372..f576900 100644
--- a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -10,7 +10,10 @@
 
      
 
 \[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
-where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+
      
+  where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
      
 
 
      
   The Naïve Bayes model assumes the features are independent, so that 
@@ -34,7 +37,7 @@
   observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
 
      
 
-\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2}} \]
 
 
      
   The mechanics of the GNB model can be seen visually in 

From a8b4090d6a95dd4a58b59c3a55551e730a6c9590 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Fri, 20 Nov 2020 19:13:33 -0700
Subject: [PATCH 093/107] Add video walkthrough

---
 .../03 Video Walkthrough.html                                | 5 +++++
 .../{03 Method.html => 04 Method.html}                       | 0
 .../{04 Algorithm.html => 05 Algorithm.html}                 | 0
 ...elative Performance.html => 06 Relative Performance.html} | 0
 ...ation.html => 07 Market & Competition Qualification.html} | 0
 .../{07 Conclusion.html => 08 Conclusion.html}               | 0
 .../{08 References.html => 09 References.html}               | 0
 7 files changed, 5 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{03 Method.html => 04 Method.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{04 Algorithm.html => 05 Algorithm.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{05 Relative Performance.html => 06 Relative Performance.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{06 Market & Competition Qualification.html => 07 Market & Competition Qualification.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{07 Conclusion.html => 08 Conclusion.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{08 References.html => 09 References.html} (100%)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
new file mode 100644
index 0000000..3a3ef40
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html	
@@ -0,0 +1,5 @@
+
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html

From 227973336f5633a840abd9953f1ae0734f471070 Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Tue, 8 Dec 2020 21:47:33 +0000
Subject: [PATCH 094/107] Update Link to The Refernce

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 .../05 References.html                                          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html
index 145c7bf..4d6f9c3 100644
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
@@ -1,5 +1,5 @@
 
        
   
      1. 
-    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
+    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
   
        2. 
 
      

From d204c4e0e21e02048a61ae7b4363b4c1234641bb Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Tue, 8 Dec 2020 21:49:01 +0000
Subject: [PATCH 095/107] Update Reference in Chinese Version

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 ...05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
index 145c7bf..4d6f9c3 100644
--- "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -1,5 +1,5 @@
 
        
   
      1. 
-    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
+    Factor Based Stock Selection Model for Turkish Equities, 2015, Ayhan Yüksel Online Copy
   
        2. 
 
      

From 55297e639875aa0a4ccf7f107e9c7b062355b099 Mon Sep 17 00:00:00 2001
From: Derek Melchin 
Date: Wed, 10 Feb 2021 16:32:44 -0700
Subject: [PATCH 096/107] Update Short Term Reversal strategy tutorial and
 algorithm

---
 .../01 Abstract.html                          |  12 +-
 .../02 Method.html                            | 182 +++++++++++-------
 .../03 Summary.html                           |   5 -
 .../04 Algorithm.html                         |   2 +-
 .../05 References.html                        |   5 -
 .../05 Relative Performance.html              |  56 ++++++
 ...07 Market & Competition Qualification.html |  13 ++
 .../08 Conclusion.html                        |  16 ++
 .../09 References.html                        |   6 +
 9 files changed, 208 insertions(+), 89 deletions(-)
 delete mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
 delete mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
index eae0057..e1419ee 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -1,8 +1,8 @@
 
      
-  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "Another look at trading costs and short-term reversal profits". The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
-
      
-
      
-  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
-  In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
-  In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
+  In this tutorial, we implement a version of the short-term reversal strategy published by De Groot, Huij, & Zhou 
+  (2012). The strategy works by observing the returns of each security in the universe over the previous month. Every 
+  week, the algorithm longs the worst performers and shorts the top performers. The original strategy outlined in the 
+  literature considers the entire universe of stocks when trading. To reduce trading costs, we limit our universe to 
+  the most liquid large cap stocks. Our analysis shows the strategy underperforms the S&P 500 index during all our 
+  backtest periods except the 2020 market crash.
 
      
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index a666b03..8b9f021 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -1,103 +1,141 @@
 
      
-  The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
+  The strategy code mainly consists of four parts: Initialization, Universe Selection, OnData, and OnSecuritiesChanged.
 
      
 
-
      Step 1: Initialization
      
+
      Algorithm Initialization
      
 
      
-  In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use self._numOfWeeks to count?the number of weeks that have passed since the start date, and self._LastDay to indicate whether it is a new week. self._ifWarmUp is true when the self._numOfWeeks is 3, which means as long as next week's data come, we can make our investment decisions.??self._stocks is a list containing all the symbols of the 100 stocks that are taken into consideration. self._values is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
+  When initializing the algorithm, we add a coarse universe selection method and specify several parameters to use 
+  when selecting securities.
 
      
-
 
      
-
-
          def Initialize(self):
-        self.SetStartDate(2005, 1, 1)
-        self.SetEndDate(2017, 5, 10)
-        self.SetCash(1000000)
-        
+
      +class ShortTimeReversal(QCAlgorithm):
+    def Initialize(self):
+        # ...
+      
         self.UniverseSettings.Resolution = Resolution.Daily
-        self.AddUniverse(self.CoarseSelectionFunction)
-        self._numberOfSymbols = 100
-        self._numberOfTradings = int(0.1 * self._numberOfSymbols)
-        
-        self._numOfWeeks = 0
-        self._LastDay = -1
-        self._ifWarmUp = False
-        
-        self._stocks = []
-        self._values = {}
+        self.AddUniverse(self.SelectCoarse)
+      
+        self.dollar_volume_selection_size = 100
+        self.roc_selection_size = int(0.1 * self.dollar_volume_selection_size)
+      
+        self.lookback = 22
+        self.roc_by_symbol = {}
+        self.week = 0
+
 
      
 
      
-
      
-  Also, we need to use?CoarseSelectionFunction to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
-
      
 
-
      
 
-
      -def CoarseSelectionFunction(self, coarse):
-     sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-     top100 = sortedByDollarVolume[:self._numberOfSymbols]
-     return [i.Symbol for i in top100]
-
      
-
      
-
      Step 2:Warm Up
      
+
+
      Universe Selection
      
 
      
-  Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
+  The coarse universe selection method creates a RateOfChange 
+  indicator for each of the top 100 
+  most liquid securities in the market. Upon creation, the indicator is manually warmed-up with historical closing 
+  prices. After the indicators are ready, the universe selects the securities with the 10 best and 10 worst 
+  RateOfChange values.
 
      
 
      
+
      +class ShortTimeReversal(QCAlgorithm):
+    # ...
+
+    def SelectCoarse(self, coarse):
+        
+        # We should keep a dictionary for all securities that have been selected
+        for cf in coarse:
+            symbol = cf.Symbol
+            if symbol in self.roc_by_symbol:
+                self.roc_by_symbol[symbol].Update(cf.EndTime, cf.AdjustedPrice)
+
+        # Refresh universe each week
+        week_number = self.Time.date().isocalendar()[1]
+        if week_number == self.week:
+            return Universe.Unchanged
+        self.week = week_number
+
+        # sort and select by dollar volume
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+        selected = {cf.Symbol: cf for cf in sortedByDollarVolume[:self.dollar_volume_selection_size]} 
+        
+        # New selections need a history request to warm up the indicator
+        symbols = [k for k in selected.keys()
+            if k not in self.roc_by_symbol or not self.roc_by_symbol[k].IsReady]
+
+        if symbols:
+            history = self.History(symbols, self.lookback, Resolution.Daily)
+            if history.empty:
+                self.Log(f'No history for {", ".join([x.Value for x in symbols])}')
+            history = history.close.unstack(0)
+
+            for symbol in symbols:
 
-
      self._stocks = []
-self.uni_symbol = None
-symbols = self.UniverseManager.Keys
-for i in symbols:
-        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
-                self.uni_symbol = i
-        for i in self.UniverseManager[self.uni_symbol].Members:
-                self._stocks.append(i.Value.Symbol)
-                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
+                if symbol not in history:
+                    continue
 
+                # Create and warm-up the RateOfChange indicator
+                roc = RateOfChange(self.lookback)
+                for time, price in history[symbol].dropna().iteritems():
+                    roc.Update(time, price)
+                
+                if roc.IsReady:
+                    self.roc_by_symbol[symbol] = roc
+        
+        # Sort the symbols by their ROC values
+        selectedRateOfChange = {}
+        for symbol in selected.keys():
+            if symbol in self.roc_by_symbol:
+                selectedRateOfChange[symbol] = self.roc_by_symbol[symbol]
+        sortedByRateOfChange = sorted(selectedRateOfChange.items(), key=lambda kv: kv[1], reverse=True)
+        
+        # Define the top and the bottom to buy and sell
+        self.rocTop = [x[0] for x in sortedByRateOfChange[:self.roc_selection_size]]
+        self.rocBottom = [x[0] for x in sortedByRateOfChange[-self.roc_selection_size:]]
+        
+        return self.rocTop + self.rocBottom
 
      
 
      
-
      
-  We get all the symbols of qualified stocks from UniverseManager and keep them in self._stocks which is a list. Then we create for each key in the dictionary self._values a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
-
      
 
-
      
 
-
      for stock in self._stocks:
-        self._values[stock].append(self.Securities[stock].Price)
      
-
      
-
      Step 3:Weekly Rebalancing
      
+
      The OnData Method
      
 
      
-  After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
+  As new data is passed to the OnData method, we issue orders to form a long-short portfolio. We long the securities 
+  with the lowest RateOfChange values and short those with the largest values. After rebalancing, we clear the 
+  `rocTop` and `rocBottom` lists to ensure we don’t trade again until the universe is refreshed.
 
      
-
 
      
+
      +class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-
      returns = {}
-for stock in self._stocks:
-        newPrice = self.Securities[stock].Price
-        oldPrice = self._values[stock].pop(0)
-        self._values[stock].append(newPrice)
-        returns[stock] = newPrice/oldPrice
+    def OnData(self, data):
+        # Rebalance
+        for symbol in self.rocTop:
+            self.SetHoldings(symbol, -0.5/len(self.rocTop))
+        for symbol in self.rocBottom:
+            self.SetHoldings(symbol, 0.5/len(self.rocBottom))
+        
+        # Clear the list of securities we have placed orders for
+        # to avoid new trades before the next universe selection
+        self.rocTop.clear() 
+        self.rocBottom.clear()
 
      
 
      
+
+
      The OnSecuritiesChanged Method
      
 
      
-  Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
+  We are rebalancing the portfolio on a weekly basis, but securities can leave our defined universe between rebalance 
+  days. To accommodate this, we liquidate any securities that are removed from the universe in the 
+  OnSecuritiesChanged method.
 
      
-
 
      
+
      +class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-
      newArr = [(v,k) for k,v in returns.items()]
-newArr.sort()
-for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
-        self.SetHoldings(stock, 0)
-for ret, stock in newArr[0:self._numberOfTradings]:
-        self.SetHoldings(stock, 0.5/self._numberOfTradings)
-for ret, stock in newArr[-self._numberOfTradings:]:
-        self.SetHoldings(stock, -0.5/self._numberOfTradings)
+    def OnSecuritiesChanged(self, changes):
+        for security in changes.RemovedSecurities:
+            self.Liquidate(security.Symbol, 'Removed from Universe')
 
      
-
      
-
      
-  Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
-
      
+
\ No newline at end of file diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html deleted file mode 100755 index 0c2b46e..0000000 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html +++ /dev/null @@ -1,5 +0,0 @@ -

- In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead. - If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&P 500 benchmark. - Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc. -

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html index 009e778..c2e37d5 100755 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html @@ -1,6 +1,6 @@
- +
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html deleted file mode 100644 index 6c1e761..0000000 --- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html +++ /dev/null @@ -1,5 +0,0 @@ -
    -
  1. - Groot, Wilma (2011). Another look at trading costs and short-term reversal profit, page 1,? Online Copy -
    2. -
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html new file mode 100644 index 0000000..3d18c34 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html @@ -0,0 +1,56 @@ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Period NameStart DateEnd DateStrategySharpeVariance
5 Year Backtest1/1/20161/1/2021Strategy0.240.058
Benchmark0.8250.028
2020 Crash2/19/20203/23/2020Strategy-1.0250.917
Benchmark-1.40.474
2020 Recovery3/23/20206/8/2020Strategy1.6880.16
Benchmark8.7650.103
+
+ diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html new file mode 100644 index 0000000..1c0cad5 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html @@ -0,0 +1,13 @@ +

+ Although this strategy passes several of the + metrics required for Alpha Streams + and the Quant League competition, it requires further work to pass the following requirements: +

+ +
    +
  • PSR >= 80%
    • +
  • Emits insights
    • +
  • Max drawdown <= 10%
    • +
  • Handles dividends and splits
    • +
  • Minute or second data resolution
    • +
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html new file mode 100644 index 0000000..ae81428 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html @@ -0,0 +1,16 @@ +

+ The short-term reversal strategy implemented in this tutorial produced a lower Sharpe ratio than the S&P + 500 index ETF benchmark over all our testing periods except during the 2020 market crash. To continue the + development of this strategy, future areas of research include: +

+ +

+ To continue the development of this strategy, future areas of research include: +

+ +
    +
  • Increasing the data resolution and adding risk management logic.
    • +
  • Applying the strategy to a different universe of securities.
    • +
  • Testing other lookback periods for the RateOfChange indicator. Some researchers exclude the most-recent month’s price action from the indicator’s calculation.
    • +
  • Adjusting the weight for each insight emitted from the alpha model. Perhaps the most extreme performer of all the securities the alpha model is about to long (short) should be given the largest weight of all the securities the model is about to long (short).
    • +
\ No newline at end of file diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html new file mode 100644 index 0000000..2a52089 --- /dev/null +++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html @@ -0,0 +1,6 @@ +
    +
  1. + de Groot, Wilma and Huij, Joop and Zhou, Weili, Another Look at Trading Costs and Short-Term Reversal + Profits (July 1, 2011). Online copy +
    2. +
\ No newline at end of file From 5aaf69e507ff2f368f5986d9a19257da90160f20 Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 5 Mar 2021 11:43:23 -0700 Subject: [PATCH 097/107] Updating fama french tutorial --- .../02 Fama-French Three-Factor Model.html | 7 ++--- .../05 Other Factors.html | 12 -------- .../{06 Summary.html => 05 Summary.html} | 6 ++-- .../{07 Algorithm.html => 06 Algorithm.html} | 30 +++++++++---------- ...{08 References.html => 07 References.html} | 3 ++ 5 files changed, 23 insertions(+), 35 deletions(-) delete mode 100755 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{06 Summary.html => 05 Summary.html} (98%) mode change 100755 => 100644 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{07 Algorithm.html => 06 Algorithm.html} (78%) mode change 100755 => 100644 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{08 References.html => 07 References.html} (81%) diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html index 21d0568..1f83f6d 100755 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html @@ -1,8 +1,5 @@ 

- This model was proposed in 1993 by Eugene Fama and Kenneth French to describe stock returns.[ref] Fama, E F; French, K R (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref] -

-

- The 3-factor model is + This model was proposed in 1993 by Eugene Fama and Kenneth French to describe stock returns. The 3-factor model is

\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \] @@ -12,7 +9,7 @@
  • MKT is the excess return of the market. It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.
  • SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.
    • -
  • HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.
    • +
  • HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have a higher book to price ratio (B/P) than growth stocks.

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html deleted file mode 100755 index 510c436..0000000 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html +++ /dev/null @@ -1,12 +0,0 @@ -

- The Fama-French 5-Factor model comprises two more factors: -

-
    -
  • RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.
    • -
  • CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.
    • -
- -

- RMW was proposed by Novy-Marx (2013) who wrote that: - "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014) who pointed out that: A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns. Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns -

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html old mode 100755 new mode 100644 similarity index 98% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html index 549200f..d2b7e7a --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html @@ -1,3 +1,3 @@ -

- In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice. -

+

+ In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice. +

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html old mode 100755 new mode 100644 similarity index 78% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html index e3c69c8..2fd2fa2 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html @@ -1,15 +1,15 @@ -

- Multi-factor strategies are stock picking strategies. Here we try to implement a 2013 paper published by AQR Capital Management. - The paper recommends picking stocks by their value, quality (profitability) and momentum. - The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value. - The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices. -

-

- In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. -

-
-
-
- -
-
+

+ Multi-factor strategies are stock picking strategies. Here we try to implement a 2013 paper published by AQR Capital Management. + The paper recommends picking stocks by their value, quality (profitability) and momentum. + The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value. + The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices. +

+

+ In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. +

+
+
+
+ +
+
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html similarity index 81% rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html index 79b3bcb..eb7216e 100644 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html @@ -14,4 +14,7 @@
  • Robert Novy-Marx (2013). The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from rnm.simon.rochester.edu/research/OSoV.pdf
    • +
  • + Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56. doi: 10.1016/0304-405X(93)90023-5. Retrieved from rady.ucsd.edu. +
    • From 304f01fe6fa9e01d15c3155cab4f3b03d5e251fc Mon Sep 17 00:00:00 2001 From: Derek Melchin Date: Fri, 5 Mar 2021 11:44:46 -0700 Subject: [PATCH 098/107] Update value factor definition to match algo --- .../14 Fama-French Multi-Factor Models/06 Algorithm.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html index 2fd2fa2..3eb4334 100644 --- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html +++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html @@ -5,7 +5,7 @@ The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.

    - In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy. + In our backtested strategy, we used operating profit margin to measure quality, book value per share to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.

    From b1fc9c2bb99abb67fdf008e118597f55bab491ba Mon Sep 17 00:00:00 2001 From: Jared Date: Wed, 17 Mar 2021 10:17:08 -0700 Subject: [PATCH 099/107] Update 01 Definition.html --- 07 Applied Options[]/02 Bull Call Spread/01 Definition.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html index 0418abb..c361521 100755 --- a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html +++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html @@ -5,7 +5,7 @@ This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss resulting from a drop in the price of the underlying stock but still creates a ceiling to the profit while the underlying price is increasing.

    - Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows: + Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 50 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:

    From 6750665e5c3f051e4ab37bbafb7835d1bed848b3 Mon Sep 17 00:00:00 2001 From: Web Editor Date: Mon, 11 Oct 2021 16:34:56 -0400 Subject: [PATCH 100/107] Automated push from server made by Alexandre Catarino --- .../02 Fetching Data.html | 196 +++++++++--------- 1 file changed, 98 insertions(+), 98 deletions(-) diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html index 325ecd6..181f0fc 100755 --- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html +++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html @@ -1,98 +1,98 @@ -

    - Here we use the Quandl API to retrieve data. -

    -
    - -
    import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
-
    -
    -

    - We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates: -

    -
    - -
    aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table['Adj. Close']['2017']
-print aapl
-
    -
    - -

    - Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm']. -

    -
    - -
    print aapl['2017-3']
-Date
-2017-03-01    138.657681
-2017-03-02    137.834404
-2017-03-03    138.647762
-2017-03-06    138.211326
-2017-03-07    138.389868
-2017-03-08    137.874080
-2017-03-09    137.556672
-2017-03-10    138.012946
-2017-03-13    138.072460
-2017-03-14    137.864161
-2017-03-15    139.322254
-2017-03-16    139.550391
-2017-03-17    138.856061
-2017-03-20    140.314154
-2017-03-21    138.707276
-2017-03-22    140.274478
-2017-03-23    139.778528
-2017-03-24    139.500796
-2017-03-27    139.738852
-2017-03-28    142.635200
-2017-03-29    142.952608
-2017-03-30    142.764147
-2017-03-31    142.496334
-
    -
    - -

    - Or in several consecutive months: -

    -
    - -
    aapl['2017-2':'2017-4']
-
    -
    - -

    - .head(N) and .tail(N) are methods for quickly accessing the first or last N elements. -

    -
    - -
    print aapl.head()
-print aapl.tail(10)
-
    -
    -

    - The output: -

    -
    - -
    -Date
-2017-01-03    114.715378
-2017-01-04    114.586983
-2017-01-05    115.169696
-2017-01-06    116.453639
-2017-01-09    117.520300
-Name: Adj. Close, dtype: float64
-Date
-2017-08-08    159.433108
-2017-08-09    160.409148
-2017-08-10    155.270000
-2017-08-11    157.480000
-2017-08-14    159.850000
-2017-08-15    161.600000
-2017-08-16    160.950000
-2017-08-17    157.870000
-2017-08-18    157.500000
-2017-08-21    157.210000
-Name: Adj. Close, dtype: float64
-
    -
    +

    + Here we use the Quandl API to retrieve data... +

    +
    + +
    import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
+
    +
    +

    + We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates: +

    +
    + +
    aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table['Adj. Close']['2017']
+print aapl
+
    +
    + +

    + Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm']. +

    +
    + +
    print aapl['2017-3']
+Date
+2017-03-01    138.657681
+2017-03-02    137.834404
+2017-03-03    138.647762
+2017-03-06    138.211326
+2017-03-07    138.389868
+2017-03-08    137.874080
+2017-03-09    137.556672
+2017-03-10    138.012946
+2017-03-13    138.072460
+2017-03-14    137.864161
+2017-03-15    139.322254
+2017-03-16    139.550391
+2017-03-17    138.856061
+2017-03-20    140.314154
+2017-03-21    138.707276
+2017-03-22    140.274478
+2017-03-23    139.778528
+2017-03-24    139.500796
+2017-03-27    139.738852
+2017-03-28    142.635200
+2017-03-29    142.952608
+2017-03-30    142.764147
+2017-03-31    142.496334
+
    +
    + +

    + Or in several consecutive months: +

    +
    + +
    aapl['2017-2':'2017-4']
+
    +
    + +

    + .head(N) and .tail(N) are methods for quickly accessing the first or last N elements. +

    +
    + +
    print aapl.head()
+print aapl.tail(10)
+
    +
    +

    + The output: +

    +
    + +
    +Date
+2017-01-03    114.715378
+2017-01-04    114.586983
+2017-01-05    115.169696
+2017-01-06    116.453639
+2017-01-09    117.520300
+Name: Adj. Close, dtype: float64
+Date
+2017-08-08    159.433108
+2017-08-09    160.409148
+2017-08-10    155.270000
+2017-08-11    157.480000
+2017-08-14    159.850000
+2017-08-15    161.600000
+2017-08-16    160.950000
+2017-08-17    157.870000
+2017-08-18    157.500000
+2017-08-21    157.210000
+Name: Adj. Close, dtype: float64
+
    +
    From eb15f94de0868141eb5bb55909c4e7ead648f79f Mon Sep 17 00:00:00 2001 From: Web Editor Date: Fri, 11 Mar 2022 01:59:09 -0500 Subject: [PATCH 101/107] Automated push from server made by Louis Szeto --- .../02 QuantConnect Options API/04 Select Contracts.html | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index 54084fb..e9ca2d1 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -65,12 +65,12 @@ 
    def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+    optionchain = i.Value
+    self.Log("underlying price:" + str(optionchain.Underlying.Price))
+    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
 			   index=[x.Symbol.Value for x in optionchain],
 			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
+    self.Log(str(df))
    From 7f51fe51a3dcbd4c46ac07aeefe2d678ed94bdde Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:01:25 +0800 Subject: [PATCH 102/107] Update 04 Select Contracts.html --- .../04 Select Contracts.html | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index e9ca2d1..7e92e12 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -65,12 +65,12 @@ 
    def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-    optionchain = i.Value
-    self.Log("underlying price:" + str(optionchain.Underlying.Price))
-    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-    self.Log(str(df))
+        optionchain = i.Value
+        self.Log("underlying price:" + str(optionchain.Underlying.Price))
+        df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+                           index=[x.Symbol.Value for x in optionchain],
+                           columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
    From 406089ace2b2521c0bd698e0120196c776346094 Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:13:16 +0800 Subject: [PATCH 103/107] Update 04 Select Contracts.html Correct indentation and variable names --- .../04 Select Contracts.html | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html index 7e92e12..07ea589 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html @@ -160,18 +160,18 @@ 
    for i in slice.OptionChains:
     if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike > 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike < 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    optionchain = i.Value
+    # differentiate the call and put options
+    call = [x for x in optionchain if x.Right == 0]
+    put = [x for x in optionchain if x.Right == 1]
+    # choose ITM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike > 0]
+    # or choose ATM contracts
+    contracts = sorted(optionchain, key = lambda x: abs(x.UnderlyingLastPrice - x.Strike))[0]
+    # or choose OTM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike < 0]
+    # sort the contracts by their expiration dates
+    contracts = sorted(contracts, key = lambda x: x.Expiry, reverse = True)

    From 86fba1494c5934965d6d08be3dfc2e7e77ebd1f0 Mon Sep 17 00:00:00 2001 From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com> Date: Fri, 11 Mar 2022 15:14:13 +0800 Subject: [PATCH 104/107] Update 02 Add Options.html Correct formatting --- .../02 QuantConnect Options API/02 Add Options.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html index a1d5a37..44c5959 100755 --- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html +++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html @@ -22,8 +22,8 @@ 

    def Initialize(self):
-    self.SetStartDate(2017, 01, 01)  #Set Start Date
-    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetStartDate(2017, 1, 1)  #Set Start Date
+    self.SetEndDate(2017, 6, 30)  #Set End Date
     self.SetCash(50000)  #Set Strategy Cash
     equity = self.AddEquity("GOOG", Resolution.Minute) # Add the underlying stock: Google
     option = self.AddOption("GOOG", Resolution.Minute) # Add the option corresponding to underlying stock

From 25963e427c4cee06d7778fecca635126745242cf Mon Sep 17 00:00:00 2001
From: Alexandre Catarino 
Date: Thu, 19 May 2022 23:54:45 +0100
Subject: [PATCH 105/107] Move Workflow to Documentation Page

---
 README.md | 30 ++++++------------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 5e2fd23..c9846d7 100644
--- a/README.md
+++ b/README.md
@@ -8,40 +8,22 @@ This repository is a collection of WordPress and Jupyter notebook tutorials for
 
 Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean][4] Engine repository.
 
-
-## New Tutorial Requests and Edits ##
-
-Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
-
-## Mailing List ##
-
-The mailing list for the project can be found on [Google Groups][6]
-
 ## Contributors and Pull Requests ##
 
 Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7].
 
 ## Strategy Library Development Workflow ##
-To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps:
-1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process).
-2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png).
-3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py).
-4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)).
-5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project.
-6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples).
-7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)).
-8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)):
-    - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload).
-    - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023.
-    - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json).
-    - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID).
-9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)).
+
+To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow the steps on the [documentation page](https://www.quantconnect.com/docs/v2/writing-algorithms/strategy-library#03-Contribute-Tutorials)
+
+## New Tutorial Requests and Edits ##
+
+Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
 
 [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer"
 [2]: https://www.quantconnect.com/lean/docs "Lean Documentation"
 [3]: https://github.com/QuantConnect/Lean/archive/master.zip
 [4]: https://github.com/QuantConnect/Lean
 [5]: https://github.com/QuantConnect/Tutorials/issues
-[6]: https://groups.google.com/forum/#!forum/lean-engine
 [7]: https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md
 [8]: https://www.quantconnect.com/slack

From 67580984b715b02d829840831b8bc487f29c7213 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles 
Date: Wed, 15 Jun 2022 17:35:12 -0700
Subject: [PATCH 106/107] Update backtest embed url.

---
 .../05 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../04 Algorithm.html                         |  4 +--
 .../05 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../04 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../113 January Barometer/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../13 Asset Class Momentum/03 Algorithm.html |  2 +-
 .../14 Sector Momentum/03 Algorithm.html      |  2 +-
 .../15 Short Term Reversal/03 Algorithm.html  |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../16 Overnight Anomaly/03 Algorithm.html    |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../17 Forex Momentum/03 Algorithm.html       |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../20 Forex Carry Trade/03 Algorithm.html    |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  4 +--
 .../04 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../32 Gold Market Timing/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../83 Pre-Holiday Effect/03 Algorithm.html   |  2 +-
 .../06 Algorithm.html                         | 18 ++++++-------
 .../13 Market Risk/06 Algorithm.html          | 26 +++++++++----------
 .../05 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../01 Covered Call/04 Algorithm.html         |  4 +--
 .../02 Bull Call Spread/04 Algorithm.html     |  4 +--
 .../03 Long Straddle/04 Algorithm.html        |  4 +--
 .../04 Long Strangle/04 Algorithm.html        |  4 +--
 .../05 Butterfly Spread/04 Algorithm.html     |  4 +--
 .../06 Iron Condor/04 Algorithm.html          |  4 +--
 .../07 Iron Butterfly/04 Algorithm.html       |  4 +--
 .../08 Protective Collar/04 Algorithm.html    |  4 +--
 65 files changed, 98 insertions(+), 98 deletions(-)

diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"
index 1f296ed..4a79e84 100644
--- "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
index 5c357f4..9717146 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"
index 932854a..c5d9f52 100644
--- "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
index 58173b8..3358861 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
index 01b99d6..0115518 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
index 69b6365..3d55776 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"
index 69b6365..3d55776 100644
--- "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
index 03b3ba2..f9505bd 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"
index bcf0bc2..d1a72fa 100644
--- "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html
index 15151d8..ccc8b22 100644
--- a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"
index 15151d8..ccc8b22 100644
--- "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html
index 516d50a..7ff9460 100644
--- a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
+++ b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
index 53170f5..b8d9c7d 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/113 January Barometer/03 Algorithm.html b/04 Strategy Library/113 January Barometer/03 Algorithm.html
index be69ba3..e85f53d 100644
--- a/04 Strategy Library/113 January Barometer/03 Algorithm.html	
+++ b/04 Strategy Library/113 January Barometer/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html
index f94dcba..6dc92eb 100644
--- a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html
index ca04542..77b7892 100644
--- a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
+++ b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html
index 6189f9c..e7aa2b2 100644
--- a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html	
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html
index 0e5258c..d001aad 100644
--- a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html
index 0cb1b39..80765bc 100644
--- a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
index 09e4c6f..1970b93 100644
--- a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
+++ b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html
index e3a87f0..41faf63 100644
--- a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html
index e4a3020..cb7b3a9 100644
--- a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html
index 6bbcd8e..219c782 100644
--- a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html	
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"
index 6bbcd8e..219c782 100644
--- "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html
index b7fd60e..f8b0c4d 100644
--- a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html
index 0a0907d..97890a9 100644
--- a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html
index 17a97c9..4b088a3 100644
--- a/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html
index c75531f..66e4458 100644
--- a/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html
index 1bb774b..d433b13 100644
--- a/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html	
+++ b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"
index 1bb774b..d433b13 100644
--- "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html
index b581b49..f2180a0 100644
--- a/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html	
+++ b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html
index 37631d8..1f5050c 100644
--- a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
@@ -2,13 +2,13 @@ 
    The Momentum Effect
    
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    Equal Weighted Benchmark
    
 
    
     
    
     
    
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diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"
index cd34f0c..7078bbf 100644
--- "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"	
@@ -2,13 +2,13 @@ 
    动量效应
    
 
    
     
    
     
    
-        
+        
     
    
 
    
 
    平均加权基准
    
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html
index 3bc488d..bdafd79 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html
index 547579a..2b216a7 100644
--- a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html	
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html b/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html
index 5041f10..15edb83 100644
--- a/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html b/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html
index a0bce57..0733d81 100644
--- a/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html	
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
index ada8627..f820ca9 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -3,7 +3,7 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
 
@@ -11,7 +11,7 @@
 
    
     
    
     
    
-        
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diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html
index 91052ac..b781228 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html
index e0ed93e..9029e2c 100644
--- a/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html
index 414a43d..c74f50c 100644
--- a/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html	
+++ b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html
index a38d444..b3b0694 100644
--- a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html	
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
index 9af8464..cff2414 100644
--- a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"
index 9af8464..cff2414 100644
--- "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"	
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html
index c07b1c7..926770b 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html
index 7cf2e86..36343ed 100644
--- a/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html	
+++ b/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html
index f8ca3b6..529a2d1 100644
--- a/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html	
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html
index 55f69ca..8caed63 100644
--- a/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html	
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html
index 3956745..57f98de 100644
--- a/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html	
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html
index 430008f..d3f5c48 100644
--- a/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html	
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html
index c9839cd..91f1e5a 100644
--- a/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
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diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html
index 36e3092..0447f1a 100644
--- a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html	
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
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diff --git a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
index b59109e..64580ab 100644
--- a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
+++ b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
index 8d6082c..309e3c2 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -1,9 +1,9 @@
-
    
-  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
-
    
-
    
-    
    
-    
    
-        
-    
    
-
    
+
    
+  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
+
    
+
    
+    
    
+    
    
+        
+    
    
+
    
diff --git a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html
index 3069322..64474c1 100755
--- a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
@@ -1,13 +1,13 @@
-
    
-    
    
-    
    
-        
-    
    
-
    
-
-
    
-    
    
-    
    
-        
-    
    
-
    
+
    
+    
    
+    
    
+        
+    
    
+
    
+
+
    
+    
    
+    
    
+        
+    
    
+
    
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 64b1d35..e485feb 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index 12d6ce9..b196a80 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 
    
     
    
     
    
-        
+        
     
    
 
    
diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index 9fa4e5e..6858e6e 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -4,7 +4,7 @@
 
    
     
    
     
    
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+        
     
    
 
    
 
    
@@ -13,6 +13,6 @@
 
    
     
    
     
    
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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 @@
 
    
     
    
     
    
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+        
     
    
 
    
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 @@
 
    
     
    
     
    
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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 @@
 
    
     
    
     
    
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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 @@
 
    
     
    
     
    
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+        
     
    
 
    
 
@@ -14,6 +14,6 @@
 
    
     
    
     
    
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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 @@
 
    
     
    
     
    
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@@ -14,6 +14,6 @@
 
    
     
    
     
    
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From 144ea92bd184b1cd91f0af35c8aa288e97d1ed5f Mon Sep 17 00:00:00 2001
From: avorobiev 
Date: Mon, 28 Jul 2025 17:58:07 +0300
Subject: [PATCH 107/107] Correct intrinsic and time value calculation in AAPL
 example

---
 .../01 General Features of Options/04 The Value of Options.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html
index f0877cc..b392bbd 100755
--- a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
@@ -12,5 +12,5 @@
 \[Time Value= Premium-Intrinsic Value\]

    -For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values. +For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $150. The intrinsic value of this contract is 150-143=$7, the time value is 10-7=$3. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.