Repository: marketneutral/pairs-trading-with-ML
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Directory structure:
gitextract_ruhsuu4_/
├── LICENSE
├── Pairs+Trading+with+Machine+Learning.ipynb
├── README.md
└── scikit-learn-example-stock-market-viz.ipynb
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FILE CONTENTS
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================================================
FILE: Pairs+Trading+with+Machine+Learning.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pairs Trading with Machine Learning\n",
"Jonathan Larkin\n",
"\n",
"August, 2017\n",
"\n",
"In developing a Pairs Trading strategy, finding valid, eligible pairs which exhibit unconditional mean-reverting behavior is of critical importance. This notebook walks through an example implementation of finding eligible pairs. We show how popular algorithms from Machine Learning can help us navigate a very high-dimensional seach space to find tradeable pairs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.cluster import KMeans, DBSCAN\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.manifold import TSNE\n",
"from sklearn import preprocessing\n",
"\n",
"from statsmodels.tsa.stattools import coint\n",
"\n",
"from scipy import stats\n",
"\n",
"from quantopian.pipeline.data import morningstar\n",
"from quantopian.pipeline.filters.morningstar import Q500US, Q1500US, Q3000US\n",
"from quantopian.pipeline import Pipeline\n",
"from quantopian.research import run_pipeline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Numpy: 1.11.1 \n",
"Pandas: 0.18.1 \n"
]
}
],
"source": [
"print \"Numpy: %s \" % np.__version__\n",
"print \"Pandas: %s \" % pd.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"study_date = \"2016-12-31\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define Universe\n",
"We start by specifying that we will constrain our search for pairs to the a large and liquid single stock universe."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"universe = Q1500US()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Choose Data\n",
"In addition to pricing, let's use some fundamental and industry classification data. When we look for pairs (or model anything in quantitative finance), it is generally good to have an \"economic prior\", as this helps mitigate overfitting. I often see Quantopian users create strategies with a fixed set of pairs that they have likely chosen by some fundamental rationale (\"KO and PEP should be related becuase...\"). A purely fundamental approach is a fine way to search for pairs, however breadth will likely be low. As discussed in [The Foundation of Algo Success](https://blog.quantopian.com/the-foundation-of-algo-success/), you can maximize Sharpe by having high breadth (high number of bets). With `N` stocks in the universe, there are `N*(N-1)/2` pair-wise relationships. However, if we do a brute-force search over these, we will likely end up with many spurious results. As such, let's narrow down the search space in a reasonable way. In this study, I start with the following priors:\n",
"\n",
"- Stocks that share loadings to common factors (defined below) in the past should be related in the future.\n",
"- Stocks of similar market caps should be related in the future.\n",
"- We should exclude stocks in the industry group \"Conglomerates\" (industry code 31055). Morningstar analysts classify stocks into industry groups primarily based on similarity in revenue lines. \"Conglomerates\" is a catch-all industry. As described in the [Morningstar Global Equity\n",
"Classification Structure manual](http://corporate.morningstar.com/us/documents/methodologydocuments/methodologypapers/equityclassmethodology.pdf): \"If the company has more than three sources of revenue and\n",
"income and there is no clear dominant revenue and income stream, the company\n",
"is assigned to the Conglomerates industry.\" We should not expect these stocks to be good members of any pairs in the future. This turns out to have zero impact on the Q500 and removes only 1 stock from the Q1500, but I left this idea in for didactic purposes.\n",
"- Creditworthiness in an important feature in future company performance. It's difficult to find credit spread data and map the reference entity to the appropriate equity security. There is a model, colloquially called the [Merton Model](http://www.investopedia.com/terms/m/mertonmodel.asp), however, which takes a contingent claims approach to modeling the capital structure of the firm. The output is an implied probability of default. Morningstar analysts calculate this for us and the field is called `financial_health_grade`. A full description of this field is in the [help docs](https://www.quantopian.com/help/fundamentals#asset-classification)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pipe = Pipeline(\n",
" columns= {\n",
" 'Market Cap': morningstar.valuation.market_cap.latest.quantiles(5),\n",
" 'Industry': morningstar.asset_classification.morningstar_industry_group_code.latest,\n",
" 'Financial Health': morningstar.asset_classification.financial_health_grade.latest\n",
" },\n",
" screen=universe\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res = run_pipeline(pipe, study_date, study_date)\n",
"res.index = res.index.droplevel(0) # drop the single date from the multi-index"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1500, 3)\n"
]
},
{
"data": {
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"\n",
"
\n",
" \n",
" \n",
" | \n",
" Financial Health | \n",
" Industry | \n",
" Market Cap | \n",
"
\n",
" \n",
" \n",
" \n",
" | Equity(2 [ARNC]) | \n",
" C | \n",
" 10106 | \n",
" 4 | \n",
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\n",
" \n",
" | Equity(24 [AAPL]) | \n",
" A | \n",
" 31167 | \n",
" 4 | \n",
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\n",
" \n",
" | Equity(52 [ABM]) | \n",
" B | \n",
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" 3 | \n",
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\n",
" \n",
" | Equity(53 [ABMD]) | \n",
" A | \n",
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" 3 | \n",
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\n",
" \n",
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" B | \n",
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" \n",
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" 1499.000000 | \n",
" 1499.000000 | \n",
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\n",
" \n",
" | mean | \n",
" 0.447432 | \n",
" 20262.618412 | \n",
" 3.377585 | \n",
"
\n",
" \n",
" | std | \n",
" 0.266508 | \n",
" 9205.899868 | \n",
" 0.654800 | \n",
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" \n",
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