Full Code of marketneutral/pairs-trading-with-ML for AI

Repository: marketneutral/pairs-trading-with-ML
Branch: master
Commit: d43429ac6a35
Files: 4
Total size: 1.2 MB

Directory structure:
gitextract_ruhsuu4_/

├── LICENSE
├── Pairs+Trading+with+Machine+Learning.ipynb
├── README.md
└── scikit-learn-example-stock-market-viz.ipynb

================================================
FILE CONTENTS
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================================================
FILE: LICENSE
================================================
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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": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Financial Health</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Equity(2 [ARNC])</th>\n",
       "      <td>C</td>\n",
       "      <td>10106</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(24 [AAPL])</th>\n",
       "      <td>A</td>\n",
       "      <td>31167</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(52 [ABM])</th>\n",
       "      <td>B</td>\n",
       "      <td>31054</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(53 [ABMD])</th>\n",
       "      <td>A</td>\n",
       "      <td>20639</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(62 [ABT])</th>\n",
       "      <td>B</td>\n",
       "      <td>20639</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Financial Health  Industry  Market Cap\n",
       "Equity(2 [ARNC])                 C     10106           4\n",
       "Equity(24 [AAPL])                A     31167           4\n",
       "Equity(52 [ABM])                 B     31054           3\n",
       "Equity(53 [ABMD])                A     20639           3\n",
       "Equity(62 [ABT])                 B     20639           4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print res.shape\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1499, 3)\n"
     ]
    }
   ],
   "source": [
    "# remove stocks in Industry \"Conglomerates\"\n",
    "res = res[res['Industry']!=31055]\n",
    "print res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1499, 3)\n"
     ]
    }
   ],
   "source": [
    "# remove stocks without a Financial Health grade\n",
    "res = res[res['Financial Health']!= None]\n",
    "print res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# replace the categorical data with numerical scores per the docs\n",
    "res['Financial Health'] = res['Financial Health'].astype('object')\n",
    "health_dict = {u'A': 0.1,\n",
    "               u'B': 0.3,\n",
    "               u'C': 0.7,\n",
    "               u'D': 0.9,\n",
    "               u'F': 1.0}\n",
    "res = res.replace({'Financial Health': health_dict})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Financial Health</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.447432</td>\n",
       "      <td>20262.618412</td>\n",
       "      <td>3.377585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.266508</td>\n",
       "      <td>9205.899868</td>\n",
       "      <td>0.654800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>10101.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>10320.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>20635.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>31054.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>31169.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Financial Health      Industry   Market Cap\n",
       "count       1499.000000   1499.000000  1499.000000\n",
       "mean           0.447432  20262.618412     3.377585\n",
       "std            0.266508   9205.899868     0.654800\n",
       "min            0.100000  10101.000000     2.000000\n",
       "25%            0.300000  10320.000000     3.000000\n",
       "50%            0.300000  20635.000000     3.000000\n",
       "75%            0.700000  31054.000000     4.000000\n",
       "max            1.000000  31169.000000     4.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Horizon\n",
    "We are going to work with a daily return horizon in this strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pricing = get_pricing(\n",
    "    symbols=res.index,\n",
    "    fields='close_price',\n",
    "    start_date=pd.Timestamp(study_date) - pd.DateOffset(months=24),\n",
    "    end_date=pd.Timestamp(study_date)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(505, 1499)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "returns = pricing.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0oGvhP3QN/KeY16Bmy1YAKbz3ve/FzGl1RdlnOUH9wV9yGaIFCZNbsmQJXnjhBQDAkSNH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\n8iR6zp6/54pGvSSvX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UWTq3c+ZJm0i7e8cncik8V1UlKJlmqGeE0pGVHk6BrJlgoVBKG+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\n5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa16cb505d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns[symbols(['AAPL'])].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we can only work with stocks that have the full return series\n",
    "returns = returns.iloc[1:,:].dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(504, 1429)\n"
     ]
    }
   ],
   "source": [
    "print returns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Candidate Pairs\n",
    "Given the pricing data and the fundamental and industry/sector data, we will first classify stocks into clusters and then, within clusters, looks for strong mean-reverting pair relationships.\n",
    "\n",
    "The first hypothesis above is that \"Stocks that share loadings to common factors in the past should be related in the future\". Common factors are things like sector/industry membership and widely known ranking schemes like momentum and value. We could specify the common factors *a priori* to well known factors, or alternatively, we could let the data speak for itself. In this post we take the latter approach. We use PCA to reduce the dimensionality of the returns data and extract the historical latent common factor loadings for each stock. For a nice visual introduction to what PCA is doing, take a look [here](http://setosa.io/ev/principal-component-analysis/) (thanks to Gus Gordon for pointing out this site).\n",
    "\n",
    "We will take these features, add in the fundamental features, and then use the `DBSCAN` **unsupervised** [clustering algorithm](http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html#dbscan) which is available in [`scikit-learn`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html). Thanks to Thomas Wiecki for pointing me to this specific clustering technique and helping with implementation. Initially I looked at using `KMeans` but `DBSCAN` has advantages in this use case, specifically\n",
    "\n",
    "- `DBSCAN` does not cluster *all* stocks; it leaves out stocks which do not neatly fit into a cluster;\n",
    "- relatedly, you do not need to specify the number of clusters.\n",
    "\n",
    "The clustering algorithm will give us sensible *candidate* pairs. We will need to do some validation in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Decomposition and DBSCAN Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=50, whiten=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_PRIN_COMPONENTS = 50\n",
    "pca = PCA(n_components=N_PRIN_COMPONENTS)\n",
    "pca.fit(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1429, 50)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_.T.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have reduced data now with the first `N_PRIN_COMPONENTS` principal component loadings. Let's add some fundamental values as well to make the model more robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1429, 52)\n"
     ]
    }
   ],
   "source": [
    "X = np.hstack(\n",
    "    (pca.components_.T,\n",
    "     res['Market Cap'][returns.columns].values[:, np.newaxis],\n",
    "     res['Financial Health'][returns.columns].values[:, np.newaxis])\n",
    ")\n",
    "\n",
    "print X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1429, 52)\n"
     ]
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit_transform(X)\n",
    "print X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN(algorithm='auto', eps=1.9, leaf_size=30, metric='euclidean',\n",
      "    min_samples=3, p=None, random_state=None)\n",
      "\n",
      "Clusters discovered: 11\n"
     ]
    }
   ],
   "source": [
    "clf = DBSCAN(eps=1.9, min_samples=3)\n",
    "print clf\n",
    "\n",
    "clf.fit(X)\n",
    "labels = clf.labels_\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "print \"\\nClusters discovered: %d\" % n_clusters_\n",
    "\n",
    "clustered = clf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pairs possible in universe: 1020306 \n"
     ]
    }
   ],
   "source": [
    "# the initial dimensionality of the search was\n",
    "ticker_count = len(returns.columns)\n",
    "print \"Total pairs possible in universe: %d \" % (ticker_count*(ticker_count-1)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clustered_series = pd.Series(index=returns.columns, data=clustered.flatten())\n",
    "clustered_series_all = pd.Series(index=returns.columns, data=clustered.flatten())\n",
    "clustered_series = clustered_series[clustered_series != -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters formed: 11\n",
      "Pairs to evaluate: 2120\n"
     ]
    }
   ],
   "source": [
    "CLUSTER_SIZE_LIMIT = 9999\n",
    "counts = clustered_series.value_counts()\n",
    "ticker_count_reduced = counts[(counts>1) & (counts<=CLUSTER_SIZE_LIMIT)]\n",
    "print \"Clusters formed: %d\" % len(ticker_count_reduced)\n",
    "print \"Pairs to evaluate: %d\" % (ticker_count_reduced*(ticker_count_reduced-1)).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have reduced the search space for pairs from >1mm to approximately 2,000.\n",
    "\n",
    "### Cluster Visualization\n",
    "We have found 11 clusters. The data are clustered in 52 dimensions. As an attempt to visualize what has happened in 2d, we can try with [T-SNE](https://distill.pub/2016/misread-tsne/). T-SNE is an algorithm for visualizing very high dimension data in 2d, created in part by Geoff Hinton. We visualize the discovered pairs to help us gain confidence that the `DBSCAN` output is sensible; i.e., we want to see that T-SNE and DBSCAN both find our clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_tsne = TSNE(learning_rate=1000, perplexity=25, random_state=1337).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vM7B1M2zbhgpDWJYN17EhpYKXEtXXB4bWoUmnJbq7FaZMGb8Eh9cKEU0GrOQQ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