{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Ranking Universes by Factors\n",
    "\n",
    "By Delaney Granizo-Mackenzie and Gilbert Wassermann\n",
    "\n",
    "Part of the Quantopian Lecture Series:\n",
    "\n",
    "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
    "* [https://github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One common technique in quantitative finance is that of ranking stocks in some way. This ranking can be whatever you come up with, but will often be a combination of fundamental factors and price-based signals. One example could be the following\n",
    "\n",
    "1. Score stocks based on 0.5 x the PE Ratio of that stock + 0.5 x the 30 day price momentum\n",
    "2. Rank stocks based on that score\n",
    "\n",
    "These ranking systems can be used to construct long-short equity strategies. The Long-Short Equity Lecture is recommended reading before this Lecture.\n",
    "\n",
    "In order to develop a good ranking system, we need to first understand how to evaluate ranking systems. We will show a demo here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##WARNING: \n",
    "This notebook does analysis over thousands of equities and hundreds of timepoints. The resulting memory usage can crash the research server if you are running other notebooks. Please shut down other notebooks in the main research menu before running this notebook. You can tell if other notebooks are running by checking the color of the notebook symbol. Green indicates running, grey indicates not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "import scipy\n",
    "from statsmodels import regression\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Data\n",
    "The first thing we're gonna do is get monthly values for the Market Cap, P/E Ratio and Monthly Returns for every equity. Monthly Returns is a metric that takes the returns accrued over an entire month of trading by dividing the last close price by the first close price and subtracting 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.pipeline.data import morningstar\n",
    "from quantopian.pipeline.data.builtin import USEquityPricing\n",
    "from quantopian.pipeline.factors import CustomFactor, Returns\n",
    "        \n",
    "def make_pipeline():\n",
    "    \"\"\"\n",
    "    Create and return our pipeline.\n",
    "    \n",
    "    We break this piece of logic out into its own function to make it easier to\n",
    "    test and modify in isolation.\n",
    "    \"\"\"\n",
    "    \n",
    "    pipe = Pipeline(\n",
    "        columns = {\n",
    "            'Market Cap' : morningstar.valuation.market_cap.latest,\n",
    "            'PE Ratio' : morningstar.valuation_ratios.pe_ratio.latest,\n",
    "            'Monthly Returns': Returns(window_length=21),\n",
    "        })\n",
    "    \n",
    "    return pipe\n",
    "\n",
    "pipe = make_pipeline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the data to get a quick sense of what we have. This may take a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Market Cap</th>\n",
       "      <th>Monthly Returns</th>\n",
       "      <th>PE Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2013-01-02 00:00:00+00:00</th>\n",
       "      <th>Equity(2 [AA])</th>\n",
       "      <td>8.975170e+09</td>\n",
       "      <td>0.032143</td>\n",
       "      <td>135.1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(21 [AAME])</th>\n",
       "      <td>6.228180e+07</td>\n",
       "      <td>0.065580</td>\n",
       "      <td>16.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(24 [AAPL])</th>\n",
       "      <td>5.505680e+11</td>\n",
       "      <td>-0.089110</td>\n",
       "      <td>13.2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(31 [ABAX])</th>\n",
       "      <td>8.283930e+08</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>35.8423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(52 [ABM])</th>\n",
       "      <td>1.037840e+09</td>\n",
       "      <td>0.053308</td>\n",
       "      <td>16.7504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(53 [ABMD])</th>\n",
       "      <td>5.296030e+08</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>39.8406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(62 [ABT])</th>\n",
       "      <td>4.916000e+10</td>\n",
       "      <td>0.008154</td>\n",
       "      <td>15.7978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(64 [ABX])</th>\n",
       "      <td>3.455270e+10</td>\n",
       "      <td>0.014778</td>\n",
       "      <td>10.2987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(66 [AB])</th>\n",
       "      <td>1.848950e+09</td>\n",
       "      <td>-0.007688</td>\n",
       "      <td>9.3284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(67 [ADSK])</th>\n",
       "      <td>7.444310e+09</td>\n",
       "      <td>0.066727</td>\n",
       "      <td>31.3480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(69 [ACAT])</th>\n",
       "      <td>4.985790e+08</td>\n",
       "      <td>-0.115538</td>\n",
       "      <td>14.9925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(76 [TAP])</th>\n",
       "      <td>7.513120e+09</td>\n",
       "      <td>0.034208</td>\n",
       "      <td>13.6054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(84 [ACET])</th>\n",
       "      <td>2.710310e+08</td>\n",
       "      <td>0.013798</td>\n",
       "      <td>14.3266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(88 [ACI])</th>\n",
       "      <td>1.426500e+09</td>\n",
       "      <td>0.089153</td>\n",
       "      <td>14.7493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(99 [ACO])</th>\n",
       "      <td>9.648460e+08</td>\n",
       "      <td>0.023727</td>\n",
       "      <td>14.8148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(100 [IEP])</th>\n",
       "      <td>4.212790e+09</td>\n",
       "      <td>0.107074</td>\n",
       "      <td>6.7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(106 [ACU])</th>\n",
       "      <td>3.430780e+07</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>10.1626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(110 [ACXM])</th>\n",
       "      <td>1.319010e+09</td>\n",
       "      <td>-0.012450</td>\n",
       "      <td>26.5957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(112 [ACY])</th>\n",
       "      <td>2.004690e+07</td>\n",
       "      <td>0.084681</td>\n",
       "      <td>4.3764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(114 [ADBE])</th>\n",
       "      <td>1.710190e+10</td>\n",
       "      <td>0.088099</td>\n",
       "      <td>20.8768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(122 [ADI])</th>\n",
       "      <td>1.223640e+10</td>\n",
       "      <td>0.044177</td>\n",
       "      <td>19.0476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(128 [ADM])</th>\n",
       "      <td>1.758370e+10</td>\n",
       "      <td>0.025843</td>\n",
       "      <td>18.7266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(153 [AE])</th>\n",
       "      <td>1.471520e+08</td>\n",
       "      <td>-0.003438</td>\n",
       "      <td>6.0277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(154 [AEM])</th>\n",
       "      <td>9.589180e+09</td>\n",
       "      <td>-0.059362</td>\n",
       "      <td>64.5161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(157 [AEG])</th>\n",
       "      <td>1.096140e+10</td>\n",
       "      <td>0.125654</td>\n",
       "      <td>4.2644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(161 [AEP])</th>\n",
       "      <td>2.069590e+10</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>11.3636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(162 [AEPI])</th>\n",
       "      <td>3.346250e+08</td>\n",
       "      <td>-0.012629</td>\n",
       "      <td>14.5349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(166 [AES])</th>\n",
       "      <td>7.938060e+09</td>\n",
       "      <td>0.005162</td>\n",
       "      <td>128.2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(168 [AET])</th>\n",
       "      <td>1.444710e+10</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>8.2576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(185 [AFL])</th>\n",
       "      <td>2.484740e+10</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>8.7108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2015-02-02 00:00:00+00:00</th>\n",
       "      <th>Equity(47858 [NMS])</th>\n",
       "      <td>6.352770e+07</td>\n",
       "      <td>0.075804</td>\n",
       "      <td>10.6952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47873 [OMAM])</th>\n",
       "      <td>1.854000e+09</td>\n",
       "      <td>-0.067692</td>\n",
       "      <td>74.7579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47875 [VBTX])</th>\n",
       "      <td>1.237870e+08</td>\n",
       "      <td>-0.064286</td>\n",
       "      <td>18.4815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47876 [MOLG])</th>\n",
       "      <td>1.302750e+08</td>\n",
       "      <td>-0.372549</td>\n",
       "      <td>29.9010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47883 [DPLO])</th>\n",
       "      <td>1.336120e+09</td>\n",
       "      <td>-0.102920</td>\n",
       "      <td>171.6673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47884 [PLAY])</th>\n",
       "      <td>1.146320e+09</td>\n",
       "      <td>0.051592</td>\n",
       "      <td>87.8222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47888 [FCAU])</th>\n",
       "      <td>1.627660e+10</td>\n",
       "      <td>0.143104</td>\n",
       "      <td>14.2440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47894 [KIQ])</th>\n",
       "      <td>2.109430e+08</td>\n",
       "      <td>-0.228376</td>\n",
       "      <td>42.2230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47898 [GWB])</th>\n",
       "      <td>1.199980e+09</td>\n",
       "      <td>-0.112478</td>\n",
       "      <td>11.4336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47904 [SRSC])</th>\n",
       "      <td>9.647810e+08</td>\n",
       "      <td>-0.030177</td>\n",
       "      <td>7.6246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47913 [XENE])</th>\n",
       "      <td>2.444860e+08</td>\n",
       "      <td>-0.113924</td>\n",
       "      <td>28.1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47921 [KEYS])</th>\n",
       "      <td>5.652700e+09</td>\n",
       "      <td>-0.012718</td>\n",
       "      <td>14.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47923 [KE])</th>\n",
       "      <td>3.170760e+08</td>\n",
       "      <td>-0.149042</td>\n",
       "      <td>14.2148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47935 [ABCW])</th>\n",
       "      <td>3.094400e+08</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>4.1449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47949 [APTO])</th>\n",
       "      <td>5.733000e+07</td>\n",
       "      <td>-0.156303</td>\n",
       "      <td>3.3201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(47980 [BOOT])</th>\n",
       "      <td>5.036520e+08</td>\n",
       "      <td>0.108791</td>\n",
       "      <td>923.0552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48002 [FFWM])</th>\n",
       "      <td>1.465090e+08</td>\n",
       "      <td>-0.017641</td>\n",
       "      <td>16.5062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48019 [TBK])</th>\n",
       "      <td>2.346070e+08</td>\n",
       "      <td>-0.039882</td>\n",
       "      <td>9.1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48090 [NDRM])</th>\n",
       "      <td>1.966550e+08</td>\n",
       "      <td>-0.238976</td>\n",
       "      <td>0.1525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48091 [VA])</th>\n",
       "      <td>1.559080e+09</td>\n",
       "      <td>-0.223353</td>\n",
       "      <td>17.8565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48103 [STOR])</th>\n",
       "      <td>2.611000e+09</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>44.1732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48124 [WF])</th>\n",
       "      <td>5.441790e+09</td>\n",
       "      <td>-0.127706</td>\n",
       "      <td>11.2718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48126 [HABT])</th>\n",
       "      <td>8.303110e+08</td>\n",
       "      <td>0.021336</td>\n",
       "      <td>47.1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48129 [UBS])</th>\n",
       "      <td>6.324900e+10</td>\n",
       "      <td>-0.021688</td>\n",
       "      <td>16.6622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48131 [NEFF])</th>\n",
       "      <td>2.372420e+08</td>\n",
       "      <td>-0.176889</td>\n",
       "      <td>7.5635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48139 [CPHR])</th>\n",
       "      <td>3.714800e+08</td>\n",
       "      <td>-0.046610</td>\n",
       "      <td>13.9360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48220 [LC])</th>\n",
       "      <td>7.025730e+09</td>\n",
       "      <td>-0.257708</td>\n",
       "      <td>860.1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48252 [AVOL])</th>\n",
       "      <td>1.582620e+09</td>\n",
       "      <td>-0.010644</td>\n",
       "      <td>14.0303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48255 [MPG])</th>\n",
       "      <td>1.234820e+09</td>\n",
       "      <td>0.053026</td>\n",
       "      <td>21.7141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(48258 [JRVR])</th>\n",
       "      <td>6.139030e+08</td>\n",
       "      <td>-0.063867</td>\n",
       "      <td>9.1169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2270710 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Market Cap  Monthly Returns  \\\n",
       "2013-01-02 00:00:00+00:00 Equity(2 [AA])        8.975170e+09         0.032143   \n",
       "                          Equity(21 [AAME])     6.228180e+07         0.065580   \n",
       "                          Equity(24 [AAPL])     5.505680e+11        -0.089110   \n",
       "                          Equity(31 [ABAX])     8.283930e+08         0.008415   \n",
       "                          Equity(52 [ABM])      1.037840e+09         0.053308   \n",
       "                          Equity(53 [ABMD])     5.296030e+08         0.008621   \n",
       "                          Equity(62 [ABT])      4.916000e+10         0.008154   \n",
       "                          Equity(64 [ABX])      3.455270e+10         0.014778   \n",
       "                          Equity(66 [AB])       1.848950e+09        -0.007688   \n",
       "                          Equity(67 [ADSK])     7.444310e+09         0.066727   \n",
       "                          Equity(69 [ACAT])     4.985790e+08        -0.115538   \n",
       "                          Equity(76 [TAP])      7.513120e+09         0.034208   \n",
       "                          Equity(84 [ACET])     2.710310e+08         0.013798   \n",
       "                          Equity(88 [ACI])      1.426500e+09         0.089153   \n",
       "                          Equity(99 [ACO])      9.648460e+08         0.023727   \n",
       "                          Equity(100 [IEP])     4.212790e+09         0.107074   \n",
       "                          Equity(106 [ACU])     3.430780e+07         0.008284   \n",
       "                          Equity(110 [ACXM])    1.319010e+09        -0.012450   \n",
       "                          Equity(112 [ACY])     2.004690e+07         0.084681   \n",
       "                          Equity(114 [ADBE])    1.710190e+10         0.088099   \n",
       "                          Equity(122 [ADI])     1.223640e+10         0.044177   \n",
       "                          Equity(128 [ADM])     1.758370e+10         0.025843   \n",
       "                          Equity(153 [AE])      1.471520e+08        -0.003438   \n",
       "                          Equity(154 [AEM])     9.589180e+09        -0.059362   \n",
       "                          Equity(157 [AEG])     1.096140e+10         0.125654   \n",
       "                          Equity(161 [AEP])     2.069590e+10         0.000469   \n",
       "                          Equity(162 [AEPI])    3.346250e+08        -0.012629   \n",
       "                          Equity(166 [AES])     7.938060e+09         0.005162   \n",
       "                          Equity(168 [AET])     1.444710e+10         0.072222   \n",
       "                          Equity(185 [AFL])     2.484740e+10         0.003021   \n",
       "...                                                      ...              ...   \n",
       "2015-02-02 00:00:00+00:00 Equity(47858 [NMS])   6.352770e+07         0.075804   \n",
       "                          Equity(47873 [OMAM])  1.854000e+09        -0.067692   \n",
       "                          Equity(47875 [VBTX])  1.237870e+08        -0.064286   \n",
       "                          Equity(47876 [MOLG])  1.302750e+08        -0.372549   \n",
       "                          Equity(47883 [DPLO])  1.336120e+09        -0.102920   \n",
       "                          Equity(47884 [PLAY])  1.146320e+09         0.051592   \n",
       "                          Equity(47888 [FCAU])  1.627660e+10         0.143104   \n",
       "                          Equity(47894 [KIQ])   2.109430e+08        -0.228376   \n",
       "                          Equity(47898 [GWB])   1.199980e+09        -0.112478   \n",
       "                          Equity(47904 [SRSC])  9.647810e+08        -0.030177   \n",
       "                          Equity(47913 [XENE])  2.444860e+08        -0.113924   \n",
       "                          Equity(47921 [KEYS])  5.652700e+09        -0.012718   \n",
       "                          Equity(47923 [KE])    3.170760e+08        -0.149042   \n",
       "                          Equity(47935 [ABCW])  3.094400e+08         0.008735   \n",
       "                          Equity(47949 [APTO])  5.733000e+07        -0.156303   \n",
       "                          Equity(47980 [BOOT])  5.036520e+08         0.108791   \n",
       "                          Equity(48002 [FFWM])  1.465090e+08        -0.017641   \n",
       "                          Equity(48019 [TBK])   2.346070e+08        -0.039882   \n",
       "                          Equity(48090 [NDRM])  1.966550e+08        -0.238976   \n",
       "                          Equity(48091 [VA])    1.559080e+09        -0.223353   \n",
       "                          Equity(48103 [STOR])  2.611000e+09         0.062500   \n",
       "                          Equity(48124 [WF])    5.441790e+09        -0.127706   \n",
       "                          Equity(48126 [HABT])  8.303110e+08         0.021336   \n",
       "                          Equity(48129 [UBS])   6.324900e+10        -0.021688   \n",
       "                          Equity(48131 [NEFF])  2.372420e+08        -0.176889   \n",
       "                          Equity(48139 [CPHR])  3.714800e+08        -0.046610   \n",
       "                          Equity(48220 [LC])    7.025730e+09        -0.257708   \n",
       "                          Equity(48252 [AVOL])  1.582620e+09        -0.010644   \n",
       "                          Equity(48255 [MPG])   1.234820e+09         0.053026   \n",
       "                          Equity(48258 [JRVR])  6.139030e+08        -0.063867   \n",
       "\n",
       "                                                PE Ratio  \n",
       "2013-01-02 00:00:00+00:00 Equity(2 [AA])        135.1351  \n",
       "                          Equity(21 [AAME])      16.0772  \n",
       "                          Equity(24 [AAPL])      13.2626  \n",
       "                          Equity(31 [ABAX])      35.8423  \n",
       "                          Equity(52 [ABM])       16.7504  \n",
       "                          Equity(53 [ABMD])      39.8406  \n",
       "                          Equity(62 [ABT])       15.7978  \n",
       "                          Equity(64 [ABX])       10.2987  \n",
       "                          Equity(66 [AB])         9.3284  \n",
       "                          Equity(67 [ADSK])      31.3480  \n",
       "                          Equity(69 [ACAT])      14.9925  \n",
       "                          Equity(76 [TAP])       13.6054  \n",
       "                          Equity(84 [ACET])      14.3266  \n",
       "                          Equity(88 [ACI])       14.7493  \n",
       "                          Equity(99 [ACO])       14.8148  \n",
       "                          Equity(100 [IEP])       6.7204  \n",
       "                          Equity(106 [ACU])      10.1626  \n",
       "                          Equity(110 [ACXM])     26.5957  \n",
       "                          Equity(112 [ACY])       4.3764  \n",
       "                          Equity(114 [ADBE])     20.8768  \n",
       "                          Equity(122 [ADI])      19.0476  \n",
       "                          Equity(128 [ADM])      18.7266  \n",
       "                          Equity(153 [AE])        6.0277  \n",
       "                          Equity(154 [AEM])      64.5161  \n",
       "                          Equity(157 [AEG])       4.2644  \n",
       "                          Equity(161 [AEP])      11.3636  \n",
       "                          Equity(162 [AEPI])     14.5349  \n",
       "                          Equity(166 [AES])     128.2051  \n",
       "                          Equity(168 [AET])       8.2576  \n",
       "                          Equity(185 [AFL])       8.7108  \n",
       "...                                                  ...  \n",
       "2015-02-02 00:00:00+00:00 Equity(47858 [NMS])    10.6952  \n",
       "                          Equity(47873 [OMAM])   74.7579  \n",
       "                          Equity(47875 [VBTX])   18.4815  \n",
       "                          Equity(47876 [MOLG])   29.9010  \n",
       "                          Equity(47883 [DPLO])  171.6673  \n",
       "                          Equity(47884 [PLAY])   87.8222  \n",
       "                          Equity(47888 [FCAU])   14.2440  \n",
       "                          Equity(47894 [KIQ])    42.2230  \n",
       "                          Equity(47898 [GWB])    11.4336  \n",
       "                          Equity(47904 [SRSC])    7.6246  \n",
       "                          Equity(47913 [XENE])   28.1404  \n",
       "                          Equity(47921 [KEYS])   14.3484  \n",
       "                          Equity(47923 [KE])     14.2148  \n",
       "                          Equity(47935 [ABCW])    4.1449  \n",
       "                          Equity(47949 [APTO])    3.3201  \n",
       "                          Equity(47980 [BOOT])  923.0552  \n",
       "                          Equity(48002 [FFWM])   16.5062  \n",
       "                          Equity(48019 [TBK])     9.1637  \n",
       "                          Equity(48090 [NDRM])    0.1525  \n",
       "                          Equity(48091 [VA])     17.8565  \n",
       "                          Equity(48103 [STOR])   44.1732  \n",
       "                          Equity(48124 [WF])     11.2718  \n",
       "                          Equity(48126 [HABT])   47.1418  \n",
       "                          Equity(48129 [UBS])    16.6622  \n",
       "                          Equity(48131 [NEFF])    7.5635  \n",
       "                          Equity(48139 [CPHR])   13.9360  \n",
       "                          Equity(48220 [LC])    860.1009  \n",
       "                          Equity(48252 [AVOL])   14.0303  \n",
       "                          Equity(48255 [MPG])    21.7141  \n",
       "                          Equity(48258 [JRVR])    9.1169  \n",
       "\n",
       "[2270710 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from quantopian.research import run_pipeline\n",
    "\n",
    "start_date = '2013-01-01'\n",
    "end_date = '2015-02-01'\n",
    "\n",
    "data = run_pipeline(pipe, start_date, end_date)\n",
    "\n",
    "# remove NaN values\n",
    "data = data.dropna()\n",
    "\n",
    "# show data\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to take each of these individual factors, clean them to remove NaN values and aggregate them for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cap_data = data['Market Cap'].transpose().unstack() # extract series of data\n",
    "cap_data = cap_data.T.dropna().T # remove NaN values\n",
    "cap_data = cap_data.resample('M', how='last') # use last instance in month to aggregate\n",
    "\n",
    "pe_data = data['PE Ratio'].transpose().unstack()\n",
    "pe_data = pe_data.T.dropna().T\n",
    "pe_data = pe_data.resample('M', how='last')\n",
    "\n",
    "month_data = data['Monthly Returns'].transpose().unstack()\n",
    "month_data = month_data.T.dropna().T\n",
    "month_data = month_data.resample('M', how='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to figure out which equities we have data for. Data sources are never perfect, and stocks go in and out of existence with Mergers, Acquisitions, and Bankruptcies. We'll make a list of the stocks common to all three sources (our factor data sets) and then filter down both to just those stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "common_equities = cap_data.T.index.intersection(pe_data.T.index).intersection(month_data.T.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will make sure that each time series is being run over identical an identical set of securities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cap_data_filtered = cap_data[common_equities][:-1]\n",
    "month_forward_returns = month_data[common_equities][1:]\n",
    "pe_data_filtered = pe_data[common_equities][:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, is the filtered data for market cap over all equities for the first 5 months, as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Equity(2 [AA])</th>\n",
       "      <th>Equity(24 [AAPL])</th>\n",
       "      <th>Equity(31 [ABAX])</th>\n",
       "      <th>Equity(52 [ABM])</th>\n",
       "      <th>Equity(62 [ABT])</th>\n",
       "      <th>Equity(64 [ABX])</th>\n",
       "      <th>Equity(66 [AB])</th>\n",
       "      <th>Equity(67 [ADSK])</th>\n",
       "      <th>Equity(69 [ACAT])</th>\n",
       "      <th>Equity(76 [TAP])</th>\n",
       "      <th>...</th>\n",
       "      <th>Equity(43476 [BERY])</th>\n",
       "      <th>Equity(43479 [FLTX])</th>\n",
       "      <th>Equity(43493 [ANFI])</th>\n",
       "      <th>Equity(43494 [SSTK])</th>\n",
       "      <th>Equity(43495 [AMBA])</th>\n",
       "      <th>Equity(43512 [FANG])</th>\n",
       "      <th>Equity(43514 [SHOS])</th>\n",
       "      <th>Equity(43572 [WWAV])</th>\n",
       "      <th>Equity(43599 [RH])</th>\n",
       "      <th>Equity(43627 [RKUS])</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31 00:00:00+00:00</th>\n",
       "      <td>9263400000</td>\n",
       "      <td>4.996960e+11</td>\n",
       "      <td>828393000</td>\n",
       "      <td>1085530000</td>\n",
       "      <td>49412800000</td>\n",
       "      <td>35048800000</td>\n",
       "      <td>1833170000</td>\n",
       "      <td>7943140000</td>\n",
       "      <td>440098000</td>\n",
       "      <td>7513120000</td>\n",
       "      <td>...</td>\n",
       "      <td>1678870000</td>\n",
       "      <td>749643000</td>\n",
       "      <td>288533000</td>\n",
       "      <td>871338000</td>\n",
       "      <td>290937000</td>\n",
       "      <td>707182000</td>\n",
       "      <td>752136000</td>\n",
       "      <td>2688420000</td>\n",
       "      <td>1279280000</td>\n",
       "      <td>975600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28 00:00:00+00:00</th>\n",
       "      <td>9434150000</td>\n",
       "      <td>4.277320e+11</td>\n",
       "      <td>852731000</td>\n",
       "      <td>1085530000</td>\n",
       "      <td>53214500000</td>\n",
       "      <td>31955400000</td>\n",
       "      <td>2141330000</td>\n",
       "      <td>8693570000</td>\n",
       "      <td>476344000</td>\n",
       "      <td>8200170000</td>\n",
       "      <td>...</td>\n",
       "      <td>1987300000</td>\n",
       "      <td>867043000</td>\n",
       "      <td>245378000</td>\n",
       "      <td>845533000</td>\n",
       "      <td>290937000</td>\n",
       "      <td>829238000</td>\n",
       "      <td>752136000</td>\n",
       "      <td>2800870000</td>\n",
       "      <td>1279280000</td>\n",
       "      <td>1736040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31 00:00:00+00:00</th>\n",
       "      <td>9110660000</td>\n",
       "      <td>4.145000e+11</td>\n",
       "      <td>937567000</td>\n",
       "      <td>1240500000</td>\n",
       "      <td>53073200000</td>\n",
       "      <td>30273500000</td>\n",
       "      <td>2422140000</td>\n",
       "      <td>8217940000</td>\n",
       "      <td>478849000</td>\n",
       "      <td>8022940000</td>\n",
       "      <td>...</td>\n",
       "      <td>2172600000</td>\n",
       "      <td>821391000</td>\n",
       "      <td>256434000</td>\n",
       "      <td>1092660000</td>\n",
       "      <td>269269000</td>\n",
       "      <td>839964000</td>\n",
       "      <td>1029570000</td>\n",
       "      <td>2705720000</td>\n",
       "      <td>1465930000</td>\n",
       "      <td>1587240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30 00:00:00+00:00</th>\n",
       "      <td>9110660000</td>\n",
       "      <td>4.161420e+11</td>\n",
       "      <td>1046720000</td>\n",
       "      <td>1215370000</td>\n",
       "      <td>55059100000</td>\n",
       "      <td>29433900000</td>\n",
       "      <td>2309700000</td>\n",
       "      <td>9231750000</td>\n",
       "      <td>577001000</td>\n",
       "      <td>8924830000</td>\n",
       "      <td>...</td>\n",
       "      <td>2154440000</td>\n",
       "      <td>876024000</td>\n",
       "      <td>227074000</td>\n",
       "      <td>1507590000</td>\n",
       "      <td>423369000</td>\n",
       "      <td>992719000</td>\n",
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       "      <td>1328870000</td>\n",
       "      <td>1560850000</td>\n",
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       "    <tr>\n",
       "      <th>2013-05-31 00:00:00+00:00</th>\n",
       "      <td>9089870000</td>\n",
       "      <td>4.156150e+11</td>\n",
       "      <td>944303000</td>\n",
       "      <td>1215370000</td>\n",
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       "      <td>19732700000</td>\n",
       "      <td>2498490000</td>\n",
       "      <td>8813240000</td>\n",
       "      <td>594034000</td>\n",
       "      <td>9411840000</td>\n",
       "      <td>...</td>\n",
       "      <td>2148790000</td>\n",
       "      <td>847847000</td>\n",
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       "      <td>1397660000</td>\n",
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       "      <td>2925430000</td>\n",
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       "      <td>1435260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Equity(2 [AA])  Equity(24 [AAPL])  \\\n",
       "2013-01-31 00:00:00+00:00      9263400000       4.996960e+11   \n",
       "2013-02-28 00:00:00+00:00      9434150000       4.277320e+11   \n",
       "2013-03-31 00:00:00+00:00      9110660000       4.145000e+11   \n",
       "2013-04-30 00:00:00+00:00      9110660000       4.161420e+11   \n",
       "2013-05-31 00:00:00+00:00      9089870000       4.156150e+11   \n",
       "\n",
       "                           Equity(31 [ABAX])  Equity(52 [ABM])  \\\n",
       "2013-01-31 00:00:00+00:00          828393000        1085530000   \n",
       "2013-02-28 00:00:00+00:00          852731000        1085530000   \n",
       "2013-03-31 00:00:00+00:00          937567000        1240500000   \n",
       "2013-04-30 00:00:00+00:00         1046720000        1215370000   \n",
       "2013-05-31 00:00:00+00:00          944303000        1215370000   \n",
       "\n",
       "                           Equity(62 [ABT])  Equity(64 [ABX])  \\\n",
       "2013-01-31 00:00:00+00:00       49412800000       35048800000   \n",
       "2013-02-28 00:00:00+00:00       53214500000       31955400000   \n",
       "2013-03-31 00:00:00+00:00       53073200000       30273500000   \n",
       "2013-04-30 00:00:00+00:00       55059100000       29433900000   \n",
       "2013-05-31 00:00:00+00:00       57553300000       19732700000   \n",
       "\n",
       "                           Equity(66 [AB])  Equity(67 [ADSK])  \\\n",
       "2013-01-31 00:00:00+00:00       1833170000         7943140000   \n",
       "2013-02-28 00:00:00+00:00       2141330000         8693570000   \n",
       "2013-03-31 00:00:00+00:00       2422140000         8217940000   \n",
       "2013-04-30 00:00:00+00:00       2309700000         9231750000   \n",
       "2013-05-31 00:00:00+00:00       2498490000         8813240000   \n",
       "\n",
       "                           Equity(69 [ACAT])  Equity(76 [TAP])  \\\n",
       "2013-01-31 00:00:00+00:00          440098000        7513120000   \n",
       "2013-02-28 00:00:00+00:00          476344000        8200170000   \n",
       "2013-03-31 00:00:00+00:00          478849000        8022940000   \n",
       "2013-04-30 00:00:00+00:00          577001000        8924830000   \n",
       "2013-05-31 00:00:00+00:00          594034000        9411840000   \n",
       "\n",
       "                                   ...           Equity(43476 [BERY])  \\\n",
       "2013-01-31 00:00:00+00:00          ...                     1678870000   \n",
       "2013-02-28 00:00:00+00:00          ...                     1987300000   \n",
       "2013-03-31 00:00:00+00:00          ...                     2172600000   \n",
       "2013-04-30 00:00:00+00:00          ...                     2154440000   \n",
       "2013-05-31 00:00:00+00:00          ...                     2148790000   \n",
       "\n",
       "                           Equity(43479 [FLTX])  Equity(43493 [ANFI])  \\\n",
       "2013-01-31 00:00:00+00:00             749643000             288533000   \n",
       "2013-02-28 00:00:00+00:00             867043000             245378000   \n",
       "2013-03-31 00:00:00+00:00             821391000             256434000   \n",
       "2013-04-30 00:00:00+00:00             876024000             227074000   \n",
       "2013-05-31 00:00:00+00:00             847847000             219620000   \n",
       "\n",
       "                           Equity(43494 [SSTK])  Equity(43495 [AMBA])  \\\n",
       "2013-01-31 00:00:00+00:00             871338000             290937000   \n",
       "2013-02-28 00:00:00+00:00             845533000             290937000   \n",
       "2013-03-31 00:00:00+00:00            1092660000             269269000   \n",
       "2013-04-30 00:00:00+00:00            1507590000             423369000   \n",
       "2013-05-31 00:00:00+00:00            1397660000             423369000   \n",
       "\n",
       "                           Equity(43512 [FANG])  Equity(43514 [SHOS])  \\\n",
       "2013-01-31 00:00:00+00:00             707182000             752136000   \n",
       "2013-02-28 00:00:00+00:00             829238000             752136000   \n",
       "2013-03-31 00:00:00+00:00             839964000            1029570000   \n",
       "2013-04-30 00:00:00+00:00             992719000             932085000   \n",
       "2013-05-31 00:00:00+00:00             971266000            1030490000   \n",
       "\n",
       "                           Equity(43572 [WWAV])  Equity(43599 [RH])  \\\n",
       "2013-01-31 00:00:00+00:00            2688420000          1279280000   \n",
       "2013-02-28 00:00:00+00:00            2800870000          1279280000   \n",
       "2013-03-31 00:00:00+00:00            2705720000          1465930000   \n",
       "2013-04-30 00:00:00+00:00            2953110000          1328870000   \n",
       "2013-05-31 00:00:00+00:00            2925430000          1328870000   \n",
       "\n",
       "                           Equity(43627 [RKUS])  \n",
       "2013-01-31 00:00:00+00:00             975600000  \n",
       "2013-02-28 00:00:00+00:00            1736040000  \n",
       "2013-03-31 00:00:00+00:00            1587240000  \n",
       "2013-04-30 00:00:00+00:00            1560850000  \n",
       "2013-05-31 00:00:00+00:00            1435260000  \n",
       "\n",
       "[5 rows x 3203 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cap_data_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we're dealing with ranking systems, at several points we're going to want to rank our data. Let's check how our data looks when ranked to get a sense for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Equity(2 [AA])</th>\n",
       "      <th>Equity(24 [AAPL])</th>\n",
       "      <th>Equity(31 [ABAX])</th>\n",
       "      <th>Equity(52 [ABM])</th>\n",
       "      <th>Equity(62 [ABT])</th>\n",
       "      <th>Equity(64 [ABX])</th>\n",
       "      <th>Equity(66 [AB])</th>\n",
       "      <th>Equity(67 [ADSK])</th>\n",
       "      <th>Equity(69 [ACAT])</th>\n",
       "      <th>Equity(76 [TAP])</th>\n",
       "      <th>...</th>\n",
       "      <th>Equity(43476 [BERY])</th>\n",
       "      <th>Equity(43479 [FLTX])</th>\n",
       "      <th>Equity(43493 [ANFI])</th>\n",
       "      <th>Equity(43494 [SSTK])</th>\n",
       "      <th>Equity(43495 [AMBA])</th>\n",
       "      <th>Equity(43512 [FANG])</th>\n",
       "      <th>Equity(43514 [SHOS])</th>\n",
       "      <th>Equity(43572 [WWAV])</th>\n",
       "      <th>Equity(43599 [RH])</th>\n",
       "      <th>Equity(43627 [RKUS])</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31 00:00:00+00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28 00:00:00+00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>18.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31 00:00:00+00:00</th>\n",
       "      <td>7.5</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30 00:00:00+00:00</th>\n",
       "      <td>7.5</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31 00:00:00+00:00</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3.5</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3203 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Equity(2 [AA])  Equity(24 [AAPL])  \\\n",
       "2013-01-31 00:00:00+00:00             9.0                 14   \n",
       "2013-02-28 00:00:00+00:00            10.0                  7   \n",
       "2013-03-31 00:00:00+00:00             7.5                  3   \n",
       "2013-04-30 00:00:00+00:00             7.5                  5   \n",
       "2013-05-31 00:00:00+00:00             5.5                  4   \n",
       "\n",
       "                           Equity(31 [ABAX])  Equity(52 [ABM])  \\\n",
       "2013-01-31 00:00:00+00:00                  3               1.5   \n",
       "2013-02-28 00:00:00+00:00                  6               1.5   \n",
       "2013-03-31 00:00:00+00:00                 11               5.0   \n",
       "2013-04-30 00:00:00+00:00                 17               3.5   \n",
       "2013-05-31 00:00:00+00:00                 14               3.5   \n",
       "\n",
       "                           Equity(62 [ABT])  Equity(64 [ABX])  \\\n",
       "2013-01-31 00:00:00+00:00                 1                25   \n",
       "2013-02-28 00:00:00+00:00                 5                24   \n",
       "2013-03-31 00:00:00+00:00                 4                23   \n",
       "2013-04-30 00:00:00+00:00                 7                22   \n",
       "2013-05-31 00:00:00+00:00                12                12   \n",
       "\n",
       "                           Equity(66 [AB])  Equity(67 [ADSK])  \\\n",
       "2013-01-31 00:00:00+00:00                2                  2   \n",
       "2013-02-28 00:00:00+00:00                7                  7   \n",
       "2013-03-31 00:00:00+00:00               14                  5   \n",
       "2013-04-30 00:00:00+00:00               10                 11   \n",
       "2013-05-31 00:00:00+00:00               18                  8   \n",
       "\n",
       "                           Equity(69 [ACAT])  Equity(76 [TAP])  \\\n",
       "2013-01-31 00:00:00+00:00                  4                 1   \n",
       "2013-02-28 00:00:00+00:00                  8                 3   \n",
       "2013-03-31 00:00:00+00:00                  9                 2   \n",
       "2013-04-30 00:00:00+00:00                 14                 5   \n",
       "2013-05-31 00:00:00+00:00                 15                11   \n",
       "\n",
       "                                   ...           Equity(43476 [BERY])  \\\n",
       "2013-01-31 00:00:00+00:00          ...                              1   \n",
       "2013-02-28 00:00:00+00:00          ...                              2   \n",
       "2013-03-31 00:00:00+00:00          ...                              5   \n",
       "2013-04-30 00:00:00+00:00          ...                              4   \n",
       "2013-05-31 00:00:00+00:00          ...                              3   \n",
       "\n",
       "                           Equity(43479 [FLTX])  Equity(43493 [ANFI])  \\\n",
       "2013-01-31 00:00:00+00:00                     1                     9   \n",
       "2013-02-28 00:00:00+00:00                     4                     5   \n",
       "2013-03-31 00:00:00+00:00                     2                     7   \n",
       "2013-04-30 00:00:00+00:00                     5                     2   \n",
       "2013-05-31 00:00:00+00:00                     3                     1   \n",
       "\n",
       "                           Equity(43494 [SSTK])  Equity(43495 [AMBA])  \\\n",
       "2013-01-31 00:00:00+00:00                     2                   2.5   \n",
       "2013-02-28 00:00:00+00:00                     1                   2.5   \n",
       "2013-03-31 00:00:00+00:00                     3                   1.0   \n",
       "2013-04-30 00:00:00+00:00                     5                   5.5   \n",
       "2013-05-31 00:00:00+00:00                     4                   5.5   \n",
       "\n",
       "                           Equity(43512 [FANG])  Equity(43514 [SHOS])  \\\n",
       "2013-01-31 00:00:00+00:00                     1                  18.5   \n",
       "2013-02-28 00:00:00+00:00                     2                  18.5   \n",
       "2013-03-31 00:00:00+00:00                     3                  23.0   \n",
       "2013-04-30 00:00:00+00:00                     5                  20.0   \n",
       "2013-05-31 00:00:00+00:00                     4                  24.0   \n",
       "\n",
       "                           Equity(43572 [WWAV])  Equity(43599 [RH])  \\\n",
       "2013-01-31 00:00:00+00:00                     1                 1.5   \n",
       "2013-02-28 00:00:00+00:00                     3                 1.5   \n",
       "2013-03-31 00:00:00+00:00                     2                 5.0   \n",
       "2013-04-30 00:00:00+00:00                     6                 3.5   \n",
       "2013-05-31 00:00:00+00:00                     5                 3.5   \n",
       "\n",
       "                           Equity(43627 [RKUS])  \n",
       "2013-01-31 00:00:00+00:00                     5  \n",
       "2013-02-28 00:00:00+00:00                    25  \n",
       "2013-03-31 00:00:00+00:00                    24  \n",
       "2013-04-30 00:00:00+00:00                    23  \n",
       "2013-05-31 00:00:00+00:00                    22  \n",
       "\n",
       "[5 rows x 3203 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cap_data_filtered.rank().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at Correlations Over Time\n",
    "\n",
    "Now that we have the data, let's do something with it. Our first analysis will be to measure the monthly Spearman rank correlation coefficient between Market Cap and month-forward returns. In other words, how predictive of 30-day returns is ranking your universe by market cap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FAAAAUOEZjkBt3rxZp06dckcWAAAAADA1wxGos2fP6o477lCjRo3k6enpnL94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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad867e31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.zeros(24)\n",
    "pvalues = np.zeros(24)\n",
    "for i in range(24):\n",
    "    score, pvalue = stats.spearmanr(cap_data_filtered.iloc[i], month_forward_returns.iloc[i])\n",
    "    pvalues[i] = pvalue\n",
    "    scores[i] = score\n",
    "    \n",
    "plt.bar(range(1,25),scores)\n",
    "plt.hlines(np.mean(scores), 1, 25, colors='r', linestyles='dashed')\n",
    "plt.xlabel('Month')\n",
    "plt.xlim((1, 25))\n",
    "plt.legend(['Mean Correlation over All Months', 'Monthly Rank Correlation'])\n",
    "plt.ylabel('Rank correlation between Market Cap and 30-day forward returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the average correlation is positive, but varies a lot from month to month.\n",
    "\n",
    "Let's look at the same analysis, but with PE Ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FNW/eXE2aNKE8AQAAACizDGeggoODNWPGDIWGhsrHx8ex/Pbbb/doMAAAAAAw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\nABT0/wAxWnPPdFJx4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad65294e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.zeros(24)\n",
    "pvalues = np.zeros(24)\n",
    "for i in range(24):\n",
    "    score, pvalue = stats.spearmanr(pe_data_filtered.iloc[i], month_forward_returns.iloc[i])\n",
    "    pvalues[i] = pvalue\n",
    "    scores[i] = score\n",
    "    \n",
    "plt.bar(range(1,25),scores)\n",
    "plt.hlines(np.mean(scores), 1, 25, colors='r', linestyles='dashed')\n",
    "plt.xlabel('Month')\n",
    "plt.xlim((1, 25))\n",
    "plt.legend(['Mean Correlation over All Months', 'Monthly Rank Correlation'])\n",
    "plt.ylabel('Rank correlation between PE Ratio and 30-day forward returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation of PE Ratio and 30-day returns seems to be near 0 on average. It's important to note that this monthly and between 2012 and 2015. Different factors are predictive on differen timeframes and frequencies, so the fact that PE Ratio doesn't appear predictive here is not necessary throwing it out as a useful factor. Beyond it's usefulness in predicting returns, it can be used for risk exposure analysis as discussed in the Factor Risk Exposure Lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basket Returns\n",
    "\n",
    "The next step is to compute the returns of baskets taken out of our ranking. If we rank all equities and then split them into $n$ groups, what would the mean return be of each group? We can answer this question in the following way. The first step is to create a function that will give us the mean return in each basket in a given the month and a ranking factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_basket_returns(factor_data, forward_returns, number_of_baskets, month):\n",
    "\n",
    "    data = pd.concat([factor_data.iloc[month-1],forward_returns.iloc[month-1]], axis=1)\n",
    "    # Rank the equities on the factor values\n",
    "    data.columns = ['Factor Value', 'Month Forward Returns']\n",
    "    data.sort('Factor Value', inplace=True)\n",
    "    \n",
    "    # How many equities per basket\n",
    "    equities_per_basket = np.floor(len(data.index) / number_of_baskets)\n",
    "\n",
    "    basket_returns = np.zeros(number_of_baskets)\n",
    "\n",
    "    # Compute the returns of each basket\n",
    "    for i in range(number_of_baskets):\n",
    "        start = i * equities_per_basket\n",
    "        if i == number_of_baskets - 1:\n",
    "            # Handle having a few extra in the last basket when our number of equities doesn't divide well\n",
    "            end = len(data.index) - 1\n",
    "        else:\n",
    "            end = i * equities_per_basket + equities_per_basket\n",
    "        # Actually compute the mean returns for each basket\n",
    "        basket_returns[i] = data.iloc[start:end]['Month Forward Returns'].mean()\n",
    "        \n",
    "    return basket_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we'll do with this function is compute this for each month and then average. This should give us a sense of the relationship over a long timeframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+8IdMnTo1jY2NOeCAAzJo0KBccMEFJdUEAABQqkIBavbs2dt8/ktf+tIOvdjr\nd7CKjr32/Ne//vV85zvfybvf/e5ceeWVufXWW3P66afv0OsDAAC8FYUC1OtvXb5x48YsXrw4f/In\nf/Km82pqatLe3t79ePXq1Rk6dGj32Jo1a7rHVq1alZqamvTp02eLOWvWrMmQIUPyH//xH3n3u9+d\nJPnzP//z/PjHPy5S+j5p2bJlWb9+fUlz29radnI1ew59K42+lUbfSqNvpSu1d/qmb6XQt9L4jCvN\nW+lbKQoFqHPOOWeLx5s2bcq55577pvPGjh2bOXPm5LTTTsvy5ctTW1ub/v37J0mGDRuWjo6OPPPM\nM6mpqcnChQvzrW99K2vXrt1iTk1NTQYMGJChQ4emtbU1dXV1efzxx3PooYeW8Hb3DaNGjcqIESNK\nmltVVZX8eOVOrmjPoG+l0bfS6Ftp9K10pfZO3/StFPpWGp9xpXkrfUt2/HKlQgHqj3V2duZ3v/vd\nmx43evTojBw5Mg0NDamsrMz06dMzf/78VFVVpb6+PjNmzMiUKVOSJKecckqGDx+e4cOHbzUnSb76\n1a/mkksuSZ8+fTJo0KB84xvfKKV0AACAkhUKUOPHj09FRUX34+effz6f+MQnCr3AawHpNUcddVT3\n12PGjNnm7cj/eE7yahi77bbbCr0mAABAORQKULfeemv31xUVFdl///3Tt2/fshUFAACwOyr0i3Sn\nT5+eYcOGZdiwYTn44INzwAEHuAMeAACwz3nDHai77ror3/nOd/LMM8/kQx/6UPfzGzduzJAhQ8pd\nGwAAwG7lDQPUxz72sXz0ox/NxRdfvMVd93r16pWampqyFwcAALA7edNT+CorK3P55Zfn//2//5f7\n7rsvw4YNy8aNG9OrV6Gz/wAAAPYahVLQVVddlR/+8If553/+5yTJ3XffnZkzZ5a1MAAAgN1NoQC1\nePHizJkzJwMGDEiSfOELX8jy5cvLWhgAAMDuplCA6tevX5J0/y6oTZs2ZdOmTeWrCgAAYDdU6PdA\nvec978mFF16Y1atX58Ybb8yCBQvy/ve/v9y1AQAA7FYKBagzzzwzjzzySPbbb7+sXLkyn/nMZ/Ku\nd72r3LUBAADsVt4wQD322GM577zzsmHDhgwePDjXXXddhg8fnnnz5mXmzJl54IEHdlWdAAAAPe4N\nA9Q111yTf/qnf0pdXV3uvffeTJ8+PZs3b87AgQNzxx137KoaAQAAdgtveBOJXr16pa6uLkly/PHH\n5+mnn85CB44CAAAWrUlEQVTkyZMzZ86c1NbW7pICAQAAdhdvGKBeu+vea97+9rfnhBNOKGtBAAAA\nu6tCtzF/zR8HKgAAgH3JG14D9a//+q/50Ic+1P34D3/4Qz70oQ+lq6srFRUVWbhwYZnLAwAA2H28\nYYD62c9+tqvqAAAA2O29YYAaNmzYrqoDAABgt7dD10ABAADsywQoAACAggQoAACAggQoAACAggQo\nAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACA\nggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAggQo\nAACAggQoAACAggQoAACAggQoAACAggQoAACAggQoAACAgnqX+wVmzZqVpUuXpqKiIhdddFGOPvro\n7rFFixblmmuuSWVlZY477ricffbZ253T2dmZqVOn5ne/+13233///N3f/V2qqqrKXT4AAEC3su5A\nLV68OG1tbWlubs7MmTPT1NS0xXhTU1PmzJmT2267LQ899FBaW1u3O+cHP/hBDjzwwNxxxx05+eST\n89hjj5WzdAAAgK2UdQeqpaUl9fX1SZK6urqsW7cuHR0dGTBgQFasWJFBgwaltrY2STJ+/Pi0tLRk\n7dq1W8154YUXct999+WLX/xikmTixInlLBsAAGCbyroD1d7enurq6u7HgwcPTnt7+zbHqqurs2bN\nmm0+397enqeffjr3339/Ghsbc/7552fdunXlLB0AAGArZb8G6vW6urp2eGzz5s2pqKhIV1dX6urq\ncs455+Qf/uEfcu211+YrX/lKuUrdoy1btizr168vaW5bW9tOrmbPoW+l0bfS6Ftp9K10pfZO3/St\nFPpWGp9xpXkrfStFWQNUTU1N945TkqxevTpDhw7tHluzZk332KpVq1JTU5M+ffpsMWfNmjUZOnRo\nhgwZkve9731JknHjxmXOnDnlLH2PNmrUqIwYMaKkuVVVVcmPV+7kivYM+lYafSuNvpVG30pXau/0\nTd9KoW+l8RlXmrfStyRZsmTJDh1f1lP4xo4dmwULFiRJli9fntra2vTv3z9JMmzYsHR0dOSZZ55J\nZ2dnFi5cmHHjxm01p6amJv37989xxx2XBx54oPv5ww8/vJylAwAAbKWsO1CjR4/OyJEj09DQkMrK\nykyfPj3z589PVVVV6uvrM2PGjEyZMiVJcsopp2T48OEZPnz4VnOSpLGxMVOnTs0Pf/jDDBgwIFdc\ncUU5SwcAANhK2a+Bei0gveaoo47q/nrMmDFpbm5+0zlJ8ra3vS2zZ8/e+QUCAAAUVNZT+AAAAPYm\nAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQA\nAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBB\nAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQA\nAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBB\nAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBAhQAAEBBZQ9Qs2bNSkND\nQyZNmpTHH398i7FFixZl4sSJaWhoyNy5cwvN+cUvfpF3vvOd5S4bAABgK73LufjixYvT1taW5ubm\ntLa25uKLL05zc3P3eFNTU2644YbU1NTkjDPOyIknnpi1a9dud86GDRty/fXXp6amppxlAwAAbFNZ\nd6BaWlpSX1+fJKmrq8u6devS0dGRJFmxYkUGDRqU2traVFRUZPz48WlpaXnDOddee20aGxvTp0+f\ncpYNAACwTWUNUO3t7amuru5+PHjw4LS3t29zrLq6OmvWrNnunN/+9rf5zW9+kw9/+MPp6uoqZ9kA\nAADbVNZT+P7YGwWf7Y299vzll1+e6dOnl6Wuvc2yZcuyfv36kua2tbXt5Gr2HPpWGn0rjb6VRt9K\nV2rv9E3fSqFvpfEZV5q30rdSlDVA1dTUdO84Jcnq1aszdOjQ7rE1a9Z0j61atSo1NTXp06fPVnP6\n9u2bJ598MlOmTElXV1fWrFmTxsbG3HzzzeUsf481atSojBgxoqS5VVVVyY9X7uSK9gz6Vhp9K42+\nlUbfSldq7/RN30qhb6XxGVeat9K3JFmyZMkOHV/WU/jGjh2bBQsWJEmWL1+e2tra9O/fP0kybNiw\ndHR05JlnnklnZ2cWLlyYcePGbXPO29/+9ixYsCDNzc25/fbbM3ToUOEJAADY5cq6AzV69OiMHDky\nDQ0NqayszPTp0zN//vxUVVWlvr4+M2bMyJQpU5Ikp5xySoYPH57hw4dvNeePVVRUlLNsAACAbSr7\nNVCvBaTXHHXUUd1fjxkzZovbmm9vzh+79957d05xAAAAO6Dsv0gXAABgbyFAAQAAFCRAAQAAFCRA\nAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAA\nFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRA\nAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAA\nFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRA\nAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFCRAAQAAFNS73C8wa9asLF26NBUVFbnoooty9NFH\nd48tWrQo11xzTSorK3Pcccfl7LPP3u6clStXZtq0aens7EyfPn1y1VVX5cADDyx3+QAAAN3KugO1\nePHitLW1pbm5OTNnzkxTU9MW401NTZkzZ05uu+22PPTQQ2ltbd3unG9/+9s57bTTcvPNN+f444/P\nDTfcUM7SAQAAtlLWHaiWlpbU19cnSerq6rJu3bp0dHRkwIABWbFiRQYNGpTa2tokyfjx49PS0pK1\na9duc86MGTPSr1+/JEl1dXV+/etfl7N0AACArZR1B6q9vT3V1dXdjwcPHpz29vZtjlVXV2fNmjXb\nnbPffvulV69e2bx5c2699daccsop5SwdAABgK2W/Bur1urq6dnjs9c9v3rw5F1xwQY499tgce+yx\nO72+vcWyZcuyfv36kua2tbXt5Gr2HPpWGn0rjb6VRt9KV2rv9E3fSqFvpfEZV5q30rdSlDVA1dTU\ndO84Jcnq1aszdOjQ7rE1a9Z0j61atSo1NTXp06fPdudMmzYthx9+eL7whS+Us+w93qhRozJixIiS\n5lZVVSU/XrmTK9oz6Ftp9K00+lYafStdqb3TN30rhb6Vxmdcad5K35JkyZIlO3R8WU/hGzt2bBYs\nWJAkWb58eWpra9O/f/8kybBhw9LR0ZFnnnkmnZ2dWbhwYcaNG7fdOXfddVf69u2bc845p5wlAwAA\nbFdZd6BGjx6dkSNHpqGhIZWVlZk+fXrmz5+fqqqq1NfXZ8aMGZkyZUqS5JRTTsnw4cMzfPjwLebM\nmDEjSXLrrbdmw4YNaWxsTEVFRY488shMnz69nOUDAABsoezXQL0WkF5z1FFHdX89ZsyYNDc3v+mc\nJNs8DgAAYFcq6yl8AAAAexMBCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAA\noCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCAB\nCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAA\noCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCAB\nCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAAoCABCgAA\noCABCgAAoKDe5X6BWbNmZenSpamoqMhFF12Uo48+unts0aJFueaaa1JZWZnjjjsuZ5999nbnrFy5\nMhdccEG6uroydOjQXHnllenTp0+5ywcAAOhW1h2oxYsXp62tLc3NzZk5c2aampq2GG9qasqcOXNy\n22235aGHHkpra+t258yePTuNjY2ZN29eDj300Nx5553lLB0AAGArZQ1QLS0tqa+vT5LU1dVl3bp1\n6ejoSJKsWLEigwYNSm1tbSoqKjJ+/Pi0tLRsc84LL7yQRx99NBMmTEiSTJgwIYsWLSpn6QAAAFsp\n6yl87e3tGTVqVPfjwYMHp729PQMGDEh7e3uqq6u7x6qrq7NixYo8++yzW8yprq5Oe3t7Xn755e5T\n9g488MCsWbPmTV//hWef3onvZs/w4vOrd4s19jT6Vhp9K42+lUbfSvdW37e+9cz8PZW+lcZnXGl6\n4j2X/Rqo1+vq6trhsW09/0brvN43v/BnxQrby6xfvz5Lliwpef7ci/5yJ1az59C30uhbafStNPpW\nurfSO33Ttx2lb6XxGVeat9q3HVXWAFVTU5P29vbux6tXr87QoUO7x16/i7Rq1arU1NSkT58+W82p\nqalJ//79s2HDhvTt27f72Dfy3ve+dye/GwAAYF9X1mugxo4dmwULFiRJli9fntra2vTv3z9JMmzY\nsHR0dOSZZ55JZ2dnFi5cmHHjxm0157Xw9IEPfKD7+QULFuSDH/xgOUsHAADYSkVX0fPhSnT11Vfn\n0UcfTWVlZaZPn55///d/T1VVVerr6/PYY4/lm9/8ZpLkpJNOyplnnrnNOUcddVTWrFmTqVOnZsOG\nDTn44IMza9asVFZWlrN0AACALZQ9QAEAAOwtynoKHwAAwN5EgAIAAChIgAIAAChorwxQs2bNSkND\nQyZNmpTHH3+8p8thL3fllVemoaEhEydOzL/8y7/0dDnsA1555ZWccMIJ+dGPftTTpbAPuOuuu/Lx\nj388p556au6///6eLoe92Isvvphzzz03kydPzqRJk/Lggw/2dEnspZ544omccMIJueWWW5IkK1eu\nTGNjY84444ycd9552bhx4xvO3+sC1OLFi9PW1pbm5ubMnDkzTU1NPV0Se7FHHnkkv/nNb9Lc3Jzv\nfve7+cY3vtHTJbEPmDt3bgYNGtTTZbAPeO655/Kd73wnzc3Nue6663Lvvff2dEnsxebPn58jjjgi\nN910U2bPnu3fcJTFSy+9lCuuuCJjx47tfm727NlpbGzMvHnzcuihh+bOO+98wzX2ugDV0tKS+vr6\nJEldXV3WrVuXjo6OHq6KvdX73ve+zJ49O0lywAEH5KWXXoobW1JO//Vf/5Unn3wy48eP7+lS2Acs\nWrQoY8eOzX777ZchQ4bka1/7Wk+XxF6suro6zz77bJLk+eefT3V1dQ9XxN6oX79+ue666zJkyJDu\n5x599NFMmDAhSTJhwoQsWrToDdfY6wJUe3v7Fn/gBg8enPb29h6siL1Zr169st9++yVJ7rjjjowf\nPz4VFRU9XBV7syuvvDIXXnhhT5fBPuLpp5/OSy+9lM9//vM544wz0tLS0tMlsRf7yEc+kpUrV+bD\nH/5wJk+e7LOOsujVq1f69u27xXMvvfRS+vTpkyQ58MADs2bNmjdco3fZqttN2A1gV7jnnnvyz//8\nz/ne977X06WwF/vRj36U973vfTn44IOT+Hyj/Lq6uvLcc89l7ty5eeqppzJ58uTcd999PV0We6m7\n7rorBx10UK6//vo88cQTufTSS3PHHXf0dFnsY4r83brXBaiampotdpxWr16doUOH9mBF7O1+8Ytf\n5Prrr8/3vve97L///j1dDnux+++/P0899VR+/vOfZ+XKlenXr18OOuigfOADH+jp0thLDRkyJKNH\nj05FRUXe8Y53ZMCAAVm7dq1TqyiLX/7yl/ngBz+YJHnnO9+ZlStXpqury5kdlN2AAQOyYcOG9O3b\nN6tWrUpNTc0bHr/XncI3duzYLFiwIEmyfPny1NbWpn///j1cFXurF154IVdddVWuvfbaVFVV9XQ5\n7OWuueaa3HHHHbn99tszceLEnH322cITZTV27Ng88sgj6erqyrPPPpsXX3xReKJshg8fnn/7t39L\n8urpo/379xee2CU+8IEPdOeHBQsWdAf57dnrdqBGjx6dkSNHpqGhIZWVlZk+fXpPl8Re7Kc//Wme\ne+65fPnLX+7+KdmVV16Zgw46qKdLA3jLamtrc+KJJ+a0005LRUWFv1Mpq0996lO56KKL0tjYmE2b\nNuXrX/96T5fEXmjp0qW55JJLsnbt2lRWVqa5uTnf+973cuGFF+b222/PwQcfnE984hNvuEZFl5Po\nAQAACtnrTuEDAAAoFwEKAACgIAEKAACgIAEKAACgIAEKAACgIAEKAACgIAEKgD3G008/naOPPjqT\nJ0/O5MmTM3HixHzrW9/a4XUeffTRfPrTny58/OrVq/Pwww/v8OsAsPfZ636RLgB7twMPPDA33XRT\nkmTz5s35yEc+ko9+9KN55zvfuUPrVFRUFD72kUceSWtra4499tgdeg0A9j4CFAB7rGeffTadnZ0Z\nMmRI7rnnnnz3u9/Nfvvtl02bNuWKK67IwQcfnO9///u5++67s99++2W//fbLVVddtcUaTzzxRC64\n4IL84z/+YzZv3pzLLrssL7/8cl588cWcd955ecc73pFrrrkmSTJo0KCceeaZPfBOAdhdCFAA7FH+\n8Ic/ZPLkydm8eXN+85vf5K//+q8zZMiQvPDCC7n66qszbNiwXH/99Zk3b16+8pWv5O///u/z85//\nPNXV1XnwwQezatWq7rVWrVqVCy+8MH//93+f2tra/M3f/E3OOuusvP/97097e3tOO+203HPPPfkf\n/+N/ZNOmTcITAAIUAHuW15/C19nZmWnTpuWWW27JIYcckmnTpqWrqyvt7e1597vfnSSZOHFizjrr\nrJx44ok56aSTcthhh+XRRx/NCy+8kM9+9rP58pe/nMMOOyzJq6fqvfjii92v1bdv3/zhD3/Y5e8R\ngN2XAAXAHqt379456aST0tzcnCVLluR//+//nXe84x255ZZbsmzZsiTJ1KlT8/vf/z4LFy7MOeec\nk6lTp6Zfv355+umnM3HixNx44435i7/4iyRJv379MmfOnAwcOLAn3xYAuzF34QNgj9LV1bXF48ce\neyyHHHJIKisrc/DBB+eVV17JPffckw0bNmTdunWZM2dODjrooEyaNCmTJk3Kr371qyTJUUcdlalT\np6a2tjZz585NkrznPe/JT37ykyTJ2rVr841vfCPJqzec2Lhx4y58lwDsruxAAbBHefbZZzN58uR0\ndXVl48aNOfTQQ/O1r30tSXLqqafmoIMOyv/8n/8zU6dOTUtLSzo6OnLqqadm4MCB6dOnT5qamvLk\nk092r/fVr341n/zkJ/Pnf/7nueSSS3LppZfmJz/5STZu3JjPf/7zSZIxY8ZkypQp6du3b774xS/2\nyPsGYPdQ0fXHP8oDAABgm5zCBwAAUJAABQAAUJAABQAAUJAABQAAUJAABQAAUJAABQAAUJAABQAA\nUND/B9hof1aM+mDtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad5aecd410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_baskets = 10\n",
    "mean_basket_returns = np.zeros(number_of_baskets)\n",
    "for m in range(1, 25):\n",
    "    basket_returns = compute_basket_returns(cap_data_filtered, month_forward_returns, number_of_baskets, m)\n",
    "    mean_basket_returns += basket_returns\n",
    "\n",
    "mean_basket_returns /= 24    \n",
    "\n",
    "# Plot the returns of each basket\n",
    "plt.bar(range(number_of_baskets), mean_basket_returns)\n",
    "plt.ylabel('Returns')\n",
    "plt.xlabel('Basket')\n",
    "plt.legend(['Returns of Each Basket']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spread Consistency\n",
    "\n",
    "Of course, that's just the average relationship. To get a sense of how consistent this is, and whether or not we would want to trade on it, we should look at it over time. Here we'll look at the monthly spreads for the first year. We can see a lot of variation, and further analysis should be done to determine whether Market Cap is tradeable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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37gBs2bKF+Ph4JUDid9p6BENDmVOnTvHMM8/gdDqJiIhg3bp1dOnSxVvNEukQ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gJ8+/Ztxj4XL17E+fPnodfr0d7eHhKvfCUymUyor6+HyWRCY2MjjEajR7vBYEB2djYcDgcEQUBH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad664c3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(3, 4)\n",
    "for month in range(1, 13):\n",
    "    basket_returns = compute_basket_returns(cap_data_filtered, month_forward_returns, 10, month)\n",
    "\n",
    "    r = np.floor((month-1) / 4)\n",
    "    c = (month-1) % 4\n",
    "    axarr[r, c].bar(range(number_of_baskets), basket_returns)\n",
    "    axarr[r, c].xaxis.set_visible(False) # Hide the axis lables so the plots aren't super messy\n",
    "    axarr[r, c].set_title('Month ' + str(month))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll repeat the same analysis for PE Ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VtHhHR0fGjBnT83jo0KHp6OjIoEGD0tHRkerq6p6x6urqrFq1Kk8//fQ2c556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\n0P8Q284AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fada3eedf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_baskets = 10\n",
    "mean_basket_returns = np.zeros(number_of_baskets)\n",
    "for m in range(1, 25):\n",
    "    basket_returns = compute_basket_returns(pe_data_filtered, month_forward_returns, number_of_baskets, m)\n",
    "    mean_basket_returns += basket_returns\n",
    "\n",
    "mean_basket_returns /= 24    \n",
    "\n",
    "# Plot the returns of each basket\n",
    "plt.bar(range(number_of_baskets), mean_basket_returns)\n",
    "plt.ylabel('Returns')\n",
    "plt.xlabel('Basket')\n",
    "plt.legend(['Returns of Each Basket']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EvYi5xUW0Wi127Nhhlb4RKYXQPkFEREREREQ9lfCdIHN3I+6szZdffokXX3wR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad6c7d2e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(3, 4)\n",
    "for month in range(1, 13):\n",
    "    basket_returns = compute_basket_returns(pe_data_filtered, month_forward_returns, 10, month)\n",
    "\n",
    "    r = np.floor((month-1) / 4)\n",
    "    c = (month-1) % 4\n",
    "    axarr[r, c].bar(range(10), basket_returns)\n",
    "    axarr[r, c].xaxis.set_visible(False) # Hide the axis lables so the plots aren't super messy\n",
    "    axarr[r, c].set_title('Month ' + str(month))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sometimes Factors are Just Other Factors\n",
    "\n",
    "Often times a new factor will be discovered that seems to induce spread, but it turns out that it is just a new and potentially more complicated way to compute a well known factor. Consider for instance the case in which you have poured tons of resources into developing a new factor, it looks great, but how do you know it's not just another factor in disguise?\n",
    "\n",
    "To check for this, there are many analyses that can be done.\n",
    "\n",
    "### Correlation Analysis\n",
    "\n",
    "One of the most intuitive ways is to check what the correlation of the factors is over time. We'll plot that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BOWygdu/erfT0dM2YMaPKXfjKysr0/vvva9CgQW4JCAAA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\n22cDQPgQoAAAMImZW2izfTYAhAcBCgAAk7CFNgB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad6dbd8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.zeros(24)\n",
    "pvalues = np.zeros(24)\n",
    "for i in range(24):\n",
    "    score, pvalue = stats.spearmanr(cap_data_filtered.iloc[i], pe_data_filtered.iloc[i])\n",
    "    pvalues[i] = pvalue\n",
    "    scores[i] = score\n",
    "    \n",
    "plt.bar(range(1,25),scores)\n",
    "plt.hlines(np.mean(scores), 1, 25, colors='r', linestyles='dashed')\n",
    "plt.xlabel('Month')\n",
    "plt.xlim((1, 25))\n",
    "plt.legend(['Mean Correlation over All Months', 'Monthly Rank Correlation'])\n",
    "plt.ylabel('Rank correlation between Market Cap and PE Ratio');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And also the p-values because the correlations may not be that meaningful by themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ChRd8LgAAAACobH5vn2vWrJk2bdp0zv4PP/zwgp4nAgAAAIDqwO9M0ahRo/SH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\nJEmKte8pDhlF6QoG6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad692c0f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.zeros(24)\n",
    "pvalues = np.zeros(24)\n",
    "for i in range(24):\n",
    "    score, pvalue = stats.spearmanr(cap_data_filtered.iloc[i], pe_data_filtered.iloc[i])\n",
    "    pvalues[i] = pvalue\n",
    "    scores[i] = score\n",
    "    \n",
    "plt.bar(range(1,25),pvalues)\n",
    "plt.xlabel('Month')\n",
    "plt.xlim((1, 25))\n",
    "plt.legend(['Mean Correlation over All Months', 'Monthly Rank Correlation'])\n",
    "plt.ylabel('Rank correlation between Market Cap and PE Ratio');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is interesting behavior, and further analysis would be needed to determine whether a relationship existed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cly+dHXf5ccHaZV5+3grw6TFeGV8KPc8my40DsOvqjZm7mU8UaDtkLs2zE+ZJ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\nxWREa3sZvw8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad6ba30550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pe_dataframe = pd.DataFrame(pe_data_filtered.iloc[0])\n",
    "pe_dataframe.columns = ['F1']\n",
    "cap_dataframe = pd.DataFrame(cap_data_filtered.iloc[0])\n",
    "cap_dataframe.columns = ['F2']\n",
    "returns_dataframe = pd.DataFrame(month_forward_returns.iloc[0])\n",
    "returns_dataframe.columns = ['Returns']\n",
    "\n",
    "data = pe_dataframe.join(cap_dataframe).join(returns_dataframe)\n",
    "\n",
    "data = data.rank(method='first')\n",
    "\n",
    "heat = np.zeros((len(data), len(data)))\n",
    "\n",
    "for e in data.index:\n",
    "    F1 = data.loc[e]['F1']\n",
    "    F2 = data.loc[e]['F2']\n",
    "    R = data.loc[e]['Returns']\n",
    "    heat[F1-1, F2-1] += R\n",
    "    \n",
    "heat = scipy.signal.decimate(heat, 40)\n",
    "heat = scipy.signal.decimate(heat.T, 40).T\n",
    "\n",
    "p = sns.heatmap(heat, xticklabels=[], yticklabels=[])\n",
    "# p.xaxis.set_ticks([])\n",
    "# p.yaxis.set_ticks([])\n",
    "p.xaxis.set_label_text('F1 Rank')\n",
    "p.yaxis.set_label_text('F2 Rank')\n",
    "p.set_title('Sum Rank of Returns vs Factor Ranking');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##How to Choose  Ranking System\n",
    "\n",
    "The ranking system is the secret sauce of many strategies. Choosing a good ranking system, or factor, is not easy and the subject of much research. We'll discuss a few starting points here.\n",
    "\n",
    "### Clone and Tweak\n",
    "\n",
    "Choose one that is commonly discussed and see if you can modify it slightly to gain back an edge. Often times factors that are public will have no signal left as they have been completely arbitraged out of the market. However, sometimes they lead you in the right direction of where to go.\n",
    "\n",
    "### Pricing Models\n",
    "\n",
    "Any model that predicts future returns can be a factor. The future return predicted is now that factor, and can be used to rank your universe. You can take any complicated pricing model and transform it into a ranking.\n",
    "\n",
    "### Price Based Factors (Technical Indicators)\n",
    "\n",
    "Price based factors take information about the historical price of each equity and use it to generate the factor value. Examples could be 30-day momentum, or volatility measures.\n",
    "\n",
    "#### Reversion vs. Momentum\n",
    "\n",
    "It's important to note that some factors bet that prices, once moving in a direction, will continue to do so. Some factors bet the opposite. Both are valid models on different time horizons and assets, and it's important to investigate whether the underlying behavior is momentum or reversion based.\n",
    "\n",
    "### Fundamental Factors (Value Based)\n",
    "\n",
    "This is using combinations of fundamental values as we discussed today. Fundamental values contain information that is tied to real world facts about a company, so in many ways can be more robust than prices.\n",
    "\n",
    "### The Arms Race\n",
    "\n",
    "Ultimately, developing predictive factors is an arms race in which you are trying to stay one step ahead. Factors get arbitraged out of markets and have a lifespan, so it's important that you are constantly doing work to determine how much decay your factors are experiencing, and what new factors might be used to take their place."
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    "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
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