pca_factor_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis As A Factor Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Principal component analysis (PCA) is a statistical technique which enjoys applications in image processing and quantitative finance.\n",
    "In this article, we focus on the later application in quantitative trading, in particular using PCA as a multi-factor model of portfolio returns.\n",
    "We use the multi-factor model to design a momentum trading strategy, backtest it under different investment universes, and present the performance results.\n",
    "\n",
    "The project is shared on my online repository https://github.com/DinodC/pca-factor-model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection\n",
    "In this section, we collect S&P consittuents' historical data from a previous project https://quant-trading.blog/2019/06/24/backtesting-a-trading-strategy-part-2/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set S&P Index keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = ['sp500',\n",
    "        'sp400',\n",
    "        'sp600']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize S&P indices close data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull S&P indices close data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in keys: \n",
    "    # Load OHLCV data\n",
    "    with open(i + '_data.pickle', 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "    \n",
    "    # Update close prices data\n",
    "    close[i] = data.close.loc['2014-06-12':]\n",
    "    \n",
    "    # Close file\n",
    "    f.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect S&P 500 index constituents data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABC</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
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       "      <th>XEL</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-06-12</th>\n",
       "      <td>39.7726</td>\n",
       "      <td>38.2958</td>\n",
       "      <td>123.2252</td>\n",
       "      <td>84.5860</td>\n",
       "      <td>44.6031</td>\n",
       "      <td>65.9975</td>\n",
       "      <td>23.03</td>\n",
       "      <td>35.7925</td>\n",
       "      <td>74.5018</td>\n",
       "      <td>66.56</td>\n",
       "      <td>...</td>\n",
       "      <td>25.8547</td>\n",
       "      <td>41.1019</td>\n",
       "      <td>83.8928</td>\n",
       "      <td>46.4230</td>\n",
       "      <td>28.5372</td>\n",
       "      <td>35.2780</td>\n",
       "      <td>51.2950</td>\n",
       "      <td>101.0367</td>\n",
       "      <td>27.7535</td>\n",
       "      <td>30.8448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-13</th>\n",
       "      <td>39.8407</td>\n",
       "      <td>38.4672</td>\n",
       "      <td>123.7407</td>\n",
       "      <td>83.6603</td>\n",
       "      <td>45.0187</td>\n",
       "      <td>66.2930</td>\n",
       "      <td>22.95</td>\n",
       "      <td>35.7745</td>\n",
       "      <td>74.7820</td>\n",
       "      <td>66.82</td>\n",
       "      <td>...</td>\n",
       "      <td>25.8884</td>\n",
       "      <td>41.6091</td>\n",
       "      <td>84.7098</td>\n",
       "      <td>46.3744</td>\n",
       "      <td>28.4920</td>\n",
       "      <td>36.0532</td>\n",
       "      <td>51.5945</td>\n",
       "      <td>101.2861</td>\n",
       "      <td>27.8192</td>\n",
       "      <td>30.9702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-16</th>\n",
       "      <td>39.7181</td>\n",
       "      <td>39.1150</td>\n",
       "      <td>124.0086</td>\n",
       "      <td>84.5035</td>\n",
       "      <td>44.8857</td>\n",
       "      <td>66.0529</td>\n",
       "      <td>23.27</td>\n",
       "      <td>35.8734</td>\n",
       "      <td>74.8634</td>\n",
       "      <td>67.62</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0740</td>\n",
       "      <td>41.9028</td>\n",
       "      <td>84.9326</td>\n",
       "      <td>46.4424</td>\n",
       "      <td>28.3791</td>\n",
       "      <td>35.9318</td>\n",
       "      <td>51.5099</td>\n",
       "      <td>100.7105</td>\n",
       "      <td>27.4060</td>\n",
       "      <td>30.9798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-17</th>\n",
       "      <td>40.0927</td>\n",
       "      <td>39.8867</td>\n",
       "      <td>124.9411</td>\n",
       "      <td>84.3935</td>\n",
       "      <td>45.1351</td>\n",
       "      <td>66.2561</td>\n",
       "      <td>23.43</td>\n",
       "      <td>35.8375</td>\n",
       "      <td>74.5832</td>\n",
       "      <td>67.54</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0910</td>\n",
       "      <td>42.2409</td>\n",
       "      <td>84.5200</td>\n",
       "      <td>46.2677</td>\n",
       "      <td>28.8310</td>\n",
       "      <td>36.0346</td>\n",
       "      <td>51.7639</td>\n",
       "      <td>100.4707</td>\n",
       "      <td>27.9601</td>\n",
       "      <td>31.6355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-18</th>\n",
       "      <td>40.3651</td>\n",
       "      <td>40.6392</td>\n",
       "      <td>128.6809</td>\n",
       "      <td>84.4852</td>\n",
       "      <td>45.3595</td>\n",
       "      <td>66.5886</td>\n",
       "      <td>23.37</td>\n",
       "      <td>36.2960</td>\n",
       "      <td>74.7459</td>\n",
       "      <td>73.08</td>\n",
       "      <td>...</td>\n",
       "      <td>26.7810</td>\n",
       "      <td>42.1964</td>\n",
       "      <td>84.7758</td>\n",
       "      <td>46.3841</td>\n",
       "      <td>28.7406</td>\n",
       "      <td>36.3521</td>\n",
       "      <td>51.8876</td>\n",
       "      <td>101.7178</td>\n",
       "      <td>27.9977</td>\n",
       "      <td>31.7126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 505 columns</p>\n",
       "</div>"
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       "Symbols           A      AAL       AAP     AAPL     ABBV      ABC   ABMD  \\\n",
       "date                                                                       \n",
       "2014-06-12  39.7726  38.2958  123.2252  84.5860  44.6031  65.9975  23.03   \n",
       "2014-06-13  39.8407  38.4672  123.7407  83.6603  45.0187  66.2930  22.95   \n",
       "2014-06-16  39.7181  39.1150  124.0086  84.5035  44.8857  66.0529  23.27   \n",
       "2014-06-17  40.0927  39.8867  124.9411  84.3935  45.1351  66.2561  23.43   \n",
       "2014-06-18  40.3651  40.6392  128.6809  84.4852  45.3595  66.5886  23.37   \n",
       "\n",
       "Symbols         ABT      ACN   ADBE   ...         XEL     XLNX      XOM  \\\n",
       "date                                  ...                                 \n",
       "2014-06-12  35.7925  74.5018  66.56   ...     25.8547  41.1019  83.8928   \n",
       "2014-06-13  35.7745  74.7820  66.82   ...     25.8884  41.6091  84.7098   \n",
       "2014-06-16  35.8734  74.8634  67.62   ...     26.0740  41.9028  84.9326   \n",
       "2014-06-17  35.8375  74.5832  67.54   ...     26.0910  42.2409  84.5200   \n",
       "2014-06-18  36.2960  74.7459  73.08   ...     26.7810  42.1964  84.7758   \n",
       "\n",
       "Symbols        XRAY      XRX      XYL      YUM       ZBH     ZION      ZTS  \n",
       "date                                                                        \n",
       "2014-06-12  46.4230  28.5372  35.2780  51.2950  101.0367  27.7535  30.8448  \n",
       "2014-06-13  46.3744  28.4920  36.0532  51.5945  101.2861  27.8192  30.9702  \n",
       "2014-06-16  46.4424  28.3791  35.9318  51.5099  100.7105  27.4060  30.9798  \n",
       "2014-06-17  46.2677  28.8310  36.0346  51.7639  100.4707  27.9601  31.6355  \n",
       "2014-06-18  46.3841  28.7406  36.3521  51.8876  101.7178  27.9977  31.7126  \n",
       "\n",
       "[5 rows x 505 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For WordPress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2014-06-12</th>\n",
       "      <td>39.7726</td>\n",
       "      <td>38.2958</td>\n",
       "      <td>123.2252</td>\n",
       "      <td>84.5860</td>\n",
       "      <td>44.6031</td>\n",
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       "    <tr>\n",
       "      <th>2014-06-13</th>\n",
       "      <td>39.8407</td>\n",
       "      <td>38.4672</td>\n",
       "      <td>123.7407</td>\n",
       "      <td>83.6603</td>\n",
       "      <td>45.0187</td>\n",
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       "    <tr>\n",
       "      <th>2014-06-16</th>\n",
       "      <td>39.7181</td>\n",
       "      <td>39.1150</td>\n",
       "      <td>124.0086</td>\n",
       "      <td>84.5035</td>\n",
       "      <td>44.8857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-17</th>\n",
       "      <td>40.0927</td>\n",
       "      <td>39.8867</td>\n",
       "      <td>124.9411</td>\n",
       "      <td>84.3935</td>\n",
       "      <td>45.1351</td>\n",
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       "    <tr>\n",
       "      <th>2014-06-18</th>\n",
       "      <td>40.3651</td>\n",
       "      <td>40.6392</td>\n",
       "      <td>128.6809</td>\n",
       "      <td>84.4852</td>\n",
       "      <td>45.3595</td>\n",
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      "text/plain": [
       "Symbols           A      AAL       AAP     AAPL     ABBV\n",
       "date                                                    \n",
       "2014-06-12  39.7726  38.2958  123.2252  84.5860  44.6031\n",
       "2014-06-13  39.8407  38.4672  123.7407  83.6603  45.0187\n",
       "2014-06-16  39.7181  39.1150  124.0086  84.5035  44.8857\n",
       "2014-06-17  40.0927  39.8867  124.9411  84.3935  45.1351\n",
       "2014-06-18  40.3651  40.6392  128.6809  84.4852  45.3595"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].iloc[:5, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABC</th>\n",
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       "      <th>ADBE</th>\n",
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       "      <th>XEL</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>67.95</td>\n",
       "      <td>29.12</td>\n",
       "      <td>154.61</td>\n",
       "      <td>179.64</td>\n",
       "      <td>76.75</td>\n",
       "      <td>82.21</td>\n",
       "      <td>267.06</td>\n",
       "      <td>77.46</td>\n",
       "      <td>177.97</td>\n",
       "      <td>268.71</td>\n",
       "      <td>...</td>\n",
       "      <td>57.78</td>\n",
       "      <td>106.90</td>\n",
       "      <td>73.59</td>\n",
       "      <td>54.40</td>\n",
       "      <td>33.30</td>\n",
       "      <td>77.40</td>\n",
       "      <td>106.97</td>\n",
       "      <td>117.41</td>\n",
       "      <td>44.40</td>\n",
       "      <td>108.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>68.35</td>\n",
       "      <td>30.36</td>\n",
       "      <td>154.61</td>\n",
       "      <td>182.54</td>\n",
       "      <td>77.06</td>\n",
       "      <td>81.65</td>\n",
       "      <td>268.80</td>\n",
       "      <td>78.69</td>\n",
       "      <td>179.56</td>\n",
       "      <td>272.86</td>\n",
       "      <td>...</td>\n",
       "      <td>59.32</td>\n",
       "      <td>105.60</td>\n",
       "      <td>72.98</td>\n",
       "      <td>55.38</td>\n",
       "      <td>33.42</td>\n",
       "      <td>78.88</td>\n",
       "      <td>107.29</td>\n",
       "      <td>118.54</td>\n",
       "      <td>44.18</td>\n",
       "      <td>108.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>69.16</td>\n",
       "      <td>30.38</td>\n",
       "      <td>154.90</td>\n",
       "      <td>185.22</td>\n",
       "      <td>77.07</td>\n",
       "      <td>81.75</td>\n",
       "      <td>269.19</td>\n",
       "      <td>80.09</td>\n",
       "      <td>180.40</td>\n",
       "      <td>274.80</td>\n",
       "      <td>...</td>\n",
       "      <td>59.80</td>\n",
       "      <td>106.01</td>\n",
       "      <td>74.31</td>\n",
       "      <td>55.63</td>\n",
       "      <td>34.03</td>\n",
       "      <td>79.15</td>\n",
       "      <td>108.42</td>\n",
       "      <td>120.31</td>\n",
       "      <td>44.24</td>\n",
       "      <td>108.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>69.52</td>\n",
       "      <td>30.92</td>\n",
       "      <td>155.35</td>\n",
       "      <td>190.15</td>\n",
       "      <td>77.43</td>\n",
       "      <td>83.48</td>\n",
       "      <td>267.87</td>\n",
       "      <td>80.74</td>\n",
       "      <td>182.92</td>\n",
       "      <td>278.16</td>\n",
       "      <td>...</td>\n",
       "      <td>59.43</td>\n",
       "      <td>107.49</td>\n",
       "      <td>74.58</td>\n",
       "      <td>55.94</td>\n",
       "      <td>34.16</td>\n",
       "      <td>79.56</td>\n",
       "      <td>109.07</td>\n",
       "      <td>120.73</td>\n",
       "      <td>43.64</td>\n",
       "      <td>110.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-10</th>\n",
       "      <td>70.29</td>\n",
       "      <td>30.76</td>\n",
       "      <td>153.52</td>\n",
       "      <td>192.58</td>\n",
       "      <td>76.95</td>\n",
       "      <td>84.77</td>\n",
       "      <td>272.43</td>\n",
       "      <td>81.27</td>\n",
       "      <td>184.44</td>\n",
       "      <td>280.34</td>\n",
       "      <td>...</td>\n",
       "      <td>59.26</td>\n",
       "      <td>110.88</td>\n",
       "      <td>74.91</td>\n",
       "      <td>57.10</td>\n",
       "      <td>34.69</td>\n",
       "      <td>80.38</td>\n",
       "      <td>108.65</td>\n",
       "      <td>121.71</td>\n",
       "      <td>43.84</td>\n",
       "      <td>110.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 505 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         A    AAL     AAP    AAPL   ABBV    ABC    ABMD    ABT     ACN  \\\n",
       "date                                                                            \n",
       "2019-06-04  67.95  29.12  154.61  179.64  76.75  82.21  267.06  77.46  177.97   \n",
       "2019-06-05  68.35  30.36  154.61  182.54  77.06  81.65  268.80  78.69  179.56   \n",
       "2019-06-06  69.16  30.38  154.90  185.22  77.07  81.75  269.19  80.09  180.40   \n",
       "2019-06-07  69.52  30.92  155.35  190.15  77.43  83.48  267.87  80.74  182.92   \n",
       "2019-06-10  70.29  30.76  153.52  192.58  76.95  84.77  272.43  81.27  184.44   \n",
       "\n",
       "Symbols       ADBE   ...      XEL    XLNX    XOM   XRAY    XRX    XYL     YUM  \\\n",
       "date                 ...                                                        \n",
       "2019-06-04  268.71   ...    57.78  106.90  73.59  54.40  33.30  77.40  106.97   \n",
       "2019-06-05  272.86   ...    59.32  105.60  72.98  55.38  33.42  78.88  107.29   \n",
       "2019-06-06  274.80   ...    59.80  106.01  74.31  55.63  34.03  79.15  108.42   \n",
       "2019-06-07  278.16   ...    59.43  107.49  74.58  55.94  34.16  79.56  109.07   \n",
       "2019-06-10  280.34   ...    59.26  110.88  74.91  57.10  34.69  80.38  108.65   \n",
       "\n",
       "Symbols        ZBH   ZION     ZTS  \n",
       "date                               \n",
       "2019-06-04  117.41  44.40  108.12  \n",
       "2019-06-05  118.54  44.18  108.50  \n",
       "2019-06-06  120.31  44.24  108.89  \n",
       "2019-06-07  120.73  43.64  110.06  \n",
       "2019-06-10  121.71  43.84  110.22  \n",
       "\n",
       "[5 rows x 505 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For WordPress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-03</th>\n",
       "      <td>66.99</td>\n",
       "      <td>27.20</td>\n",
       "      <td>153.17</td>\n",
       "      <td>173.30</td>\n",
       "      <td>75.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-04</th>\n",
       "      <td>67.95</td>\n",
       "      <td>29.12</td>\n",
       "      <td>154.61</td>\n",
       "      <td>179.64</td>\n",
       "      <td>76.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-05</th>\n",
       "      <td>68.35</td>\n",
       "      <td>30.36</td>\n",
       "      <td>154.61</td>\n",
       "      <td>182.54</td>\n",
       "      <td>77.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-06</th>\n",
       "      <td>69.16</td>\n",
       "      <td>30.38</td>\n",
       "      <td>154.90</td>\n",
       "      <td>185.22</td>\n",
       "      <td>77.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07</th>\n",
       "      <td>69.52</td>\n",
       "      <td>30.92</td>\n",
       "      <td>155.35</td>\n",
       "      <td>190.15</td>\n",
       "      <td>77.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         A    AAL     AAP    AAPL   ABBV\n",
       "date                                           \n",
       "2019-06-03  66.99  27.20  153.17  173.30  75.70\n",
       "2019-06-04  67.95  29.12  154.61  179.64  76.75\n",
       "2019-06-05  68.35  30.36  154.61  182.54  77.06\n",
       "2019-06-06  69.16  30.38  154.90  185.22  77.07\n",
       "2019-06-07  69.52  30.92  155.35  190.15  77.43"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].iloc[-6:-1, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABC</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>...</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.00000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.020384</td>\n",
       "      <td>41.208459</td>\n",
       "      <td>145.434801</td>\n",
       "      <td>135.133436</td>\n",
       "      <td>66.670458</td>\n",
       "      <td>84.568308</td>\n",
       "      <td>162.852343</td>\n",
       "      <td>49.123858</td>\n",
       "      <td>118.508010</td>\n",
       "      <td>142.302737</td>\n",
       "      <td>...</td>\n",
       "      <td>39.362328</td>\n",
       "      <td>58.865112</td>\n",
       "      <td>75.435801</td>\n",
       "      <td>53.179431</td>\n",
       "      <td>26.876008</td>\n",
       "      <td>51.309177</td>\n",
       "      <td>66.614527</td>\n",
       "      <td>111.70591</td>\n",
       "      <td>36.711273</td>\n",
       "      <td>59.821177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.868035</td>\n",
       "      <td>6.368716</td>\n",
       "      <td>24.131106</td>\n",
       "      <td>38.117633</td>\n",
       "      <td>17.717831</td>\n",
       "      <td>9.473348</td>\n",
       "      <td>116.557311</td>\n",
       "      <td>12.652720</td>\n",
       "      <td>31.178171</td>\n",
       "      <td>70.409857</td>\n",
       "      <td>...</td>\n",
       "      <td>8.317325</td>\n",
       "      <td>22.243159</td>\n",
       "      <td>4.713198</td>\n",
       "      <td>7.834761</td>\n",
       "      <td>3.222998</td>\n",
       "      <td>16.350151</td>\n",
       "      <td>15.998532</td>\n",
       "      <td>9.92955</td>\n",
       "      <td>10.738373</td>\n",
       "      <td>20.214756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.258600</td>\n",
       "      <td>24.539800</td>\n",
       "      <td>79.168700</td>\n",
       "      <td>82.743800</td>\n",
       "      <td>42.066600</td>\n",
       "      <td>65.718100</td>\n",
       "      <td>22.220000</td>\n",
       "      <td>33.935700</td>\n",
       "      <td>68.852300</td>\n",
       "      <td>60.880000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.247700</td>\n",
       "      <td>32.502600</td>\n",
       "      <td>58.967500</td>\n",
       "      <td>34.178400</td>\n",
       "      <td>18.532600</td>\n",
       "      <td>28.874500</td>\n",
       "      <td>44.181800</td>\n",
       "      <td>89.23610</td>\n",
       "      <td>18.885300</td>\n",
       "      <td>30.652000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.391500</td>\n",
       "      <td>36.574400</td>\n",
       "      <td>132.526400</td>\n",
       "      <td>103.637500</td>\n",
       "      <td>53.175300</td>\n",
       "      <td>77.745400</td>\n",
       "      <td>80.700000</td>\n",
       "      <td>39.410700</td>\n",
       "      <td>92.630500</td>\n",
       "      <td>82.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.489100</td>\n",
       "      <td>42.116300</td>\n",
       "      <td>72.522500</td>\n",
       "      <td>48.734500</td>\n",
       "      <td>24.214700</td>\n",
       "      <td>35.032500</td>\n",
       "      <td>53.061000</td>\n",
       "      <td>103.35200</td>\n",
       "      <td>26.823800</td>\n",
       "      <td>44.771300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.123400</td>\n",
       "      <td>40.847900</td>\n",
       "      <td>150.479700</td>\n",
       "      <td>119.476200</td>\n",
       "      <td>58.475100</td>\n",
       "      <td>83.862300</td>\n",
       "      <td>118.580000</td>\n",
       "      <td>43.045400</td>\n",
       "      <td>112.149800</td>\n",
       "      <td>107.970000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.059500</td>\n",
       "      <td>52.731200</td>\n",
       "      <td>75.815100</td>\n",
       "      <td>54.533600</td>\n",
       "      <td>26.571900</td>\n",
       "      <td>48.112000</td>\n",
       "      <td>61.584700</td>\n",
       "      <td>112.77000</td>\n",
       "      <td>38.252800</td>\n",
       "      <td>51.338500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65.603800</td>\n",
       "      <td>46.127100</td>\n",
       "      <td>161.921100</td>\n",
       "      <td>167.857700</td>\n",
       "      <td>82.898700</td>\n",
       "      <td>89.855500</td>\n",
       "      <td>261.940000</td>\n",
       "      <td>58.174900</td>\n",
       "      <td>149.608800</td>\n",
       "      <td>212.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>45.463000</td>\n",
       "      <td>68.820200</td>\n",
       "      <td>78.488900</td>\n",
       "      <td>59.653000</td>\n",
       "      <td>29.655300</td>\n",
       "      <td>67.341800</td>\n",
       "      <td>80.207700</td>\n",
       "      <td>119.13230</td>\n",
       "      <td>46.435300</td>\n",
       "      <td>80.866200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.940000</td>\n",
       "      <td>57.586600</td>\n",
       "      <td>199.159900</td>\n",
       "      <td>229.392000</td>\n",
       "      <td>116.445400</td>\n",
       "      <td>107.649700</td>\n",
       "      <td>449.750000</td>\n",
       "      <td>81.270000</td>\n",
       "      <td>184.440000</td>\n",
       "      <td>289.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>139.263300</td>\n",
       "      <td>86.137400</td>\n",
       "      <td>67.795300</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>83.549000</td>\n",
       "      <td>109.070000</td>\n",
       "      <td>130.91280</td>\n",
       "      <td>57.139500</td>\n",
       "      <td>110.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 505 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            A          AAL          AAP         AAPL         ABBV  \\\n",
       "count    1257.000000  1257.000000  1257.000000  1257.000000  1257.000000   \n",
       "mean       52.020384    41.208459   145.434801   135.133436    66.670458   \n",
       "std        13.868035     6.368716    24.131106    38.117633    17.717831   \n",
       "min        32.258600    24.539800    79.168700    82.743800    42.066600   \n",
       "25%        39.391500    36.574400   132.526400   103.637500    53.175300   \n",
       "50%        46.123400    40.847900   150.479700   119.476200    58.475100   \n",
       "75%        65.603800    46.127100   161.921100   167.857700    82.898700   \n",
       "max        81.940000    57.586600   199.159900   229.392000   116.445400   \n",
       "\n",
       "Symbols          ABC         ABMD          ABT          ACN         ADBE  \\\n",
       "count    1257.000000  1257.000000  1257.000000  1257.000000  1257.000000   \n",
       "mean       84.568308   162.852343    49.123858   118.508010   142.302737   \n",
       "std         9.473348   116.557311    12.652720    31.178171    70.409857   \n",
       "min        65.718100    22.220000    33.935700    68.852300    60.880000   \n",
       "25%        77.745400    80.700000    39.410700    92.630500    82.100000   \n",
       "50%        83.862300   118.580000    43.045400   112.149800   107.970000   \n",
       "75%        89.855500   261.940000    58.174900   149.608800   212.280000   \n",
       "max       107.649700   449.750000    81.270000   184.440000   289.250000   \n",
       "\n",
       "Symbols     ...               XEL         XLNX          XOM         XRAY  \\\n",
       "count       ...       1257.000000  1257.000000  1257.000000  1257.000000   \n",
       "mean        ...         39.362328    58.865112    75.435801    53.179431   \n",
       "std         ...          8.317325    22.243159     4.713198     7.834761   \n",
       "min         ...         25.247700    32.502600    58.967500    34.178400   \n",
       "25%         ...         31.489100    42.116300    72.522500    48.734500   \n",
       "50%         ...         39.059500    52.731200    75.815100    54.533600   \n",
       "75%         ...         45.463000    68.820200    78.488900    59.653000   \n",
       "max         ...         59.800000   139.263300    86.137400    67.795300   \n",
       "\n",
       "Symbols          XRX          XYL          YUM         ZBH         ZION  \\\n",
       "count    1257.000000  1257.000000  1257.000000  1257.00000  1257.000000   \n",
       "mean       26.876008    51.309177    66.614527   111.70591    36.711273   \n",
       "std         3.222998    16.350151    15.998532     9.92955    10.738373   \n",
       "min        18.532600    28.874500    44.181800    89.23610    18.885300   \n",
       "25%        24.214700    35.032500    53.061000   103.35200    26.823800   \n",
       "50%        26.571900    48.112000    61.584700   112.77000    38.252800   \n",
       "75%        29.655300    67.341800    80.207700   119.13230    46.435300   \n",
       "max        35.000000    83.549000   109.070000   130.91280    57.139500   \n",
       "\n",
       "Symbols          ZTS  \n",
       "count    1257.000000  \n",
       "mean       59.821177  \n",
       "std        20.214756  \n",
       "min        30.652000  \n",
       "25%        44.771300  \n",
       "50%        51.338500  \n",
       "75%        80.866200  \n",
       "max       110.220000  \n",
       "\n",
       "[8 rows x 505 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For WordPress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "      <td>1257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.020384</td>\n",
       "      <td>41.208459</td>\n",
       "      <td>145.434801</td>\n",
       "      <td>135.133436</td>\n",
       "      <td>66.670458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.868035</td>\n",
       "      <td>6.368716</td>\n",
       "      <td>24.131106</td>\n",
       "      <td>38.117633</td>\n",
       "      <td>17.717831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.258600</td>\n",
       "      <td>24.539800</td>\n",
       "      <td>79.168700</td>\n",
       "      <td>82.743800</td>\n",
       "      <td>42.066600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.391500</td>\n",
       "      <td>36.574400</td>\n",
       "      <td>132.526400</td>\n",
       "      <td>103.637500</td>\n",
       "      <td>53.175300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.123400</td>\n",
       "      <td>40.847900</td>\n",
       "      <td>150.479700</td>\n",
       "      <td>119.476200</td>\n",
       "      <td>58.475100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65.603800</td>\n",
       "      <td>46.127100</td>\n",
       "      <td>161.921100</td>\n",
       "      <td>167.857700</td>\n",
       "      <td>82.898700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.940000</td>\n",
       "      <td>57.586600</td>\n",
       "      <td>199.159900</td>\n",
       "      <td>229.392000</td>\n",
       "      <td>116.445400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            A          AAL          AAP         AAPL         ABBV\n",
       "count    1257.000000  1257.000000  1257.000000  1257.000000  1257.000000\n",
       "mean       52.020384    41.208459   145.434801   135.133436    66.670458\n",
       "std        13.868035     6.368716    24.131106    38.117633    17.717831\n",
       "min        32.258600    24.539800    79.168700    82.743800    42.066600\n",
       "25%        39.391500    36.574400   132.526400   103.637500    53.175300\n",
       "50%        46.123400    40.847900   150.479700   119.476200    58.475100\n",
       "75%        65.603800    46.127100   161.921100   167.857700    82.898700\n",
       "max        81.940000    57.586600   199.159900   229.392000   116.445400"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].describe().iloc[:, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1257, 505)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close['sp500'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill NaNs with previous observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "close['sp500'].fillna(method='ffill', inplace=True)\n",
    "close['sp400'].fillna(method='ffill', inplace=True)\n",
    "close['sp600'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize daily returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate daily returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in keys:\n",
    "    returns[i] = close[i].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Momentum Strategy Implementation\n",
    "In this section, we present the model (factor model) and tool (principal compenent analysis) used in implementing the momentum trading strategy. \n",
    "\n",
    "### 1. Factor Models\n",
    "Factor models use economic (e.g. interest rates), fundamental (e.g. price per earnings), and statistical (e.g. principal component analysis) factors to explain asset prices (and returns).\n",
    "Fama and French initially designed the [three-factor model](https://en.wikipedia.org/wiki/Fama–French_three-factor_model) which extends the capital asset pricing model (CAPM) to include size and value factors.\n",
    "The general framework is known as the [arbitrage pricing theory (APT)](https://en.wikipedia.org/wiki/Arbitrage_pricing_theory) developed by Stephen Ross and proposes multiple factors. \n",
    "\n",
    "### 2. Principal Component Analysis (PCA)\n",
    "Principal component analysis is a statistical procedure for finding patterns in high dimension data.\n",
    "PCA allows you to compress high dimension data by reducing the number of dimensions, without losing much information.\n",
    "Principal component analysis has the following applications in quantitative finance: interest-rate modeling and portfolio analysis.\n",
    "\n",
    "PCA implementation has the following steps:\n",
    "1. Pull data\n",
    "2. Adjust data by subtracting the mean\n",
    "3. Calculate the covariance matrix of the adjusted data\n",
    "4. Calculate the eigenvectors and eigenvalues of the covariance matrix\n",
    "5. Choose the most significant components which explain around 95% of the data\n",
    "\n",
    "Note that in this article we are only interested in applying principal component analysis, for a complete illustration you can start by checking out https://en.wikipedia.org/wiki/Principal_component_analysis#Applications.\n",
    "\n",
    "### 3. Momentum Trading Strategy\n",
    "Momentum trading strategies profit from current market trends continuation.\n",
    "The momentum trading strategy proposed below assumes that factor returns have momentum.\n",
    "The idea is to long the *winning* (*losing*) stocks which have the highest (lowest) expected returns according to factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before implementation, we set the following parameters:\n",
    "1. Lookback period\n",
    "2. Number of significant factors\n",
    "3. Number of winning and losing stocks to pick "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookback = 250\n",
    "number_of_factors = 5\n",
    "top_n = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the trading positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "positions = {}\n",
    "\n",
    "for i in keys:\n",
    "    # Update positions\n",
    "    positions[i] = pd.DataFrame(np.zeros((returns[i].shape[0], returns[i].shape[1])),\n",
    "                                 index=returns[i].index,\n",
    "                                 columns=returns[i].columns\n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in keys:\n",
    "    for j in range(lookback + 1, len(close[i])):\n",
    "        # Calculate the daily returns\n",
    "        R = returns[i].iloc[j - lookback + 1:j, :]\n",
    "\n",
    "        # Avoid daily returns with NaNs\n",
    "        has_data = (R.count() == max(R.count()))\n",
    "        has_data_list = list(R.columns[has_data])\n",
    "        R = R.loc[:, has_data_list]\n",
    "\n",
    "        # Calculate the mean of the daily returns\n",
    "        R_mean = R.mean()\n",
    "\n",
    "        # Calculate the adjusted daily returns\n",
    "        R_adj = R.sub(R_mean)\n",
    "\n",
    "        # Calculate the covariance matrix\n",
    "        cov = R_adj.cov()\n",
    "\n",
    "        # Calculate the eigenvalues (B) and eigenvectors (X)\n",
    "        eigen = np.linalg.eig(cov)\n",
    "        B = eigen[0]\n",
    "        X = eigen[1]\n",
    "\n",
    "        # Retain only a number of factors\n",
    "        X = X[:, :number_of_factors]\n",
    "\n",
    "        # OLS\n",
    "        model = sm.OLS(R_adj.iloc[-1], X)\n",
    "        results = model.fit()\n",
    "        b = results.params\n",
    "\n",
    "        # Calculate the expected returns\n",
    "        R_exp = R_mean.add(np.matmul(X, b))\n",
    "\n",
    "        # Momentum strategy\n",
    "        shorts = R_exp.sort_values()[:top_n].index\n",
    "        positions[i].iloc[j][shorts] = -1\n",
    "        longs = R_exp.sort_values()[-top_n:].index\n",
    "        positions[i].iloc[j][longs] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarks:\n",
    "1. Investment universes used in backtesting are the S&P 500, S&P 400 MidCap and S&P 600 SmallCap indices.\n",
    "2. Ordinary least squares (OLS) method is used to calculate stocks' expected returns from significant factors.\n",
    "3. Only a single stock is bought for each of the winning (losing) stocks, this could be improved by adjusting the number by the rank. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Analysis\n",
    "In this section, we present the performance of the momentum trading strategy based on principal component analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjust the positions because we consider close prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in keys:\n",
    "    positions[i] = positions[i].shift(periods=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the daily PnL of the momentum strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pnl_strat = {}\n",
    "avg_pnl_strat = {}\n",
    "\n",
    "for i in keys:\n",
    "    # Daily pnl\n",
    "    pnl_strat[i] = (positions[i].mul(returns[i])).sum(axis='columns')\n",
    "    # Annualized average pnl of the momentum strategy\n",
    "    avg_pnl_strat[i] = pnl_strat[i].mean() * 250"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average daily PnL of momentum strategy using different investment universes i.e. S&P 500, S&P 400 and S&P 600 indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sp500</th>\n",
       "      <th>sp400</th>\n",
       "      <th>sp600</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average PnL</th>\n",
       "      <td>-0.033961</td>\n",
       "      <td>-0.491665</td>\n",
       "      <td>-1.941058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sp500     sp400     sp600\n",
       "Average PnL -0.033961 -0.491665 -1.941058"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(avg_pnl_strat,\n",
    "            index=['Average PnL'],\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remark: the average daily pnl of the momentum strategy is negative regardless of the investment universe used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the cumulative PnL of the momentum trading strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c192b4978>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set size\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "for i in range(len(keys)):\n",
    "    plt.plot(pnl_strat[keys[i]].cumsum())\n",
    "    \n",
    "plt.xlim(pnl_strat['sp500'].index[0], pnl_strat['sp500'].index[-1])\n",
    "# plt.ylim(-10, 5)\n",
    "\n",
    "# Set title and legend\n",
    "plt.title('Cumulative PnL Of Momentum Trading Strategy')\n",
    "plt.legend(keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarks: \n",
    "1. From July 2015 to January 2017, the momentum trading strategy generated negative PnL.\n",
    "2. From July 2017 to January 2019, the momentum trading strategy turned it around and generated positive PnL.\n",
    "3. From January 2019, the momentum trading strategy continuted to generate positive PnL for S&P 500 and S&P 400 MidCap indices only. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "In this article, we implemented a momentum trading strategy based on principal component analysis.\n",
    "The momentum trading strategy generated positive PnL from July 2017 to January 2019, and negative PnL from July 2015 to July 2017.\n",
    "A way to enhance the current momentum trading strategy is to include exit and entry points depending on the expected profitability of the trading system."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}