{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EPAT Session 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Executive Program in Algorithmic Trading**\n",
    "\n",
    "**_Vectorized Backtesting & Object Oriented Programming_**\n",
    "\n",
    "Dr. Yves J. Hilpisch | The Python Quants GmbH | http://tpq.io\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/tpq_bootcamp.png\" width=\"350px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Financial Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.2 ms, sys: 5.62 ms, total: 19.8 ms\n",
      "Wall time: 230 ms\n"
     ]
    }
   ],
   "source": [
    "%time data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1972 entries, 0 to 1971\n",
      "Data columns (total 13 columns):\n",
      "Date      1972 non-null object\n",
      "AAPL.O    1972 non-null float64\n",
      "MSFT.O    1972 non-null float64\n",
      "INTC.O    1972 non-null float64\n",
      "AMZN.O    1972 non-null float64\n",
      "GS.N      1972 non-null float64\n",
      "SPY       1972 non-null float64\n",
      ".SPX      1972 non-null float64\n",
      ".VIX      1972 non-null float64\n",
      "EUR=      1972 non-null float64\n",
      "XAU=      1972 non-null float64\n",
      "GDX       1972 non-null float64\n",
      "GLD       1972 non-null float64\n",
      "dtypes: float64(12), object(1)\n",
      "memory usage: 200.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "      <th>GDX</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>30.572827</td>\n",
       "      <td>30.950</td>\n",
       "      <td>20.88</td>\n",
       "      <td>133.90</td>\n",
       "      <td>173.08</td>\n",
       "      <td>113.33</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>20.04</td>\n",
       "      <td>1.4411</td>\n",
       "      <td>1120.00</td>\n",
       "      <td>47.71</td>\n",
       "      <td>109.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>30.625684</td>\n",
       "      <td>30.960</td>\n",
       "      <td>20.87</td>\n",
       "      <td>134.69</td>\n",
       "      <td>176.14</td>\n",
       "      <td>113.63</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>19.35</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1118.65</td>\n",
       "      <td>48.17</td>\n",
       "      <td>109.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>30.138541</td>\n",
       "      <td>30.770</td>\n",
       "      <td>20.80</td>\n",
       "      <td>132.25</td>\n",
       "      <td>174.26</td>\n",
       "      <td>113.71</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>19.16</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1138.50</td>\n",
       "      <td>49.34</td>\n",
       "      <td>111.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>30.082827</td>\n",
       "      <td>30.452</td>\n",
       "      <td>20.60</td>\n",
       "      <td>130.00</td>\n",
       "      <td>177.67</td>\n",
       "      <td>114.19</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>19.06</td>\n",
       "      <td>1.4318</td>\n",
       "      <td>1131.90</td>\n",
       "      <td>49.10</td>\n",
       "      <td>110.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>30.282827</td>\n",
       "      <td>30.660</td>\n",
       "      <td>20.83</td>\n",
       "      <td>133.52</td>\n",
       "      <td>174.31</td>\n",
       "      <td>114.57</td>\n",
       "      <td>1144.98</td>\n",
       "      <td>18.13</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1136.10</td>\n",
       "      <td>49.84</td>\n",
       "      <td>111.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     AAPL.O  MSFT.O  INTC.O  AMZN.O    GS.N     SPY     .SPX  \\\n",
       "0  2010-01-04  30.572827  30.950   20.88  133.90  173.08  113.33  1132.99   \n",
       "1  2010-01-05  30.625684  30.960   20.87  134.69  176.14  113.63  1136.52   \n",
       "2  2010-01-06  30.138541  30.770   20.80  132.25  174.26  113.71  1137.14   \n",
       "3  2010-01-07  30.082827  30.452   20.60  130.00  177.67  114.19  1141.69   \n",
       "4  2010-01-08  30.282827  30.660   20.83  133.52  174.31  114.57  1144.98   \n",
       "\n",
       "    .VIX    EUR=     XAU=    GDX     GLD  \n",
       "0  20.04  1.4411  1120.00  47.71  109.80  \n",
       "1  19.35  1.4368  1118.65  48.17  109.70  \n",
       "2  19.16  1.4412  1138.50  49.34  111.51  \n",
       "3  19.06  1.4318  1131.90  49.10  110.82  \n",
       "4  18.13  1.4412  1136.10  49.84  111.37  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "      <th>GDX</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>2017-10-25</td>\n",
       "      <td>156.41</td>\n",
       "      <td>78.63</td>\n",
       "      <td>40.78</td>\n",
       "      <td>972.91</td>\n",
       "      <td>241.71</td>\n",
       "      <td>255.29</td>\n",
       "      <td>2557.15</td>\n",
       "      <td>11.23</td>\n",
       "      <td>1.1812</td>\n",
       "      <td>1277.01</td>\n",
       "      <td>22.83</td>\n",
       "      <td>121.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>157.41</td>\n",
       "      <td>78.76</td>\n",
       "      <td>41.35</td>\n",
       "      <td>972.43</td>\n",
       "      <td>241.72</td>\n",
       "      <td>255.62</td>\n",
       "      <td>2560.40</td>\n",
       "      <td>11.30</td>\n",
       "      <td>1.1650</td>\n",
       "      <td>1266.73</td>\n",
       "      <td>22.43</td>\n",
       "      <td>120.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>163.05</td>\n",
       "      <td>83.81</td>\n",
       "      <td>44.40</td>\n",
       "      <td>1100.95</td>\n",
       "      <td>241.71</td>\n",
       "      <td>257.71</td>\n",
       "      <td>2581.07</td>\n",
       "      <td>9.80</td>\n",
       "      <td>1.1608</td>\n",
       "      <td>1272.60</td>\n",
       "      <td>22.57</td>\n",
       "      <td>120.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>166.72</td>\n",
       "      <td>83.89</td>\n",
       "      <td>44.37</td>\n",
       "      <td>1110.85</td>\n",
       "      <td>240.89</td>\n",
       "      <td>256.75</td>\n",
       "      <td>2572.83</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1.1649</td>\n",
       "      <td>1275.86</td>\n",
       "      <td>22.76</td>\n",
       "      <td>121.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>169.04</td>\n",
       "      <td>83.18</td>\n",
       "      <td>45.49</td>\n",
       "      <td>1105.28</td>\n",
       "      <td>242.48</td>\n",
       "      <td>257.15</td>\n",
       "      <td>2575.26</td>\n",
       "      <td>10.18</td>\n",
       "      <td>1.1644</td>\n",
       "      <td>1271.20</td>\n",
       "      <td>22.48</td>\n",
       "      <td>120.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  AAPL.O  MSFT.O  INTC.O   AMZN.O    GS.N     SPY     .SPX  \\\n",
       "1967  2017-10-25  156.41   78.63   40.78   972.91  241.71  255.29  2557.15   \n",
       "1968  2017-10-26  157.41   78.76   41.35   972.43  241.72  255.62  2560.40   \n",
       "1969  2017-10-27  163.05   83.81   44.40  1100.95  241.71  257.71  2581.07   \n",
       "1970  2017-10-30  166.72   83.89   44.37  1110.85  240.89  256.75  2572.83   \n",
       "1971  2017-10-31  169.04   83.18   45.49  1105.28  242.48  257.15  2575.26   \n",
       "\n",
       "       .VIX    EUR=     XAU=    GDX     GLD  \n",
       "1967  11.23  1.1812  1277.01  22.83  121.35  \n",
       "1968  11.30  1.1650  1266.73  22.43  120.33  \n",
       "1969   9.80  1.1608  1272.60  22.57  120.90  \n",
       "1970  10.50  1.1649  1275.86  22.76  121.13  \n",
       "1971  10.18  1.1644  1271.20  22.48  120.67  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>30.950</td>\n",
       "      <td>20.88</td>\n",
       "      <td>133.90</td>\n",
       "      <td>173.08</td>\n",
       "      <td>113.33</td>\n",
       "      <td>1132.99</td>\n",
       "      <td>20.04</td>\n",
       "      <td>1.4411</td>\n",
       "      <td>1120.00</td>\n",
       "      <td>47.71</td>\n",
       "      <td>109.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>30.960</td>\n",
       "      <td>20.87</td>\n",
       "      <td>134.69</td>\n",
       "      <td>176.14</td>\n",
       "      <td>113.63</td>\n",
       "      <td>1136.52</td>\n",
       "      <td>19.35</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1118.65</td>\n",
       "      <td>48.17</td>\n",
       "      <td>109.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>30.770</td>\n",
       "      <td>20.80</td>\n",
       "      <td>132.25</td>\n",
       "      <td>174.26</td>\n",
       "      <td>113.71</td>\n",
       "      <td>1137.14</td>\n",
       "      <td>19.16</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1138.50</td>\n",
       "      <td>49.34</td>\n",
       "      <td>111.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>30.452</td>\n",
       "      <td>20.60</td>\n",
       "      <td>130.00</td>\n",
       "      <td>177.67</td>\n",
       "      <td>114.19</td>\n",
       "      <td>1141.69</td>\n",
       "      <td>19.06</td>\n",
       "      <td>1.4318</td>\n",
       "      <td>1131.90</td>\n",
       "      <td>49.10</td>\n",
       "      <td>110.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>30.660</td>\n",
       "      <td>20.83</td>\n",
       "      <td>133.52</td>\n",
       "      <td>174.31</td>\n",
       "      <td>114.57</td>\n",
       "      <td>1144.98</td>\n",
       "      <td>18.13</td>\n",
       "      <td>1.4412</td>\n",
       "      <td>1136.10</td>\n",
       "      <td>49.84</td>\n",
       "      <td>111.37</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "               AAPL.O  MSFT.O  INTC.O  AMZN.O    GS.N     SPY     .SPX   .VIX  \\\n",
       "Date                                                                            \n",
       "2010-01-04  30.572827  30.950   20.88  133.90  173.08  113.33  1132.99  20.04   \n",
       "2010-01-05  30.625684  30.960   20.87  134.69  176.14  113.63  1136.52  19.35   \n",
       "2010-01-06  30.138541  30.770   20.80  132.25  174.26  113.71  1137.14  19.16   \n",
       "2010-01-07  30.082827  30.452   20.60  130.00  177.67  114.19  1141.69  19.06   \n",
       "2010-01-08  30.282827  30.660   20.83  133.52  174.31  114.57  1144.98  18.13   \n",
       "\n",
       "              EUR=     XAU=    GDX     GLD  \n",
       "Date                                        \n",
       "2010-01-04  1.4411  1120.00  47.71  109.80  \n",
       "2010-01-05  1.4368  1118.65  48.17  109.70  \n",
       "2010-01-06  1.4412  1138.50  49.34  111.51  \n",
       "2010-01-07  1.4318  1131.90  49.10  110.82  \n",
       "2010-01-08  1.4412  1136.10  49.84  111.37  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WjdimRqivg9IBmAFlyZXrdsc2/Zu/B++8BYDzGw+KivEvPh17/x/wq6twPv3F5HNra4LP\nslbXlLywfhj7n4ehpARTXJzv5vQZ6tESEZH2Ve3E/87Z+NdfmnosGixBLMgCMMUlGGMgMgRl//13\n7Kb1+M89nnBuNEgzOWi0dFq0J7OlJb/t6GMUaImISEx0Irw5+uOYT50VFDY2BJ+RXFnW94O30wC7\n8LnUixx4WGzTzNg7tu1//wLsn27DRgOsaJCmrPKFwfcBMJ/7Wp4b0rco0BIRkRj7xD/AGMwxn8BM\nng6Af/ev4seXLMT+5Xb8i07Df+zBtNdIHHYyhxyVWmHbpuBa0YCrrbcSpWdFAi1Cofy2o49RoCUi\nIjH2zddgrw9gKiZDcSQf1rqVseP+r3+EjQz/2b/fnf4iCRPhzYy9MKd/Nemw/7MrsTu3q0er0EQD\nXgVaWaVAS0RE4nZsw4ybEGyPGp3ZOZGeryi7ZUPSvvPRk3Gu+QUc+KGgoLkJ/6pzIZrMVIFWYYj2\naDkKDbJJT1NEROIaGyHyZqEpH4s549yOzxk9LmnXHHBoShUzeTqhr18ZL2hpgWgOrrAmXxeEaKBl\nFBpkk56miIgAYFtagqCnZEC8cPDQ9JUnTIltmsScSzP2wvn4ZzO/qeNASzO2g8So0gOik+E1dJhV\nCrRERCTQFHm7sDQeaJnikvjxUDz1ovOVS2DPfYM6J3wac8Z5mIMOx/lqmhQQ7TCfOTvYWLuy/YqS\ne9E5Who6zCo9TRERCTQFKRsojS8KnbhAdNIQ38hynIu/j/OzP2KcEM6xJ+GcfzmmfGy7t3AuuxHz\nyTNi+2a/YPFiG0kdIXmkOVo5oacpIiKBSG6spOCqKHUBEfPxz2IGD8WUlmJGjOrULczM2ZjjTo0X\njBkPgP3bH7HWdrrJkkWxQEtDh9mkQEtERAKRxKQmcY5WNPgZMgxz6NHB8YOP7N59EoYjjUnICr91\nU7cua5ubFKx1h4YOc0JPU0REAmnmaMVUTMY559uE7lyAmTitW7cxrXvJInO9WL+my9e0TY34F3wW\nu2B+N1rWz2noMCf0NEVEJJBujlYkqHKOPzXNCdnhXPQ9AGzllq5fZHewdJD91/3ZaFK/Yzevx//Z\nFcGO0jtkVergeyuu694FnARs9TxvTqTsJuCTQBOwCviy53m7IseuBM4BwsA3PM97LEdtFxGRbEoz\nR8sMGUrozgW5vW9pWXDPXd1I8VC3u+M60ia79t34TnRtS8mKTMLWu4ETWpU9AczxPO8DwDvAlQCu\n684GzgD2iZxzu+u6mlUnItIL2OhfsCVphg5zyBgDw0ZAN3Jp2VUr4ttbNmajWf1LQ2R+3gePhL33\nz3Nj+pYOAy3P854FdrQqe9zzvOh7vi8DEyPbpwD3eZ7X6HneGmAlcHAW2ysiIrlSFflffbo5Wrk2\nbAS2qhuB1l9uj2+/8nQWGtR/2F07sE/+EwDzxYswiUPH0m0dDh1m4CtAdFB8AkHgFbU+UpbCdd3z\ngPMAPM+jvLw8C01pW1FRUc7vUej0DPQMQM8A9AzSff+WdavYHlkketT48ThtZYTPkq0DBlI0bSYj\nI+3YNXocLatXUHz/nQw+7UuEImkfMmGbm9ka3SkdQJnjMKSDn29//x2A4BkMeOpf1M6fFysrr5iA\n6UeT4Xvi96BbgZbrut8DWoB7O3uu53nzgOhP11ZWVnanKR0qLy8n1/codHoGegagZwD94xnYzeuh\nrhYzfc+UY62/v/V97NPx6bTb6xowDU05bZ/z6/vwIdYOf8BA7JaNhLdspGH7NkIXXJXxtfz/PBzf\nCYWor9pJ3Xmfxhz/Kcx+B8Pm9Zi990s6pz/8DnRk1KhR8SBrQBnO5TeyfceO9k/qY7r6e1BRUZFx\n3S4HWq7rfolgkvyxnudFE5dsACYlVJsYKRMRkR5iw2H8ay4AwPnFvZhBQ9qv/6/7sf/8a7AzbVZ+\nejSGjYhtmpLMh66sH8be/4fgvDMvwC6Yj92wDrZtxt77W+xDf4a63Tg33Y0ZPjLrze7Nmle8Fdt2\nfnoXZuCgPLam7+rSnybXdU8ALgNO9jyvLuHQAuAM13VLXdedBswEXu1+M0VEJGMrlsS3q3d1WN2+\n+Vps27nsJ7loUceGDo9vlw3M/LzmhGWBSgcEmeyrEr5z5G1E+9Qj3WxgYfP/dBvhm7/XqXOaFi8E\nY4JgXEFWzmSS3uGvwNFAueu664FrCd4yLAWecF0X4GXP8873PO9t13U9YCnBkOKFnueFc9V4ERFJ\nZWtr4zu1NR2fEIq8HD5tFqaoODeN6khxwn1LyzI/r6U5tmmKirChIqivTalmN6ztRuMKl//Yg9jl\nS+CtRRmfY62Flmaa310KFZM77PGU7ukw0PI873Npiv/QTv3rgeu70ygREemGHVvj27szCLQa6gHS\nzufqMaF4oGUfexC/fAzO0Sd2fF5CoEWoCIqKoS4y52b/Q+CNV4Lt5tzOOcsH64exkRcYMj5n2WL8\nW68BwJ86E4ZpODXX+s+rBSIi/UXCmoE2gx4tc8ChweepZ+asSR22oTj53/323juS9ze9j339ZVK0\nJAwdhkJB4tNwUOacelb8WFPfC7TY0blJ3NbaWJAF0LL2Xcxg9WblmgItEZE+xq5cBlP2CLb//UDm\nJ+Yjf1ZUqP0hS//7F+LffkPqgVY9WiYxLcSAhCHIlUuxfh+bybI1SMxqzv1ObPjXJj6P1pYvSS0b\n0IlhWukSBVoiIn2IbWyEje9B9A27LRuwtR0sTxNuCYIUY3LfwLa0Xmh6ZPrcRramOrkgMbCwPlRM\nju+XlCTXbW4nCOkF/L/cjn/Pr2P7dkvQc2lm7oM57StBYWQYOO35/1kAAwfhXP87KB8bnHvUx3PX\nYAEUaImI9Cn2jcjw2uKEF77fW9X+SWE/PiE+X+rrkvfDfmzTRhe7Bvxvn4ldvzZeL2FdPltfl/zG\nYus0Eb18+NA+8yj2+Sew0YBx68YgmBw+Mt4z1U6gxZKFMHo8Zsx4nOtuZ8yDL2AmT899w/s5BVoi\nIn1JNJ3DuPiiHHbtyvbPifRo5dWgwcn7iQHDquVJh/w//DxtPRMKBZPho4pLgtQFn/5isN9HJsT7\nF3wGiKzpOHo8xhhMdNi3oS7tOTYcDJua6bOCz+Li/PZg9iMKtERE+hDrBS+FO+dfgXPj74OyB+/B\nNjVi31pE+Iqv0vDCf5NPCofz3qNl9twX53u3xAuaGrB+0KtlW/fI2YTervfXBOcfdwoc8KGkNBHG\ncYLUBSNGBQUtfSPQAgjfeFnQUzk2ElAPjSR8bWNBbftoZK7e6MyXNpLsUKAlItJHWBtZpGPg4CA/\n0qgx8YONjdh3l8L2rTT+78Xk8za9HwRbeWamzozvWIt99AHsmndShxX9eKDFzuDNO3PS6UEPTZo8\nYKY4Mlerl8/RSpq4vmo5VO3EzNon2K+YBKEi/P/z0p5q//GXYKMuNceY5JYCLRGRviIyNGZO+HTK\nsJBd9CL2keAvYZsw3Oa/8gyseDNtks98cL55LebE0wCwD/0Z/4bvBMODxSWYD30kqLQ7YUJ8czMM\nHY4ZGBl6jMznMh88Ml5n8LDgc9P7uW5+bqXJ3m4iPVpm8FDMYR+Byi3pzx03Mag394icNU/SU6Al\nItJXRJaboSz1L2T7l9vj2/W12OqdQSqAdK/855GZc1B8OCzCPvlPaG7C+cq3glxfNVXYHZXYZYuh\nsT45LUX0DcuKhGV3Z+4NI8rxX302q2211mKXLY5PTs+1lhZonVR2dsJi2UOGQ0NDvGczUVEx7Hcw\nZsLk1GOSUwq0RET6iuiwUELPh/nUWSnVwtu24F96NnbBXzNKaNrj2psvFnmr0L//9/i3XoN95RlI\n6L0zR30Mc/ynMMd9Kl7mhIKXAzJY97FT3nwtaMNzj2X3um1pacZMnYlz7a8AcC78XvDdooqKgvlr\njUGPpf/QnwmfezJ26etQX4vpzBqSkjUKtERE+opIoJW4QLA55OjkOnvuS3jTegDsv/9emPOWnHYC\nregcrEUJ88wSenDMgIE4p30ZU9oqtcPyJbBqeZBnLAvsohfxX3gy2NnwXlau2aGWZigqwkycSujO\nBZj9D2l1PMiIb727gpcfHvkbAP7Pr4XtW+MT5qVH5fl9XhERyZroPKuBCakSBiUEXSd8Bmp2YVe8\nGT9egCkPjOOQZvArkGayu3P6uR1fNBqMVe+E0eO63DYIsq/7v70xXtADb2xaa6G5BYpL2qxj5h6O\nfcTDPvc4Nl3v3bDhOWyhtEU9WiIifYRNM3RIScL8peLi1Plb2zbnvmGdFWrnr6Y0y+iY/T6Y+bWz\n8Xbl2neT93siB1k4HAwLpgk0Y4YMi29HEtaaT54RL1OPVl4o0BIR6QPsjm3xN84SginjOOBE/ldf\nXAJjKpJP3LGth1rYCSa1h8h8+VvBRk1VcvkXzs/skuddFmxkoQfPf+TvyQXh7A6/2pYW/BefxCYu\nGh1daqidHq2UpK+Amb1/fHuYAq180NChiEgf4F9+Tnxn8NDkg8NHwo7KIEXCxKltD8sVijQ9Ws5h\nkdQOicvojJ+Ec/SJGV3SDBgQfO+mLMzRevO1pN2kgCgL7POPY++9Aws4t/wJM3R4fC5dcds9WqZ1\nEDZjL5gwNb6f2OMlPUY9WiIivVzr+Tim9ZyhSA4likviWdJbHXfOvyJHresCp9VfTbMPiG2aoz4G\n02bh3HQ3oet+k/k1I+se+jdeRvjck6l/+tFstDTQVu6qLvCf/jf23jvi+5dGlg9auTT4bG/osLVh\nIzBlA3Guuz3IQRb9PZAepUBLRKS3Wxdfoia67E4iMzYyXNjYAENTJ0Sbj38Wc9BhOWtep5nkv5oS\nFz42w0cRuupmzPCRnbtmq96eujYyqGdk1BiYNgvmHAj7Hwrbt6bPXdUF9vGHUsr8hc/h335DsNPe\n0CHg3HR3bDvaw2XGTwxykBVpECsfFGiJiPRydslCAJxb/5y87E5U9C3EhnpMSWnKYVM+NpfN67zW\nPXJjsrA+X0mrAMXvRmDU2ICZMoPQN38QLIHTUA9ZyEdmrYWqnQCYL16EOfaTQfm8m+KVOujRSgpA\nFVgVBAVaIiK9nH36kWBj0JD0FaK9IG0tqtzNdAdZlzB0aL5wPuaI47p/zeLkANM2NnTpMrZyCzQ1\nxN/mjPQQ2sf/0a3mAUGw1tSIOf0cnCOPT8mQD2DamaMVq/PJzwUbDV37jpJdCrRERPoI03puU7R8\nxl7B57Q90x6n0N5GS0hYamYfkLJuY5e0GnJz0s1V64Bd8y7+lecGE/IjCVHNnnOCYxuzkLQ0unh2\nWdADaeYcmFqng6FDADNxStCmLgaTkl0KtEREejFrLThObCHmdMxeH8C56Y+YAw4FoPSIjybNg2or\nQMubxPZkK0dVNFP8mPEwZjzO8M4Fl7ZqJ/4NlyZcL+jRMsNHBYtaZ+GNPv8n3w2uGVkqx4weB+Mn\nJVfKZDL8gMhSO9l4w1K6TQO4IiK92eb14PuxNQDbYobHe3CGfev7VJ72FVi9ojD/Mk4c5stS1nUz\naAjmrAswHzgY/5c/pPH5J3E+9hlMpm/irVre+orxzckzsG+/jm1pxnTmrcDWojnCEoYMnbMvxq55\nB3t/5CWHTOZdjQvOjwbWkl8F9s8YERHpDP++O4ONTryFZ0JFmIGDMXMOwhxYQG8bRiX2NmUx67rz\n4RMww0dipswAwC56KeNzbSRhqPnM2cHnuIRg6JgTYWclvPm/LrctaQ3GsfGksmbGXjgfPTl+bEir\nHGlpmJGjcX7519hkeskv9WiJiPRmO7bBqDGYuUfkuyXZk7hWYw7WETRnX4x94T+dW1C7IZg/ZQ49\nGnPUx2PDewDM2gcAu3k9XZ1NZhc+G29fe71WI0ZndL3EhcUlv9SjJSLSmzU2Yvbct3tDVgUmafJ7\nDlIUGGOCdA+dWY6nvj74HDAwOcgCzICBQTtrd3e9UatXBNf64kXtVsvkrUMpLAq0REQKlP/gPdjX\nnm+/UkN9h/OzerUcBZCmuDS+fmA7rB/Gbt8W9GgZE5sEnyJU1L0Fq4tLoGxQkNZB+hQNHYqIFCC7\nbiX23w9gi4oJzT0Cay32mUcxhxwV61GxLS19NtAyB38Y+87bOXsj0pSUYDPo0bJP/gvr/QH23i/o\nzWor1UQ4asyXAAAgAElEQVSoCMItXW9QXS20N9w3flLavFpS+BRoiYgUIPvog8HGrCBPE2vewd77\nW3j3bcy53wnKKjeD9WFMRfqL9GJO9DvmSnFJ8gLVbVm/NvhcthgqJrddLxTqXo9WuKXdBaM7ta6j\nFJQOAy3Xde8CTgK2ep43J1I2ErgfmAqsBVzP83ZGjl0JnAOEgW94nvdYTlouItJH2brd8SHDUAj/\nrl9gX/pvcGzLxnjFhmDekCY+d54pjvdo2ZpqKC4K5lq1lvA2pzn+1LYv2M0eLRsOJyVqlb4jkz7Z\nu4ETWpVdATzped5M4MnIPq7rzgbOAPaJnHO767r6zRER6QT/h9+M76x4MxZkAWAMtqkR//kngkWi\nIaNs4ZLMlJTEUjb43z4T/6qvpa+4O76GYbs5t7rbo+Ur0OqrOgy0PM97FtjRqvgU4J7I9j3AqQnl\n93me1+h53hpgJXBwltoq0i9Z38dGei6k77M1VUHKhqjWCUXXvot/4WnYe34dD8AUaHWaKW711mE0\nWWgrdsuGYGPKHjBpWtsXTNOjZat3YTevz6xB4XByRnzpM7r6Ux3red6myPZmILr0+wTg/YR66yNl\nItJF9q+/w7/4dOzWjR1Xll7PLnkNAPPJM2CP2bFy54qfpdZ9/eVgo0SBVqeVlMKGddgVb8WKbF2a\n9Azr12KOPJ7Q1bdiSkpTj0eFQsHwXwL7tz/iX3MBduWydpti33glyPCfg5xhkn/dngzveZ51Xdd2\n9jzXdc8Dzotcg/Ly8u42pV1FRUU5v0eh0zPonc9gy9P/BqD4339n+KXXdft6vfEZZFshP4NafHYD\n5ad/maqbrqYJwHEoP/hwtrauXFcLwIgx4yjqxPcp5O/fU3aVDoCqnfg3XxUrK/7bXSl/xraGWygb\nOYohHTyv7aUDCIUchifU2165iRbAeeCP2Pp6hl16HcXTZiad1/TW6+z8zfXB/UeNZmQP/lz0e9Az\nz6CrgdYW13XHe563yXXd8RD7878BSFwBc2KkLIXnefOAeZFdW1lZ2cWmZKa8vJxc36PQ6Rn0vmdg\nt8eHkBprqrPS9t72DHKhkJ6Brd2NfeBuzGe/jBk4CH/rZjAO22vr8asjw1kWtm/f3uY1dhoH04nv\nU0jfP1+K0uTnahqe+lxsczP1Tc00dvC8wtbSUl+fdH64KZgD1rIyWCdxx72/I3T+FUnn+f/0YtvN\nvt+jPxf9HnT9GVRUZP6mb1eHDhcAZ0e2zwYeTig/w3XdUtd1pwEzgVe7eA8RWRNki2bCFFi3Mr9t\nkZywjz6Afe5x7ItPBvvvrYaKSUH+qDXvRCr5bV/AcdK/LSftMumGW0ckrxdpfT+Yd5VJdvqiNG8d\n1tYk7ZrSspTT7OsJ6y12ZzK9FKwOAy3Xdf8KvATs6bruetd1zwFuBI5zXfdd4KORfTzPexvwgKXA\no8CFnufpN0eki2xNNQBm6h5Jbz9JH1K1M/jcsS34i331csyMvYKy8ZPSnuL8Yn5scWMUZHVNuhcI\nwkFAaze8R/jck2FV0BOVUXb6dG8dtgq0bOJLDgRZ52lugiHDgoJtmzNquvQuHYbpnud9ro1Dx7ZR\n/3rg+u40SkTA1tXC+6uDnRGjoaUZu2MbZmRmi8pK72AXRzr9d1fDpveDeVeRQMv59o+wj3hQPi6o\ns9/BmIGDMIMGw6TpWIABqb0k0jEbmd+WVPa/F+CYE7FvBC8Z2FefCQ5k0qMVKkp6QzSWtX/w0OBn\nO24CLF+C/6/7cE46I6hUGcy6MXvuG+RNq97VvS8lBUmZ4UUKlP/Ty2Hje8EbZZFlP/yfXUnoxt/n\nuWWSLXZHJUTfdGtqwr4TvAFn9tw3+Bw+EvP582P1QxddHT958NDgs60lYaRdLRvWpRaueBO7a0ds\nqNZGXkTJKL9VKAQtCUOHkZ+r+YSLOfBD2CULsffegX14PkQCLX/eTcF9dvbveVJ9nZJ2iBQY/+lH\nCF/yhSDIAigqif+LenvKe2fSS/kL5gdr6EXYzeuhuioInEaM6vgC0Wzwfjvzt6Rt0aBorw8kFfvf\n/VJywAQwbETH12u9qHR02HDIMMzI0ThHnxjszzkQW1+HbWyIzbt0vnhRcKy9hKjSa6lHS6SAWN/H\n3v8HiGSsBqCpISmRoX3teczcI/LQOskG6/v4114Im1u9kL1hHXbDOigqwmTSgxJZGsacemYOWtn3\nDb/yp+x48WnMUSfgX3x68p+52oR8WsNHZfbnLRRKmgxvI2skmrEJb6dNmAJvLcL/xhmxeVnO16/A\nVEzG+eX8IFiTPkc9WiIFxD705+T/4UPwr+uEQMv/XWriSulFKrekBlmJJkzN6DKmuITQnQtwDvtI\ndtrVzxRNmopz7EmYoiKcX9+fdMxG50YCjB6LyWB41oSKYNP72HWrgoJ3l0LpAJg0PV6pvi6+Hc1E\nH1mo2gwcjCkd0KXvIoVNgZZIAbGPPpC23LTx9pn0Qu+vSSlyLv1xbNscd0pPtkYgdembVcvjvUtp\nUjKkFanv//gSAOzC52D8JExCtndz+EdTzxs9vtPNld5FgZZIIZo5G+fKYKIsA8ow+86F0ePy2ybp\nEv+5x/FfeSa2H+0tMR88Ml5pZDnm0GOC8llzerR9QpCzrHXZwUdCcQnOiadldpGyeEBma2uCNw3X\nvptUxTn5c7GfM8bBuermpEBM+iYNCIsUCNscHzIMXXYjAM4FV0F5sJSoc+HV+D+4KDY3R3oH+6fb\ngo1Djgr2X3gymPQ8ZQ9Y+FxwbMRozBe+hvnoyZhMJsJL7o0cTej2v2def8jw2KZ9+N5gI5oPLdHQ\nSL1xEzDTZnWjgdJbqEdLpFDsiiyxktBzZQ44FDNpWrA9YTLmkKPSJ1qUgmRbJ6zcXR38nEeMig9X\nTZ2JKS7GDBiImTIjD62UmLETcK66Gfadizn2k5061XxgbmzbPvUIEPRgpSiNLEwdfWtU+jwFWiKF\nIpI12jnrwrbrDBsBO7fjz7uJ8OXn9FDDpKvs4oXx7eYmeC8YNnSO/jhm/0OC7bMuyEfTpBXnB7cF\nQ3nTZhH6xvcx0WztGTJT9kiaawdgZh+QWjE631LJSfsNDR2KFAgbXYplaDs5e8ZNDDLER4ecpKDZ\nP/4itu1f8FnMkccHy7nMPgAzoIzQnQvy2DpJZCZM7v5Fpu8JI8qhnQSkZurMIKN/5Zbu3096BfVo\niRSKhsir3+0MKZjxyQkNbcKSH1L47HOPw/Q9MVo2p08yJaU4X/pG+5XKx8K4iZizL+6ZRkneKdAS\n6QRbX4ddtjg3F4/m2Clr5y/h1pmjG+pz0xbpNtvYGCSxbJWaw4wsz1OLpEdEc2GVlKY9bIwh9KPb\ncdKlepA+SYGWSIZsOIz/7TPxb70mWA8t27YHc7QoaTtpoYmubxdt0/1a97BQ+RedBuEw5rCP4Hzz\nB0HQBfG/iKVvik52189ZIhRoiWRq1bL4GmjR+VRZZJ/6PyB9Tp8kCT0i9tVns94OybLSMsycA3HO\n/W6wr1f6+7ZhkaWRPnJSnhsihUKT4UUy5N96TXxn+1bI4qv4Njoxds99O648bRbsaHuyreSfbYgv\ntWIqgqFDc9BhONffoUzgfZwZMgznV/dhygbmuylSINSjJZKphIV+7eoVWbusranGv/r84Baf+1rH\nzTj7G5j2UkBI3tlnHw82DjoMkxA8mzEVGa2bJ72bgixJpEBLJAO2dnd8UvOY8e2+vt3paz/7KITD\nwU5Fx2samrKBmAlT4udHz5XCsW0zAM4XL8pzQ0Qk3xRoiWTAv/kqeG8VTJgCJaVdTqtgrcWueBNb\nU4Vd/GpQ9u7bADjX3Z55b0dRwqj/0je61BbJHbtyGey9H2bg4Hw3RUTyTHO0RDKxfm3wWTYI/DBs\n3RQsp1K7GzO2IuPL2If+hP33A7F95+Z74O3XMR88MiVHVrsS1kW069dg9j0o83Mlp6wfho3rMB/7\ndL6bIiIFQIGWSAdsdcIbhsXF4Idg9Qr8S84EwLn975gM1h+01iYFWQD2738MPlcu61yjhsYXsE2c\nOyYFoLoKfD/IEC4i/Z6GDkXaYa3Fv/TseEFRcWoiws0bMrvY7urU67/2PEDnF7AdMx7np3cFbYku\nRi0FwT7xD4CkeXQi0n8p0BJpT02rhV+LSzCtA63IxOcOpUvJEMnLZY44rtNNMyPLYfJ07NuvY63t\n9PmSPbammvCNl2E3rccufB6m7AEzZ+e7WSJSABRoibRnW/LCr85HTkpZi9BWZZglfue2+HZRcXIv\nVmn65To6Yj70Edj0PnbB/C6dL9lhX3wSVi3Hn38H7KzEfPhjSuMgIoACLZF2xRKJAs5tf8PsOQda\nLwi8M7OhOxvp0XJu/D3OL+ZDKDJFsqg4vt1J5oNHBtf+1/1dOl+6z77xcmyuHcuXAPEkpSIiCrRE\n2mCtxf7+FgCc2zxMtNepLLlHq6M5Ura2BltfBxveg7KBMKI8uFZRcVBh4tQu936YsoFBfq+994vf\nb+N7hM89Gf+VZ7p0Tekc/4X/pha2s16liPQvCrRE2tLUFNs0iQvEliX3aNmXnsLW7W7zMv6V5+Jf\neS521TKYvmd8LcPIIsNm0rTutXPIMFi2OAjmAP9vkTcZf38Ldv2a7l1bOmRGBGvbJQ0pj5uQn8aI\nSMFRoCXSluZIUtIhw5LLB6Qur2HfeCW1bNd27LbNUF8HtTWwYR0mcUHhpobgc/S47rVz43vB/f5y\nOwAmYWgzm0sFSXq2eheMm4jzrR9iDv8ozu8eSn1hQkT6LeXREmlLpEfLfOqspGIzeQYp7/j5fmzT\nrlqOf/P3oKW5dS0YNCTh+pFArrvDTJG0ETbSXpvQFmpru3dt6Vj1Lhg6HDNtVnIgLSKCerRE2hYN\nhFolIzWTp2M+/LFgZ/DQ4LM5PsxoX3wyfZAFyZPeo9uthiI7bb+Dg3YNiiz3srMymLMVCkF920Oa\nkiXVVZjEBLIiIgm61aPluu4lwFcBC7wJfBkYCNwPTAXWAq7neTvbuIRI4YoET6YkTdb3GXvDs4/F\ne5Pm/w47fhI4Ieyzj7V9za2bYpvmoyeD72P2P7RbzXQuuAr/iq/Gl+XZWYmZfQC2qDiWp0vALn09\nSKsxa072rmktVO+EoQdk7Zoi0rd0uUfLdd0JwDeAuZ7nzQFCwBnAFcCTnufNBJ6M7Iv0PrEerdT5\nNubQo1LK/N/+BP+mK1PKna9fgTns2GBn+p7xa4wag/O58zCt8nJ1lnEcGFCGbWkO5oTt2gHlY4Me\nrcRhxH7G1u4mfO7JhC/7Cra+Dv9vf8T/8+3Zvcnu6mAO3uix2b2uiPQZ3Z2jVQSUua7bTNCTtRG4\nEjg6cvwe4Gng8m7eR6TnRYcD0/RomXTrC6bJheVcdDVmv4MxBx6G/dSZmOGjst3KQHEJNDfhX3Ve\nsD96bLAGYrj/9mjZ558INnZWYv/7L6jaCTVV2E3vQ3mW1iGMvOnJoKHZuZ6I9Dld7tHyPG8DcDPw\nHrAJqPI873FgrOd50fGRzYD+qSe9UzTQ6mDBaOend2Hcc6CmKvXgtJmxzZwFWRAsdp0wL8yMnxQE\nfuFw7u5Z6BLmvtm3/hf7+fjfv5Dwlo3YcBjb3MZcukz5kefraLqriKTX5R4t13VHAKcA04BdwN9c\n1z0zsY7nedZ13bSLsLmuex5wXqQe5dn6F2YbioqKcn6PQqdn0Lln0DCglCpg+JixFKc5p+lHvwE/\nTMmsvWgOwQ7vD7FjI264g6JJU3EG90xPx46ygeCHsdNm0rLmXUYfdCjbiospKS5iWKu294ffA+v7\nbE0cJly5LOl4ePUKQv+YT/M7b1My93CGffOaLv2sWuqq2Q4MHT6CAb3omfaH34GO6BnoGUDPPIPu\nDB1+FFjjed42ANd1HwQOA7a4rjve87xNruuOB7amO9nzvHnAvMiuraxMs+BuFpWXl5PrexQ6PYPO\nPQN/e1BvV20dJt054yLLrFRWYkPxXi9zxHFUj66AhiZo6JnnHQaoqwNrYd+5VFZW4htDY11dyvft\nD78HiUsnmY99GvvYg0nHm7dsovmdtwFoeu0FKuffifPpszt/n+3BqgA1tbXs7kXPtD/8DnREz0DP\nALr+DCoqKjKu253+7veAQ13XHei6rgGOBZYBC4Do/7HOBh7uxj1E8mf9umBC+bAMXt1PzI81c5/c\ntakt1sLad6GhPp6wtLoKu+bdpGr+n3/D7kjm+D4tMdD6dDwPmnP5TwHwq3clVbf/fiB536btiE+l\noUMR6UB35mi9AvwdWESQ2sEh6KG6ETjOdd13CXq9bsxCO0V6jK2pInzrNdjH/wF7zMakyQTfWuJa\nhd19i7BL3n49+NyyIb7odWN9sJ/APvsYtfPv7OHG9Ty7PehId26YF7y4EP35lI+FoiLqHvpL+vPe\nW0X4otPxb/tx6rE17+L/4efYut3xQCw6By6U5uUIERG6+dah53nXAte2Km4k6N0S6ZXsf/8FyxYD\nYPbct/MXyEeglWhAcgJU/1/34Zx0Rp4akye7a4LPIcG8K+eS67DP/weGjWg3t5j977+CAHXJwpRj\n/u9vga0bsS8/hfn8+ZhjTkzo0VKgJSLpqb9bpLXahGzqI0d3/vyBg7PXlq5oFWjZh+cHn40N+WhN\nftTVgnGgNHgWZu/9cM69FGNMkCg2gTnyeCgK/s1p162OldtIQGaXvo5dtRxswjJLT/4z2IjmKdPQ\noYi0Qf93EGktku0dwJR1PGyYIg89Ws5Vt8SCCiILGpsjj48d9+fdFKzJ18fYlmbs1o2pB+proaws\naUg3yjn9q4TGTQDAnHkBDB0O4UjAlDjUWlOFXb4E/+fX4t94GWzbHD8WXe4omlJDQ4ci0gYFWiKt\n2IRAi84EWkOGBZ95CLTMtJmYDx+fXLb/IbFtu/A52Ph+Tzcr5+x9d+J/73z8R4PJ7La2Blu9K0gk\nWtb2z2HgKZ+HPfbGfOiYoDfK+kFOreYmmDAluNabC/FvuTrpPHPeZXDgYbB6Bf7j/8D/9Y+CA/04\nA7+ItK+7meFF+p6aKhg/CfOBuZ16g9D58rewK5dlNHk+J6J/2Ud7cfadC4OHxOYr2fcThsWWLMR8\n4IM93cKss6+9EHw+cA/hpW/A6hXQ2BAstN1eoHXCp6ibe2RwbnR+VaQ3y8ycjd2wDptmuR4zfRZs\n34Jd9CL2b3fFD9TWZOkbiUhfox4tkQR23SpYvxZz0GE4n/0ypijzf4uYfQ/C+dSZHVfMEXPEcUGA\neEiwDqMxBufMC2PH7eoVsW3/1z/C1lSnXKPXSQxwli0OgiyAxa/Gh/c6Ehn2s6uXA2AOTljHcsIU\nzFcvBSJrVo4ag0kXfI/NPKeOiPQvCrREEtgVSwAwH/lknlvSeWbiVELX/SZ5qZ/i4vj2m68ln7Bm\nBb1ZR8vnmP0OzuxC0R6tVSuCeW4z9sQcG/z8zYeOwRz8YZxf3Is58LCg3rRZQU9h9PRfzsdMnNbp\n9otI/6BASySBjSbzTPiLtFdLs05jaPxEAGz1LuyShdiGup5uVXZs3dTuYXPMJzK7Tij436DdsgHK\nx2CcUGx+m5k2K3hTMSEhrXEcnGt/BUXFsO9cTL7fMhWRgqY5WiIRdtP62Ha6t9V6tRHlsDNYZmLk\nzX9k2xeOg2VL8F99BnPwUZhzL81zA7tgc/Dzci79MQwfiX/NBTCiHHPaV2DrxsyHfU20R2s5RPKm\nmb0+gHObhykdkP6U4aMI/faBtMdERBIp0BIhmJvl//gSAMxZF3ZQuxcZNgIAc8rnsXf/CgBn4CAY\nNAT76jMA2KodeWtea7a+DnvvbzGnfAEzelwwPLhhLWbqzNS6kUCLqTMxA8pwvnM9jK1IHjrNRCje\nsW8ivX1Am0GWiEhnKNASAez6NbFts3+Gc3t6AVMxGefnf8EMHoo98LD4m4mJk8jbeTuvp9kn/oF9\n5RnsK8/gXH8H/gP3wKKXcH4xH9N6cvum92FkeWxtxy5l8YdY3jEAs/f+XW26iEhamqMlAvElWwAz\ndEQeG5J9ZnCwDI0pG5garABmyoyeblKb7Iv/jW37D/wJFr0U7KRJn2DXvAOTu9/2pB6wMeO6fT0R\nkUTq0RIB2PRe8Fla1n69vijNhPl8sE2NEFkMGoBFL8a3W03Yt7t2wNZNmA+f0P0bT98TZh+A2ecA\nvT0oIlmnQEv6PeuHsf97EYqKcG74Xb6b0/MKJat5JJgynzwj6NlKDLrq64O3I0tKMU4I+/hDYAxm\nzkHdvq0ZUEbokh92+zoiIuko0BKp3Q0N9ZgTT8MMHZ7v1vQ8P5zvFgQaG4PP8nHJQRZASzP+xWcE\nk/uHj4J1K2H/QzETJvd8O0VEOkFztESaIn/Bj+5H83MS13C0Nn/tSBTJ6m5KB2DmHpF8LDp0WLUz\nCLIAM3Z8T7ZORKRLFGiJRAOtfvQ6v3PVLZgzLwh2IkOHdnc1tnpn/hoVXT6npBTna5fhXHc7ztU/\nD9q2c3tq/QH9cD6diPQ6CrREIoGWSXjNv68z4ybgHBWZSG6DQMu/5Ez8q76Wv0bFAt7g52DGT4yn\nXtiVGmiZQ47uoYaJiHSdAi2Rutrgsx/1aMU4DvgWuzHy1mW0VykfovdO/DlEsrsnLojN5BmE7lyA\n6U9DvSLSa2kyvPR7sczow0fmtyH5YByw4Q7XDcw1u3UjdnmwoDclCYFWKPK/qHeXAuD86LcwtqKH\nWyci0nUKtER29eNAy3HA97EJCUHtru2dX8amm/zvnR/fKU0Ywi0KJVccOrzvrUMpIn2ahg5Ftm2G\nAWWYAQM7rtvXOE7w1uHu6liRXbYkpZpdtpjwzd9LPykdsIteInzFV4O1CTvJNtQnFwyJp9honaXf\nDCyc5YJERDKhQEv6NdvYgH31Wcy+c/PdlPwwBvvm/7Bvvx4rsnf9POXtQ/+un8OKN7FLFqa9jP/b\nnwS5r1rnv8rE+rXJTSouTt7/8MeCz8OP7fy1RUTyTIGW9Gv+H24NkpW2ztvUX7Q0B4szL1sMgPng\nkUH55g2xKnbl0vjwas2ulEvYhISn9ulHOt0Eu3Vj+xXKxwafoeL264mIFCAFWtK/RZJfssde+W1H\nvrS0xLcnTMEcfyoAdlX8LT/fuytep3Z36jW2xCfS2yf/GXy+t5rwHTdit23usAl2/rzYtjniuNQK\ng4YEn9H0DyIivYgmw0u/ZVcuhR2VmNO+kjIXqF8yBsqCOVD2wXuw4yZgDjg0eS3E3TWp521cl7Rr\n/TD+72+BTe/jV24ldPWt7d83EkA58x5O36wJU7AAs/bJ9JuIiBQM9WhJv+X/9AoAzEGH57klBaJy\nC4waHdv1b78Bay1UbgnmSU2ekfR2YpRdtRwAc9IZwf68m4PhSIAN64JrtME21IHjYD72aYwxad8o\nNDP2wvnxHel7u0RECpwCLen3TEJw0a9NmoYpKsZ8+ovxssZ6qK0J1oEcPBSq08zReiLSEzUiSI9h\n//dC/GBLM/a//9fmLe3830G4BbPPAe02zYytUFoHEemVNHQo/deMveJLvPRXBx0GVbtwvvA1GBEE\nnM7HP0v49Zehvg7/9p8E9QYOxlRMxj7z76AXauP7mOl7Jl+rNGHtwT1mY0aPxb70FPa+edgPHp52\neNa+9FSwMXN2Lr6diEjeKdCS/qulBQYOzncr8ip0/hVpy83EqdhXnobN64OCskEweTo0N+HfdBW8\ntxrnyptiwZb5yEmYklKig4Shy2/E+mHsrh2wbDH+pWfj3PEQJhRPQGp3bAsy048YhSnSG4Ui0jd1\nK9ByXXc48HtgDmCBrwArgPuBqcBawPU8b2cblxDJC/+5x4M3Dvc/NN9NKUwDB0FTU2zXDB4CQ0cE\ngdR7qwGwG9bBpOlBhaHDsfW1SZcwTgjns1/C/9ElQcHiV+DAw4Jzd1TiX35OUG9GP33jU0T6he7O\n0fol8KjneXsB+wHLgCuAJz3Pmwk8GdkXKRi2uQn7p9uCnTdezm9jCtXo8cn7E6fBuAlJRfZPt8GW\nSI/X8FGYWXMAcK74WayOmTwDc9LpQf1IVnnbUId/+Vfi19mxLdutFxEpGF0OtFzXHQZ8GPgDgOd5\nTZ7n7QJOAe6JVLsHOLW7jRRJ5P/ldsJXfx3b2Lm8Sralhe3fPQf/gs/mqGV9h5k8Pbbt/Oo+zJCh\nmFAI86VvJtWzz/8nqD9uAqZ8LKE7F6T0UJlPfi5Y6qe6Cltfh/3nfck3i7y1KCLSF3Vn6HAasA34\no+u6+wH/A74JjPU8L5rBcDMwtntNFImzzU3YZx4NdlYthdntv62WZPP7tKxcllw2cWrW2tanVEwJ\nPucchCmLrwFp5h4OzU3Ye38LBAtQAym9XYmM48CQYVCzC//uX8KilwBwLr8xSLHh6OVnEem7uhNo\nFQEHAhd7nveK67q/pNUwoed51nXdtEl0XNc9DzgvUo/y8vJuNCWDxhYV5fweha43PgPr+zS9tYiS\nfQ/CGINfvYvoQNOwkeWUdOL7NLz7JlXAkK99l+I99gLfJzRpGk5Z/1pMOtPfg+Zb7yY0pgJnUKsX\nBj57Fg3jK6i6+RpCWzfhDx7C6CnT2r3W9lGjcXZXE96ykeiCPaMP/TB1X/sOxXvOobiHfy9745+F\nbOrv3x/0DEDPAHrmGXQn0FoPrPc875XI/t8JAq0truuO9zxvk+u644G0q8x6njcPiK69YSsrK7vR\nlI6Vl5eT63sUut74DPwX/oO9+1eYL38T57Bjk5Z0qdpeienE9/FffxVCIWrnzMUMiKQiqK0L/utH\nMv49GDIS6huC/1qxRUFajPDWzTBiZIfX88dU0LL8TRgxCgDzmbODc+Z+OKjQw7+XvfHPQjb19+8P\negagZwBdfwYVFRUZ1+1yn73neZuB913XjSbTORZYCiwAzo6UnQ2kX1dDJBOr3wk+qyIvrjbWxw7Z\ndSuxTY3Yqp3Y1SvSnBxnF72EfeJhSg8+Mh5kSdeVRvKPNdZDKIN/r02cBru2w6b3MQcfhXPCZ3Lb\nPq9pGZsAABqtSURBVBGRAtHdPFoXA/e6rlsCrAa+TBC8ea7rngOsA9xu3kP6INvchH14PuYTbtIc\noJR6VTuCzwf/hD3mRKiKZya3D/0Z+9CfY/uhOxekv8Zb/8P/bZB4s+zYT5JmtT7prMRErxkErmbS\ntCA1REM9jB3fUXURkT6jW4GW53lvAHPTHDq2O9eVvs8+8yj2sQehpARz8ufbrrhlY/yc118JloNp\n65p+GOOEUstffjq2XXLgobB9e5faLAkSAi3noyd3XH/KHvHtcRNz0CARkcKk130kPzasA8D+8z7s\nhvfSVrF+GCo3xxd93rwBtm5MWxeA+vqUIuv7wbDi7AOC1ANaLy87Enu0xnQ8V8EkTKg3CrREpB9R\noCV5Yd98Lbbt3/K95GN+5L207duCZXL2OQDGTcBufj8IpkaNidU1p58De+8X7NTtjl/zL7cTPvdk\n7O9vgW2bMXvtm7sv0x8lBloTpmR2TjQgG69AS0T6D611KD3OtrTEJ7cD1FTFj23bjH/NBZgvnI8Z\nEbxya8ZWYMdOgC0bseVjoWwQ5quXBgk05x6BLR+Lv2wx/u9+hnPZT+C9VbFcW3bhc8E1Muh1kU6I\nToAvH5u0fmF7nO9cD5VbMMUlOWyYiEhhUaAlPW9t8CahOfVM7D/+AoCt2w1NTdhFL0K4BfufBdjo\ncNOYCszQ4di1K4OJ14OH4BxyVPx6ZZF661ZiX3kGoku6DBoSn9OlNw2zyhiD85M7oSTzoMmMGBVL\n7yAi0l8o0JIeZ3cEOUvM/odi174LlVvwf/AN2FkJcw4KKm1MmLc1bAQMHAy11RAKxdbUi5mUkCxz\nd03QWzZkGM7XLse/+aqgXIFW1plyLfogItIRBVrS86JzqQYNxpQMwNZUxYcS3/pfcl3jYIzBjq0I\n5mvt2AbDRyZXGTgIRo+DbZuxby8K3lQsGwSjRgcVho+CaTNz/KVERERSKdCSnrd9WzDHZ/AQGDo8\neb5WK87VtwJg9p1LbC0nm7qqk/P9X+JffDqseDNWZsrH4nzzBzB5Wtq0DyIiIrmmQEt6nF29AiZP\nxxQVBz1VrU2ejpk6E/P582MTrU1iL1aayddmQBnM2AtWLQ8KRo8LyuccmO3mi4iIZEzpHaRH2XAY\n1r6LmR6s3GSGj0ip45x5Ic5ZF6a8zeZc9xsYWY458vi013Y+cXp8+8Krs9hqERGRrlGgJQD4C5/D\nT1jOJme2bICmRpgayRQ+bGRqncQcTQnM+EmEfnpXm5Owzb4HxXfGaJkXERHJPw0dCnb5Euy8m4Kd\nT51F+Lc3YkaMwjnj3OzeZ9Vy/BsvA8CMjExUH5bao9WZlAGtOT/4NXbdKkxxcZevISIiki0KtPo5\nay3+LfFhNltbA4texAJ25mzY7xBMUfpfE+v7GCfzTlH/n3+N70R7nIYOT61YOiDja7ZmJkzBZJqp\nXEREJMcUaPV32zYl7fo3XRXfvuOnmNO/iq3eBUVFmJPOwDgOtqUF/zfXw9uv41x/BwwdAcVFHb/Z\nF80mPmEKZniQuNIURXqeRpZjTnTBD2PSBV8iIiK9kAKtfsa+tQj/6Udwvn4lJhTCLnoJAPOlb2Lv\n/mVssedY/bdfj+e2Gjocc/SJQQqFSJl/1XnB+Qd/GHPud7DNTdh778Cc8BnMuAnx69TXwfIlmIMO\nx5x1YdI9nB/eBkOGYYYMy9XXFhERyQtNhu9n/F/+ABa/il30Ev5dv8A+cE8Q5Mw9PF5p9gHxhYI3\nr4+Xv7sUABtd4iaBffVZrO/Dm69hX/gP/jVfTz6+8FloasQcfyomurROhKmYrCBLRET6JPVo9SPN\nK5fFtu28n8W2zdzDMQnzopxPnAYz9sb/8SWwfm1QOG0WduHz2FM+Dzu3B/Vu82D7VuzihdgH78H/\nzfWxIUEAW1cbZG0H7NI3YNQYmDYrh99QRESksKhHqx9pePmZYGPi1KRyc9pXgo1Ibiv2mB3ksCob\nGKvjfPmbYH38676FffmpoBesdEDQGxVd3mbJQuyzj8bOsUtejd/k7deDeVjGZPtriYiIFCz1aPUT\ntqGeugf+BLP2wey9HzbSU2U+/UVMcZBOwbnkh1C9K/4mYWSo0BxxHGb8JJi1D7zzNmzbnLyQ84y9\nk2+2x2xYuRR73++xQ0dAuAUa6qG6KtdfU0REpKCoR6u/WLYYAHPAh2Dw0GD7g0fifPyzsSpmwEDM\nmDRL4uy5LwDO6Ql5tRKGCE1xMeaMc2PDgs7XrwgSkdbW4P/8+/i/ui4o/+JFWf1KIiIihU49Wn2c\nfW81/v95sOjFIEXD0SdiFz4XHGw1hNia890bsE89gjnkKADM5OmYD30E+9J/+f/27j1aqrru4/h7\nzzlHbnL1gCgIkooaeEmUSEMNpKUFopXf0qWSPomXTJ80L4GaaZZmJq18evL2iJgGXyXJa4+ZmpKW\nPa5WFImiCSqgCCqicpEz+/njt8/hHDgcz21mz8z+vNY6a87s2TPz3d/1mz3f/du/+e3cweOarjt+\nEoyf1HA/+vwxxHf/T5N1ouEjOr5BIiIiZUSFVgWK83ni2TcTL381TMWQqNphAFRXw6cPhU0fEX1m\nXAuvAtHwkUTDRzZd9vVziI6YRDRkt5af+6kxxA/OgQ8/CPfHHN6+jRERESljOnVYZuKPPiJ/7x3E\nq98iztdRd83F1J12NPHLi8LjK1eQP/0Y4scfbCiyonETib50Mr0vvCrcz1WRG/v5bc743pIol/vY\nIgsg6j+Q3CXXb76/xdxZIiIiWaAerTITP+TED90NuRzR6EPhpTBgPT/je0RftDAvViJ3+oWw295E\nfcN4qpraWli1qmixRv0Hbv5/GxeKFhERqWQqtMpMnMzcHs9/tOk1/QYNbVpkXTuTqE+/Yoe3ldwl\n1xOvXPHxK4qIiFQgFVrlZsOGcPvuavI3JpOOJtMp1Iu+YNC7bwrBbS0auhvR0I8/1SgiIlKJNEar\n3GxYF4qo7psvY5M7+vjN/19+A7ljT9TEoCIiIiVAhVYZieMYVr1JtNe+5M4Lc1Ox936w174N60SD\nhqQUnYiIiGxJpw7LyQv/gDXvwN77w5DdyJ1xEYwcRRRF5M6+FLp2SztCERERaaTDhZaZVQH/Byxz\n94lm1g+YA+wKLAHM3d/p6PtkVbzqTeL7ZxMd9WXyd90I23Uh2icUV4w6pGG9aL+DUoxSREREmtMZ\npw7PBZ5vdP9i4A/uvgfwh+S+tFP8zOPET/+B/KVnwYrXiI6YTNSrT9phiYiISCt0qNAys8HAF4Fb\nGi2eDNTPM3A7cExH3iPr4gV/bXI/GnVwSpGIiIhIW3W0R2sGcCGQb7RsR3evnzjpDWDHDr5HZsXv\nrIYli2Hg4M0LdxmWXkAiIiLSJu0eo2VmE4GV7v6cmR3e3DruHptZvI3nTwWmJutRW1vb3lBapbq6\nuuDv0dnemzuTdUC/C67k7fNPAaB///7tfr1yzEFnUw6UA1AOsr79oByAcgDFyUEUx83WQR/LzH4E\nnARsAroCvYDfAAcBh7v7CjPbCXjC3ff8mJeLly9f3q44Wqu2tpZVRbz8DED+d3OJ595O7tLrW3V9\nwC3VnXY0ALkb58HqlbB6JVGjqRzaKo0clBrlQDkA5SDr2w/KASgH0P4c7LzzzgCtmrCy3acO3f27\n7j7Y3XcFvgY85u4nAvcBU5LVpgC/be97lKr8/bOp+/45xEtfanG9+kvi5K/8NvFHG9v0Hg0F8PAR\n4ULO/Qd2qMgSERGR4ivEhKVXAxPMbDFwRHK/osQPzoHXl5D/wXnEixY0fSyOiTdtIs7XNV3+yLym\n95tZp4lkEHw09vOdE7SIiIgUXadMWOruTwBPJP+vBsZ3xuuWg/zPryA6+gSiCZOJclXE984i/uP/\nkpt+HQDRoUcSP/k7eLPpqdH8eSfBug9gyCfIfcGa/JowXvse+dt/DrU7Eh10aFG3R0RERDqPLsHT\nRluNadu4kfiemfD3vxKv+5D44bnw4fvEixcCyXQMO+1C/OrL1J12NPmnHiH/wJxQZAG8+m/yv7ya\n+IO14fXzefLXfhfWriH6wnFEVVVF3DoRERHpTCq02mrjRqirIzr2JNhzn4bF+cceCD1XifiZx8M/\n/QfC9j1h2dKwfNYNxH99avPrDdg53C5bSlxXB2+9ASteAyA6QHNmiYiIlDMVWm217v1w26Mn0a57\nbF6+aEGYXDSXpPSFf0DP3tC3Frbv1fQ1lr9KNG4iuetmkTvpLADy104j9lvh7bcAyP3n94l6bF/g\njREREZFC0kWl2yBe+x75C8J8VnTrDlsWQi8uhIGDoEs3WPoS0TEnElVXEw0czFaTaAwcRNSrT5Nf\nI8aPPQBduoQ7Q9s+HYSIiIiUFvVotUH83J8239mwHrr1CP837tl6Yxn06AlA1LtfuB09FvrsQHTC\nGUSTjg/L+g8MtzsMIHf2pdAvTEQaPzwXunQj2rIXTERERMqOerRaKf/ob4nn3AoQLuw8+jDivz0d\nHuy7Q7hUDhBNvZCoWzfy//obDNs9LBs8jKprbwPCYPdoj09Cozmxov0OIrfvgeSnTg4Ldt29SFsl\nIiIihaRCqxXyd/yiYaB77pLriZLTevH69QBEPXqGU4NRRO6gzwJQdfN9zb5WlMvB3vttvTyKYMBO\nsHIFubMv6fyNEBERkaJTofUx4ndXby6yZtzVZIB6NOJTxEB0+FFEYw6H2o5dP7vqqhs79HwREREp\nLSq0tiGOY9i0qWHwe+47P9zqV4BR/4Hb7LkSERERUaHVjHjpS+R/fHGYMysR7TkyxYhERESkHOlX\nh82In3i4SZFVfzkdERERkbZQj9YW4uf/Tjz/97DnPkQ7DiI68BCaTEwqIiIi0kqZKLTitWt4b94s\n4mF7Qfftyc+dSe7bVxB16br1usk0DbkvTyEaNrzYoYqIiEgFyUShRU0N6x68p+myJYubXKuwwetL\nYLsuKrJERESkwzIxRivq2p1eZ09rsiz/k+nkn36sybL4lcXEzz4JuwwrZngiIiJSobLRowV0Gz+R\n96u2I/+zyxuWxXNnwsHjiBctID/nVqipASB3xsXpBCkiIiIVJRM9WvWikQc0nffqvXfJP/UI+Z9e\nBq+/Aq+8CJ/cn6hPv/SCFBERkYqRqUKrOfGsGyDOE42fBIB+YSgiIiKdJZOFVu6626FHzybLouNO\nJXfmxUSTvpZSVCIiIlJpMlloRb36kjvpLNhpF6IxnyP38zlEVVVEBxxMVF2TdngiIiJSITIzGH5L\n0ahDqBp1SNphiIiISAXLZI+WiIiISDGo0BIREREpEBVaIiIiIgWiQktERESkQFRoiYiIiBSICi0R\nERGRAlGhJSIiIlIgKrRERERECkSFloiIiEiBqNASERERKRAVWiIiIiIFEsVxnHYMACURhIiIiEgr\nRa1ZqVR6tKJC/5nZc8V4n1L+Uw6UA+VAOdD2KwfKQafloFVKpdASERERqTgqtEREREQKJEuF1k1p\nB1AClAPlAJQDUA6yvv2gHIByAEXIQakMhhcRERGpOFnq0RIREREpqooqtMys1b8CkMqkNqAciIiU\nkooqtICatAMoBWZWlXYMKaq0Nt0emf8cmFltcpvZz4KZ7Zp2DGkzswPNbEDacaTJzI4ws1Fpx5Em\nM+vd6P+iH4hWxBgtM/sMcB6wAvgl8IK716UbVXElOTjK3S9LO5Y0mNlo4BxgOXAHsNDd8+lGVVxm\ndiBwESEHdwPPZOlzkOxAuwG3AkPc/ZCUQ0qFmR0A/JjQDk7JUhuoZ2YjgJuB1cD57v5iyiEVnZl9\nCvgh8FngG+4+J+WQis7MPg1MAzYCDwN3uvuGYsdR9kf/ydHKDcBDwCrgXODU5LFMnEIxsynA7cAl\nZmbJsup0oyoOM8uZ2feAWwgfpGrgm8B+qQZWRGYWmdnVhIOMB4A3gbOBIakGVmTuHrv7h8ndWjM7\nE0IbSTGsoknawXTg18Bsdz+5vsjKyr6wkXOBe919Un2RlZUcmFmVmd1EKDRvBO4C9k4ey8RnAcDM\n9gX+C7iHcOA5Dtg9jVgqIekjCT1YtwHXAb8BJpvZcHePM/LhepXQiI4k5AB335SFbU96rZYCX3f3\nO4GrgKFAZk4ZuXsMPAFMcPfbgdsIl7V6K824ii0pNHYiFJr/AZxpZn3cPZ+FL5ikHdQA8939Fgi9\nGmZWnTxW8ZIiox+h/d+QLDvWzAYTejsrvuBKiuvfAWPdfR7hO/FzZtY1Y738o4CX3P0O4PdAV8J3\nJVDcdlB2pw7N7DBgvbv/Jbm/A/Bn4Eh3fzn5kJ0D9HT381MMtWCayUEEVCXF1XzgcXe/1Mxq3P2j\nVIMtgGa2vyuha7jG3TeYmQN3uPv9acZZSFvmoNHyscCvCKeNngUecPffpxBiwTXOgZnl6r9EzGwe\noUfvIuAD4GZ3fznFUAummc9CD2AusBA4lFB0riH07tyTWqAFtI39wd+A84ETgFrgDWCju09NLdAC\namF/EAHjga8CF7n722nEVwzNtIMBwGvAT4ApwOvA88Aid7+mmLGVzVGemfU0s98A9wKnm1lfAHdf\nDcwBvpWs+i7wKNA9ObqtGNvKAeGaS/XjME4HzjGzHSutyGpm+/slD21w93xSZNUAg4EXUgu0gLbV\nBhr12LxN6N37DOHL5ngz2yudaAujuRw0KrKGA/9299cJR7FnAXebWZekbVSEFvaHHwCzgP2B77j7\nROBJ4MgkNxWjhRysJ/Tq/gJ4xN2PBKYDI83sqNQCLoAW9geRmUVJT+YiQrHVtf6x1AIugBbawUrC\nEJJqYJq7jwFmAp9NxjQXTdkUWoQei8eAEwlH68c1euweYC8zG5/scFcDgwhHcpWk2RwkRUZsZlXu\nvpBwPvpqgArbsWy5/V+BhlMm9fYG3nT3F5MP4Ojih1lQ22wDye1Cd388WfdJoC/wfgpxFlJL+4Ll\nwB5mdh9wLfBHYKm7b6iwA49t5sDd7wKOc/c/JoseBfqTrXbwC0JhUQvg7suA+UClnTrb1v4gTr4T\ncslBx19ofn9ZCVr6LCwC9iL0bAE8B6wEijogvqQLLTM72cwOS8ZZbCAMeH4UeBE40Mz2TFZdAMwG\nZpjZ7oTqPQK2SyPuztSKHAxP1osI4xJw928AU8zsHWC/ch6f0obtrx/83w/40My+DjwN7FPuR3Bt\nbAONTSB8xtcWNeACaG0OgJ6EXx//Gxjl7pOAXawCft7elnawxSmiCYR9Q9kXWq3Ngbu/TxhCMsXM\n9rfww4gjgCUphd5p2tAOcsn4xGpgMeE0ekVow/4A4BHg8mT/+DVgBKEzpmhKboxWkoyBhF9K5IGX\ngR7Aue6+KllnD8I51w3ufmWj515AqF6HA1Pd/fkih98p2piD9e7+g0bPGwJcD+wAfNPd/1n8LeiY\n9m5/svxHhLE5M4EZ7r6guNF3jg60gS7AWOAawpiEi5KjurLT3n2BmfV29zWNXqfJ/XLSgXaQI/ys\n/2eEAcBZaQdb7g++Sjh9NIJw+mhhkcPvFB1pB0mxdT3wvrtfmsoGdIIO7A+6Ea5nOIDwI6lz3P1f\nxYy9pHo6klNfMeGodJm7jwfOJIw7abjwo7svJnQB7mRmu5tZj6RBXQuc6e5jy7jIamsOdk5y0JXQ\ni/cOcLW7H1amRVZ7t7978tD9wPHufmoZF1ntzUEXwg7oTeB77j65jL9c27sv6AasT14jl6xTrkVW\nR/YFMbCM7LWDnRt9J9R4mDtqepKDci2yOtIOuiUPn1fmRVZ79gd7mFl3d18HnAJMcfcjil1kQYn0\naFmYvflKQrX5ENAL+Iq7T0kezxHOvX610bgDzGwaYc6s7YHPlWtxBZ2Wg3FpNKLOoDagHIByANoX\ngNoBKAdQOTlIvUfLwk8ynyMM2n2JkNSPCPN+jIaGgb6XJ3/1zzuO8EuSx4F9005kR3RiDspyx6o2\noByAcgDaF4DaASgHUFk5KIXZw/PAdR4mFcPCZQOGAZcB/w2MSqrWecA4Mxvm7q8Q5kU50t2fSinu\nzpT1HGR9+0E5AOUAlANQDkA5gArKQeo9WoSK1W3zxV//RLhO2Uygysy+lVStg4FNSSJx96dKKZEd\nlPUcZH37QTkA5QCUA1AOQDmACspB6j1avvnaZPUmEKZrgDCA7TQzewDYk0aD3ipJ1nOQ9e0H5QCU\nA1AOQDkA5QAqKwepF1r1kqo1BnYE7ksWryVceXsk8IqHSecqVtZzkPXtB+UAlANQDkA5AOUAKiMH\nJVNoEc7HbgesAvY1sxmEScW+5e7zU42seLKeg6xvPygHoByAcgDKASgHUAE5KInpHeqZ2RjCbN5P\nA7e5+60ph1R0Wc9B1rcflANQDkA5AOUAlAMo/xyUUo8WhJmspwM/9TCtfhZlPQdZ335QDkA5AOUA\nlANQDqDMc1BSPVoiIiIilaQUpncQERERqUgqtEREREQKRIWWiIiISIGo0BIREREpEBVaIiIiIgWi\nQktERESkQEptHi0RkWaZ2RLCZTg2AXXAv4BZwE3JxWVbeu6uwCtAjbtvKmykIiKbqUdLRMrJJHfv\nCQwFrgYuAspqlmgRyRb1aIlI2XH3NcB9ZvYG8Gczu45QfP0A2A1YA9zq7pcnT3kyuX3XzAAmuPsz\nZnYqcAEwEHgWmOruS4u3JSJS6dSjJSJly92fJVyeYyzwAXAy0Af4InCmmR2TrHpoctvH3bdPiqzJ\nwDTgS0B/4Cng18WMX0Qqn3q0RKTcLQf6ufsTjZYtMLNfA4cB87bxvDOAH7n78wBm9kNgmpkNVa+W\niHQWFVoiUu4GAW+b2acJ47ZGAtsBXYC7W3jeUOBnyWnHelHyeiq0RKRTqNASkbJlZgcRCqP5hJ6r\nG4Cj3H29mc0AapNV42ae/hpwlbvfWZRgRSSTNEZLRMqOmfUys4nAbOBX7v4PoCfwdlJkjQZOaPSU\nt4A88IlGy34JfNfMRiSv2dvMjivOFohIVqjQEpFycr+ZrSX0Rk0Hfgqckjx2FnBF8vhlgNc/yd0/\nBK4C/mRm75rZGHe/F7gGmG1m7wH/BI4q3qaISBZEcdxcj7qIiIiIdJR6tEREREQKRIWWiIiISIGo\n0BIREREpEBVaIiIiIgWiQktERESkQFRoiYiIiBSICi0RERGRAlGhJSIiIlIgKrRERERECuT/Ac7P\nvtXX82adAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156d0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['AAPL.O'].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data['AAPL.O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.columns = ['Prices']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 1 columns):\n",
      "Prices    1972 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 30.8 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA1'] = data['Prices'].rolling(42).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['SMA2'] = data['Prices'].rolling(252).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices  SMA1  SMA2\n",
       "Date                             \n",
       "2010-01-04  30.572827   NaN   NaN\n",
       "2010-01-05  30.625684   NaN   NaN\n",
       "2010-01-06  30.138541   NaN   NaN\n",
       "2010-01-07  30.082827   NaN   NaN\n",
       "2010-01-08  30.282827   NaN   NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>156.41</td>\n",
       "      <td>157.610952</td>\n",
       "      <td>139.862520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>157.41</td>\n",
       "      <td>157.514286</td>\n",
       "      <td>140.028472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>163.05</td>\n",
       "      <td>157.517619</td>\n",
       "      <td>140.221210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>166.72</td>\n",
       "      <td>157.597857</td>\n",
       "      <td>140.431528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>169.04</td>\n",
       "      <td>157.717857</td>\n",
       "      <td>140.651766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prices        SMA1        SMA2\n",
       "Date                                      \n",
       "2017-10-25  156.41  157.610952  139.862520\n",
       "2017-10-26  157.41  157.514286  140.028472\n",
       "2017-10-27  163.05  157.517619  140.221210\n",
       "2017-10-30  166.72  157.597857  140.431528\n",
       "2017-10-31  169.04  157.717857  140.651766"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IGtM/6SP6y9AJGYUQQvSYeusV0DS0ZZ9BGz0eAP2Zx5qP79mO+uvj6N+8jpLX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fb2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Positions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Positions'] = np.where(data['SMA1'] > data['SMA2'], 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+NyqPvP0B4MVzru8CzppnuZ8c5PU1Wh2/fdlOta332vqjRXjyWJXLM/pLkipk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c59438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Positions'].plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Positions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>46.079954</td>\n",
       "      <td>45.280967</td>\n",
       "      <td>37.120735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>47.081381</td>\n",
       "      <td>45.349708</td>\n",
       "      <td>37.186246</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>47.327096</td>\n",
       "      <td>45.412599</td>\n",
       "      <td>37.252521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>47.714238</td>\n",
       "      <td>45.466102</td>\n",
       "      <td>37.322266</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>47.675667</td>\n",
       "      <td>45.522565</td>\n",
       "      <td>37.392079</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices       SMA1       SMA2  Positions\n",
       "Date                                                  \n",
       "2010-12-31  46.079954  45.280967  37.120735          1\n",
       "2011-01-03  47.081381  45.349708  37.186246          1\n",
       "2011-01-04  47.327096  45.412599  37.252521          1\n",
       "2011-01-05  47.714238  45.466102  37.322266          1\n",
       "2011-01-06  47.675667  45.522565  37.392079          1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ePDg0D369euHx+PB6/VejscRQgghWlZpEVp8h4blNYGQP7Dw8/FiD3tyXVRWBfpdlGGg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dTmeIK9j6lvEoSGNgB3Xw0sAWpxt9ietlHF8P+8dPgh5zcMv52vFXtb914EjiCY2QJ4BtgAlOyA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c7dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.dropna().plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CgoLQNI3k5GSWLVvGW2+9dcrPNXv2bKZOnVrnXGf5Xu7fv5/ly5dz+eWX07Vr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\nCCH6OUn4hBBCCCH6uf8P4Uadq60aSIcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116beaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.dropna().plot(figsize=(10, 6), secondary_y='Positions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Returns'] = np.log(data['Prices'] / data['Prices'].shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Returns</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>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.006626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Prices  SMA1  SMA2  Positions   Returns\n",
       "Date                                                  \n",
       "2010-01-04  30.572827   NaN   NaN         -1       NaN\n",
       "2010-01-05  30.625684   NaN   NaN         -1  0.001727\n",
       "2010-01-06  30.138541   NaN   NaN         -1 -0.016034\n",
       "2010-01-07  30.082827   NaN   NaN         -1 -0.001850\n",
       "2010-01-08  30.282827   NaN   NaN         -1  0.006626"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001727395398339415"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = math.log(30.625684 / 30.572827)\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0017288882052027"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.exp(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0017288882052027"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "30.625684 / 30.572827"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prices</th>\n",
       "      <th>SMA1</th>\n",
       "      <th>SMA2</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Returns</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>2017-10-25</th>\n",
       "      <td>156.41</td>\n",
       "      <td>157.610952</td>\n",
       "      <td>139.862520</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.004402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>157.41</td>\n",
       "      <td>157.514286</td>\n",
       "      <td>140.028472</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>163.05</td>\n",
       "      <td>157.517619</td>\n",
       "      <td>140.221210</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>166.72</td>\n",
       "      <td>157.597857</td>\n",
       "      <td>140.431528</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>169.04</td>\n",
       "      <td>157.717857</td>\n",
       "      <td>140.651766</td>\n",
       "      <td>1</td>\n",
       "      <td>0.013820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Prices        SMA1        SMA2  Positions   Returns\n",
       "Date                                                           \n",
       "2017-10-25  156.41  157.610952  139.862520          1 -0.004402\n",
       "2017-10-26  157.41  157.514286  140.028472          1  0.006373\n",
       "2017-10-27  163.05  157.517619  140.221210          1  0.035203\n",
       "2017-10-30  166.72  157.597857  140.431528          1  0.022259\n",
       "2017-10-31  169.04  157.717857  140.651766          1  0.013820"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Positions'].shift(1) * data['Returns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-25</th>\n",
       "      <td>-0.004402</td>\n",
       "      <td>-0.004402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-26</th>\n",
       "      <td>0.006373</td>\n",
       "      <td>0.006373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27</th>\n",
       "      <td>0.035203</td>\n",
       "      <td>0.035203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-30</th>\n",
       "      <td>0.022259</td>\n",
       "      <td>0.022259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>0.013820</td>\n",
       "      <td>0.013820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2017-10-25 -0.004402 -0.004402\n",
       "2017-10-26  0.006373  0.006373\n",
       "2017-10-27  0.035203  0.035203\n",
       "2017-10-30  0.022259  0.022259\n",
       "2017-10-31  0.013820  0.013820"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)\n",
    "data = data.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     1.299757\n",
       "Strategy    1.668989\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    5.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].sum())  # absolute return per USD invested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     2.668406\n",
       "Strategy    4.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].sum()) - 1  # relative/net return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>0.021500</td>\n",
       "      <td>0.021500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>0.026705</td>\n",
       "      <td>0.026705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>0.034852</td>\n",
       "      <td>0.034852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>0.034043</td>\n",
       "      <td>0.034043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>0.041179</td>\n",
       "      <td>0.041179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>0.059851</td>\n",
       "      <td>0.059851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>0.057468</td>\n",
       "      <td>0.057468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>0.065573</td>\n",
       "      <td>0.065573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>0.069224</td>\n",
       "      <td>0.069224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>0.077292</td>\n",
       "      <td>0.077292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2011-01-03  0.021500  0.021500\n",
       "2011-01-04  0.026705  0.026705\n",
       "2011-01-05  0.034852  0.034852\n",
       "2011-01-06  0.034043  0.034043\n",
       "2011-01-07  0.041179  0.041179\n",
       "2011-01-10  0.059851  0.059851\n",
       "2011-01-11  0.057468  0.057468\n",
       "2011-01-12  0.065573  0.065573\n",
       "2011-01-13  0.069224  0.069224\n",
       "2011-01-14  0.077292  0.077292"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Strategy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>1.021732</td>\n",
       "      <td>1.021732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>1.027065</td>\n",
       "      <td>1.027065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>1.035466</td>\n",
       "      <td>1.035466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>1.034629</td>\n",
       "      <td>1.034629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>1.042039</td>\n",
       "      <td>1.042039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>1.061678</td>\n",
       "      <td>1.061678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>1.059152</td>\n",
       "      <td>1.059152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-12</th>\n",
       "      <td>1.067770</td>\n",
       "      <td>1.067770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-13</th>\n",
       "      <td>1.071677</td>\n",
       "      <td>1.071677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-14</th>\n",
       "      <td>1.080357</td>\n",
       "      <td>1.080357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Strategy\n",
       "Date                          \n",
       "2011-01-03  1.021732  1.021732\n",
       "2011-01-04  1.027065  1.027065\n",
       "2011-01-05  1.035466  1.035466\n",
       "2011-01-06  1.034629  1.034629\n",
       "2011-01-07  1.042039  1.042039\n",
       "2011-01-10  1.061678  1.061678\n",
       "2011-01-11  1.059152  1.059152\n",
       "2011-01-12  1.067770  1.067770\n",
       "2011-01-13  1.071677  1.071677\n",
       "2011-01-14  1.080357  1.080357"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data[['Returns', 'Strategy']].cumsum()).iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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28BIBBWnhX5rrK2LvHfMYKiJHampuMhvaBE3+CuBTcpNZdPoELpqZB8AuX7BW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\nPOlpEiJO3tpQw8/f2ILXaJsSLNpT1tjK41+Vcfd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Op7PXX6M/6Ol6siwLvTujkHZAQ6sK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170c37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TRBE8tawmYl9oMk5RFE6ZVIzdouAKCd4a/Vk3MGeitvvkD4dYus28aZq2AZjW3XHdMWTV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\nxntVNbVz1dubg9ttXj3YJbi8qiU4Vs5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eFJtCrdcc43ah/xrPn7N31EkLvktONW8Mq8R6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2AAAU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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117164518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))\n",
    "data['Positions'].plot(ax=ax, secondary_y='Positions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Financial Base Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.FinancialData at 0x116f6b9b0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def f(self, x):\n",
    "        return x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.f(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self, x):  # special method\n",
    "        self.x = x\n",
    "    \n",
    "    def f(self):\n",
    "        return self.x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fd = FinancialData(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.x  # attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.f()  # method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FinancialData(object):\n",
    "    def __init__(self, symbol):  # special method\n",
    "        self.symbol = symbol\n",
    "        self.prepare_data()\n",
    "    \n",
    "    def prepare_data(self):\n",
    "        self.raw = pd.read_csv('http://hilpisch.com/tr_eikon_eod_data.csv',\n",
    "                               index_col=0, parse_dates=True)\n",
    "        self.data = pd.DataFrame(self.raw[self.symbol])\n",
    "        self.data['Returns'] = np.log(self.data / self.data.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.9 ms, sys: 3.99 ms, total: 17.9 ms\n",
      "Wall time: 193 ms\n"
     ]
    }
   ],
   "source": [
    "%time fd = FinancialData('AAPL.O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL.O'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 12 columns):\n",
      "AAPL.O    1972 non-null float64\n",
      "MSFT.O    1972 non-null float64\n",
      "INTC.O    1972 non-null float64\n",
      "AMZN.O    1972 non-null float64\n",
      "GS.N      1972 non-null float64\n",
      "SPY       1972 non-null float64\n",
      ".SPX      1972 non-null float64\n",
      ".VIX      1972 non-null float64\n",
      "EUR=      1972 non-null float64\n",
      "XAU=      1972 non-null float64\n",
      "GDX       1972 non-null float64\n",
      "GLD       1972 non-null float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 200.3 KB\n"
     ]
    }
   ],
   "source": [
    "fd.raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>AAPL.O</th>\n",
       "      <th>Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.572827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.625684</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.138541</td>\n",
       "      <td>-0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.082827</td>\n",
       "      <td>-0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.282827</td>\n",
       "      <td>0.006626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL.O   Returns\n",
       "Date                           \n",
       "2010-01-04  30.572827       NaN\n",
       "2010-01-05  30.625684  0.001727\n",
       "2010-01-06  30.138541 -0.016034\n",
       "2010-01-07  30.082827 -0.001850\n",
       "2010-01-08  30.282827  0.006626"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd.data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class for Vectorized Backtesting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinancialData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinancialData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_studies()\n",
    "        \n",
    "    def prepare_studies(self):\n",
    "        self.data['SMA1'] = self.data[self.symbol].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data[self.symbol].rolling(self.SMA2).mean()\n",
    "        \n",
    "    def plot_data(self, cols=None):\n",
    "        if cols is None:\n",
    "            cols = [self.symbol]\n",
    "        self.data[cols].plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1972 entries, 2010-01-04 to 2017-10-31\n",
      "Data columns (total 4 columns):\n",
      "AAPL.O     1972 non-null float64\n",
      "Returns    1971 non-null float64\n",
      "SMA1       1931 non-null float64\n",
      "SMA2       1721 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 77.0 KB\n"
     ]
    }
   ],
   "source": [
    "sma.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rq7FVO4NUAOle+c8hM/OQ+HBYhH3yH9DYgPOVbwW5vnZXYndUYJe9CfW1yWkp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\nREREpEhUaImIiIgUiQotERERkSL5f0KYFmjohnMQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116bdddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xWG2DZ8BNAi0hhBARTRPhtauuh6RUo3ilz1h2palWltJ1CATQ7HbUjo1tLzJ7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11740f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_data(['AAPL.O', 'SMA1', 'SMA2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(SMABacktester):\n",
    "    def run_strategy(self, net=False):\n",
    "        self.results = self.data.copy().dropna()\n",
    "        self.results['Positions'] = np.where(\n",
    "                self.results['SMA1'] > self.results['SMA2'], 1, -1)\n",
    "        self.results['Strategy'] = self.results['Positions'].shift(1) * self.results['Returns']\n",
    "        self.results.dropna(inplace=True)\n",
    "        perf = self.results[['Returns', 'Strategy']].sum().apply(np.exp)\n",
    "        if net is True:\n",
    "            return perf - 1\n",
    "        return perf\n",
    "        \n",
    "    def plot_results(self):\n",
    "        try:\n",
    "            self.results\n",
    "        except:\n",
    "            self.run_strategy()\n",
    "        self.results[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    5.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     2.668406\n",
       "Strategy    4.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy(net=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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28BIBBWnhX5rrK2LvHfMYKiJHampuMhvaBE3+CuBTcpNZdPoELpqZB8AuX7BW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\nPOlpEiJO3tpQw8/f2ILXaJsSLNpT1tjK41+Vcfd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Op7PXX6M/6Ol6siwLvTujkHZAQ6sK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\n3/dyYJd+5fWQmETD8w9jV5WHnGa9/iwAdnUl1nMPYD14B3aEBZ3thjq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GPTjsnCuzK0lO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171b8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sma.plot_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Backtests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    5.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 30, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    4.888468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.2 ms, sys: 2.88 ms, total: 19.1 ms\n",
      "Wall time: 183 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma = SMABacktester('AAPL.O', 30, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36.1 ms, sys: 1.65 ms, total: 37.8 ms\n",
      "Wall time: 36.7 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Returns     4.177810\n",
       "Strategy    4.457191\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Improvements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(FinancialData):\n",
    "    def __init__(self, symbol, SMA1, SMA2):\n",
    "        FinancialData.__init__(self, symbol)\n",
    "        self.SMA1 = SMA1\n",
    "        self.SMA2 = SMA2\n",
    "        self.prepare_studies()\n",
    "        \n",
    "    def prepare_studies(self):\n",
    "        self.data['SMA1'] = self.data[self.symbol].rolling(self.SMA1).mean()\n",
    "        self.data['SMA2'] = self.data[self.symbol].rolling(self.SMA2).mean()\n",
    "        \n",
    "    def run_strategy(self, SMA=None, net=False):\n",
    "        if SMA is not None:\n",
    "            self.SMA1 = SMA[0]\n",
    "            self.SMA2 = SMA[1]\n",
    "            self.prepare_studies()\n",
    "        self.results = self.data.copy().dropna()\n",
    "        self.results['Positions'] = np.where(\n",
    "                self.results['SMA1'] > self.results['SMA2'], 1, -1)\n",
    "        self.results['Strategy'] = self.results['Positions'].shift(1) * self.results['Returns']\n",
    "        self.results.dropna(inplace=True)\n",
    "        perf = self.results[['Returns', 'Strategy']].sum().apply(np.exp)\n",
    "        if net is True:\n",
    "            return perf - 1\n",
    "        return perf\n",
    "    \n",
    "    def plot_data(self, cols=None):\n",
    "        if cols is None:\n",
    "            cols = [self.symbol]\n",
    "        self.data[cols].plot(figsize=(10, 6))\n",
    "        \n",
    "    def plot_results(self):\n",
    "        try:\n",
    "            self.results\n",
    "        except:\n",
    "            self.run_strategy()\n",
    "        self.results[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.7 ms, sys: 2.92 ms, total: 16.6 ms\n",
      "Wall time: 181 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 31.5 ms, sys: 1.51 ms, total: 33 ms\n",
      "Wall time: 31.9 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    5.306802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39 ms, sys: 1.56 ms, total: 40.6 ms\n",
      "Wall time: 39.7 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Returns     3.668406\n",
       "Strategy    4.888468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy(SMA=(30, 252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 33.7 ms, sys: 1.6 ms, total: 35.3 ms\n",
      "Wall time: 34 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Returns     4.177810\n",
       "Strategy    4.457191\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time sma.run_strategy(SMA=(30, 180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(product('ab', range(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10, 150),\n",
       " (10, 175),\n",
       " (10, 200),\n",
       " (10, 225),\n",
       " (10, 250),\n",
       " (15, 150),\n",
       " (15, 175),\n",
       " (15, 200),\n",
       " (15, 225),\n",
       " (15, 250),\n",
       " (20, 150),\n",
       " (20, 175),\n",
       " (20, 200),\n",
       " (20, 225),\n",
       " (20, 250),\n",
       " (25, 150),\n",
       " (25, 175),\n",
       " (25, 200),\n",
       " (25, 225),\n",
       " (25, 250),\n",
       " (30, 150),\n",
       " (30, 175),\n",
       " (30, 200),\n",
       " (30, 225),\n",
       " (30, 250)]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(product([10, 15, 20, 25, 30], [150, 175, 200, 225, 250]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 150) [4.54948915 5.08289727]\n",
      "(10, 175) [4.4311009  6.88134872]\n",
      "(10, 200) [3.72101001 4.47263444]\n",
      "(10, 225) [3.77610794 5.58645691]\n",
      "(10, 250) [3.6376193  3.59115222]\n",
      "(15, 150) [4.54948915 4.99473679]\n",
      "(15, 175) [4.4311009  4.01447783]\n",
      "(15, 200) [3.72101001 4.73026305]\n",
      "(15, 225) [3.77610794 4.73065818]\n",
      "(15, 250) [3.6376193  4.89942916]\n",
      "(20, 150) [4.54948915 4.25024893]\n",
      "(20, 175) [4.4311009  3.93375523]\n",
      "(20, 200) [3.72101001 4.84007863]\n",
      "(20, 225) [3.77610794 5.68931323]\n",
      "(20, 250) [3.6376193  4.30918536]\n",
      "(25, 150) [4.54948915 3.65147892]\n",
      "(25, 175) [4.4311009  4.54749315]\n",
      "(25, 200) [3.72101001 4.69869619]\n",
      "(25, 225) [3.77610794 4.88412096]\n",
      "(25, 250) [3.6376193  4.05712521]\n",
      "(30, 150) [4.54948915 3.1958258 ]\n",
      "(30, 175) [4.4311009  4.54731188]\n",
      "(30, 200) [3.72101001 5.59403157]\n",
      "(30, 225) [3.77610794 4.85687906]\n",
      "(30, 250) [3.6376193 4.734574 ]\n",
      "CPU times: user 779 ms, sys: 21.6 ms, total: 801 ms\n",
      "Wall time: 794 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for SMA in product([10, 15, 20, 25, 30], [150, 175, 200, 225, 250]):\n",
    "    print(SMA, sma.run_strategy(SMA=SMA).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SMABacktester(SMABacktester):\n",
    "    def optimize_parameters(self, SMA1_list, SMA2_list):\n",
    "        self.opt_results = pd.DataFrame()\n",
    "        for i, SMA in enumerate(product(SMA1_list, SMA2_list)):\n",
    "            perf = sma.run_strategy(SMA=SMA).values\n",
    "            self.opt_results = self.opt_results.append(pd.DataFrame({'SMA1': SMA[0], 'SMA2': SMA[1],\n",
    "                                                  'BENCH': perf[0], 'STRAT': perf[1]}, index=[i]))\n",
    "        self.opt_results = self.opt_results[['SMA1', 'SMA2', 'BENCH', 'STRAT']]\n",
    "        print('Optimal results:')\n",
    "        print(self.opt_results.iloc[sma.opt_results['STRAT'].idxmax()])\n",
    "        \n",
    "    def print_opt_results(self):\n",
    "        try:\n",
    "            self.opt_results\n",
    "        except:\n",
    "            print('no optimization results available yet')\n",
    "        print(self.opt_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sma = SMABacktester('AAPL.O', 42, 252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal results:\n",
      "SMA1      10.000000\n",
      "SMA2     175.000000\n",
      "BENCH      4.431101\n",
      "STRAT      6.881349\n",
      "Name: 1, dtype: float64\n",
      "CPU times: user 629 ms, sys: 5.86 ms, total: 635 ms\n",
      "Wall time: 636 ms\n"
     ]
    }
   ],
   "source": [
    "%time sma.optimize_parameters([10, 15, 20, 25], [150, 175, 200, 225, 250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.88134872211726"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.opt_results['STRAT'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.opt_results['STRAT'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SMA1  SMA2     BENCH     STRAT\n",
      "0     10   150  4.549489  5.082897\n",
      "1     10   175  4.431101  6.881349\n",
      "2     10   200  3.721010  4.472634\n",
      "3     10   225  3.776108  5.586457\n",
      "4     10   250  3.637619  3.591152\n",
      "5     15   150  4.549489  4.994737\n",
      "6     15   175  4.431101  4.014478\n",
      "7     15   200  3.721010  4.730263\n",
      "8     15   225  3.776108  4.730658\n",
      "9     15   250  3.637619  4.899429\n",
      "10    20   150  4.549489  4.250249\n",
      "11    20   175  4.431101  3.933755\n",
      "12    20   200  3.721010  4.840079\n",
      "13    20   225  3.776108  5.689313\n",
      "14    20   250  3.637619  4.309185\n",
      "15    25   150  4.549489  3.651479\n",
      "16    25   175  4.431101  4.547493\n",
      "17    25   200  3.721010  4.698696\n",
      "18    25   225  3.776108  4.884121\n",
      "19    25   250  3.637619  4.057125\n"
     ]
    }
   ],
   "source": [
    "sma.print_opt_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.533408\n",
       "1     2.450248\n",
       "2     0.751624\n",
       "3     1.810349\n",
       "4    -0.046467\n",
       "5     0.445248\n",
       "6    -0.416623\n",
       "7     1.009253\n",
       "8     0.954550\n",
       "9     1.261810\n",
       "10   -0.299240\n",
       "11   -0.497346\n",
       "12    1.119069\n",
       "13    1.913205\n",
       "14    0.671566\n",
       "15   -0.898010\n",
       "16    0.116392\n",
       "17    0.977686\n",
       "18    1.108013\n",
       "19    0.419506\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma.opt_results['STRAT'] - sma.opt_results['BENCH']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  }
 ],
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