22_SimpleStockBacktesting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Stock Backtesting"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.investopedia.com/terms/b/backtesting.asp"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'MSFT'\n",
        "start = '2016-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-04</th>\n",
              "      <td>54.320000</td>\n",
              "      <td>54.799999</td>\n",
              "      <td>53.389999</td>\n",
              "      <td>54.799999</td>\n",
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              "      <td>53778000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-05</th>\n",
              "      <td>54.930000</td>\n",
              "      <td>55.389999</td>\n",
              "      <td>54.540001</td>\n",
              "      <td>55.049999</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-06</th>\n",
              "      <td>54.320000</td>\n",
              "      <td>54.400002</td>\n",
              "      <td>53.639999</td>\n",
              "      <td>54.049999</td>\n",
              "      <td>50.366753</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-07</th>\n",
              "      <td>52.700001</td>\n",
              "      <td>53.490002</td>\n",
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              "      <th>2016-01-08</th>\n",
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              "      <td>52.150002</td>\n",
              "      <td>52.330002</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2016-01-04  54.320000  54.799999  53.389999  54.799999  51.065639  53778000\n",
              "2016-01-05  54.930000  55.389999  54.540001  55.049999  51.298611  34079700\n",
              "2016-01-06  54.320000  54.400002  53.639999  54.049999  50.366753  39518900\n",
              "2016-01-07  52.700001  53.490002  52.070000  52.169998  48.614864  56564900\n",
              "2016-01-08  52.369999  53.279999  52.150002  52.330002  48.763962  48754000"
            ]
          },
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      ],
      "execution_count": 2,
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        "collapsed": false,
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    {
      "cell_type": "code",
      "source": [
        "# Create Signal and Moving Average for long and short\n",
        "# Initialize signals column to zero\n",
        "df['Signal'] = 0\n",
        "df['Short_MA'] = df['Adj Close'].rolling(window=20).mean()\n",
        "df['Long_MA'] = df['Adj Close'].rolling(window=50).mean()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create short and long signal with short window\n",
        "short_window=40\n",
        "df['Signal'][short_window:] = np.where(df['Short_MA'][short_window:] > df['Long_MA'][short_window:], 1, 0)"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the difference between consecutive entries in signals \n",
        "df['Positions'] = df['Signal'].diff()"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Positions \n",
        "positions = pd.DataFrame(index=df.index).fillna(0.0)        \n",
        "positions = 100 * df['Signal']"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Daily and Total of Profit & Loss\n",
        "df['Daily P&L'] = df['Adj Close'].diff() * df['Signal']\n",
        "df['Total P&L'] = df['Daily P&L'].cumsum()"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
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              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "      <th>Date</th>\n",
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              "      <th>2016-01-04</th>\n",
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              "      <th>2016-01-05</th>\n",
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              "      <td>52.369999</td>\n",
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              "      <td>52.150002</td>\n",
              "      <td>52.330002</td>\n",
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              "      <th>2016-01-11</th>\n",
              "      <td>52.509998</td>\n",
              "      <td>52.849998</td>\n",
              "      <td>51.459999</td>\n",
              "      <td>52.299999</td>\n",
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              "      <td>52.759998</td>\n",
              "      <td>53.099998</td>\n",
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              "      <th>2016-01-13</th>\n",
              "      <td>53.799999</td>\n",
              "      <td>54.070000</td>\n",
              "      <td>51.299999</td>\n",
              "      <td>51.639999</td>\n",
              "      <td>48.120983</td>\n",
              "      <td>66883600</td>\n",
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              "      <th>2016-01-14</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>53.419998</td>\n",
              "      <td>51.570000</td>\n",
              "      <td>53.110001</td>\n",
              "      <td>49.490814</td>\n",
              "      <td>52381900</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>2016-01-15</th>\n",
              "      <td>51.310001</td>\n",
              "      <td>51.970001</td>\n",
              "      <td>50.340000</td>\n",
              "      <td>50.990002</td>\n",
              "      <td>47.515282</td>\n",
              "      <td>71820700</td>\n",
              "      <td>0</td>\n",
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              "      <th>2016-01-19</th>\n",
              "      <td>51.480000</td>\n",
              "      <td>51.680000</td>\n",
              "      <td>50.060001</td>\n",
              "      <td>50.560001</td>\n",
              "      <td>47.114582</td>\n",
              "      <td>43564500</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-20</th>\n",
              "      <td>49.980000</td>\n",
              "      <td>51.380001</td>\n",
              "      <td>49.099998</td>\n",
              "      <td>50.790001</td>\n",
              "      <td>47.328907</td>\n",
              "      <td>63273000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-21</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.580002</td>\n",
              "      <td>50.299999</td>\n",
              "      <td>50.480000</td>\n",
              "      <td>47.040028</td>\n",
              "      <td>40191200</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-22</th>\n",
              "      <td>51.410000</td>\n",
              "      <td>52.330002</td>\n",
              "      <td>51.259998</td>\n",
              "      <td>52.290001</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-25</th>\n",
              "      <td>51.939999</td>\n",
              "      <td>52.650002</td>\n",
              "      <td>51.650002</td>\n",
              "      <td>51.790001</td>\n",
              "      <td>48.260765</td>\n",
              "      <td>34707700</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-26</th>\n",
              "      <td>51.790001</td>\n",
              "      <td>52.439999</td>\n",
              "      <td>51.549999</td>\n",
              "      <td>52.169998</td>\n",
              "      <td>48.614864</td>\n",
              "      <td>28699500</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2016-01-27</th>\n",
              "      <td>52.009998</td>\n",
              "      <td>52.200001</td>\n",
              "      <td>51.020000</td>\n",
              "      <td>51.220001</td>\n",
              "      <td>47.729610</td>\n",
              "      <td>36775200</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-28</th>\n",
              "      <td>51.860001</td>\n",
              "      <td>52.209999</td>\n",
              "      <td>51.250000</td>\n",
              "      <td>52.060001</td>\n",
              "      <td>48.512363</td>\n",
              "      <td>62513800</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-29</th>\n",
              "      <td>54.730000</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>51.335884</td>\n",
              "      <td>83611700</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-01</th>\n",
              "      <td>54.880001</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>54.500000</td>\n",
              "      <td>54.709999</td>\n",
              "      <td>50.981773</td>\n",
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              "    <tr>\n",
              "      <th>2016-02-02</th>\n",
              "      <td>54.169998</td>\n",
              "      <td>54.259998</td>\n",
              "      <td>52.650002</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>49.388306</td>\n",
              "      <td>56313800</td>\n",
              "      <td>0</td>\n",
              "      <td>48.856217</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-03</th>\n",
              "      <td>53.250000</td>\n",
              "      <td>53.389999</td>\n",
              "      <td>51.259998</td>\n",
              "      <td>52.160000</td>\n",
              "      <td>48.605545</td>\n",
              "      <td>57559800</td>\n",
              "      <td>0</td>\n",
              "      <td>48.721564</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-04</th>\n",
              "      <td>52.099998</td>\n",
              "      <td>52.810001</td>\n",
              "      <td>51.369999</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>48.456451</td>\n",
              "      <td>46803400</td>\n",
              "      <td>0</td>\n",
              "      <td>48.626049</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-05</th>\n",
              "      <td>51.939999</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>49.560001</td>\n",
              "      <td>50.160000</td>\n",
              "      <td>46.741840</td>\n",
              "      <td>62009000</td>\n",
              "      <td>0</td>\n",
              "      <td>48.532398</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-08</th>\n",
              "      <td>49.549999</td>\n",
              "      <td>49.570000</td>\n",
              "      <td>48.189999</td>\n",
              "      <td>49.410000</td>\n",
              "      <td>46.042942</td>\n",
              "      <td>59290500</td>\n",
              "      <td>0</td>\n",
              "      <td>48.396347</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-09</th>\n",
              "      <td>49.020000</td>\n",
              "      <td>50.240002</td>\n",
              "      <td>48.669998</td>\n",
              "      <td>49.279999</td>\n",
              "      <td>45.921810</td>\n",
              "      <td>45822200</td>\n",
              "      <td>0</td>\n",
              "      <td>48.255637</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-10</th>\n",
              "      <td>49.889999</td>\n",
              "      <td>50.389999</td>\n",
              "      <td>49.520000</td>\n",
              "      <td>49.709999</td>\n",
              "      <td>46.322506</td>\n",
              "      <td>38237000</td>\n",
              "      <td>0</td>\n",
              "      <td>48.112598</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-11</th>\n",
              "      <td>48.680000</td>\n",
              "      <td>50.110001</td>\n",
              "      <td>48.509998</td>\n",
              "      <td>49.689999</td>\n",
              "      <td>46.303867</td>\n",
              "      <td>48878600</td>\n",
              "      <td>0</td>\n",
              "      <td>48.021742</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-12</th>\n",
              "      <td>50.250000</td>\n",
              "      <td>50.680000</td>\n",
              "      <td>49.750000</td>\n",
              "      <td>50.500000</td>\n",
              "      <td>47.058666</td>\n",
              "      <td>34243300</td>\n",
              "      <td>0</td>\n",
              "      <td>47.900134</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-16</th>\n",
              "      <td>50.900002</td>\n",
              "      <td>51.090000</td>\n",
              "      <td>50.130001</td>\n",
              "      <td>51.090000</td>\n",
              "      <td>47.950283</td>\n",
              "      <td>37291200</td>\n",
              "      <td>0</td>\n",
              "      <td>47.921884</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-15</th>\n",
              "      <td>104.989998</td>\n",
              "      <td>107.800003</td>\n",
              "      <td>103.910004</td>\n",
              "      <td>107.279999</td>\n",
              "      <td>106.823784</td>\n",
              "      <td>38505200</td>\n",
              "      <td>0</td>\n",
              "      <td>106.380492</td>\n",
              "      <td>109.118909</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-16</th>\n",
              "      <td>107.080002</td>\n",
              "      <td>108.879997</td>\n",
              "      <td>106.800003</td>\n",
              "      <td>108.290001</td>\n",
              "      <td>107.829491</td>\n",
              "      <td>33502100</td>\n",
              "      <td>0</td>\n",
              "      <td>106.385342</td>\n",
              "      <td>109.129771</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-19</th>\n",
              "      <td>108.269997</td>\n",
              "      <td>108.559998</td>\n",
              "      <td>103.550003</td>\n",
              "      <td>104.620003</td>\n",
              "      <td>104.175102</td>\n",
              "      <td>44773900</td>\n",
              "      <td>0</td>\n",
              "      <td>106.159386</td>\n",
              "      <td>109.044346</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-20</th>\n",
              "      <td>101.800003</td>\n",
              "      <td>102.970001</td>\n",
              "      <td>99.349998</td>\n",
              "      <td>101.709999</td>\n",
              "      <td>101.277473</td>\n",
              "      <td>64052500</td>\n",
              "      <td>0</td>\n",
              "      <td>105.864395</td>\n",
              "      <td>108.864086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-21</th>\n",
              "      <td>103.599998</td>\n",
              "      <td>104.430000</td>\n",
              "      <td>102.239998</td>\n",
              "      <td>103.110001</td>\n",
              "      <td>102.671516</td>\n",
              "      <td>28130600</td>\n",
              "      <td>0</td>\n",
              "      <td>105.925640</td>\n",
              "      <td>108.702387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-23</th>\n",
              "      <td>102.169998</td>\n",
              "      <td>103.809998</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>103.070000</td>\n",
              "      <td>102.631691</td>\n",
              "      <td>13823100</td>\n",
              "      <td>0</td>\n",
              "      <td>105.688445</td>\n",
              "      <td>108.516096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-26</th>\n",
              "      <td>104.790001</td>\n",
              "      <td>106.629997</td>\n",
              "      <td>104.580002</td>\n",
              "      <td>106.470001</td>\n",
              "      <td>106.017235</td>\n",
              "      <td>32336200</td>\n",
              "      <td>0</td>\n",
              "      <td>105.686956</td>\n",
              "      <td>108.388395</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-27</th>\n",
              "      <td>106.269997</td>\n",
              "      <td>107.330002</td>\n",
              "      <td>105.360001</td>\n",
              "      <td>107.139999</td>\n",
              "      <td>106.684380</td>\n",
              "      <td>29124500</td>\n",
              "      <td>0</td>\n",
              "      <td>105.872997</td>\n",
              "      <td>108.298427</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-28</th>\n",
              "      <td>107.889999</td>\n",
              "      <td>111.330002</td>\n",
              "      <td>107.860001</td>\n",
              "      <td>111.120003</td>\n",
              "      <td>110.647461</td>\n",
              "      <td>46788500</td>\n",
              "      <td>0</td>\n",
              "      <td>106.263140</td>\n",
              "      <td>108.266502</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-29</th>\n",
              "      <td>110.330002</td>\n",
              "      <td>111.120003</td>\n",
              "      <td>109.029999</td>\n",
              "      <td>110.190002</td>\n",
              "      <td>109.721413</td>\n",
              "      <td>28123200</td>\n",
              "      <td>0</td>\n",
              "      <td>106.454296</td>\n",
              "      <td>108.246000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-11-30</th>\n",
              "      <td>110.699997</td>\n",
              "      <td>110.970001</td>\n",
              "      <td>109.360001</td>\n",
              "      <td>110.889999</td>\n",
              "      <td>110.418434</td>\n",
              "      <td>33665600</td>\n",
              "      <td>0</td>\n",
              "      <td>106.724424</td>\n",
              "      <td>108.202356</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-03</th>\n",
              "      <td>113.000000</td>\n",
              "      <td>113.419998</td>\n",
              "      <td>110.730003</td>\n",
              "      <td>112.089996</td>\n",
              "      <td>111.613327</td>\n",
              "      <td>34732800</td>\n",
              "      <td>0</td>\n",
              "      <td>107.042398</td>\n",
              "      <td>108.168929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-04</th>\n",
              "      <td>111.940002</td>\n",
              "      <td>112.639999</td>\n",
              "      <td>108.209999</td>\n",
              "      <td>108.519997</td>\n",
              "      <td>108.058510</td>\n",
              "      <td>45197000</td>\n",
              "      <td>0</td>\n",
              "      <td>107.115707</td>\n",
              "      <td>108.056275</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-06</th>\n",
              "      <td>105.820000</td>\n",
              "      <td>109.239998</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>109.190002</td>\n",
              "      <td>108.725670</td>\n",
              "      <td>49107400</td>\n",
              "      <td>0</td>\n",
              "      <td>107.211964</td>\n",
              "      <td>107.961327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-07</th>\n",
              "      <td>108.379997</td>\n",
              "      <td>109.449997</td>\n",
              "      <td>104.300003</td>\n",
              "      <td>104.820000</td>\n",
              "      <td>104.374245</td>\n",
              "      <td>45044900</td>\n",
              "      <td>0</td>\n",
              "      <td>106.880460</td>\n",
              "      <td>107.788669</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-10</th>\n",
              "      <td>104.800003</td>\n",
              "      <td>107.980003</td>\n",
              "      <td>103.889999</td>\n",
              "      <td>107.589996</td>\n",
              "      <td>107.132462</td>\n",
              "      <td>40801500</td>\n",
              "      <td>0</td>\n",
              "      <td>106.697276</td>\n",
              "      <td>107.662650</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-11</th>\n",
              "      <td>109.800003</td>\n",
              "      <td>110.949997</td>\n",
              "      <td>107.440002</td>\n",
              "      <td>108.589996</td>\n",
              "      <td>108.128212</td>\n",
              "      <td>42381900</td>\n",
              "      <td>0</td>\n",
              "      <td>106.671950</td>\n",
              "      <td>107.557339</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-12</th>\n",
              "      <td>110.889999</td>\n",
              "      <td>111.269997</td>\n",
              "      <td>109.040001</td>\n",
              "      <td>109.080002</td>\n",
              "      <td>108.616135</td>\n",
              "      <td>36183000</td>\n",
              "      <td>0</td>\n",
              "      <td>106.804867</td>\n",
              "      <td>107.437198</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-13</th>\n",
              "      <td>109.580002</td>\n",
              "      <td>110.870003</td>\n",
              "      <td>108.629997</td>\n",
              "      <td>109.449997</td>\n",
              "      <td>108.984558</td>\n",
              "      <td>31333400</td>\n",
              "      <td>0</td>\n",
              "      <td>106.952736</td>\n",
              "      <td>107.333547</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-14</th>\n",
              "      <td>108.250000</td>\n",
              "      <td>109.260002</td>\n",
              "      <td>105.500000</td>\n",
              "      <td>106.029999</td>\n",
              "      <td>105.579102</td>\n",
              "      <td>47043100</td>\n",
              "      <td>0</td>\n",
              "      <td>107.005510</td>\n",
              "      <td>107.161390</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-17</th>\n",
              "      <td>105.410004</td>\n",
              "      <td>105.800003</td>\n",
              "      <td>101.709999</td>\n",
              "      <td>102.889999</td>\n",
              "      <td>102.452454</td>\n",
              "      <td>56957300</td>\n",
              "      <td>0</td>\n",
              "      <td>106.786944</td>\n",
              "      <td>106.973894</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-18</th>\n",
              "      <td>103.750000</td>\n",
              "      <td>104.510002</td>\n",
              "      <td>102.519997</td>\n",
              "      <td>103.970001</td>\n",
              "      <td>103.527863</td>\n",
              "      <td>49319200</td>\n",
              "      <td>0</td>\n",
              "      <td>106.571862</td>\n",
              "      <td>106.820994</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-19</th>\n",
              "      <td>103.650002</td>\n",
              "      <td>106.879997</td>\n",
              "      <td>101.349998</td>\n",
              "      <td>103.690002</td>\n",
              "      <td>103.249054</td>\n",
              "      <td>68198200</td>\n",
              "      <td>0</td>\n",
              "      <td>106.525560</td>\n",
              "      <td>106.687899</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>52.004964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-20</th>\n",
              "      <td>103.050003</td>\n",
              "      <td>104.309998</td>\n",
              "      <td>98.779999</td>\n",
              "      <td>101.510002</td>\n",
              "      <td>101.078323</td>\n",
              "      <td>70334200</td>\n",
              "      <td>1</td>\n",
              "      <td>106.515602</td>\n",
              "      <td>106.483430</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-2.170731</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-21</th>\n",
              "      <td>101.629997</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>97.459999</td>\n",
              "      <td>98.230003</td>\n",
              "      <td>97.812279</td>\n",
              "      <td>111242100</td>\n",
              "      <td>0</td>\n",
              "      <td>106.272640</td>\n",
              "      <td>106.334598</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-24</th>\n",
              "      <td>97.680000</td>\n",
              "      <td>97.970001</td>\n",
              "      <td>93.980003</td>\n",
              "      <td>94.129997</td>\n",
              "      <td>93.729706</td>\n",
              "      <td>43935200</td>\n",
              "      <td>0</td>\n",
              "      <td>105.827541</td>\n",
              "      <td>106.109073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-26</th>\n",
              "      <td>95.139999</td>\n",
              "      <td>100.690002</td>\n",
              "      <td>93.959999</td>\n",
              "      <td>100.559998</td>\n",
              "      <td>100.132362</td>\n",
              "      <td>51634800</td>\n",
              "      <td>0</td>\n",
              "      <td>105.533297</td>\n",
              "      <td>105.939025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27</th>\n",
              "      <td>99.300003</td>\n",
              "      <td>101.190002</td>\n",
              "      <td>96.400002</td>\n",
              "      <td>101.180000</td>\n",
              "      <td>100.749725</td>\n",
              "      <td>49498500</td>\n",
              "      <td>0</td>\n",
              "      <td>105.236565</td>\n",
              "      <td>105.820389</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>102.089996</td>\n",
              "      <td>102.410004</td>\n",
              "      <td>99.519997</td>\n",
              "      <td>100.389999</td>\n",
              "      <td>99.963089</td>\n",
              "      <td>38196300</td>\n",
              "      <td>0</td>\n",
              "      <td>104.702346</td>\n",
              "      <td>105.618600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>101.290001</td>\n",
              "      <td>102.400002</td>\n",
              "      <td>100.440002</td>\n",
              "      <td>101.570000</td>\n",
              "      <td>101.138069</td>\n",
              "      <td>33173800</td>\n",
              "      <td>0</td>\n",
              "      <td>104.273179</td>\n",
              "      <td>105.446061</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>49.834233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>754 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2016-01-04   54.320000   54.799999   53.389999   54.799999   51.065639   \n",
              "2016-01-05   54.930000   55.389999   54.540001   55.049999   51.298611   \n",
              "2016-01-06   54.320000   54.400002   53.639999   54.049999   50.366753   \n",
              "2016-01-07   52.700001   53.490002   52.070000   52.169998   48.614864   \n",
              "2016-01-08   52.369999   53.279999   52.150002   52.330002   48.763962   \n",
              "2016-01-11   52.509998   52.849998   51.459999   52.299999   48.736000   \n",
              "2016-01-12   52.759998   53.099998   52.060001   52.779999   49.183296   \n",
              "2016-01-13   53.799999   54.070000   51.299999   51.639999   48.120983   \n",
              "2016-01-14   52.000000   53.419998   51.570000   53.110001   49.490814   \n",
              "2016-01-15   51.310001   51.970001   50.340000   50.990002   47.515282   \n",
              "2016-01-19   51.480000   51.680000   50.060001   50.560001   47.114582   \n",
              "2016-01-20   49.980000   51.380001   49.099998   50.790001   47.328907   \n",
              "2016-01-21   51.000000   51.580002   50.299999   50.480000   47.040028   \n",
              "2016-01-22   51.410000   52.330002   51.259998   52.290001   48.726696   \n",
              "2016-01-25   51.939999   52.650002   51.650002   51.790001   48.260765   \n",
              "2016-01-26   51.790001   52.439999   51.549999   52.169998   48.614864   \n",
              "2016-01-27   52.009998   52.200001   51.020000   51.220001   47.729610   \n",
              "2016-01-28   51.860001   52.209999   51.250000   52.060001   48.512363   \n",
              "2016-01-29   54.730000   55.090000   54.000000   55.090000   51.335884   \n",
              "2016-02-01   54.880001   55.090000   54.500000   54.709999   50.981773   \n",
              "2016-02-02   54.169998   54.259998   52.650002   53.000000   49.388306   \n",
              "2016-02-03   53.250000   53.389999   51.259998   52.160000   48.605545   \n",
              "2016-02-04   52.099998   52.810001   51.369999   52.000000   48.456451   \n",
              "2016-02-05   51.939999   52.000000   49.560001   50.160000   46.741840   \n",
              "2016-02-08   49.549999   49.570000   48.189999   49.410000   46.042942   \n",
              "2016-02-09   49.020000   50.240002   48.669998   49.279999   45.921810   \n",
              "2016-02-10   49.889999   50.389999   49.520000   49.709999   46.322506   \n",
              "2016-02-11   48.680000   50.110001   48.509998   49.689999   46.303867   \n",
              "2016-02-12   50.250000   50.680000   49.750000   50.500000   47.058666   \n",
              "2016-02-16   50.900002   51.090000   50.130001   51.090000   47.950283   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2018-11-15  104.989998  107.800003  103.910004  107.279999  106.823784   \n",
              "2018-11-16  107.080002  108.879997  106.800003  108.290001  107.829491   \n",
              "2018-11-19  108.269997  108.559998  103.550003  104.620003  104.175102   \n",
              "2018-11-20  101.800003  102.970001   99.349998  101.709999  101.277473   \n",
              "2018-11-21  103.599998  104.430000  102.239998  103.110001  102.671516   \n",
              "2018-11-23  102.169998  103.809998  102.000000  103.070000  102.631691   \n",
              "2018-11-26  104.790001  106.629997  104.580002  106.470001  106.017235   \n",
              "2018-11-27  106.269997  107.330002  105.360001  107.139999  106.684380   \n",
              "2018-11-28  107.889999  111.330002  107.860001  111.120003  110.647461   \n",
              "2018-11-29  110.330002  111.120003  109.029999  110.190002  109.721413   \n",
              "2018-11-30  110.699997  110.970001  109.360001  110.889999  110.418434   \n",
              "2018-12-03  113.000000  113.419998  110.730003  112.089996  111.613327   \n",
              "2018-12-04  111.940002  112.639999  108.209999  108.519997  108.058510   \n",
              "2018-12-06  105.820000  109.239998  105.000000  109.190002  108.725670   \n",
              "2018-12-07  108.379997  109.449997  104.300003  104.820000  104.374245   \n",
              "2018-12-10  104.800003  107.980003  103.889999  107.589996  107.132462   \n",
              "2018-12-11  109.800003  110.949997  107.440002  108.589996  108.128212   \n",
              "2018-12-12  110.889999  111.269997  109.040001  109.080002  108.616135   \n",
              "2018-12-13  109.580002  110.870003  108.629997  109.449997  108.984558   \n",
              "2018-12-14  108.250000  109.260002  105.500000  106.029999  105.579102   \n",
              "2018-12-17  105.410004  105.800003  101.709999  102.889999  102.452454   \n",
              "2018-12-18  103.750000  104.510002  102.519997  103.970001  103.527863   \n",
              "2018-12-19  103.650002  106.879997  101.349998  103.690002  103.249054   \n",
              "2018-12-20  103.050003  104.309998   98.779999  101.510002  101.078323   \n",
              "2018-12-21  101.629997  103.000000   97.459999   98.230003   97.812279   \n",
              "2018-12-24   97.680000   97.970001   93.980003   94.129997   93.729706   \n",
              "2018-12-26   95.139999  100.690002   93.959999  100.559998  100.132362   \n",
              "2018-12-27   99.300003  101.190002   96.400002  101.180000  100.749725   \n",
              "2018-12-28  102.089996  102.410004   99.519997  100.389999   99.963089   \n",
              "2018-12-31  101.290001  102.400002  100.440002  101.570000  101.138069   \n",
              "\n",
              "               Volume  Signal    Short_MA     Long_MA  Positions  Daily P&L  \\\n",
              "Date                                                                          \n",
              "2016-01-04   53778000       0         NaN         NaN        NaN        NaN   \n",
              "2016-01-05   34079700       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-06   39518900       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-07   56564900       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-08   48754000       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-11   36663600       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-12   36095500       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-13   66883600       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-14   52381900       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-15   71820700       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-19   43564500       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-20   63273000       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-21   40191200       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-22   37555800       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-25   34707700       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-26   28699500       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-27   36775200       0         NaN         NaN        0.0  -0.000000   \n",
              "2016-01-28   62513800       0         NaN         NaN        0.0   0.000000   \n",
              "2016-01-29   83611700       0         NaN         NaN        0.0   0.000000   \n",
              "2016-02-01   44208500       0   48.940084         NaN        0.0  -0.000000   \n",
              "2016-02-02   56313800       0   48.856217         NaN        0.0  -0.000000   \n",
              "2016-02-03   57559800       0   48.721564         NaN        0.0  -0.000000   \n",
              "2016-02-04   46803400       0   48.626049         NaN        0.0  -0.000000   \n",
              "2016-02-05   62009000       0   48.532398         NaN        0.0  -0.000000   \n",
              "2016-02-08   59290500       0   48.396347         NaN        0.0  -0.000000   \n",
              "2016-02-09   45822200       0   48.255637         NaN        0.0  -0.000000   \n",
              "2016-02-10   38237000       0   48.112598         NaN        0.0   0.000000   \n",
              "2016-02-11   48878600       0   48.021742         NaN        0.0  -0.000000   \n",
              "2016-02-12   34243300       0   47.900134         NaN        0.0   0.000000   \n",
              "2016-02-16   37291200       0   47.921884         NaN        0.0   0.000000   \n",
              "...               ...     ...         ...         ...        ...        ...   \n",
              "2018-11-15   38505200       0  106.380492  109.118909        0.0   0.000000   \n",
              "2018-11-16   33502100       0  106.385342  109.129771        0.0   0.000000   \n",
              "2018-11-19   44773900       0  106.159386  109.044346        0.0  -0.000000   \n",
              "2018-11-20   64052500       0  105.864395  108.864086        0.0  -0.000000   \n",
              "2018-11-21   28130600       0  105.925640  108.702387        0.0   0.000000   \n",
              "2018-11-23   13823100       0  105.688445  108.516096        0.0  -0.000000   \n",
              "2018-11-26   32336200       0  105.686956  108.388395        0.0   0.000000   \n",
              "2018-11-27   29124500       0  105.872997  108.298427        0.0   0.000000   \n",
              "2018-11-28   46788500       0  106.263140  108.266502        0.0   0.000000   \n",
              "2018-11-29   28123200       0  106.454296  108.246000        0.0  -0.000000   \n",
              "2018-11-30   33665600       0  106.724424  108.202356        0.0   0.000000   \n",
              "2018-12-03   34732800       0  107.042398  108.168929        0.0   0.000000   \n",
              "2018-12-04   45197000       0  107.115707  108.056275        0.0  -0.000000   \n",
              "2018-12-06   49107400       0  107.211964  107.961327        0.0   0.000000   \n",
              "2018-12-07   45044900       0  106.880460  107.788669        0.0  -0.000000   \n",
              "2018-12-10   40801500       0  106.697276  107.662650        0.0   0.000000   \n",
              "2018-12-11   42381900       0  106.671950  107.557339        0.0   0.000000   \n",
              "2018-12-12   36183000       0  106.804867  107.437198        0.0   0.000000   \n",
              "2018-12-13   31333400       0  106.952736  107.333547        0.0   0.000000   \n",
              "2018-12-14   47043100       0  107.005510  107.161390        0.0  -0.000000   \n",
              "2018-12-17   56957300       0  106.786944  106.973894        0.0  -0.000000   \n",
              "2018-12-18   49319200       0  106.571862  106.820994        0.0   0.000000   \n",
              "2018-12-19   68198200       0  106.525560  106.687899        0.0  -0.000000   \n",
              "2018-12-20   70334200       1  106.515602  106.483430        1.0  -2.170731   \n",
              "2018-12-21  111242100       0  106.272640  106.334598       -1.0  -0.000000   \n",
              "2018-12-24   43935200       0  105.827541  106.109073        0.0  -0.000000   \n",
              "2018-12-26   51634800       0  105.533297  105.939025        0.0   0.000000   \n",
              "2018-12-27   49498500       0  105.236565  105.820389        0.0   0.000000   \n",
              "2018-12-28   38196300       0  104.702346  105.618600        0.0  -0.000000   \n",
              "2018-12-31   33173800       0  104.273179  105.446061        0.0   0.000000   \n",
              "\n",
              "            Total P&L  \n",
              "Date                   \n",
              "2016-01-04        NaN  \n",
              "2016-01-05   0.000000  \n",
              "2016-01-06   0.000000  \n",
              "2016-01-07   0.000000  \n",
              "2016-01-08   0.000000  \n",
              "2016-01-11   0.000000  \n",
              "2016-01-12   0.000000  \n",
              "2016-01-13   0.000000  \n",
              "2016-01-14   0.000000  \n",
              "2016-01-15   0.000000  \n",
              "2016-01-19   0.000000  \n",
              "2016-01-20   0.000000  \n",
              "2016-01-21   0.000000  \n",
              "2016-01-22   0.000000  \n",
              "2016-01-25   0.000000  \n",
              "2016-01-26   0.000000  \n",
              "2016-01-27   0.000000  \n",
              "2016-01-28   0.000000  \n",
              "2016-01-29   0.000000  \n",
              "2016-02-01   0.000000  \n",
              "2016-02-02   0.000000  \n",
              "2016-02-03   0.000000  \n",
              "2016-02-04   0.000000  \n",
              "2016-02-05   0.000000  \n",
              "2016-02-08   0.000000  \n",
              "2016-02-09   0.000000  \n",
              "2016-02-10   0.000000  \n",
              "2016-02-11   0.000000  \n",
              "2016-02-12   0.000000  \n",
              "2016-02-16   0.000000  \n",
              "...               ...  \n",
              "2018-11-15  52.004964  \n",
              "2018-11-16  52.004964  \n",
              "2018-11-19  52.004964  \n",
              "2018-11-20  52.004964  \n",
              "2018-11-21  52.004964  \n",
              "2018-11-23  52.004964  \n",
              "2018-11-26  52.004964  \n",
              "2018-11-27  52.004964  \n",
              "2018-11-28  52.004964  \n",
              "2018-11-29  52.004964  \n",
              "2018-11-30  52.004964  \n",
              "2018-12-03  52.004964  \n",
              "2018-12-04  52.004964  \n",
              "2018-12-06  52.004964  \n",
              "2018-12-07  52.004964  \n",
              "2018-12-10  52.004964  \n",
              "2018-12-11  52.004964  \n",
              "2018-12-12  52.004964  \n",
              "2018-12-13  52.004964  \n",
              "2018-12-14  52.004964  \n",
              "2018-12-17  52.004964  \n",
              "2018-12-18  52.004964  \n",
              "2018-12-19  52.004964  \n",
              "2018-12-20  49.834233  \n",
              "2018-12-21  49.834233  \n",
              "2018-12-24  49.834233  \n",
              "2018-12-26  49.834233  \n",
              "2018-12-27  49.834233  \n",
              "2018-12-28  49.834233  \n",
              "2018-12-31  49.834233  \n",
              "\n[754 rows x 12 columns]"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Portfolio dataframe with Holding, Cash, Total, Returns\n",
        "initial_capital = 10000 # Starting Cash\n",
        "positions = pd.DataFrame(index=df.index).fillna(0.0)\n",
        "positions = 100 * df['Positions']\n",
        "portfolio = pd.DataFrame(index=df.index)\n",
        "portfolio['Holdings'] = positions*df['Adj Close']       \n",
        "portfolio['Cash'] = initial_capital - portfolio['Holdings'].cumsum()\n",
        "portfolio['Total'] = portfolio['Cash'] + positions.cumsum() * df['Adj Close']\n",
        "portfolio['Returns'] = portfolio['Total'].pct_change"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot Chart Signals"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot backtesting for Long and Short\n",
        "fig = plt.figure(figsize=(14,10))\n",
        "ax1 = plt.subplot(2, 1, 1)\n",
        "df[['Short_MA', 'Long_MA']].plot(ax=ax1, lw=2.)\n",
        "ax1.plot(df['Adj Close'])\n",
        "ax1.plot(df.ix[df['Positions'] == 1.0].index, df.Short_MA[df['Positions'] == 1.0],'^', markersize=10, color='g', label='Long')\n",
        "ax1.plot(df.ix[df['Positions'] == -1.0].index,  df.Short_MA[df['Positions'] == -1.0],'v', markersize=10, color='r', label='Short')\n",
        "ax1.set_title('Stock '+ symbol +' Closing Price')\n",
        "ax1.set_ylabel('Price')\n",
        "ax1.grid()\n",
        "ax1.legend()\n",
        "\n",
        "# Plot backtesting Portfolio Values\n",
        "ax2 = plt.subplot(2, 1, 2)\n",
        "ax2.plot(portfolio['Total'])\n",
        "ax2.plot(portfolio['Returns'].ix[df['Positions'] == 1.0].index, portfolio['Total'][df['Positions']  == 1.0],'^', markersize=10, color='g', label='Long')\n",
        "ax2.plot(portfolio['Returns'].ix[df['Positions'] == -1.0].index, portfolio['Total'][df['Positions'] == -1.0],'v', markersize=10, color='r', label='Short')\n",
        "ax2.set_ylabel('Portfolio Value')\n",
        "ax2.set_xlabel('Date')\n",
        "ax2.legend()\n",
        "ax2.grid()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "version": "3.5.5",
      "pygments_lexer": "ipython3"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}