Algo_trading window norm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Algo trading.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShreshthSaxena/Algorithmic-Trading/blob/master/Algo_trading%20window%20norm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "OWCFgNpI-4Co",
        "colab_type": "code",
        "outputId": "768048ac-07b4-4ed3-dbf2-05b4b99c3e37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install quandl"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting quandl\n",
            "  Downloading https://files.pythonhosted.org/packages/8c/55/53084c085ba50df5f19ca30d1d38b2867317e9b52d2aacbf5f5edb601a5c/Quandl-3.4.6-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from quandl) (1.11.0)\n",
            "Collecting inflection>=0.3.1 (from quandl)\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/35/a6eb45b4e2356fe688b21570864d4aa0d0a880ce387defe9c589112077f8/inflection-0.3.1.tar.gz\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.6/dist-packages (from quandl) (6.0.0)\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (0.22.0)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.14.6)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from quandl) (2.5.3)\n",
            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.18.4)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2018.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2018.11.29)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2.6)\n",
            "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (1.22)\n",
            "Building wheels for collected packages: inflection\n",
            "  Building wheel for inflection (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/9f/5a/d3/6fc3bf6516d2a3eb7e18f9f28b472110b59325f3f258fe9211\n",
            "Successfully built inflection\n",
            "Installing collected packages: inflection, quandl\n",
            "Successfully installed inflection-0.3.1 quandl-3.4.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VHzMw452lfTP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import quandl \n",
        "aapl = quandl.get(\"WIKI/AAPL\", start_date=\"1960-10-01\", end_date=\"2019-02-01\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bhVlSFKB_FaF",
        "colab_type": "code",
        "outputId": "d13a2f36-cc86-42e2-a6d6-0a329c905745",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "cell_type": "code",
      "source": [
        "aapl['Close'].plot(grid=True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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dvHlMyJs2bcKnn34KANBqtbh69SruvPNObN68GQCwZcsWjBo1CsnJyTh06BCKi4tRVlaG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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "s7CrWVpfFzdh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.models import load_model\n",
        "import keras\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import math, time\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yujXd2iwGCm5",
        "colab_type": "code",
        "outputId": "706b6529-a982-442d-b72c-858e9076898d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance\n",
        "\n",
        "def get(tickers, startdate, enddate):\n",
        "  def data(ticker):\n",
        "    return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))\n",
        "  datas = map (data, tickers)\n",
        "  return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))\n",
        "\n",
        "tickers = ['AAPL', 'MSFT', 'IBM', 'GOOG']\n",
        "all_data = get(tickers, datetime.datetime(2012, 10, 1), datetime.datetime(2018, 1, 1))\n",
        "\n",
        "all_data.head()\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticker</th>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">AAPL</th>\n",
              "      <th>2012-10-01</th>\n",
              "      <td>96.678574</td>\n",
              "      <td>93.785713</td>\n",
              "      <td>95.879997</td>\n",
              "      <td>94.198570</td>\n",
              "      <td>135898700.0</td>\n",
              "      <td>64.749588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>95.192856</td>\n",
              "      <td>92.949997</td>\n",
              "      <td>94.544289</td>\n",
              "      <td>94.472855</td>\n",
              "      <td>156998100.0</td>\n",
              "      <td>64.938141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>95.980003</td>\n",
              "      <td>94.661430</td>\n",
              "      <td>94.980003</td>\n",
              "      <td>95.921425</td>\n",
              "      <td>106070300.0</td>\n",
              "      <td>65.933838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>96.321426</td>\n",
              "      <td>95.078575</td>\n",
              "      <td>95.892860</td>\n",
              "      <td>95.257141</td>\n",
              "      <td>92681400.0</td>\n",
              "      <td>65.477219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>95.142860</td>\n",
              "      <td>93.040001</td>\n",
              "      <td>95.028572</td>\n",
              "      <td>93.227142</td>\n",
              "      <td>148501500.0</td>\n",
              "      <td>64.081856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        High        Low       Open      Close       Volume  \\\n",
              "Ticker Date                                                                  \n",
              "AAPL   2012-10-01  96.678574  93.785713  95.879997  94.198570  135898700.0   \n",
              "       2012-10-02  95.192856  92.949997  94.544289  94.472855  156998100.0   \n",
              "       2012-10-03  95.980003  94.661430  94.980003  95.921425  106070300.0   \n",
              "       2012-10-04  96.321426  95.078575  95.892860  95.257141   92681400.0   \n",
              "       2012-10-05  95.142860  93.040001  95.028572  93.227142  148501500.0   \n",
              "\n",
              "                   Adj Close  \n",
              "Ticker Date                   \n",
              "AAPL   2012-10-01  64.749588  \n",
              "       2012-10-02  64.938141  \n",
              "       2012-10-03  65.933838  \n",
              "       2012-10-04  65.477219  \n",
              "       2012-10-05  64.081856  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "TSWarefBD8bg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "aapl = pdr.get_data_yahoo('AAPL', \n",
        "                          start=datetime.datetime(2000, 10, 1), \n",
        "                          end=datetime.datetime(2012, 1, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FB5fuVrOZXqI",
        "colab_type": "code",
        "outputId": "9201164e-f352-417b-a6f6-5a47b46250eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "all_data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5284.000000</td>\n",
              "      <td>5284.000000</td>\n",
              "      <td>5284.000000</td>\n",
              "      <td>5284.000000</td>\n",
              "      <td>5.284000e+03</td>\n",
              "      <td>5284.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>242.318107</td>\n",
              "      <td>238.656345</td>\n",
              "      <td>240.552463</td>\n",
              "      <td>240.544330</td>\n",
              "      <td>2.571984e+07</td>\n",
              "      <td>231.375313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>253.487655</td>\n",
              "      <td>249.752604</td>\n",
              "      <td>251.711193</td>\n",
              "      <td>251.715076</td>\n",
              "      <td>3.389500e+07</td>\n",
              "      <td>255.406230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>26.629999</td>\n",
              "      <td>26.260000</td>\n",
              "      <td>26.379999</td>\n",
              "      <td>26.370001</td>\n",
              "      <td>7.900000e+03</td>\n",
              "      <td>22.696848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>69.557499</td>\n",
              "      <td>68.567497</td>\n",
              "      <td>69.019999</td>\n",
              "      <td>69.074644</td>\n",
              "      <td>3.148150e+06</td>\n",
              "      <td>60.366683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>146.135002</td>\n",
              "      <td>144.220001</td>\n",
              "      <td>145.105003</td>\n",
              "      <td>145.209999</td>\n",
              "      <td>1.289585e+07</td>\n",
              "      <td>132.298378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>243.025017</td>\n",
              "      <td>239.711269</td>\n",
              "      <td>241.761955</td>\n",
              "      <td>242.224743</td>\n",
              "      <td>3.636635e+07</td>\n",
              "      <td>210.309174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1078.489990</td>\n",
              "      <td>1063.550049</td>\n",
              "      <td>1075.199951</td>\n",
              "      <td>1077.140015</td>\n",
              "      <td>3.652131e+08</td>\n",
              "      <td>1077.140015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              High          Low         Open        Close        Volume  \\\n",
              "count  5284.000000  5284.000000  5284.000000  5284.000000  5.284000e+03   \n",
              "mean    242.318107   238.656345   240.552463   240.544330  2.571984e+07   \n",
              "std     253.487655   249.752604   251.711193   251.715076  3.389500e+07   \n",
              "min      26.629999    26.260000    26.379999    26.370001  7.900000e+03   \n",
              "25%      69.557499    68.567497    69.019999    69.074644  3.148150e+06   \n",
              "50%     146.135002   144.220001   145.105003   145.209999  1.289585e+07   \n",
              "75%     243.025017   239.711269   241.761955   242.224743  3.636635e+07   \n",
              "max    1078.489990  1063.550049  1075.199951  1077.140015  3.652131e+08   \n",
              "\n",
              "         Adj Close  \n",
              "count  5284.000000  \n",
              "mean    231.375313  \n",
              "std     255.406230  \n",
              "min      22.696848  \n",
              "25%      60.366683  \n",
              "50%     132.298378  \n",
              "75%     210.309174  \n",
              "max    1077.140015  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "lihGwjNjGMun",
        "colab_type": "code",
        "outputId": "8f963c81-e6a5-41df-e46c-c84596f707e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "cell_type": "code",
      "source": [
        "# Import matplotlib\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "# Isolate the `Adj Close` values and transform the DataFrame\n",
        "daily_close_px = all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')\n",
        "\n",
        "# Calculate the daily percentage change for `daily_close_px`\n",
        "daily_pct_change = daily_close_px.pct_change()\n",
        "\n",
        "# Plot the distributions\n",
        "daily_pct_change.hist(bins=50, sharex=True, figsize=(12,8))\n",
        "\n",
        "# Show the resulting plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T5XA4JElJSUmqr69XWhqFxswuLbgAAADDSb/TJCwWi2w2W6/xqqoqrVixQhs2bNAnn3wi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\nPwpFhe0gVN0cAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zKhOeYkvGfRN",
        "colab_type": "code",
        "outputId": "ce06c870-4a4c-42d9-f398-57aaec1595b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot a scatter matrix with the `daily_pct_change` data \n",
        "pd.plotting.scatter_matrix(daily_pct_change[1:], diagonal='kde', alpha=0.1,figsize=(10,10))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZrN+YMNRlkshpe7eRWeoGx2SUeCuN5GjREn3gcGl5cNRuMmEiBmQhOGocIgTq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2QpG7amFVVj6eURReFlmHSj2wWkpKzB2sfJaz+MxmdMYwsqdaRKUlWGurHY1jbwuRsDjHW8dHdC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jPKOyJc8UTRt6S0PAb3j7zigMpHhIkhBYhoSoAO9QKg2LDyVDu0Z33rTadpP5MurI7sNebSzn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\nhBBCCNF0/x8FHQz5CqN0jAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BHlkBC8aQw2F",
        "colab_type": "code",
        "outputId": "836808b9-33d9-4e54-e2ab-3763fbdb7180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "cell_type": "code",
      "source": [
        "# Import matplotlib\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "# Define the minumum of periods to consider \n",
        "min_periods = 75 \n",
        "\n",
        "# Calculate the volatility\n",
        "vol = daily_pct_change.rolling(min_periods).std() * np.sqrt(min_periods) \n",
        "\n",
        "# Plot the volatility\n",
        "vol.plot(figsize=(10, 8))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ES6yGFP0kwyVE6kn5gMt/2ovVqfT0sYUAvPiffWbAdf2FY7FZVc4pK2FQQVbQ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\nxXBsuW+fBY9Xx2Lp+2kzks+ITbGjKd6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Y/Nb/az+aV8eeZmH2F8aZ1w8fXcCpZwzmqpum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\n0sfXHA5dy2FvjUgA75Xkc8OFI537tSdikcN18jE6pZjHFj7A+DRNQf7jC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PcAACAASURB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ejzLU0krHnt3hlfTvPtTcTNPKd6hd9i/UYBBfeRmyCvZQJw1tErXV3lMinAiiSlEgGFI4i0bi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\ncImIiIikmRouERERkTRTwyUiIiKSZr8BXV8i0O2qSGkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KgLMi77Y2Z89",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "MODEL"
      ]
    },
    {
      "metadata": {
        "id": "vStEZJ8s2XcM",
        "colab_type": "code",
        "outputId": "338baf96-0ca9-41ce-845a-321150143be4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LwHRyi-l4iCb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "b31d3de8-58bd-494c-a772-65efef2d94f0"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "#train_data, validation_data, test_data, train_labels, validation_labels, test_labels, test_images_filenames = import_data()\n",
        "\n",
        "model = load_model('/content/drive/My Drive/lstm_norm.h5')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "up81uGjlEhS7",
        "colab_type": "code",
        "outputId": "79fd2d15-c956-489b-c6c5-91e220d2c664",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "cell_type": "code",
      "source": [
        "df= aapl.drop(['Close'],1)\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2830, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2000-10-02</th>\n",
              "      <td>1.910714</td>\n",
              "      <td>1.678571</td>\n",
              "      <td>1.906250</td>\n",
              "      <td>606197200.0</td>\n",
              "      <td>1.154998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-03</th>\n",
              "      <td>1.785714</td>\n",
              "      <td>1.584821</td>\n",
              "      <td>1.781250</td>\n",
              "      <td>509530000.0</td>\n",
              "      <td>1.062717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-04</th>\n",
              "      <td>1.696429</td>\n",
              "      <td>1.562500</td>\n",
              "      <td>1.598214</td>\n",
              "      <td>366506000.0</td>\n",
              "      <td>1.125230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-05</th>\n",
              "      <td>1.750000</td>\n",
              "      <td>1.571429</td>\n",
              "      <td>1.678571</td>\n",
              "      <td>218251600.0</td>\n",
              "      <td>1.050810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-06</th>\n",
              "      <td>1.638393</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.620536</td>\n",
              "      <td>153164200.0</td>\n",
              "      <td>1.056764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                High       Low      Open       Volume  Adj Close\n",
              "Date                                                            \n",
              "2000-10-02  1.910714  1.678571  1.906250  606197200.0   1.154998\n",
              "2000-10-03  1.785714  1.584821  1.781250  509530000.0   1.062717\n",
              "2000-10-04  1.696429  1.562500  1.598214  366506000.0   1.125230\n",
              "2000-10-05  1.750000  1.571429  1.678571  218251600.0   1.050810\n",
              "2000-10-06  1.638393  1.500000  1.620536  153164200.0   1.056764"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "metadata": {
        "id": "E7FWH0YOxl03",
        "colab_type": "code",
        "outputId": "bf36962e-d788-4b4f-fd01-34789021ebe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (18,9))\n",
        "plt.plot(range(df.shape[0]),(df['Low']+df['High'])/2.0)\n",
        "plt.xticks(range(0,df.shape[0],500),df.index[::500],rotation=45)\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Mid Price',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAGCi+cNreyRJHT3hrPNm1taMdGhQU+XRaMiqspB00qzqUXlfYLjy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\nIiIiIm3m/wOTHiv52p+9tAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-m3vg5kXsqmU",
        "colab_type": "code",
        "outputId": "b73f90b6-5c1e-46c9-fed2-e1e3e0847a5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "def normalize_data(df):\n",
        "  smoothing_window_size = 500\n",
        "  for di in range(0,2830,smoothing_window_size):\n",
        "      if di +  smoothing_window_size > df.shape[0]:\n",
        "        dil  = df.shape[0]\n",
        "      else:\n",
        "        dil = di+smoothing_window_size\n",
        "      df['Open'][di:dil] = np.squeeze(scaler.fit_transform(df.Open.values.reshape(-1,1)[di:dil]))\n",
        "      df['High'][di:dil] = np.squeeze(scaler.fit_transform(df.High.values.reshape(-1,1)[di:dil]))\n",
        "      df['Low'][di:dil] = np.squeeze(scaler.fit_transform(df.Low.values.reshape(-1,1)[di:dil]))\n",
        "      df['Volume'][di:dil] = np.squeeze(scaler.fit_transform(df.Volume.values.reshape(-1,1)[di:dil]))\n",
        "      df['Adj Close'][di:dil] = np.squeeze(scaler.fit_transform(df['Adj Close'].values.reshape(-1,1)[di:dil]))\n",
        "  return df\n",
        "    \n",
        "dfw = normalize_data(df)\n",
        "dfw.tail()\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2011-12-23</th>\n",
              "      <td>0.854122</td>\n",
              "      <td>0.890344</td>\n",
              "      <td>0.858471</td>\n",
              "      <td>0.064929</td>\n",
              "      <td>0.878158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>0.888840</td>\n",
              "      <td>0.913803</td>\n",
              "      <td>0.880339</td>\n",
              "      <td>0.062420</td>\n",
              "      <td>0.898777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>0.883538</td>\n",
              "      <td>0.902638</td>\n",
              "      <td>0.904643</td>\n",
              "      <td>0.041292</td>\n",
              "      <td>0.873712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>0.867125</td>\n",
              "      <td>0.897122</td>\n",
              "      <td>0.882262</td>\n",
              "      <td>0.033933</td>\n",
              "      <td>0.889691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>0.871102</td>\n",
              "      <td>0.916927</td>\n",
              "      <td>0.882968</td>\n",
              "      <td>0.012862</td>\n",
              "      <td>0.888918</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                High       Low      Open    Volume  Adj Close\n",
              "Date                                                         \n",
              "2011-12-23  0.854122  0.890344  0.858471  0.064929   0.878158\n",
              "2011-12-27  0.888840  0.913803  0.880339  0.062420   0.898777\n",
              "2011-12-28  0.883538  0.902638  0.904643  0.041292   0.873712\n",
              "2011-12-29  0.867125  0.897122  0.882262  0.033933   0.889691\n",
              "2011-12-30  0.871102  0.916927  0.882968  0.012862   0.888918"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "metadata": {
        "id": "ngtteIXM8ezn",
        "colab_type": "code",
        "outputId": "77da7ca2-e19b-4d28-9d13-0bc7a71244dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "def normalize_data(df):\n",
        "    min_max_scaler = preprocessing.MinMaxScaler()\n",
        "    df['Open'] = min_max_scaler.fit_transform(df.Open.values.reshape(-1,1))\n",
        "    df['High'] = min_max_scaler.fit_transform(df.High.values.reshape(-1,1))\n",
        "    df['Low'] = min_max_scaler.fit_transform(df.Low.values.reshape(-1,1))\n",
        "    df['Volume'] = min_max_scaler.fit_transform(df.Volume.values.reshape(-1,1))\n",
        "    df['Adj Close'] = min_max_scaler.fit_transform(df['Adj Close'].values.reshape(-1,1))\n",
        "    return df\n",
        "dfn = normalize_data(df)\n",
        "dfn.head()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2000-10-02</th>\n",
              "      <td>0.016139</td>\n",
              "      <td>0.013158</td>\n",
              "      <td>0.016492</td>\n",
              "      <td>0.715571</td>\n",
              "      <td>0.013388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-03</th>\n",
              "      <td>0.014056</td>\n",
              "      <td>0.011556</td>\n",
              "      <td>0.014385</td>\n",
              "      <td>0.599581</td>\n",
              "      <td>0.011057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-04</th>\n",
              "      <td>0.012568</td>\n",
              "      <td>0.011175</td>\n",
              "      <td>0.011300</td>\n",
              "      <td>0.427967</td>\n",
              "      <td>0.012636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-05</th>\n",
              "      <td>0.013461</td>\n",
              "      <td>0.011327</td>\n",
              "      <td>0.012655</td>\n",
              "      <td>0.250078</td>\n",
              "      <td>0.010756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-10-06</th>\n",
              "      <td>0.011601</td>\n",
              "      <td>0.010107</td>\n",
              "      <td>0.011676</td>\n",
              "      <td>0.171980</td>\n",
              "      <td>0.010907</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                High       Low      Open    Volume  Adj Close\n",
              "Date                                                         \n",
              "2000-10-02  0.016139  0.013158  0.016492  0.715571   0.013388\n",
              "2000-10-03  0.014056  0.011556  0.014385  0.599581   0.011057\n",
              "2000-10-04  0.012568  0.011175  0.011300  0.427967   0.012636\n",
              "2000-10-05  0.013461  0.011327  0.012655  0.250078   0.010756\n",
              "2000-10-06  0.011601  0.010107  0.011676  0.171980   0.010907"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "metadata": {
        "id": "U8CAkFYPFjj4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "INITIAL VALUES NOT CONTRIBUTING IN NON-WINDOW NORMALIZATION"
      ]
    },
    {
      "metadata": {
        "id": "wy7PToeEDmyw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "e7e7a1de-be4e-4eb7-b0cf-14778edab280"
      },
      "cell_type": "code",
      "source": [
        "com1 = pd.DataFrame(index=df.index)\n",
        "#com1['orig']=df['Adj Close']\n",
        "com1['window']=dfw['Adj Close']\n",
        "com1['no window']=dfn['Adj Close']\n",
        "plt.figure(figsize = (18,9))\n",
        "com1.plot()\n",
        "plt.show"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xv/jFr6TX5DnFUojk60GDBuOf/1ysOt9VV12Lq65Sez3rXSdVKOiEpSQhAEJhFiGwWPzx\nfiz87mkpt83pJCNDSwp9lrcjESKEEL5g6Nn4b8My7QN5GnppmTKpCbvm6Czz2vJ6vPyxcX5o2o2X\nsoyeNO8HZwJS3rgOSQBAQasiXSo944Y3CSSjCWc7tzSpCYeNUk9yFPSEsIdyhfApw/trj+DjDcaV\nkihXE7YMbw70ZSLm6FMBjuct04Rd5CQyvMbUlQHIvnc06YS1/Ogq3eN4noaX1jYGu5qwiwzXHG0d\nmot0h03WpMA5VTVhcvHR2RtKeU9Y48suSGx8eT3CPEVm0MoGpDnaEI5GkadQ+xzhzIlGVwg7ANoV\nwpaRC5qj3BydvXZkE06xL66pCJvsm2BYPWmH3FrEABLThL1eQZwwWY4X0wtHUsHTOHpS21z90z9v\ntLBFxrhC2AG4N8k6ckAGg1TTcmFRkQz7jnTK/k+lPvQ7qw6r3uvLAd8BK0hkfHlpYaZ6d3VDUt+3\nCobV14S5AJHHmqfx8Yp+9TF9mameJOLO7zZFFgtKvN/a5RYqT4VcEFqkvDlVk1T837+3SK/H1JXp\nxAmbu9daca3pTujiFIy60O+Viw+v1x7ixEgTZjtrYv/wFMB5wUdi3tx3TL8FoV1nAgD6g5kZA/bo\nNQfBcTpOIBYjy4pEzLpPvWFtTuJTjVww38pClNzNS4wfVp7SfSUdiVzkGHWrcptMFh+cRYJsSPN9\nPuIHHyL3gIX2U76YsGUjNMALTlm9/a4QtiV3/HElfqFI02YVEYZDw8ke8DwvM6+RQj/b2WicjijA\nJg6vwMyx1VluTXKQa8C1O5tyQrtPBY7nUwqLOdLUa2FrcgujseX12FN8BJigZu7nSMMEgDX2eg6F\nY7+3tEi//rCV2LMXbUwgxOBEm3ofIRlI4dobiOD593fjoefXY/vBNmza1xI7zl2oW055sR+jBpdm\nuxlJoZwWsx2XmW04jtcsymF2beKanvUx6sM8nz3FR4AJIhyUC+F5w84F2z4YbFeNzrcECvkK6bUv\nQ+Z1N2NWFiFd+X/8x5XS60MnenQ1YTdvR2qIfUk5uCOV2gnH8UDmwhptR0hRxUfE7NLkVA3zigfD\ncthS36r7ud8nH3QXzhma9fz2HM8hxIYAphTIi1kNpfrAjLF2yxDzbqaiUuy5lDlFYHVU3P98fgh5\n/tgAd/esrEOcbx0sg1XayamuCev+epPCNRXP6lxmzY6Thp97aLn4GD6wWHVMptc3oWiGLD4iF7Zk\nVaTQ/hkI7Z8h/c80xfLxi1NyRUlexhbqrhDWoa66KP5BKWI0eb6+/ID02hXC1vH8+3sAQFUD1Uko\nNeFTXQjr/X6zvXKq958efUHjpBcdPXL/lAEV2okvMolYxpBn5PmraaKEJ9cxEFxHrHoX01oX+yz6\nbM2bMySdzZS3LWNXchhapoj1Fnv/mc0swxDHhSKuQE6F3Q1C8YbjrX3Se06bgo+29Mn+Z0/BRdqA\nytiET2qy3/3aBFz2pREAzN/XKSMrLWxZ7lAWxzFJ6bg00kIfi/5IAAEmcSdU6TusfKeVpmjZmJER\nyZNeimPJk0FTmSuEddBaHW812B+x6hpakEWyXa3YGpxsgvzgiwbZ/6eiJlddli+9Jn9/dVm+lD7R\nrBQuKtCu+nOqx+T74zhe3Xn1NHzj7JHS/zRFYcFXxsqOSXZk/u/KhbhXowZwPCRzNCcXwh7KIwv1\nJBFN19X5ldK8kMl8/a4Q1kFrYrN6o76jRzueTUnETaFnOU4WW5MVmhtzCgph0iTPKp1polqM2Rjq\ncER7a+K+p9ek0ELnw8Sx1NVWFOKyuSNl7+X7rfUQ3NdxACxnfuuoNxINN1OEInkoWn+xyntw95R7\ncP/pd0nm6ExWrnOFsA6chtOU1Tdm8SfGJQxFIoxz9y/tSn+c/S47I47D8mJhBf/Sf82No0Sxc/yx\nLH82MblSFBUrTmey+fGEzamKWavbYz88C7+5VcgyZcUc2ReJhYD+cfMzeH3/26a/+5dtzwsvOLkQ\npija0Au+xFeCAm9+zBztasLZR2vVZPWN6TGZkSXimqAtx8mxoeJkIsYxbj/YZv01OB7fe2wZnnl7\np+XnthpSCNMUJXm+mw5RcgPxNRGFcFmx8d5wdVmB5JRFKyVKEgu5B1b9Wvb/+qbNCZ+DVwphGG/b\niM0Uj8lk0RxXCOugaY62WBM2q2m45mjrGFYrhFHcevnkLLckcf769k48/u/N+HCdEIuZzn3tYJgB\nzwNf7GpK2zVSgXx2yKo9LMfBF83ktP2AucWJ0eT8+1e24PNtJwAAfcEI1u9pPmXiikULwXkz6uIc\nGaOzV7sqUULXVZQiTMZBi6Ll1kO/x2/4vIifiQpPphJ1AK4Q1kXrhlEW95bZZ9kVwtZRUSJ4Qk4b\nXZXlliTO2l1N2Hm4Q9Li27rN+RQkg5OcvZRRBgX5glPOxxuPmhKYHMdDb3m941A7/rF0NwAhvO3p\nt3ZgnU0XJlbT2NwDAChWOK49fPPpePQHZ2l+R4w+sBrSRN0W6NCsGSxTarwxS9d5Q+ZiWvWkOJpw\nVAhH51pfBlNyukJYB61n12pztNkVtSuErUPs8Uw6XlhBd3/qGkYi2H2fVG9PeGBlIQrzYkLDjCbP\n8rwp8+PWekGzPtJ8auSa3lrfBq+HxqQRFbL3K0vzUVteoPkdZS8mM4oq8spV7/1h018QZiNo6G7E\nL9f8Bm/Wv6s6huFj2i/bOlh6fc24K+ClvYaasDieRBN8JitCuUJYBy1TsfXmaHPHuXvCifPkG9vw\nx9e2qt6Ppa3MdItSY6+ifi6QXm3+nx/sSdu5rYB8dNjoPZ05thplxXkoyo+Fp5xo60M8OE6+wJZC\nnHRw2NBJCobl0NUXBsNy8Ci0QiNlJGyBwkBmtxI53ncSd6/4Gb44uREAsOLoaoRZuV+HmKhjWP5Y\n8MFiBLeegy/5rpE+N1J6xM9cxywbobVosnqzftY4eTJx0VSqZO3OU8P8ZSWb97diq8aeoBgrSKak\nc8IW35LPDqreS2eVl20m91OzhazedvSelhcLz09BXkwIK1MrasFxvCwuVD9dofPzjpuFdFxUTnse\ng0XKaRNqk7oez/P47OhqNPe3oi+iv3AKMTGL0N0rfoYAE5RM1d1hwXyeB8FJjA8VotIXa48ZTTgb\naW1dIayD1qrJak24rkaeGtNs3LCLeZQPnvgvTTtrMp05Rl12US/5gBWUFmonsLALMk2Ylcd2kkLC\njEbDcrwsQ5LesBCnhBVbjiXWWAfSFXWw0ir3aTQPnj+zDnddMw1fnjpIeMPkEK3vPIhX9r2FX639\nP0NHrAgn137/tOVZ3LfyQXSGutAZ6gYA+BHLjCXefz5OuUtxvuf5zC+0HC2E+4MRvL+2IS0xn5rm\naIs1YSdoYE6EXEApYx2z8ZBZgderbi/5O6322K0qE/b8JgxT78/ZAuLnioVQtBwnjbQ2ES66J/zj\naAYobxztuS/I4FhLbu8LiwrBmLoy1WdGzw5NU5g2uhp5vsSSdvQzQnayeAlWREErcqhbyB73+bEv\n0B39zMfH9qtFU3q8x4PngXW7m/DmykMAMisYTV3rkUcewfz587FgwQJs27ZN9tlLL72E+fPn47rr\nrsOiRYvS0kg9ttS34rXlB7DtoLXpJAHtm2b5NkGSE6edkyjYAVL7VQth4a+zRLB2TWlyZW91uBKn\nYba3E+RkLcV2arTVrDmapinMGFONy+aOlIU8ya5JdHG84gZORyzOUFGal5H0cixvbi+5M6T2jQAA\nL+1BV9Qc7eViQlhUnOJ5+3Mcj7/8JxYTbytNeN26dWhoaMArr7yCRYsWyQRtb28vnn32Wbz00ktY\nvHgxDhw4gC1btqS1wSSiGcpsIYREyIQmnOy8uUfDScclhiyGVDE2eF4IR7GrcNGjqaNf9R45fqwU\nwsda+9DQ1GPZ+dKCTBOWC2HyzsZzsgKAnv6wrP/05hOyvKiTQriSQSwSUlmSD1I8/vV/z0voPGZ7\naVnj56aOawsKIVBfGXaO7H2/xy+lrPQSmvDbnwuard7zMT3q3NjVJ48+sFWyjjVr1mDevHkAgNGj\nR6Orqwu9vcKP9fl88Pl86O/vB8MwCAQCKCtTmy/SRhr7iWF57FHEvNll4g7p5Lp1EWANNGEO9rmP\nibBxb4vqPXJP2EqhsGKz/fc8yV8bS7qvPi7enjDP8+gLMjJHJL2+HF0XqxKU68UdlkXHQHlJHvIJ\n07LXbPxsgo/Ywa7Dsv9L/SWGx5f45LWLA5GAlGOaIsSaKFy1tmt++u1ZGDpAOE8wLLds2Moxq7W1\nFRUVsTixyspKtLQIE0JeXh5uu+02zJs3D+effz6mT5+OkSNH6p0qbaTLOvvYYnm6NKtvTLL7eJl0\nn3ciSnM0+T/P8Y4LT9KDHD9WblEMH2g8AdoBXkMTlp4L4gbH02gSWbyQe8XPLbV3CJdVlBb6UFrk\nx+3fnIpHbjkzbdeZVi3PYOehjPeUS/wKIcwGwYiFHni1WNMryCNaT/pDSiGcuUnCG/8QOeTD3tvb\ni2eeeQYffPABiouLceONN2LPnj2YMGGC7vcrKgrh9VpTaaO0RDA7lJTkoaYmPROHeN7lGxvRHWBU\n76dCUZF2SFI8KisK0/Z77USyv3Hz3ljd50X/2oRQhMUbj14KAPB4adA0hZqaEqn/y8oKHNmfdbUl\n2Lxf8IcoryhCUYFPU1PZfagdyzY24rqvjkdFSb7qcyXVVTHnF7/fY8u+6eyNRRKIE2xpiXAfA4Q5\nubLc+FkJhsw90/4CPzyKBA527JdEMNP+oXWCAvbVBH9rQTTLVoXJuSoMuUf0zXPm4+l1L+DS8fPw\nyo53ZJ9RoFBXLffa5r2stPVQkC+fV6urixFpVYc9VVUWofikYNWlFL4DldFc2Jm4x3GFcG1tLVpb\nY45Pzc3NqKkR4lsPHDiAoUOHorJSKK02Z84c7Nixw1AId2jsbSVLb69w47p7gmhpSX0Pa+SgUhw6\n0Y2KkjzJO7ClpQfNnQE8/vIm2bGpXq+mpgS9PYnnRAWAvt6QJb/XztTUlCT9G3/511gJup5opinx\nXOEwC4oS/u/rE+5xV1fAkf158WlD8e4qYc/rV39fi32NnfjzT85Bvl/+WN/31EoAQFmBFxedPizu\neTs7Y6bWcJi1Xd8cb+2ThfOJpQhDwTBaWnrQTswxfX3Gz0p/MGaGNjru7eX1CATl4TF265dEiPd8\nVZTkweuhkv6Nwah5v729D8Vx6hIDQHt/l+z/Yb4ReOzsh3C057jqWB48wgqZ2tHbA29Uew70ybXa\nhc+slharJF2dAfQHhPmhuV1+wq7odoOV91hPoMftnblz5+LDDz8EAOzcuRO1tbUoLhZMAXV1dThw\n4ACCQUGY7NixAyNGjLCoyQlgoTna66FVSRCCofR4Qia7jZeo+78L0N4tjFGed+aesNYWRGG+F2dO\nHgAA2NcoOOudaBMEEMtxaGqXL3hDJrMZLfnsQCpNTTuHTsjDVMR9f9ETmuypeLH9ohY9W5E4R0l7\nT9CSbFBOgWE5U57lVsByLJr7Y0KyIq9cyprF8Npzr7JtncEu9EaTfHgg14S1BDAg7FqIc0Fzh3yP\nP5NpbeNqwrNmzcLkyZOxYMECUBSFhQsXYsmSJSgpKcGFF16Im2++Gd/5znfg8Xgwc+ZMzJkzJxPt\nlmHllrBW3zstz7CLmnv/vBrfv2wSeJ7XCDWzv6er30cjEIo55D1x97kA1MK5tSuIkYNK8ey7u7F2\nVxN++u1Z0mdm941bOpOz0GQKpS+F6AGvVfkm3i+W9pPjeFF/vOEohg0oNjwmV4gwLHr7IxhUVRT/\nYAvoichjrkeXj5BeszrhYgMK5Yumhh6hspiP9oFiY0pKYZ5Xtd8rQlOU5FygzBCXySnf1J7wvffe\nK/ufNDcvWLAACxYssLZVJrG+o6JelqoLpX5mjudVwjxZZ5pTpZSa1WzY0wyOF/aUAGfFCotz0aCq\nQtx5zXSMGVKOlpYelRBuiZqS10YLFxw4FtMac6UQiN7wr4kWFSAfs3jPmGjKJgX4z78zB0+8ukUV\nCxyOcCjI8yIQYjSTWOQKTe0B8AAGVxXGPVaXBB4uskISIHfKYnntSJACbwF+d85DeHXff7DuZGyr\nMMJFZI6YX5k9BO+sPqzdRAOnPVvFCZ9qCDGkivcsuCE/+v0K/EFRUMAVpelBz3u8rTsIHs70juZ4\nHiMHlWDR98+UVbBRmuWUXqBVKYtAAAAgAElEQVSkJpFMaJsdE8PoLUIHVKgr+8RrvmhdICsvjRpc\niu9eMlF1bJhhpeIQmYwjzTSBaLhOUUFmUpcqhbCXKOAwpFiohlRbUI1vT7xWdlyBtwA3TlIrgGaV\nFFprso9iqxAlOyNqNFZNFLHTyO+AFc9bOMJJJo93Vh/Ght1Ncdv946umab5vw3nRVugVNjjS1Csk\n63egFOY4tSUFUAsD5ZgiC9szOaIJhyPavyM/mkxjQEVMg4uXBlE0VRbkyf0stIZIOMLB7/OApqic\ntkZ9vOEoAGuKeJjpJrUmHDPQFvoK8NT5v8XCs+6TTNB5HuPCJeQ61Oj+G20zZnIL0tFCWJSVZKC9\nFedMZ/8Hwwze/OwgHvr72rgDVK9UnR21EzuhrH8qMrS2GDzvTC1Gz6FMqfUrMwPtOxrzOs2VLE96\nt88XDX2kaQq3XT0dQHwhIOadL8xXaH0a3wszLPxeGjSd3uIZ2Wb9HiHErzGFusmUSXv08d6T+PuO\nfwEAZtVOQ74nH+cNnSs/V3TcjyobjhsmXoufn3GP7POx5aOk136PX54XwOA2UZS+1dzVhE0i1lh9\nY4W6zFsy6N0vK2UeuYqXignoHaxXzcW65uQmOh00pKZYSFvpMBnM8zw4npf2L0mUDkVGOY2NhHCE\n4aRwHbIUoB0pKRQ0oXOmD5K9T+7rSvc4rjlaWxPepciWB0Q1YS+d85pwJvnLtuel15OrJuDxc3+l\ncroiOXPQHFTmyxfZC8Z/U3o9pHiwIomN/rVpitKdC1xN2CTpSB1HKdZw7d1BSzXPMBObSMXT6nlm\n6lY1dScAQ/QmSIblBE3YYVL4g3VHAABHNDQTpSb8yUbBlOjX8BQ2EsL3P7MGt/9BiCcebnMvYDEk\nqa5G3k4yT7SoPcUzR0eiz6My7E/pxDYo6qTk89LweGhEmNx8Bg+f7I5/kEXs66hHW7Bd+r8yP7mK\nXbWFscQdQ4oHy6wURnOl0baUqwmbJE3O0bITH2/tS1kT5nVWZuJLvXg8vUHiymBj9PonwnDgHKgJ\nr93ZpPuZ3tjJ19BmWVZ/T1hMfsHzPCIMZ+vUqOJiwqfIDkY+L5IiHOdZ0avApAx3GjlIyBtN0RQK\n8zyqXMO5wq+e3yC9vvLLyacgjveM8TyPP27+q/T/D6d9F+MqxiR1LZpIGv7lujPMa8JE2kr1OW0U\nJ3zKQcn3M8SJOxVkyeY18v2SE96154/Bq8vqjc/nSmFD9O7XlnohaL+iJLl0odnCKEmE3v52gd+D\nbkVWITN7wizHoz/EwOulwYbtWShk0z4hd71RMQFxDo33qCz5TNjKUi46yovlzj8cIawL8nxS8pdc\nZtZ44wQmibC1ZSciXATP7XwZAHD3rB9Kn100/HxMqVZ7oyeDl/bKHLNEK5IWFKU/hmydO9pWWNxR\nkumKOC3L8alrnqT2q/GanEjjmc9c4hNP1pApDx2BwQA80abOiQvoaMImhPBLH+2Tsm7ZkRNtfZLX\nrlZyDhGz5uhgdKGhXMzMmz1U5mvCiJWaABTmeXAsxGjG/ucSfgsy84n9/9ft/5S9/8Smp6XXV4z+\nWsrXEfFQtOmynjRFwaszhlxzdBahIDdzsxxvgSasvUchvk+uwo0e6qpSQYPLYcdMS0jUUmB3w4JR\n85ratf0iCvzqCdSMEF6xRZ6r1259Y+R4RmJWExZRasJ5iv6TyiVSFMIMBx5Aj6IGba5RWmhNnHB/\nRH9Rd4Mi9jdVGI41PV8LmrBenLDrmJUdoveupz8W8sRZoAmT3ydXacdbo4OTuN9Da4vx9bOGy9IN\niowYWBo9n81mRpthusC9U5SYOGEWWv9reTgb7Qk7BfLn1lUbpVVM7OZqZU/6f3eejV/+j5CGt/6o\nEIlB0xQOnxSS+utlYnIy1WWxKlvKQiDJ0h3WL4KQ77Fma+ie2bfh6yMvxIDCGvOaME3pWlPEpCyZ\nwNHm6HTMoRQFnCQS3zNc6nvCJOSpxL2t3n55nPNV547W/O6owaXYuK8lZ9IPpotcW6MYjT/lM1Ab\nTVSRr6EJ74mG9ClxkpMRQywkBlXrp1WkJU3Y3GDwaGg+xQU+hKLm6u7oM0oeZlYrdxJWx5LzvPH4\n9dDWFKMZVTYco8qGS9c0AwVK5dwnYoUp3izO1oQtlsJa907QhFM0R5OasMa5yIFvdCVxYCSTfvBU\nItdiOEVHstMm1MY9VtR29Vb4fUF1YpvufguT3aQZhnhWDBNCiHvCJoeCnoObVgrbr8waAgCYObZa\n4xvORlzkLPyf01I6D9lvLK+vNAwtqUvpOlqYTltJGzv3ZYrst8B2UKo94dTndHMu8/EQYxlDNvVa\ntQs5JoOl/cpbL5+s+kzpOd3aFcRL/92ney6G1egcgw6zW1eSJnWjbTuTuTok9EKylBM6TQGDawQz\neK6NM0AQwkNrizF8oHXF7HkdIez3+FGeZ30hDCNzNOlzQxk4ZmWS7LcgBcymRjOL+FCRWgSbAU1Y\nfrD+R6KJ0dWEjcm1PXOGE2KbtbS1y740QvXeJ5uOqgSEuH+q1TcRLcFsU8hFhJHzDJ2gZ5aeJlxd\nJi8KwfExgW3a98BBBEOs5dqhnib8EyJMyUqM5tiSopizGU1RKNPJM59JHC2ErYDjeRxt7pVuHAUg\nQqy2WZZP+WEjvx1fBusf4JqjzSHeS6MQFifBspzu3tWowaXSa1KbU44zcWLVGn9OKuzAmHUui3aF\n2UfXbKjRhj3N0rG5kotbpOFkD3hY89yQChIP7XsmVkiyGqP5urwo5ghGUUIqWyXnzkhPu/TIjVkq\nBT744gh++Y91+Hh9I0RxqdRcU56iDDRhvSINWuT5hNsV0qki4yIgdvH4ocmlwbMbEYaHx4R2Qjpj\nKRdz4sSqNUGRqVTtjp4QPn2ifL880QiTRI6XNOEcs7i09wgJSIYPsM4UDcjLaZKkKwzIcG1EXFK0\nfswYE9vbf/C7p+HGiyekpV16OFoIW3EPt0azKG2NJgBQnpNludTN0TpxwoBGjl/FpR6++XTptRi7\nqJXI30WA5Tjsjibft3shArOwHAefTjwjCemtqxS2Yjyk1ljWysj1na+OR/QLiTQ17WjuaQOYf8FY\n2f9Ssg6D9pNC1KwmPG5oOcQsiblmjha7o4oIU7ICls/sfGW0OCLzoot33GyehnThaCFsBeLDuruh\nQ9MQbIVjllaWLKIBht+tIQq4i45ZQdcxS5c1O2J5lvX2tsiVL2A/5yMlDMuZ0oTl31Es9qJjR2uC\niigsK7+66XScN7POlmHUerHOKi/m6F+jZ5cl95dN5sqeMKxcyteda+ZoqaqbhTee54F+xvpCO4bX\nNLgvAytjseXi3C9aAIT30tcuPXJDVUgBrWdv3NBy7GsUYipZLvU9YRLluZTXV16JFCSiEHY1YX16\n+mNZjHxe7Sfq1isEL2OrHfvSBcPyupl99ISM0mxbFK2Xq3V8RHGsWYGUDfQ0YWWLY2krjc4V+91m\nfzJNxZL+O10TDoYZ9AcZVJYKmq/4cyzRBsl46mjGrO9MnI/ZA6bjeN9JeKj0xeHqjZE8v0fTC/7Q\niVgykWzcUkdrwlZMFbJ9iegN+PFV03Dl2UIFEaszZinNY8p9EeW1SK/NPNc7Oi5kukE97VFZts7u\nMCynq9XrOfIptTSxXKamJqzYE7ZzBSUmur8omctFdATHlv0tuuci+0jPO1oJRcX6x8ma8PYDrfjR\n7z/DvX9eLS0mxLnJ6tsvCuFifzG8tBfDSoagrnhQnG8lj94etM9Dx73P2VhYOVsIW7BiU94TiqJQ\nmO+VHKasCFEi1+PK4ZHIT8hzvaPj0k3k89XzKHYa/UFGP2yEGJrTCSc/Usu7+Ixh+HzbCQCQih+Q\nKOcsWr0utQ3iJFmt2LdUPkZHmoS6uHpZwgC5aVsrY5YWFEVJE7lTHbOOtfTigT+vkv4XhRaZH9sq\nePBoDQhjrsRnlGbUOvQWRz4vHXeBmY17mhuzVCoQA47sfjIMIdXFkSxESXGyREyi4kTspJCSTPP2\nqsPSaw9hwhXvZyZzwlrBR+sbwXI8Gpt7NT8ntf2hhFcraZIjF22tXeoSfKqEFOJEZUOF2KygUO5z\na56L+NlmNWGaphwfJ6yskiWOFa2qbskibgZs69yMrS07kO/JT6v2S8KwnObQLSvyu0LYjujdEnFy\nsz5tpfwz9ZgwvhZF2U87sSteouC9+HBddPqwbDUnKRZ/st/wc7I2Mhnfebw1VuJw9rganDdTSA84\nbIA6LlLtp2BD6RtFEhSqTWD5v2biicnfLe6Zx4OsvOPUHO7K1KWi5kjmSrACuqQdH59cin4mgCJf\noWV5ouPBcrxmJqwfXDFZtjDXwjVHZwPS9EZIy9i+j7UFHJTnoihKFs8a71I0RdktasRWXDhnqPRa\nWY4O0C5sYGfGDRHS+k0ZWal7zKLvn4FbLp8kK0TfGxAm2t/ceiYmjajE3iNC2NZzS/eoxqCuJmxD\nJEGhWCgoWzxuWEXcc7HRc00dVaU5VrSgKUoKfesPObOAwz8/2Cv7X1yw6PVt0nhj/eP3WFMW0QwM\ny6u2oi45czhqKwrjju3BhpW50sMpL4SV5mBx/JHOF1aGBB063q1sgCzrUTwoKvfSMlqJuNK99fLJ\nKC5QP/hOE8ITRwjC92tn6Gvwg6qKcOakgZomElFgkOU5WzvlISMqTZjMvJVwi9OLaDpVTqZKwXHW\nVMH0OahKv9KSuDVELl7iQVGUNK76NYph2B0tpyVRCMfM0RZciAIoT6x/Aox6GyRdsCyn0oTFfAyj\nBhnPtWYtIlbiaCFsiSe9zjlIIfz8+3tSugYpNLv6QooPDf9VQVFUVtzonYK4Xz6oqlCdCAVAgUU1\nUjOF5LFqQjvVGhbit8i9cOUa7uWP5SZvUYuwYwjX0rUNAOI/+xRFoazYL4sFVsKZ7NtbLptEnBco\njPalE0sZdvaEVe+J5mjRvG5V7mjKGxPCQzK0HwwIudaVmrA4XsRSn3bCWTNSmiEdFqx0viDP8NnW\nE7LPVCtTjcvNv2CM9KC4mrAxYqk7j4eGz6vWeksKM7/STYVUTYTi94oKfECHoAHHC61xQqaxQCi+\ndcrvpQ1TcorPdrw98LLi2L47TVHI8wnxplplIe1OW7daIxWtCxv3NgOw8P4TQjjCZW7BwrK8Ov+1\njf0c7P+0pRm9WyOujo1W0qYxOIUZefpVwpmIcveEDRE1YZ+Hkj2IP/vObGze14pxWvmkbdyfMUck\nE5qwxsAQv0ZqBkYORT++elpiDcwSythmre7x+zzoN9BWzSankKc1jJXAYxgbDxwdejRqR3d0B1FX\nXSSFc5FZ+pKFAiUzR4fZzC1YWI5TCWEbuzk42xxNhmc0d/TjF3//Qsp0ZRY9DcPK1HRGZ1B+ZlRF\nCRAGk6sJ6yMmc/B6aJk5evTgMlx93ui0JY1PFxxhAYmHuH9MIn7t3JmxyjBKITx3ykAAwK+/d4Yq\npaddFyjK51KrewRNWH/BIfVtnFmQ7Hvxsl6ackyyjkCIkbLsac0dv391KwBgdNQ3ZUBF6kIYgEwT\nzlTqSjG5kpMqqDmnpRp8/czhAICBlYV4/4sjONbah6ff2mHJuS3NimNUNJ2PJ3blUHD3hI1giH0t\nn8/RwxuA+X1LAKgtL8Cjt54pe09cdJw5aSDOmS7syylNtGKaSmX4hp3XK2oLlbqxPq8HEUa/AEt7\n1DSbSEiWmOCDpilHJOvgeR63PfEZHnxuPYDYeBo/XO09HopwKMjzWrZQJfeEu8PdBkdaB8tp72uT\n93jh/5yGX9w4JyPtMYOjZ6nSaEFmioo53AQSDBvQdcwS0/xxHGaPr0m+kYijCatVYUOEOGH7P/yZ\noLUroEpiIe5veT10TmTMEseH2Xkxz8DxbEDUKUWpHYpewmazRtmBWePkz6SmOTqqDWmZ34+19OLJ\nJdsBJBaSFSGEsBM0YTFU7WR7P5o7A7GMYxom52CYQUGeNdEDFAVQ+bFY9dMGzLLkvPEQn3+lJkwu\nLIYPLMFIwkv6+5dOQjZx/iwFwaFKFJqJPhh6qz6a0IQHVqbmUWe0YFY6fpnxjnbAAjwj3Pf0Giz8\nxzqZpiOGW3g9lFQ5yMmYdR4SURZ6IL8mTkxvrTwoO4ZNQNvONn4fjeEDSkzF9UoZ5jQSd9Qf65Je\nx+tb0ktcrOXtoSndik52YNO+Fvzy2S9wpCm2SP3Xh3slTXjqKHUd80CIsSx6gOc50HmCpeEXZ9yL\nq8Zeasl54yHO/4kswK0u3ZgojhbC8iooSQphnfdpigJFpT9JeyjBgurunrAaMi2jJIS9tK3NqWZY\nv6cZH288CsD8HpeX1tcAxEUJOTEDxqkg7WR1iTAcwhFO0+PZyClN6xeQ2nHcxQfxsegQRlP2Nkc/\ntWQ7jrb0YdnmY9J7gRAjLeD9Pg++PFUeNhQMs6bj6F/b9x/8bNUicLz2QoRF7B4NLKqFl86MD7C4\nMErEMSvb84SjhTD5EKzZeTK5kxjcAE/U5JR6FSX9EygTgcS7lhsnrCZE5AlmWB4eWig3V1WajzFD\nyrDgK2MNvm1fSP8Gsyt71b4u8VrvHOFo/9m9utQn0QWJMvcxYFyCLl75xngyWNz2AmKmfI9DzNFi\npjRA2EeXLCs0JeuDD744ApbjTVuPlh9dhc5QF1YfX6f5OR8VwsMKRyTZ8uSIbUepKvPofsequOhk\ncbQQriX2NbQS05vByAnBQ9OCEE6jNhBUxTuayB1t4xV4NogQmnD9sS5pcvR6aDzw7dm46LShel91\nDPkmYzeVCerJ4a2XN1f0o7B7NrETbX26n2k9xkZm5kQ0YXKeEQtD0DRl6wIOormeTChCpuClaUrW\nB68uqweg1iAbe47hj5v/ivZgB7Ro03mfiwrhfI9FntYmER2zlGVMjbTdbHtSO1oIW+HFZ3QGmqYE\nL8yUNWH9z4JhJqHzC3vC9n34s4GonYhOKLmIVgpOLdTPROx/vRV/Z28IpYU+2+8Ja8W43nfdTFxz\n/mjtdIPRn6NdQ1m9lWWEGF8uLlQ8NhfCylKPABBheVlstJbDmrInPmxYhn0d9fjF6t9I78kyAIa0\nvZ5ZCPfKS5lPCWoFkiasGMtG9zjbviOOFsLpxhMNQ0j1UTM6Q6J5qYU94RQblGOIe4TJ7tHZad9T\ni6rSvPgHaTB6cKlslU+a6JraBZMuz/No7wmholTHOcVGXbOlvhUAUEn0x4ThFfjaGcM1j5d+rcZv\nIAWomcXHLZdNwrzZQ3D5l0cCAI629KEvyJiq1pQNtEr2hSOsJHj9PlqV8ARQp+L0UmoLDENkv/ri\n5EbN64cg+B2UeEs0P08XohXMk0ACbK30tpnklBfCRotg0QMyZc3T4OuhCCsTAmb2hF1NWI64p5mo\nHpdthwyzJFtA5PRJA2T/kya6p6LhOcGwMDGXFak1Frv1z+VzRwAAFlxgbo9ftApoPi3EbzOjCVeW\n5uP6C8epLBItnZlJQpEoWnm/e/rDkhNjnt8r2xMWOdku328n+2Zfh2CyjnDxLU4B9AAASn0aGerS\niLi4Um69GK2zsr0nfMqnrTTC47HIMcvgs4kaQfNGCB7bqbUn1xBX94lmS3MKpPd3Iii1IdJEJ/pQ\niAu6eMXO7YDYRrO5jSXvaC3PaUJIpfLTbRuLrvGbGJZHd69QwCHP59E0RyvHmoeKmWr3tNejwFuA\nV/a+Jb1XnlcGjufwl23PY2jxYHx91EWgKRpBXtSEzVeII3l71SH4vR5cbFA9TAuWcDyTYbDQErcY\nspVX3qYjKD2s2n4C3/vtMrQRTlxVemY4CKvAdKetvPXyyQmdy9WE1Ygp+f70pjXZ0uwGk2T+cqWG\nR5Z3EydbcXg7IZ1nLI92ct8jIc3RVApS2K5PovIXVZQIJvzOXqGKW75fWwgrh4GHjgnhDxs+xd93\nvIhD3Q3EdSh0h3uws20PPmj4FE9sehoA0Id2AECZLzElQ+StlYckZ7FEkDRhAwdFJV4Pjcd+cBYe\nvfWshK9nBaaE8COPPIL58+djwYIF2LZtm+yzEydO4LrrrsPVV1+NX/7yl2lppFU8+95ucDyPtbti\n4UxGq2qPh0Z3X1jTISQhDIRmQZ5X9rGeB6uIm6xDgLyHRvmBcwHRDJsoygVkviIRQ1N7v7Sg05uk\n7DTUEq0oZXQc2TeJpK0UEVOA2tU5i/ztRfleKTnHxn0tAATvaW0hLO8LXhEH3Bpok/3fE+5Bc3+r\n9P/BrgbwPI8wHwDPU0lrwskieUcn4JgFCBnEslU9LK4QXrduHRoaGvDKK69g0aJFWLRokezzRx99\nFDfddBNef/11eDweHD9+PG2NtQpy8Bk9QmIsYNIxyCauoWTaaHUmGxI3WYfAX9/eJb1WTibDBhRn\nujlpoTDPi/Jiv+QMZBYx3rdJsb83WFHg/qd/XUukxdSapOylHSeSRxswNkeTJUSTMcXTFhZ4SQfk\n7cz3e1Qx4KVFebjkLLVDm7Ir9JJxiDA8iz9ufkb2XluwA11oAkVlvm90zdE2Jq4QXrNmDebNmwcA\nGD16NLq6utDbK9j7OY7Dxo0bccEFFwAAFi5ciMGDB+ueK9toFmUwEGiW7ZOZHIt5fk9crz43WYca\npZdnth0trILleJQV5yWsqRUVCCt6pUOXZkYsUbA5YM5KpKwjEFtCaD3in26KZZJKZsIW82zbVggT\nr/0+Dw6flIcSFRf4cN6MOtX3vqfIo8zGEcJa7GjbnfB3rCIZc3S2iTtbtba2oqIiZtevrKxES4tg\n0mhvb0dRURF+85vf4LrrrsPjjz+evpZaiPhQhsKsoXy042rKTdahRqkJm42ptTsMy6kz/5gglsI1\n/gT6p2hWLifsCSdS1lFA9I6OnwAnUWIFXuz5LJL3M8/nkfaERbTmtu9eMgEzx8oLY8TThLWIRGsH\nM20DE/6ukkdf2oSGkz2mjz8SLeiiVGa0vMXtQsJGcFIA8DyPpqYmfOc730FdXR1uueUWLF++HOed\nd57u9ysqCuH1pjc4uqZGOzZNHJeFhX6UlBXgpp++Z/h9oyxFetfQImwwAGpqSjCoVjjXoKqiuOc9\n2dYvaEjlhVkPMk83en2hNLP6832oqSmB3+dBOMLi1m9OM3V/iouFiamstCCh+5kJeJ4Hy/HIz/Pp\ntk3v/VF15WjtOonBtSWqY7weWhbbWn9UKGRQkK++DkUJzlx26Zu8aEKOysr4zwkAFBSIxxejxqAI\nS3lZ4ve/uEgYO6VJfDcT+Ii5objIj3NnD8W63c2yY5TtHj2sUvWez2/eqjR1wARsb9qD5nATAIDr\nrEFZeWHC/RMmPLT3NXbimXd24m8PXGjqu68vPwAAKFU43JaU5Cd1nzJxb+MK4draWrS2xjbem5ub\nUVMjrJYqKiowePBgDBsmuJGfddZZ2L9/v6EQ7uhQ531NBQpqa29Li/bKSVw/9PeHcaihXfec4veN\nqqToXUOLNoN0ey0tPThrQg3aO0bgnOmD455XNH8tXXkAcxUJ2HOJmpoS3b74wytbZP8fPtaFlpYe\nVJbkoT/EoMBDmbo/vVFP0a7uQEL3MxOIgpLnOM22GfXPt+aNwdCaQnxlpno8XTZ3BN787KDqO+Ew\no3m+SET7+tmgt0+4X91dAbS0GE9dNTUlCIUEjay1rRc0qx/mFQxEEv6N4rnb2vrQUmC/SM9wOJZQ\ngwbQH+07EuVvHlSWp3qvPxg2fc2egDC3r26MJvDgaXR19ifct2KdZ5FAUHtsGhHol7e7pyeY8DmM\nnrFk0BPocZc5c+fOxYcffggA2LlzJ2pra1FcLDi+eL1eDB06FIcPH5Y+HzkyMSeSTBKrqsKbMr9Z\nZcDYsLfF8HO/z4NvnDMqoZJaJYWZTQdnBxiWw4Y9zejokU8on20VnAFZjktuH9+GFkWxaH0imX9E\nSgr9+PpZI1Te0ABw/kz1PiBg7z0zETLbkxkkE2Sc7ZuK4sQzksX8S+zpmU9mj8vzeVS5lLXQmhON\nzNEFXnle6MPdR2T/81xylrr31jTI/k8mK5nS3G7n8R13CTdr1ixMnjwZCxYsAEVRWLhwIZYsWYKS\nkhJceOGFeOCBB3D//feD53mMGzdOctKyO2b2e626b1qaR7JMG12FbQfa4oYy5SJL1zTgrc8P6X7O\ncXxCQtjOPShO7snsCRuhl6LPCXvCYmY0v8ntLKNShiTDBybuTU9rOXnaCLJZeT4P8pPYuuoN92Fb\n607dz0v8RQgwsYxhtQXVaA7ErKbg6KTCKcnyi0BycfL2H80xTNlR7r33Xtn/EyZMkF4PHz4cixcv\ntrZViaBlj7bs3Pa7lWPqyrDtQNsp6Zy1fm+z5vtDaooAAAzHI8+klmR3xInHjAaTCHoVY/TXLtkZ\nZ6EIqwqrEXOE+8xqwgaKcF1NEY619OGX/zMHviR8VMTFnh0dsxiWkzkz5fk8cWNgJ4+sVL239uQG\nw++wnNzE/62J10jJOgAAnDVjNxlN2K45vbVw/IyVmNdb7FinCjGjiSXXOalRRxaIheIkqgnbGakc\no8W/R0/j1Xo/Wz25dG0Dfvj4CtQf65K9L9U9Ni00o97RGg8Lzwte9CMGJpdMws6a8I9+v0L2v99P\nIz8v1mcP33y69Prpe87FL26cgzuvnqY6T2W+cbarq8ZehsFFMQ/oMeUjMaxkiPQ/z1vjOJpMHyu1\n52QSsmQK+3kUZAgzFXfseNvEweTURUQ6CIQYvPppPXr6I5qFCJzIxxsaAQhJ9zOBnczR76w6DADY\nsKcZY+rKpPcjCWrCeusXjuNxvLUvpVA2j02TdbAcpxJAeT4PCgj/gLqaYtlnIwdpL0RCjNqZi2R6\nzRRMr5mClcfWSpWVzhnyJby4+1XhAI7OmruFUhPW0vTtgvM14STmjvfXHjGV8MJG85KEOFna7NnP\nCHr3oy/I4IN1glNIaY4I4fe/EH7PzsPaRdNT4cI5Q1Xv2Wms65l6QxEOXg9tXqvReVY+jI6VVOpP\n29UcLVoLSFq7gijIS9suQcAAACAASURBVFwrPdh1GAAwq1atJZOcXXcmzh/6ZQDAke5G6X2eyV68\nPimE//yTc2w9LzheCCdLMprkbd+YAiC7pg2jVHy5TqVBsQ2R6rKCuMec6lw3byxmj5cnZaB17D7Z\nGGaSqTd68dbOAN5f24BQhE2o9qv0ixQ/YvvBNtWxSbfRbkJYIx+0l6akLHJmq7axHIvVJ9YDAC4a\nLne2/b+zH8Tj5zys+b2mfiISJGI+2sNqSGuAVpSAnThlhXAyK9ihtcUYM6Qs/oFpRKqRaq9nPyOc\nPS1+XPTxVv2YbKdgZqskVZTmZ7ukjuZ5XtJQxTj9R1/ehNeWH8Dx1j5dxzItzHpHJ4N9NeGYsxQF\nYPb4Gnz7ovGgKAr/uP8C3LtghqnzkMK0UBGKlO/NR75XO6zr6yMvAgCUU9FnNYmxPLi6SPWeWP3J\nCPK5cZJviOOFcCJKKXmsmbGh2ieTioSn9uCdNyP5/NqnsiZsJma2yeJkMNng4PFYnt907XEPrZFP\ndHbZE+4hTMSiabW9OzYBJ5JKloL2gtWKR0ec5BmbxQmLToqzxtXgL/eeh9u+MVXWZ2bvs5j/+frx\nV0G5jKEp/edwdPkI/PG8R3CG78oEWx5Dy3T+rw/3AhD8Ap5+awd2N6i3acgF0UWnq7dc7IrjhXCy\nmDEj9Sn2jChYoxykUmCAPoU14Qixz6NnlkzEXGlXyDGWrkLjF58xHAMqYhpOEjlB0gJLmBH7Q4zq\n84S2gnQWrM2dAY2DE4O2oSbM8TwW/mMdAKC6LD8hq4GS/xx4HwAwqnwE/J7YQvD+0+6K+10vnZr5\nV6vEotjPa3Y2Yf2eZvzf4s2qY8Q5fcqoShTl+/CTa6fj5q9PTKktmcDexnJTJBcoHGb009iJiMnA\nySsBSV1ORiqJNsQ5KBMmS7tBJj1JpRC7Erv1ZL4//TnBfV4aM8fWSA5tehpSpvuGzEAV0BDCCSVj\n0TlUWV0qGexojr77yc+l18qCDckyoLAGNEXj+1NuwKDigRhQWBP/SwTJ9I5Wco7dDR3oDUTQ3acf\nLSBVUIre+CmjjMvC2gWbrH8zA/lMankRxv0+ZU0tjlSqM53Ke8IkehpRQsVBbGKCVZKpeZ30GNXq\nimxUniEtVFpCOJHFl5452grBqVkWNYvwPI+e/pjlbkCFfsGKeHA8h3xPPgYWDZBMzzNqpyYmgFMY\nOoyGJhxmOPz4jyslfwEti5doKXNaKVNntVaDZOdRZQ3aRK6V6mOXinf1qbwnTOKhKdx1jTp0Ylht\n4ikI7UamtKuZ46ql13ZJZkCao7U0VqOiKkrIXPGyc1iwj2s372jldJCKta2h+yiCbBAjS4el2Krk\niBjcY3GLUMsLPBJJLLe4XXBWazVIdqglowkLF0zsilrCkqIo/OLmM3DL5ZM0vmGMOFmeiuZoEpoC\npo2uVr1PChYncrS5F/uOdmbkWqS2ZBMZLFuAaD07rV1B1Xt66GWXSyYXsRK7maOtXAz8K5psY2PT\nljhHxieZVmntCWuh3NsXtxidVuLV8UI4IYiJJhkhlowHqdZV8v0enD5pIM6clHjRa7EFp7gMlsyS\n188bK72X7/ck1ad24pf/WIfFH++X/teremQ1uqbnDI8zUpikemk9c7QVCw4xp7eR1pZJrFoM7Grb\ni6Z+IUd7mEs+mUkqXWzUp6SGf/9f1iBEhGSJilUqDmnZwFmt1SLJu52sJpno5bQejgtmJT+xUq4m\nDCBmESgiUg/ecNH4bDXHEpRm0vNn1uH8WUN0jrYWu8QJkyE/WkN8SI06hlQXHXP0eTOE5++7l0xQ\nfsM0xfnCuCP3YbOJcuwke+v+tPVZ6fWC8d9IoUWJwXE8/v3Jfhw60a25J6wH6TcglbpMoiBHNnG+\nEE4AMs7UoEymLvI4Y3NCUOuwVDK4iD9hVxrSGToJsaYwuZd51hRna8H/fH+v7H8zHvxWYRcTXrwJ\nOJEcwHrRDOJ+7vAB2kXWzSCWmLTLYlhpjh5YlbxjlsiMmqkpn8OsOWNXQzv+u74RD/9zg6FpXbkX\nfPhED5Z8dgA8zyMkmaOdJdac1VoNzHpw8jwvWzUlZY5G4qYsqx9SURP+YleTped1KnbZk7OCz7ef\nkP3flsD+Z6rYJb76jRWxMDStZ2fPEfP75XqRBKLG5DSzpRHiczB7fA0ev21u0ulbS/zFmq8TJsGJ\n0qyPjvK4//fGNry7ugEHjnXHHLNcTTjDmLzXh070yP5PxpGB3BM2+22rvZjt4kBjF7oM4gadhJbH\nrlayinShpwmnmh0uUWTlCzUuPbDSvIan5x0tRkZYMlnbZA0ozmc+L510jDDP8+gJC7kR7ptzh2Vt\nM4PZeY1My0kSYljCMctZYs1ZrU0BZWmrpDTUJASg1Vnt7BJKkg20KqGIOWVT0eTsYFE80tSrei+T\nGRG1Jq5sjDSyrJ7WbbniyyMTPmdaNGGbPYesIlFFMtR3HgIADC8ZiuGl1qR9NLuIM2vR1BPCbV1B\n1zHL7ijHJq+jCc8Yox/iIjuFyYlbOQjHDy0390W9Ntjs4c8kWlaF82fVweel8cMrpyR8Pjv1pJZZ\nPZNaaJ5NTHiy4a1xv4vyzftT6D0rYQvN0TZYvwEgskWlEB/cxwh510eXj0i5PQm3wuQXQjo+Ax98\ncUTShPNs4t9gFsenrdS6dzzPxxVWetboH1+tXzuToqiEhSC5V3PjxRNQmJdal9tJcGQaLY11QEUh\nnrn3vIy3xWq0tkcyqaHbxTGLXIx090dUC69EUnrGwvmU5ujUhbDdnkNx/NAmkoDzPI+l+z7F6sOb\n0Rfpw09PuwsURYHlBCFWXZD5dI96SpESURM+c9IArCX8Yr4ye4irCWcLLZm4aV+L+k0FqTr0mNVS\nxOefoigUF/hSSlkpnCf22i6emZmC5/mUFzF2JRhW7/+m4r2bKLr7aBkeYsrnUrk4SSQloV6yjgjD\ngaYox6U3NCIRc/Tejno8v/k17Ouox7HeE1I8MMuz0XNY2C8mx4/ZeGsxLtirELQ+L+0m68ge6kH3\npzd3aBwlPy4ZxyyzmVxIREFpVb0Bstm55BlsBp6PhYbkGoGQeq/r2xeNy9j1tZyUsrHzoVxYtiiy\nIiViiRKPXbPzpOz9CMNZpy3ZZCEs+ryYMUc/ueVvsv9DrOBXwUbjNj1U6kIs0bGj9NnRQ3xOlPeP\n5XgiTthZYs1ZrU0F5Z5wEg9PMZEYwuzXxeNS1YBFyDjKU04Ig89YcYNMc7y1T/VeQQa1frt4lCoX\nxz/72xdJn0sUBCu3yUO/wgybshC2m2uGmGc7nrm+M9Slei/ECBEGJ3qFxYrPk57ymUaYVXDEMFOf\nworBsJxjQ8+c1doUUD4zgSTKmfm8dOJxwtFJxaqKNKTZJkflkS4cD1SW5uH0ibX4/qWJ5922K33B\nCN5ZfTirbbCLM4uViyxZSCGxarZUE7YJyzcfAyDPICfSFepGf0RwujrSfVT1+UdHlgMAGnoaAQA1\nFu4Jm72d//xAnqgmXh1tlSbM8tJc67QIEsePxGS7+90MTXriw29V0XSZue4Uk8Kiw90Prpji+OxY\nJEqTazawyz4ax3Eo0whFSwbS+ERq2BGWs8xkaZdHcP0eId+z2Hccz6Ez1AWWY/HAql/jf1c+iCAT\nRIRT+x6sOi5YG9oCQha+YSWZSZVqRLx5XW2O5mJWR1cIZxbT/W3RfdEyGxrx8UZh5dkXsD7xQqYT\nKWQbnrdub91OsBZU9UkVPaGU6ZYxLG+ZlkpOxmR8aSSSW5ow6dQ3dogQAvnavv/gZ6sWYd3JTdJn\n/977FpioEL5+2pWyc0TYCPqYfgwqGpCVMEhlTvB4bVDeP4blwUVHq8NksPOFsFnzlVXm4M5eYf/E\nrPby3/WCiWfn4XZLrk/OijbxCUmY9u4g2rsTT8nI87kZJ006pXzznFEZvfbIQYIHttLbVCCzfd3e\nHURHT8gyAUmOlb6gIHw4jkd/iMHRlsQW03rY4Rls6w5Jr8VsWZ8dWwMAeHHPa9Jnx/tOSB7QlQXl\nmFUbC8e8a8XPEGbDKPVb65Fvtn+GKOqAx3vMtfaEyUgUJ+H4eI9MF7cfO6QM+492JewU5TQTSTq5\n98+rAQD/uP+ChL4nmKPT0aLszqSkqXRITQr5epPgZzfMQYTlbDE+f/dvoX7tibZ+S85H/qTeQAQ1\n5QXY1WDRYthG/OLvgjl59vga6b0BhbVSSUKRABOUwpG8tBcBRr0Q5pKpbKNBooJQWbgj3veV2ycs\nx0uywAZDOSFyQBM2G6+b2kT7tTOGAYil1ftk41FZQYh4WOUdTWKHVXgmsVwTtsnDSgrhytLk8v4m\nC01TtnHKOtmeuvA90deE2z69D6/vfE+2sOgLCMInmTBDLeyibT21ZLv0evTgMum1X8PDuT3Ygc6g\n4B1dmleM/ojamtfQ3ZiGVsaHUWzJJKcJu45ZWcGsIEolscUvbpyDa84fAyAWyrF8y3G8+N99ps+R\nnj2oU0sK8zzv/AGrAbkn7LHZpncmF3rxfrkZU/3r+94GALy6413Z0yGao60ne8/g1vpWWWIicujQ\nOgk3RE/o0rxi9DPWWByMMdc/EUXZztpyoQqUVl4ACurtE5aNhS86TAY7f05Lpa5vPObNFrwEB1fH\nnAZ8RFKDI009qu/oUZBAuj0jnC52k41t5vmoG5rTnjATkHvCtRWp14G1ikz3NGkt+vX3zlB9fumX\nRsQ9R54n5lnNsCzxWuhjqxYVdhiFz7+/R/Y/WXWLJqb22oJqnDZgpuzYAcU1uGHifNU5Tx84y5K2\nJdo/EYUmfNV5o/G1M4fhB1eoc8LTNKUSzqQmbBcrhVlyYE/Y7HGJP33XXzgO180bK7uppBdpItp1\nfhoSLzhRIId0qqDEQ/ytNlMULUE0R99w0bic8tpNFI+Hkvoi0RCiIBPCC7v+ja2tO6X3eplu6TXL\n8WA5LmcS3ATDjKqMJ5lDgJRDJf5iFPvl3sd5Xr+qUMPloy7GvGHnWt5WMygzZlWW5OOa88ag4aRa\n0aEotTla2BOOfe4kHC+EzT5UyT57ylWVXAibP49VmjCJE/eEzQrh/mAEgRATyxrlUM9HMxxrFcoY\nivuVD3/vDMel3rOCaaOrsWFPM2746nhTPhQ8z+NIz1EMKR6Mhu5GmQAGAI8/NtZau4L4/mPLrW5y\n1p5BpRYMyLc1WMLBqtRfgvEVY7Cs8XPDc140/PysPV/KvfqCPPV86fXQYFgOFEXBo7EnLI4Zd084\nw9zw1fGmjrPKi5rci4hX+ePxV7ZIr/P9Fq13nCh5CcwK4fk/W4rbnvhM+p9zqOejGT5aL8SSHzwh\naG511UWoie6JZZ/MjTdvdBKdPrrKlDBYcXQ1HtvwJN6ofxeRqNcvybTRlVLp0HjJeSIcg+6w+e2l\nbNuj9zR0qN4TrSj/3PVvmYNVib8EZf5S1fEkI0uHp0UAm52ulJqw6CxINkk8JsJwak2Y5d0QpWxx\n3sw6vPDh3rjHGU3+laV5aCdi7YwgE93HM0fvPBQLh8jXWNklA+man+nwLCsIJZEuFIj9Vq1CB07n\nnOmD8dGGRlwwK/uZikgyPZeJz5OHpkxd/LX9/wEAbG3ZgTHlI1Wf+3wULjp9KPY2dqo+uzga7SBy\n1/IHAACjy0biQNch3DfnDssK26eDoGI+mzm2Gl87YzgiHCNL0AEI1ZEq8mN1zB86637p9f+d/RAa\nuhstqSEsI8Gxo9SERUGqJ1CVlhKG5UCJmrDD9qwcrwmb5ck3tut+Jga4D6yM7xRDeq8mIgSvmKue\nJJLhjEkDYte35IyZRaz5mSjLtxwHABw60R3nSOfBcEKfFCZQsD4XEfeDKZpKaO+f4zkpE5T8fV7X\nNKnnhX6g6xAAYPGeN0xdO1vPYEmBPLXnHVdNQ2G+F30RdRKSkaXDUOwrQoE3H5OqxqO6oFL6rNBX\ngIlV4+D3WJMqNFnI/ew5RLyz2WHAcjwi0YWJ07ZyTu2nPsrUkVW4YNYQTBpeEfdYcmWWSMxhVVl+\nUm1T4vXQmDO+Bhv2tjhSCgcj8UNFlBaGtq4gVm8/qXO0czl4vBt7j3QgHLUO2CVeN1uI/h0emopr\nxnz/0MfS6+5wDyKs2hzN8ZyusO3pjzk1KTVHAGjsPW54/WzrWlWleWhTZJ1r7DmOR9f/Qfr/B9P+\nBxzPYVr1ZFAUhcfOfjDDrTQ3RfE8j67e2P0g59iARp1t4Rj5/wzLQYxysktFMLO4QjjKWZPNFQQg\nCzGEExDCljoLOGzPgyQUjvXZxr0tsiw/IhFCW+7uC+N/n16d1jZly6r/6xc2AACGDRCyZNmliAJJ\nJruGrIIT77rvHvqv9PpLg07Dy3vVmivPx0yUStp7YttPHzUsT7itsYsk/9VUUJqjAcgE8LXjrsTU\nanmlMb3Y4XSQSJpgZdIjcnrTM0crn1mGE6ooeT0UPFZVy8kQzmptAiQSw5vIc5SsJpwOuelARViW\nSP9Pb27HlvpW1THk/n2/4gH9/66fqTw8aeyylBHzkec5bAVvNaKfI01RqqpjRYameu07aWSOJh+e\nk4r0jiIsZ+B/kOWFsNK3QtnWagvLEaYbZYywGYWlrloeclV/tAvdfWFHWpNy9ql/8Ln1po9NZG+X\nHCDKAuRGWOmxJ53JgY5ZYUVmnFaNQhikEFZ2W0lhdveu0kF3NN6TdPo7FeGie+M0Takm4gdumC37\nvyIv5mgUYmNa7ZjykZhWPRmAUGVMb2/5kjOHS6/HV4zRPObHy3+KCMfYzgEyEGLQ1CF/bsKcPGaY\ntssS00TXKbNlkbder+/z/B6cP7NO9l5rV9CW1qR45KwQThdmHUbauhKvEmQWcZDaa2owh9J6oLWQ\nIYWwMg7co5HGLldwmlen1UiaMC3vi9IiPwZVyTWfmsJq6XWQEMKXjrxI8vR9csvfwEFbmx0/LCbE\nWYOiBXctfwDP7XwZALClZQd+vuoRNPXHUkVmo5zomp2Cf0RxQSw/dJgVLEYUKIyrGIMxFZmtxpUK\n6rzRsXtvlAfiirNH4rQJtbL3UklPnC1MCeFHHnkE8+fPx4IFC7Bt2zbNYx5//HHccMMNljbOSs4k\nvIqVJHLf9PaYSCIMm/Z9TMCRirBKCGs9ZGQ+XOXHdsutnPNkYIwFQgyefmsHDh4XPN9pSqEJawx0\njo8JV7Ia0JCSwRhUFPPv2N6ldroqyvfKJvoQE4KX1jd3b2z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NI+XjuUlGJOLtzhloOJI3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1XzcpGT7jrJFQvEMct3hogQFu+AWjTSsdqOu5JoyXEqF0/qX/KcaZJ468WPXDcJ+SPiEaOS4abF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\nAoFACBP/H1DoQwjlIFLMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Vr9eo1wing8s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_data(stock, seq_len):\n",
        "    amount_of_features = len(stock.columns) # 5\n",
        "    data = stock.as_matrix() \n",
        "    sequence_length = seq_len + 1 # index starting from 0\n",
        "    result = []\n",
        "    \n",
        "    for index in range(len(data) - sequence_length): # maxmimum date = lastest date - sequence length\n",
        "        result.append(data[index: index + sequence_length]) # index : index + 22days\n",
        "    \n",
        "    result = np.array(result)\n",
        "    row = round(0.9 * result.shape[0]) # 90% split\n",
        "    train = result[:int(row), :] # 90% date, all features \n",
        "    \n",
        "    x_train = train[:, :-1] \n",
        "    y_train = train[:, -1][:,-1]\n",
        "    \n",
        "    x_test = result[int(row):, :-1] \n",
        "    y_test = result[int(row):, -1][:,-1]\n",
        "\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))\n",
        "    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))  \n",
        "\n",
        "    return [x_train, y_train, x_test, y_test]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R0kpczq4ngmS",
        "colab_type": "code",
        "outputId": "86666e46-e3d4-4916-e429-5787a6510d96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "window = 22\n",
        "X_train, y_train, X_test, y_test = load_data(df, window)\n",
        "X_train.shape\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2526, 22, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "EVoUnlLQnekn",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def build_model(layers):\n",
        "    d = 0.3\n",
        "    model = Sequential()\n",
        "    \n",
        "    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=True))\n",
        "    model.add(Dropout(d))\n",
        "        \n",
        "    model.add(LSTM(256, input_shape=(layers[1], layers[0]), return_sequences=False))\n",
        "    model.add(Dropout(d))\n",
        "        \n",
        "    model.add(Dense(32,kernel_initializer=\"uniform\",activation='relu'))        \n",
        "    model.add(Dense(1,kernel_initializer=\"uniform\",activation='linear'))\n",
        "    \n",
        "    # adam = keras.optimizers.Adam(decay=0.2)\n",
        "        \n",
        "    start = time.time()\n",
        "    model.compile(loss='mse',optimizer='adam', metrics=['accuracy'])\n",
        "    print(\"Compilation Time : \", time.time() - start)\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VLtiqyUg9sB0",
        "colab_type": "code",
        "outputId": "89c4783e-b92f-47ec-b622-1b84a24ef51e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "model = build_model([5,window,1])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Compilation Time :  0.033682823181152344\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZEp_fMri9rv9",
        "colab_type": "code",
        "outputId": "6f2a5976-ea17-45d0-a6df-6cc2428ba844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3111
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,batch_size=512,epochs=90,validation_split=0.1,verbose=1)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2273 samples, validate on 253 samples\n",
            "Epoch 1/90\n",
            "2273/2273 [==============================] - 3s 1ms/step - loss: 0.2228 - acc: 0.0022 - val_loss: 0.1878 - val_acc: 0.0040\n",
            "Epoch 2/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0676 - acc: 0.0031 - val_loss: 0.0701 - val_acc: 0.0040\n",
            "Epoch 3/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0426 - acc: 0.0018 - val_loss: 0.1209 - val_acc: 0.0040\n",
            "Epoch 4/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0323 - acc: 0.0035 - val_loss: 0.0434 - val_acc: 0.0040\n",
            "Epoch 5/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0258 - acc: 0.0031 - val_loss: 0.0439 - val_acc: 0.0040\n",
            "Epoch 6/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 0.0191 - acc: 0.0031 - val_loss: 0.0273 - val_acc: 0.0040\n",
            "Epoch 7/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0160 - acc: 0.0031 - val_loss: 0.0281 - val_acc: 0.0040\n",
            "Epoch 8/90\n",
            "2273/2273 [==============================] - 1s 290us/step - loss: 0.0137 - acc: 0.0031 - val_loss: 0.0221 - val_acc: 0.0040\n",
            "Epoch 9/90\n",
            "2273/2273 [==============================] - 1s 289us/step - loss: 0.0126 - acc: 0.0031 - val_loss: 0.0244 - val_acc: 0.0040\n",
            "Epoch 10/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0109 - acc: 0.0031 - val_loss: 0.0179 - val_acc: 0.0040\n",
            "Epoch 11/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0096 - acc: 0.0031 - val_loss: 0.0192 - val_acc: 0.0040\n",
            "Epoch 12/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0090 - acc: 0.0031 - val_loss: 0.0146 - val_acc: 0.0040\n",
            "Epoch 13/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0080 - acc: 0.0031 - val_loss: 0.0134 - val_acc: 0.0040\n",
            "Epoch 14/90\n",
            "2273/2273 [==============================] - 1s 290us/step - loss: 0.0073 - acc: 0.0031 - val_loss: 0.0132 - val_acc: 0.0040\n",
            "Epoch 15/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0070 - acc: 0.0031 - val_loss: 0.0143 - val_acc: 0.0040\n",
            "Epoch 16/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0068 - acc: 0.0031 - val_loss: 0.0146 - val_acc: 0.0040\n",
            "Epoch 17/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0067 - acc: 0.0031 - val_loss: 0.0126 - val_acc: 0.0040\n",
            "Epoch 18/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0064 - acc: 0.0031 - val_loss: 0.0123 - val_acc: 0.0040\n",
            "Epoch 19/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0062 - acc: 0.0031 - val_loss: 0.0122 - val_acc: 0.0040\n",
            "Epoch 20/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0060 - acc: 0.0031 - val_loss: 0.0117 - val_acc: 0.0040\n",
            "Epoch 21/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0058 - acc: 0.0031 - val_loss: 0.0118 - val_acc: 0.0040\n",
            "Epoch 22/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0059 - acc: 0.0031 - val_loss: 0.0114 - val_acc: 0.0040\n",
            "Epoch 23/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0056 - acc: 0.0031 - val_loss: 0.0119 - val_acc: 0.0040\n",
            "Epoch 24/90\n",
            "2273/2273 [==============================] - 1s 289us/step - loss: 0.0056 - acc: 0.0031 - val_loss: 0.0116 - val_acc: 0.0040\n",
            "Epoch 25/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0057 - acc: 0.0031 - val_loss: 0.0112 - val_acc: 0.0040\n",
            "Epoch 26/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0055 - acc: 0.0031 - val_loss: 0.0106 - val_acc: 0.0040\n",
            "Epoch 27/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0052 - acc: 0.0031 - val_loss: 0.0124 - val_acc: 0.0040\n",
            "Epoch 28/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0053 - acc: 0.0031 - val_loss: 0.0112 - val_acc: 0.0040\n",
            "Epoch 29/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0050 - acc: 0.0031 - val_loss: 0.0095 - val_acc: 0.0040\n",
            "Epoch 30/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0049 - acc: 0.0031 - val_loss: 0.0097 - val_acc: 0.0040\n",
            "Epoch 31/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0047 - acc: 0.0031 - val_loss: 0.0091 - val_acc: 0.0040\n",
            "Epoch 32/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0046 - acc: 0.0031 - val_loss: 0.0085 - val_acc: 0.0040\n",
            "Epoch 33/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0044 - acc: 0.0031 - val_loss: 0.0087 - val_acc: 0.0040\n",
            "Epoch 34/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0048 - acc: 0.0031 - val_loss: 0.0082 - val_acc: 0.0040\n",
            "Epoch 35/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0044 - acc: 0.0031 - val_loss: 0.0079 - val_acc: 0.0040\n",
            "Epoch 36/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0043 - acc: 0.0031 - val_loss: 0.0077 - val_acc: 0.0040\n",
            "Epoch 37/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0045 - acc: 0.0031 - val_loss: 0.0077 - val_acc: 0.0040\n",
            "Epoch 38/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0043 - acc: 0.0031 - val_loss: 0.0074 - val_acc: 0.0040\n",
            "Epoch 39/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0042 - acc: 0.0031 - val_loss: 0.0074 - val_acc: 0.0040\n",
            "Epoch 40/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0040 - acc: 0.0031 - val_loss: 0.0071 - val_acc: 0.0040\n",
            "Epoch 41/90\n",
            "2273/2273 [==============================] - 1s 290us/step - loss: 0.0042 - acc: 0.0031 - val_loss: 0.0069 - val_acc: 0.0040\n",
            "Epoch 42/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0040 - acc: 0.0031 - val_loss: 0.0077 - val_acc: 0.0040\n",
            "Epoch 43/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 0.0040 - acc: 0.0031 - val_loss: 0.0067 - val_acc: 0.0040\n",
            "Epoch 44/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0040 - acc: 0.0031 - val_loss: 0.0067 - val_acc: 0.0040\n",
            "Epoch 45/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0039 - acc: 0.0031 - val_loss: 0.0063 - val_acc: 0.0040\n",
            "Epoch 46/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0038 - acc: 0.0035 - val_loss: 0.0067 - val_acc: 0.0040\n",
            "Epoch 47/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0044 - acc: 0.0035 - val_loss: 0.0062 - val_acc: 0.0040\n",
            "Epoch 48/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0039 - acc: 0.0035 - val_loss: 0.0061 - val_acc: 0.0040\n",
            "Epoch 49/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0038 - acc: 0.0035 - val_loss: 0.0059 - val_acc: 0.0040\n",
            "Epoch 50/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0039 - acc: 0.0035 - val_loss: 0.0060 - val_acc: 0.0040\n",
            "Epoch 51/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0035 - acc: 0.0035 - val_loss: 0.0059 - val_acc: 0.0040\n",
            "Epoch 52/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0037 - acc: 0.0031 - val_loss: 0.0057 - val_acc: 0.0040\n",
            "Epoch 53/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0037 - acc: 0.0035 - val_loss: 0.0056 - val_acc: 0.0040\n",
            "Epoch 54/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0038 - acc: 0.0035 - val_loss: 0.0056 - val_acc: 0.0040\n",
            "Epoch 55/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0035 - acc: 0.0035 - val_loss: 0.0062 - val_acc: 0.0040\n",
            "Epoch 56/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 0.0035 - acc: 0.0031 - val_loss: 0.0059 - val_acc: 0.0040\n",
            "Epoch 57/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0036 - acc: 0.0035 - val_loss: 0.0055 - val_acc: 0.0040\n",
            "Epoch 58/90\n",
            "2273/2273 [==============================] - 1s 303us/step - loss: 0.0035 - acc: 0.0035 - val_loss: 0.0053 - val_acc: 0.0040\n",
            "Epoch 59/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0054 - val_acc: 0.0040\n",
            "Epoch 60/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 0.0035 - acc: 0.0035 - val_loss: 0.0053 - val_acc: 0.0040\n",
            "Epoch 61/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0051 - val_acc: 0.0040\n",
            "Epoch 62/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0033 - acc: 0.0035 - val_loss: 0.0057 - val_acc: 0.0040\n",
            "Epoch 63/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0060 - val_acc: 0.0040\n",
            "Epoch 64/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 0.0033 - acc: 0.0035 - val_loss: 0.0052 - val_acc: 0.0040\n",
            "Epoch 65/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0052 - val_acc: 0.0040\n",
            "Epoch 66/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0049 - val_acc: 0.0040\n",
            "Epoch 67/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0049 - val_acc: 0.0040\n",
            "Epoch 68/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0049 - val_acc: 0.0040\n",
            "Epoch 69/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 0.0035 - acc: 0.0035 - val_loss: 0.0050 - val_acc: 0.0040\n",
            "Epoch 70/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0051 - val_acc: 0.0040\n",
            "Epoch 71/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0031 - acc: 0.0035 - val_loss: 0.0066 - val_acc: 0.0040\n",
            "Epoch 72/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 0.0034 - acc: 0.0035 - val_loss: 0.0063 - val_acc: 0.0040\n",
            "Epoch 73/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0059 - val_acc: 0.0040\n",
            "Epoch 74/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0061 - val_acc: 0.0040\n",
            "Epoch 75/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0033 - acc: 0.0035 - val_loss: 0.0058 - val_acc: 0.0040\n",
            "Epoch 76/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0052 - val_acc: 0.0040\n",
            "Epoch 77/90\n",
            "2273/2273 [==============================] - 1s 293us/step - loss: 0.0030 - acc: 0.0035 - val_loss: 0.0054 - val_acc: 0.0040\n",
            "Epoch 78/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0054 - val_acc: 0.0040\n",
            "Epoch 79/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0048 - val_acc: 0.0040\n",
            "Epoch 80/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0052 - val_acc: 0.0040\n",
            "Epoch 81/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0033 - acc: 0.0035 - val_loss: 0.0051 - val_acc: 0.0040\n",
            "Epoch 82/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0030 - acc: 0.0035 - val_loss: 0.0048 - val_acc: 0.0040\n",
            "Epoch 83/90\n",
            "2273/2273 [==============================] - 1s 292us/step - loss: 0.0031 - acc: 0.0035 - val_loss: 0.0050 - val_acc: 0.0040\n",
            "Epoch 84/90\n",
            "2273/2273 [==============================] - 1s 294us/step - loss: 0.0032 - acc: 0.0035 - val_loss: 0.0051 - val_acc: 0.0040\n",
            "Epoch 85/90\n",
            "2273/2273 [==============================] - 1s 290us/step - loss: 0.0031 - acc: 0.0035 - val_loss: 0.0045 - val_acc: 0.0040\n",
            "Epoch 86/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0030 - acc: 0.0035 - val_loss: 0.0046 - val_acc: 0.0040\n",
            "Epoch 87/90\n",
            "2273/2273 [==============================] - 1s 291us/step - loss: 0.0031 - acc: 0.0035 - val_loss: 0.0044 - val_acc: 0.0040\n",
            "Epoch 88/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 0.0031 - acc: 0.0035 - val_loss: 0.0047 - val_acc: 0.0040\n",
            "Epoch 89/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 0.0030 - acc: 0.0035 - val_loss: 0.0044 - val_acc: 0.0040\n",
            "Epoch 90/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 0.0029 - acc: 0.0035 - val_loss: 0.0044 - val_acc: 0.0040\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1ae06ebf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "ICGRykHs3gv6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('lstn__norm.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6aGXAp8XBI0R",
        "colab_type": "code",
        "outputId": "67dbbd49-7f59-486f-dd3a-3964629b673d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "diff=[]\n",
        "ratio=[]\n",
        "p = model.predict(X_test)\n",
        "print (p.shape)\n",
        "# for each data index in test data\n",
        "for u in range(len(y_test)):\n",
        "    # pr = prediction day u\n",
        "    pr = p[u][0]\n",
        "    # (y_test day u / pr) - 1\n",
        "    ratio.append((y_test[u]/pr)-1)\n",
        "    diff.append(abs(y_test[u]- pr))\n",
        "    # print(u, y_test[u], pr, (y_test[u]/pr)-1, abs(y_test[u]- pr))\n",
        "    # Last day prediction\n",
        "    # print(p[-1]) "
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(281, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GN2ZvBW6-xeu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df= aapl.drop(['Close'],1)\n",
        "\n",
        "# Bug fixed at here, please update the denormalize function to this one\n",
        "def denormalize(df, normalized_value): \n",
        "    df = df['Adj Close'].values.reshape(-1,1)\n",
        "    normalized_value = normalized_value.reshape(-1,1)\n",
        "    \n",
        "    #return df.shape, p.shape\n",
        "    min_max_scaler = preprocessing.MinMaxScaler()\n",
        "    a = min_max_scaler.fit_transform(df)\n",
        "    new = min_max_scaler.inverse_transform(normalized_value)\n",
        "    return new\n",
        "\n",
        "newp = denormalize(df, p)\n",
        "newy_test = denormalize(df, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RqU_EhbNF0Oo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "WINDOW NORMALIZATION TRAINING RESULT"
      ]
    },
    {
      "metadata": {
        "id": "7FaOBDPR6cP_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f270db02-8825-45cc-e09b-045c7e6ca30b"
      },
      "cell_type": "code",
      "source": [
        "def model_score(model, X_train, y_train, X_test, y_test):\n",
        "    trainScore = model.evaluate(X_train, y_train, verbose=0)\n",
        "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))\n",
        "\n",
        "    testScore = model.evaluate(X_test, y_test, verbose=0)\n",
        "    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))\n",
        "    return trainScore[0], testScore[0]\n",
        "\n",
        "\n",
        "model_score(model, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Score: 0.00248 MSE (0.05 RMSE)\n",
            "Test Score: 0.00210 MSE (0.05 RMSE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0024819761799111583, 0.0020957825824724397)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "metadata": {
        "id": "afScx_Ur6k5f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "aaf189b8-a12a-4859-8abe-081a88ce3ea6"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt2\n",
        "\n",
        "plt2.plot(newp,color='red', label='Prediction')\n",
        "plt2.plot(newy_test,color='blue', label='Actual')\n",
        "plt2.legend(loc='best')\n",
        "plt2.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hx6BWL8CCBcLy/VfHAgDcahCtbh/6EOU2djtce22E4mITo7SM3u/eh1nU31rM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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uCADq_i-F72j",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "FULL BATCH NORMALIZATION RESULT"
      ]
    },
    {
      "metadata": {
        "id": "RIhfdJTYBUlV",
        "colab_type": "code",
        "outputId": "38241c66-a6c7-47c7-ad8c-a98fc20e2cbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "def model_score(model, X_train, y_train, X_test, y_test):\n",
        "    trainScore = model.evaluate(X_train, y_train, verbose=0)\n",
        "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))\n",
        "\n",
        "    testScore = model.evaluate(X_test, y_test, verbose=0)\n",
        "    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))\n",
        "    return trainScore[0], testScore[0]\n",
        "\n",
        "\n",
        "model_score(model, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Score: 0.00017 MSE (0.01 RMSE)\n",
            "Test Score: 0.00731 MSE (0.09 RMSE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.000174571302189554, 0.0073100276775260415)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "hIleNOdbBUYu",
        "colab_type": "code",
        "outputId": "5864c671-1449-4408-ca47-e79dd84c01f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt2\n",
        "\n",
        "plt2.plot(newp,color='red', label='Prediction')\n",
        "plt2.plot(newy_test,color='blue', label='Actual')\n",
        "plt2.legend(loc='best')\n",
        "plt2.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NVQApvhKJpItYt07lo4+E861v3+Bds8nJ0Xc7V1YqtLQo5OVpbvHdtSv4y+We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\n4isQCAQCQQUjxFcgEAgEggrm/wGKMNVHn2MXqAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HdU5lAHEC3gC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "pred = pd.DataFrame(index = df.tail(281).index)\n",
        "pred['Close'] = newp\n",
        "testy = pd.DataFrame(index = df.tail(281).index)\n",
        "testy['Close'] = newy_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "me9m8WKQ8_0o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c23839cc-4107-44bf-b4c2-ddbf3cb24b9f"
      },
      "cell_type": "code",
      "source": [
        "    trainScore = model.evaluate(X_train[400:600], y_train[400:600], verbose=0)\n",
        "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Score: 0.00155 MSE (0.04 RMSE)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "piPe_ATWGUcf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "ANALYSING DAYS - 400 TO 600 (WINDOW EDGE AT 500)"
      ]
    },
    {
      "metadata": {
        "id": "a9ERLUDO9LEG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3451
        },
        "outputId": "f54dd18a-a56e-4230-d8d5-6ee8c04dbe1b"
      },
      "cell_type": "code",
      "source": [
        "com = pd.DataFrame(index = df[400:600].index)\n",
        "com['pred'] = model.predict(X_train[400:600])\n",
        "com['y_train'] = y_train[400:600].reshape(-1,1)\n",
        "com['orig'] = df['Adj Close'].values[400:600]\n",
        "print(com.to_string())"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                pred   y_train      orig\n",
            "Date                                    \n",
            "2002-05-10  0.590958  0.484127  1.110703\n",
            "2002-05-13  0.539722  0.440476  1.140233\n",
            "2002-05-14  0.482225  0.484920  1.219773\n",
            "2002-05-15  0.448012  0.519841  1.204056\n",
            "2002-05-16  0.487721  0.488889  1.200722\n",
            "2002-05-17  0.500601  0.248413  1.191196\n",
            "2002-05-20  0.357788  0.247619  1.178336\n",
            "2002-05-21  0.259022  0.226984  1.117371\n",
            "2002-05-22  0.214870  0.260317  1.158332\n",
            "2002-05-23  0.208779  0.250000  1.199293\n",
            "2002-05-24  0.221933  0.203175  1.150235\n",
            "2002-05-28  0.213875  0.243651  1.142138\n",
            "2002-05-29  0.215843  0.296031  1.142138\n",
            "2002-05-30  0.242753  0.243651  1.152617\n",
            "2002-05-31  0.264083  0.234127  1.109750\n",
            "2002-06-03  0.263021  0.282540  1.091176\n",
            "2002-06-04  0.262869  0.376984  1.084984\n",
            "2002-06-05  0.291609  0.319048  1.082126\n",
            "2002-06-06  0.317218  0.280953  1.055454\n",
            "2002-06-07  0.325791  0.264285  1.019256\n",
            "2002-06-10  0.320306  0.342063  1.023067\n",
            "2002-06-11  0.315537  0.279365  0.974485\n",
            "2002-06-12  0.322123  0.336508  0.956862\n",
            "2002-06-13  0.320836  0.307143  0.930666\n",
            "2002-06-14  0.327985  0.130159  0.957339\n",
            "2002-06-17  0.237688  0.079365  0.978295\n",
            "2002-06-18  0.141449  0.076984  0.959720\n",
            "2002-06-19  0.087925  0.073810  0.815405\n",
            "2002-06-20  0.070525  0.038096  0.814929\n",
            "2002-06-21  0.060621  0.096032  0.802545\n",
            "2002-06-24  0.056437  0.029365  0.822549\n",
            "2002-06-25  0.052490  0.027778  0.816357\n",
            "2002-06-26  0.045941  0.081746  0.788256\n",
            "2002-06-27  0.046593  0.114286  0.812547\n",
            "2002-06-28  0.057961  0.100794  0.843982\n",
            "2002-07-01  0.078808  0.064286  0.812547\n",
            "2002-07-02  0.092205  0.036508  0.806832\n",
            "2002-07-03  0.085957  0.000000  0.835885\n",
            "2002-07-05  0.065340  0.059524  0.892563\n",
            "2002-07-08  0.053814  0.082540  0.857794\n",
            "2002-07-09  0.057470  0.103968  0.834933\n",
            "2002-07-10  0.070451  0.080159  0.824930\n",
            "2002-07-11  0.083226  0.079365  0.871607\n",
            "2002-07-12  0.088264  0.047619  0.833980\n",
            "2002-07-15  0.086002  0.093651  0.868273\n",
            "2002-07-16  0.085379  0.128571  0.850650\n",
            "2002-07-17  0.097665  0.144445  0.744438\n",
            "2002-07-18  0.118402  0.157937  0.713955\n",
            "2002-07-19  0.138734  0.152381  0.712527\n",
            "2002-07-22  0.153774  0.169047  0.710621\n",
            "2002-07-23  0.165033  0.157143  0.689189\n",
            "2002-07-24  0.173088  0.138096  0.723957\n",
            "2002-07-25  0.169440  0.122222  0.683949\n",
            "2002-07-26  0.154895  0.068254  0.682997\n",
            "2002-07-29  0.130228  0.056349  0.715384\n",
            "2002-07-30  0.098750  0.056349  0.734912\n",
            "2002-07-31  0.073608  0.060317  0.726815\n",
            "2002-08-01  0.064334  0.004762  0.704906\n",
            "2002-08-02  0.051750  0.038889  0.688236\n",
            "2002-08-05  0.045571  0.015080  0.666327\n",
            "2002-08-06  0.039374  0.030953  0.702048\n",
            "2002-08-07  0.037688  0.030159  0.715861\n",
            "2002-08-08  0.036903  0.026984  0.728720\n",
            "2002-08-09  0.037051  0.023810  0.714432\n",
            "2002-08-12  0.038101  0.011905  0.713955\n",
            "2002-08-13  0.037619  0.014286  0.694904\n",
            "2002-08-14  0.035624  0.040476  0.722529\n",
            "2002-08-15  0.036301  0.064286  0.743485\n",
            "2002-08-16  0.044597  0.081746  0.753011\n",
            "2002-08-19  0.059595  0.046826  0.761108\n",
            "2002-08-20  0.067762  0.069841  0.757774\n",
            "2002-08-21  0.072865  0.068254  0.767776\n",
            "2002-08-22  0.074841  0.051588  0.760632\n",
            "2002-08-23  0.070106  0.074603  0.749201\n",
            "2002-08-26  0.071239  0.056349  0.739675\n",
            "2002-08-27  0.074295  0.057937  0.707287\n",
            "2002-08-28  0.071521  0.040476  0.700143\n",
            "2002-08-29  0.061044  0.041270  0.700143\n",
            "2002-08-30  0.052267  0.042186  0.702525\n",
            "2002-09-03  0.050329  0.047409  0.669184\n",
            "2002-09-04  0.050804  0.036561  0.689665\n",
            "2002-09-05  0.051246  0.026115  0.675376\n",
            "2002-09-06  0.049628  0.022499  0.684902\n",
            "2002-09-09  0.046594  0.018883  0.684426\n",
            "2002-09-10  0.042425  0.039775  0.682521\n",
            "2002-09-11  0.041254  0.055846  0.680615\n",
            "2002-09-12  0.045813  0.066292  0.673471\n",
            "2002-09-13  0.054098  0.081961  0.674900\n",
            "2002-09-16  0.071090  0.057854  0.690617\n",
            "2002-09-17  0.078473  0.039775  0.704906\n",
            "2002-09-18  0.074595  0.049016  0.715384\n",
            "2002-09-19  0.065908  0.057854  0.694428\n",
            "2002-09-20  0.060047  0.063479  0.708240\n",
            "2002-09-23  0.059689  0.070711  0.707287\n",
            "2002-09-24  0.064010  0.063077  0.697286\n",
            "2002-09-25  0.070335  0.092407  0.711098\n",
            "2002-09-26  0.078832  0.100040  0.700143\n",
            "2002-09-27  0.093125  0.093210  0.701096\n",
            "2002-09-30  0.103198  0.114905  0.690617\n",
            "2002-10-01  0.113684  0.118521  0.691094\n",
            "2002-10-02  0.124776  0.130173  0.674900\n",
            "2002-10-03  0.132770  0.151466  0.681092\n",
            "2002-10-04  0.146001  0.151868  0.668232\n",
            "2002-10-07  0.155226  0.164725  0.655848\n",
            "2002-10-08  0.164436  0.115709  0.651562\n",
            "2002-10-09  0.161577  0.109281  0.647275\n",
            "2002-10-10  0.143948  0.081961  0.672042\n",
            "2002-10-11  0.120682  0.101245  0.691094\n",
            "2002-10-14  0.105608  0.099237  0.703477\n",
            "2002-10-15  0.098881  0.127762  0.722052\n",
            "2002-10-16  0.104378  0.113700  0.693475\n",
            "2002-10-17  0.112292  0.101647  0.672042\n",
            "2002-10-18  0.116615  0.086380  0.682997\n",
            "2002-10-21  0.111471  0.096826  0.693475\n",
            "2002-10-22  0.105408  0.129771  0.700143\n",
            "2002-10-23  0.111484  0.116111  0.708716\n",
            "2002-10-24  0.118459  0.114504  0.699667\n",
            "2002-10-25  0.121875  0.092005  0.734436\n",
            "2002-10-28  0.117576  0.104460  0.743485\n",
            "2002-10-29  0.112945  0.095621  0.735389\n",
            "2002-10-30  0.108454  0.082764  0.761108\n",
            "2002-10-31  0.105114  0.081961  0.765395\n",
            "2002-11-01  0.097907  0.074327  0.779207\n",
            "2002-11-04  0.088529  0.060667  0.804450\n",
            "2002-11-05  0.078988  0.073524  0.804927\n",
            "2002-11-06  0.072983  0.065488  0.820168\n",
            "2002-11-07  0.070328  0.086782  0.762061\n",
            "2002-11-08  0.073552  0.095219  0.754440\n",
            "2002-11-11  0.081922  0.083166  0.722052\n",
            "2002-11-12  0.089113  0.067095  0.744914\n",
            "2002-11-13  0.088803  0.069506  0.742533\n",
            "2002-11-14  0.084976  0.078747  0.776349\n",
            "2002-11-15  0.082131  0.058256  0.759679\n",
            "2002-11-18  0.076579  0.043391  0.745390\n",
            "2002-11-19  0.069250  0.040980  0.727292\n",
            "2002-11-20  0.060322  0.055042  0.739675\n",
            "2002-11-21  0.056097  0.049819  0.778731\n",
            "2002-11-22  0.054190  0.051426  0.762537\n",
            "2002-11-25  0.054763  0.037766  0.760632\n",
            "2002-11-26  0.053738  0.038168  0.733960\n",
            "2002-11-27  0.051417  0.048614  0.748724\n",
            "2002-11-29  0.050477  0.067497  0.738246\n",
            "2002-12-02  0.054921  0.071515  0.723005\n",
            "2002-12-03  0.064058  0.071515  0.722052\n",
            "2002-12-04  0.076288  0.069506  0.713003\n",
            "2002-12-05  0.082397  0.057453  0.696809\n",
            "2002-12-06  0.080595  0.062676  0.712050\n",
            "2002-12-09  0.077208  0.064283  0.702525\n",
            "2002-12-10  0.073561  0.060667  0.727768\n",
            "2002-12-11  0.071229  0.059863  0.737770\n",
            "2002-12-12  0.069743  0.052632  0.723481\n",
            "2002-12-13  0.067757  0.060265  0.704430\n",
            "2002-12-16  0.066392  0.039373  0.707287\n",
            "2002-12-17  0.062219  0.036159  0.718242\n",
            "2002-12-18  0.057079  0.030534  0.693951\n",
            "2002-12-19  0.051761  0.042186  0.676329\n",
            "2002-12-20  0.049228  0.027320  0.673471\n",
            "2002-12-23  0.047163  0.040578  0.690141\n",
            "2002-12-24  0.046693  0.058658  0.683949\n",
            "2002-12-26  0.050209  0.072720  0.685855\n",
            "2002-12-27  0.058343  0.048212  0.669661\n",
            "2002-12-30  0.067497  0.049819  0.670137\n",
            "2002-12-31  0.068520  0.061872  0.682521\n",
            "2003-01-02  0.069104  0.059462  0.704906\n",
            "2003-01-03  0.068478  0.053435  0.709669\n",
            "2003-01-06  0.068509  0.052632  0.709669\n",
            "2003-01-07  0.065842  0.041382  0.707287\n",
            "2003-01-08  0.062329  0.049417  0.692999\n",
            "2003-01-09  0.058839  0.049417  0.699191\n",
            "2003-01-10  0.057777  0.051024  0.701096\n",
            "2003-01-13  0.058073  0.057051  0.696809\n",
            "2003-01-14  0.059714  0.062274  0.695857\n",
            "2003-01-15  0.063235  0.086380  0.687284\n",
            "2003-01-16  0.072233  0.069506  0.696333\n",
            "2003-01-17  0.079507  0.066292  0.671566\n",
            "2003-01-21  0.082393  0.075532  0.667756\n",
            "2003-01-22  0.082960  0.065086  0.661088\n",
            "2003-01-23  0.078424  0.076336  0.674900\n",
            "2003-01-24  0.076585  0.055444  0.657277\n",
            "2003-01-27  0.074907  0.069908  0.672995\n",
            "2003-01-28  0.073650  0.075934  0.694428\n",
            "2003-01-29  0.075711  0.061470  0.711098\n",
            "2003-01-30  0.077101  0.057854  0.682044\n",
            "2003-01-31  0.074914  0.060265  0.683949\n",
            "2003-02-03  0.072058  0.057854  0.698238\n",
            "2003-02-04  0.068354  0.056649  0.695380\n",
            "2003-02-05  0.065476  0.050221  0.688236\n",
            "2003-02-06  0.062892  0.044596  0.687284\n",
            "2003-02-07  0.059332  0.044194  0.673947\n",
            "2003-02-10  0.055666  0.064283  0.683473\n",
            "2003-02-11  0.057595  0.066693  0.683473\n",
            "2003-02-12  0.063529  0.075934  0.685378\n",
            "2003-02-13  0.072506  0.075532  0.692523\n",
            "2003-02-14  0.079988  0.073524  0.698714\n",
            "2003-02-18  0.084306  0.071916  0.727292\n",
            "2003-02-19  0.084364  0.075532  0.707287\n",
            "2003-02-20  0.085215  0.050221  0.703477\n",
            "2003-02-21  0.079005  0.057453  0.714432\n",
            "2003-02-24  0.071950  0.051828  0.702048\n",
            "2003-02-25  0.065526  0.055042  0.715384\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0i_HFg70BOvh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "cb20a94c-18c1-4ea9-9b07-79db66a7262b"
      },
      "cell_type": "code",
      "source": [
        "com.plot()\n",
        "plt.show"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
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VK9GnpZP8w+a/L09eOFH3u+4Gyv6zhKr338W1cwf9brwJQ7/+kfN8BQVo4uLI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\npQQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3bFWU2MAFOp1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Now perform exponential moving average smoothing\n",
        "# So the data will have a smoother curve than the original ragged data\n",
        "EMA = 0.0\n",
        "gamma = 0.1\n",
        "for ti in range(2200):\n",
        "  EMA = gamma*train_data[ti] + (1-gamma)*EMA\n",
        "  train_data[ti] = EMA\n",
        "\n",
        "# Used for visualization and test purposes\n",
        "all_mid_data = np.concatenate([train_data,test_data],axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JgGZp2-8myuX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "STRATEGY"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "0c39b19e-483d-4837-fc6e-e70558898eba",
        "id": "XzB-rp9ziu-G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "d=all_data[['Close']].reset_index().pivot('Date', 'Ticker', 'Close')\n",
        "d"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Ticker</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</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>2012-10-01</th>\n",
              "      <td>94.198570</td>\n",
              "      <td>378.428680</td>\n",
              "      <td>210.470001</td>\n",
              "      <td>29.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-02</th>\n",
              "      <td>94.472855</td>\n",
              "      <td>376.049164</td>\n",
              "      <td>209.839996</td>\n",
              "      <td>29.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-03</th>\n",
              "      <td>95.921425</td>\n",
              "      <td>378.786346</td>\n",
              "      <td>210.509995</td>\n",
              "      <td>29.860001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-04</th>\n",
              "      <td>95.257141</td>\n",
              "      <td>381.543427</td>\n",
              "      <td>210.389999</td>\n",
              "      <td>30.030001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-05</th>\n",
              "      <td>93.227142</td>\n",
              "      <td>381.344727</td>\n",
              "      <td>210.589996</td>\n",
              "      <td>29.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-08</th>\n",
              "      <td>91.167145</td>\n",
              "      <td>376.471405</td>\n",
              "      <td>209.820007</td>\n",
              "      <td>29.780001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-09</th>\n",
              "      <td>90.835716</td>\n",
              "      <td>369.640839</td>\n",
              "      <td>207.990005</td>\n",
              "      <td>29.280001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-10</th>\n",
              "      <td>91.558571</td>\n",
              "      <td>369.874329</td>\n",
              "      <td>205.820007</td>\n",
              "      <td>28.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-11</th>\n",
              "      <td>89.728569</td>\n",
              "      <td>373.311951</td>\n",
              "      <td>205.759995</td>\n",
              "      <td>28.950001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-12</th>\n",
              "      <td>89.958572</td>\n",
              "      <td>369.968719</td>\n",
              "      <td>207.800003</td>\n",
              "      <td>29.200001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-15</th>\n",
              "      <td>90.680000</td>\n",
              "      <td>368.095886</td>\n",
              "      <td>208.929993</td>\n",
              "      <td>29.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-16</th>\n",
              "      <td>92.827141</td>\n",
              "      <td>369.943878</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>29.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-17</th>\n",
              "      <td>92.087143</td>\n",
              "      <td>375.304016</td>\n",
              "      <td>200.630005</td>\n",
              "      <td>29.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-18</th>\n",
              "      <td>90.377144</td>\n",
              "      <td>345.254456</td>\n",
              "      <td>194.960007</td>\n",
              "      <td>29.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-19</th>\n",
              "      <td>87.120003</td>\n",
              "      <td>338.692139</td>\n",
              "      <td>193.360001</td>\n",
              "      <td>28.639999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-22</th>\n",
              "      <td>90.575714</td>\n",
              "      <td>337.142212</td>\n",
              "      <td>194.399994</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-23</th>\n",
              "      <td>87.622856</td>\n",
              "      <td>337.976776</td>\n",
              "      <td>191.250000</td>\n",
              "      <td>28.049999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-24</th>\n",
              "      <td>88.118568</td>\n",
              "      <td>336.461639</td>\n",
              "      <td>190.720001</td>\n",
              "      <td>27.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-25</th>\n",
              "      <td>87.077141</td>\n",
              "      <td>336.690155</td>\n",
              "      <td>191.600006</td>\n",
              "      <td>27.879999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-26</th>\n",
              "      <td>86.285713</td>\n",
              "      <td>335.393585</td>\n",
              "      <td>193.270004</td>\n",
              "      <td>28.209999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-31</th>\n",
              "      <td>85.045715</td>\n",
              "      <td>337.951935</td>\n",
              "      <td>194.529999</td>\n",
              "      <td>28.540001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-01</th>\n",
              "      <td>85.220001</td>\n",
              "      <td>341.573395</td>\n",
              "      <td>197.149994</td>\n",
              "      <td>29.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-02</th>\n",
              "      <td>82.400002</td>\n",
              "      <td>341.737335</td>\n",
              "      <td>193.429993</td>\n",
              "      <td>29.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-05</th>\n",
              "      <td>83.517143</td>\n",
              "      <td>339.273346</td>\n",
              "      <td>194.139999</td>\n",
              "      <td>29.629999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-06</th>\n",
              "      <td>83.264282</td>\n",
              "      <td>338.657349</td>\n",
              "      <td>195.070007</td>\n",
              "      <td>29.860001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-07</th>\n",
              "      <td>79.714287</td>\n",
              "      <td>331.404541</td>\n",
              "      <td>191.160004</td>\n",
              "      <td>29.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-08</th>\n",
              "      <td>76.821426</td>\n",
              "      <td>324.037445</td>\n",
              "      <td>190.100006</td>\n",
              "      <td>28.809999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-09</th>\n",
              "      <td>78.151428</td>\n",
              "      <td>329.372742</td>\n",
              "      <td>189.639999</td>\n",
              "      <td>28.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-12</th>\n",
              "      <td>77.547142</td>\n",
              "      <td>330.798462</td>\n",
              "      <td>189.250000</td>\n",
              "      <td>28.219999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-11-13</th>\n",
              "      <td>77.557144</td>\n",
              "      <td>327.395599</td>\n",
              "      <td>188.320007</td>\n",
              "      <td>27.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-16</th>\n",
              "      <td>171.100006</td>\n",
              "      <td>1032.500000</td>\n",
              "      <td>149.119995</td>\n",
              "      <td>83.199997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-17</th>\n",
              "      <td>170.149994</td>\n",
              "      <td>1019.090027</td>\n",
              "      <td>148.970001</td>\n",
              "      <td>82.400002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-20</th>\n",
              "      <td>169.979996</td>\n",
              "      <td>1018.380005</td>\n",
              "      <td>150.509995</td>\n",
              "      <td>82.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-21</th>\n",
              "      <td>173.139999</td>\n",
              "      <td>1034.489990</td>\n",
              "      <td>151.949997</td>\n",
              "      <td>83.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-22</th>\n",
              "      <td>174.960007</td>\n",
              "      <td>1035.959961</td>\n",
              "      <td>151.770004</td>\n",
              "      <td>83.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-24</th>\n",
              "      <td>174.970001</td>\n",
              "      <td>1040.609985</td>\n",
              "      <td>151.839996</td>\n",
              "      <td>83.260002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-27</th>\n",
              "      <td>174.089996</td>\n",
              "      <td>1054.209961</td>\n",
              "      <td>151.979996</td>\n",
              "      <td>83.870003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-28</th>\n",
              "      <td>173.070007</td>\n",
              "      <td>1047.410034</td>\n",
              "      <td>152.470001</td>\n",
              "      <td>84.879997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-29</th>\n",
              "      <td>169.479996</td>\n",
              "      <td>1021.659973</td>\n",
              "      <td>153.550003</td>\n",
              "      <td>83.339996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-30</th>\n",
              "      <td>171.850006</td>\n",
              "      <td>1021.409973</td>\n",
              "      <td>153.970001</td>\n",
              "      <td>84.169998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-01</th>\n",
              "      <td>171.050003</td>\n",
              "      <td>1010.169983</td>\n",
              "      <td>154.759995</td>\n",
              "      <td>84.260002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-04</th>\n",
              "      <td>169.800003</td>\n",
              "      <td>998.679993</td>\n",
              "      <td>156.460007</td>\n",
              "      <td>81.080002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-05</th>\n",
              "      <td>169.639999</td>\n",
              "      <td>1005.150024</td>\n",
              "      <td>155.350006</td>\n",
              "      <td>81.589996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-06</th>\n",
              "      <td>169.009995</td>\n",
              "      <td>1018.380005</td>\n",
              "      <td>154.100006</td>\n",
              "      <td>82.779999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-07</th>\n",
              "      <td>169.320007</td>\n",
              "      <td>1030.930054</td>\n",
              "      <td>153.570007</td>\n",
              "      <td>82.489998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-08</th>\n",
              "      <td>169.369995</td>\n",
              "      <td>1037.050049</td>\n",
              "      <td>154.809998</td>\n",
              "      <td>84.160004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-11</th>\n",
              "      <td>172.669998</td>\n",
              "      <td>1041.099976</td>\n",
              "      <td>155.410004</td>\n",
              "      <td>85.230003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-12</th>\n",
              "      <td>171.699997</td>\n",
              "      <td>1040.479980</td>\n",
              "      <td>156.740005</td>\n",
              "      <td>85.580002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-13</th>\n",
              "      <td>172.270004</td>\n",
              "      <td>1040.609985</td>\n",
              "      <td>153.910004</td>\n",
              "      <td>85.349998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-14</th>\n",
              "      <td>172.220001</td>\n",
              "      <td>1049.150024</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>84.690002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-15</th>\n",
              "      <td>173.970001</td>\n",
              "      <td>1064.189941</td>\n",
              "      <td>152.500000</td>\n",
              "      <td>86.849998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-18</th>\n",
              "      <td>176.419998</td>\n",
              "      <td>1077.140015</td>\n",
              "      <td>153.330002</td>\n",
              "      <td>86.379997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-19</th>\n",
              "      <td>174.539993</td>\n",
              "      <td>1070.680054</td>\n",
              "      <td>153.229996</td>\n",
              "      <td>85.830002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-20</th>\n",
              "      <td>174.350006</td>\n",
              "      <td>1064.949951</td>\n",
              "      <td>152.949997</td>\n",
              "      <td>85.519997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-21</th>\n",
              "      <td>175.009995</td>\n",
              "      <td>1063.630005</td>\n",
              "      <td>151.500000</td>\n",
              "      <td>85.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>175.009995</td>\n",
              "      <td>1060.119995</td>\n",
              "      <td>152.500000</td>\n",
              "      <td>85.510002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>170.570007</td>\n",
              "      <td>1056.739990</td>\n",
              "      <td>152.830002</td>\n",
              "      <td>85.400002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>170.600006</td>\n",
              "      <td>1049.369995</td>\n",
              "      <td>153.130005</td>\n",
              "      <td>85.709999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>171.080002</td>\n",
              "      <td>1048.140015</td>\n",
              "      <td>154.039993</td>\n",
              "      <td>85.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>169.229996</td>\n",
              "      <td>1046.400024</td>\n",
              "      <td>153.419998</td>\n",
              "      <td>85.540001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1321 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Ticker            AAPL         GOOG         IBM       MSFT\n",
              "Date                                                      \n",
              "2012-10-01   94.198570   378.428680  210.470001  29.490000\n",
              "2012-10-02   94.472855   376.049164  209.839996  29.660000\n",
              "2012-10-03   95.921425   378.786346  210.509995  29.860001\n",
              "2012-10-04   95.257141   381.543427  210.389999  30.030001\n",
              "2012-10-05   93.227142   381.344727  210.589996  29.850000\n",
              "2012-10-08   91.167145   376.471405  209.820007  29.780001\n",
              "2012-10-09   90.835716   369.640839  207.990005  29.280001\n",
              "2012-10-10   91.558571   369.874329  205.820007  28.980000\n",
              "2012-10-11   89.728569   373.311951  205.759995  28.950001\n",
              "2012-10-12   89.958572   369.968719  207.800003  29.200001\n",
              "2012-10-15   90.680000   368.095886  208.929993  29.510000\n",
              "2012-10-16   92.827141   369.943878  211.000000  29.490000\n",
              "2012-10-17   92.087143   375.304016  200.630005  29.590000\n",
              "2012-10-18   90.377144   345.254456  194.960007  29.500000\n",
              "2012-10-19   87.120003   338.692139  193.360001  28.639999\n",
              "2012-10-22   90.575714   337.142212  194.399994  28.000000\n",
              "2012-10-23   87.622856   337.976776  191.250000  28.049999\n",
              "2012-10-24   88.118568   336.461639  190.720001  27.900000\n",
              "2012-10-25   87.077141   336.690155  191.600006  27.879999\n",
              "2012-10-26   86.285713   335.393585  193.270004  28.209999\n",
              "2012-10-31   85.045715   337.951935  194.529999  28.540001\n",
              "2012-11-01   85.220001   341.573395  197.149994  29.520000\n",
              "2012-11-02   82.400002   341.737335  193.429993  29.500000\n",
              "2012-11-05   83.517143   339.273346  194.139999  29.629999\n",
              "2012-11-06   83.264282   338.657349  195.070007  29.860001\n",
              "2012-11-07   79.714287   331.404541  191.160004  29.080000\n",
              "2012-11-08   76.821426   324.037445  190.100006  28.809999\n",
              "2012-11-09   78.151428   329.372742  189.639999  28.830000\n",
              "2012-11-12   77.547142   330.798462  189.250000  28.219999\n",
              "2012-11-13   77.557144   327.395599  188.320007  27.090000\n",
              "...                ...          ...         ...        ...\n",
              "2017-11-16  171.100006  1032.500000  149.119995  83.199997\n",
              "2017-11-17  170.149994  1019.090027  148.970001  82.400002\n",
              "2017-11-20  169.979996  1018.380005  150.509995  82.529999\n",
              "2017-11-21  173.139999  1034.489990  151.949997  83.720001\n",
              "2017-11-22  174.960007  1035.959961  151.770004  83.110001\n",
              "2017-11-24  174.970001  1040.609985  151.839996  83.260002\n",
              "2017-11-27  174.089996  1054.209961  151.979996  83.870003\n",
              "2017-11-28  173.070007  1047.410034  152.470001  84.879997\n",
              "2017-11-29  169.479996  1021.659973  153.550003  83.339996\n",
              "2017-11-30  171.850006  1021.409973  153.970001  84.169998\n",
              "2017-12-01  171.050003  1010.169983  154.759995  84.260002\n",
              "2017-12-04  169.800003   998.679993  156.460007  81.080002\n",
              "2017-12-05  169.639999  1005.150024  155.350006  81.589996\n",
              "2017-12-06  169.009995  1018.380005  154.100006  82.779999\n",
              "2017-12-07  169.320007  1030.930054  153.570007  82.489998\n",
              "2017-12-08  169.369995  1037.050049  154.809998  84.160004\n",
              "2017-12-11  172.669998  1041.099976  155.410004  85.230003\n",
              "2017-12-12  171.699997  1040.479980  156.740005  85.580002\n",
              "2017-12-13  172.270004  1040.609985  153.910004  85.349998\n",
              "2017-12-14  172.220001  1049.150024  154.000000  84.690002\n",
              "2017-12-15  173.970001  1064.189941  152.500000  86.849998\n",
              "2017-12-18  176.419998  1077.140015  153.330002  86.379997\n",
              "2017-12-19  174.539993  1070.680054  153.229996  85.830002\n",
              "2017-12-20  174.350006  1064.949951  152.949997  85.519997\n",
              "2017-12-21  175.009995  1063.630005  151.500000  85.500000\n",
              "2017-12-22  175.009995  1060.119995  152.500000  85.510002\n",
              "2017-12-26  170.570007  1056.739990  152.830002  85.400002\n",
              "2017-12-27  170.600006  1049.369995  153.130005  85.709999\n",
              "2017-12-28  171.080002  1048.140015  154.039993  85.720001\n",
              "2017-12-29  169.229996  1046.400024  153.419998  85.540001\n",
              "\n",
              "[1321 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "metadata": {
        "id": "It4TQVFDRWbe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the short and long windows\n",
        "short_window = 5\n",
        "long_window = 20\n",
        "\n",
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "signals = pd.DataFrame(index=testy.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "signals['short_mavg'] = testy.rolling(window=short_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "signals['long_mavg'] = testy.rolling(window=long_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create signals\n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] \n",
        "                                            > signals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "signals['positions'] = signals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Tq1ItKXg1mZ",
        "colab_type": "code",
        "outputId": "810dba55-800c-4eb1-8951-d76811d8014d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "signals"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>signal</th>\n",
              "      <th>short_mavg</th>\n",
              "      <th>long_mavg</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>2010-11-19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.184861</td>\n",
              "      <td>11.184861</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>10.967988</td>\n",
              "      <td>10.967988</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.459546</td>\n",
              "      <td>11.459546</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.410005</td>\n",
              "      <td>11.410005</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.690015</td>\n",
              "      <td>11.690015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.025260</td>\n",
              "      <td>11.885193</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.542674</td>\n",
              "      <td>12.092764</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.429903</td>\n",
              "      <td>12.066019</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.821277</td>\n",
              "      <td>12.194045</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.992216</td>\n",
              "      <td>12.341116</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.116717</td>\n",
              "      <td>12.444977</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.284080</td>\n",
              "      <td>12.589145</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.644327</td>\n",
              "      <td>12.673060</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.879561</td>\n",
              "      <td>12.796015</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.961718</td>\n",
              "      <td>12.881316</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.120923</td>\n",
              "      <td>12.968710</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.198485</td>\n",
              "      <td>13.062481</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.304623</td>\n",
              "      <td>13.126272</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.271453</td>\n",
              "      <td>13.184288</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.347479</td>\n",
              "      <td>13.247857</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.350030</td>\n",
              "      <td>13.403232</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.377584</td>\n",
              "      <td>13.600706</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.577095</td>\n",
              "      <td>13.738987</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.822020</td>\n",
              "      <td>13.948578</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.941936</td>\n",
              "      <td>14.060837</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.149617</td>\n",
              "      <td>14.184322</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.315965</td>\n",
              "      <td>14.294028</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.371586</td>\n",
              "      <td>14.474408</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.295054</td>\n",
              "      <td>14.567022</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.241988</td>\n",
              "      <td>14.623280</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.603043</td>\n",
              "      <td>33.627780</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.204525</td>\n",
              "      <td>33.399437</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.710587</td>\n",
              "      <td>33.170710</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.187565</td>\n",
              "      <td>32.701775</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>28.558915</td>\n",
              "      <td>32.430566</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.651661</td>\n",
              "      <td>32.001813</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>26.945449</td>\n",
              "      <td>31.477260</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.005663</td>\n",
              "      <td>31.109486</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.219464</td>\n",
              "      <td>30.706629</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.509811</td>\n",
              "      <td>30.524082</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>28.578316</td>\n",
              "      <td>30.403148</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.911643</td>\n",
              "      <td>30.232593</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.773480</td>\n",
              "      <td>30.140363</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.679210</td>\n",
              "      <td>30.028361</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.030782</td>\n",
              "      <td>29.809712</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.170078</td>\n",
              "      <td>29.750774</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.370104</td>\n",
              "      <td>29.857930</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.310402</td>\n",
              "      <td>29.950033</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.201199</td>\n",
              "      <td>30.071860</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.747066</td>\n",
              "      <td>29.961644</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.149038</td>\n",
              "      <td>29.887273</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.506099</td>\n",
              "      <td>29.933324</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.014709</td>\n",
              "      <td>30.026063</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.379041</td>\n",
              "      <td>30.369728</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.208731</td>\n",
              "      <td>30.624097</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.209357</td>\n",
              "      <td>31.026697</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.347760</td>\n",
              "      <td>31.533902</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>34.588738</td>\n",
              "      <td>31.921832</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>34.930109</td>\n",
              "      <td>32.297390</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>35.372508</td>\n",
              "      <td>32.589772</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            signal  short_mavg  long_mavg  positions\n",
              "Date                                                \n",
              "2010-11-19     0.0   11.184861  11.184861        NaN\n",
              "2010-11-22     0.0   10.967988  10.967988        0.0\n",
              "2010-11-23     0.0   11.459546  11.459546        0.0\n",
              "2010-11-24     0.0   11.410005  11.410005        0.0\n",
              "2010-11-26     0.0   11.690015  11.690015        0.0\n",
              "2010-11-29     1.0   12.025260  11.885193        1.0\n",
              "2010-11-30     1.0   12.542674  12.092764        0.0\n",
              "2010-12-01     1.0   12.429903  12.066019        0.0\n",
              "2010-12-02     1.0   12.821277  12.194045        0.0\n",
              "2010-12-03     1.0   12.992216  12.341116        0.0\n",
              "2010-12-06     1.0   13.116717  12.444977        0.0\n",
              "2010-12-07     1.0   13.284080  12.589145        0.0\n",
              "2010-12-08     1.0   13.644327  12.673060        0.0\n",
              "2010-12-09     1.0   13.879561  12.796015        0.0\n",
              "2010-12-10     1.0   13.961718  12.881316        0.0\n",
              "2010-12-13     1.0   14.120923  12.968710        0.0\n",
              "2010-12-14     1.0   14.198485  13.062481        0.0\n",
              "2010-12-15     1.0   14.304623  13.126272        0.0\n",
              "2010-12-16     1.0   14.271453  13.184288        0.0\n",
              "2010-12-17     1.0   14.347479  13.247857        0.0\n",
              "2010-12-20     1.0   14.350030  13.403232        0.0\n",
              "2010-12-21     1.0   14.377584  13.600706        0.0\n",
              "2010-12-22     1.0   14.577095  13.738987        0.0\n",
              "2010-12-23     1.0   14.822020  13.948578        0.0\n",
              "2010-12-27     1.0   14.941936  14.060837        0.0\n",
              "2010-12-28     1.0   15.149617  14.184322        0.0\n",
              "2010-12-29     1.0   15.315965  14.294028        0.0\n",
              "2010-12-30     1.0   15.371586  14.474408        0.0\n",
              "2010-12-31     1.0   15.295054  14.567022        0.0\n",
              "2011-01-03     1.0   15.241988  14.623280        0.0\n",
              "...            ...         ...        ...        ...\n",
              "2011-11-17     0.0   30.603043  33.627780        0.0\n",
              "2011-11-18     0.0   30.204525  33.399437        0.0\n",
              "2011-11-21     0.0   29.710587  33.170710        0.0\n",
              "2011-11-22     0.0   29.187565  32.701775        0.0\n",
              "2011-11-23     0.0   28.558915  32.430566        0.0\n",
              "2011-11-25     0.0   27.651661  32.001813        0.0\n",
              "2011-11-28     0.0   26.945449  31.477260        0.0\n",
              "2011-11-29     0.0   27.005663  31.109486        0.0\n",
              "2011-11-30     0.0   27.219464  30.706629        0.0\n",
              "2011-12-01     0.0   27.509811  30.524082        0.0\n",
              "2011-12-02     0.0   28.578316  30.403148        0.0\n",
              "2011-12-05     0.0   29.911643  30.232593        0.0\n",
              "2011-12-06     1.0   30.773480  30.140363        1.0\n",
              "2011-12-07     1.0   31.679210  30.028361        0.0\n",
              "2011-12-08     1.0   32.030782  29.809712        0.0\n",
              "2011-12-09     1.0   32.170078  29.750774        0.0\n",
              "2011-12-12     1.0   32.370104  29.857930        0.0\n",
              "2011-12-13     1.0   32.310402  29.950033        0.0\n",
              "2011-12-14     1.0   32.201199  30.071860        0.0\n",
              "2011-12-15     1.0   31.747066  29.961644        0.0\n",
              "2011-12-16     1.0   31.149038  29.887273        0.0\n",
              "2011-12-19     1.0   30.506099  29.933324        0.0\n",
              "2011-12-20     0.0   30.014709  30.026063       -1.0\n",
              "2011-12-21     1.0   30.379041  30.369728        1.0\n",
              "2011-12-22     1.0   31.208731  30.624097        0.0\n",
              "2011-12-23     1.0   32.209357  31.026697        0.0\n",
              "2011-12-27     1.0   33.347760  31.533902        0.0\n",
              "2011-12-28     1.0   34.588738  31.921832        0.0\n",
              "2011-12-29     1.0   34.930109  32.297390        0.0\n",
              "2011-12-30     1.0   35.372508  32.589772        0.0\n",
              "\n",
              "[281 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "5xs4GhGDmfrH",
        "colab_type": "code",
        "outputId": "7cd16f9f-a0ad-462b-8547-ae31dadfd82b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize the plot figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a subplot and label for y-axis\n",
        "ax1 = fig.add_subplot(111,  ylabel='Price in $')\n",
        "\n",
        "# Plot the closing price\n",
        "testy.plot(ax=ax1, color='r', lw=2.)\n",
        "\n",
        "# Plot the short and long moving averages\n",
        "signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)\n",
        "\n",
        "# Plot the buy signals\n",
        "ax1.plot(signals.loc[signals.positions == 1.0].index, \n",
        "         signals.short_mavg[signals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "         \n",
        "# Plot the sell signals\n",
        "ax1.plot(signals.loc[signals.positions == -1.0].index, \n",
        "         signals.short_mavg[signals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "         \n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zy/nnn8WCBa8wd+5N/OpXF3HzzbdhtVp56KG/kZral0cemce6dWuYNm0GP/64kry80WzY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\nKLGW8dCZkdz2l0vZl7gXXfoedjjW8FnRl+RpmtC4PFzz+kaqwuIAiA3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mpRYBgbjg2AFd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\n7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wt1bLvu5CZWZXwPQO8WPXm0w2jUKrn7TrgplUoKtxANRIo3oN21E48lX1H+2l9RZ59C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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9h5eOAGqigcz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "psignals = pd.DataFrame(index=pred.index)\n",
        "psignals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "psignals['short_mavg'] = pred.rolling(window=short_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "psignals['long_mavg'] = pred.rolling(window=long_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create signals\n",
        "psignals['signal'][short_window:] = np.where(psignals['short_mavg'][short_window:] \n",
        "                                            > psignals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "psignals['positions'] = psignals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7Byqp5ahjgGO",
        "colab_type": "code",
        "outputId": "3619f252-821e-4197-8f1a-ab247ddf70e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "psignals"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>signal</th>\n",
              "      <th>short_mavg</th>\n",
              "      <th>long_mavg</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>2010-11-19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>9.770053</td>\n",
              "      <td>9.770053</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>9.925555</td>\n",
              "      <td>9.925555</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>10.142779</td>\n",
              "      <td>10.142779</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>10.425375</td>\n",
              "      <td>10.425375</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>10.679439</td>\n",
              "      <td>10.679439</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>11.184157</td>\n",
              "      <td>10.948473</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>11.726324</td>\n",
              "      <td>11.211818</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.248489</td>\n",
              "      <td>11.458848</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.622384</td>\n",
              "      <td>11.645936</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>12.952874</td>\n",
              "      <td>11.816156</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.228158</td>\n",
              "      <td>11.984694</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.444788</td>\n",
              "      <td>12.142223</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.658012</td>\n",
              "      <td>12.304680</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13.949472</td>\n",
              "      <td>12.468627</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.219741</td>\n",
              "      <td>12.617351</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.444618</td>\n",
              "      <td>12.753420</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.622520</td>\n",
              "      <td>12.871722</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.786715</td>\n",
              "      <td>12.994134</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.886960</td>\n",
              "      <td>13.105031</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.954905</td>\n",
              "      <td>13.201740</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>14.999142</td>\n",
              "      <td>13.464019</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.045085</td>\n",
              "      <td>13.709680</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.032081</td>\n",
              "      <td>13.931324</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.059549</td>\n",
              "      <td>14.129591</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.160943</td>\n",
              "      <td>14.322116</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.296779</td>\n",
              "      <td>14.492174</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.439683</td>\n",
              "      <td>14.638019</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.614062</td>\n",
              "      <td>14.772717</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.770806</td>\n",
              "      <td>14.916697</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>15.853132</td>\n",
              "      <td>15.047180</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.504994</td>\n",
              "      <td>33.475943</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.915122</td>\n",
              "      <td>33.260574</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.468948</td>\n",
              "      <td>33.108936</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>30.109908</td>\n",
              "      <td>32.933836</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.633698</td>\n",
              "      <td>32.623513</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.130478</td>\n",
              "      <td>32.303471</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>28.462753</td>\n",
              "      <td>31.978119</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.784179</td>\n",
              "      <td>31.568884</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.471462</td>\n",
              "      <td>31.200050</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.589307</td>\n",
              "      <td>30.858969</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.869125</td>\n",
              "      <td>30.634986</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>28.430627</td>\n",
              "      <td>30.479994</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.396606</td>\n",
              "      <td>30.364420</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.359322</td>\n",
              "      <td>30.276097</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.215311</td>\n",
              "      <td>30.202248</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.753305</td>\n",
              "      <td>30.064475</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.119003</td>\n",
              "      <td>29.981876</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.305331</td>\n",
              "      <td>29.988766</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.311458</td>\n",
              "      <td>30.063037</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.279236</td>\n",
              "      <td>30.179388</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.070461</td>\n",
              "      <td>30.205842</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.635415</td>\n",
              "      <td>30.161949</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.133141</td>\n",
              "      <td>30.154814</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.674603</td>\n",
              "      <td>30.204211</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.541442</td>\n",
              "      <td>30.406324</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.914234</td>\n",
              "      <td>30.651781</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>31.603277</td>\n",
              "      <td>30.947080</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.453729</td>\n",
              "      <td>31.322202</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.435014</td>\n",
              "      <td>31.695099</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>34.137875</td>\n",
              "      <td>32.043466</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            signal  short_mavg  long_mavg  positions\n",
              "Date                                                \n",
              "2010-11-19     0.0    9.770053   9.770053        NaN\n",
              "2010-11-22     0.0    9.925555   9.925555        0.0\n",
              "2010-11-23     0.0   10.142779  10.142779        0.0\n",
              "2010-11-24     0.0   10.425375  10.425375        0.0\n",
              "2010-11-26     0.0   10.679439  10.679439        0.0\n",
              "2010-11-29     1.0   11.184157  10.948473        1.0\n",
              "2010-11-30     1.0   11.726324  11.211818        0.0\n",
              "2010-12-01     1.0   12.248489  11.458848        0.0\n",
              "2010-12-02     1.0   12.622384  11.645936        0.0\n",
              "2010-12-03     1.0   12.952874  11.816156        0.0\n",
              "2010-12-06     1.0   13.228158  11.984694        0.0\n",
              "2010-12-07     1.0   13.444788  12.142223        0.0\n",
              "2010-12-08     1.0   13.658012  12.304680        0.0\n",
              "2010-12-09     1.0   13.949472  12.468627        0.0\n",
              "2010-12-10     1.0   14.219741  12.617351        0.0\n",
              "2010-12-13     1.0   14.444618  12.753420        0.0\n",
              "2010-12-14     1.0   14.622520  12.871722        0.0\n",
              "2010-12-15     1.0   14.786715  12.994134        0.0\n",
              "2010-12-16     1.0   14.886960  13.105031        0.0\n",
              "2010-12-17     1.0   14.954905  13.201740        0.0\n",
              "2010-12-20     1.0   14.999142  13.464019        0.0\n",
              "2010-12-21     1.0   15.045085  13.709680        0.0\n",
              "2010-12-22     1.0   15.032081  13.931324        0.0\n",
              "2010-12-23     1.0   15.059549  14.129591        0.0\n",
              "2010-12-27     1.0   15.160943  14.322116        0.0\n",
              "2010-12-28     1.0   15.296779  14.492174        0.0\n",
              "2010-12-29     1.0   15.439683  14.638019        0.0\n",
              "2010-12-30     1.0   15.614062  14.772717        0.0\n",
              "2010-12-31     1.0   15.770806  14.916697        0.0\n",
              "2011-01-03     1.0   15.853132  15.047180        0.0\n",
              "...            ...         ...        ...        ...\n",
              "2011-11-17     0.0   31.504994  33.475943        0.0\n",
              "2011-11-18     0.0   30.915122  33.260574        0.0\n",
              "2011-11-21     0.0   30.468948  33.108936        0.0\n",
              "2011-11-22     0.0   30.109908  32.933836        0.0\n",
              "2011-11-23     0.0   29.633698  32.623513        0.0\n",
              "2011-11-25     0.0   29.130478  32.303471        0.0\n",
              "2011-11-28     0.0   28.462753  31.978119        0.0\n",
              "2011-11-29     0.0   27.784179  31.568884        0.0\n",
              "2011-11-30     0.0   27.471462  31.200050        0.0\n",
              "2011-12-01     0.0   27.589307  30.858969        0.0\n",
              "2011-12-02     0.0   27.869125  30.634986        0.0\n",
              "2011-12-05     0.0   28.430627  30.479994        0.0\n",
              "2011-12-06     0.0   29.396606  30.364420        0.0\n",
              "2011-12-07     1.0   30.359322  30.276097        1.0\n",
              "2011-12-08     1.0   31.215311  30.202248        0.0\n",
              "2011-12-09     1.0   31.753305  30.064475        0.0\n",
              "2011-12-12     1.0   32.119003  29.981876        0.0\n",
              "2011-12-13     1.0   32.305331  29.988766        0.0\n",
              "2011-12-14     1.0   32.311458  30.063037        0.0\n",
              "2011-12-15     1.0   32.279236  30.179388        0.0\n",
              "2011-12-16     1.0   32.070461  30.205842        0.0\n",
              "2011-12-19     1.0   31.635415  30.161949        0.0\n",
              "2011-12-20     1.0   31.133141  30.154814        0.0\n",
              "2011-12-21     1.0   30.674603  30.204211        0.0\n",
              "2011-12-22     1.0   30.541442  30.406324        0.0\n",
              "2011-12-23     1.0   30.914234  30.651781        0.0\n",
              "2011-12-27     1.0   31.603277  30.947080        0.0\n",
              "2011-12-28     1.0   32.453729  31.322202        0.0\n",
              "2011-12-29     1.0   33.435014  31.695099        0.0\n",
              "2011-12-30     1.0   34.137875  32.043466        0.0\n",
              "\n",
              "[281 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "WJNyLPAdipED",
        "colab_type": "code",
        "outputId": "332462e4-b0e4-4b96-ebb9-a2e67befd464",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "cell_type": "code",
      "source": [
        "# Initialize the plot figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a subplot and label for y-axis\n",
        "ax1 = fig.add_subplot(111,  ylabel='Price in $')\n",
        "\n",
        "# Plot the closing price\n",
        "pred.plot(ax=ax1, color='r', lw=2.)\n",
        "\n",
        "# Plot the short and long moving averages\n",
        "psignals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)\n",
        "\n",
        "# Plot the buy signals\n",
        "ax1.plot(psignals.loc[psignals.positions == 1.0].index, \n",
        "         psignals.short_mavg[psignals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "         \n",
        "# Plot the sell signals\n",
        "ax1.plot(psignals.loc[psignals.positions == -1.0].index, \n",
        "         psignals.short_mavg[psignals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "         \n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x+H/W8tTCDWzZ13lFNdoNVUUuL6PaHAsc2uoO4bnsClSzhdTCPQCU17havVaq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\n5iaTiWeeeabZ+wsWLOjoqQUCwQlAQw/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+g7iKvMYxUa0ob6GmmhijYm1nRCuBVOVVDmz1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\n1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PwAiIuLc5jM15N1zZo1BAQEAKDT6Rxut9r1Zm6c4OLh4eFw58Fe72e68X0Gxzw00NLP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\nvr4cBi2kAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KXugzj4rwR8a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "BACKTESTING\n"
      ]
    },
    {
      "metadata": {
        "id": "8FsLQx_juXqh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Set the initial capital\n",
        "initial_capital= float(100000.0)\n",
        "\n",
        "# Create a DataFrame `positions`\n",
        "positions = pd.DataFrame(index=signals.index).fillna(0.0)\n",
        "\n",
        "# Buy a 100 shares\n",
        "positions['AAPL'] = 100*signals['signal']   \n",
        "  \n",
        "# Initialize the portfolio with value owned   \n",
        "portfolio = positions.multiply(df['Adj Close'].tail(281), axis=0)\n",
        "\n",
        "# Store the difference in shares owned \n",
        "pos_diff = positions.diff()\n",
        "\n",
        "# Add `holdings` to portfolio\n",
        "portfolio['holdings'] = (positions.multiply(df['Adj Close'].tail(281), axis=0)).sum(axis=1)\n",
        "\n",
        "# Add `cash` to portfolio\n",
        "portfolio['cash'] = initial_capital - (pos_diff.multiply(df['Adj Close'].tail(281), axis=0)).sum(axis=1).cumsum()   \n",
        "\n",
        "# Add `total` to portfolio\n",
        "portfolio['total'] = portfolio['cash'] + portfolio['holdings']\n",
        "\n",
        "# Add `returns` to portfolio\n",
        "portfolio['returns'] = portfolio['total'].pct_change()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HpUyorDcxvuH",
        "colab_type": "code",
        "outputId": "906e6998-3c4a-4446-b70b-703ae0d8dc44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "portfolio"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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</th>\n",
              "      <th>holdings</th>\n",
              "      <th>cash</th>\n",
              "      <th>total</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>2010-11-19</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>3018.427086</td>\n",
              "      <td>3018.427086</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>2963.939095</td>\n",
              "      <td>2963.939095</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>99945.512009</td>\n",
              "      <td>-0.000545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>3013.949203</td>\n",
              "      <td>3013.949203</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>99995.522118</td>\n",
              "      <td>0.000500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>3030.619812</td>\n",
              "      <td>3030.619812</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100012.192726</td>\n",
              "      <td>0.000167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>3023.855782</td>\n",
              "      <td>3023.855782</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100005.428696</td>\n",
              "      <td>-0.000068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>3049.670792</td>\n",
              "      <td>3049.670792</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100031.243706</td>\n",
              "      <td>0.000258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>3031.190681</td>\n",
              "      <td>3031.190681</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100012.763596</td>\n",
              "      <td>-0.000185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>3057.863235</td>\n",
              "      <td>3057.863235</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100039.436150</td>\n",
              "      <td>0.000267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>3045.956993</td>\n",
              "      <td>3045.956993</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100027.529907</td>\n",
              "      <td>-0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>3053.576469</td>\n",
              "      <td>3053.576469</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100035.149384</td>\n",
              "      <td>0.000076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>3064.150238</td>\n",
              "      <td>3064.150238</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100045.723152</td>\n",
              "      <td>0.000106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>3051.004791</td>\n",
              "      <td>3051.004791</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100032.577705</td>\n",
              "      <td>-0.000131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>3051.671028</td>\n",
              "      <td>3051.671028</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100033.243942</td>\n",
              "      <td>0.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>3060.149574</td>\n",
              "      <td>3060.149574</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100041.722488</td>\n",
              "      <td>0.000085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>3054.052734</td>\n",
              "      <td>3054.052734</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100035.625648</td>\n",
              "      <td>-0.000061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>3069.293976</td>\n",
              "      <td>3069.293976</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100050.866890</td>\n",
              "      <td>0.000152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>3088.249969</td>\n",
              "      <td>3088.249969</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100069.822884</td>\n",
              "      <td>0.000189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>3097.394180</td>\n",
              "      <td>3097.394180</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100078.967094</td>\n",
              "      <td>0.000091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>3082.535744</td>\n",
              "      <td>3082.535744</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100064.108658</td>\n",
              "      <td>-0.000148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>3092.823029</td>\n",
              "      <td>3092.823029</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100074.395943</td>\n",
              "      <td>0.000103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>3100.348091</td>\n",
              "      <td>3100.348091</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100081.921005</td>\n",
              "      <td>0.000075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>3098.633385</td>\n",
              "      <td>3098.633385</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100080.206299</td>\n",
              "      <td>-0.000017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>3083.106995</td>\n",
              "      <td>3083.106995</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100064.679909</td>\n",
              "      <td>-0.000155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>3072.629356</td>\n",
              "      <td>3072.629356</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100054.202271</td>\n",
              "      <td>-0.000105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>3139.404106</td>\n",
              "      <td>3139.404106</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100120.977020</td>\n",
              "      <td>0.000667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-17</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>3724.095535</td>\n",
              "      <td>3724.095535</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99896.837807</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>3706.377029</td>\n",
              "      <td>3706.377029</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99879.119301</td>\n",
              "      <td>-0.000177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>3721.331787</td>\n",
              "      <td>3721.331787</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99894.074059</td>\n",
              "      <td>0.000150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>3749.529266</td>\n",
              "      <td>3749.529266</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99922.271538</td>\n",
              "      <td>0.000282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>3732.572556</td>\n",
              "      <td>3732.572556</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99905.314827</td>\n",
              "      <td>-0.000170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>3703.709412</td>\n",
              "      <td>3703.709412</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99876.451683</td>\n",
              "      <td>-0.000289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>3621.598434</td>\n",
              "      <td>3621.598434</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99794.340706</td>\n",
              "      <td>-0.000822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>3609.690857</td>\n",
              "      <td>3609.690857</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99782.433128</td>\n",
              "      <td>-0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>3629.504013</td>\n",
              "      <td>3629.504013</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99802.246284</td>\n",
              "      <td>0.000199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>3640.839005</td>\n",
              "      <td>3640.839005</td>\n",
              "      <td>96172.742271</td>\n",
              "      <td>99813.581276</td>\n",
              "      <td>0.000114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99944.465828</td>\n",
              "      <td>99944.465828</td>\n",
              "      <td>0.001311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>3776.486588</td>\n",
              "      <td>3776.486588</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>99944.465828</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>3796.489716</td>\n",
              "      <td>3796.489716</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>99964.468956</td>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>3842.023468</td>\n",
              "      <td>3842.023468</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>100010.002708</td>\n",
              "      <td>0.000455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>3872.507095</td>\n",
              "      <td>3872.507095</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>100040.486336</td>\n",
              "      <td>0.000305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>3835.451508</td>\n",
              "      <td>3835.451508</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>100003.430748</td>\n",
              "      <td>-0.000370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>3859.074402</td>\n",
              "      <td>3859.074402</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>100027.053642</td>\n",
              "      <td>0.000236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>3857.931519</td>\n",
              "      <td>3857.931519</td>\n",
              "      <td>96167.979240</td>\n",
              "      <td>100025.910759</td>\n",
              "      <td>-0.000011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   AAPL     holdings           cash          total   returns\n",
              "Date                                                                        \n",
              "2010-11-19     0.000000     0.000000  100000.000000  100000.000000       NaN\n",
              "2010-11-22     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-23     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-24     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-26     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-29  3018.427086  3018.427086   96981.572914  100000.000000  0.000000\n",
              "2010-11-30  2963.939095  2963.939095   96981.572914   99945.512009 -0.000545\n",
              "2010-12-01  3013.949203  3013.949203   96981.572914   99995.522118  0.000500\n",
              "2010-12-02  3030.619812  3030.619812   96981.572914  100012.192726  0.000167\n",
              "2010-12-03  3023.855782  3023.855782   96981.572914  100005.428696 -0.000068\n",
              "2010-12-06  3049.670792  3049.670792   96981.572914  100031.243706  0.000258\n",
              "2010-12-07  3031.190681  3031.190681   96981.572914  100012.763596 -0.000185\n",
              "2010-12-08  3057.863235  3057.863235   96981.572914  100039.436150  0.000267\n",
              "2010-12-09  3045.956993  3045.956993   96981.572914  100027.529907 -0.000119\n",
              "2010-12-10  3053.576469  3053.576469   96981.572914  100035.149384  0.000076\n",
              "2010-12-13  3064.150238  3064.150238   96981.572914  100045.723152  0.000106\n",
              "2010-12-14  3051.004791  3051.004791   96981.572914  100032.577705 -0.000131\n",
              "2010-12-15  3051.671028  3051.671028   96981.572914  100033.243942  0.000007\n",
              "2010-12-16  3060.149574  3060.149574   96981.572914  100041.722488  0.000085\n",
              "2010-12-17  3054.052734  3054.052734   96981.572914  100035.625648 -0.000061\n",
              "2010-12-20  3069.293976  3069.293976   96981.572914  100050.866890  0.000152\n",
              "2010-12-21  3088.249969  3088.249969   96981.572914  100069.822884  0.000189\n",
              "2010-12-22  3097.394180  3097.394180   96981.572914  100078.967094  0.000091\n",
              "2010-12-23  3082.535744  3082.535744   96981.572914  100064.108658 -0.000148\n",
              "2010-12-27  3092.823029  3092.823029   96981.572914  100074.395943  0.000103\n",
              "2010-12-28  3100.348091  3100.348091   96981.572914  100081.921005  0.000075\n",
              "2010-12-29  3098.633385  3098.633385   96981.572914  100080.206299 -0.000017\n",
              "2010-12-30  3083.106995  3083.106995   96981.572914  100064.679909 -0.000155\n",
              "2010-12-31  3072.629356  3072.629356   96981.572914  100054.202271 -0.000105\n",
              "2011-01-03  3139.404106  3139.404106   96981.572914  100120.977020  0.000667\n",
              "...                 ...          ...            ...            ...       ...\n",
              "2011-11-17     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-18     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-21     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-22     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-23     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-25     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-28     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-29     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-11-30     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-12-01     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-12-02     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-12-05     0.000000     0.000000   99896.837807   99896.837807  0.000000\n",
              "2011-12-06  3724.095535  3724.095535   96172.742271   99896.837807  0.000000\n",
              "2011-12-07  3706.377029  3706.377029   96172.742271   99879.119301 -0.000177\n",
              "2011-12-08  3721.331787  3721.331787   96172.742271   99894.074059  0.000150\n",
              "2011-12-09  3749.529266  3749.529266   96172.742271   99922.271538  0.000282\n",
              "2011-12-12  3732.572556  3732.572556   96172.742271   99905.314827 -0.000170\n",
              "2011-12-13  3703.709412  3703.709412   96172.742271   99876.451683 -0.000289\n",
              "2011-12-14  3621.598434  3621.598434   96172.742271   99794.340706 -0.000822\n",
              "2011-12-15  3609.690857  3609.690857   96172.742271   99782.433128 -0.000119\n",
              "2011-12-16  3629.504013  3629.504013   96172.742271   99802.246284  0.000199\n",
              "2011-12-19  3640.839005  3640.839005   96172.742271   99813.581276  0.000114\n",
              "2011-12-20     0.000000     0.000000   99944.465828   99944.465828  0.001311\n",
              "2011-12-21  3776.486588  3776.486588   96167.979240   99944.465828  0.000000\n",
              "2011-12-22  3796.489716  3796.489716   96167.979240   99964.468956  0.000200\n",
              "2011-12-23  3842.023468  3842.023468   96167.979240  100010.002708  0.000455\n",
              "2011-12-27  3872.507095  3872.507095   96167.979240  100040.486336  0.000305\n",
              "2011-12-28  3835.451508  3835.451508   96167.979240  100003.430748 -0.000370\n",
              "2011-12-29  3859.074402  3859.074402   96167.979240  100027.053642  0.000236\n",
              "2011-12-30  3857.931519  3857.931519   96167.979240  100025.910759 -0.000011\n",
              "\n",
              "[281 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "4TQzfnA54uj9",
        "colab_type": "code",
        "outputId": "085d8330-cf7c-4020-ce50-3bfc786e5094",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "cell_type": "code",
      "source": [
        "# Import the `pyplot` module as `plt`\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a figure\n",
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')\n",
        "\n",
        "# Plot the equity curve in dollars\n",
        "portfolio['total'].plot(ax=ax1, lw=2.)\n",
        "\n",
        "ax1.plot(portfolio.loc[signals.positions == 1.0].index, \n",
        "         portfolio.total[signals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "ax1.plot(portfolio.loc[signals.positions == -1.0].index, \n",
        "         portfolio.total[signals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvYbi4mJUV1dDLpdj3bp1uP/++8EwDB544AGo1YHVy8aNG/Hoo4/C7XajoqIClZWVAIA1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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DFHPHIG4zPQu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Set the initial capital\n",
        "initial_capital= float(100000.0)\n",
        "\n",
        "# Create a DataFrame `positions`\n",
        "ppositions = pd.DataFrame(index=psignals.index).fillna(0.0)\n",
        "\n",
        "# Buy a 100 shares\n",
        "ppositions['AAPL'] = 100*psignals['signal']   \n",
        "  \n",
        "# Initialize the portfolio with value owned   \n",
        "pportfolio = ppositions.multiply(df['Adj Close'].tail(281), axis=0)\n",
        "\n",
        "# Store the difference in shares owned \n",
        "ppos_diff = ppositions.diff()\n",
        "\n",
        "# Add `holdings` to portfolio\n",
        "pportfolio['holdings'] = (ppositions.multiply(df['Adj Close'].tail(281), axis=0)).sum(axis=1)\n",
        "\n",
        "# Add `cash` to portfolio\n",
        "pportfolio['cash'] = initial_capital - (ppos_diff.multiply(df['Adj Close'].tail(281), axis=0)).sum(axis=1).cumsum()   \n",
        "\n",
        "# Add `total` to portfolio\n",
        "pportfolio['total'] = pportfolio['cash'] + pportfolio['holdings']\n",
        "\n",
        "# Add `returns` to portfolio\n",
        "pportfolio['returns'] = pportfolio['total'].pct_change()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "obD6ZGURm365",
        "colab_type": "code",
        "outputId": "6274d9ff-efc8-4ccc-dd8b-6af99b3abd0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "pportfolio"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AAPL</th>\n",
              "      <th>holdings</th>\n",
              "      <th>cash</th>\n",
              "      <th>total</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>2010-11-19</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
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              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>3018.427086</td>\n",
              "      <td>3018.427086</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>2963.939095</td>\n",
              "      <td>2963.939095</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>99945.512009</td>\n",
              "      <td>-0.000545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>3013.949203</td>\n",
              "      <td>3013.949203</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>99995.522118</td>\n",
              "      <td>0.000500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>3030.619812</td>\n",
              "      <td>3030.619812</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100012.192726</td>\n",
              "      <td>0.000167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>3023.855782</td>\n",
              "      <td>3023.855782</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100005.428696</td>\n",
              "      <td>-0.000068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>3049.670792</td>\n",
              "      <td>3049.670792</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100031.243706</td>\n",
              "      <td>0.000258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>3031.190681</td>\n",
              "      <td>3031.190681</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100012.763596</td>\n",
              "      <td>-0.000185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>3057.863235</td>\n",
              "      <td>3057.863235</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100039.436150</td>\n",
              "      <td>0.000267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>3045.956993</td>\n",
              "      <td>3045.956993</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100027.529907</td>\n",
              "      <td>-0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>3053.576469</td>\n",
              "      <td>3053.576469</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100035.149384</td>\n",
              "      <td>0.000076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>3064.150238</td>\n",
              "      <td>3064.150238</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100045.723152</td>\n",
              "      <td>0.000106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>3051.004791</td>\n",
              "      <td>3051.004791</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100032.577705</td>\n",
              "      <td>-0.000131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>3051.671028</td>\n",
              "      <td>3051.671028</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100033.243942</td>\n",
              "      <td>0.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>3060.149574</td>\n",
              "      <td>3060.149574</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100041.722488</td>\n",
              "      <td>0.000085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>3054.052734</td>\n",
              "      <td>3054.052734</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100035.625648</td>\n",
              "      <td>-0.000061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>3069.293976</td>\n",
              "      <td>3069.293976</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100050.866890</td>\n",
              "      <td>0.000152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>3088.249969</td>\n",
              "      <td>3088.249969</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100069.822884</td>\n",
              "      <td>0.000189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>3097.394180</td>\n",
              "      <td>3097.394180</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100078.967094</td>\n",
              "      <td>0.000091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>3082.535744</td>\n",
              "      <td>3082.535744</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100064.108658</td>\n",
              "      <td>-0.000148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>3092.823029</td>\n",
              "      <td>3092.823029</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100074.395943</td>\n",
              "      <td>0.000103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>3100.348091</td>\n",
              "      <td>3100.348091</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100081.921005</td>\n",
              "      <td>0.000075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>3098.633385</td>\n",
              "      <td>3098.633385</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100080.206299</td>\n",
              "      <td>-0.000017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>3083.106995</td>\n",
              "      <td>3083.106995</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100064.679909</td>\n",
              "      <td>-0.000155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>3072.629356</td>\n",
              "      <td>3072.629356</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100054.202271</td>\n",
              "      <td>-0.000105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>3139.404106</td>\n",
              "      <td>3139.404106</td>\n",
              "      <td>96981.572914</td>\n",
              "      <td>100120.977020</td>\n",
              "      <td>0.000667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-17</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>3706.377029</td>\n",
              "      <td>3706.377029</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99873.785210</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>3721.331787</td>\n",
              "      <td>3721.331787</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99888.739967</td>\n",
              "      <td>0.000150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>3749.529266</td>\n",
              "      <td>3749.529266</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99916.937447</td>\n",
              "      <td>0.000282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>3732.572556</td>\n",
              "      <td>3732.572556</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99899.980736</td>\n",
              "      <td>-0.000170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>3703.709412</td>\n",
              "      <td>3703.709412</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99871.117592</td>\n",
              "      <td>-0.000289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>3621.598434</td>\n",
              "      <td>3621.598434</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99789.006615</td>\n",
              "      <td>-0.000822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>3609.690857</td>\n",
              "      <td>3609.690857</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99777.099037</td>\n",
              "      <td>-0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>3629.504013</td>\n",
              "      <td>3629.504013</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99796.912193</td>\n",
              "      <td>0.000199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>3640.839005</td>\n",
              "      <td>3640.839005</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99808.247185</td>\n",
              "      <td>0.000114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>3771.723557</td>\n",
              "      <td>3771.723557</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99939.131737</td>\n",
              "      <td>0.001311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>3776.486588</td>\n",
              "      <td>3776.486588</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99943.894768</td>\n",
              "      <td>0.000048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>3796.489716</td>\n",
              "      <td>3796.489716</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>99963.897896</td>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>3842.023468</td>\n",
              "      <td>3842.023468</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>100009.431648</td>\n",
              "      <td>0.000456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>3872.507095</td>\n",
              "      <td>3872.507095</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>100039.915276</td>\n",
              "      <td>0.000305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>3835.451508</td>\n",
              "      <td>3835.451508</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>100002.859688</td>\n",
              "      <td>-0.000370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>3859.074402</td>\n",
              "      <td>3859.074402</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>100026.482582</td>\n",
              "      <td>0.000236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>3857.931519</td>\n",
              "      <td>3857.931519</td>\n",
              "      <td>96167.408180</td>\n",
              "      <td>100025.339699</td>\n",
              "      <td>-0.000011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>281 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   AAPL     holdings           cash          total   returns\n",
              "Date                                                                        \n",
              "2010-11-19     0.000000     0.000000  100000.000000  100000.000000       NaN\n",
              "2010-11-22     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-23     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-24     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-26     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-29  3018.427086  3018.427086   96981.572914  100000.000000  0.000000\n",
              "2010-11-30  2963.939095  2963.939095   96981.572914   99945.512009 -0.000545\n",
              "2010-12-01  3013.949203  3013.949203   96981.572914   99995.522118  0.000500\n",
              "2010-12-02  3030.619812  3030.619812   96981.572914  100012.192726  0.000167\n",
              "2010-12-03  3023.855782  3023.855782   96981.572914  100005.428696 -0.000068\n",
              "2010-12-06  3049.670792  3049.670792   96981.572914  100031.243706  0.000258\n",
              "2010-12-07  3031.190681  3031.190681   96981.572914  100012.763596 -0.000185\n",
              "2010-12-08  3057.863235  3057.863235   96981.572914  100039.436150  0.000267\n",
              "2010-12-09  3045.956993  3045.956993   96981.572914  100027.529907 -0.000119\n",
              "2010-12-10  3053.576469  3053.576469   96981.572914  100035.149384  0.000076\n",
              "2010-12-13  3064.150238  3064.150238   96981.572914  100045.723152  0.000106\n",
              "2010-12-14  3051.004791  3051.004791   96981.572914  100032.577705 -0.000131\n",
              "2010-12-15  3051.671028  3051.671028   96981.572914  100033.243942  0.000007\n",
              "2010-12-16  3060.149574  3060.149574   96981.572914  100041.722488  0.000085\n",
              "2010-12-17  3054.052734  3054.052734   96981.572914  100035.625648 -0.000061\n",
              "2010-12-20  3069.293976  3069.293976   96981.572914  100050.866890  0.000152\n",
              "2010-12-21  3088.249969  3088.249969   96981.572914  100069.822884  0.000189\n",
              "2010-12-22  3097.394180  3097.394180   96981.572914  100078.967094  0.000091\n",
              "2010-12-23  3082.535744  3082.535744   96981.572914  100064.108658 -0.000148\n",
              "2010-12-27  3092.823029  3092.823029   96981.572914  100074.395943  0.000103\n",
              "2010-12-28  3100.348091  3100.348091   96981.572914  100081.921005  0.000075\n",
              "2010-12-29  3098.633385  3098.633385   96981.572914  100080.206299 -0.000017\n",
              "2010-12-30  3083.106995  3083.106995   96981.572914  100064.679909 -0.000155\n",
              "2010-12-31  3072.629356  3072.629356   96981.572914  100054.202271 -0.000105\n",
              "2011-01-03  3139.404106  3139.404106   96981.572914  100120.977020  0.000667\n",
              "...                 ...          ...            ...            ...       ...\n",
              "2011-11-17     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-18     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-21     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-22     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-23     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-25     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-28     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-29     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-11-30     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-12-01     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-12-02     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-12-05     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-12-06     0.000000     0.000000   99873.785210   99873.785210  0.000000\n",
              "2011-12-07  3706.377029  3706.377029   96167.408180   99873.785210  0.000000\n",
              "2011-12-08  3721.331787  3721.331787   96167.408180   99888.739967  0.000150\n",
              "2011-12-09  3749.529266  3749.529266   96167.408180   99916.937447  0.000282\n",
              "2011-12-12  3732.572556  3732.572556   96167.408180   99899.980736 -0.000170\n",
              "2011-12-13  3703.709412  3703.709412   96167.408180   99871.117592 -0.000289\n",
              "2011-12-14  3621.598434  3621.598434   96167.408180   99789.006615 -0.000822\n",
              "2011-12-15  3609.690857  3609.690857   96167.408180   99777.099037 -0.000119\n",
              "2011-12-16  3629.504013  3629.504013   96167.408180   99796.912193  0.000199\n",
              "2011-12-19  3640.839005  3640.839005   96167.408180   99808.247185  0.000114\n",
              "2011-12-20  3771.723557  3771.723557   96167.408180   99939.131737  0.001311\n",
              "2011-12-21  3776.486588  3776.486588   96167.408180   99943.894768  0.000048\n",
              "2011-12-22  3796.489716  3796.489716   96167.408180   99963.897896  0.000200\n",
              "2011-12-23  3842.023468  3842.023468   96167.408180  100009.431648  0.000456\n",
              "2011-12-27  3872.507095  3872.507095   96167.408180  100039.915276  0.000305\n",
              "2011-12-28  3835.451508  3835.451508   96167.408180  100002.859688 -0.000370\n",
              "2011-12-29  3859.074402  3859.074402   96167.408180  100026.482582  0.000236\n",
              "2011-12-30  3857.931519  3857.931519   96167.408180  100025.339699 -0.000011\n",
              "\n",
              "[281 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "I4N6XBBlm5a7",
        "colab_type": "code",
        "outputId": "c151ef25-0447-4f2d-bf99-b47c3d7844cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "cell_type": "code",
      "source": [
        "# Import the `pyplot` module as `plt`\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a figure\n",
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')\n",
        "\n",
        "# Plot the equity curve in dollars\n",
        "pportfolio['total'].plot(ax=ax1, lw=2.)\n",
        "\n",
        "ax1.plot(pportfolio.loc[psignals.positions == 1.0].index, \n",
        "         pportfolio.total[psignals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "ax1.plot(pportfolio.loc[psignals.positions == -1.0].index, \n",
        "         pportfolio.total[psignals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rOhSq65zweFVVFd5//300NzfjiSeewNq1a6MeIxQ6b/exqioltNr8koijWbPH40FPL3vT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\nEMLgJBkQQgghDE6SASGEEMLg/j9AeeXf6mbk6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mxMcTe89BFvr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "EVALUATION"
      ]
    },
    {
      "metadata": {
        "id": "t3g-kyMq8w00",
        "colab_type": "code",
        "outputId": "44d9d1f3-24f4-4b5e-e167-a2049733e657",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Isolate the returns of your strategy\n",
        "returns = portfolio['returns']\n",
        "\n",
        "# annualized Sharpe ratio\n",
        "sharpe_ratio = np.sqrt(252) * (returns.mean() / returns.std())\n",
        "\n",
        "# Print the Sharpe ratio\n",
        "print(sharpe_ratio)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.03901387936157988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Boy0SlGMBNqI",
        "colab_type": "code",
        "outputId": "edbad3fe-33d4-48c4-d845-21a430126082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Isolate the returns of your strategy\n",
        "returns = pportfolio['returns']\n",
        "\n",
        "# annualized Sharpe ratio\n",
        "sharpe_ratio = np.sqrt(252) * (returns.mean() / returns.std())\n",
        "\n",
        "# Print the Sharpe ratio\n",
        "print(sharpe_ratio)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.03693736902986299\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MN7SOE0oBeKb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}