Algo_trading.ipynb

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    "colab": {
      "name": "Algo trading.ipynb",
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      "name": "python3",
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    {
      "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.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "VHzMw452lfTP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import quandl \n",
        "aapl = quandl.get(\"WIKI/AAPL\", start_date=\"2006-10-01\", end_date=\"2012-01-01\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5422c8f-3f4f-4e04-cd77-f4df1a344298"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
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        "colab_type": "code",
        "outputId": "4cc03bf5-be63-4243-aecd-d404a100a1b5",
        "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": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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            ],
            "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": 2
        }
      ]
    },
    {
      "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": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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
        }
      ]
    },
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        "colab": {
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          "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\n3RjKRrcDGBqtLYLtvrk888Nd3urQIIWXovFaj2rHBmyda0W4fR9qr5Og2uzPWd20Dsti8r7/ZJnS\nLIkY5HGQhTrjlLUPItNE7Rjs0cYtgs+5MYaSkjRWi6zO7iGOqjYLGSnrHIN8e52cT9YDCBz3N0te\nfzDjlbsjQLDaTzDWsVXWVLX3LF8fN4eqoOxGCVqN1ZiVXsqgl5HGESvdmG4noZcnTIqaprFY6/WN\nsyTyvYltoGCtW4i2V43x4uhnLIhotGHSDnA2xu74u58XlsXx54egqjEUlfaGDJ14x989SyRV7fVq\ny8Ywms4WVrZN+/nGGHRjKQ4ZkAI4AcY5nBPESnFzrUuSKrYmtTeiUFBXhpGr6PcShBCUlUFIf8MO\nkpTNcYm2fmDBSxL6xxbCl+xDT2lgB+vr69y8eROA4XDIZDKhaRrW1tYAuHHjBvfu3cMY89D3zb/n\nvLKwXRPiwKXmqjGL8v7WtKGX+zdSox0WaBpDEiviWFHWDfc2Siazito6EiXZHFmaxrIxLvn2Kxts\njEuq2pdIDpEkffh3saAixbCX4qwlwvHqvSnPvUlxd7NECUGeKpQSOOuQSpDEim6qoLVE7HeTRTA5\nqwzWOrT1i2SWRNTa7tiMJm0/EEA/jw88FKaN9TIh6uHhj7PG+ujpMqXgS/jfuzN+Kr3MwPFR1pqi\n8puosW7HfazktiSUFDutdg8yUbz71RZCsDbImJUaKeHVNyasbxZszWqwPpga9hKcM4ynJUX5dAFp\nKqDTTbl2JWOYZ3TSmCxSdDuKa6s5VwYpFt+qVJaacdkg0thrKuMWv4F120YofiDSPTQBftrM1yJg\noRl93jBm++88PwyVtVd0Ma26w7y1yljLaNowLry5gzU+uLu3UTJuW6nySFFJRWHMoQNS8PuK0d6d\nsNAObR1vGuR04pjGGLZmNZ1EkSSKTtv2FUcOIVrNbuvoZDFlZdrkxc73jLfdPlv30X6EoPSEuHnz\nJnfu3OHWrVtsbm6yurqKMYaNjQ0AXnvtNW7fvk1d1w9936NYXc2JzpgjwzKNtqxvzpCxL7P38uRA\nXr1JnrLVlnSTyPtHg1+ktRR0uwmxirh1NafWhplxrXSLIEkksRJMpg3fvzfG4cv53sDv6eikkKcR\nnTwmiQTT0gKCKInp5QlXBhmVsWityZOENJVYC1dXcrR1GG0Y9lK6Hd9snnRK34RuHP1ILKQ9Vnop\nSkmcc8hku0+y2zpJPY5GW0ZTn32MI7Vw+bh372xp7M7ZmGdKn7CnFODqSoe/f23ExrjyrjmBM4Vb\nevPtDmb8YI9iVhg//HOAQ9S8/cU77GyvKXPJKCkEa/0U6xybWURVa4QTCAlN03BnveH+ZsFoWlNW\nT/e7qRiuDVLecn3AtbU+0hpA4oRja1LRSWNfws8TpqlGSkmWKiIld/xdIuWzw402xJE6k5WQJFbt\nkNn5tUOd61tb60jbvWU+mDb/eI4xbrFxmDbD6g9RlqLWiAgMFq0b6uahpzowUuEHpPADU0I4hr0E\npSR5GtPv+grZaj9lkCeLIcBZpRHCa5UOezGRkgfaIx7FtGyoG0sSyxPXfT6fd9Q55GMf+xif//zn\nGQwGfOhDH+LTn/40n/vc5/jABz7AZz/7WcbjMS+88AJa6x3f97jFeWNj9sivnybOuUUZ3jpHN4uY\ndpIDDfc455DGT8ljHJtT34OVxJLIOWIhcEbjGsODBzNGmyV1pUkTST/KsMBoVGBsQyQFsRJ0Y0f5\nhIvGXLZDRRLhHOWsJumlxAgyJRmNSjIl2Nj0EjXdLCYWgHbUtebBgymz0g9nTSfVwl50MqsZF80i\ni1xGkiSS2Ho7nzv36lZKoKuY8QE27OWslBBQPYH250my6CntP02mdLuvNASlZ4808a5Gc4/u3ZSV\nbRUv/L0+H/zxLmsaYyxZuj2BL4TY+3Fq02a9HLPSZ2TXBh2eu91na1yzvlGwPquZjErKxmH001VP\nAJT0wYuxjshZRrVB65osjrHOZ2a9rqRg2E1JIrVv36jXLD67W7MUgv45LNkvI6VgkMc+M98GoN0s\nom7bpObSS0XlbUER/hA0H8bcmtZ00pjVPqxvFdSNozIO/YRZj0hBEkf08pQs9lJQCD9Qe22QESt/\nmIqjiF7mExpZEi2C53mckMbRUw++Lrur1Y0lje2JHo7O7p1/wXjb297G7/zO7yz+/Yu/+IsA/PIv\n//JD37v8fReBJPZivwLxUFlhP4TYKZOy3Lc07Pq+njT2ZXKlJLU2PBhXi81hY1xSNn66NYkka8MO\n42npN4dDNpTGCvIYenlClsR0soi8k5AlEc/f7CJbZymcw1hLP8/IEkUU+f4gL1tlMUah2jf3Ijsi\ntrOj+51KO6lvrj9MCT6JFFVj20zA2c2kz3kwqpBCHNpidJlrw22t0h969tEVhsDJM+/V2w+3lDJc\nzh42jV2UWItKP/Z+nj/OrGz8BDM+IH72xpDvNJskaYMbV1jhqBt36P7y3XRi6HVihr0UKSUIQS+L\naZoIhwEn0NpSVpqVnl/HOmm06PkLnA6idRFb/vfynlM3Zofc2JVhRpZECwvrrYlXfVBKeEWY8vCV\nOAWkiZc4u76ScfNKhzz2w7o+E62YFg1RJBn20ocsZyMliSI/iyClQB1BiV5KcapWpCEoDRwbQgjy\n1J/m+p2Y7iEE8h9FnsULDb+68UNCAj/YMJo1WAuNNVhjmc4MxgqKqmFa+GnLxxHjHZ6EYjFRvzrM\nuLnSZdo0pFHEzbUuz9/qo5Rk3Eq/aOtoakPcpmMHsUJJ0Q4uKdJEUVTezzprZbCyxAv/zz/ej8P2\nhC4yAXAunGLWRyWr/acb6ljIQm0FWaizwvJ0/ONe2zyLKCqvObx8eF1uKT3IvZwmCm0dRdsjeH9c\nMswThv3EDxsKaLRGN5ZEQtSNmEw1bWHhkUGqwAceUXtd3Y7gyrBDEkdI6TOlUkmwhkE3IU0kcawY\ndhNkm/lS5+D9eFmZS5Jp46WWqkYvDkiDPCFPIzanNZESSCkZz0rG04qy8g6BByVRkEjv/jXsp7zt\n1pBnbgy8GoMUJK2eaL+bUNU+U6m1RWY77x0h/N5q2j76g671VW1ojCWJ5ENSZLK1ItXaEUUHf8yj\nIgSlgWMlidWx6u8lrZCxUhIhYFI0lLUlSwS6MQghMM5isaSJxGIRxlsGLq8h880mFl4rTuI3t04W\n8dyNHmkS081juiLhxiBDKdUKEac464MqryEnSSKBbLUTtdFI6TOpSkp6nZ2Z4rnH8nEghDhjIxJ7\no41lc1LxA7eHT/U421ajYQL/LFDWepFp8hWER9/n88PbXp/vZn7A5iBZfyUl/U5M2U24t1kwmTVI\n4VjfKtgqGu5sFggh6XRihBKMZ5pBx7FVememeo+oNGmt3kTbx9PpSJIkop9FXBt2ME6Qd2LyWHF9\npbMYLkwSn+lSUqIOETQETpa56oFpe0wb7e1Bi1amLEu9XfWgm5BEEqckG5Mt7q4XvryP295E9mF5\nj+kkvvXk6kqXW1e6/Gc/eNVX/qQkktDJfGvB+mZJoy15FrO6zxDoYYeYtLHMqlY9QfuAd/eB0VuR\nHvghj5QQlAZOhUYbito3Z+dLQr8HwU+oOpSUTGY1m9OaLJbkqWKQRdQO0igiiRRIiRLgrEG3k/tx\n5G98o31wKoFOsu2+0e9EgCBLFUopjJPknYReJyGNJN1uQllram0YTSuiSLLSTymqBhDE8+desL1S\nGWuZFt6RKU+fvv/nIrC+VeIcXG+DyidltZ/6qdgQlJ4Jlkvw7gl77eYc9mA7L8XGkSSOBI1xvP5g\nCk4wmdbMGk3VWKRzZFlELR0dZ2m0w1XssBjtpTDoxQgLtXHgHGka+95vBw5BGimUkNTaywd4yTeJ\nA66vdvwmfwA9zMDpUGuzUBWoGu+655y3sd4YF8SRwjhfZrfWsjGueP3emEnRUBSOLJWUtUXBnqL5\niYBermiMIYklUvhAUCrfRy+Fz7wC9HspaaQYzXzZXgg/mNW5JHazISgNnArT0pdFjHFEyh6479FY\nu5BIShPFeNZgrfUOLbUhjiPKsubBuEQqgTOQphFrUQeovEyTEDghKIUmcr7JPEsjsliBFeR5TJ5G\nVI0hSyK6HT/RqCQ4IRDOS3doC9LaVhg7YTRTbaCpFhI33sFl+3er6u3Fr6g1cbT/cVQb2y5WF3sj\nmweR154yKJ33fYWg9GywGGxyjnzXQJJtP39cAxS27ckzxqIiSS+NSKKIcVkjlKArFMZYqsZRTiu0\nc6hIYY2mm8N45qWplIQkkWQqQmURa6lEIhgXDcLCsJ/w5utdauOIpKKfx3SziEEvPZGsaNUY6sa7\nDZ0Fy+F5Fk62rVvnZe3ysmPb/aOxklTaIpxFW0tdWoqiYVo0CCEZz2pmldeU7qQCQ0KtK2alIxNe\n4qluq3GJgmFPEStFLmJAg1PEaYyuNVE8d2Lyz5lEEptBrPx1SCEYdOM9qwhPQqQkeRotyvdn7TU6\n/bs4cKGZ+9Lv1hoUO8StD47WjlmlqRqDKiVpIrD+YOut1RL/uGPrpyYj5TOSsxKuDDLSKEI7QyQF\n00oiHPS6CWu9DCtgJU+IlOD6Ws79UUUsFf2uIotjamO50s/o5pEXLhaCfjemn3sh7m4Wt2U73x+3\n14a7vAAsy45Y59oA3WdTZqX/HYXwQxRnURrmqFgEpatPF5SCD2z/+h8fUNb6qWVRAk/HfoNNy8Ll\naSyfyON97sazX6XBtD2BIIilpNuJ+YE3D3nptTHCwta0YFpZtDVIJYicn76WeYxzkk7a4Kwjin3l\nI1KKQR4jleD6aodZ1RBLyY0rPdb6HfJMeYejRJFE6kQC0rkqAYA25kw4QM0q7VUIcJTCtEoCZ584\nkvRzL6BfNQajDcZa4kSRp743P4nVouaVRL5Na7OxxEmEspY8tSSRo9HebSlWUDaufeyENFEM84RK\nO+rG0M1j0khybZijnUMbRxYrstSX7rvt7MRe5fWnJU0UKWcz83o+7pjAuWSudQY+sFreQLz8hkHJ\nhxut90Ib77pitGFWeaekWVFTN5I4FsSNd+WYzgwqlvRlwtaoBOvIs5gsVsxqQy9P6XdjpLO8/qBs\npT4i3nqzT9F4bbh+nrA27DDoZr63NFUMexlVrRcuSr2OX56SWC3aD1zH+ezmHkH4nCyJ/FAW2/1x\n1jrGsxrrQCnBIPfe2eDLnvO+n4vK3K/+aTOly49xf6vkmWu9p368wNHTaPuQcPlhf35S+AL7fkGt\nUpKyMr4aoQRKKW6v5Kz0Ul65O+I739ftUJWXAXJWkCWKfp6gG8M4Vkj82lFrQ5bFrHYT3nQtRxvB\ntWGH1UGHYS/h2kqOFIJxUbdrw8npOs4NB84KUgjm8vHnrVMhUhJtfHpzc1qC81KCSSyJhNfZxjmM\n8feVEI5hPyGpBY2GSCqKpiaJUqyxSKHo4IilYHWQMcwjrg5zpJRMS90Gq34/Wekm3FzrUFZ2MbQU\nR2ff9OQ4CEFp4NjQSxuONnZHUDq32zwI1jnGs4amMai2t6ZqfF+Pdb5BXQgHTnBtNSUrJEVt6KUS\nEAx6Kav9lLrWTBvvoFRVhl7XksWC1V7GjWtdZoVhMqvJEn8yxkEnj+kkik6qFuX4ucbobts2IcSB\nSizprt4gbexictMYX3aMI0nV2EU/0UXmqMr3/jG2tUpDUHo28fe2WUjrHBa9pOnWmL0jsrrNeM0q\nLwvVzxOKSiGE5e5mSa0FTkp6WULakzgpKCuLxSEixZtXO2AdK72UKPIBiZKCq4MOQnqdTu877gca\nO6nkymDn/Xvc7TdSCLpZRNVY4kOsp8dJns2rSI9WEzmrREpS1NorNwBZpBjkKUVlcNYynlW8ul7w\nyp0Ro4kmzxT9TkqkJMYaHoyklwBzjn5H4lAILKu9jEEvYW2lw2ovY9iNMc6L9SeRoNtJ2j3RV8wu\nc/9xCEoDx4bXJ/WSTYcNrKxzlJXGOUdjHA+2Cu6PSjpJxPXVjCSSRJHg9fWCuvYBr5CC3OHlX3JH\nWVtv6SlF60XvmEwaqsaw0k8YdhJvZYpkc1y1QaCX4uimEa7jBY0bbWi0JYkUaazIs6jN9hxNT1zU\n9q1C0U4AACAASURBVPXMg1HvbhOTxJenpzSJJYP86TNM21qlQRbqrOKco9O6GR3EQnQ3y0Ftsse6\nMp+kts478VjjKCtD1cx4Y33Ga/enGNsOOaURFokUDqkcnbZEay1EUjIcZFwddvwapvx7v24sUkhG\n07p12/GDl8vqAvMq0XG33+ynWHBaSCHOTcl+L6T0MwN5GiGclwdLk4hZ2WBcxIPNkjfWZ2zNNOOy\nQsmUaysplXFYJ6itI8LbYOtUcW3QIVaOXtdPzteNIU0VeRYzLX3riGO7lUsKgYwu9nr/OM7v3RM4\n83Ra9xUhDq+zWVS6Lat5oXxjfQDocIynDf1ewpVhh9GsQSkNVtDLEyQOgyBPIq6vpuATqKxvzWiM\nodS+9C+V5Mow84uCEzTa95mmiZewWh1kWOdotJ+2nWsMVrVpB6t8hkYb99SLsBReU9S2igJzzkLm\n47ixznHnwYwba/mRZAYWWqVh2OlMMncnA8gS6KSHv8cjJb1trttf2Fu2phRR2/s3rTWv35swmta+\nHK8UaS/lyjCj0YaysmSxn7DOs4helrA6jLm12qOXJ2jjEICQvk+2qDTGWspWJznP/GAkzptgzNsS\ndrffWOueaD0MnAxl5QNFYbyc11o/YXNS08m82oI3UCnRWvspJiEQUiGsH+6KECAE1wcZb7rR58Zq\nhyRWVLVhNNMIIZlMGyIhF0Yqe+GcWxxqjlNS8SwSgtLAsfKkWb55n5SQXoMtS1Qr1eGIE6/ndmXQ\nYZinvLI+pqo0nTRmMtPEUhApRa8T4/CyHp04ZiYNg27KII+4vtIljRXro5JKG0QjWelnZKniTVdz\n0jjyE7Z4S0SlBNOi8U4eU+37jJTEHESN/wAIIS6lqPb9zYJaW25f6x7J4y2X7wNnD7OkML788WGR\nQuw7ISnbwDGOJEns9UE3JpU3sIgVUkqeuZ7zzLU+9zcrKq1xzuKQzIoGKX25vxPHxLEiSyKSWFK3\ngWYcSe6PSqrGUtWWLJY4Z5mVrv29ZOuoZna038yHF6Xw0kJPkiUOHDeONPaSfkmsEEKSxhHWgnGO\na8MOWSQZTRv6XV/SF8CgmzErGq6udHAI1nopb3/z6kIrOo0tSnk3FhV5ebBO6ofjIiUWigVz575p\nq48Kfi/c3fJ1kQlBaeBMkqcRs9aRetj1ZbG1qmYy0+1i4ctEtTas9jImsmFrVhNJ0M7RGOOzG+26\nn+cx3W6EMX7TujrskCXeom1aaBrtH3etn5IlcWsL6hBSYB300ghtnC8ZxhJnHSLiXFh4nmVevT8F\n4PbVowlK8yxmkMfcWZ8dyeMFjpY0loue0ON878xNO5QU3N8qsdaRZ4o4SunlKe98doVIeZcp54sh\naGMomwSFJEsVWaZwDmZVQ5pki+utGkOsJGnkv95JY4rKkMR+OzXW0c9j0sQPqswn8et2eNG22VOV\nPByUzn3HIyUuXYbstJibdwAMujEO2b6uCtsenDpp1M5FKK6t5vzgW4ZsTmo/0NTaTPfTCCtyYiVx\nzovwV41FSp/wuLEGo5n2kkxZvLifRrMaY7aHw7IkWjwvPN3h7TwSgtLAmWSe7Vimk8ZYK9qeNP/G\nnZUa0+oRdtOINJEUpWaln6HaDKuSXuutk/qNal5KM9b3kDbGIqVitZ8SR4rRrMZa50/BS04snSSi\nqDVZ4nvLpLi8zehHxav35kHp0Q0l3VzL+c6rWzTaXvghsfNGHCmGXf+anMR7x5fO/WBSvxujG8ug\nm9LJYox29HPf62etRckEbSx5J0YA9zYKLBZjFWnc0LRZrbTNnurMBySdNNr2CHduIUW2OxMaKV/W\nnw9M7sY5x7RosA6qxj/eZWjhmZeq4XQygpuTaqHmgIOrSwOXSkKeQtVojGGR6X7L9T7dvMRoS7eT\nkMZqYU3qW818oFo3FtVW+/IsoZP6BIt1jtGs9vbUxi7eC/MKYSdVbE1rpBCkexxeLjIhKA2cOYz1\nWcookossg3OOyaxZTKlL6f2JlfTT8FUtGeQpQkC/m9E0hrI2JJFk0I2RuybjG+0nKY11XF3pUDfG\nf12A0f5JoljRSVRrYer7TS9TGeUkmGdK33RE5XuAm1e6fPuVLd7YmHE7TOCfOY4iGNXGLgYDH/V4\nog3sam2945L14u7Ng4IbK76f1BjHaj8lWxKf18a2Vo9ensfL0Elq60haTUtffrVtVm1vXeJlupmv\ntkj5cMAKvkVxOSlmreOMSkkeKd5j3geltk04nCTOOaz1BxPjHm7HShM/F2Gsbq8Rht2YrFVgmfdI\ndztes1obbwZjrFdViaSkk/oXcn6vVq2eq/+cb/GQS4oFjbaLe8RaxyU4mywIQWngTGGsZTzzwtre\nWz5mVmlf1to1MJDGikZb6gauDDPf+9UGlpvjGqUEDq8ZunvDmAtrC7zQ9lo/I88iZqVGt84ekTyY\nxFPgyfn7V7foZhFXh9mRPeatKzkAr6+HoPQioo1fI8Bv5o/SBe1lMbHy4vn1kpua1pZp1bSHTUlj\nLMt3oC+xRotSuneg8l/zXuNycSj2faqCRlvK2g9SdvawTvaScfsH0HMXpLIxl6p8v1yetqcgujrI\nE7YmtTdzcV45IVJyR3vJvD95UjQIoKgl3VYfN4nlnkYOk8ISq3nLhyVS24+3fG8ksVo81pxlqbNG\nO+JLFKldol81cB4wbd8m+MWqqPWitGPbPk5v3ekzJKrdDGaVo2ksN9a6CMGOJvKq1kytl4+Zn8Ln\nE7B55jMe85JbJ1VI6cso81Orn8Kfl2Eu0ZH1mNmaVNzfKvmRt105UgecRVD6IPSVXkR8dtIhhKCs\ndetvL/e8h2QbIIJ/Pxe1l3frdiLKymdN68YwyBOyZKe8UjeL6WbbzzlvBYiUpKy9+D74darXiZmV\nvpKjjUNJ+0RVlctYjcmSiFnls5Cn0aMvpeRKKyU3mtZESvvMupL0spg88weMNFZMi4ZZpf31Dn12\nVEm5yGRq48Xv49azfo7YNZWXtdnX/YaY0lh6cwfhg97LRAhKA2eKKPI9nMY6PzkrJF4tzpfIBt1t\nLUDrHKOiYXPqG87LWNHLE98/life+7rVLAQoa7PIanSzeDEdu2xHKVopmWUmswbTSrlcdMvPk+S7\nr44AeNubBkf6uDfXfFAahp3OJsbaxYa7V0bxcdSNXeiQprFkVvrs5PLasBdKSt50tdtmPR2jaY2o\n268pwawyDPepjOw2+3C7y+yw014ptJofmDiSDKNHv3bHi2t7Rr1oPfhKWxRBVRviqLM4rMyH9AQC\nvUt5ZTmDr6Sgl8dIMZc/23lf7bXPLOMVH5SXIbtkcwshKA2cKaTwm4tttUHBB5/WuYfe2FVtiKXA\nGkdtLP1uuujTAZ8lmZeD5r1h881iOYMyZ54NWfaQts4tykvOXZ4+r5PgWy89AODtz6wc6eNeHXaI\nlODOg+mRPm7gaJiWy/105lA9hMbaRWay0oZIzocW3SJ7uszyEE0St9PwrUB6pHzlZN7es/yjez3W\nMmmiMNa1U/3++rutVqlss2qB80FRG9I4wkX+Hqoag8USqai93yyR8/fIsJuwOWmII0m2q6a+W+5M\nwFP1xx5l9eg8EYLSwJlk+Q35qDe2lJIbV3LKStPLk4dKHZGSJJFkXNTESmKtQ6qH3+xzFxjn/ITl\noJt4d412g6kag1KCKExzHwnOOb753fvkacQPPDM80seWUnBjLef19dljg4vA+WLucGYtZK00nHO0\nJh0Pv85FZVp9Y7BOLbXv+H71PIvQ2uLYLh3PS/NSCvqdeE+tZSkeVgc5jHVy4Owxb9PKkohu5oeV\nYiWpG0dR1cSR7yPtpF7/evewWtzqjxrj9r0fA48nBKWBc0uaKJzz/TvDNojcazjAl/n8rV41Zs+N\nw7rtXlavW+iYp07yLKKThkXmKHn57oQHo4qf+OEbx7KRv+lKl1fvTVkflVwddh7/A4ETo5tFbfle\nHNofXQgfKDbG93cqKR958FgenNmt9+gHlh7uE6/aaWprfdbspKfBAyfL4n40LGT++p2EbsdLfc1K\nfz802i6cBffqz/DOfEk4CD8l4VgXOLfIdlCp14kXPTh7Md+4gEW5bzeR8tOVUviMye5TcFhkjpZv\nfvc+AD/69qvH8vjP3uwD8NKdybE8fuDJUdJPzM8HSA6LlGLHe/RRj5ElvodcyoMHwEotrRch83nh\nmd+Pg25CEquFbWyWRMSR/7dzDikOVlIPe8XTEY6AgQtPpHxTukQsGtn3YrcsR+D4+MZ37qGk4J+8\n9cqxPP5bbngpqJfujnnvO64dy3MEzj7zPsDD/YxgWvhMbHACvTyIPVoywFfOGm1J9mnlCBwtISg9\nAb785S/zyiuvMB6P+cQnPsFb3/pWAIqi4DOf+QxXr16lKAp+4zd+g1/5lV/hHe94BwAf+MAH+Kmf\n+qnTvPQLQdmYhV5cvU/5/rA450WTnYMsVZe2Kf1JWN8qefnuhHc9v3ZspdG33PCZ0pfvjo/l8QMX\nl7qxi6pLVVvy7ODrhbGWqjZtZjZsr+edpvH9xkmrib27NN9oS6NNW2kLw21HQTgHHjNlWfLiiy/y\n6U9/mk9+8pP84R/+4eJrX/3qV3n/+9/Pr//6r7OyssKf/dmfIYRgOBxS1zXPPPPMKV75xSFaOt3u\n5aTyJBSVd4yqGsOs1EfymJeFeen+PcdUugcviL02SHnpTghKA4djuZryqMrKXkwL7060PGAVOL/s\nvheWA1LrHNOyoWos01Iv5KICT0c4yh0DX/7yl/n6178O+Gxov++zNjdu3ODevXuL77t//z4/+qM/\nuuNrv/3bv82b3/xmtra2+M3f/E1+93d/95HPtbqaE50D1yFjLGVtUFLssPM7CZxz1Noi4KHT7Pyk\nG0fqUD7pk6Khqn0wGkfqsRqJZ4V7904/SPvmd/x74Ed/4PiCUoBnb/T5xnfu82BUsjY4OseowNmn\nat2b5j2lhyFWgkb76f7DSju5pcGq5Y8DR8uymcF+rns+a21R6skluiLlLWXngvg7cDv1agNHQwhK\nj4GPfvSjfPSjHwVAa80LL7wAwGuvvcbt27cX33fr1i3u3Lmz+No73/lOvvvd7/KWt7yFPM9pmuax\nz7WxcT4Ewrem9UJkuteJzoR9p7WO0axmPmg/yJMDb2DWOmaVxjmvU1jNqmO+2ovBtGz425c3efZG\n/9gDxR988wrf+M59/vblDf7Zu28d63MFzg5VYxYOQdpaBvnBD4zaWKbttHXVmNb3/OBBbZ7FFK3V\naNAqPR6cc0yLZmH72s/3dtqbFNrvOQ1IwRPvOV7q6+HPS+ltYSttiIMc2JERgtJjJooiPvCBD/DZ\nz36W8XjMCy+8wF/+5V/yrW99i5//+Z/nM5/5DN/+9rep65of+ZEf4fd+7/f42te+hjGGj33sY6d9\n+UfGctbAnpEqx24ZKOsc8oBWLFLu3RQfeDR//nf3MNbxvh86/uGjdz67CsC3XgpB6WViZ7byyX/W\ntpmww7SLe8vT81E1Oa84YFnda7+M9EnsOZfRFva4ES7UGM41Z6EcexDqxvdgeovPJ5OCOQ5mpaYx\nliSSQY/wBPh3//M3+at/fMB/99+8n+srx6sfap3jv/3dPyVNFP/9v/1nZ+aeCxwv1jlmpc+SdVJ1\n6AzZrGxojAtrwhmmrH3vbqTEvqop8z1HKZ/RDO//s8O1a/19vxbecYETIYnVmZxOnFsEBo6f0azm\nb763wXM3+8cekILXFHzX82v8P996g5fujnnu5uDYnzNw+uzltnQY8iANd+bJkojsMQnps7rnBB5N\naIIIBAInwot/+TrWOX7iXTdP7Dl/4of9c339r+6e2HMGAoFA4MkIQWkgEDh2tLH88TdeJYkk//yf\nnFxQ+u63rtHrxLz4n15nVj5+cDAQCAQCp0cISgOBwLHztW+8yv2tkp/8kVsn6pwVKcnP/vhbmFWa\nf/8n/3BizxsIBAKBwxOC0kAgcKy8dGfMv/+TfyBLFP/qnz9/4s//M+99htvXuvzxN17lf/oP36HR\nZ0T+IRAIBAI7CNP355zzMn0fuHzc3ZjxH/7sFf70L16j0ZZ/+6/fzft+6PqpXMv9zYJ/95W/4M6D\nGbevdvnYf/lOnr8VBp8CgUDgpHnU9H0ISs85ISgNnDW+d2fE//71l/j//u4eDljtp/zX/+IHec8P\nHr826aMoKs3/8id/zx//+asIAT/zT5/hX/3k80FvNhAIBE6QEJReYEJQGjgNnHMPmQ+8fHfM//Ef\nX+bPv+1tRJ+92edf/thbeO87rp0pt5NvvbTB//h//i1vbBRESvC2Nw25Mszo5zErvZS3P7PCczf7\nh7anDAQCgcDjCUHpBSYEpYEn4W++94DX7k/ppBF5GtFJI7JUIRA4dgab2li2pjX3NwteX59x58GM\n19enTEu952O/7faAf/2Tb+WHn1s9s4LVjbZ87Ruv8qd/+Tqv3Js89PVOqnjbm4ZcHWb08phOEnn3\nlliRJYqs/XekBHVjvbVlqRnNakbTmvGsxljHtWGHaysd+nlMJ40WwfnuP4sQYuFA4xyL18D/53C0\n/3fbTjXWfxILrQ+3wzovGl7UmrI2COZ6jZI0UouP40iijaOqDWWtmZWasjH807df48rweO1fA4HA\n5SYEpReYEJQGnoRf/70XWR9VT/SzUgiur3ZY6SUIIRACBLDSS/nxd93gXc+tndlgdC8abdiY+EDy\n/mbJt156wN+9vMndjeK0L+3E+S9+4ln+q59+22lfRiAQuMCEoPQCE4LSvZlnl+Q5Co5Okjc2C16+\nM2ZWaYr2v7I2O7y+fbApUEow6CZcHWTcvJJzbaVzpsrxx8WkaNia1kxmNVVrWVjWxmcXG/9/bazP\nQsaKThoxyBMG3YR+HiOF4N5Wwb3NkknRUFQaYxz+zoQd/3OA2P6bi/nHi6B/6XOLj7cPBIuPhSCJ\nZOt4491sam2oG0vdGCrt/99oS6QkaaLIYkWeReRZxA8/t0Z6jl1wrHPhPR84U4R78mFCUHqBCUHp\nw1jrGBcN1jqyRAX/6kDgEjArG6rGoqSg1x4KAoHTwjrHZNZgrCONZbCvXeJRQenFT3cELh21Nljr\nz1pVY075agKBwHFjnaNqvP6ssS5o0QZOHa0tZrEPWWzI/x2IEJQGLhzLpeXLUGYOBC47Uvg2E/At\nDpEKWdLA6aKUWLRCKSlC5v6AhLpm4MIRKUk/j7HWEUchKA0ELgO9TozWFqUESob3feB0UdLvQ8Y4\norAPHZgQlAYuJJGScH7nNQKBwCGRQpCc4yGtwMVDSUko1h2O8OcKBAKBQCAQCJw6IVMauLTUzVzS\nR4Xe0zNOo72MURyp0JIRCATOFVVjMGGvORDhrxO4lGhjmZaaqrFMioagjHZ2MdYyKfxrNS2bhbJC\nIBAInHUabZm1e8007DWPJQSlgUvJ8rrgbR0DZ5WHX6vwagUCgfPC9noVztOPJ5TvA5eSOPIuPNpY\n0lgFuY4zTKQkWaJotHdPCpPVgUDgvBBHijR2aOv3mvNkwXwahKA0cGnJs3D7nxc6aUQnPe2rCFw2\nnHP8r//XP/L3r27xSz/zdm5f6532JQXOIWGvOTgh5RAIBAKBwB78xd+v87+9+D3++nsb/N4f/VXo\nZw4EjpkQlAYCgUAgsAf/9396HYC3XO/x+vqMv/7eg1O+okDgYhOC0kAgEAgEdmGt42++t8GVQcYv\n/8t3APAf/+buKV9VIHCxCUFpIBAIBAK7eG19yqzS/NCzKzx/a8Cgm/DX//ggSPoEAsdICEoDgUAg\nENjFK/cmALzleh8pBO96bpWtac2r96anfGWBwMUlBKWBQCAQCOxiHnw+c91P3L/9zSsA/MPro1O7\npkDgohOC0kAgEAgEdvHKGz5T+sy1LgDP3ewD8L0741O7pkDgohOC0kAgEAgEdnF3o6DXiennCQC3\nr/aIlOB7IVMaCBwbISgNBAKBQGAJ5xzro5Irg2zxuTiSvOlKl9fuT7Fh2CkQOBZCUBoIBAKBwBKj\nWUOjLVeH2Y7P37ySU2vLxqg6pSsLBC42ISgNBAKBQGCJ9a0SgCu7g9K1HIA7D2Ynfk2BwGUgBKWB\nQCAQCCxxf6sAQlAaCJw0ISgNBAKBQGCJ9ZHPlF4dPFy+hxCUBgLHRQhKA4FAIBBY4v4+5fsbqz4o\nvbsRgtJA4DgIQWkgEAgEAks82Cco7aQR3SziQRh0CgSOhRCUBgKBQCCwxNa0JlKSPI0e+traIGN9\nVOKCLFQgcOSEoDQQCAQCgSXGs5pBN0YI8dDXrgwyqtowq/QpXFkgcLEJQWkgEAgEAi3OObamDYPW\nyWk3a4MU2JaNCgQCR0cISgOBQCAQaClrgzaWQXfvoHTu8hT6SgOBoycEpYFAIBAItIymNcAjMqU+\nKJ3LRgUCgaMjBKWBQCAQCLSMZm1Q+thMaQhKA4GjJgSlgUAgEAi0bGdK4z2/Pu8pfTAO5ftA4KgJ\nQWkgEAgEAi2jWQPsnymdf35rEoLSQOCoCUFpIBAIBAIt80xpf5+gNFKSXidmc1Kf5GUFApeCh5WB\nA8fCt7/9bb70pS8xGAx4/vnn+chHPgLAl7/8ZV555RXG4zGf+MQn+OY3v8lXv/pV3vrWtzIcDvnk\nJz95yld+/rDOUVYah3dgkXtoDQbODlVjaLQljiRprE77cgKXnHlQOtxn0AlgpZeEQafAI6kbQx3W\ntUMTgtIT4ktf+hKf+tSnuHXrFh//+Mf58Ic/jLWWF198kS9+8Yt8//vf5wtf+ALvfe976Xa7RFHE\nrVu3TvuyzyVFpakb2/5L08327g0LnD7GWmalFyFvtCVSAiVDASdwejxu0Alg2Et55d6UqjEh4Ag8\nhLWO6dK6pqQgUmFdOwghKD0h1tfXuXnzJgDD4ZDJZELTNKytrQFw48YN7t27xwc/+EE++MEPsrKy\nwq/+6q/y0z/901y5cmXfx11dzYmisCgu05nVVI0BII0VvUdkPC4b9+6NT/sSAoEzzWhaIwT0Ovsf\nZleW+kqvr+YndWmBwIUnBKUnxM2bN7lz5w63bt1ic3OT1dVVjDFsbGwA8Nprr3H79m1efvllbt++\nDUCn00HrR1vZbWzMjv3azxvWOora/910ElFMw0DCWUVJ7y/eGEusZMiSBk6d0ayh34mRcv+2n2HP\nT+BvTuoQlAYeQkpBnkW+LUnJkCU9BCEoPSE+9rGP8fnPf57BYMCHPvQhPv3pT/O5z32OD3zgA3z2\ns59lPB7zwgsvMB6P+a3f+i1u377NzZs3uXHjxmlf+rlDShFK9ueINFGkhGx/4GwwmtYL2af9GPba\nTOk0DDsF9iaNVWjteAKEc86d9kUEnpxQjj15jLVMC41zjjyLiaNwCr6oTMuGRluSSJFn4Qx/0Wm0\n5RP/w9d457Or/Pq/ec++3/f//u0b/P4f/RX/5j9/O//ifW8+wSsMXGRmZUOtLUkkyS9wYuXatf6+\nXwu7aSBwSKraYKzDOhZtAoGLhzaWurE45xUCjLWP/6HAuWZ8gCEngOGipzRkSgNHgzaWarHeWLS5\nnOtNCEoDgUOy3GumHtF39rRoY6lqgw3FjFNBCsFcTUwKEIeQFguv3flkMXn/mOHIlV4Q0A8cLcvr\njRAcuZShc66V3zNH+rhHTahHBQKHJEsihBA4546tZ0gby7h1llGNeGzmJnD0SCnodeKFhupBN4lG\nWyZF+9pp8dgAJ3B2WFiMdh9dOl0MOoWe0sAR8dB6c8QJj9mSVGKecWb7XUNQGgg8AY96Q1vnMMYR\nKXGo7NoyxrodHzvnnvixLjraWITgWCb3oyeYnLXLr50JmdLzxGjaWow+5iCRxopOqkKmNHCkPMl6\nAwdbA5fXImMcnNGW1RCUBgJHiLWO8azGOlDqybNkcSRRSmCMI0tUCEj3oag0Ze3LUb1ORHwGNHvj\nWKL09msXOD8cRDh/zrCbBqvRwKlz0DUwSxSzSiOEIE3ObudmCEoD557jzCJa55gWDdZBJ1Ekjyl5\nGGuZJ8qMcVjrnqgMI8XFKvvORT6e9HWalZpGG+JdU/DN0jCAL3udfhB40V67y8R2+f7xr99KL+HO\ngxna2KBDeUmwzh24jaduDEVtkAK6nfjY7K4bvbwGOuJ9orokfvz+dRYI76TAucVax9a0ZnNSU1TH\nMwVf1QbdBpf7PUdVG6ra4JxDqe1eoOPoCzqPaGMZta/T3GnrsD9fNQbbTsEvT6UmrRyXEOwISKtm\n+zUJBA7KQQedYDtwHYW+0guPdY7RtGZrUjMrmwP9TFEbrHVo46jqoxsu2r22xTvWwPMf0oVMaeDc\nUmuz6N+rGkMnPfrbeXm6XuwRYC6XToz1WbxBHmOsO9bJ/PNE3QaU4AP4wzbYz6dSnXt4KjVLIuJI\nIhCLA8DO1+Ri6/0FjpaDDjoBrLTDTlvTmrVBdqzXFThdtLaLPv+qsWTp4zOmUsD8+HxUyYm91rZO\nGpHEEiHEsWVjT5IQlAbOLb6p279Bj6t8lsQKh8/K7hVMLQ8k2aUSdaTO/+JwVCgloZ36fJK/y+Om\nUnc39y/LMC2/PoHA4xhNGzqpOlAbyMLVKfSVXniU2j4YK3mw4K/bialqg5TiyCbd91vbLpI9cwhK\nA+eWOJL08xhr3ZGVLYpKUzWGSEm6mZd+etSCkiVqsTiEoZa9SWOFbCW0nrSn6TBTqWmsKGvDrGjI\nszj0/AUOzGhW0z9gP/BcQH9zGibwLzpK+r3GGEd0wL1GCvFU1TvnHNNSo41t1R4i0lihjQPnyJKL\nGb5dzN8qcGmIlOQobNON9U4aZW1aG9GG8bQmS31zeHefEnCk5GJzCuzPUfY6WeclspSUlLWmqAxN\nu3B3s4hISZJIItvgYn7ICAQehXVeOeP66vBA3z/XKg2Z0suBkpKjWEaq2vi+0Mag2iGodI8As9F2\nMcRU1oYsUZdivwlBaeDSMym8v7nA4ayftp+fUHtZRJbGCFhk3ZxzREoGmaYTZK5ioI0XpnfO/Mr3\n6AAAIABJREFUtwLU2rK+VVBrS78TY0zMaj9DSYE2Dm1s6O0NHIj5fTU8YKZ0ZW41GgadAgekqBru\nbZY02vpAM1UUteHW1e6imuTwWdb5etdoi5SgzdmQvDtuQlAaOPcYa5kUGucceRodqkTsnFucRo11\nNNq0U5MWAWxMataUpKwMQkBZ++9NYrlv9hSORkA/sF3C8q+R/3vO26oabf0QlXUUZeMdthLF5qTC\nOShqjZICYyVFpY9lEC5wcRi3wWX/gJmo7UxpKN8H9sY5x6Ro0MaRxpKytR7WxlDVmk7q9ypjLFaI\nxcEoixVK+SbWRhushU1Z0+vEF34du9i/XeBSUDd2MYVf1OZQQakQ/z97bxJi2Zbe9/5Wt7vTRJOZ\nN/NWJ8vSE+8JgXlQMyEsKCEwxh65KTCaCIGx8cRDg6mRjUDYaCAMBtVASHgoNFSpxmXwoExppnq2\n9KxnVd0um4g4zW5W+wZrnxMRmZGZEZGRNyMyzx+SezLviR37NHvtb33fvxEYLbE20I8/ezATHC8H\nqtJgB8+00ggpcP6UWO5fktSTLUAiR6uerg80peajw2b8mci69whgUuv3ipz+tpA3CpFucHRDoDSS\nqtAoJfM4S0tCyBsIbRSFVnRDABJHi55ZU1AaTTd4rMuig7fpGbjD3cVWed9czq0hU0XEzkB/h5fC\n+bi9Vwwupy41pcYoiTGKUmftwoaKlBLEGHmysDSVwfo86RmcZ3CSslCYEOmsZ916jJYczsv3qvGx\nK0p3uPM4q8a+zqi2LhSD9Wgl6V1gWhke7GcFrqsDUggKIymM2hrpVxcUvpvRcts5Pnm2pjSKVe+Y\nTwqqMhdGm+K5t4FJ9WEVpTElnIsoJS7N8VQyj7HWvR+VrJpGCWaN2R6j0JL5pNz6xDoX+MmTNe3g\nWbYudx20RCRBDAnrwnsrEtjh+li0Y8ToJTulQgj2JgWLndBph5cgpsTgAkbLUTxrsC7iQqAOmhAT\nVamRMq+Jg8tFrJISKQRKSexYrDofWa4t1gaeLnoKo+hdntpdVpx3F7BbmXd4Z9iYoL+pCCWruyHG\nfIG+DGcFMmcRYkJKiYgBEhgtqAvDsnOI0SNTKbk1fg8x0g4OH+O5Eb4bfVOFFNtsYSVOuafZUiS9\nN35yV8Wqc9v85WltLiV+EiIXoKtWI0XuNvS9R0qBc5mTJYQkpezAUJWaFPNnPKsNSkrKUiGlJIT8\n+X+I7/0Or8dpp/TyN/j5pORvvli+1VS5He4mfIh0Q0ArQYyR2bQEAWUh6VZ5k+18pOsdTaWZNSWT\nSsNYyKaUsuK/0kx9xPmQ10+Ru6kx5kL3ffvW7YrSHd4JrAus+5yQVBXqjXkyryOAhxhZtbnLWRpJ\nXWqcH7t2WpKAdvAYJbNAJqYtd9GHxKqzLNaWZeuQQrA3MUybkkLn6EsfIm3vWfeewki+dn+CT1CX\nihAj3ZAJ6+2YCqWkoDDxgxrhhzOUh/ga/9CtH1+CROL+Xs1Pn6ywzvN57+i/8GPBD/f2a6QQGK0o\nB8/+rOTBfs3J2lIaifMJrRLW5dznTediV0TscBZXyb3fYH9a8L8+zbznab0LadjhFBurwM0aH1Nk\nuXas1jaL46SgGwJGCxatJaSEUQohxBgJmjurg/UsVkOe/HSO0ijmE01p8p+61FtB52XWtI14Sitx\nK4VTu6J0h3eCs5nlZ2Mj3xY28ZRSStre8fSkpx08dWV4eFAzqzUpRgSCtvcYlQ3wfUhICd0Q6MeC\nMsTIsnP4BClGZpMC6/OxM18RDg4n22i6wWVOZKHVWOgmQszRc80HNMKviuwfqqTAvKKjvdmw+BBJ\n5IW6LhVVoYkx8Ww5sFw7jFbEsUNaF5qjZYfRmtXacbhX8fCwpu0DISZizGOxptKjKj9h9K4o3eEU\nV8m93+Cs2GlXlO4A2fKpdwEpQUkIMa99g4157WotzuVku653xFKjS421AVHkQlEIgfOn8cpNZTha\nWUSCutAUWvPwYHJOSKWkYNq8mi+/ef5mzz9rLk+l+rKwK0p3eCcotMT57A1avKXdWkyJdixurAvb\nXWmMiUVniTGRusSREhzOKwqjeLrs0ULwbDEQE2gtWK4s1gWCSEwrg0SglGCwgeOYsy+zwCarxCfV\neFllj2Mgj/97l8U2GyHWTUTPpZSwI4H+usb0XxbqUl+qI259HF0RAt3gKYzGh1zMHq96TtYD3idi\n8hxMSlJM/PSLBavOsbaBe/Oan4v7fHx/glaSEENO2dIS60OOJN3Vozs8h+uM708N9C1fffBWTmuH\nO4SUEu2QnWBiFEyq7AaTUuLzZy2r1tEPnrb3rISj0AIJxBCJ5HtCSoknxx3WeVY2IFMW1aWYJ3gh\nRkqd19Gs5M83mRCz44t8xWY7nbknbf5+27ArSnd4JzBaMZ9ISDeXC/w8+mHk7AyeEBNGSWKKeZGI\nkWcnAzEmrI95Zwu0vcfaQEyRmATHy46j5YDSknlT8tE3KupK85Mna6yNVCMVwBg1ktnF1oR7Uhnq\nUjG4SKlzqpFRCkHmFd2EoXw7eOwY4ZkSlHcgVSqm3CUWghcERymlrCztHMu143g1ICXMGk1daJat\nww6RaWNw3qGMYtkNfPas52jd4wIIITlYDdSVZj4xlIXMm58oWfceqQSDizS3rEOww7vFonVoJbY2\nPZfBJmp0sVPgf1A4HYGfX8dDTKxaSxypW0Lk7vm68wzO42PgaDmwWluSSJTG8GBfMm80icQXz9Y8\nXfQs1tkrW8nEtC5Z9Z57s4p5rdmflRzMc4dewNbbVCuZbaReASnF9p5klLjRUJObwq4o3eGdQQrB\n22Rpb6YYRkv6tcWJPFpZ9S2kyGAdVWn4/FlLiolJnU3yhUgsW0eMiWVnWbSWJPJF39rAF8c9T447\npAIxqUYrKZ/FVkDbB5SUxOTYmxRURaYPpN6DAusDg40MNtJU+o1ykc/yNO9Kznu79R3NhfTZ7qnz\nES0llVF8bte5mx3zvz89WbPqPEfLns46tJT0bsXT456nyx4fIpMyBxyEkBNQrIvEMa/a+tMudYhv\nnzKyw93CYp0jRq/CNd6f5OJgFzX64SDG0xG4EIF5U2wbK9YFmkrTu+xD2lvP4GDVe46WA8cLSyJP\n1/rBESOsOomPCecCz5Y9R0vHtJK4AErDwVySYsCFgDYac0aHsNFlbGwGLyPirApNdYvF+ruidIf3\nFpnHk8e++1PD58eZRxpiJIVshN/5ATsE6kISUoIEPiZmTYH3gXZQJKAfPEcnHf/zf0eWXcgVb0wI\nJN94NEMgEDL/W2lyt893EbmJkTOKENJYDJ0uKs7HNypKq0LRDlnAUxZf3q43xkS/7XaqK93I45mZ\n0fPjIylz0d5ZDyMX1HrPpK44WgwIkXDe83Sw7E0qbGc5Wg6EELE2MakThRaEEFm2jqbK6nwA5wLe\nJ6RIVKVhGHldO+yQUmLRWr56f3Kln9t0SndRox8OEqciWOcjz5YdUkia0W9USokUkSQFi1HU5ENg\n2VlCzKP7lCLWRQZvCTEyqwM+RtresWp7FmuY1YafOZhzb14ihWLaaIQQbJbM7Il96vqQYjp7a7mz\n2BWlO9x69NYTR7L4ZUf9gwscL/s8pq0Mg83qRCnA+0AMsD8ts+CqKigLzbpzaC35/FlLU+TMe5my\nF6GzeRe7aC0h5u7rfFLQD5a//nTB/f2GSW3oB8dgI08WHfdmJYuVRQDTpqAZuaaDDVsV/puOT7JK\n88svrNrhtNsJXMgVHWzI/KdCnXMZaEpN2/ttQXsWzmfCvnWRRObqIhPrPtAOlnXrOF5ZphPDF8/W\ndM4z9J6AoCkkRgmUlKw7x8mq42C2hxu9SROCFCOr3qO1GgVt8q3RR3a4O+hHe56riJzglFO6ixr9\nMGBHwaySgpAS1oftOhhTYn9aUpeSFCW9TSw7x7ob+F+fnHDSOrRUKJW4P6+ICT55vGTZWo6XHUpr\nus5jjMDbRFEoXAjsTUse7Dd8frQm+HzPiDGx7h2d9RRaZnGUFPRjit1tVNVfFruidIdbjcGGMaEH\nfIwXihA2QqZELnh8yCbDq85vbZ8QcH9eo6Rg3hS5qAoR5wLHrcO5wKqz9DbwxXFHClmtXZUSkHQ2\n0A2WwcOkKjBaUBnJ4GHZWe7Pa1JK+ACddUgpWLSOmEReXOKAtdmKaNYUzCeZa3RXLaFe1e2EvHhv\nCm8XEnuTYptoRUo01Xmf0mynZbd8rBAjMUWQgnlZ8vSkZdkFTlYDLkS6XtI7R0TS+0jvEwiNXlp8\nECgpWHeeVefGcZUcI0sDLmb7rqsWIDu8v9gUlXtX/E5svkO7qNH3H9v1izwom9Umdyu9I8TIYp2V\n9UJk7+zPjlr6wfGTxyuenPQMNlKWkUltWPeRk2WP9YmQIs4J9qcKIRMxQBSC47VFIDheZlqJEhKh\nst9zPp9ENXp0zxrDelThA0zrF20SY0x3YgO+K0p3uNV4XfEDmVfzbNFhXRw93AqerQbW3cC6zybE\nP/Nolj1KS82nX6xYD27LPTxaWn7SOQSRgGCxHOiGbPlklCKmiBLQDZ4EhKBoSpUXABEgGnoXaJIG\nsgJSCYEPARcinx91xBiojKEq1bYwvctoxoQqIQTVBcKQc59V2sTshTOJVh6j83vgQ+TpoufZouOv\nP1sAELzHJ4F3kboUHK0cTxdrluuBGCJLYdEyEZOgDwGZBN4KFikRouBTErNJsT3Haa2pSkWIaRSy\nZGXsZpF+mXBhhw8D17GDgswzn9ZmFzV6xxFHq6QYE3WhLxSMPq9al0IwHcNTluuBL06yM8hq5dib\nFSipaArB589aTlY9i86z3xQUWvK4y96jvc1FbucCg8tUJYC92lAVJVrDuhsYhtxZBXh81LHqHJPS\nUFcarXP601lNQYgJsz3XU9sorQTT2txqj+ZdUfoK/Jt/829e+f9/+7d/+0s6k7sNHyK9zXGdWZF4\n+QuiLHIhEVNeLC7CMOSOWIiRRWupSoWWgsEmJpUmpkQ3OFxMPH625v/56THD4JAJTrqBdZ/5h4UW\nCBJ1mePfehfxMox5xUUuNBOEEHi6cBzODGWERWfZ6x3GSO7PSwRi9DK1CBInqwElsrGxkFl0k1La\nvid3QTH/PLR6dWFdGImPMheBZ15fiHk8r2R2KZBCbBf7deu2XNKTdQ8Ilq2j7T0xBhatw9psI1bX\nCRsSIUSiTwSRiNESWlB7ENaJpycdhVbMasMwK3l0b0Jpxmi/Qm39+TY3pHwegfnE3NkO9nWRLbji\nNu7wQ8PJNYtSyAb6Txe7TuldgnUB6yNGS0qjsuXf2GXsrL9wTTZaUhUKF0Y3FZnX7rJQkBKfPWt5\netTzxVHLF8eK/WnBpDbEEFm1A90QqRR89jTSW4f12eFFidz86HrLevDMG0NnA1XpeXaSSCw5mNdo\nJTlZDzlZUOTXsDebb6lTdaHpbE67O0vpCvHUNsqH7JGtX6PSf5fYFaWvwH//7/8dpRTf+ta3+OVf\n/mWUunvFw21A2/vtLk7KF22AXgUpxGtNqctSkULMtixCsFhZZhPDtNE4F1muB3yInJz0fLHo6HvP\n4+MeHwKDzdZD7QAKKCvwMSBlLmxJIJXMEW8JJAktJdYJBh8IMS8QPqTReqpkWhvWg8MoSV2ZvK0W\nMlsUNQV1qXly0m89S2cYqkKz6hzOx22Sx12GEGK7WOZdfMT7mIVeSeAkDCu7fa3zOluiEBKL3hJD\n4ovjjsV6oOsjY8IrYXP8PqC1yAIzlfAe0hg1O/jI/QPDwbxCa4FUIqtdrWd/Wr1wrimlW+/d97ax\nPuOIMGvMB1eYLq45vodsoP+Tx+tzHsQ73F5kPuZILfJx3CRLNqvLxmbpouaJ0XLkbObrI6U00ssS\nIXg+P1qz7i3rXiKVyFSyduDJIvuQeusQ2pFi/j1KMlrW5Q2hkgll9HiPFEQS3kV++njN//0LH2VB\n3jrz8aUQNKXenue2QH4OUgqkFNvx/W0f4e+K0lfg+9//Pj/84Q/5kz/5E77zne/wd//u3+Uf/IN/\nwN/5O3/nXZ/aDiOsC3gf6axn1Vu0Uixbx/60YN6Uedd61LMalixXNhuyO0/bO4QIHC0dvSOP5QHb\ng3OesoyUJnf0CiUBQZRgo2AIgaZUhBAolaE0gs+PWj5WDSFG1p0jIXi437AePPf3a4zRVIWiKXX2\nRbW5UA8xd3M34+P8miJVcbcjSJ3PqUwpQVPpzLeNia73tINDIGkqxaQuqIzi8UlPZz1t5+gHhyKx\n7jzLdcRfcPzOwUQljEwgJdJn66dC507f4bzkwV5FShLnsqvCy5T2SuYOiPURo+QHV5DB+VQ1H+IH\n9x6cjJZO1ypKzxjof7Rf3+h57fDlwOhMw1i1Fp+yZ+2sNucKOD86egA5fz4kjldZTKsUfHE8IMmU\no946fIykEPj0ycDm6hoS4E5/byVApDiK7EpKXdEYxVcOKzqfeHrSUxtJU+ZieH9WoWTeQB3Oy0tN\nHaUQzGqzva4vYxv1LrErSl+Db37zm3zzm9+k73v+7M/+jN/7vd/j008/5e/9vb/Hv/pX/+pdn96d\nQFPp01H1DXQSYkwMLozek6MPpVZZ7bheseot+5MiC4xcoHeOJ0ctCIGUkb61rNqeroeQ65Vz6AOE\nLtK2MGkkVZl/p/NZwa1kHvnMm1z4llVWl6eYOy5t75FKUhrJ/f162+kNMRPlN36Zgw3bEZD3kcF5\ntJTvRS67HdO6IG8c6lKzbAeWnWOwYUz0Eqw7i1FwtOz59Mmaz457vPcM1rLqwoUFKeTPzA0wCChM\nRKvsEVs3hq8+mPIzj/ZpqgJSGj8LRV2+vPuc06Zu+l24OyhNjoCVUry1hLXbjOtySuG8gf6uKL39\nkFLQVHqb/77ZgOlxoiLZrPfxXOcxhMRgc0KgC3kDG2Li2UkPRGKAqi4oOsuidayetQwW3CsmL87D\nMkJVZkeTB3s1jw4bikJjikSMiWlt+Nmv7jOpNDNpuL9XESPPdWs9MXGOlvT8ay7k3biud0XpJVEU\nBbPZjMlkQtd1PH369F2f0p1BFgPcXOdlORLS87GzzVNpdO5qak0K8L8+O+HhfsPnxx3O5+QllxKL\ndWSICRDYV8xpNwvJcRsZXI8ERrtLjJR0xlN0klmt8U6xN8/j5+OlzZnHSbDsPPf3T4vLHHmaq2At\nJZO5oSoUKUE7BLSSxHh5E+TbDC0lduwP6NF2aVoVpMRow5S5tcYolp3j6VHHF8cdwTsSsO4D9mUV\n6YghAQnCkBf1ykgqoxlcYNlaKqMREgqTvjQ/UusCg8uf5WUiVW8L6jKLO3J4xN3+7l0HG5/R63RK\ntwb6OwX+nYHREusCvc1rVFXkMbhWYvT+5IV0JBsCzxZ9TmsqFIvBsWwt1gdWvUNrOGw0Rws5xlqf\n0o1ehgCECK6DSgWOREdhJNNYsT8teHQ4YX9aMa3MOWrI2bqzt1kkBbmYvi2uIr3NlKBCX0wreBnu\nzqr5jvBXf/VX/PEf/zHf+973+KVf+iX+4T/8h/yH//AfMOZuc/7uAjapO8+PsdOZYrLQirrU9M7z\n5Miw7gd8pi2y6BzTUnK0SEit2C8k3if6Huyrtq/PoXPZk1hlb3zAorWgKjSdS3z9XplH8S4yrbPZ\nfmHkqFAPlCZfZvlmn0a+pdoWLc6H7etUEuQdHttvUBYKpbKIabOjr6u88McqUldm7DokPj1q8WRF\n/3I50DmP9XDZzKUIWAuFAds75Lzk8dGKlCKP7k1RUqJHI2shYFKZt8KrOs29Bh8CWt0tv8C7vhF6\nEyxae+2NxNZAf+dVemfgXNyKf7ohbHUO03HMLbc801MEH6nL3HxQUhFS7mTGGBDkbqVRikmp8mb5\nCueTABvgqA2YYqCqDMuVRSnNrClY9R6lJCEkjD7/PT132d6SS9iHeGrlGDxav/h+vgy7ovQV+Pa3\nv81iseDXfu3X+E//6T+xt7cHwOPHjwH4yle+8i5P773GWYP556M4jZEs1xajFEYLehtZt56DecWy\nG2j7QAgwWI+NMGkMIeTdbKnIHEQFL50NX4BIFsDorH0iISmMJPiIt4Fpo5lPaspCkusdkRXo4dRY\nuSk1nfAIBKU5vUCNVhQmL5Kluf2cn8tiM0bqx+Kz0JJ6phEi3wgSubskEZjRiklqxfqkx1/hswFA\ngEgCVSicTyy7iBADVVnw1Qc5HCHENI66HJP65d2EjSvAm+P9+Bw/BCzWlr3J9axyTg30d53Su4Kz\nm1J15rEQF28kU0oMPhJTtvybziSgmU8Knh63fHHS0fUeRC503VXXL3JxKRN0NlMCvvbRjMm4kX98\n1GbeaVMQRr/rzfpaaLkVEj8fRnIZpJRInN+UujEUwIzG/Nd5Lef+foW1cFeUvgLGGO7du8ePfvQj\n/vzP//xch04IwR/+4R9e+lj/43/8D7773e8yn8/52Z/9Wf7ZP/tnAPzBH/wBP/nJT1gul/zzf/7P\n8d5f+LwPDe6M8GITxZlSTsh4fNTSDZ6y0GgNAolSAmsdWkmaStJ1joMHNXJQKCFYrhNdZ1mvA13v\nUSJ3P6+agC5FHhM/PCi4t1czKSUxRdo+UleBqdIczCoGFzK3VCSeHHeoseiqCsW6c1gfaEq9Hcnc\ndbX9y3B2x+x8ZG9akMiepVIIhIB2cHz6dMFy7ZAqYgwEAd69+tgbKEBL0FpQakWhMz1gNinYmxjm\nE0NZ5ASntnfYQjG4fIOpS33uvW97l4ULUjBtzJWKUyEEk8pgfdhyg3e4/UgpcbK2fP2j6bV+fn+6\nGd/vOqV3BRthU4jxUhzqEBOFVkzrgt56EmJUzWvq2rD+dIGPCUYT/eGKN5bSgCFz4qeVpiwNMYIg\nEVNEa0mMpwfdLEuDDax7l/2in0vOuwxCjKxalykJpaIqNDEmVl2uqgcX2ZtcPfVOScmk0rgQx/V4\nV5TeCP7oj/7oxo713e9+l3/9r/81H3/8Mb/1W7/FP/7H/5gYI//1v/5Xfv/3f5+/+Zu/4T//5/+M\nc+6F5xXF6zkieWeTKMz7oR42Sm7V6EqKrV1SDDHnCa8sRaEQAqaVYbG2LHtPjIKqkPREepfQRvD4\n2OO9xzpYtB3rNtBdYyebAETmSzZVyf1ZCQisS0yqnBhUFRuT9khdKqyL+BSoN68npa0Jcn9JCxnn\n41bYZXT+PVrdXTGUFJkHvGgdy87y6dOWwSVciASXkAmGSxakAEbBXmOYTgz35zVffTChLDSlliil\nSDHTKbohc49jzIEGdZnFPZuYvpjSlpsVYsL7eGWLn9xZuPvX34eEbsid/L3J9ZRup6lOu6L0LsFo\niblkWLza2CqNjakYE1Iw5twL7s0rjlcdT44ti+5q34MC2K9h1lQUhWFaF0zq3IWdNflPVRhKoyhN\nng4qKWl7z2Jt6Z0fraEujnt+FayL2/vR4CJVcT6wBsbJ0TWmPteNwN4Vpa/Bj3/8Y+7fv8/9+/f5\nL//lv/CDH/yAX/iFX+Bf/It/QVW96Hn4Mjx9+pRHjx4BsLe3x2q1wjnH4eEhAA8fPuTx48eEEF54\n3uY5F+HgoAEhOFkNlORuzcHsclYRtx1h7Ja2g8eOaUC99TwIiahamlLz8P4MIeChkuzt1RwtBxCJ\nYQgkEquVxxNZdI6QEj6KaxWkkDurpZEoo9BKowpDbSQ+RZRWSKWRRmFKzcd7NT/9fIXRCaMke9MS\npQRGCnoXEEJQFvpCD9Ywcpo2n+GzRU+ZsgFyTNm42Wh1LUL748fL6734a0KPLgSrzqG1wLo8eqor\nzaK1pJSLgqcn/dYWK121pkuZQ1wXOb7VaMUv/e17CJG5pAiBc3GbvNN2gZQiIYhz1gtSZAuwEC8W\nOuzwfuLUOP9604q6zPSi3fj+biKmRD+m9dWFvrCrJ4Rg1uQS9qfrgaPVQIyRdvD89WcLnhz3WO8R\nEry/mtFxUYJSmvm0zkEjUlJXBQ/vTaiLPMlpSkWpJU11WrL5GBEiF8g+RJprTNvOmuhvaAxanYYE\n3IRF3iYGXEpxKY/yXVH6CvzH//gf+f73v4/3nn/yT/4Jf/mXf8k/+kf/iB/+8Id85zvf4Xd+53cu\nfaxHjx7x2Wef8fHHH3N8fMzBwUGOuDw6AuCTTz7hq1/9KtbaF573KhwdtTgftu12ANfbW8NLHFxg\nsAGlzhv9XgXr3mHdRsktkERKIYg+MHQWoyXr1cBibXEhYgdPSIlnJy1Hi4HHi57leiCExGq4ZkUK\nI+8m04a0TKyWA2JiRtd2j+09T45WTGvDwawkJbF93TpFfMx8pBhz/nEsNIvjNo9fgElt6G3YJuvM\nGoMg3zRTgsF55Mh5EgKG6e3wMHodBzOmzJtdtpZ+CDRVdh1oe8+6dYRxMV+vBhZtuDIfK6TM3mzK\n/L6IcXGd1oaTlc2esMEwbwoE2Rz+2TJgXUCrxKp1TJt8jtPG4H1EqcsT83e429h0OOfX7JRCFjvt\nOqV3E/1wql5PyV/YKNiscdpIhiG7ewzO8/nTlnVnOVn2LNZDzqa/wt5EC7YWgEdrT+c8D/cbKmMo\ntKIpFClmv9KqzCr2DR9epJw+BVl3MamuXs4ZrZg12Vj/7ISnLjU3ZW627v3WLUdcwhZyV5S+Av/t\nv/03/vRP/5SjoyP+/t//+/zgBz9Aa823vvUtvv3tb1/pWL/5m7/J7/7u7zKfz/n1X/91/u2//bf8\n+3//7/mVX/kV/t2/+3csl0v+5b/8l3jvzz3vMkWc0YpyFMoUX5JQJqVs/C6leOnvSynRjskZISaM\nuvo4FPIFklIe36fRTqmuNEoIqlKPWb55R7ZuLVYIvA+4kLAh4kIikfAp0RiJdfEFb9JXoRBQVGCU\n4KODKftNybQ0JHJnb1JpQog8W1qkEmMXVGXVfcgWVlpJYkooKZFSbnelg8tFqBCCdvDbqLt4Znw8\nqTSDixS6wMes+PyyLI6ex9ldb2EUq9aNfCuZvUNH/m/z3AIZUxrHQoK2dxwvez55vOD47fwaAAAg\nAElEQVRo5XMRGjNF4Tp7BmVgWmumk4JJXWKk5GRluTevcCGnraxau13Aq7H7UBhJb0PuXPeC/alC\nCvFepPKk8f3eFdavx9EyVxGHszcoSicFf3l8Qoh3O/TirsD5mLnbowfxTaMbPIMLCNh6S08qQ1VI\nIjD4wOOjjhjg6KTnpB2wHsIV9yVGQVFoyiIfuyoURaE5mBd8/aMpWkmeLYacBIXiZG2pq8x1F1Iw\nH6OetbpafLd1Ycv3NFplYv7bwrm4vNc/fVeUvgJ1XSOl5N69e/z8z/88Wp++XVe1hPq5n/u5c53V\nf/pP/ykAv/Ebv/HCc6/Sgd3gOq37N8Gqc/iQth29iwpTMXIHN5yV61IKTvPRBb0NrFvL4AKFViNJ\nO2W/OBdRSpJSoO0Cg83eUCLlcawgEkkYzWs9MCFfp4UBreCgKTGlojSSvYliZbP9EykXOTEl9hI4\nn7PeS505Qd0QmNb5e+NDvmFJcWpztSWqIyh0ufXJk+JUvW7GcT2MRsnWj+b07kv/3M/uep2PW9Xn\nuvfosSAfXKAq1HYM1ow8p1gZEomffLEixciTxUAIERsTbR9IV/FQGaGAQgpmk4qP9mtSypsA/3iB\nIHsAKpU7AWr8HkoCe7OCwQbEqPy/HXOFm0GM+XqIKdNNvuzvyF3Ds2UPwMEbFKV5MpK7rofzy9O6\ndrg6Ukqse0dKmROppHijEXNVKhI5argus6C2t3kxWg+Ors958ifrAa1KDiYl63WPjzCbGopC0pSG\nEBz+CgInQe6S5s16ZFJq9qclD/ZrvvFwyt60YN1lzntKeb2dNadr1VndhbnC6/chnotZnU/ebiOr\nqQy99dlH3Lz+PHdF6SXxvHfk+8DZvC4yh+W0oxdCRL5EwTipDdbF0bPxxS+k83l0otWr+SYhpswr\nTYn14OlspBMerSXrPgufjMl2QEJEhAwImf0yJ5UmxcAwBFyA8JqC1ACTGpCZT1MWivmkJJJV3k+W\nA4czMKZgUhumtUKqrFw8WfZMKsPH9xtihBRzd3ezqzU6+3duFgGtckb8ZoGqCoUPaUusfx4JcrEN\nDDFhdHiphUk35PfrZSkf18KZXW9Wz+fF3Bg5ZkaPFIczpy6lYFobprXh06creutpx+6DtZ5SZ+Vo\nq0HYS22mMYBSUGh4eK/io4OGvWlJ1zsGD0oqVp3nYF6O74HEh1ygFkaxNymJdWJwfju66wZ/pwzv\nXwbnz4sXml2N9EpsOqVvUpQezqrtsXZF6dtF4rnm29UonC9Ajq4Z5/5tkxUv8jTLxywuTQlmE5Ob\nGimyHhyFyUrzwTusz+tfeM05GZHDWISIKKGoizzt00bz0X7NvXlNoTVrPC4kCpWnTxuTfzj1ggau\nvb6nMYDkbe7Ks/jz8vqHu78Cv0X86Ec/4ld/9VeBLFTaPE4pbbmgHyKkPE2+kFKgXnFB6FcQpfOO\n14+7wNw9fJVyebG2+BiRQvBgv2KwgdXa5YublK+rBGVRMG0gJcmq89Sl4n/HSBo5X1LmDtrzKGW+\nPpUBn6BE0FSK+bRirzZEBDFkgnlKEiNzxvqsKQhRsGwd9/YnFCYbtdeloTDZYuN5r9UNCqOIKRdx\nG26R0S9fIQRcqvtsXR6HQxZO7d0Q//Tsrrcu9da3L1uWpK2580XnNdjA8dJCTCyWAykmrI1IJZlP\nS066gVkdGVzC+pcXp0bCvFFUlUEJgZSa3vrsE4igKgASg/dAxf60ZNYYlqOgxYyfg5SCujQMbiCm\nnIyi5N0f3yuVrbbOBhfs8HLcRFF6MM8/+2w58HM3clY7vAxS5JjQwYWtv/FNY1bndS7EvIE9bnvu\n75UIIbHBI5TA+8hiaUcPZIH1gZQszkN4hXtIIcEUUBlNaSSTylBX2Ue5KRVa51F89rgWlEYQY6aD\nPb+sXqcY3QiZfMh2WG8jSORNsCtKX4Hvfe977/oUbi2yx9urOaWvQky587kZab/2+TFhTLbwWI8x\no4mE9bnTVRrJk5OWhECQuD8vuT+v6G3gk6crtFSYQpOcz3noHoY4JjUpKFUu9KTOBsZiLLyVVBxM\nSr5yf0o/eFobkMDerGR/XlFoRe8is7rIyk2xKRTzeyKEoCjkS8crdakpjBypDq9/H4UQTGrzQm7z\ni08890OvPe5l8fyuVwmxjbyTSrBp2loXMtfUyO3n60KkqTQna0mSgm4ICCmw1jP4iEIgUi46kwR7\nwcah0LDXaObTkkllUAImdUlVZN7twcxwr6xY9Z6DaUFTKSqTeWdiUhDeIR/3y4JWMptsx3ROXbvD\nxThaDmMU8vVpDttO6aK/qdPa4RXY2CO9LcRRM+FCZNEOLFYOieDRYUPXe2KMuAhGKaQSzCrNYpXH\nPNHnwuqigZwCZhNJVRhmk5LaaA72C7o+oITAGIn3EWMkEjFqJRK9C6jBM2/Of0c33ulXndze5onQ\n7T2zW4CPP/74XZ/CrcUmJ/i6WHVuVKJnzmb2X3t5cXpWwPXosKGp9NZG6Gg1sO4iq86P/NPsG/ez\nX9nDusDTk47KCCZlLlB8SDjnUTaiBWi9sf9RVEbhU6JQEq01VaGwPjGEyN/6yownJ5bgs71QVSha\nO+acF1ngY32k1JIHB1X+PT7iQswE9dGc+HlcVRjxqu7zBqVRuXMZ07VSPt4Ezp9yltrBM63M1l9V\nyuweUGmJ1jk2r/eW9XpACElVQBIBu0gvhBtMCyhLxf39kv1pzTcezjJnVAjW1jOfGvanFVprUhJA\nfp82nfyXdUAnldn6wN71LukG8iX0jx1exNFy4PANbfQOz3RKd7jbiCmx6jJnddlaehvQUuB9Fp9W\nhWbaGPYmBu8E6yEiZCTGLKCNKa9bm/VLkotRpcEYwaPDGff2Sx7uTZAqr0vrPiBF7tB+4+EUPd4T\nprWhGzwPK70VyW6wST2UItPktMoJgkJc/Z5ym7ArSl+BX/zFX7xwoUop55f/xV/8xTs4q7uDEGMW\nkwjxws5sozI3WlIXmvI1hVMWa2SeT10q1GgeH3zkaDGQEqwHy6wqKQwcrxxPTwZI2V7JKI1SCqSn\n0JpWJmokWouc/lMbfJJUSlBUmkopWucwWmWltso78/1pyazOgp2NAKkqFFJkE20xel1qpdAKYvRb\n/q0dzYkvi2wNdf0F5l3thmNKOJ8TrTrrYb8mptOxUeZq5fmyKSSqk/ggsCGgFcyaGiU8i97S9Vls\nplTuRmmtKIxhUhfUVcGj/Zp17zhubaYUVAaBYDopSKMS+nVF+VnD+xizv9RtsVTb4e3Cj2Ecv/D1\n/Tc6zka5/2zXKb2z2Fz7YfRL1koyqwusC6e8eSkJLnA4rVgfeD55vEKIwHrINCQlJFpHfMyFqJBk\nPnuKFEoxnxY8PGj45v/1gBgFkQQhopSjLDSTQm0pUinl2NC9SZkT8OR5XcaGnhVHwZd1WZ8hRC5m\n72qIzq4ofQV+/OMfv+tTuNNYd36rzpajTdIGVaG2HL7LcIKkEC+M1x7s1xyvekyRZ+9KauYzg7We\ntrd89nTFqrV8ftRxtBrQWiKsYT4vkJ0imNEWw0gKY6ikoDaavVnB1z+a0bmAc56mLFBSIFBAXqwm\ntaapzLbgLLTEjkrIsx0qo8U2negqi8Qm7lKI3Mm7S9zAQktcSISU0DJ3phO5S5p5joKq1kz7glVr\nc1CAhEpL1GhRYkzEWImochRoqQR7kzymrxvDpM4+fkOMVJXhcOzaWxe4t1fhfBaLlebyVimbzsNd\nfM93uB5OVpbEm/FJIUfaKim2/NQd7hY2134Yozx9iKPDi+TevGLROiaVRsksikop5vVZJrrB03ce\nkSJpLGxLkznxk1IzmxTEIFFa8OCg4uN7U+ZNiZCSqtB0naNpMh9eCEE/+K2KP6W09XUW4vyYXimx\nvb9qJbZxzhul/q4o3WGH55DOPT4vW6lLPV5orzJdz+Kl55/jfBjH/oJpUzCvDO3geXRvyt6k5q8+\nPeFkbXl81DOp9Wg4PBLXZTZG/9q9mqeLnnUfqQrJwayi0IKqNuPYJXF/r2ZvUqBEXiyEFDS1ZtYU\n2Yhd5It/Y40kRKIuzDnbi5y8JIjxaqKTjdVHSnmBvEsFkhg3EHWhaMdc5txdBh8EH+03fPJ4zeOT\njhgTeozH8yEv6pKNU4EchV2Srz+YcLBf0Q3586rLEsjPj6QsLgiJ/Wl+n+oxdq8qLz+OH/zZRT3c\nqfd8h+vhJkROkDfNB7NyN76/o9hc+9aHMZZY4bwfxUswq4txLA4n64H/96cLlquB1dozeNBaUmKY\nBEFsBwqjmNeae/sNe5MKpRIxSSaFZn9eYn1CjR51e/OSyiq6ITCpDVJJGIvjjaD1IipOU2qMiqM4\nVhJitrLaiGbvKnZF6Q5vDU2ZVdFSXpzi8KqCtBs8Txc9QsD+pCCNo3vvA53NufI+5m6Y0pIiKUpj\nmNaaj/YnfB5bBAMImFYF/cRRF4qYInVh+PpHU6aTkuOVxYfIzz6csewdSmmaUjKrSyaV5v68YlIX\ntIPP43QBs6bYLhJGyy1/kpGY/vzrUlJy1U1rYdSdXmCmtWawEaMF6z6w6Bz35xVNqce85kBpDCFF\nmlJRGIkLglU3ICQkBNNJSaWznZXSihASjw4b5pOCopCkCEZrtBIczqoc46oVKeXv3lU5lUZJQsg3\np7vaZdjhargJj9INDmcl//OnOwP9qyLG7L0sgKrU74Q6Y5Sk7Sxt5xmcz7QsoxiGyNNlT2kUh/OK\nL44tq84xOE9VKkynub+n0FJwsuqQSlJXghAl9+Ylj+5N+dr9KZOmQJBoh5AndiN/dH/83k0qg3Ue\nH7I7zCb6tKk0Mb48+vQsB/6qotnbil1RusNbw1X9yc7ieD3kzlmCz551HMxKFmtLNzjaITBrCqaN\nyR6TKZPJfYSu93jn6QfLpCnZmxbUheLRYcVff3JC7zP3JwpJbQxylj3mTKkxPnNgFzFwOK8xWm1t\nr6a1wY+Z9CS20alK3kxAwPO46wuMkpKmknz21NKPuaFHy5629/z4r494tuxISTApNX/7K3OenfR8\n8nSdxU5asj+vmNWGde+RSjAMAd966jIwqQXTutya+E+qPCJr+7CNy7uOyKcu9XYDsCtKPww8W2w6\npW/uLXowr0g/OdkZ6F8Rmw1/RnghDe7LQF1q2t6P3p8SJRMuZMtCUv6eKCnoh0BZKAqj6IbI1x5N\n6buBtg88PJwSj1pioZHAg4MaJeG4tSx7x4O9illTUBjJfFKem+KEGFn3eUMsRBZvbkIvBhcurQ94\nHzZDu6J0h1uJQkkG8kW6MQjOo+yED5G2s9zbK0lJ0VSKo1Wg77Ld0715RTt49mcl07pgWhtKIym0\noht8jh71kf1ZSVkq9prMDdUyx05KmQnqxHSuyMzmyYnFaEkFOUf9dQEB18X7sMBsXkKIkVXreHzc\n0XtPaTR1KfnbH+/xf/7MAX/x/52w7B2T2rDXFFSloa4UhwGeHXcsnKVEsB4c/8fM0JSGlBKTSlOO\njgbzJo+w1DUK0g12xeiHhccnHQAP9t+8iDwVO+0M9K+PN3TCfwNUpWJwgRADdWmYSPAuIKQmpMwV\n3ZsVhJA4mJU82GsYvOeYhJCBvYlh1pRYl+lW2fkj4Vxi8IFPn7V89d6Uj/YrpvV5zvrzYQBSQhrr\n9LvYlHgT7IrSHW4lDubVeNEKykLifOLeXpkNf43K4heTEy5Igqrs6PrIYt3T28zNyd06hY8RnOBw\nv2a1tqx6T1FEmiIT1/fnFdYGepeNSw8nBUWhmTXmhV17gm1Byvi4MDeYmHSHEVNisOGcqO1wXiFX\nA4u13cax1kbjy8hHBzUP701RSvH1BxOW64GjtaUwim88nDAfVafWefpQ0pSGrz2csjetcC4iJLRD\nIERoKv3GNmU7fHh4cpzH9/f36jc+1qYQzZSAvTc+3oeCptS0+JED/m5KEudzDvzexLDuc8LcpC6Q\nUvHJk9W24JxUmsXa0Q+BusyuIUZplEzYEPi5r81YdTHbRItMC/j8qEXFXKRGUvbDfu52sXEmcT4X\ntGWh8lr6Eurb+4xdUbrDrYQUgvnklOdVms1/FcvOEROkFCmN5nBesmh7fAxMqmzq/9FBw6TWWB+x\ng0cYKArFg4OGufWEEGltQElJjGBDIMXErNJMJ+WWY6af63xKkReJwYUb74zedbT96RgukagKjVaS\nw3lF23sGH5hPctTq3qykKRUiJZ4tBrQSfP2jKc0y23vFBEpJ9ivDz39tn/nRmpQkDw+azFfVETvG\ng24UszvscFU8Pu6YVPpGRsYHZzqlHypCjHRD9tzMYsPXbxI39Kh3hW7w9DYQYiSE3GSwPiJ7h0jw\n6GBCTDkIpDKak7VDG8F68OzPsjK/rhSHs4JvfLSP8wEfI01p6KxHIlj0A87FLdc9hPSCzqAuNXV5\n/u8fIj7MV73DnUVZaIxWnKwtPsCyzepuLXNqUlUo6tLkG0QSnKxaBhdwITGXEuscvQ2kmPmhk0rT\nDo6uj0glkFIiRe6g+BipjOL+fn2uE9rc0E3sfUNMZzvIp/8eQjbwH1ygqQyHs4rHx9mm63htmVa5\nq51SHr33NvD0ZMAYxf15zcf3p9zby5SMtvd8+rRjf1ow0nu/9HCAHd4PxJR4ctLztQeTGzneqYH+\nh+tV2vanvsxCXJ4L+S7hx8zpGFO2r0uBo2VPXemt33RpsgBLymzZlFKmV1VG8mC/JqREXWgSiWmT\ndRRt7/jiWccQPFJIHh7WlEaNgkzJunfbUf+7LMpvG27/N2aH9wLWhexLqgTNJXfQL8NZeykfIlII\nJnWBUpKm1BzOKwqjaHtPoRUhZqP7aa1ph4AgUJQa7yOrziFVHteklDBGoZVisDkn3Y2pTJui9DYo\nRd8lnA+5EzIW9Gc/x7rQW5/Ps4WiVoKqzJsJpQTOx+wsAAgkNiQaDYXSKO+3ecwpJpatpSwkWil8\ncCRyNybGxN603AZZ7LDDVXEyOm/c33/z0T3AvXF8//Tkwy1KbxMue88pjMIHPwpzBavOUYx5yVIk\njMqi07rIXc77+xWrzhNCIgmRGxRCMK30uVG79ZHB5QKWlAtRJbPFXYinkx7nIz7cXV/Rm8auKN3h\nS0E77i5DTGgZX5vg9CooKanLPGIptMaFtC1I96bFdvGpSsXBrKQYFJNSM60NUvmcaxwiSkmmtcb7\nRO88TWV4sF9jtGTwAesChZHnRvSd9dvFBPxWIfmhoB3CNr50cOFcbKrRkr0L3BaEyL6jMWYnA+sC\nHx3ULDtH8Pm7YIxiUmnqssGHxGJtCWNkXjcEphuzfBco9Gkk7fM3Gh9yxKkAJrV+L8RiO7wdPD6+\nOZETsI0e3hz3Q0RT6e34/k3W+JvAZe85ZyOu5ej52Q2ebvBUhWZ/Wp5z89ifVhTGsWo9pcnR0U2p\ntoLLDfLUTpN6T1ELUoSkBD5EVl1k3VkSeXP/oTU3XoVdUbrDlwIhBCltxjpvfryq0NvIzg0XKPu/\n5YP7kLtp80nB3vSUqDOrDU2pcC7SjRGog7XsNSWM56WV5OPDBuuzov5sYXNOJfnmL+OdwfkwCoOu\nVrSd/eyu2qHcLOxaSfanJU1l0FJwMK8YXO6cFibHkM5qw6pz4/cm/+zhvGQ+yXGzL+PydoPfCtF6\nG5hUu6J0h4uxLUpvQOQE+Xr4aL/m86Pug+3gKymZ1rfjmjt7z3nd3vRsUTipNIWWHMzKC9fHvPku\nkUJky0CRQ1KeR1VovvFwlovbUtH1YWsdaEebJz/et65jYfe+YleU7vClYGOmruR5w9+bwPPm9D5E\nlm3O9nyer5MLsTyir0qdLYRUJp7DaeEkxMWqx7NK0bq4m5dP2/ttbnLz3MjpdZhWZmubdV1VqJQi\nR++dsW86eywpBYVUNClzvAottzeNixb/s1BSbDltu+7DDq/Ck3HM/uCGxvebY/3vL1YsWsfe5Hoe\nzTvcDM7ec163bpzF86b0Lz9+FtVKebGXdPYezd3afohMG7PtIm/uOYXMscw7nOJu3lV3uHPYmKl/\nGdgQ159/fBYxJeRoITStzYX2Gxvz/rNduXetFL0J+DMqpBAiXKG4lFLciMhLCoF8jX1T9Zqi/7Tz\nfobXWmqkzBQDrcQH27Ha4fX44ih3Su/f0PgesmE6wOOjbleUvmO87XvORRZ0Z+8ZIaTtZC2Mnteb\ne0dKmf6UhZq7MuwsdiX6Du8dNkIZeLGbF1PmK56sLG2fu6lSiNH39PS5zudu66pzrPtslp/SXR7Y\nn2LzOoXgxrvWG7zt98v5wMnacrK2uDG3GthSEqyPrDp/JgJ2hx3O47Nna7QS3N+7waJ07Lp+cdze\n2DF3uBsYbGCxtizXlm7waC3PTILkC91UHxL9EFh17r25t9wEdiX6Du8dpBTMG0PixRGu95EwEnsG\nF6nKdO45MSZWvaMbPFKAVorF2mJdtpCaNebOj4U3xH7BzcWinkVv/XZMNanNjahKBxfoB58FbZVm\ncHHbhRhcPDee23QrNo932OF5pJT49GnLw4PmRsVwH41F6ePjnQL/Q8PgcoEZQuJoPXB/r6Ypc2zp\n8/eMENPW09mN96Rd8EfGrlO6w61EfMOd48sy45USW7GOuoAL1NuQjY2FYLDx3A42xoT3Ly9yYkr0\n1mNdeOlzbgukEG9trD2M7gQx5YX6JroA3eCJKS/gzsVtB8L5gA/nf4fR2WsW2Fq77LDDWRyvLL0N\nPLrX3OhxN0r+DTVgh7uDN73nSJlFmdYHYow5oGUIF96HMg91fCw4J3RyPtBb/8bnc1ex65TucKsQ\nU2LVOkLMCRqTS1ouOZ8LSDMq8GNK213rxpoDMs9o1pitWv95bIodpSR7U8WkMqz6fBwxEtRfhvOJ\nRtcXAt11SAG9C4SY8EHgfbywY+p8ZN07BNBU5pXpWFKInD9NVtKWWm+7DSkJ1r3f8rWUlMwnxVa1\nv8MOz+Ozp2sAPr53M8b5GxzOK6QQH7Qt1F1DGu8VPqStMDbGhAsvuq+8CnWpmE0MhZHj2panbWac\n7pyFFIJZU2z9STeFq/OBVZcpR9ZH5s2Hx0veFaU73CqcHa9bF6mfG69fhMEF2pE7uClknY9bRX1v\nA1Whtp3B59X6Z1EWCkTuipaFyotHbfAhouSrrTs25w1sbYk+NMSURj9SRp+/ctvhfL4ozV3UXMAP\nLryyKJ3WhsGFc0ra/Pg01OAshBA3Yj22w/uJT55mzufHN9wp1UryYL/i06frncjujsCHtHXsyEb2\ngXWfxZJCwLwpLrW5VVKyNymIdaIbMp99E0ldleqF+9jGZeQsdveQ3fh+h1uG143XL8Km+Dz7WJ1Z\nRJS82qi6NLmzuvndQuRC6HULU12obPchL7aT+hAQYyKmzMU1Wm9HUBfxpZ7/jF4FKbMY7awwqzRq\n/I7sokZ3uBo+e0tFKcBX7k9Y957F2t74sXe4eaizo3QpEIhtQZjN9y/PS1dSYrSiqQz1mCKVj3k5\nFGMM6V22HHxTfJiveodbi9eN1y9CHpdkY+JNaodWp8cx5sXj5OIp3Wi0W2HUW1Oz3xVsupfOR+YT\ns+1QX/Q+16XeFqMXvW8+xJd6AMIoaNvZ7uxwDXz6LI/vHx3efFH61QcTfvQ/n/DTJ+tzwR073E5k\nAes4Sh89kQsjsS6P769yjwgxIsg/M63H+8+ZUJfXnsuYfvchY1eU7nDr8Krx+kXQSl6Yg66V5CKd\nS455c6TEOb7pDm+OjRffZUeXLyviNwb/QuTR/S4XeoebxE8fr7k3L9+KR+RX7mee6k+frPnFv3V4\n48ff4ebx/Ch9Uhma8mr0i43rCGTjfqPVhfefHV6N3Uq/w3uDyy4gWRyTH9sL1PQpJZat5Xg10Nud\nz+V18KZcuo33aBr5qBvElFiMn81gb7/LwQ63DyergZO15esfzd7K8b8yiqc+ebJ+K8ff4cvBVdew\ns+vURfeVs7D/P3vvHiNZdtd5fs459xWPfNWjq6rL3bi7h4cZHoZZVsuiHnk9Wms1f+AdaaCl9Rox\nLSRrLcTKO5oFaZsZj7CFZKGxQFpkRM9MSxZa/oDdkVbWCC0soJHFzIAZFpDNNDaYflRXVVblKyLu\n67z2j3MjMjIrMyuzqrLydT7/VEZlZMSNe2/c+zu/x/erLZvjhq1Je2F7R/cjBqWRC0ei5KxvNd0j\nA9cah+ncOKrmyUgaRY7GdIBJiJ2OWq22M6eUKi4YIo/A23fHADx/bXgsr3/jch8hQqY0cnGYv07t\ndV+Zp25Du5l1fmb5HAnEumXkwpEmksV+tm9P6eMMSUWeDP0iJUtdsCPdZyAqyj1FHoW3uqD0uDKl\naaK4fqnPO3fHMzvjyPmnyJJZwuNhMlJSitmkfbyO7SRmSiMXEin3b2CfNqn3cnXmfe7PMol6UIIr\nTRTDXhKPTeSROe5MKcD7ry9St5Y7a9Fu9CKRKHkoXdNBEa5hgyK5sEot+xGD0khkD9JEUmRJXMWe\nQtJEhWMTM1CRR+CtOyN6uXqinve7eeFGyML+1a2tY3uPyNlFCEGRJRderWUvYvn+KfDGG2/wzjvv\nMBqN+MQnPsGLL74IQFVVfPrTn+bKlStUVcU//af/lB/7sR/j27/92wF4+eWX+bt/9++e5KafO7z3\nM9H2NJVo7RAiTIGXdbB26+dJnPY+BVRNcMgKCwRF3Q02zRshwM5jOjU8iET2omktt9dKvvXm0rG2\n5bzw7CIA33xvxA99941je5/I6cR7v+f1qqw0o0qTZ5KFfnZot6iLRNwjx0xd13z5y1/mtdde4yd/\n8if5l//yX85+96UvfYkf/MEf5J/8k3/C8vIyf/RHf4QQgqWlJdq25X3ve98Jbvn5pG4tVWOpW8v9\njXr2eHPczlygyiYO0Jw0xjrqNliV1q1lXGvq1nbHa+fxmT+mU2evSGQv/vq9LbyHF59dOtb3ef6Z\nIUoK/vp2zJReRMrGzK5X0+C01Za1cUPVGrYmevb/kZ3ETOkx8MYbb/AHf/AHQC22dk8AACAASURB\nVMiGLiyEUs61a9dYXV2dPe/evXt88IMf3PG7n/u5n+O5555jc3OTf/bP/hm/+Iu/+PQ/wDlmfpDe\n7pDi8ND5bsRM28kzfwiEIByejt0KKn7H76JSQmR/vnFrE4AXu0zmcZEmivc9M+StO6OZv3nk4rDX\nNcl7mHo7ee85vM/TxSIGpcfAj//4j/PjP/7jABhj+OQnPwnArVu3uHnz5ux5N27c4Pbt27PffeAD\nH+DrX/86zz//PP1+H631Q99rZaVPEhV6D411nnEZ7P/SRNF2Au2DXtrpl/pz3Uu6ujo66U04FEqG\nYbNp+V5KmHRZ0N2WokWmcN7PWi8ikf34xrshc/nSzePNlAL8rZtL/M3tEX91a4tve2752N8vcnqY\nXpME29erLJUsDFLqRnSmLfG+vRfxCn7MJEnCyy+/zGc+8xlGoxGf/OQn+dM//VO+9rWv8dGPfpRP\nf/rTvPnmm7Rty/d8z/fwy7/8y/ze7/0e1lpeffXVh77++nqc7nxU5p2p22r7UVQXPB2kidyh/bef\n/Z6UIk7iRx6K955v3Nrk8mLOysLx239+x/Mr/M5X3uEv3lqPQekFI1HygeuV6CxEL7qN6MMQPiqD\nn2nOSubruPDe4zl6yd17Tzs35BR5chzlmEyPA4IojRI5Vu6ul/zMr/x7fuA7nuF/+u+/69jfb1xp\n/udf/Hd8+/PL/K//w/cf+/tFHp1pVvM4ht+MdRgbKj5xsClw9er+GsExUxo5s8x72PfzhDw7fFAz\nqc3MFs5z9IDI2PC3sVdsJ/PHpJerPb3F5/dd1diZo4lznl4sv0eOib94awPgqWUth72U564N+fq7\nm7TaxsXvKWXqWS+7Nq4neU13zs+uh01r6fcSBPtrZEfi9H3kDNN2MkAAjXn4JKPznqa1aGN3DDkd\n1Xu40ZZRqRmV+oFJ8ItOq93smLT6Qf/npt3ed01rdwwm2X2Og3WOujVYd7CfdCRyEF/95hoA3/n+\nlaf2nh/4lhWM9Xz93c2n9p6Ro9F01ynnd/rXPwms89vXQ2PZHIdrX1mH618bLUYfIAalkTPL/Goz\nOURZpKwNZWMYVwYl6OzgxCNnSXf/HIE02S5/JerBUpie21/aOopMIUXoC909wARhITEuNVUTgtmj\nLiAiEQjn0Ve/uc7KQs71S/2n9r7f+f5LAPzZX91/au8ZORqpOvia9Viv3fXFCwECObNJ3ipbysYw\nqQ1NlIbaQayVRc4sWaqQUgQh/OThQamdC4iUkgz7j3b6Z4nsJvUhi8oHO0gTxUJf4L0n3WPfTPfd\n9OdESZaG+w+dOOdnElDeh+BCRimVyBF5+86YcaX5oe++fqyi+bv5jueXyVPFf/rLe/zof/O3nup7\nRw5Hv0hJE4sQx1NWnw5hauOY1KGUr8T2++xXIbqoxKA0cqY5ykWkyBOqxiDF0bOj86SJYnEgwXNu\npaMeh4OOSZaq2e8Ps+8SJclSSasdWSpjL1bkkfjKm0Ef+rtfvPxU3zdNFN/14iW+8p9XuXW/5OaV\nwVN9/8jh2GsB/eTfQ7I4yMCHClvZGIQQ5Fm8ps0T90bkwpCniuVhzuIge2hAZKyjrM2+PT9SiBiQ\nPiJS7r/vtAmuTPNtEYMiZWUhZ1BE2afI0fHe84d/cZcskXzvS1ee+vt/37eG9/zjN1cf8szIeWd6\n38hSRZEpskTG7PkuYlAaiezCe8+k0jTaMtkVIEWOjzC5b2i0ZVzp6M4UeSK8dWfMnbWS73np8pEU\nOp4U3/u3rpAoyR/8+W2iAmMEtif+ozXyg8SgNBLZhWenlWW8kTwdvN/1c9ztkSfA//vH7wDwQ999\n40Tef1CkfP+3XeH2Whmn8CMAzAuJxMX3TmJQGonsQgpBP0+6yXz5VPqNIqHnKu+G13q5iu0Rkcdm\nVLb8h6/e4cpS8dT7Sed5+XufBeB3/9O7J7YNkdNDkSkSJVBSRGvkXcSgNBLZgzxTLA4y+rGP8anS\nLxKWBtmeovuRyFH5t//+LVrj+G9/4LkTXeR84FtWuHl1wH/46h1ur0Vr6IuOlIKFfsbiIIvDm7uI\neyMSiUQi5471UcPv/PE7XFrM+dAHnz3RbZFC8NEfegHv4dd/5y9jS1Aksg8xKI1EHhFjHZuTls1x\ngz6Eo1Tk+Clrzca46az94o3/IvN/f/mv0cbxwz/0wqlowfn+b7/KB75lhT/9xn1+5yvvnPTmRE4B\nk+56Vdb6pDfl1BCD0kjkEWm1nYm7V9GV48SxztF0NqfaOIyNQelF5Zu3t/j9P7nFjct9fui7r5/0\n5gAhW/rq3/8Ai/2U/+O3/5J/9//dOulNipwgxrqZLXOjXVR56YhBaSTyiMz3qKk4lHPiCCGYHoap\nhWzk4mGd44u/9SYe+B8/8u2oQ1gQPy0uLxX8L698kH6R8K//7V/wm7//jTh9fUGRQjCVKJ1aLUdi\nUBqJPDJFljAoEvp5EicoTwFSCIb9lF6uGPbSeJG/oPybf/fX/PV7W/xX33mND3zLyklvzgM8f22B\n/+3H/gueWenxpT/4G77wb/6cqolalRcNKQXDXne96qfIKKIPgPCx8epMs7o6OulNiEQikadKqy1f\nf3eT9VFDkSmWhjmpkvz7r97mt/7j21xZKvj0P/qBU62eMSpb/vf/8894851Nrl/q84/+/nfwre9b\nPunNikSOnatXF/b9XQxKzzgxKI1EImcBbRx31kveu19yf7NmcZByZanH1eUeznnKxlA1hkGRcHmp\n2CELNqk1d9Yqvnl7iz/9xn3+4m/Wac3ePXhXl0OJ/NpK/2l9tEfGWMdv/v43+K3/+DYA1y71ee7q\ngMtLBVeWelxeLLiyVHB5qaAXqzGRc0IMSs8xMSiNRCLHydpWzf/zR28HU4MsocjUzOSgbAxVbai1\npdEWa8Pghic4obXasT5qWB/VrI/aI/VPDnspeapmweo8N68M+K4XL3Hj8oC6MWxMWlptuXl1yH/9\nt6+fiJ3o4/Dm2xv81n98i6/9zTr1PkOT/TxhcZCFlqEiZVAkZGnwT08TSZYqepmilyf08oQ0Cd15\n21VhseOx9+EYTf9108dMH/sdz3Hd8SwbQ1lrFgcZ/91/+Xz0bo8cmYOC0rj0ikQikci+vPnOxiyT\n9yhIIVgaZrx4c5FnL/d59vKAy0s9RmXL6kbFvc2aRIlZMDWuNPc3a+5t1rTGcmkx5/LiEtdW+ty4\n0ue7XrjElaXeE/yEJ8+3PbfMtz23jPOe0aTl3mbN/a2wD+5t1tzvHo+7fWbdyeeSslTy4e9/H3l6\nthYAkdNNzJSecWKm9PGwznVT23G1f5Hx3mOdR0kRMz97cGetZFRp6sZQtyEr6pynX4RAMu+yp4mS\nCBFyckII0kSy2M/i0NkTxHtPoy2TytAaizYObR1ta6laS1mHzLJxLqSsmf2zQ7tXdNPfgqBaMXss\nHnwsRFhcpImkXyT085SrywUL/ezp74AI0N27EGfyuxUzpZHIHpS1ptEOKWDYT0+VdEzk6TKpDdq4\nzv4vTsLu5tqlPtdOeiMiQAgSiyyJVrwXmLo1VI1FCBgU6axV4zxwfj5JJHJEpoMSrhNbj1xMnPOz\n4++cx0YR60gkcoqZ3ruCUcj5Mm6JQWnkwjI/CJCo+FW4qAgBSoXMqBTEjHkkEjnVpHP3q/OUJYVY\nvo9cYAZFSp46pDibfTmRJ4MQgoVeirEepWJ/cSQSOd1M1RXEOVxEx6A0cqGJGdIITIdyYjAaiUTO\nBuf13nU+P1XkkVg5A2LTx8lF//ynnfN8fM7zZzsKp3k/nNZtO63bdRBncZsfl/iZD0fMlEZmJMnF\n1pubfv6qMVjnyVN17vp1zjJP4vw8rcf2on/3ppym/dC0Fm0dWSdMf5q2bZ7Tul0HcRa3+XGJn/mQ\nf3MM2xGJnFlabWeOKtY6lob5CW9R5EmhzfaxNdaxNMiiJmlkT4wNzkUQlDnOa6k0EjltxKD0KfHm\nm2/y+uuvs7i4yAsvvMDHPvYxAP74j/+YX/qlX+If/IN/wEc/+lHu3LnDL/zCL3DlyhV6vR4/9VM/\ndcJbfnF5HFcJbSzWebJExSGqC4RzQdg8UfJUZWIjTwbnPK2xKClIL2DmKxI5buJV8ynx+uuv86lP\nfYrXXnuN3/3d36VtWwCuXr3KD//wD8+e9+u//uu88sor/PRP/zS3bt3ivffeO6lNvpBkqaLIQml3\nUKSP9BraOMZVEDceV/oJb2HkUUmT+WObHEuWdFRp6jYcdxP1Ts8siZL0uwnnfpHMFpbjSnffaxO1\njSORYyAGpU+J+/fvc/36dQCWlpYYj8cAPPfcczued+/ePa5dC94pzzzzDKurq093QyP08oRh79Fd\nMtycL/Vp8KiObLN9bJ98lst7v+PYu3jszzR5phj20h3e7s7H4xs5Gs57fvn/+jM++6//w7kTuj8O\nYvn+KXH9+nVu377NjRs32NjYYGVlZc/n3bhxgzt37vDcc8/x3nvv8eyzzx74uisr/SfaQH2QJ+1F\n4El8fuc8o7LFOk8vD97gp4XV1dFJb8K5Jdg/qli+P8f0soSqNaF8n8bjG3k4f/5X9/mj/xySS9/9\nwiV+8G9fP+EtOt2cnrvlOefVV1/l85//PIuLi3zkIx/htdde47Of/Sxf/OIX+f3f/32klGit+dEf\n/VE+97nP8du//du8//3v58qVKwe+7vp6+cS28erVhQsdtBzH5x83mvETfcXIaea0LUIiT5Y8U+RZ\n7CWNHJ6/eGtj9vOf/9VaDEofQrx6PiVeeuklPve5z80ev/LKKwB8/OMf5+Mf//iO584/LxKJRCKR\nyNnkr25tzX5+6+7FTfoclhiURiKRSCQSiRwD766OuX6pz+Iw4xvvbGKdO3fWoE+SuGcikUgkE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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\nYrWp7Pr0OL99+F0z4PO/Lh2358+KSYZLiPB61fj0gQceYOvWrSiKwuLFi5kyZYp5m8vlYsmSJezZ\ns4fVq1cDsGHDBu644w7GjjWWaxk3bhz3338/FRUV3HPPPXi9XoqKinjkkUew27s/eaUSf8C1dV8t\ns08b3OvHxTDeosXpZoCvKLaj08cU8tePA5drGo1v+RlpVnB5sfj+kHz47/1s3XiYCVNKmTN/QvQH\nZIpjhqvZRV5OGrkd/pBmpFlpbg0/DT/VdQyorVaVotJsAGqrW3jlL59wybVTugxNAXg8Xv765Eaz\nq/nUc4dzyphCvB6NlU9v6rSWaPjFqy0WNWTvqXhx+Y7rK/PGcn4Pn81/vbaLPTuNbLDFonDljVPN\n7FKfhxQVBd1iPM9wz9d/vUVVjDUXO2W4jBouT5eIq7mhjYwsG1+/bWbQ9ZlZds73BWBFpTlUHDYm\nzOz9rIrxk0tDfpnyv9bOqpYutwkhDD1muDZu3Eh5eTkrV65k2bJlLFu2LOj2hx9+mIkTJ3Z53PTp\n03n22Wd59tlnuf/++wFYsWIFN9xwA3/5y18YMWIEq1atitHTODmk+U5af3hjV0L2r+k6ra6uGS7w\nBVYmBafL+AM00Bec5efa8Xi8fLa1AoA9OyrjkuWKdQ1XfbOLVpfHHM71K8hNkxYR3ehYa2WxqsGF\n1EcaWfPS9pCPa6p3Bi0hk55hY0BeRpcMia7rRqYsTIBisajdFpH3SO+cm+2es90YAp07dUiX90pH\nrS3tZrAFRlZq72dVNNa3kZ5hw9rNY0MJquEKE3A524xjS8uwBQ0pBmq4Qs9SdLs10jNs3e5/6rnD\nzZ89voL4UB+LrBzfzEWvTvXx5q53EEL0HHCtX7+eefPmATB69GgaGxtxOAINIu+66y7z9p5s2LCB\nCy+8EIA5c+awfv36SI75pDX/nBGRPdC/MG6U+3e6vOg6ZKV1DbjSOmUr2nxT1C2+KfIDW728tnKb\nOXXd69Vp9wVlB3ZXs/bVnd1Oa+9Jx+VEYmnjZ8bMOv8w2FBfHVfn5yuChavB8jt8oL7LfTa9d4CV\nT28CYGBhJmefP5KRYwuBwPvIn615+9XP8LjDTyDpTYbraHk9H79/MKjtRKQ8Xt3oqq52f8o8fMCo\nYbNYVWbNGwPAJxsO09TgpKA4q7uHhqSgoKv+DFfo34V/aDUzy+gQ7w9+zSFFJXQfLo/b2+0yXwDD\nRxUw6yLjebjbfcGwFlw0D0bdV6Xvmg3v7pehRSFC6HFIsaamhrKyMvNyfn4+1dXVZGcbQwjZ2dk0\nNHSdzbV3714WLlxIY2MjixYtYubMmbS1tZlDiAUFBVRXV3e777y8TKzW2P7hKyrKien2YqmoKIfi\n/Ew0TWf7oQZOGZTLyMEDenyc3Wq8jOnptqieX2WdUSSePzAj5HbsduPEfayulSLfST09zUYzMMCt\nccw39DBybCEH9tSQmWEnvzCL3zz4bwBOnTKY084aFnb/3R37QK8xgyoryx7T1zDX1zX74nNGUFSU\nw4q75+D16ix5cj26rif1+6W/hPodKB3+nubmGu+X79w7h4ojDRwtb2DjewdQUczHer0aH71XDhiZ\nlg9rW/4/e2ceIEddpv9PVZ/TPfd9JZPJfQdyEJJACEgiCAKKHIoIousNXqzXruKuu+6Ku94oyiI/\nAREERBEFwn2HXJD7mCSTZO777Lur6vfHt6q6uqd7pueKGejnj2S67rveet7nfV5uuWyxuYyAVzAk\ndptMSUmOyRJVVucnXb/LbUdVUp+fwYEQj/9hFwDdHX4+8k+r48aHXSIIy8pK73qy2WRkWUo6bXen\nj62v1DNvSTmSJgLHq25Yyex5Jbz6bMwQdsGSCtS33wRJouzC94y4TtAF7HqAk2z9JSU5dOq6t9Ky\nHLQ94HSIYxiRIkhI5OV6gQAOh92cX1U1olGVLM/I+z9zdgmvPnMEh91GSUkOXm+8D1dJSQ5+RaMN\njTIkGup7GOgNMmdBWVr7+E5F5tlx+uIfdW5G3bw6nTTLjBkz+MIXvsDFF19MQ0MDH/vYx9i8efOo\nl9MzwVViJSU5dHSc3nR3lsPGibYBfvTATgB++40LRpzH6LMWCkXHtX879SpEGyRdjtNuIww8veUE\nSqeRXoj/kj1jdSyg+sV/Pc+8JeXm7z07m6isyU+67pHOTZ9eGebzhSb0HA7qFYpOOX6flahg+073\n62Wykeq8dHfHWKPeXr+YRoLyaXlmk/S/Pbqb8983n7yCLPbsiLmk70YjrMFtv36d4jw3V543y2Rp\n/YEILc3iA87psnHOptlJ16+qGoqi0t7eP0SI3trUx18f3GX+bm7sHTJdt09sfzAQTusch8JRZGno\n9eD3hXn4nu34B8NsfbWeLK9I0UUVha5uH+dunEN/b4Ca2UWUlrg5csuvANDmLsGWlTXieh2yLNwx\nJDh5rJsXnj7I4uVVQOzctDb3i2OCOCaaqvHQjr9xqPMold5ywgFxr1qfD7u2NZjrGGn//QHBoPX2\n+Glv76elKf4Du6NjgL5ePyGgAZVpyHR1+sh/F987U+Fd827FZJ+b4YK5EVOKpaWldHZ2mr/b29sp\nKSkZdp6ysjLe9773IUkS06dPp7i4mLa2NjweD8GgCA7a2tooLS0ddjnvRsypHpnRmizsrRe+RrNT\nbIPTEX+5FOS4TEd6A9UzCiivis1/yNJjr6G+e1gvoWExSZr5VIaUkiShMfX7RE4WrMfFcCg3MG9J\nOdNnFtLS0MfLTx9msD/IyZOi4rPHJmHUFu483MHmbQ185RevEtZTUEpUNT8gZs0vjdOGWWFYGCSm\nHIOBCH/7426UqMqKdTVUTs/HPximral/XPurahqSxX+rvaWfA7ta2LezKc6nKuAThRa5+SKYWryi\nirXvmU1VTQEEYkHq0Zs/S6ipccT1upzimzikH+9XNtfFjY9EFHz6R4PH60RRNSQZ/lb/DABXzb3M\nEmiKZfT3Bnj9uaNATJc1HLL03qu7tzfy4F1b2buzecg0Nt0M1lha5r7JIIOhGDHgWrduHU8//TQA\n+/bto7S01EwnpsLjjz/O3XffDUBHRwddXV2UlZWxdu1ac1mbN2/m3HPPHe/2v+PwkY1z+fEXRNWQ\nTZbSenCZ04wzKDnZNojdJrNiXvKAulR/iVQUebjlQ0v538+vi3Pd/tCNK5hWW0jN7CKu+vjKIfNH\nwsq4W7JM+HNcX2Cil6Xxjsq8NlJAPzDTZhaSXxgfcDlddi65einZuS4aj/dw3y+3cPJQJ1E0lpw9\njZXz4z+0fMEof361XnQtUFT6e0XAlZufuuenqfmKqpw42sWrz9TRfLKXQ3taCYcU5i4u56xza5m9\nQKxrqI5rqBPpcDYTqgqyZdrH7nuLF588xPbXRJp0xuwic1xeYRauJIUnymB8c/Sep59KuT4DWQ6x\nnHpigZERJKmKygO/fpOduh9alteBqmpIjgBBJcSK0mXMLZhttsUy7p2334yxWwuWVYy4Ddk5Lpad\nJZjr3u6YB5l1D/O98dXmozZvziCDdwFGTCkuX76cRYsWce211yJJErfddht/+tOfyMnJYePGjdxy\nyy20trZSX1/P9ddfz9VXX80FF1zArbfeynPPPUckEuG73/0uTqeTm2++ma9//es89NBDVFZWcsUV\nV5yKfZxyyMt2Mb00m5Ptg/zskd188aplac8biiimvcRo0dUfpCjXlVIYvHxuMceOwUWrazijUoid\nDQGtH40XD7bRu62BN/a1cv175/Gpf17P73+1Bd+g1cxyjAaQk0Rxme+FIQGXJeKaPEeKKY/8gtRp\nsfxCT1xF4gk0Vhd7aU/SIqqly0eeLoQ30scGS5QMssGoKCrPPr6fcEjh5LFuXFnikbZ4ubBuyNL9\nvV566jB2u8zM+SVJdaFHD7az+c/7WbGuhpXrZsQ5y4O4bo1BkbAyJKCYMaeY47rz/fpNc5Juc2LA\n5ayqSrl/BgwD4n5ZRcp2ofWHCAaiZOfYCIWiJrs2Y04R+YUeneES2+a2C61VjJkTw5v0DgGLL8/l\nDe1FZigfxGEbvlrx7A0zcbnteHNcVFTn8vpL9Ww/GGvlZFRuGkclw3BlkMFQpKXhuvXWW+N+z58f\n81f62c9+lnSeO++8c8iw0tJS7rnnntFs37sWm86axv89cYBdR7s4eKKH+XrrjOHQ3R/icz96iZve\nt4B1S0b+crUiEIoy4I9QXZKavXTr6Q3rl76RItSAJy3O3/c9fYgNZ1Ty3g8u5s/3v4UkiSqr8T+H\nJ+dBnsqQUtU05EzENQTpvFBL5xax7Xg3Ffrx6weK87JwO4Y+dg4c72GlbCMSVs009LAMl55SPLy3\nzayG7esJgO5Vm5Mn5i2tiOkpnnviIG3NA5y7aY7FsFdsm8ES7XjtBP09AS68bGHc+hSLv1UiW3bu\npjnMWVhKwB9m/pJyPEl87ADUoAgkPQsX4d+/L+W+WSFLsY8fm9NGFAj6I2TnuIjqadjZC0rYeLko\nbFItAZddtsfto6aCbyBEQPeXe7DlQQBynTm8f+Z7TfuIpNshS6xYG6uiXrtxDg8fjO+duXJ+KfW6\nDjTR1T6DDDLIOM2ftli7uIJPXCL8zW7/w1s8+FzdCHMIlkDT4O6/HeDOvyT3QUqFXUeFTm84DVmy\nR6hqGjoODUoUVaOsMpdPf+08ambrjNg4I66J9uHSkqQU63qOcrzsD8jZGaf5VEjHxupkf5BGNA6j\n0oDKzOo8qku8cT5v122cy+c/sIT8HBeoGr3dfjrbBBOUqA2zwmglc/TQ0ErnJSuqyNJNbLNz3Xzq\nn9dz/vvmAbD/7Wb6k7TmsVpMdLYPDhlv1XB1JYyvqinA7rCxfE1NymALQAsLNkp264FkGnpG876S\nNDR9/YZLf1R3sbfpLJiqibvDCLhseksfg63rr5O49443CAWjVEyL3efPnHyRW178Jif7R9aUGcjz\nOvny1cu4/TNrzGFlFrZTzTBcGWQwBKOuUszg1GHZ7GLcThvBsMK+47EedfUt/eR5nRTqlgbGs81q\nyLj1QDufuTz9dbV2iYrQOdOSVxFaYU3vGQHXzMpc1i2vpKzQw+Ov1rPraBeRqGpqvMwM3Rifw0Zg\n9FZdJ56+JjacMXI6Jh2oSSKH+w88DIC9oj6TGhkBw7WqaekU19R1VyympjyHEj1FuOHMKkIRhX5f\nmNULy8jOcuAPRnj+yUOU6udh5rwSHM7Uj6ds/do3xPDnXDibLS8d49JrllGR8NFgs8nMX1qBbJN5\n7q8HeOFvB1l1uWCAJURVY0+nnyyvA7vdhm8gREN9N+XVeaZPlapqJrNrBFxLVlSxdFX1sKlPK9Sw\nCJRkl37fphFwWdlVRf8zFnDFWvoY2wgMYbjMa1uLLSvZNv9g+88o85Tw6aU3UuYZvjAKYMnMorjf\nRqEJZBiuDDJIhgzDdRojO8vBj76wjqJcF37dRDIUUfje77Zz6y9ft0ypf9Em6E5GI1xt6xFf/WXD\naHKScVyqxSPo7EXl1Fbk4tBfABGL+aHxYh5rANOvC5pPtA5w71OH+Pmju8e0nFSwHrrOoAhu1UD2\nxIv03yEwzmOqcEvTNPbWd1NakMWKeSVmsAWiuvXa98zhU5ctIlt3Os/1OjmBRunSMvKLPKx7z6xh\n129NN3qzncxfWsE/fXX9kGDLitkLSskvzKK5oY/tTwubCjUk8exf9gOwZEU1A31BwiGFJx7azf63\nYtV4QsMl9raz3Yckwdnnz0w72ALQIuIaltw6CzZKhisx4DL6NNpsEvdtPsQ2PZ0n6UapBsOVqH+U\nbRKrzplhDl9QGOuj2ObvYPOJF9Lep7jlShYNV8b3NIMMhiDDcJ3mcDvtZGc5adF9jzot6RBFVbHJ\ncqyZrP5HVYmXpg4f/lDUfKGNhLZuP3abbLJm6cIMuCzViobQNy7gko1tHNXiY/NL8d8Gb9V1EghF\nE1oOjR5W0bwv4o/Tp8FEaM7e2XhuZyN7fSFuumRBXMVqKKIQVVTKCz1pNWzO1avcIi47H/6ns0ac\nvnZuMevfO5eqmnzyCrLSWocsS6y9YDZ/f2QP3a0BKIDgMTfB/hDzlpSzYm0NW1+uN6e39nVUNbDb\nxD3W3TFIfuHoTJkH336LjgcfENsxCobLGixFER55JsOl31+BsMILu5t5YafudWYyXHrAlfAh9rHP\nr0FxRNDQWFg0j88v+wQnBxp54thm9nUdpMUXr81KhKZp7O7cxyN1f6U72MN3zv5nyjwlCdrOzI2T\nQQaJyARcUwAet51wRCWqqLRbAi5/MIrX4oNlPOJyPU6a8DHgD6cVcGmaRltPgNKCrISAI2G6JAyX\n2ZTX8lA3GS6LLiYm3B3bg1gxXk6SxrrF5by2t5XndzZSmOOmMNfFvOkjFxUkhR5R/aXlDzTXn4wb\nJdmUjBYlBYzDElE0tuxvY9nsYlYvjDmLB3QhuzvNFkle/Tr1BdNrGG6321h0ZvoN3g3UzC6iqiaf\nphO9SIqMOiCu1bPW1w47n6pqyHbZZMCmzxzeGicRHX980Px7TBouNCIauBCieYhpuPzheC8tWYpn\nuBwJ/nnf2vrvhFXBtrlkEehOz6nmc8tu4rY3fkB3cHjtYpu/g9/suRebonFmXYDXvE/zwdUfRZKl\nTJViBhkMg0zANQVQoDeG/dWf97L/ROxh+L3fbafPF+a/P74KiL0Ea8pyOHCih7rGPiqKRu7fNhCI\nEAhFmT99ZP0WxOt2TIZLGhpwhSOKZR70bRxjwKUvqqzAw8r5pby2t5VHXzpmjr/raxtG7HOXDAci\nr+FefoDmZC9629h7P77TkXge61v64wIuo9mzexgdlhVZemBmNEWfTOQXeWg60Ysr6EWLiAvTrQv5\n8wqyRLUjxNGxqiaaQhvjhhP0J4MaitljGAHXaBguh10irG9PUL9W9+wQIvdwYk/JBA2X2xs7B35v\nrxls2SUbcwviU7eF7gIO9xwhokRSWkX4m05y4186yfOJ9Ub2vYi67Mq4lGKG4cogg6HIBFxTALUV\nuby+t5W36jrjhnfqjtx/fyPWow6EMSnAYCA9tsDooVhWMMJLJMkzNL8wi862wThNTVKGK42Uz3Aw\nGK5sjyNpJWU4opLlGl3AFVGjnFB3I6W4CyRbNPOlniaONcc7uRuawyxXegyXkRr2j6PBeboo0I1a\nZ+87F+MOsevi+A9+bDlHDrTzyua6uMvdEM0bAVd2bupqxERoqorSF2uHIzmcxkJHnNe4b+zuCCE9\niKnb107dvvbYRPqxc9hlIlEV2RZfpeiwsIyt0w8AcHbFSq5fcPWQ9RW6xEdXT6iX0hTCeeXNHWaw\nBeAIRel64q/I1aszDFcGGQyDjGh+CqBoBF3Va3uFb5HxjDMCHiXNr8xntosv5bLC9AXABs7eMIvV\n59Wy3OLR4zQYLmuqY5wMlyHClSXi2gmV6IFeKDI6ZqTV186/vXF70nGbas5HVp1Ibl/GaT4VLAem\nstjLibYBopYA+1mdfUnXhNduk3HYZQJ6wNXvC9ORxL5hIpA/DDvlznJQWDyUFVY1Efwc3if0TYbP\nVzoIHIlZuhRddgXuGnGvpMNweR36ttRuJawqFBTHb/ul1ywloge1X//Icq4+fzZLZor0utWHq7X6\nIK3TDuLP6eGzSz/ONXM/kHR9hW4RcHUHe5OO11QV7bVtQ4aHmxqTMt8ZZJBBDJmAawogL9s58kTE\nBOBGwGXopTZva+CV3UP7nxno1p2/Z1cN38cxmYYrJ88t/IcsrT2qdPPUNw/EvsINL6CxtlKMJvh9\nfemqpVy2bgbzde1WKDy6gOtI7zF6Qr2USDOINMxlQc4Sc1xJVhFuNR/J7SMcTY8lfLfBrDWQhHdb\nJKrS0D5osqqtOmua2MZnOHhcdjp6A7R1+/nSz1/l63e+MSlMSVFpvP5q1vwUFgiWVau68amhmyof\nRc9TVe8fW7DxvRRddgWSzWYsdMR5r5h1sagidIQIO9u56uMrue4zq1myooqPfvpsptUW0jMYAjSy\nvTIXrZ5Ofq74IDFF85JEZ+UxOitECr4quwJninRhoVvcT6l0XFo4BNF4FlKVhIt+fJViJuDKIINE\nZAKuKYAiy9d0cZ6bay6YzR1fXs+vvnoeH77Q2kZE127o1WKG4PvB5+q45+8HUy7f+C6tGsZlPn76\n4dODK+aWUF7o4bU9LTER9DiNuGJaMfF76axirjh3psmgjJbhiqjipVEuzSHaMpNN5e/n1hWf53PL\nbmJ1+QpcSgGSBK3+4Su23q0wAiG7TWZmRS4gNIVf/Okr/H7zYU60DjC3Om/YzgWJWDa7CF8wyjd/\ns8UcFpiEFKPH6+TMjeUM5nZSeFkfm65YNOI8QsMluiW4PQ6zWXNa0AMrW76ukZSND6KRAy6nzcm5\nVWcDEHF1Y7PJ5OZncc7GOcycW8L+493sONSBZ+4evrfzPwgrEYJREeA5ZRFUSQmP+Sx7aia7QGe4\nDvUcSTre2OajVU56Vy+gaU4RfrdMdGAgzodrIopNfAMhlGjGXyKDdw4yAdcUQK7HyaZV09hwRiW3\nf3Yt7z1rOlkuOy6HjfcsrzbNEY2PSrs9PuAaCaqmmazYcEj3ESrLEotqC1FUjU69EfF4jU8Vi9+X\nFUYFlsGopIuoJQ4PkwAAIABJREFUHnBJuoxRkqA2r4ZFRfOxyTbcqvjSb/G3jm2D3+nQz6NNlqjV\nAy5j8HM7RToxnXZUVly3cR4fXD8zbtihk8lTW+NF9dxcjs/fipxMv2e20bSI5nUNlxJVzfsrXRhB\nimQUdRgWJ2p6Hwn5LsGmqVIobng4ovA/D74t1pHfjKqpvN6ylRcbhUefETwVuGNsXJY9C5ctNWM+\nM28G2Q4vuzv3m/dIHPT7UJMkejeexYlLVzKYJRPt7sIeDkyYD5dvIMS9d7zB3x/ZM74FZZDBaYRM\nwDVFcO175vCxi+YPGS7LEjl6g15Tw2VLruFKFYCpKsPaQSQinSnz9TRony+kzzM+41NFGdqCB6BH\nb45851/2cbJtIO3lmQGXZjjhxy84Sw+4GgbTb3fyboQkSVRYdEXzp+dzzpIKZlfnsXZx+aiW5bDL\nXLKmJm7Ykea+CdnO0cC8EqwpRb21j6Koo2O3IJY61AMuyZY+wwXg0JkqhfgAzWR1bbG098OH/0KX\nbtxb5C4EINvhNf++YeE1wxawOG0OlhQvJKyE+fnbd/Hbvb+Pu2eNbdYkkGWZfFcezaUOUFUcfbGi\nnvFquIx+j43He3js/p3UJxQMZZDBVESmSvEdAG+WA/yiok4illLU1PggKxiKxgnODRjpkpGR/kM0\nzyuquPoGdXftcTJcRmop0cTxkjU1bNkv0n7fvWcb3/roCmanoa8xUoqyZgMUEt9BHrUILWrncG+d\nOK7jrLJ8p8F0mpeIs+OYXZ0/hKUaDSRJ4sc3n0NLp4/b//DWqLV56SKxeXXCRsRNE1VUNE0wSkpU\nxeUe5WPTZLh07ZZxvNIMSpw6I6VJUXGv6ttn2K6ULDxGYvfHquwKsp0x8f8Xz/w0siSZrNdwMITz\nR3qFCewZpUtYXrpU32axTlUWjbXz3Xk02sT2yJo2YVWKVp/j1sZ+nmrcS0l5DpdesxR3mmbOGWRw\nuiHDcL0DYFQFappgvGLPc81khgB8weR6GOtDPC2kMa3TEW8NYQRKo30QB0JRHn3pKE9vbQDAbotf\nd1VJNjdePN9MiT67o4GXdzXz5JYTDPjDKZcbUcUXtESK9ieSDaWvmN5wL63+9iHzv9thnEbjvBpB\nbukoWt2kQp7XSaGuW5ysgGs4JDJcj7x4FICTbYMoylhSivo+GAyXqeGK7ZumaahBUZWpRaO03fc7\nGn/yI/pefdlkuJBV010eYp0c7M6hTNmNCz8c97soqyCtYAugOCu+R+Lde+/niWOb9W22MFzIFLjy\nUPVrQNLUCRPNJ0tJdrQOcM9PX8tUQGYwZZFhuN4BMIKNqKpikyUzeFJULa5U358q4FK1Idqo8cJg\n2YwXRKLxaUuXjwF/hLkjNMt+fmcjf3vjBLKuvS4vHFrSv35ZJWsWlfG9321n64F2turVkc2dPj5x\n6cKky43qLztDUJwYQ0oSqP2FUNTK8f4GKrxliYvIgFhw8oUPLmH7wXbWLhldGjEV3JZiiKNNfWw7\n2M6yWUUsmFE4Icsflq1NiLi2HogVTijRsaQUE7oxyEOrFHuf3UzHQ3/Au3QZ2Wcsp+8l0c/Qv38v\n1atW6PMrDPgjFOWJ+Q2GS5aGbs94rtczS5YQnBtiIDLI0d56DvUc4cnjz3LpzE3mNquSJBguVx6q\nsVuWgGvPjibO2Tgn+QrSgPGcWLqqmnXvmc1gf5D7fimKKR67bydX3rBizMvOIIN/FDIMlwU9wV7+\n483/5Td77v1Hb8qoYDBcUUXTGS5DRB8fcA2maJtiCIJHwmjIKTPgSrBzMJbxL3e9yX//fueI5qyt\nXUIMP6daBGapAkOH3cZnLl8cN6yhPTHREkPUYLg0o3Q+fryEBIo9btoMYoilFMWBy/U4uWB59eiY\n0mFgVJ8GIwp/ff04m7c18IvH9ky8TcQwm6tp4t7oHQzHDbONUTQfY7j0lerD+7e8TsdDfwDAt3sX\nbffeA4C9uBhUFVtAF8vLKnf9dZ95DMLmx0zsmMzJn8k1cz8wrhS4w+ZgffUaLqndyM1n/JOZ0gxE\ngyZzpUlgk2TyXLmo+uGQ0TBk/dIoj1Ei1IQimexcN5d/5AwAujp841p2Bhn8o5AJuCx4qfF1Wnxt\n7OrYy2B46tzUhkg+FFHiGC5N1YhaUor3PXWI53YMFYEbbUvSRTpTGqk/I+BL1drnSNPwomgje7Bp\n1bQR11lZ7OWXX1nPdz++iuoSLx19qY0zI6ZoPnlKUZJA04yg8dSntaYKJkvaZlSf7j3Wze6jXYDo\nz9jWMzlmqFZYg5W2nlj16w2b5gKMmuEyUoeJGi5NVYn29dL6f78xp5WzRErWs2gx3kW6N9ygD1mS\n8XgkDjf20dQpnk3bWrbjmH4A9IBrZt4MvrT8M6yvXjOq7RsOkiSxafoGAG7f/jPueOsuse2SaCjv\ntrlRpfiUog9t7GJNHVrChxpA5fR8Sityxr3sDDL4RyETcFmwu3Of+Xd3aPgGrqcTTJE8onedETwl\nphTbewP8/pnDQ+ZPv0ox/QedyXApKoFQlBNtgm16c39bXNDVNqKdQ0ycnQ7cTjvTy3LI8TgJhJS4\n/bdiaJVi/HhJAuPTPWl5/LscpoZrkiIuWZKSNl5/63DHhCzfkHeP5ClnfLC8Z0U1qxeINN1oGa4h\nVYp64OXb9TbHvvolc7KsOXOZ+aOfMusnv6D6y7diyxV2G8rAAFk2N3anuA77BsNElAiPHXsUe/kJ\nuhCtvW5YeO3otitNLCoW1dHt/k66/CL4NRgul82JZgm4AFRAU7RxsZGJDJcBq9dXBhlMNbzrA65A\nNMiujn3U952gzW95mE+huzoQjgUEaxaXp0wppkL6VYoGRn7JGgHX23VdfP7HL7O3XpSqv/x2M0GL\nEHokxiLxxZ7uQ9yrv6x9KVKWJsNFzI3bCgkJtEzAlQrmeZjE4s3Pf2DxkGEv70rdMWGiYaQUQQTg\nin4vjdUWwhDLS3aRqtYSHNvz33MhssOJLVsIFmWXqPRVQyHy3XmENB+g4QtGqO8/ETdvnjPXrC6c\naEzLrmJV2Zlim/TTbmi4JEnCpu+PpO+ncXdHx2Faaqas9WdZIBSl3xcWH0JT6NmcQQZWvOtF88+d\nfIknjz83ZHiyNjanK2ZW5nL8RB9zq/P44PqZdOqpNFWNfaGvX1ZpvqwCoajZLFhMpyFPoPEpxFKK\njR2DcfPOKM+h31I9OBLDNWz5/jDI0QOugUCEvOyhjYbNICqVhmuUDNcfDj5KjjObS2e+d8i4+r4T\nPHn8OT405zJKPcWj2IvTF4karsnAvOkFLJ5ZyN5j3eawtp4APQMhCnLSbx6dFMNp5i2ieeM5YJie\nAthso9tns2JPD7hklws5Kws1EEB2u5l+278jO13Y8+LtTCSnbgcRjVDoLqBpsAUpaxB/KEpbd7wT\n/KeX3pBUPD8RkCSJjy28hlXly/nji3cCop2Psb6QJu6P9tBJYCZGmBUJKzjS7KWZCGugu+1gO7/6\n814ALq3KzzTGzmDK4l3PcNX1Hov77baJcvSpFHCtmCt6wc3QHb9lCxtkMDw5HgcXrZ4OwHfu3spL\nbzeZ849aw5XGS9Y+hAUQxzMaVU1vLoDewRDDwXi4jraI0ug/2TuQfPkRNaqzWHo6JGG8JEmmhiui\nDR9wqZrKq81v8uTx5zjQFZ+ybRps4X923MG+roMc7K5LsYSpB+OdN8HFrUNgBNp5ll6de+u7Jnel\nBjRrNlCKMVyjTikaGq7YfBWf/izOqmpqbvsezpLSIcEWgGzXDY3DEdZWrALAMWMfvkAkrvVOti2P\nmtyRNY7jgSzJLCqax9y8WWKbLAGXIZqvG9yF5O0zGa5jhzrGHBwZAZc/FDWDLYCO3kAc85hBBlMJ\n7/qAK5LAXngdQrQ6FT+ijHefzaLhOqlX6lWVeHn/2hkAdPUH+d1Th/DrVYvp2kKMJghN9CpaqJfz\nR6Iq/b5YwDXSg9M8D5KEt68I/wk5rYd4SZ44j4cakreGiapR7LId46gNSSlKWFKKw4vmQ0psf+47\n8BA9wdg63263tiaZghdVCqhJRM2TAYMhnVOdR2GuYLXu+ftB+kYI1EfCcBouM31NzDhYMFzi79GL\n5uM1XADexUuZ8W//gaMkReNsQHKKgEuNhFlasogaby22nF6ODx7leH+DOV1Ndk2qRYwL0d5eWu++\ni+hAvznskpr3AKDJkullt6piJSDSjbKnH+NueGVzHc1jbM1k3OOJ3TJ69WfH7585NKblZpDBPxLv\n+oBL1VSz7BnA6xA+T1OJ4TKhvzski4arQW93M700hyyXPU6IbBihjtr4NA3YEwK46WU5ADR3+fil\n5Yt1pH6PZjPcqEbtodX0bXOzZ0fTsPMAzKgQ69t1JHlLkIgawSHb4xzTrZAkyfx0V0ZIKRrNggH6\nwgO82vym+TtssZRQxttg7jSCNeUzmTAYytICDz/87FpzuOG1NqmwMimKwu5tIsgZq2heGp1QEsmh\npxQj4hqanT8DgDpeR0NDPr6K8NFlfHTBlaPbnhEQamyg/ltf59itX6L/jdfoeOB+c5xLN2HVJPBF\nhBxgfomo3pQ1kOxhBi3Pzp4uP2qaLYysSFalaMWRxlPf8imDDMaLTMClqcjIrC5fgdfuocJrGDdO\nnYArFrPoLTb0h5SqapxoG8TpkE3DUKPvIsTa5aRdpTiKQ+K0aDfmTcvHk6IdykjPYiMg6joRE9cf\n3N0y4vorirzMqsyludNvGkQa6PeFTYYrVbWdBGkzXL0hwQAsK14EQF3PMe7YdTdHeusJK9aA651j\nL6GeAg0XwBlzhOZt/vR8JEniU+8XRrbWQpHJgkas0i7Q6uPQXmGAOhEMVzqQDQ1XWLA6JV4hio/Y\nBtBUCV9HAStKl5HrGb+7vxUD27cSaY+ZvQ5s20rnnx4RP0zjU/DrAZdRdSmrGpIjTD9Qryu53njh\nKHf/+FX2j7LYwXwu6Ids/bJKFs4omJBOBhlk8I/Cu140r2oqsi4KVTXVbGExEvNyOsJ49xkpxXBU\npaXLR015jpkyzPE4adHNRM2Aa5RViukI2LOzHFy3cS7BcJQLV05jv85KWeeUJWnk46yPrt8ZS02E\nUjjmJ2JGeS5Hm/t56PkjfHTTXKKKxq2/fI0Bf4SSNWFcDgvDlTCvJIFmiOaH0XD5In4eP/YUIPrX\nHeip42if6EG3v+sQZ+vpFhDX2jsFpyql+MlLF9LQPmh2JCjWU8WhyAQFr6lbKQqGy6jKs6zv1DFc\nRkpRBO15rlxznNJTxpUb5nKJrsucCISammj+xU+JdAxlD7v//gQFF1+Cpojj4HVls7byLDHSqL7U\nQHIKtrcbqAWiEbHvJ+q6WLisMul6VVVDkuKvJVM2oGssPS47t157Jn99cBeNvcEp+XzOIIMMw6Vp\npvhTluTJrHKfNCRqmgy2qr3Hj6JqVBbHmthaGS6/yXBNvIYLhHfRJWtm4HLYzJfYjRfN5yc3n8Ot\n155BSb57xAenMT7sEw96e55CcAR3egPTy0V5/QtvNXG4oZcXdjYy4BfzhqIR7LIjVgWZzGk+DVuI\nr73yXQ7rAuZzq9dQlhVfhbi7I+bt9s4MuCZ3PVkue1z7J6NHZzgymccytlNqspTzKF/2Y2W4jIDL\nYLhm5dcyK2cWkeZaIseWcs4ZVaNaXiJ8e/fQ+OP/IdLdjRoM4tu9Ky7Ymv7t71L0gSuxFxQAEGo4\nae77+mnrzN6Mkk0wXEU+mBFuYMOKElRg3opK5iwsBWCgP0gqbP7zPh76v21xz7HE6yvx/0y8lcFU\nxLue4dI0Na6cOub3NHVfjsZzvaNXPOSsNHyuJ6ZXMxoDp9vax8BY3rHWFjC5XicLvYXIspRWk1sb\n4gHbn99KQbQEJaLS1txPWWXusPPNKI+Nv+fvB2nvDQAajpoDRKQADrkgpb1BOrYQwWhMuH3lnPeT\n68xhdcVKGuoeN4f7o7FUqPIOcqw3nQ4mO+JKgMsZ67E4HgwvmrdMZ1Jcses0ndYy0b4+Qo0NBA4d\nNIMY02k+TdhyxPUb7Rd6Ja/Dw1dWfZoHeg8TLVaZXZ1PR8fAqJZpRfMvf44WDlP/ta8kHe+umYG7\nZgaO4hJa77qTrscepWDTRWKk9SDpD5wlBwdZchBaljyDNzqPv245wNc/upKeLr+u5Rr6YRcORak/\nLHSW9Yc7qZ1bzEBfrIWQsR5jPikTcWUwhfGuD7jUhIDLCCem4u1sPIsSH2pWofz6ZZW88JZI70Wi\nKpomXj2T/eJMZlwqUorDz6dpYGx91BFGi4jl9Hb7Rwy4ppVm85EL5/DAs3V6sAWObB/2spMArKtc\nzWH9gz6ZLcRIGq5Xm0Uz3QJXPhuq1wFw/rRzmJM/k9/ue4A2f3xq5p3EcJmi5sn2hUiA0WMxUZc3\nGWjt9pM7U7A7mqVF1rzFIzfobv3tXfj37Y0fOMpj5SgsBEki0hHvrv+RjXNHtZyUSHLPu2tnUnr9\nDcgOR9wwgEDdYdMbzJoeNRguAxV7dnMzuwHo/8/H6Fn5GZSoin8wRHauO27agD/GVj/92D4Kij30\ndPqZNlNvUp7AbBm/M7YQGUxFZFKKaHFfuZJVwDFVkCCat8ky+dkxJsth0ZzUlOfwqcuE8DiiqLGy\n99G8DMYQnCX7MJUkKQ1bCM38Kojaw3jn66xSmqfnwpXT+Mo1y9i0ahpzq/NYs0KkV+fbzmF99ZrU\nonkJUz+SaB3SF+rnP9/8EY8d+RsAH11wVVzQXp1TyXfOvpUZufH6mndklSIwsGMb4dbWU7Jep11n\nuMLjZLhMo/zU1/Le+m4iuhWEEXB9/IvrqJldNOyyw62tQ4MtxqDhstuxZWejDI6dxUqF/q1b0ELi\nXiq+6hryzttA5S1fpvpr38A9vQZnRUxv5SwtpfS6jwHE9suyL1YPsX7v0H1s1j92rMGVAUMeYNx+\nPZ1CX9pgMbuF2Hky/s8QXBlMRWQYLk3FJsW+0CRz+NS5oxPiLUBU6fXqBqOuBLdnt0Oc9nBETdmz\nLPl6xn5MXHqV4qBFyyHL6dlCGN/aiiPmdzUaQ8XFtUUsrhUvycf2vQgBkFRdH5OiV6P4LWGTbETU\nCE8ff54FRXNRNZWfv3UXQUW8rD637CbmF85Jut7Z+bUc7z9p/n4nVSkq+vF3hX20/OoeAObcdc+k\ni+gddrH86CliOJo69U4J+vpcKaptreh9/hkA3LNm4ygsZGDbVgBkj3e42ZJCsjsgOrHXjaYotP5G\nOMa7ZtRS+N6LR5wn95xzaP/9vbFlWNoSuaqnUXPb92iKdHFP/e+5qreGyie3m+MjaIBEZ9sgJeU5\nccsN6F0naueWcOzQ0D6ZWgoN11R6PmeQgYEMw6Wp2Kwarql4SJJU2lUUecy/HQlVVcbviKKaX/CJ\nvlnDYSyv1LIqkf7r1JtYQ3pVippGHMPFeB+4kphPVQ3KTR+crJciYJPsnBxo5PFjT3H7tp/zQsOr\nBJUQTpuTGxZey6Ki+SlXdWbpEmRJZknxAn2b3zkMlxGA2KOxIDjaOzaTy9HAOE/jTymlnl8ldj8d\nPNFjTi/LUloBpTIgGKnymz5J2Y2foPT6G5j29X/BWVY26q2U7HbUaHpFIuki3ByzaIh2JvepS4Ts\ncFLz3e/hmi5MVg3LCgOuadPwlFWAJNGyqAJt5TpznBGcv/jkIRqP98TNd+KoYLKqamKFEfHysIyG\nK4N3DjIMl6YixYnmxf9TqV9XzI09NqyiKPY17UwVcEVV0xoiK40v9/E85Ny6jiwcin0Zy7KUlg+X\nGXA5wmbANNbYRdP9gTQ1Zg4LqRgu4thPDY3tbW8D8L/r/33E3nUzcqfz0w3fp9XXzp7OA++ogMs4\nbjZLulWb4MAgGYwX72Ten9ZCjoOGU7qWfiY90t0NsoyjpBRJlsk/7/wxb4vksKMFhu83mi4Uv4/G\nH/43oYaYS/1o0pWu6mlM++a/Mrh9K9lnLh8yPssuinMC0SAzP3wV9dtfA6CszMVge4RsJOoPd1A9\no8Ccp7WxD7tDZuEZFXS1DxIORWlrHmCgTzDhbr0PqhlomR9c6e93BhmcLpiCdM7EQktwWTc1AlNI\nw9XRKh6auXmxasTSgtjfiQ1kjYCrvqWf1/cJ7Y21mfXIGD3H5dSX32Rp9SGno+ECHPr6FHvIXPWY\nX7iSSM8YAVdMy5MwmX5NBJShL7syT2najYJlSTYZ1HdSSjGqN3K2W/Vtp+AtKEviDh0vwzXc3KkW\nnW6BQLSnG3t+wag1W0nXabNDdGJMXsMtLWawlTV3HrLHQ+l1149qGbLDQe6adcjuoQakWXYhiA9E\ngzjy8sldI1iubLvGIf2I93bHqnYjYYWeTh/FZTnIssx5F81j4+WLmLu4DEmC4tJssnQtqnHoYxLb\nqfN8ziADAxmGiwRbiCkYcHV3ijJ1I20HovecgcQKRCPg2lffzb56Qel7RhVwjR5GABMJKzzy/3bw\ngY+eiSylkRpMZLj08zLWgMtguFRF1n/Hb19se+PnW1W2nG1tO6n0lvPVFZ8b1Tptuh3AVBbNt3b7\nuftv+/mnSxdSWuAhqjdydoViKeJT9RKUZclMU40XyVKEya4tTUvP5FVTFKI9PWTNTq7rG/X2Oexx\neqnRItIlGn07iopMA9bC911K8Qc/NCHbZ4VDdiBLMgG91ZXRCzLPLaECdqeNgKWPamfbAJoGpRXx\nuq6zzq1lxdoabDaZ3UfF9hvH3kwpT53HcwYZmHjXM1yitc/QKsWpklLsaB3g4G7BUuXkxUqu3U67\nyVoZZpGxcUP9gNIJuCbqiHS0DnDkYLuZHhou6FItthCKRcM15pSiJGbcX9/L5370EtsPJveFSHy3\nXjh9Pf9y1lf4xqov4rbHl7aPBCOgn8opxZ88vIujTf088foJAKJ61V5u87HYRKdo/0R16+QtP/mi\ntbQCrmhfr/hIKCickG2R7A60aHRMzyMlEKD+61+l8cc/BDBd4rGNzg8sXUiSRJbdTUD3njOMW0tz\nDSsJia4OH7/7xev4fWFTz5kopBebaNwzyQ1Qp8rzOYMMrMgEXJqKLA19AE0Fhqu/N8Cjv9sBQGGJ\nd0jq8D8+uZqbr1xCdUl23PDivCzWJ7TZmDe9gHQx1jq08983z/zbZpPTE0CrGjmA3SGjydq4U4qy\nLdYyJGixFhjqQyZ+z89ZTKG7gKrsCiqzy022alTrNFKKU9j4tL1HvESL9KDeYLhAw6GLwU/VSzCd\n6taRMUyQbxjDxzmgpmcUH+0SjLG9cIICLpv+IaSM/toxDFcjhmWHEbxMQKozFbLsWTGGyy4CrtoS\nkX4M6/e5fzBM/eFOs0WXx+tMsiRjk/Uq6iER14RvegYZTDoyAVeihusUO2ePB/29QTQNKqflcfVN\nK4eML8hxceackqTz3nDRPK46fxYA71lezcwRTERh/EHo/KUVnLsplmpJRwDtaR5AQkI1jCfHWaVo\npBRvufIM/u2ms1JOZ2hGdj0/jcqOS8f1gnfI4sXTH554P6VTgRd1o1wAj9tO4OgRbFteMIe5pul+\nY6cq4JLS61AwVhjn2mGLD7jSeTYM7hZFFY6S5PfdaCHpFi51n/mkaS+RCpqq0v/6a6YnmuqP1x+a\nDNekBlxuAooecOkMV4nXTlWJF7+l4XhDfXdMBzhMb8pEnzzTFiKTU8xgCiITcCVWKTJymut0gVHx\nVzu3ZNSBoiRJXLy6hju+vJ6PbByd3mQ8QamZRrS0E0qVHurvDSArmjm9WLnBUI1t/YZrfHGOl2ml\nMeYvUQ9tMH6qprHtQAdPvXmSscLr8FDuKY3z5JpKuPfpQ+bfUUWl8elnsHXp7WrQYgLqU/QSTKvp\n+QgYzvjUuNaMtBaIj43hRPOh5ibUcJi+l15EcrnJWZU6mB8NJHss1d927z3DTju4fRutv72Ljkf/\nCIAaCMSN18wm2pOTUgTIsrkJK2EUVTHd6rVohPOWVWJNxPsGQkR1f7HhmoFriSlF83yd/s/nDDJI\nRFpK6e9///vs2rULSZL41re+xdKlS81xoVCI73znO9TV1fGnP/3JHH777bezY8cOotEon/70p9m0\naRPf+MY32LdvH/n5wnPlE5/4BBs2bJjYPRoFRFubxCpFc+w/YpNGBSPgcrrG/gAdXXXi+BEfcIlh\nqV6eDfU9Q4YZU471C1fRxDFLTA0mLm5BTQHfvmEl2w+18+SWk5xoHR87lePMptXfnqSV1OmPxTML\n2as7f0cVja62Hqx3SsyTaXTnJBAN4pQdo07TyrKEMonBnbHkOG86LXWVYqi5mRPf+Rfzt2fhImxj\nMDlNBmvA5aqeNuy0kR5xjnxv7QTiAy5NUay50gnZtmTIcujWEEow1nw7EuHCldPY+vxRCvRNiEZV\nFIPhcozMcMVSiuK/DMGVwVTEiG/brVu3cuLECR566CGOHj3Kt771LR566CFz/O23386CBQuoq6sz\nh23ZsoW6ujoeeughenp6+MAHPsCmTZsA+MpXvsL554/dl2YiYaTIZAvRZ7Bdp7Moc9sr9eza1khE\n1yCl4359usB4aWmWRraJAVdvt5/jdV3s3h7zC6qel8texi+aNRguu67b+9T7F3LwZA/eJMewtiKX\nsgIPT245STg6PpW2yyb8hEJK2CyfnyoosTQ/VxQV2e9Dkwxj3Zi4aTTnZCA8yDde/XdWlC7jpsXX\njWp7ZHnkHpwjYbj0uOkxlhBwpYpT+l97Je73RLY5yl6+koGtbwJgLxy+pVBiT0PFklLUIpEYwzVJ\nonkQDBdAMBrErgdcakT4s/kL3HQPhlnqdBCNKEQjRkox9fYMFc0bEddkbH0GGUwuRvzUfuONN7jw\nwgsBmDVrFn19fQwOxkrBv/zlL5vjDaxatYqf/vSnAOTm5hIIBFDGIPqcbBhl+skYh9M13Ar4w+x8\n4ySqEnvi5OYP9cSZDExEEBpjuFRLSjF+uc8/cZA3XjiKbyBMxGVjNxrLNugNg03j07FtS1+oH4Bs\np0gnnr1KpwTGAAAgAElEQVSonBsvXpAyTZrlsuFy2ugZCI1pfQZcNsEChZXwCFOeflAs11pU0bCH\nYi9yyaptGsU5aR4UQcmO9l2j3h5ZGvv5T0Sys26mFC0RlqYlTylGujrpeXZz3LDiD101IdsGkLNy\nFTP+47/FNoxoLBu/fdGumIu8GgmDemo0XCDYSyvDBeDNctAditI5EGRgIMThfW3A8ClFNUE0P4ZL\nLYMMThuMSI10dnayaNEi83dhYSEdHR1kZ4sXVnZ2Nr0JLT1sNhsej/gCfuSRR1i/fj02/avq/vvv\n55577qGoqIhvf/vbFA5TzVNQ4Bn262csKCmJlSCH9LYkbpfDHJ7bK4KX3Fx33LSnA1qb+3jg11tR\nVY3FZ1Zx1rm19PUEmL+o4pSsP7tLsDT5eZ4xH5uOfJGa83hcpvt8QaFIv5SU5NDe0k9bswiKNl62\nkD+93YjS1EdpmVif0y2uhyyPc0zb0BXupsCdR3X58GyBFcV5WfQOhsd1PeR6xT4+9PIhtuwYID/b\nxWXrZ3LVe+aOeZmnCjbLPehw2pEiIfwu8WItXL2SLG+QHiA/P4ucNI9RfSgWHNiyFQqz8oeZOh52\nuw1kaVznoxNxn3u8Q6+jFt3XzumMfzza7bYh03bW7QVFwZ6dTXRwEMluZ9YlG8e8Xcmg5Do5DjhQ\nhqzf+jvscWB0I8xRfBzfsc0cV5jjpN/rpAXIyR37/TsSitpyoRGcXomCskLaAHc0SElJDi5dvqAS\nawYO0BNUyMqzUVrgGbK8nGzxbjGex1lZusUE2mn3fE7E6b5972b8o87NqHNRo2E5nn32WR555BF+\n+9vfAnD55ZeTn5/PggUL+M1vfsMvfvELvvOd76Scv6dnYlpaGCgpyaGjI6bFCerly9GIag4fHBRM\nRm+fnw736VVV9uJThwiHoiw6s5Kzz6/F4bRT6hH71Pv8s3Q+9ihqIEDl528m+8wVE75+49j09QXo\ncIzt2AwOimPe3x8gEBBfvt+583V+/s8X0NExwJHDQow9Z2EpsxeWEtkuhObd3eIlaOjWfIOhuHOZ\nLvqDA5R6SkY1b67HQVPHIM0tvTjG+AGgRUSA8eaBJiCX3sEQDz5ziPOWlJ/WlbElJTkMWswq6052\nU6tEUGXx6HAtW0Fg34sA9HQPEkzjuD578iWzRRLAZx7/Jl9feQvTc6vT2yhNIxpVx3T+DfT2Cn2T\n3x8Zspxe3QJDsjzrJATbMmTaFsEi5V/0Pvz79lJy9bXj2q5kMJ65wQF/3LITn2eD/bHn5d7/+h/C\n3d3m787WXoK9YrwvyT5PFKSI+Ig61HyCohLRQ7Rjx9u4ztvIqrkl7D3aNSQb+O273qCqxMv3PrF6\nyPL6+sW5GNTv91BIPDMUdei5OJ2QeG4yOH0w2edmuGBuRG65tLSUTkuD0/b2dkrSKHl+5ZVXuPPO\nO7nrrrvIyREbsGbNGhYsEDfhBRdcwOHDh0dczqmAtV/eqaiC0VSVnuefxX9gf3rTaxoP3b3NpOCX\nr5mOw/L1rakq7Q/9wRTJNt/xc8JtbRO/4RMAq4bLSN0cbx0gqAdSoYD4f8acYkCkDiTJ0jR4HBou\nVVMJKxEzvZcuCnMEszfWtKKiquw9Im5wySXOUW1FLuGIii84MW1bJhNRS0px//EeHGoUp9tyDEfR\nULgr0MNjR/5Gw0BT3PAfbP9Z2tszEaL54a4fQ98lSRJfuXoZ3/34KiSS91JUfTobVllF9Ve/FrPI\nmEBIkoTkcqPo60oFzSLbCJ0UBrWSzkSG21qJdAvX9slMKS4pWgjA/u5D2PPycVZPI3D4EGo4zLql\nFXz346uSHsimDh+apjHgD9PUMUg4orfgMm0hJm2TM8jglGHEO2/dunU8/fTTAOzbt4/S0lIznZgK\nAwMD3H777fz61782KxIBbr75Zhr0Xl5vvvkmc+ZMTPuLscJtd3P5zIs5f9q55rDJdjJWgwEafvB9\nOh64n/YHH0hrnoG+IN0d4mFbUZ2HVw8AzGX6/aAoyO6YGLvxf283xaoThYkwg7VWKb5/3QxzeIfO\nOBislzvLCCi1OGWKJhk2EaNfd0SNoqGZAvZ0kT+OgEvVNO596hCNR8Q94ypvoLokmxl6O5PecWrD\nTgWilvSPQ41gQ8PjFSk5SZJiRppp3DNNg81xv6+ae7n59ytNb6S1PRPpw5XsPa5YXvKLZxYxvSxH\naLgS3vqaotD56MMA2LwTU5WYCu4ZMwg3NeI/fCj1RNabQlVxz5pN/vkXAND885/Q9dijAEi2yQu4\nirIKyHflcbK/EQDvwkVo0SiBusPIksT0shz6HbHjGLIUq/zHvdv54s9e5dt3b+WPLxzRdyNRwxWb\ndypY92SQgRUjphSXL1/OokWLuPbaa5Ekidtuu40//elP5OTksHHjRm655RZaW1upr6/n+uuv5+qr\nr8bv99PT08OXvvQlczk/+MEPuO666/jSl75EVlYWHo+H//qv/5rUnUsHm2bEV0zGeilODtruv5fg\nUfEwibSnx0IZBoFlVblc/pFlwnXaUi4e7Reap5yzzqbo8itou/9efG/tJHikDs+ChRO8BxPnw1Vb\nkcvl59Tyl1fr6eoLUFWQRdAvAi6XW69wMhguc+Vj76UYUkRw47I50TSNlsY+ohGVqun5wwp3x8Nw\nbd7awCu7W4BclIF8bDmdOPqj2GXDff70eWnsOdZFXWMvH1w/K264wXDlh/v5zMk/A7pdQVj/QEmT\n4QpGQ/z56N/N36vKzmR91RrKPaX8du/vefjw46ytOCulTcSh7iPkuXImpEpxOBjBXJzRvBbvPD/4\n1g469QDGUVIyKcyWFUWXf4DG/72dlt/8itrv326x4rBsY0Jhks3rNYXrcZhEhgugzFPCoZ4jhJQw\nnoUL6dn8FIHDh/AuWgxAwGWny6ZRm+3i+Y4ByoOdeJUgR1uqmVaaTUP7IAdOCEsY4zQniuYl9GdA\nhvrKYAohLQ3XrbfeGvd7/vz55t8/+1nyVMA111wzZFhlZSWPPvroaLbvlGMym1dH+3oZ2Pomzqpq\non29aX8VG341ZRW5NP7wv4l0dTLz9h8Ram6m99mnGdR9d2x5edjz8sk9ey2+t3biP3RgYgOuCTgk\nie18snXh/IAvAgVZ+H0iqDFZPA2dhkhgF8YQcAWjRsDl4vDeNp7/20EA3B4HN3xhrRkMJsJkuAZH\nH3DtONRu/q2FBQOpSopZAXc6faX/+I+iYnDjymnkeGIv9IiiUuNv5sPNz5rDNCubkmb/0Vea3qDN\n38EF087lyjnvN4fPL5xDbd509nYdJKJGkgZcfaEBfvb2bwAokq6cAKdxs235kDHJlq2pGtZi5tb/\n91sznVjx2S8kDYAmEp6588g7Zz19L71AqOEkWbNmD50ogfbVolHTfNSKyTQ+BShyC9Pg3mAvBSWl\ngN5jUodNluh1QM68YrSOAW5sFEF456e/zdkrZvKf9+3gZNsAobCS2haC9BsbPPzCEfYf7+GsBaWo\nmsaCmsK0OmtkkMFEY2o5MJ4CTGbz6uCxY6Cq5Jy1GtnhBCW9vJhRlm+zSwTqDhPt7qZn81Oc+M63\n6Hv5JZQBoQ9yloqHm3fRYuSsLHqeforg8foJ34+xd1O0tPPRX2o5HvFCuOPRXaiahm8gjCxLZOnD\nNS0+pTie5tV1PUcBwXAdP9JlDg/6IwT8qe0aCnNEoHToZC8PPHOYZ7Y1pH199PnC5Gc7+fRli0AT\nt1t7r888DqcTw2XAuk2dvQFaj7fEBVu+7DL2RgWjI0mStd/KsMut1532N1SfIyYPBuh6/M8c/uSN\nlB0TjEZUS24f81rzFvPviXCaHw7G/ltd6K0pxWhvrxlsTfv6t3BPr5m0bbHCUSQqaxNb9pjbqOim\nvrkimLAXFCIlCwQnmeGy662sFE3Flpsn/tZZeBAO/lFFIxiJP9cztj9JuPEkOR4HiqrxuR+/REDX\ndiZqOCF98+OXdzVzom2Ah188yqMvHeOxV46NPFMGGUwCMgHXEEwewxVqEroGV/U0JJttSAogFQyG\ny0rAdPzxwSHTGWkN2e2m7IaPo0Ui9L/+2ji3OoYJ0XDp+hHjYenVGS5fIMLuI134BkN4sp2xwJfE\nlKK+LaN84UaUCA/X/YWcUAEDTxVy7FAHLredhWeKJt6GWD8ZSguycNhldh/t4tkdjfzhuTqaOocX\nMBsYCETI8ThZvbCMOZVCzxhRo3Gp1dMNEYvJ69b9rZzRF1/c0rnmQ+bfHf4uTg6I63o4yqFpsIXd\nHfvIc+ZS6M5HU1WO/fNX6HpcpChrt4gPg6ia/Dy0+TvMvyVZHfdxG25uX1Ckta29FDWL07zxEVN0\nxQfJmnPqbD3kLKGbUwKpAi6dCb/hJnLPWU/JVdckTSlOZvNqAJvRrF1Tkd1uJLudaE+sa0RXX5B+\nX5jdR7virpnBHds5+e+3UeUXHm2aBjsPifOe2NpHYniGS9M0fv/MYd6u68SfUJgyMMzHVQYZTCam\njkX5KYL1RT/RCDeLyixXVRXYbGjh9FJUBsMVbWqIG+4oK6f4ig+SNW8+yuAArsoqc5yzQvytjUVd\nPgLGo5oYwnBlxV4IOw61Ex0MU1weK8oYynCNTcMVUaNE1Cjz1bmE/CLQzc13m+L8h+7explnT+fs\nDTOHzJvlsnPFubU8/MJRc1hrl5/6ln5WzS+lscPH3U/sp60nwDeuW05tRS7bDrZRVughFFZMFq+i\nKIf6FvjIxjn0dZ2+DJc14Grr8pOliut0S/4i/DYXM91eQAScj5x8jMVdjaxh+HPSMNCEhsaF09cj\nSRLhjo641jOB4hxAMTsBWHHPvgfibCQ0R3DCGK5k17JPZ1XsFnG5qsYYruBxwZC4Z9ROyDakCyPg\nSuyRaEI/do6iIspvvEnM40jCcE2iaB5iRtKqpiBJEu7amQSO1KH4fNi8XlMT2Nzpw6WK4MdZVU1Y\n/yBdpTbjW3Y2L+9q4ajuyZeo4RLLT30NdPQFeW5HI8/taBwyLhTJ2NRn8I9BJuBKgGkKMQkpi1BT\nE5LLjb2oeESGS1NVIu1t2HLziATEQ8m/YysA3jPOJH/D+XgXx3pa2nMTNAnSOHJvqbZpApaRSsMF\nsPtwB/NVDW92rIpQMFzEdEIYAdfo1qvqx0GKyuZ+rDq3lt6uGFvw1paTrD6vNmlRwMWra7jorOls\n2dfGXU/s55d/3gvA46/W09UfC5x//PAuVs4r4bU9sfYuC2qEpsVhE7fb7Ooc3uoR51SZhIB4LLAG\nWXEBV7efAlUwPjvz5tHvyGa6ngpa9P5c9rb2oMVumpTLD+ied/luwfKpfhGwZZ+5gsG3dmDTrwcl\ngeGKqtG4YAtAswdQ1fG2R0q9rYOB+IDLeBYYDHOwXjBcpz7gEsag0Z4egidPQMniuPEGw4VFoyU5\nTz3DFQu4xPY4KyoI1B1GGegfolv1KuK6yJo1i8rP3czxf/k64dde4up/XkNDew71LbqdSkzEJf5j\nBGsPy4dMjsfBRaun88LOJrr6g/gCEe59+hCLawtZPndki6MMMpgoZFKKCZgsHy4tGiXc2oKrqkqk\nyGw2UW2YAi133sHxf/0mzT//CeEeITiVNYVp3/xXqr7wxbhgKxmkydkNsezxaLj0NI3B2nktAVdU\n7w1pDbhIqFKMxZGj2zHFDLjEy+iyDy+jZlYR02YWxvWijEZSnxNJklg6u4i87BhrYARbOR4H5Tqj\nZQ22ABbXCu2N0b9R0Syi+dOE4WrpiqVII4o14PLh1iJEJQdhWex3RGeAsgoAiREDriO99TxS97iY\nR2/9YnhKOcpFyyZZ92JI1HAZrZgAztADDM0RQNW0OH+wMSMhuH5hZyNb9onzZ7fF6zklWULTNIIn\njuMoLsE2gj3ORMNeIAL37ice5+S/38bef72Ntt/fZ7LYxgec1fZBSsZwTbJo3ppShFigmMxHrDjc\nJ+bJzcVZVob3jDMBaPzhf3NOZWw75fh4Cxi+vY/x0eC0y/zk5nO4eHUNt392LTVlOQwGIrz4VhN3\nPLZnbDuYQQZjRCbgSsBkiebDbW2gKDir9LSfzZYy3Rc4eoTBnTvE33WHGTwkNDQ5S5ckr05KhkmJ\nuMa/LLce3BzY1ULj8R5cjthD1Xg1eHNiLwlV0+LfiXpK8eCeVu694w3u/9UWjhxoZySoxotcEZe8\nU28zUljs5Yab1zJrvvjSjYyQbvC6Hdzw3vlxw4py3fzws2u5/JwY4/Gp9y/kXz62ghsvns/0MvFi\nNqrvFC3WR/J0SSm+vCvmjxWfUvSRJbt5adZ1LLG5uaS6gOaGPhxOG6qR3h0m4NrTuZ8f7/yV+dtt\nc6OpKr16/0F7nhBV24y0uYXhUjWV72/9MQCz8mpZU7EKAKdHsIPd/cEx72+q2/u+zYcZ0L3gTIZL\nPxySJBHt7EQdHMR1itktAGd5RZzgvW/PXvpeeI5wSzMD27bS/+rLYjstzamTabhk1+h86EaLRIbL\nYLXUBO3ZpWtncJFdpPzseSKYtLKG8wIxc1zJTClafLiGuXeMj4YLllfHzTO9LOYCXpJ3anrQZpCB\ngUzAlQITJZrXFIVofz8hXX/lqhLtS4ZLKQYSHPj79guzQ5dnNA/Kyau2HI+Iy5VlVB/CXx/chW8g\nxFevOYOPnDeLQn3Bnuz4/ZSs/2apzJpfgsttR4mqDPQF2bN9qE4jEYqqMOPAWSjHRPDjdMVeSjab\njF334RqO4TIwd1o+tRW5/NP7F/Ldj6/i3246C6fDxhmzi1kxr4QNZ1Ry1oIyZlXmsX5ZpfnAN7/8\n1ehpxXD5g1Fe3tVi/jYCLn8witzfS5+71BzX3igYCZfbbjIYxh4oylDB+3MnX477nWV30f/qK/j2\n7AbAUVwCshxjuCwartea3ySoe6eVe0so0NORdrcY1tEbJBRR2Lytgd4xWHZA/KWcqAmy2eLPkSRL\nhHXvPFdVFacastNJ2Q034TS0mnrwpfr9tPz6l5YJLcxQEobLXph+H9ExbafO5BoBl+wxGC4RcL3v\n7BpK87O44pxa8sIiZZi7dh0A+RsuIHetqGIdfPxRNlQIlqq6VE9F6idMBvqHEb8bTvX2BH+96zbO\n4aoNwmfO0FYmQ1uPf2IY1AwysCCj4UqAPMFVih0PP0jvs8+Yv60BF4qCpqqEGhuw6x5aAOFW8fIL\nfPDzHNhSx6BTfP0VFI/CzXoU7VZOJWwJgt2mk70sWlTG9r8folg/9tlxDBfxeQQJNl0Ra6Z+7x1v\n4EvDkDQYjJA9UGz+Tgzq7E7xkoimIaj1uO18+4aVQ4a7nDY+/4ElKeezSeJ2e6npDWZI5wGnB8O1\nedvJuJfLTx7exdkLy8j1OsmJ+pCTWDWsXDeDTk20j9H08xNW4l+AISXMsb4T2GU7V86+lM5At+hj\nefxJAEqvvxHvsjOQ7HbkqArIJsN1rO84Dx56DIBr5l7BqvLlaPoLPGTrBabR0RugtdvPg8/V8eb+\nVr59w6pxHYdEdtNIZPfrXRC82U6ivaJqzkjvnWrkrTuHvHUiIAm98hwnfncfkZ7uuGmsDFYyDZfB\nKk4WhqYUdbF/UDCSH9owiw9tmIUyMECkvQ3PosXmNtuysym/6ZMMbN2CFo1y9iv3cuXtP8GbZ3Q2\nEOsoRKJnIER1SfK0rsFwORICLofdxsVn1/DEG8dTiucfeOYwz+5o5NK1NUNMgDPIYDzIBFyJMFOK\nE7M4a7AFmKkIg/Zvv/939L38EgBZ8xcQ7eoi0tFOyOnl9d0+8AjbgrxwNxWrNqa/YjPgGucOWDBR\nx2TF2hqaT/bS0thHd8cg/b25+C1BU1wwpKcUU+nGvDlOOlsHh7Re0TSN40e6iISiVNcWErEwV2ef\nPxOHI17HYtebUkfSYLjGCpvOSLzVvpuCHJGWPB0Yrs3bBPu64cwqdh7uoN8XZsv+NgpyXMwLtqPY\n4gP9D1x/JuVVebzYKKr17Hoj61Akvnpua+sOFE1h0/TzWV+91hxu9BDNWbVK6PMcDmQjpaiJgOt/\nd8QYm3Or1iBJksnWtkeakFwz6egNENR1f/UtA0nb76SC2S/Rcl2FoonnXkzTrVuAlJTlEG0R7LPx\ncfSPhF1njlp/c6c5rPqrX4sTpltTitVf+6YYNtmieTlWpQggO8X9rIXiU8CBY6Lq1z1zaFBTecuX\nafrRDwHovOMnRM5ZD7LMrNlL2Pk6OBi+84PB0jpSVGQ67TYiQ843HGnq41m9srGuoS/l8jPIYCzI\npBQTMNGieZfFFDH/ggux6V976AFX4FjMhC9w8ACRDqFHerv64rjlXPL5jdhzUnchH4I0KsfGivGI\n5gHOWl/LRVcKAfTxI11DNFjZll6Rmhaf9klkHp1OO6qlEbaB1qZ+nnp0L889cZCH79nOwW2CmXDN\nCHHm6qFtWByO9FOKY4W1SXoYEZycDgxXaYG4Jj9y4Rx+cvM5bFxchEsJ4e5oZG3PXgLu+OCipDyH\nQDTIw4f/AoDHIeZ/pWkLHf6Yoezb7aKSc0XpMnOY4veZ17ghppbsDjOl6Iv48ScEblb9jhHcyfn/\nn73zDoyjPLf+b2a2aYt6l2xZlm3cbdyxqYEQaiihhYSE3IR0QgkpN/VLgRQSkhtCbhJuEgIJCRBI\n6B3bBDAGG9y7ZEmW1dvuStt35vvjnZndlXbVLGE71vlH2t2Z2ZnZmXfO+zznOU8HHd4Q+5sSDubd\nviPrSxkZ8Nsbt06fURiR4zCtXWylZUf0XeMBS/bg8WBgZ4nklGLWjJk4Z5004fulDEgpSnr0Sg2n\n/j6RFqEbdEwdfD+65s6j4tavABBuqKf9r/fT/sB9aG+tx+60kg109WawxyCJcGVo2WW1yERiqREu\nVdN48MWEnKO+1U8ocuw3l5/E8YPJCNcAmLYF40W4KqcQbhSpl9yzz0l8jz4LjDQdwpJfwNRvfJt4\nIEDP88/SYy+krzERKq+eVUhOwSjIFkmkaFwJ1/hty5FlJa/QSU9ngI3rE274SrbdFLSLbxRRi0w1\nAMkGokla4ZQ0Y6AvQv0Oke6SM1zxFut7EeFK7GBUCwHKMRHhCoXj5LptWBSZaEcHJz/9G5ZGxfmL\nyVa8NpGKvejqhXhyHKiSygM7HzLXd9rEtdrc18K9O+7nGytuAcAX8ZNlcVDpKTeXjXUJQuaonm7e\na7LVitLvxxq10h3qpcEnIm6XvdKD163A+xL7+rmFn+DuLfeiWOPUHvamRDnqWnwU5IzdLiISTdWk\nGfDr4nx3tp2+lmYkux1LYSFHG/krlmMtKcWSl0f2ilU4qgcL+ZNTihMd2TIgD0wpZiBcZlVlukpK\nBOma/vNf0vXEvwjs3k20vQ0tHKRyej61O9robu/LuA/DES6bVcHXn0iB1zX7+OmD75gkbMmsIt7Z\n14G3P4LDNvmYHC0i0ThWi3xEfXf/EzEZ4RqABFEZn+3Fk9yqFU/CKyve5zf/t5VXYMnNxV5eTukn\nPkmzJ+Fe7XTb+MBlCc3SiGEYjE5YG+4jxwVXLKB8qoieFJV52IGKvTo1mpLoT5v+xjUJVzz1OMO6\nW/hASEr67Th0Mb/RPHsikDbCdZQ1dpqm4Q1EcGfZ8PYEeeGxbQQkEbE6kDOXDTOuNJetnJZHbr6T\nFxvWsrVzp/l+hUdYO0iacJR/eN/jrG96g+b+Vjy2VI1N35Z3AXCfvMR8z710GVIwxLkbfDT1NVPn\nawBNY2prlAUHUtNQdkU8vJ1Zkkm2FtYIEXhd81hSQInrITIwxWREuLzie9zZdmK9Xiy5ucfEg0S2\nWKi+/cdMue1r5Jx+RtoG2pnIzITu14AqRTOlONDo2bj2hziXlpxcSq67nvLPfQF9o1RMEWNE31AR\nrnjCFiIdbBY5JaL52Ku1JtmaOy3P9Ac8FiZExxvufnQbn/35er70P//m9e0tw69wAmGSug9AIpAy\nPjfavp5aKgBt4RxUu5U3D29kbsFJOKbXEKqro+iqa8g+7YyUdYy+ftd/aTU2u2WMg/v4R7jGm7xl\n52bxwQ8vos8XxuKy8czP1qV460S7Oino66DTlTAnHLgPsllJlpoeCIcypAKi6Qdgp+6tFeifuLYf\ndiXx8AtrQcB91Af0Hn+YcCROaYGThgNdNPYoNE69lJ5oH3lWN+inNbndUmcwVaTtsgnNkO4Swfqm\nRDupPHsqgfZv3gSArbLSfK/w8isIHtjHjNpaduzaSvik2Vhj6c+LcQ4dWYnPz185le11XdQ1+9Ku\nkw7prmUj4nHRqqk0v9lkLtPnC2G1KVgtEvE+P7ayo59OHCmMptq2isphlhw/KAMIl+TQI1yhAYRL\nv2dHFHmTEya0FVOE6F/qHYGGKwPhcjutRGIqwXCMLLslxYA5P9uR6IgxybdGhR0Hu3h3fycA/aEY\nWw90smbB8XO/TDQmCVcSopE4bXtD5HVUEqsan5Lg3rCXCmDn0hKe3/Yn9vYcYHnJEpqqWjj5psuY\nteC8QesEA1EsVpks5xHMTo9R49NB25IkkabS84HJBKTxjh9wmdfLgwuuzfiNpkB3AHFpb/anWxwt\nmn5LTpdOuPomjnAZpp8AYfXY0HA16mmZKUUu1FiCpOZZ3WiIyOOiZZXMnJuwhjAiGAYkSREdAdIc\nykXTzzX/j7S2EGk6hH1aNe6FixPrKwqu+QsJ1dYyrb6P9YV7cUcTG0sWwxsRLos1cX/OrMylssjN\n/iYvX/qff7NoRgGfvDBVy5QJyXOZhjZxLiqKPDRDIsLlD+POtqMFg6Bp77nh6ZFAslio+dU9SJbM\nFgjjjUEpRT3CpQ6IcGkjiHCZSGqQnqNrDq1xjc42P4Ulg+UWwxGugmxxL3b7w1TYLeb4sXp+KRee\nUmUWkkxkk/T/NGiaxhOv16e8ZxS1HAnifX2o4TCy05nQQB+nmCRcSXjl6T3U7e2ngoW0SyqMQ0Ww\nxyr6zm3u2EZvtjjdb7e9A0BLsJ2KjUuo29fBBz+8CItFQdM0+nzhIyNbAMZDcVwjXBMHw5fKSLGF\nm0oQGHQAACAASURBVA4R94oU0ZyO3cBgGwbInFLs6epHkqCiKo+m+h6mLnSztXk35YtK024nO1fc\nyL3d6RsDjwescuKhF9bE9xztCFeTTrgqi90EtxiCYY3pJxWx6swaZswqpqMjlbxG1dS0q2F8enLR\nfBbOnEt1zlT+vOvvXDXzUqbnTDOXM9KJeWcPrrb1LF9B1+P/xKL/jlXNCeJ76Me3E/P24lm+Euvq\nFQAU5luxlmXz4XNmIssSZ51cwf3P76UvGOX17a189P0nYbcN4aie5rQ3torjnFLi5m19kUg4RjgU\no7jMgxoVx3000nRHAsU5CjuZcUByL0VIaLi0cPoIFyOIcJlRMIN8W2SIqWzY1MTFF84ZtPxwVYpF\n+v3e0tlPRaGLoE4MPvaBk7BZFdOY+Gjfn8cT6pp9HGhKTeuHjlATq4aCHPz6baihEHJWFlXf+T7W\nouO3HdMk4dIRCkap0zvTA4R7h1h4FLDLmQdnj83NBr0hcmNtN9NPKqKpvodQMMq0mUdmTpiYNE5A\nleIEyFcUs+WP2N9ISyL3P6P7AMNquAYMjPG4htNt59T3z+Dgvk7sM0I8s307S9zl6TaD3WHB5bbR\n0zVxhKsoK/GbhtSJI1yhSIxoTMUzAtLe1GFEuNzsDgYBC+cscTDz3PkZ14nGUwmXioYMLC6cT/YU\nYf/w3VVfHbRe3CdSfrbSwaTXIDGF1lxA5Zy6xL6Hag8A0PPs0/Ds00jXlhAjlOKFdubJFZyxuJzf\nPr6Tt/e0E4zEhiZciW82/6tv9ZPtspFrtG7SoF+PeLo8djSTcL130aLjEQmDXz1lqKc1B4rmjcmg\nJI0gpWjY9ejbrFpaQcPGQ7S2po9kJyJc6a+BWZUi1X3fs3sIR+MEw8KM2IiIGYRrMsA1cuxp7AGg\nvNDF1GI3W2u7CIWPjHBFWlpM/zY1GKT7heco+ch1R7yvRwsnvGg+FIzSUNvFnm2if1pRzTi3vdAG\ntD/R4ba6cFqc5uv6/Z3s3trCUw8JB+7ZC9JHYkaOCahSnMDRRxmQGjQebgCeiB81IFJwI9VwiapF\nibwCF0tOqQLZaECc+ZLPLXDS5wsTnaBS8IKsfL6x4hYcioNgfPxTirG4yh+f3s3n73qVb/3fxmHJ\n3Mubm3hrdztZdoWCHIcZJVSsQ8/DogMaTJsVvcNcH/GAIJmG83gyJP3BOMMzlbvP+jE2h1jGs/KU\nQcvmO/Ko9dbzQv3a1G1IEll6hWswPLLfMBpTOdDk5U/P7KbLF2JaqSdJM6mh6uJri0VB01Ouk4Rr\naMhJPUNBRKckm21wlaIZ4RrBDE5OjdivXix0QV0d/Xz2Z+t46o36lMWHSynWVGRzyanVBMIx/vD0\nbuqafWQl6WXNDOYk4xoxwtFElPDTH5yHw6YQDMeOaFIZ0S1k8i+4CAA1TT/O4wknPOF67IF3eOaR\n7WakKa/STsjRR9QvctJvNL/NTzfdze0b72J3175htjYYhjv2QMiSjKUnEeqv29fJ6y+LmXxJRTal\nlUfoBj2hRvPjH+IyU4r6zVnbKOwDQnoazverX7N6S9+g48mUUlTjKnJSOuF32/4slh+CcOUViN9j\n8xsNYz2MYVHhLqPEWaRHuAb7hx0J6pp9vKZXBfkDUQ4czly1d7ijj7+ankMSkiSZ0UU5Q1TAgJFS\nLHDo/e+sQg8zXBspo5deWsKl6CQvFkeWZOJ+H9biEko+/gnKv3gTM34t+jFai4pZUChSSG+0vDVo\nO1l6y6bhtCMGcd+8t507/rKZf29rwW5VOG/F1NQGyWZbn8QkQLZMJgaGgtMi0nV9kYRtg2y3D04p\nmk0qR5BSTNJwAXh0DVYOEjNiGv96tS6lB2jUJMrpty1JEpecWs2puqC7pjyba8+ZmdjfSdH8qBHT\nC10MkuuwKXT5Qnzqp2vZUdeVfp24SntP5qyC2ieuIUuuiEhqadqHHU844QmXtzu1tNjuVIg4+lGj\nIvr11z2P0OA7RHN/K9s6d416+8ZDyKbYWV22nNuWfpGvL78ZWZKxdYgHltWmEI3EiUbizJpfwqUf\nOfnIy85NDdf49QN7LzRcqqZxqL2PN7YI0arPIgTK8cPNLN8VSOg+dGQSzcfjmrnNYCxozrZ7w5lJ\niFVPQb375iEO7usgPkG91Fw2p9gfOT6uEa7HXxN+ZoYgeHNSinwgupJMQq88S4gVjXOoDCBc/z68\nwfTGAoioUSyyha8su5HPLfwEBUaqdJinU7RTVC8pWekiXILEaPEYmqoS9/tRPB5kmw334pORHVnC\nVkWRuXLWJeTac9ISvCzdMymQqUp1AFp1zZ7DpvCrm05ldlWiZY9G4v6VZWkypThClOhVxa2BhKGx\nZLcPFs2bZHY0VYp6Q2xFpmJOERHAhUQ+0J5kE2G4yGeKcBn4xAWzueuLa/jmx5axal4iqzAZ4Ro9\nBurmLlo9zTyPdz28Fe+ACvAef5ibf/UaX//dm+Z9OBDxoPhNFbcojNDUiRmT3yuc0ITLeKCWT0lE\nk/KKnEQcImzZ3ZVqrNcfHX040xiwb1v2BT4y50qqc6ZS6S6jaNt8XM3iBq+elTBRXLq6ypxdHREm\n0CZoIjad0HCp/PyhLVh0guSzpAp+rQMa1spKeg2XqqrIisT2zl3c9up3zfdPKcvcb2/+koS+67nH\ndrJl46GMyx4JbIZ4XlbHdUDf3SA0FAaB6hmioXNfUJzHD58zkzMXi2bIxjmUk9oe9Qa9/H3vP/np\nprvN96LxKFbZgsfmZn7hHCTjeh2C3Pfv3EG4oR5b5RSTXCXDJFyxGGpAEGslOzt1GZsVLSJIjyLJ\nZhVcMrL1alNDT5IJxlkv1/uTfvf65Ul6n4QXn3FO4j4fh+78kfh0knANiRxbNg7FQWt/gnDJNvtg\nWwhtFClFYwKZdJ9/8JJ5XH3dyQBkIdGapL8cTjRvblaSyHUPlpFMiuZHj2g8tWH4KfNKuferZ5mk\nq8ef6qn35Bv1BPTUvzfDWKUaMgSjMjg+ccbU7wVOaMIlyxKz5pUwZ3E5H/zwIs69dC41xVOJ2sWF\n0dkjRL6LioSAuD86BkG1MUNOMr309gSxe3UvGQnsSc7qufmDZ/9jwbHuNN+/cwcdD/+dSFsbkNBw\n7TzYTcjfbxKumWesTFlPGrAPxsO+5ZCXg/sSER01riErMts6EgadH597DRXuzJ4w7mwHH/r4EpPw\ntjZNTC81oz0NkjquES7DS2jZScVIEmza086j62vTkro+3eA135OwqjD2xZIU4eoN+ZM+F79JJB7B\nllwMYgqMMx9LYJdo81P0oSvTL6DbgmixGDFdXG/xDCBcVmsirSfJps9TMuZMExGqLm9o0GfpYOyy\n05FEApO8j43PA1vfTfhGTRKuISFJEqWuYtqDneY1I9vtg41PjfM5TEqxzlvPvTsf0FdJfeB69M4C\npUg0JpFs40HutI8t/SuN4JqeRCrSkVxZkjh/pWhvFxvgrdfcmQhgxOLpz7NqRrgE4erqDfDIugOD\n2nAdLzihCZckSZx98RxmzSuhoiqPmtnFOCx2cj3ix63tEFqe4qxC7IqNvjFFuIwQeOIh1pFUWVM+\nNZfZCwUJOOuCcexzNgHNq5M2PuY1NU2j5fe/5fAvfkbPC89x+H/uAoTXyqy+Bs7q2sytdX8jLyrO\nUeXC2VT/+E4sVVPN9ZNhkKMNa2t57rGdqKqGpgltlCJLhOMiknPHmm+xonQJw6G4LJtPf+V0ABrr\nutm3s23cB12LJB4CkqyO2ww6Eo3TF4xy0pRcZFmipkIQ+qc3NNCQppLLHxTExeNMkAdjXwKEzH6G\n3nDCTPR324UOLhgPkWVN8sMZxoJEjUTwvfE6stNJVoZefpIkgSKE6XGfILrKgF6BstWKFjMIl5KW\ncOUYfmrDiubFvppp1KS0VqLAV0ukvZJuJO04n2W/Fyh1FqNqKh1Bod2R7Ha0WCzl3Jn31TApxdcP\nv0WdrxGA3mBq5NLpsuHJFaTr0OZmYnoqMRASVYc269gecbKZUhzT6ickovFUDZcBi56FMHR1mqax\nYWcr+w4lrABiaeQb/du30a9P1Ixm8a0dfp59s5Ev3/M6P//7u2yr7Rz/A5lAnNCEKxPKcoQGYWvL\nbrIsDs6csga31TU2wkVqhKupvpsd74imrf7iFi68aiGFJW4+9/UzTeI1LjBnaMeWhivS0oz/rTfN\n19F2EeHa85M7ubx1PSt7hU5uVkxEqySrFWthEZbSEvF6wENdGZCOCAWjpoBeVmRCcTGrdlhG3mMv\nWT/38pO7qd8/vje1xeipOI4RLtPAtFhMFr527cks0lve+AODzVz7dMKV7LBtkI/79z7MV/79XW5a\n9w3u2fhn8/OdXXv4/pt30h8N4Ew6n4YGJxMR8b+9kbjfT+6Z7zM9mdJBslgE4fILgqgMinDZzAiX\nkiHCZdc9lIYnXALG6VeSWz6lEc3LST0JrflHZtlyIqDIKWQSXSHRlSBtP0VDjzOMXjWsRswq7wbf\noZQG6ZIkcc0NK2hBwwpsfVeMrf2hGK4s65i1sAnR/CTjGilieoTLMiCNaxAwg1S9uauNe59M1UMP\nJFxqOMzh/7mLWGcnjpoZprxA0e/5/lCMnfU9/PKRbeN/IBOIScKVBovKRBVUQds0rpguBLouq/OI\nUopIojHyk3/fZqaqeqoOogyjMRgzJlLDdQSC/khTEwAFH7xUNDC2WIj5ffh27ExZzhYUD10zfSOl\nT5EqA2ZTbc0+0yJCUSSCsRASUkI3NQb0+TNrocYCxUgpjmOEy2hrU12uD0yyzAKdcKWr2DNSiqkR\nLvE3rltoxNQYvSGx3UtrLgCgLSCIcHJfSCPcb1QUDUSoXoj53csy6+dAJ1zxODG/nlLMTq3UlaxW\nESVRVVHJmIZwCWsIheAwonnjQaqZEa7ENW2k4zUtsZykqkgWC5Vf/iqe5SuG3PYkwGERBMuIMKcl\nXMa5HSbCFYknCJekafx66/+lfG5RZHz6dNDbE0RVNfz9EVyOsVeTGmPcpGh+ZNA0jS0HxMR0YITL\niB7H4irhaJzH1tcOWj86gHBpSV0vsleuQpIkVElGQqOyKKHrtVszV1T3rn2Zuq/fxr5PXU/zb+6m\nd/26UR/XeGOScKVBRYGIplijDnY+EqChtguX1UVUjbK9cxeh2Mj0IZA8Q5J4+9/15vsxd4CoZXwf\n5Kk4Nn24Qg31AGTNnIWclYUWi1F3y5cGLafk5OJethxbifgtksqGUpYzNBwG1j2z17Q3kGSJOm89\nkiSNmiTa7IkbOToO7SmSMRERroMtPkCj07Kbg94GgrEgVqvYdjpPKn8gggS4HAnCFYnrhpWWOA7F\nTolTRHqtspXqnKqU9ZMnH0YFUcw/OHUZ7ekhuGcPSBK2kqG95SSLhcjhJuK9ItWgeFJTipb8fHE8\n+/YiI5lO5gOR53HQ1hOgbYhycwNmhEsnXJHWlpTSc9MWQhOEyzlnblrR/yRSYRg+G4RLsg12m9eG\niHCFYiG69PRhKuECb5pK42VzxTgRCsbYuKuNQDjGzMrcQcuNFGZK8fguintP0OMPc8cDm83XFiX1\n90xEuDSe3tBAly/M6vmlrJpbwpkni4Kd+EANV9KzJvtUIfFQJRlFU80uAQBFuekzF+HmZtr/+gAx\nvTK6753NtD9wn+nrdbQwOXKkQU5eFqvOnE5bs4+D+zp5540GXEuEmP232+6jKnsKX11244i2Zc6Q\nZYnGWhEKv+ZTy/lt7b1ooYm7mxOVY8fWDM2Idjim16C4BrccCZ93BeF5y1k2pyTlfSmDTihfrzLL\nL3LR3dFPKBilTY/2hFTdoXgMadWPfHYVTfU9vPj4rnEnXFZDwyWNX4TrYLMPZ26Q5w6/ynOHxXsV\n9ipgDlsPdPHYq3V88sK5LDTSjMEorixrSkVsd9SOO9xNdUEVnzztC0TUKE80PM2S/JPJsSfSeytL\nl7KydKn52iBGcX9q8+ju55+l85GHAHDOmz9kOhHAVlxC0OvF/7bw1xpYpehZuhz/hjdo+tlPqPhA\nDc2F6X/Xi1ZX8dvHd/L4awf59MXzBn3+47++g6OwC6yYWj9Jkgjs3UPTnT8m55wPAGVompaYMGkq\nyCNxrp8EgE1vMh4xIlx6A+uupx6n9PpPCtKaxhbCH+njidrn2Nq5g/5ogEtrLiCiRs2osKxJaaNO\nNj2aFewLs2XLYWRJ4qLVVYOWGynkEYrmG9v8ZNktKSTgRMPTG+qp1cfcC1ZVDZrcGgQsFlN5a3cb\nFkXi2nNm4XRY2LCzlXXvHh4U4TLgPnmp2YBdlWQUVKYUu80G2ZnOe6hOeFrap1XjqJpGvL+Pvk1v\n4123loq549Czb4yYJFwZcPIqIdJ+4Dcb6O+L4LYmyEGD7xCN/iameiqH35B+w8ZiGr3dAUors8kr\ndCEflMZEBEaO8Sdc47GlWE8PSnY2st1O7jnnong8OOctwG0Db6eXnNPOSL+ifhPv7NrDnu79zM4X\nJoV5hS6uuH4peQVOGmq7eeFfO3nmke0ARG2CcH10dobKuCHgyLKSmy9u5vGPcCVSiuMR4fIHIrT3\nBimf10OypPhwuAGYY4b6H3plPwtrCujyhmjpClCSVBGrqioqMtZ4iLlli5EkCbti4/MrP2b2Urxs\nxoXU5FRTnTM15fuNlGJ8QITL++o6APIvvoTcM88a9jjyL76Ew3fdSbSjHcliwVpQmPK5a/4C8/+p\ne7t4syA9gVs+u5i/vLCPN3e24bJb+ci5s8zPorE4+w71Ivu6sJ8knvmGfsu/6W0Ael56AWZ8HEjy\n5lRjSMok4Rop7IoR4RIRLXu5GCv9b24gsGsnVd/7YUJfmkT6H9n3OJvbt5qvn294hWAshEuPmDkV\nx6BuEyA89DQ02g95CWdZyPXYKMxJPIwb67rpau8zx/XhMJKUYjAc4/YHNhONqUwtdrN4ZiEfPLXa\nJGsnCoxqw3tuOd3s9JAMQ9N1sNVHe0+Q5bOLzapgo6IxFsvwLNRPpT8QIaZJKGhcvGYaVovMiy9t\nY/mmVzmw/reUf/5Gws2HkRQFa34BPS+9CEDhZR/CNW8+vo0bBOFavxY+/6nxPPxRYZJwDQNFkYlG\n4risqXYNtb31IyJcxqDS0xtC06BAFzVLkoyKRjgeobmvlXxHbkoU4Ygxgb3ApDEKxDRNI9bTja1c\nhJGzamaQVTMDgKIiD3Sk74smvlRvuaHBPw88zX+vuNn8qKhURFim1uTjzrbT5wtTVVNA7/R90AFT\nPBVj2l+rbqI5YYRLGh8froMt4rz1uLanvG9X7FxwxnQeXV8HQFWJB1XVuOvhLQC0JZkNxqK67k2L\nU+JJn/o7Z2p6MixZLMhOl0m4tFiMwJ7dRNvacC1YSOEll43oOJxJFYyeFasGRcQki4WKm2/l8C/v\nQlNkNEQEauCMWpIkqkrc7Kzv4eV3mujpC/PZS+ZhUeRBhqiapmFDxbfhdbxrXwYSqUtI8mFSVZgo\nveV/IIwI15N1zwMwc1ENzjlzCezeRdznE71SzZSiOK/Nfa28054qgg7q8o2QKoibpGlpJ6o2u4Va\nNGYpMiXBGL1Zqammpx8W252/pNy8r4fCSBIEz7zZYFohNLb30djex4LpBWaF8IkC4x5xZOhdaqQU\na/XOF4Z1CyQmO4NsIQac+Kb2PlRJIssiocgya+aXkn//XeRHfahA089+MviLFQV7hRj73UuWUvyR\nj5E1c+bg5d5DTBKuYaBYZELBqHnjGxjKsTwZhg9NU6N4GBUUCcIlI6qs7njrF3QGuyh1FvPtVbeN\n454bOHZSimp/P1o0iiUvb/iFB8CqWAkiCFdPKH1ncatV4ZpPLae9xU/51Fx+tnk9iqRQ6ioe0/5a\n9ZLy6Dh7voxnhEtVNTbuagUl0Xvy3Kqz2NdTS72vkfNWTiEW13j8tYPYbQpbD3TSohtEnr0kMWEw\nyullLU6+KzWyNBIoHg+R5sM0/uiHhJsOmVod57wFw6yZgGSxkHfeBfS+/CJ5534g7TLOufOFlsof\nASyompoi4DfwsfNm8/sndlLb7OOdfR3squ9mYU1honpRf6DGIlGu3/8PWncnadJ0ojdIND8Z4Rox\nHIouktdUHq99llx7Dt//wpfpfuoJep57RpDzJLkFwFut76RErz638BN0h3p4aN+/mFs4G+gwp3qq\npqa06VJkmR6g6uRyDm46jCfpAR5Pip7EYirW4Xu6m/uUKeXf1NHH0xuEbdANF8/lby/tpy8YpT8U\nTbv8fzJUDTMtnw6GaN5wky/MTpBhM8I1UDRvXAf6Nps6+8mTZJzEUKNR8PZQEBVpzMLLryDe348a\nCuHfuAE1FCLv/AvxLF+BJVc8a2Srjdyz3jdORzx2TBKuYaAoMvGYyprylezp3s/F0z/A77b/eUSE\nS9VU4mqcsOJg01uix11BsUhNypJIKXbqPjWdevm0sV5MjZmzxB2du9nfW8elNRekXNSqpvJiwzoq\nPRXMKxjgbzQBGq50ofzRINYjEl5jIVySrp+Z6q5gS6yNqBrDKg++fK02CxVVecTVOIf7Wih1FScI\nzihhVEDGM4W7x4hsmx7ltIWOqH3Q7oYe7vzbuwAUVgYxTEsumHaOWU0YjIU4bWEZj792kGA4xu+f\nEuXY3/zYUqaXJSKqvW9sACDkAKdn9GJjxeUiCoRqhXZCstko/9wXcc6bP6rtFF1xFYWXXp5RmC7J\nMpaCArJ83YBTEC4GE6Gi3Cz++6NL+czP1hFXNdMGo39AhMvT5cUdC4AkYZ8ylXBjQ0qFlPnA1eLm\nNTiJ4VHhLsNpySKqxpAlid6wFx8h7BWC5Mf7+0zRfE/Yz8HWg7zYuA6Ab664lZb+VuYVzEaSJNaU\nr0TS4AD/RjJ+Dk1LqcQ2oiiaXnVriao884/tqHGVQwcTifZoJE6azlKDYKQFdxzsYndjD9eeMzPF\nq83wtjtjcTmr5pbQH4zy4Ev7iURPPJV9XB0cZU5GQY4g38GwmNQVJumujN8tMnCMHfCoqT3s5X2x\nAMTgwOduMN/fc9LpfFBvbA1QdM21og/rMWrdMkm4hoFikYnFVEqcRXxz5a3E1TgSEj2h9ITLG/bx\nbsd2Tq84xTSPDCe1pynWH3IDmyjH1BhxNc6hvsO8fvgt3mrdzC1LP8cUdwX/u+1PAJxWsQp/pJ9p\n2VOQJImdXXt4ou45AO487f/hTEl7TpxofqwShWiPIJXWvPxhlkz3neJLS7IKIdrGY/uf5OqTMqeq\nnmt4hagapcozZWw7C2bz6/F0g4eER5Gc1UeY/ozkMR1auwP0h6LUlOewJckfbPkyK+ua4fOLPolV\nsZoNhAOxAC6bIFBv7RYVOsW5WdSUJ9IeajhM+1NPQ9mF9JaNrdNBtD1R/ZM16yQKP3SlmS4eLYar\nArQWFWNra8MeUYlrKpkMP2RZ4vOXzefuR7fj6xeEy0gp2vSBfmaXmPCUfeZzeJat4ODXv4JqEK4k\n0fxkhGt0UGSFO0//HgAP7f0Xrx5+g0AsSK5eCBFpaaEvLGxEfrL5VwQdifGwzFVCubs0ZVsGDMKl\noqXQbCM1FYzGUdGQA1EaDgxumJxRKzQAxnz11a1iouzJsnLR6mlYFJlQJMbzb4m2X2edXIEkSdh0\ne4LwceqAfiQwCk8yYVppNj/69CrqWnxIEpQmaUcdeno3FBlQSa2lRrj2N3kZGJ8KyjbaiqpT3pOt\nVuRjlGzBJOEaFkZfqGAgitNlQ5EVsm2etKXJAPduf4CDvgZsspUpnkokQNVTHktOmWpWhaVrZ/F4\n3bO83Piq+frOTb9O+fyeLX+gPdhJji2bJSULWXvoNfOzH268i9MqVjEjt5rpOdMSws1jJ6NIrHfs\nES5jBFxdtoKXm/byWvNGLqh+Px49WjQQe7v3A7C8dPHYdhZMj7TxjnCVu0pxWZz0FzdxkCb+vPMA\nn1pw3YjW/c4fNhKLa/z8C2vM3mTf+OhSnmr/G0U9MXKfeBX/Qh95YVEdFogFyXelDkDXvj9VxxD3\n+YhHxYCnZo1tSDAaE2evOY3ST3xyTNsYKewVFQR2bKegNzZs4YnHKaLEhvlrICyIV7bThh8o9YuH\nvmv+QkAne2FxXjUSPl3E4zCE588kMsMwyQ1Eg5TNmgWSRKiuln5LGBeg6kPVVbMuZaqnMnO0RJJS\nI1xJMFJT/eEYchqNafnUXJobe4mNkBBJAwjEE6/X88Tr9SyqKWCrXm2+Yk4xU0uEftTwgzpeW84c\nCeKqNmz/35J8Z0qRjoEs3X4nFB543hJ2Sl3eED3+MJ2F0yiJ9lB05dVo9ixueb6HeVljeJYcRUyq\nQIeB8dD9891v0KqL/nLsHjpD3bxQv5ZoPDVn3+AXM5+W/jba+ttAg7hOuCxJhnDpBgWDbKXTpAC0\nB0VEwxvxpZAt472nDr7AL9/9Hf/z7u9Z1/SG/slEMC5d6Oj1miRqOGixGO333wdg5tVH95Xi3Lmt\nWVw8/QOomsrbbe+mXbTR18RBXyNV2VOYlTe2KAuICIkkgZom7dfZ1scffvEaa5/eM+q0oEW2pPha\nvduxndreeqLq8O7ohrj0y/e8zqa9Im2Y67bRHujk3LcD9L/+Oi3/ew8z7nuJHH+MQDSIRZEpKxCD\n3QWrqlhYk6rR0mJRc1IgK2MLXzqmi1LrrBljP98jhb1CRC0LR0C4svUUk08nXIYharZbvJ8bChJz\neZAduq5Ed7sHRPNqI8KlxScjXGOE0QaqN+wlrsjITidqMJjiw/X5Rf/FGZWrB1XApkCWExGuAb+7\nEeHq7A3h1ce8y647mSWrp5KTn0VxmSBG7c1DFOYkf9UA0mcIwg2ydcbicv7rgjnm50YLofAJmFLU\nNG1E/cfTwaFXNQYHRbjEH0mC/YeFZtd32X8x/We/JHvVatyLFgHHX3PxyQjXMAj0J9qiHNjdTmlF\nDo1+YXT0eN2zvHRoPd9Z+RXcNpE2dCh2ArEgrxz6NwAXoqHq6SIlqSlwckpRQjL1Ubct/SIVQuuo\nBgAAIABJREFU7jJuWf9N8/PktkKfXvBxukM9PFH7LOdUncmF1e/n7nfvZU/PfnP5Wu9B6nrq+BLj\n25oieVtaLEb9t/8bLRaj5hd3Z/RY0lSVjof+Ru/LL5rv2SpGYKcxAOasV9NYWbaUfx54mkf3P8nu\n7n1Mz57GB6adhSzJaJrG77b/GU3TOKvy1FF/z0AoFjktoWqq7yYSjrFneyuV1XnMnFuSZu3MWFQ0\nnx1du83Xd73zGy6qPpfzq88Zcj2h/Uv8DoU5DnI9dsLxcMLPKMtFPBShwBsnEBNp7e9/cgWxmIY9\nTSWRrzdIbYHw1Ror4Sr/7Bfo2/IO2WtOG9P6o4GtUlw/Bb1x4po6ZErWiHD19kXQNM0Uzedl2zis\ngqJq2LISmhIpiXCJCJf+fjw+bM+/SaSHxyqi0Pft+hvs+htfdmShhoJoWWLM+MLJN1BTMGuoTQBi\nDDBae2mk3pOF2eI33F7XhQU4f1kFpRU5lFbksPL06TylVyn++8X9lFRkm5XNmb8r9fU9t5xOa3eA\nN3e2EY7Gufp9M1IicSd6hGuolOJQyNJTiun6vQIgSRzuEM++6jKPec4NQjzeco+JxiThGgbJjabD\nQTEQ1+RMo9ZbDwjH7Zb+VmbaxAx/oGWCBMR1o0tLUiPV5BnUnIJZ7Oray/yC2eYMb1nJYup9h7j5\n5M+Q58hlXdPrhGJhFhUJI8czK9eYF9/18z7Mg3seZVtnoj2O4cw8IRouINTYgBoQVSexnh5spaVo\nqor/zQ1433iN4muuBU2j66kn6Nu8SRx/fj4VN96CJXsM9heGzYWq4bG5WVW6jDdbN7Gray+7uvbi\ntGYx1VNBYVYBvWEvCwvnsbz05CM+VkWRU1yQO9v6eOWp3XR1JPpqtjZ5R024Vpcv5y+7/oEkJ7bd\nGhjeBdnjtOLVJwHfvX45U0rcgEYoHsaiQTQrm20zL8fvj2APvWDqCBVZRslQnfX25i56nGVI9KOU\njryLQjIUt5sc3RF6omErK0OTJAq9MXZ37+Wve/7BNSddxuqyFYPSUQ6bgkWR2Xmwm+/+8W1mThHa\nteI8O3SJptTJ7aFkvX0QQHdHf5Lx6WSEa6yYVzg75XW75ic/KKHl6gJ3ZYSPoaQI18CJ5IzKHK46\nawYPrz1ADFCyUpV98xaXcahOaEj/cd9mrvnUcvIKBxsvm1+VxmqkrMDFZadPT7u80ZO0t28iu4cc\nm1BHkFLMBKtFxm5V6PSGqGv2Mb3ceDYkfl+jEMGRZOchSZKYfB5nhGtyyjYMTjt3JjYj7KlXOn1q\nwXV8Yu6HuXrWpQD0hn3E1TgbWzbTH0ttJ1LmLKEpRww4ySnFZA3XqtKl3LDgY3xi3kfM966f+2G+\nu+or5DmE4PnMyjWcN+19SesnLnCPzc1nFn7cTEVeWP3+xA5MkNO82p8gHHG/D/+mtzlw4+do/eO9\nBPfspnftKzT88Hsm2Sq88mqm//Qu7FPGKGI3I1zi5rt29of42enf4+wp4iH/8L5/8bPN99AeEGnX\nYuforQ3SQVakFA3XjncO09XRj8UiM6VapEa9vYKk9PeFqdvbMeKooto8/Kx+ICK6fcOimgIqilzI\nkoQvojd7jsM7xe+jNwBxxYYj4CEY9OOL+DNaaQC0d4r9d9mewZp37A9gstVGKM9FQW+MB3Y9hKqp\nPLjnUf6w8680+Zu5+917ae0XTdElSeLi1VXYrDJNHX1s04XUTqe4/+wWJVVPqSjmPRMJx0wPNik+\nSbjGCrfVxZmVa8zXQUVFC4YgbjQ7HmGfU0k2I1zp/OvOWzmVX998OpedVs37lqRG0atnFXHDl08j\nX+/DlzxhSodkwvXtjy8bdtfKC11YFNn0xDtREIyFCBVuB1f38AtnwDnLxG/1o79spsur6yfNn1cy\nXegtA3o0yrJ03EW4JgnXMJi/pIL/ulkMFofqRBop2+ZhWenJ5DvEA3dH125uf+su7t/9kLleibOY\nb638Mqq1Gm+WiH4YhA1SU4o2xcbiovlmw1cwGPzofp5vrLiZc6vO4rxpZ4+vieogSKjhRCQk5vPS\n9eTjaOEwkl1oYbzr10I8jmvhImb8773kf+D8I/tGI52jGc2GFbIsWSwonJuy3PZOYXtQmDU+lSoi\nwpUgXCG96fN1XziFi65ehCPLiq8niK83yKP3beb5f+5k+6bDI9t4x3QKmy7mztNENZfRd24oRKIq\nNRXZ3HTlItPB+YFdDwMQi4TpsyYsHebWRSm593Hu2PgLvrPhx+zrGdw0tvWwl0BIxR3uJm5hzBYa\n7zWU8jLsUQ1PIPHbvNu+jR+9/Uv29Oznz7sS9+LFa6r5yPsFue3yievW4E4ypOSPJEvqwz+k37MS\n2mRK8QjwoZkXs6psGYsK5xFwCAVrdoPQII40wiXJUlKVYnqtlNNh4eI11WbEKRkWq8KSU0QGIRQc\n2i8reegdSdseiyJTVeKmqaPvhEorvtiwjmj+AcJVr7Gzaw/e8OgJ5+WnT2fJrCLiqsahDlHEkqhS\nTFSWWgfIHRR5MsL1HwlJknC5RT5mt14mDDAjdzoeq5tNbVtoC3SQa89hTv4sfrj6G3xn1W2UuUpo\niye0AtlJrSaUpDvaKo9whjcMSl0lXFJzPrIkU+YqRSNhvDoeSPbhUpOa0Lb87z1EDjfhnDuPKV/7\nb/P9rFknUf7Fm5Ct43B8SRquZEzxlDM1yUne8PIpGacIVywap88XNqNWYd3Y0Ih6Zuc58HtDvPtm\nI/19gjDV7esY0bYVWUaK2ZPaoAxNuGJxYZRqs6RGWg73i2vSNsAgQZVknN399If9qJqalnDteEeQ\nw0rvHuLy8UO4SmuEv1dhb4wbF9/AFTM/mPJ5IJoaaS7ITnUelxUxiEualkq4rOL4Z88T149fn3Er\nanQywnUEkCWZ6+ZcxacXfpzWgtRrzGIZgRMp6BEu8e9YtakOnYiFQ0MXqCRHuNKRt3SoLssmrmo0\ntveNad+ON3SHeljblCje+s3WP/Lo/ieIxEdn/ipJEit1SUZHT1B/N1GlaJiiWgeMe/+xEa477riD\nq6++mmuuuYZt21JbL4TDYb72ta9x+eWXD7tOS0sL1113Hddeey033XQTkcjwM/qJRmDfXtr+ej8t\n9/6OwO5dGZc7+2JRkWI0RgZwWOzctOQz5kNqZelSvrj4U2YaEEDVxVRnvn86009KkIDZeYnS/PEi\nXMkoc5WgSRCNjd85tvgCVDeFUde/TqR5cBTHffJSZEeCVBZccllKY9ojQoZmsg6Lg68tv4lLay4w\n38u2eZiRm15rMVqEdN1eR6sfTdNoPuTFZldMzUJegQtV1di1pQWbXuI8UhsJWZKIaxqKrGCRLWbf\nuUww2ojYkkLrhkmuhIRdTi27bi4SEdMLt8S56NVeCp/egF9P8QJs39zE/p3teLIkyn37iCsSluPE\n3NNeWgbA5QWnMTt/JgsK5yJLMiWdUT7yTBdSd2/KZGPWlFzmVCWqY03RtaalWAAYholaWzOQSD1l\nRf1Yi8fWsWASqdg+M4s3Fib0U8oIxz/F5cLhDWCNqmPuQ2sQrpZDmVPsMJigD4V4XGXPthay/BFs\niEbyJwI2t201m5Ob77Vv5db132J3175RbcsgtaZTf4JvmeOeJV2ES9Oobfbi6z/6XGIkGPZp+NZb\nb9HQ0MBDDz3E7bffzu23357y+U9/+lPmzJkzonV+9atfce211/Lggw9SVVXFP/7xj3E8lLGh89FH\n8K59Bf/GDTT9/Kc0/fLnRLs6By1XPjWXLKc1hXCBIDY/WvNtLqo+l7OmDK6Ki2viFFdU5aborlaU\nLjH/L3EVjdfhmDDa2Qy8IY4ElU+8yQdf9RJ/4nl6nn8u5TMlN5ec005HSar4spWXj9t3m1EINf1A\ne1rFKXxy/kf5zqqvcMeab406HZsJFVWCPHt7gnS2DZ65zju5HHe2nfKpubzvwjlYbcqIw9zJIXGH\nYqfBd4jW/szCeVPLkNTTr7W/nWAsxIrSJcTU1AEpR/bQ6axg6l4fNU0R8rcepOV/f83BXYfZsLaW\n114UrvDlvr1IgKyBRTo+IlyyU5DLHFVERwqz8vnGilu4ri6fwt44Z7zeRXN/q7m8RZG56YqFFOdm\nUV2WTcwgYxop+SPXQlFujldovXq7AsgS2GP9ZOnWF5M4MkStMpvmOmkutPLWPCe2EaYU3UuXocRU\nSrtiY+56UVjipqDYRVN9T0oF+kBUlXqYXp7N5RlE8snYv7ONtc/spXVfJ2VINLSdGDquep+wQJIO\nrCa38ULzfQ2NjuDgZ+hQMKs8zclqwhci3bgHIsLV3NnP7fdv5r9/v4FgeHDU8lhLOQ77VNqwYQPn\nnCNK1WtqavB6vfT1JR48t9xyi/n5cOts3LiRs88+G4CzzjqLDRs2jNuBjBVqKLUqK7BjO+1/fWDQ\ncpIkUVKRTZ8vTJ8vdR2nNYvzq89Ja8Kp6lWLyoCLxapY+caKW/jy0s/jtmaulhkrylylIsKljh/h\nsvqCGT8ruuIq0cTY7ca1cBH2KVNR3EOXXo8GAzVcA+Gw2FlSvJASZ9GQbSZGiwVLRbpy8xsN/OO+\nzQAsXZ3w0Copz+a6z5/CJdcupnpWIfIodAXJIfFyt4jY3Lnp7oxpYGO7StJMr06vlp2eU0VUE4OW\nVbd+iJUsZmv5+9k49RLzOqzPnc9zT+xny8ZD5jbKG94EwBVUj5sIl6wT+0hrC633/ZFoVxdlrhIc\nBWLyUtERpb52S8o6NqvCD29YyTeuW0JcE4OzpKVqs5xz5yHZbGjBREqy0BlDRkPJPrGaEk8kNFni\nkXPz2LDIPeI0dtYMkRUo7YyOuem7JEnMXlCGpkHdnsypf4si862PLeOi1dOG3WZLU8IE2w34AydG\nP8UtHdsBUIMurGqqzi0+yghkwsdswNinJSJc1gGi+eK8xHcGw3FTn2ngjVcO8LufrufFx3dyrGDY\nK72zs5N58+aZr/Pz8+no6MDtFuTC7XbT29s7onWCwSA2m5iRFhQU0NExtNYlL8+JxTK+D4CiolQS\n0GRViLuczP/h92l7/gU633iTSEP9oOUAKqfmUb+/Cxk57efJaD7USzgUQ9Nnz0UlHlw5qYRsuG0c\nCcL2QgzFznh9T7i6GNvWevN15VVXkL9sKe1r11F93vtMrVbxD76Dpg3dXysdhtrPkNtBF5CT7SB3\nAs/bQHjcDt7+d31KVdOKNdXk5KVvgWOxCEHwSM651aqAJFFU5OGaRRfx/XW/JBQPE7H3MzW3YtDy\nmn4vOJ02c/vNdSL1tXTaXDbJzwNQXOrhcGMvIcUF+AlZPayd8XEi+ZuxdS9N2eZpdX9D1tNrm+c4\n+Wx59aB9n8jrdKwIhAs5BPjfFJO2vk1vMevWm/FvfNNcJue+xyl84Kq0ae1n172s/6dhtVpSjvFQ\nYSFF3jqCS5fhycliuncrQaBoWhmuY+xcHIu/zWhRWpyb0r4nE3KWL6QZWL2tn8BvfkvBD74/Jn3o\nslOqeP3lA3S0+o/4/EXCMRoOdGGzK5SUZdNY302fThqG2ramabyzt507/7KZ/mCUymI3P7/pdJyO\n8ZeXTASSJ4Va3GZqWg04nMqozm1cv0clWTxbQ2qQg8DbeztgqTCgLi3JTnmmfOayhTy6dj9v7hCR\nbIvNmvKdna0iMHRgdwdLVvYxe0GZ+dnRum9GnT8Yi1gx3Toj2U5PT2DYZUaDoiIPHR2p4d5YRJCi\noKeQ7CuupXvbTmLe3kHLAUR0N9yurj6c2UMLPe/79evEYipOhI7G6w0SiLx34U1vQESj1Lia9ljG\ngrjuhyKfvID86pNwnnsRISD7Qx+mqzcEjM3DCdL/Nsno153Ce3sDRMfpeADUaHTYQfvyjy/h1ef3\nEwpEOfeyuURi8cz7KkE0OsTnydA0ojHx+5TI5Vw240L+eeBp9jY3khUdXGXaod8PsUhi+3va63Ao\nDmzBLHw2oT/KL3JxuLGX9gFmggbZqgzUYQ/24Al1YlPDMHsGD0/p4uTqVdTYZ6bs+3C/y9FCLJg6\ng1ZDIfbc8WMArKVlHLT6qTzUx5bv/oCKG2/JTP5VjdiAe0TOycXRvIsLLp+DbLPR/L8vAOCLWQgc\nQ+fiWP1tRoqirAJm58+iu2uk47xC2OPA7g8R2r2P5p0HzGbYo4HR3qe/P3JE509VNf762zcJBqJk\n5zooLPVwqL6HUK8Ye4fa9g/v30Rdkjylqb2PnfvbmVY6kdXl4wejwGduwUlsjauocZUb5l/H+sMb\n2NdzAK8/OKpz26+nd319YTo6/ES7BFmKxlR213djtch0dqZKOgpcVj590Vyml3p48KX9HGrxUpqT\nqPTvS/JEe/afO9AkjcISz4TfN0ORuWFTisXFxXR2JvKx7e3tFBUNrTnKtI7T6SSkp/Da2tooPgZE\nqJoaT50By3JGnZCshzRH0srFKGUNIMSXsvLeFoQqkiIcSsfTh0vfluXi88hP6tD+XkAa4MN1pNA0\njZ6XXuTAFz9Lz0svDLmsxaLwvgtnc8GVC4aNuMqyPIqUYuqyhs3Izs7daZePD0gpxtU4HcFOylzF\nSKqGz1EIaJRNSRRt2OwKTnfq5GDeFe9nzfUfYOGnP8yMe37H5ovn0lJkZUZu9bimYycSiifbbHJd\n+KErzfetRcVM+/7ttJwu7EIC27bR++LzmTc0oEoREr0+Y3qz9bjPB5KE4jn+o0nHArL0xurfWHEr\n1wzRgD4dWlafZP4faW0ZYsnMMMbidC27RgpV1Xj+sR30+cRDfcGySkorBVmSh6mAbOtv53Duc8jZ\nnXzjuqWcu1x4EwaGWe9YQkxvQ2aVLGYvxcXFCzh/mpAMxZPalAVjQXZ3Dy2iN1KKm/a0s2FHq9nM\n2hgdq4boDDCwX6qBcDBGboGT6lmF+L0hHvnTZur2jqyCfKIwLAtYs2YNzz8vBqydO3dSXFxsphNH\nu87q1avN91944QVOO23i24AMi7iaouGQZDnRsHYAEs2Mh3+gurNTW93I77GHjyzJ4mIdV+NTva/c\n0fAj0lOzmX6b0SKwexcdf/8rxON0/P1B6r5yK/s+dT1NP/8pcf/YZz+yLKHGR7aPyoCy5gpdx3Wg\n92Da5Q3He6ONRm/Yi6qpwnNMjRO2OLFLMbNaEuDaz6zkqv9aximXV9BVdYD6WW+TN8uDa958XAsW\nItvtHPQ2ICGxpHjhmI75aEBSFKb897eouPlW8s+/kBn3/I7ij36Mqu/fjiTLxEsKuPcyEfHr27ol\n7TYskqJXKaZez5b8fEB0UADhM6d4PEfnuv8PxPdO+RrfXvllbCM1PE1Cz8JpPHGG0NJF29rG9P1G\nhXF8hPdpOnS0+qnXTXTPvngOC5dVUloh9qsgqtHbnTlq92z9KxT5C1gQcvPuuj0YCt50ou9jFQbh\nikTFI0bRJy2G+XZMS6Qcv7fhTn695f9oG6IgyJbUGP7ep3bxj7UHzNdXnlnDjZcvyLiuR++Xmqyd\nU1WNcCiK3WHh/ZfMZc4yIdHYsHawNc57iWFHkCVLljBv3jyuueYafvjDH/Ld736Xxx57jBdfFL3x\nvvSlL3Hrrbdy8OBBrrvuOp588sm06wDceOON/Otf/+Laa6+lt7eXSy+9dGKPbgTQNBUpWT8gyxmj\nKCbhGsHM6GhHCmRJQZOkMVfzpIVZqnsUju0IWhWp0Si9r7yUUn0abU8drI1oRmD3Lg7d+aNEY91R\nQlYk1BGuO7A1RYmziKmeCjqCXXQFBzs3mxEu/cHfr3tNuW0uouEYYcWJQ4oSSRq4s5w2spw2Fs+a\nSdXibPpyO/jWG3fwduu7+jbjtPW3M8VTgXUMD8CjCUfVNFzzBUmU7XZyz0zoCEOxEIEshYBdoqej\nKWU9Q39Sk1udIcIlCFfXE/8Sy3u9k4L5cYTL6qTUNbpWWAYkSaLHo1e0tbYOs3RmKIp0RBEur+4X\ntWT1VGbNE8fiSPLr+vWPXskomwnFwpQeOglbxEnnoSC99UIDHTjGCJemaWzZ38mOjr38bNOvae5L\nnO+oTri2HxCTEkPQbhTdxNU46w69zj1b/oA/KlKBfdHMJFSWJG6+chFXnTUDgMN6+tCVZeP8VVVm\nFCsdDEuJPp1waZrGhrW1aBrkF7pQFJn7Nx1iByqrPjD67h7jiRFpuG677baU17NnJ3pj/epXvxrR\nOiBSjX/6059Gs38Tj7iaYmgoyVLGlKKRyhnJjaqqGtm5Dnx625f3mqQow1T1jQVGWw2OBpfMYHw6\nHOJ+P4fu/BGR5mayNr3NlK8KY1ajX17O6WfgfXU9AJ6Vp+DfuIFIczOhg3Vk1cwY9W4qsjzimbMi\nCx+uZJxavooH9z7Kj97+Hz499xM8t87H5WdMp7LITUy/Lo3r0GhM7bI4Wf9SHapsId/ixaLPFgdG\nWc+fdjb90X62d+7mL3seYUHhHB7Y/TAxLU65u3TUx3oso10vS+/PUsj19tEX8uN2iLSEoT+xGf5P\nGVKKwX17ifX2ooZCWHImCdexABkZn1sBWSZUf3BEGkwDmqbhXb+WuN+PrOQfkWVAQ62IbjmKNe7a\n/BuiapQzK08FqwxRFVXV+O1P1nPR1QuZUp2fug8dIbKR8ea1kNNTZrY4OtaqG9/c1ca9T+4ia4Ww\nANrUsp1Ao5+LVk8zI1zotkfnrhBpUSPC1RXq5uVDr6ZsbziLooU1BSysKWDLgU46ahvF9pThHzYG\nGTvc2Udrd4Duxl62vd2E1aZQeVIh67cIz8ggEJigVncjxQkfI9fUOCTrqyQ5Y3RDGYWGy6jSK6WL\nor76955wSbJoYD2uEq6EN8p7DbPf3Sg1XP07thFpFpV8wX17CezbKzYTFQOGkpPQO5Vc93EKr7gK\ngHjf2NyiRYRr5LYQA5ddU7GSU8tXEowF+fO2f7LlQCc/fVBEo0xbCD0l0hEUg36WNYvmJj/2aB/z\nne1MnZ7PmeefxIc+tiRl24VZBXx24SeoyakmpsbY21PLlo4dACwvOfJG38cSDD1cS6EVaxwatycs\naCK6VYpd1mfNA65nI2oGED4somPKWBquT2LcIUkSqiwhFRYQaT7Mgc/dMKJ7NdTYQNOdP6b9L/fT\n9fg/kaKREY3jmeDrDQIavz/8O2q99TT6D4vWbjMstCcNuhteSU1hNdZ2kb1J6NBUC6hS3DT0bGg9\ntgogWrsCSI5EdfbT7+7ghbcbeeiV/QnCpcp882NLmV8t0vdGhKtWt6sBOKVsOcCwxs4GSvMTFeDK\nCNL4RkpxT2Mv3/nDW2x5S1jeXPOp5dz91C7+/Nxec9lfPLx1RPswUTjhCRdqakpRGkI0b6QUYyNw\nEjc6qC+T97Gwdd17TlJkfaYxrhEu4+84mYqOCrLhND+61Yy0g5EmCh2sE9uJ6S169MIN+9QqZIfD\n7KVnfD7q3ZRHnqrI1AvsshnCRLCXFpSSevr0vm8DNVxb2oUPjsviJBSKYY8HUBQZSZKYs6gMp9s+\naNsAi4uEZUutV2jFTi1fyez8mWmXPV5x3ZyruGzGhUyZtRgA5U+P4H9X+KgFoiIy6NAJ18DrWZJl\n8s4TnQsiBuEaR0+5SYwdhlRDueQ8873eda8Mu17no48Q3Jd48BINEw+P3aMwEAoTs0RBArtiY6be\n2aLLs5cGNE7/rxpCWX66Ovp5/KF3THLX3NJjbkOTrWiSSleoi4IcO2/vaaep49hpC9QfimKdusd8\nbSlswTptJx3xQ+zSneQ1TSHXlRhnjOeOPyKO46Lqc5meI3wLR9IrFmBmZSKaXFqQ3n4nGcmGqPa4\nirdb3N+yTRkUNVwya/xNxkeDE55waQNE88b/6aJcRoRrJKJoTU1tGfJea7oUQzQ/viEu8feoaLjG\nVqUYaROEq+BS0XpK09tJaXExQ7MUFDLtB3cw5WvfEF+jVyFqsbH1oJRlCU0bme2JYXyarl2RxyoK\nU2xVe5A9XaiaRlzVUAqa6dAEUdLQkOIynq4SNA2s8TDSCKphjejPy40i5O+0Dj+oHW/w2NycM/UM\nLB4xeEuRKC333A2IdAdAvl2PbsqDr2ejIrHj4b+L11nDNzCexMRDNh5Zs2dQeOXVAHj/vX7Y9aKd\nnUh2BzN//0cqbv4ysqYS689s5DwcIuEYqhLj9IpT+PnpP+DGxTfgUBz0ItJXB30HaZ0iqo2bD/ro\n1n38OpO0mTb7FJA0orEYZ68QRGDTnszC8vcaLb5ulNwOypwJ/ypLcROH3C/zeN0zACgoZoQJGGSc\n7LI6zV6xwViI1v62RHQsA06ZX8rXPyIi7oUjaByejLIkvcsXf/lv8/+CbAc/+ewp3HDx3FFtb7xx\nwhMuNDWl+sj8Px3h0h9mB/cP37ZA0zTRAPUo5YxlSZ4AWwj971FJKY5NwxVta0Wy2cxIlhbVhZV6\nSlGyWLCVlSPb7frrI4xwjaKwwohUtaapaPr2qoQGUnL0c7DZRyAWwFazjS2x59nSvp2+aD/T9i9n\n7ZNitmlVIzACA8nkXp8ATst/LpmwJond+7LEb2M89AodurYmzfXsWb4y5bU8SbiOCRgtu/wRP/kf\nOB/nnHnEurqIeb0Z1/G+/hrRtlZsxUVIsoyjZgaSFucIihSJReLE5Rg1upWKIivMzKsmiA/Z3cOz\njU/Rl9tJa6WIqgX6I0KE3ixcz/cWNCIrdiwWC2gSReURJAl21fcM9bVHBF9/hM/+fB0vbjo0/MJA\nR1hMVpeVLuTGxTekXeacxdNTKgyVpNZgVtlKdc40nBYxoXtk/+P8YOPP+e6Gn9ATytzLUpYkckwS\nN7pnjXGXHiQx/t71xTXc8elVFOVmmS2EjhZOeMKlxeOgDKhSRFQvDkR2rvDUajvs46E/vE3HEDl3\nNTnCdRQIiqxruMaTbyVE80fPFoJRCl3jgYAo6dcJlRoROgJDNG94OZlfc4QRLsOS4R/3bU6pFkyH\nXN0fqyWN8aPL6qRUEWkKyRrh9gc2c6gzMUjdu+MBDve1YIskiIA9FhiRdUGBIz/l9fElkyAUAAAg\nAElEQVRWnTgaWKdNpTNXT3M4xbkxtG+FdhHpSxd9tubnk33a6eZrOes/Lwp4PKLIWQhAgy9VW5cu\nytX93DPUfvlm2v70fwC4lywDxO8tayqaNrZxORyKoUYlVCXGrLxEf82T8kRa3j53IwBVninErGK8\neeaR7Wzb3ogcF/daxFeIJAm7IEmT+OPu+5k6RaGu2Tdhflxv7mojElX520v7h11W1TS8ensrl9XF\n7PyZuKxOPEoupzgv5pKS6/jknI9x8ewzUtZzWsV4ZFNs/OKMHzLFU87MvOlcWnOBmXbtDXt5qXHo\nqKT53BrhT/Thc2YiATZJIoiGERI5a0kFuW77oLZARwvHxl4cTahqSupvqAd7dm4WF1y5gOIyD90d\n/Wx6vX6IzWp6euloVkVISP8pKUVTw6WhxWImYRoOWiyOpFjMSiYtoke49AiWEdEycKQRrjVnz6C0\nMpuezgDeniBvvHIgo9ne9HIRfYlliIZVSUJ/hCLI39NvHRi0jKQlbuGC/qbUApAMcNtcXD3rMqbn\nTANgfsGcoVc4juFx5vHX8/MJ2iWsMY2+SD9NfpH2KdBTq5muZ/fCxeb/kxGuYwNVHuEs3x8TZCDn\nVOHlGPf5UpaL9/fT9a/HiHt7kWw2Km/7GgUXXyI+lCQkVLO/qIGOVj/7d7WZlg+ZsE9vJRPP6yfb\nltD2nZSXqGoudZRz27IvUD4ll7gsxqo3njlIfscUQCMadSAh4bDaUBATgvbSp1E1lafeqB/FGRk5\nenXn9YFNoAfipU2H+OIvXkWVxH4bKcHbV3+TH5z2VT666jTOnbeAJWXzB3mpWWUL/2/V17h1yefM\niYxFtvD+qjO5ecln+ezC6wFY1/Q6X3jlq/+fvfMMjKM62/Y1s027q1Xv1bYsy7bcG+5g000IJpSP\nEEwgBQgk4Q0QkjiFEt4klEBeElIhIYSQOPTmgCmmuuGCGy64S1bvWpVtM9+PKbsrrYqLLNk+l39Y\nOzsze2Zn58wzT7kfHvjktzy949kYFYxHZnGdOy2fP995FhbAuDN89/IJLDmvpLfNTjinvcGlKkpU\nGMY0vnrIFSosSuWyr07FYpVp9/acBKjlcBFT5+eEMgAhxUE5HP1Dq/7yRz6/6Rsc+OnSfm2mhoJI\nVguSPezhUvx+Og8c0HbbzcOlvTYMus6DB45ICDU+Ic5Uem/z+ti8rpw3X4zdPNWoTupJRkIJab/L\nlGQJyd6OZNHGlC+H+5RKyCQkxXHVlwpI7qyO1pTrhfl5s/jelJt4aP69pDlT+t7gJCU3PptvjL8W\n7HbsQZV/737RrKByWvRk3x68gvassFSGq2R0zHUEJxanVYsydAY1uR17jiZo2fTu2zS9+7a5nvfT\nTajBIGmXXcHIx/6Ea3TEQ4WsebgMg0tVVd777y6ee3IDb7+yg+XPbul1DFs3l6OiYh0ebZhlR2iL\njU6ciCzJJKe42DFtBbaIItfiyRmE0KY0iyzjsYaNNjm5hu0H61m/syZmqsGxoHTpVBGL9s4gy97d\nQ6c/RJKud2bXDS6bxYatH43G012p5Hu694IFyHZHy88cbC1jdeUnlHsrYu/sCG42Qb+i3W4tMnar\nTEl+Ut8bnWBOa4NLVVXdw9W/pPlIbDaZQNfO5hH7VVUtFg0MmsF1vEOKg2lxdQ37BGprCHX0I+k1\nGNQ8XHrT9NY1q9lz8w34Dh7Q9tuDwaV0tNP03rsc+vndlD/yUL/H6a+twaq7r30dEe0t2rsb58aT\nZjDGb62uuYMd+zVDrzVuH3GTPkBO0HKPJhcWmO55GRlJlnDaw81f+4ssyeYN7FRFkiQmZ4wnPj4Z\ne0BlU03EzbQPj601Nc3829JHdw3BiSGui8Fl8XiQXVq4t+aZpwk2N9H8wftmGNE1prTb3CFJMlKE\nwXXg83p2bA63CWpu7OhR2mX/53U013bSHt/ItIJxXfYrYdGlLe16NlGhR9On2lL0NjsnvUPbgu3M\nOUvzusiSpOV8qrBo2DkAOIo3URHYw+9f2sbSP6/heGIIJ8u9zN81Te2EFJVzpuZx9gwtWd7UqzsO\npDlTuHfWD/nWhOu5dORFZv5oUOlyLz2K+5avU4tKTBiVxgM3z8bpOOJW0QPOaW1wmYnxMZPmez/j\nVpuFYCC2UWaEESV58JLmtQEcbyEufbeD0OIkVksff2UPT0WR24VCSFZrjyEhiyt6uWFwNbz2KjVP\nPwWA79DBfo2xfecODvzoTnzbtZt6Z0c4LPnko6tob4s2uiw9eLha27Wcrdr66LCmLU8LKaY4k/jG\n+CX8z+SbiJPjtNC1MWFZBjcpdKhiT0wmzq9i0dty/c/km8xrs6cKYlkPRQ2771cnbJyC3nFY7EhI\nHGjREr8lWSbn5u+YuVztn31G9VNhcW17dnb3nUgSshoCNFmWjau167todDrDR6WhqrEfkABqqrTQ\nZUdyA7N1falILkz5CoGK4eTFaeHFaVmTObfgLEI2P0G7j2k5k8L3BymsxTcn9wyztZfkOL6eLQNF\njZaViUVjqxZ2TEmIwx/SpXMsPau8Hw2pzhTGpY3hnIIzObtAywELdTW4zJtN//fr03Pf3G47Cb0o\n0w8mp7XBZXixpFhJ8314uKxWmWAPidWqacdpBs9gtflRUFFCIXY07I7xgz5ypEHM4TL1svILSJir\nJTMbgqa9oQaDYLEgWa0U3vu/5vKURV+g8J77kONiG1yR2NL712Td0Pnx79TERH1dkl/Lu1QgWfXf\nWtccrrIaL81eP/lp3V3idtnGpPRxxNvcFCePQFX0alh9H6LfX2zsGVq4Z8LnHdhkm/bdGRdqL79n\n1+gxUaFFweBieGW9gTaafFplomv0GPK+p1X1tq5fF72+I4YWnSwj6+deCSnU1XhJz4rnvMWluNza\njTryYSmSfTVasn5aYVzMYpNURxrB8hLzWd5hsbN45CJ+NfdnfHPcEublzjSf5SVJMg2uJEciXy65\nTHvDOjCK86rp4ep5HcPgSvLYzbwqx3E2uCIxZCSCapec3Iis+bqOBpbvf8vU9uoJY751xA3dIqDT\ne3Y2rgoploerD4OrFw+X4Y7WPFwMbg4X8LtPH2f5gbf7XrEvBtHgco0ZS9Fv/0DhXfeSOEdLlPVX\nHO51G1VRtObEuhHlyMnFmpxM3IgRpH3pchy5ed226TpBy/HxMY2wWBihDYuuM2NM2i69GrFr1aLh\n4Qp28XAZv5/pJZl8b8q3uH3qzeZ7Fw4/J2qiVxQV2RLh4RIGV0zip2iq+3PrPPxw+ne1hTGuf8HQ\nZ06OJtlR2Rbuh2rPzcORX0BbRKNyyRE7XC5JEpJucIVCKqqiYtGrk8PyM7E/u6VV09M6s2hm1HLj\nAd1QRg90Ecf22OOZlDEei2yJ6eGCcIVfdvrAGAxKZOSlB0wPlyeOJr/mzYu3uXtc/1gxWgF1cwhE\n3GpWV6zj9f1v8cOP7mVTzVa21n2GEiPH2ggpOuKGXijRYOiO7AQQ9nBFt/aJfK8nrDaZYCBktvCJ\nRImIlauDmDSfkJJFsKoKu19hc+02Lh5x/rHtcDCbVxMWn7Tn5ADg68PDZYibRnowh9//6163sefl\nk7TwbJrefQcAxevF7/VS//qrJM6ZhzWp50RMWU/Mt6jahb99kzY+h8NKu9dPqMskbORwhbr81iJd\n/yOThqGqKvNzZzMsIZ8zsqdGH6MhPxLLWyswcY+bgKt0HO3bt5HSqoKbcEixt0d+wZAjz6Nd/1Vt\nNYxJ0ZoRS7JM0jnnUv23JwCwZWaSd9udPe5DxjC4tERrWf8NmB3EuqSChIIK3tZOAl6QpBAlmcNR\nFYVD991jphy4xpRi++JXgd67kahdPFyG58mt61V1OiuAEQzPPr7tpMwMml7m77IazYuU7HFQXVFD\nnMVBkmPg+oiaHq5uYqhhD5dfCXv8Ht/2DwBumfh1xqZGVyCGPVxD16w5rR/tZIcDR34+zpERbU36\nqFI0sFotqGpsgcuhksPlGTUGOaSQ0Baisq2a32z8I4e9lX1v2BOqYUgO7s/G4nZjSUzCX9mHh0sP\n+UZ6qCRZ7jXsJkkSqYs11749Kxt7npb0Wv/i8xz4WXRlpOKPzvNQdCmJ5PaqqOV2fQLoOglb5dg5\nXEZyq2HIS5LE/ytZ3M3Yggj5kRj5iIJoEmbNBqDyT79HVZRwXuAge6AFR0amSwvxV7VHq7IbOlsA\nzqJibKmpPe7DkMsJ6oVPpsElxW4h9tHbn/PMn9Zh8ToJOX1YZAvBpqao/M72HduxVmm5ZYFehI9V\ncx7V7hGK3m3CrXd88AbacCZ7CQSP772jr6T5LXvr2KI35U6Kd9AWaCfeHj+gKTGGUOqHFWvwh/w0\n+ZrZ13yAFp9eGR7x2Yn2sAHaFuie53YyGFxDd2QnAEmWKbzr59HL+unhik/QvBlN9R2kZUZXMJke\nLsPgGiyPkF5ZFefXxvN50z7WVm7gS8VfOKr9HecU/GPCkZND+47PaFj+Gglz5mJNjOF5Cnb3cPUH\ni8vF8F8+iBwfj+xw4N20gco/PIbS3o6v7BCO/AJaVn1M1d8eR7LZ8MyYiT07m7pnlwHgTE1i1sEX\nWF2otRMyOhR09XBZeqhSNF72ltwK0dWwakg3LvspC3E6Ej9ZM1h9ZYc4/Mivybzua9obwkg9qch0\naW1wPjq8hoX588zXFqcTa3IywcZGFF9nr/swPFzGQ5BkGlza+4ZR5G318cGKXRz8vAFJVmlzNmHJ\n7UANhejYpbXusSQmkXLRF6h95mkcnXplcQ9J9xBdjyVH6AvKsszUjIlsqNmMxdlGoPPom2vHQu0j\naf711WHj0WaVCSgB0wgcKAwP156m/Xzv/Z+YyzMbFa4CkMLj/tbE6yn3VvL0jv9EecSMKFM4pChy\nuE4e+pnDlVug3eC3b+ruZVGVrqGKQTK49Jyi89Jnkaz3jYv1ZNBvVJWjFGc+7tiytIqeuheeo/rv\nf+v2frClhYM/vwuInQjf5/7T07E4nUiyjGfqdJIWamXbB+/5GU0fvEfNsme078Pvp+WjD0xjC8Ce\nl4cr0EK8U/uyjD5qXb2hRg7Xhl21fO93H1HTqJ0bI6Qo92FwReUKxgqPC6KQHQ5cY7Reau07toe/\nM+HhOqmIFNu8d82DfFC+ynxtTdG8Wn1p5xlJ86aHS4rt4fp8RxUHP9fkWFrd9VRO/JQLzppO9T+e\npOqJvwCQcuFFWBO1sFucT7vWm1p8MT+388ABgnp/V1MWAgjp3qyZ2ZqXTnb4eizKOlp6y+EKBBXK\n9XnKXKYEsB1HSYjYY+ohD1oNH3tnlUzRtjnsXF1P3fYgkiKbSfbelk6eemw1K5fvZPs+rcfspsZP\naT+W+9wAImbnLvQ3aX7k2AySU13s2FxJS1O0HpSRHG23W7Sk7UGaz2WXluyYtbOKH59xG6C5q48a\nVR0yHi53aVgDx1fWvTdYy+qPCTZoE6VkOXZHrueMcJJszVNPorS14Zk1m8QFC7tJMdjSNP2m8SGt\nanHKrAKgu4fLqFKsrG+n2evnrfVaBZSR09WXwaVGeFIND1d/eimezki28A0kUK83ABFJ8ycdkb39\nlu1+ybxxG2ryyedd0Ov24ZBi9LVmGlz6tVVVq80hVfk7OFS8kTum3sLolGKzQjpxwdkkzJ6DNUnr\nWmBt1SonDVX3SLScr7tp/NVdZHXWIUn6PQII+LV7RorR59TeQWsPlZJHiyl8GmNeqW5sp8MXJDXB\nwV3XTUdVVQJKENtxmDt7o7vCvEaWy6gMl+jY68DZnsiejQ2Ur++kdP0F7FvhJxgI8enaMtq9fnZu\nqcJfo431jYq3+PeuFwd03EeLmGm6YspC9G5ayLLM+Gl5qCqU7W+Ies+oTotz2oDBCynGDRsGQNun\nm2h7/b/IknxMHi5J1cRUh4JHIH7SZEb+/s9a89rGBpTOnkVQDY2eY8FZNJKCn92Do6AQADkujqSz\nFpJx9RKKH/sTox5/krw7f4RnxkySz7sQyW7HtWstN35/HgVF2lP31g2HTbc3gMcV/fTo82tGkykr\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": "bcc69200-6519-4482-e574-3a73444c199a",
        "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": 5,
      "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",
        "outputId": "2b32c4b8-003e-44de-fa94-f065aad297bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "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.h5')"
      ],
      "execution_count": 6,
      "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": "4850ffa5-848b-489e-a452-32ddcae7641e",
        "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": 21,
      "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": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "E7FWH0YOxl03",
        "colab_type": "code",
        "outputId": "10ebe285-47f6-4017-bc65-c5b464b17a9f",
        "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": 23,
      "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": "AfUJ2MHx3Tm4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "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",
        "\n",
        "smoothing_window_size = 500\n",
        "for di in range(0,3000,smoothing_window_size):\n",
        "  df[di:di+smoothing_window_size] = normalize_data(df[di:di+smoothing_window_size])\n",
        "\n",
        "\n",
        "#df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-m3vg5kXsqmU",
        "colab_type": "code",
        "colab": {}
      },
      "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(0,3000,smoothing_window_size):\n",
        "      df.loc[di:di+smoothing_window_size,'Open'] = scaler.fit_transform(df.Open.values.reshape(-1,1)[di:di+smoothing_window_size])\n",
        "      df['High'][di:di+smoothing_window_size] = scaler.fit_transform(df.High.values.reshape(-1,1)[di:di+smoothing_window_size])\n",
        "      df['Low'][di:di+smoothing_window_size] = scaler.fit_transform(df.Low.values.reshape(-1,1)[di:di+smoothing_window_size])\n",
        "      df['Volume'][di:di+smoothing_window_size] = scaler.fit_transform(df.Volume.values.reshape(-1,1)[di:di+smoothing_window_size])\n",
        "      df['Adj Close'][di:di+smoothing_window_size] = scaler.fit_transform(df['Adj Close'].values.reshape(-1,1)[di:di+smoothing_window_size])\n",
        "      return df\n",
        "    \n",
        "df = normalize_data(df)\n",
        "df.head()\n",
        "\n",
        "\n",
        "# You normalize the last bit of remaining data\n",
        "#caler.fit(train_data[di+smoothing_window_size:,:])\n",
        "#train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ngtteIXM8ezn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "baa5c916-effa-4cb5-ad1d-46b380341cca"
      },
      "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",
        "df = normalize_data(df)\n",
        "df.head()"
      ],
      "execution_count": 24,
      "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": 24
        }
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "929e5151-bdd0-41ac-a39d-08ec0f9d74f7"
      },
      "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": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2526, 22, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "117fd92c-753f-424e-bbbd-6754c78ce9e6"
      },
      "cell_type": "code",
      "source": [
        "model = build_model([5,window,1])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Compilation Time :  0.03806447982788086\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZEp_fMri9rv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3111
        },
        "outputId": "45b5dab3-c361-4c32-9ce8-6daf364efac2"
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train,batch_size=512,epochs=90,validation_split=0.1,verbose=1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2273 samples, validate on 253 samples\n",
            "Epoch 1/90\n",
            "2273/2273 [==============================] - 2s 960us/step - loss: 64.3240 - acc: 0.0000e+00 - val_loss: 538.3782 - val_acc: 0.0000e+00\n",
            "Epoch 2/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 58.5381 - acc: 0.0000e+00 - val_loss: 504.7290 - val_acc: 0.0000e+00\n",
            "Epoch 3/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 50.1339 - acc: 0.0000e+00 - val_loss: 456.1207 - val_acc: 0.0000e+00\n",
            "Epoch 4/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 40.9910 - acc: 0.0000e+00 - val_loss: 403.6377 - val_acc: 0.0000e+00\n",
            "Epoch 5/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 34.4792 - acc: 0.0000e+00 - val_loss: 349.6307 - val_acc: 0.0000e+00\n",
            "Epoch 6/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.2763 - acc: 0.0000e+00 - val_loss: 304.1847 - val_acc: 0.0000e+00\n",
            "Epoch 7/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.4579 - acc: 0.0000e+00 - val_loss: 283.6660 - val_acc: 0.0000e+00\n",
            "Epoch 8/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.9033 - acc: 0.0000e+00 - val_loss: 289.5954 - val_acc: 0.0000e+00\n",
            "Epoch 9/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.4139 - acc: 0.0000e+00 - val_loss: 306.8126 - val_acc: 0.0000e+00\n",
            "Epoch 10/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 31.0067 - acc: 0.0000e+00 - val_loss: 322.5642 - val_acc: 0.0000e+00\n",
            "Epoch 11/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0749 - acc: 0.0000e+00 - val_loss: 330.1525 - val_acc: 0.0000e+00\n",
            "Epoch 12/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.1527 - acc: 0.0000e+00 - val_loss: 328.5032 - val_acc: 0.0000e+00\n",
            "Epoch 13/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0771 - acc: 0.0000e+00 - val_loss: 321.4227 - val_acc: 0.0000e+00\n",
            "Epoch 14/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 31.0874 - acc: 0.0000e+00 - val_loss: 314.1693 - val_acc: 0.0000e+00\n",
            "Epoch 15/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0592 - acc: 0.0000e+00 - val_loss: 313.7380 - val_acc: 0.0000e+00\n",
            "Epoch 16/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0266 - acc: 0.0000e+00 - val_loss: 314.1989 - val_acc: 0.0000e+00\n",
            "Epoch 17/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0210 - acc: 0.0000e+00 - val_loss: 317.2454 - val_acc: 0.0000e+00\n",
            "Epoch 18/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0747 - acc: 0.0000e+00 - val_loss: 318.5920 - val_acc: 0.0000e+00\n",
            "Epoch 19/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.0242 - acc: 0.0000e+00 - val_loss: 320.1688 - val_acc: 0.0000e+00\n",
            "Epoch 20/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0540 - acc: 0.0000e+00 - val_loss: 319.4489 - val_acc: 0.0000e+00\n",
            "Epoch 21/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 31.0622 - acc: 0.0000e+00 - val_loss: 317.6007 - val_acc: 0.0000e+00\n",
            "Epoch 22/90\n",
            "2273/2273 [==============================] - 1s 303us/step - loss: 31.0715 - acc: 0.0000e+00 - val_loss: 314.4299 - val_acc: 0.0000e+00\n",
            "Epoch 23/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 31.0013 - acc: 0.0000e+00 - val_loss: 316.0677 - val_acc: 0.0000e+00\n",
            "Epoch 24/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 30.9976 - acc: 0.0000e+00 - val_loss: 319.2234 - val_acc: 0.0000e+00\n",
            "Epoch 25/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.9892 - acc: 0.0000e+00 - val_loss: 319.6605 - val_acc: 0.0000e+00\n",
            "Epoch 26/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.9819 - acc: 0.0000e+00 - val_loss: 318.7427 - val_acc: 0.0000e+00\n",
            "Epoch 27/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 31.0302 - acc: 0.0000e+00 - val_loss: 314.3884 - val_acc: 0.0000e+00\n",
            "Epoch 28/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 31.0759 - acc: 0.0000e+00 - val_loss: 313.0204 - val_acc: 0.0000e+00\n",
            "Epoch 29/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9113 - acc: 0.0000e+00 - val_loss: 316.0731 - val_acc: 0.0000e+00\n",
            "Epoch 30/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 30.9919 - acc: 0.0000e+00 - val_loss: 318.8257 - val_acc: 0.0000e+00\n",
            "Epoch 31/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 30.9649 - acc: 0.0000e+00 - val_loss: 318.1235 - val_acc: 0.0000e+00\n",
            "Epoch 32/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 30.9651 - acc: 0.0000e+00 - val_loss: 317.9825 - val_acc: 0.0000e+00\n",
            "Epoch 33/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 30.9665 - acc: 0.0000e+00 - val_loss: 318.0542 - val_acc: 0.0000e+00\n",
            "Epoch 34/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 31.0781 - acc: 0.0000e+00 - val_loss: 315.1874 - val_acc: 0.0000e+00\n",
            "Epoch 35/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 31.0391 - acc: 0.0000e+00 - val_loss: 317.5499 - val_acc: 0.0000e+00\n",
            "Epoch 36/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0712 - acc: 0.0000e+00 - val_loss: 318.0441 - val_acc: 0.0000e+00\n",
            "Epoch 37/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 30.9649 - acc: 0.0000e+00 - val_loss: 317.1438 - val_acc: 0.0000e+00\n",
            "Epoch 38/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 31.0666 - acc: 0.0000e+00 - val_loss: 317.2658 - val_acc: 0.0000e+00\n",
            "Epoch 39/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9645 - acc: 0.0000e+00 - val_loss: 316.5030 - val_acc: 0.0000e+00\n",
            "Epoch 40/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0686 - acc: 0.0000e+00 - val_loss: 320.6574 - val_acc: 0.0000e+00\n",
            "Epoch 41/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0393 - acc: 0.0000e+00 - val_loss: 318.8188 - val_acc: 0.0000e+00\n",
            "Epoch 42/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9790 - acc: 0.0000e+00 - val_loss: 314.8911 - val_acc: 0.0000e+00\n",
            "Epoch 43/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0603 - acc: 0.0000e+00 - val_loss: 315.1514 - val_acc: 0.0000e+00\n",
            "Epoch 44/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 30.9836 - acc: 0.0000e+00 - val_loss: 319.1615 - val_acc: 0.0000e+00\n",
            "Epoch 45/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0623 - acc: 0.0000e+00 - val_loss: 319.6555 - val_acc: 0.0000e+00\n",
            "Epoch 46/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 30.9793 - acc: 0.0000e+00 - val_loss: 319.1598 - val_acc: 0.0000e+00\n",
            "Epoch 47/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 30.9639 - acc: 0.0000e+00 - val_loss: 315.6152 - val_acc: 0.0000e+00\n",
            "Epoch 48/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 30.9502 - acc: 0.0000e+00 - val_loss: 315.1858 - val_acc: 0.0000e+00\n",
            "Epoch 49/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.1154 - acc: 0.0000e+00 - val_loss: 316.7071 - val_acc: 0.0000e+00\n",
            "Epoch 50/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.0382 - acc: 0.0000e+00 - val_loss: 318.8182 - val_acc: 0.0000e+00\n",
            "Epoch 51/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 31.0319 - acc: 0.0000e+00 - val_loss: 317.8280 - val_acc: 0.0000e+00\n",
            "Epoch 52/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0198 - acc: 0.0000e+00 - val_loss: 314.2981 - val_acc: 0.0000e+00\n",
            "Epoch 53/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0413 - acc: 0.0000e+00 - val_loss: 317.6460 - val_acc: 0.0000e+00\n",
            "Epoch 54/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 30.8711 - acc: 0.0000e+00 - val_loss: 318.3106 - val_acc: 0.0000e+00\n",
            "Epoch 55/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.1031 - acc: 0.0000e+00 - val_loss: 318.7666 - val_acc: 0.0000e+00\n",
            "Epoch 56/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 30.9935 - acc: 0.0000e+00 - val_loss: 314.5356 - val_acc: 0.0000e+00\n",
            "Epoch 57/90\n",
            "2273/2273 [==============================] - 1s 300us/step - loss: 30.9755 - acc: 0.0000e+00 - val_loss: 315.9213 - val_acc: 0.0000e+00\n",
            "Epoch 58/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9842 - acc: 0.0000e+00 - val_loss: 317.9859 - val_acc: 0.0000e+00\n",
            "Epoch 59/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 30.9792 - acc: 0.0000e+00 - val_loss: 318.9015 - val_acc: 0.0000e+00\n",
            "Epoch 60/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0094 - acc: 0.0000e+00 - val_loss: 319.1881 - val_acc: 0.0000e+00\n",
            "Epoch 61/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9475 - acc: 0.0000e+00 - val_loss: 315.4305 - val_acc: 0.0000e+00\n",
            "Epoch 62/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 30.9467 - acc: 0.0000e+00 - val_loss: 314.1536 - val_acc: 0.0000e+00\n",
            "Epoch 63/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0323 - acc: 0.0000e+00 - val_loss: 317.3511 - val_acc: 0.0000e+00\n",
            "Epoch 64/90\n",
            "2273/2273 [==============================] - 1s 295us/step - loss: 31.0446 - acc: 0.0000e+00 - val_loss: 320.9129 - val_acc: 0.0000e+00\n",
            "Epoch 65/90\n",
            "2273/2273 [==============================] - 1s 306us/step - loss: 31.0130 - acc: 0.0000e+00 - val_loss: 319.5695 - val_acc: 0.0000e+00\n",
            "Epoch 66/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.1048 - acc: 0.0000e+00 - val_loss: 316.1619 - val_acc: 0.0000e+00\n",
            "Epoch 67/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0961 - acc: 0.0000e+00 - val_loss: 318.3207 - val_acc: 0.0000e+00\n",
            "Epoch 68/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0663 - acc: 0.0000e+00 - val_loss: 320.5746 - val_acc: 0.0000e+00\n",
            "Epoch 69/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.1184 - acc: 0.0000e+00 - val_loss: 316.6708 - val_acc: 0.0000e+00\n",
            "Epoch 70/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0132 - acc: 0.0000e+00 - val_loss: 316.4885 - val_acc: 0.0000e+00\n",
            "Epoch 71/90\n",
            "2273/2273 [==============================] - 1s 305us/step - loss: 31.0178 - acc: 0.0000e+00 - val_loss: 316.3228 - val_acc: 0.0000e+00\n",
            "Epoch 72/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0166 - acc: 0.0000e+00 - val_loss: 316.2715 - val_acc: 0.0000e+00\n",
            "Epoch 73/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0054 - acc: 0.0000e+00 - val_loss: 315.9587 - val_acc: 0.0000e+00\n",
            "Epoch 74/90\n",
            "2273/2273 [==============================] - 1s 303us/step - loss: 31.0593 - acc: 0.0000e+00 - val_loss: 320.7485 - val_acc: 0.0000e+00\n",
            "Epoch 75/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0128 - acc: 0.0000e+00 - val_loss: 322.4338 - val_acc: 0.0000e+00\n",
            "Epoch 76/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 31.0042 - acc: 0.0000e+00 - val_loss: 316.2853 - val_acc: 0.0000e+00\n",
            "Epoch 77/90\n",
            "2273/2273 [==============================] - 1s 303us/step - loss: 31.0602 - acc: 0.0000e+00 - val_loss: 312.2816 - val_acc: 0.0000e+00\n",
            "Epoch 78/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0397 - acc: 0.0000e+00 - val_loss: 318.4539 - val_acc: 0.0000e+00\n",
            "Epoch 79/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.9484 - acc: 0.0000e+00 - val_loss: 319.0746 - val_acc: 0.0000e+00\n",
            "Epoch 80/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.0404 - acc: 0.0000e+00 - val_loss: 316.9723 - val_acc: 0.0000e+00\n",
            "Epoch 81/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.9398 - acc: 0.0000e+00 - val_loss: 319.4926 - val_acc: 0.0000e+00\n",
            "Epoch 82/90\n",
            "2273/2273 [==============================] - 1s 296us/step - loss: 31.0253 - acc: 0.0000e+00 - val_loss: 320.1034 - val_acc: 0.0000e+00\n",
            "Epoch 83/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.9411 - acc: 0.0000e+00 - val_loss: 318.7505 - val_acc: 0.0000e+00\n",
            "Epoch 84/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 30.9609 - acc: 0.0000e+00 - val_loss: 314.0669 - val_acc: 0.0000e+00\n",
            "Epoch 85/90\n",
            "2273/2273 [==============================] - 1s 301us/step - loss: 30.9282 - acc: 0.0000e+00 - val_loss: 314.6390 - val_acc: 0.0000e+00\n",
            "Epoch 86/90\n",
            "2273/2273 [==============================] - 1s 304us/step - loss: 31.0470 - acc: 0.0000e+00 - val_loss: 316.7654 - val_acc: 0.0000e+00\n",
            "Epoch 87/90\n",
            "2273/2273 [==============================] - 1s 299us/step - loss: 30.8639 - acc: 0.0000e+00 - val_loss: 323.8879 - val_acc: 0.0000e+00\n",
            "Epoch 88/90\n",
            "2273/2273 [==============================] - 1s 297us/step - loss: 31.0314 - acc: 0.0000e+00 - val_loss: 321.2801 - val_acc: 0.0000e+00\n",
            "Epoch 89/90\n",
            "2273/2273 [==============================] - 1s 302us/step - loss: 31.0400 - acc: 0.0000e+00 - val_loss: 314.9930 - val_acc: 0.0000e+00\n",
            "Epoch 90/90\n",
            "2273/2273 [==============================] - 1s 298us/step - loss: 31.0952 - acc: 0.0000e+00 - val_loss: 314.1278 - val_acc: 0.0000e+00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fadd6c62f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "ICGRykHs3gv6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6aGXAp8XBI0R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ea3e756d-054d-4fb2-b8d4-34ba099ffffd"
      },
      "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": 27,
      "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": "RIhfdJTYBUlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "fb4cb2c2-c436-4764-ff4c-8b1ecc9d2f66"
      },
      "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": 29,
      "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": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "hIleNOdbBUYu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "c72cf388-91b2-45da-d8a6-03b51f7b98b6"
      },
      "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": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NVQApvhKJpItYt07lo4+E861v3+Bds8nJ0Xc7V1YqtLQo5OVpbvHdtSv4y+We\nPQpOp8KQIeEPbg9efMWjzRb2oTpN28lGTqfHHb5lS+gy43KJnt8jRgRXpmWIb2WlL/GNv3gvSLez\nRCLpAg4fhksuSaK+XuGyyxx+s3nb4on5Rmt1It4IItnLGPgQivgaCVqdEd9gY77NzfFg+bZODvO2\ngIuLTYDD19s65OBBBYdDCfqmLCkJkpL0Di3fYMIasUZavhKJJOaUlqrU1yvMmuXgmWeaQ3LNJiWJ\nx2havkbsUFi+4sK9e3fwx9u2TST+dEZ809JEVu+hQ/4v04blG8ws32jRtsWkt/iGY/mWlIj3hOIR\nycpqL74Oh7g5kW5niUQiwXNx7tUrdHGyWsFq1aMqvkZP5fx8kTCVmqqHFPPdsUO8vzPiqyhC1LpD\nzLet+Hqveds2FUdohq970pV3RnMgsrI8k7AMjHI0I1QRT0jxlUgkMae6Wjwa7spQSU6OrtvZ2/JV\nFDFrePduNeiymW3bTJhMHpd1uGRmBiO+4rErY77G91hcLM6RsWZFgZYWhZ07Q5MaY9JV25ai/sjM\n1Glpad2O06jxjbfuViDFVyKRdAFGYo5hMYVKcnJ0LV8j5mvM1R04UKOpSQl6vu6OHaIsyWrt3Dp6\n9mzfvKItTU0KFoverrNTLBkyRCSmffqphRtvtLkt4PHjxeuhup4NyzdU8YXWSVfxOlQBpPhKJJIu\nwLg4Z2SE934hvhFcUBv271cwmTzDHozyprZlM2Vl8OKLFm65JYHycvGZDh1SqKpSO+VyNujZUzSv\n8DcxqKmpa61eEMf/9NNGCgpcvPuuha1bxXmaPFm8bliywWLEfI2bn2DIyhLn21t8DStYiq9EIpHg\nLb6dcTsHZ4VWVYXuoj5wQKVXL8+wh9GjxYV906bWHZQuvBDuvtvGW29Z+fhjUTwSiUxng2DKjZqb\nlS6N9xpkZemccYZoA7ZunThPo0eL14wbk2DZu1clJ0cLKYnMV62vdDtLJBKJF4b4hh/z1bHblYCJ\nPC4XnHJKMjfcELxpqOui1MW7K9OYMcLvW1TkuWQ6nfDDD6CqYjvDVR2JZCsDf80jDJqaujbT2Ruj\nA5jR19oQ30CTmbxxuYTnIZRkKxDiD1BZ6fmO4rWvM0jxlUgkXUAkYr7gaa3YEQcPKpSXqyxebA46\n47a+HhwOxW11gnB/9uiht7J8d+9WsNth6lQhzKWl4nIaa8u3qUkhKSk+xMVIMDNqj0eOBEXRQxJf\no8Y3lHgveMS3teUrHqX4SiQSCR4x6Uy2M3gsm44wrNHGRoX164O73PmyyhVFWL+7dqnuC7poHgGn\nnupCUXR3MlbsxbfrY74G/fsv3nWUAAAgAElEQVR7PrOq6mRmClEMZdygEe8NVXx9JVwZN3ld2fe6\nI6T4SiSSmFNTI6y1hITw3h9si8kDBzyXuJUrg2voZ4ivt+ULnrjv5s1CdI2kojFjXGRl6e5jbd+u\n0qNH+AMVvAnU5UrT4ifmC8JDYDKJtWRkiGEGvXqFZvmWlYlte/cO1+0s3m+3wxtvWFFVnYKC8GYq\nRxMpvhKJJObU1CjtxC0UDMs3UCLV/v2ei/5337UfN+cLQ+jausRHj24d9zXEt6BAIy9Pp6xMichA\nBW+Mc+SrZzFAc7N4jJeYr9nsmVBlnL9evXQaG5Wg5y8bbmNDTIOlbXz8+eet7NihcuWVobUvjRVS\nfCUSScypqlLCdjlD6Jav2ayzerWJb781BRzwbrgq265v1KjW5UZbt6r06CEstNxcUQe8fr2K06kw\ndGjnXc4A2dliDR1lCxvdrWy2+BEXI+5rlJEZXcyCtX4N8QzVc5CYKH4Xhw4pNDXBM89YycrSuOuu\nAF94FyHFVyKRxJSWFiGa4SZbgbf4+t/OiPmedZaThgaFCy9M4v77/fu6Dcu3rWXep48QkbIyleZm\n2LlTZfRoEQ82MqO/+Ua4tiMR7wVhNXrHk9sSb5YveOK+xvkzaqWDjfsa4huOZyQzU6eiQuHrr80c\nPix6h6enh7ybmCDFVyKRxJTOZjqDt9s5sOVrseg89pidu+8WFtC+ff7f01ENcmqqmJxTVqawfbuK\ny6W4S2ny8sS2ixcL17Yx3L2zWCzC+jUyqdviGaoQP5avUW5kiGdOjiG+wdZlh2f5Aowc6aKsTOWB\nB8QN1oUXOkPeR6wIKL5NTU3cdNNNzJkzh0svvZQlS5bgcDi49dZbueSSS5g7dy61tbWxWKtEIjkK\n6GyNL3iPFQyc7Zybq5OTo3PttS1BvaejmwNFEVZcaamnV/Hw4eK13r2F2K5ZY0ZRdCZPjlyCT16e\nOKavvtKeoQoRO1yn8bid21q+obmdw7F877qrBVUVQzCGDHG5k+TikYDiu2TJEkaPHs2bb77JU089\nxSOPPMJ7771HRkYG77//PjNnzqSwsDAWa5VIJEcBne1uBd6lRh1v43SKC35enrgAWyyQkKAHLE/q\nKOEKIDdXo7LSI74DB4rnDcsXYOJELeRkIX/07q1htyvuYRTeNDZ2/USjtpx0kpOpU52cfbawOj0x\n3+Ddzmlp4fWqHjlS4/LLRUH3hRc6I5L0Fi0C5t7PnDnT/e/S0lJ69erFkiVLuPHGGwG47LLLorc6\niURy1FFTIx6jnXBVVqagaUorYUxJ0QNm3fq7OejdW/RaXrdOCMmAAeJ5Q+ABd4vFSGHEk0tLVXr2\n1Hj6aSslJQqPPmqPy5hvz56wcGGT+//hWL6dKdO69147gwdrzJkT4hzDGBNc4Rswa9YsysrKeOGF\nF7jllltYvnw5jz/+OFlZWdx777306NEjmuuUSCRHCR3V0YZCMOJrJFt5C2NycuDGHDU1YkqQYV17\nY9SerlkjYrsDBogEMu+a1DPPjKz4GjcPpaUKo0bBK69YKC1VSU6GKVPEseIp27ktocR8jXGEffuG\n7y5OSYFrr41v4YUQxHfBggUUFxdz++23o2kaAwcO5Prrr+e5557jxRdf5M477+zwvRkZSZjNwdXY\nhUJ2dhx2y44j5PkJjDxH/onG+THaPPbvn0h2dnj76NtXPDqdVrKzfc/tMyzc4cMTyM4WCTjp6bBn\nj//PVVsLmZmQk9N+myFDxGNVlUpGBkcyaVPJzoacHOHaPv305Ii6O4cNMz5PEmlpYpISiDpWXRef\nPSfHRnZ2nLS5akPfvqn07AllZeaAv6eaGhEuyMszHfV/mwHFt6ioiMzMTHJzcykoKMDlcqGqKscf\nfzwA06ZN45lnnvG7j+rqAA1YwyA7O5WKisMR3+/Rgjw/gZHnyD/ROj8lJVYgAVVtpKIivMSkpCRQ\nlBSKilxUVDT53KaoSBwnLa2JigrDQkyivl6lvLy+Q4GsrEyhd2+Nior2162UFDMgfLx9+rgAk/sc\nvfaais0GlZWRTfJJTjYBSfz4o5316x3oegppaTp1dQrLl4s1OJ2ezxhPGL+hceMSWbLEzIYN9eTl\n6TQ1wa5dKiNHtj5XO3cqQAopKQ4qKpq7ZtERxN8NRMAIeGFhIa+++ioAlZWVNDY2cv755/PNN98A\nsHnzZgYaWQcSiUQSAGM4QWfKcVJSYNAgnaIik88sYIAffvC0f/S8T0fTFHeJjjcffmhmwQIztbUd\nx6O93cttew8ff7zGmDGRz6413OZlZYq77/GkSeIz7dkj/h9PMV9fGK74RYuEvffcc1ZOOy3piNgK\nd/Mbb1jc/bI7E5LoLgQU31mzZlFVVcXs2bOZN28e99xzD3PnzmXZsmX88pe/ZNGiRcybNy8Wa5VI\nJN0clwu+/97EkCEud/emcBkzxkVtrUJJSXsTVtehsNBEr15aq4HsRolS27hvSwvccouNm2+2oesd\nNwAxSoqAkEfehYsh+KWlqnsovSG+Rsw7nrKdfWEkoS1aJMR12zYVXVfcNw9FRSq33mrjzjtFeCAz\nM35LhCJFQLezzWbjiSeeaPf8008/HZUFSSSSo5ctW1Tq65WI1MGOHq2xcCFs3Giif//WLtcDBxQO\nHlSZOdPRyr1sTLeprxcxWoM1a0zush3ouAzKn+UbLVJSIC1N1Pru3SvWaIivQbxbvgMG6AwZ4mL5\ncjN2uyf5yijrMnpXl5cLMY7EUIp4R3a4kkgkMWP1amH5REJ8fQ24N1i7Vhxn0qTWAtlRlvSSJa0T\nQo2+xG2x2TzCHCvxBVFfXFqqut3Ow4ZppKZ6BCreLV+AM85w0diosGaNyd2r2sh8b+uJkOIrkUgk\nESSy4ivEz4ghV1Yq7taRhYXiueOOa32cjjpjLV5sJiFBJz299UQeXxiu51i5nQH69dOpqVEoLDRh\nMunk5uru5hUQP/N8/WFMhdq5U3U33DAs37q61t+HjPlKJBJJBFm92kROjsaAAZ2/uGZliWlCmzaJ\ny9g119g477wkQFi+JpPO2LFtxVc8ejfaOHhQYfNmE5Mnu5gxQ7iv/YnvoEEaiYl6p2pRQ+Wss8S6\nSkpU8vN1zGZP/SyIntPxjnGzsn276hZbj/i23lZavhKJRBIhGhvFRKCCgsjMugUYMULj4EGV+noh\nogcOqDgcIqFn8GCtXaMMw+3s7eY0sqKnTXMxe7aDlBSdCRM6tsz/8hc7n3zS6BbyWHDeeU73kHpD\n9L3FN95jvuCZCrVuncfFb7idDx8+9tzOQTfZkEgkks5gWDmR7HtsuJEbGxWamz3WVG0tFBS0P46v\nbGfjwt+zp84pp7jYudN//8nevfVWiVexIDNTrG3JErPbgjTaNkJ8d7gyyM3VMZl0Nm702HzGb8L4\nDsaMcdHUJCZIHe1Iy1cikcQEY1pNJMXXiHU2N3vG65WUKOi64rNW19dABiPLOd5dtxdeKFqDDRwo\nLEjvUq3uYPmazUKA7XbPjU/bmO9LLzXx7beNcT0QIVJIy1cikcQEQ3wj6VI0LL7mZo/lu2uXsCl8\nxW19JVwZop2UFLFlRYVLL3XidDZz7rlChHNyhAgrik5CQleuLHj69NHYt89j83nczuL/6ekcE8IL\nUnwlEkmM6Myc1o4wLL7mZtwTfjzi2357X27neBzL5wuTiVaTeoyYb2Ji9xGsPn1an+O2lq93+dTR\njnQ7SySSmBBNy7exUcHhCMbyFY+t3c7iMd4t37YYMd94v2nwxjtDPD9fOxKrFzHfhITuY8FHAim+\nEokkJkRHfMWjMSMYgnM7+7J84z3m2xZvy7e74G35FhQIIa6uVjh8+NiyekGKr0QiiRGehKvI1cca\nlq8ROwTRxAF8D0fwdLjyPGfEfI3XuguZmSJ7uDtkOhsY5UaJibq7Q1hVlUJdnUJaWleuLPbImK9E\nIokJ0Yz5eotvba34tz+3s3fClSfmG7FlxQRVhYsvdrbqdBXvGG7n7Gzd/TuoqlI4fFghN7f7fI5I\nIC1fiUQSEw4dUjCZdHr0iNw+jXhnTU37jCNf4muxQEKCflS4nQGefbaZu+9uCfl9ps1FpP16Fqai\nTVFYVceI7lw6+fmaW3zLyxWamhTS0rrf+e8M0vKVSCQx4dAhlYwMHTWCt/xGzNfb8jXoqEVkSop+\nVCRcdYbkh+8jYdGXWNYWUv3pV2gDYjOTPTERXnutidxcnW3bxA/BGCsoY74SiUQSBQ4dUiLaYAO8\nE66CF9/k5PYJVxaLjsUS0aXFLaYd20hY9CVaVhZqRTlpv4/tPPaf/czF2LEey9eYx3wsdLXyRoqv\nRCKJOk6nEMhIT6sxko3aiq/NpncYw01Obut27n7x3s6Q+PKLANT/5a/YZ5yJ5fvVmDYXxXwdxm9h\n924hQ8ea21mKr0QiiTpGM4VIN8w3RLOt+PrKdDZISRHZzvqRTRoblW4Z7w2XhI8+RMvOwT7z5zT/\n+ioAEv/5aszXYYivkZ0u3c4SiUQSYaJR4wu+S43A/0jAlBQdTVPcJUZNTcdOvFepqECtrMAxcRJY\nLLSc+TNcvXNJeP+91vVXMSAvTycpSaesTIqvRCKRRIXoia94bGv5BhJf8EzSaWxUulWXqM5g/rEY\nANeIkUeeMNM861eoh+uwLl4U07WYTDB+vGd047FW5yvFVyKRRJ1ouZ0Ny9foDWzMvPUnvoZLurZW\nQddFzPdYsXxNR8TXOXyE+7mWn50DgHXxVzFfz/jxntpeGfOVSCSSCFNZGd2Yr4HR79if+BqvVVcr\n2O2gacdOzNe8dSsAzuEF7uec4yeiZWZi/forTyA8Rkyc6LF8pdtZIpFIIkx5uRBfox9xpGjbWjE3\nV/zfX8KV8VpNjfc4wWPjwm/eugVdVXENHeZ50mSiZfoMTGWlMc96njBBiq9EIpFEjYMHhfhGuhWi\nEfM1MPbva5yggZFlW12tdNvWkmGh65h+LMY1cFC7E9dyxlkAWL/+MqZL6tNHd/f6ljFfyTHPwYMK\nxcXypyGJHAcPit9T796RtW4SEsQweYMxY8SFfOhQV0dvcbe39Bbf7jZUIRzU8oOoNTW4vFzOBi2n\nzQDAumxJTNekKHDcceK78hcqOBqRV1hJO266ycY55yS52+5JJJ2lrEwhOVl3DzaIFIrS2og76ywn\nK1fW87OfdSy+xkW+pkY5plpLmrYeSbYaMaLda3rPTJwFo7Cs/R5aQu8V3RkefNDOa681RTwkEe9I\n8ZW0wumEVatMNDYq7rmoEklnKStT3MlQkcZbfG02GDxYR2nfbdKNIb5VVaKhP8RRzFfXUXfvwrR9\nG2iRddGb9u0FwDVgkM/XHVOmojQ1Yd7wQ0SPG4j+/XXOPdcZ02PGA/LqKmlFcbHqdsUZnWckks7g\ncIg63969ozMyzjvpKpjZtr4s33iI+SqHDpE+6yIyTxhHz6mTSL/kF9DcHLH9q+UHAdBycny+7phy\nEgCWld9F7JiSjpFXV0krvv/e5P73jh3y5yHpPBUVCrquRDzea9DW8g2Eke1cXa245/p2ueXrctHj\n/LOxLvmalikn4Zg8BeuK5aTeeG3Eyn+UinIA9Gzf4tty4hHxXfVtRI4n8Y+8ukpaUVgoxVcSWcrK\njEznaIlvaJZvQoIQW5FwJZ7r6pivZdV3mH/6kebzL6L2w0+p+ddHOI6fjG3hB5jXrI7IMdSKCgC0\nDsRX79UL5+AhWFavAlfHMXNJZJBXV0krCgtNpKWJgddSfCWRwOjdG+kyIwNvl3Ewli8I13NNTfzE\nfBMWfgBA8+VXgKqCzUbDrXcCYPv3uxE5hnrE8tUyszrcxjF1GurhOszr10XkmJKOkVdXiZsdOxR2\n71Y57jgXAwZoYYnvqlUmZsxI4rzzEmPdLEfSxezerbBkiand80aNb/TczmK/JlPwM3kzMlpbvl0a\n83U6Sfj0I7SsbBxTp7mfdpwyHS07h4T/fCgC551ErShH69kTfyepZfqRkqOvY99q8lhDiq8EgD17\nFC65RPjeLrzQweDB4uJUVRX8PnbtUrjookQ2bTKxZo3Z3UxfcmzwP/9j45e/TGz3m4m++LZ+DIaM\nDDHT1+gJ3ZWWr+Xbb1ArK7H//Hwwmz0vmM00X3gxalUV1iWdH3qglpej5fTyu43j1OnoZnOX9Hk+\n1pDiKwHgwQcT2L9fZf58O5dd5mTQIOEiDMX6LS424XR6JsRs3y5/XscKdXWwcqUJTVMoKWn9vRsx\n385kO1tWfUfKrTeScutNWFa1zsY1LN9QJhMZGc8HDoi1dqX4Wpd8DYB95s/bvWa/8BKxzeefde4g\ndjtqbU2H8V4DPS0dx/GTMf+wDqWysnPHlPgl4NWxqamJm266iTlz5nDppZeyZImnA8o333zD8OHD\no7pASXTYvl1xXxSbm2HRIjODBmnccIMosB88OHTxNSycE05wHTmGFN9jhWXLzLhc4vv3Fl9d93S3\nCreJQvJ98+nxi7NJfOMfJL7xGumXXYj5h7Xu18OxfI2M5/37u769pGXVt+hmM47jJ7d7zTluAnpS\nEpZ1a328M3jUSiPZKjvgti0zzkTRdaxLv+7UMSX+CXh1XLJkCaNHj+bNN9/kqaee4pFHHgHAbrfz\n0ksvkR3ElymJL+x2OOecZK65RlytvvlGNNU45xynuznBgAFCfPfsCV18TzpJiO+2bVJ8jxUWL/bE\nektKxO9gwwaVfv1SWLbMREpKeN2t1J07SHruaZwDB1HzwSfUvfAK2O2kz7kMpbYG8Fi8CQnB79fo\n77x/fxdbvvX1mDduwDlugu+Ua7MZx9jxmLZu6dSwe3eyVQDLF6DlzLMBRKxZEjUCXh1nzpzJ1Vdf\nDUBpaSm9eomYwQsvvMDs2bOxWq3RXaEk4vzwg4naWoV160w4HPD55yLOdPbZni4zRllIRUXwcVtj\ncs1JJ4n9SMv32EDX4euvzaiq+M3s3Su+96VLzdjtCk6nwujR4ZWu2N57B4DGW+/EMe0U7BddSuP/\n3IFaUe5+zWP5Bi+ghuV74IDR2zms5XUay9rvUZxOd4MLXzgnTELRNCybNoR9HHeDjWz/MV8AV8FI\nHGPHY/3qC9SDZWEfU+KfoK+Os2bN4rbbbuOPf/wju3btYuvWrZxzzjnRXJskSqxaJawUu10MUPj8\nczNZWZq7wTlAdrawfA1BDYbycvFzGj5cIytLk+J7jLBrl0JZmcrpp4vfjyG+W7eKxy++aGDBgqbQ\nd6xp2N5/Fz0pGfu5v3A/3XTVPHSrFds/XwNdDzvhCqC5uWsTriwrRUMLx4lTOtzGOXESAOa1hWEf\nx13j20F3q7Y0/+rXKC4XCe++HfYxJf4xB95EsGDBAoqLi7n99tvJzc1l/vz5QR8kIyMJs7l9CUJn\nyc5Ojfg+jyY6Oj9rvcJH77yTTEUF/OY30Lu3Z/vMTFGRUFNjITs7uPqNQ4fEBXDQoFQKCuDbbyEt\nLTUkd2Cskb8h/wRzfozf06mnmlm7FvbvN5OdncqOHSKWOmNGMqZw/vyXL4eSPTB3LtkDenstKhUu\nugjzggVk/7iBzMyTAUhLMwX9fQ4Y0Pr//fqltko0DoVO/YbWrQFFIX3mmZDRwX7OOBWAlC0bSAn3\nWI21AKQN6S/OXyDmXQn3/YmUd94g5Z4/+i1PCoT8G/NNwJ9bUVERmZmZ5ObmUlBQQENDA9u3b+e2\n224DoLy8nDlz5vDmm292uI/q6siPx8nOTqWi4nDE93u00NH5cTphxYoUbDZx1/+Pf+iAwvTpjVRU\nuNrsI5kDB6CiIrhY0/79yeTkQGVlAwMGJPDNN1YeeaSZ445zccIJ0Wmw0Bnkb8g/wZ6fDRssgI2s\nrCb69LGyfbtKaWk9W7emMGyYRlVVeH//SR99QjJQe9a5tLRZh+Wyy+mxYAFN//cqWv8TgARMJicV\nFcFZ2KpqAkSMdfp0J9XVYVjmdPI3pOtkFq5FGzqMaqcZOtpPUk8ys7LQV62mKsxjJe/eSxJQbU3B\nGdQ+TKT8v9kkvv4K9Q89StP1N3W4pVJ1iMR/vILlu29pmnsVLT8/3/1aTP/GGhpIuW8+SnMTh598\nplM3DJHC341HQL9gYWEhr776KgCVlZVomsaiRYt47733eO+998jJyfErvJL4YtMmlYYGhfPPd2Kx\n6LhcCklJOqec0j4ml5OjU16uBNUsw+US8WEjVmxkS993n40rr5QNN45mjKS8/v01+vbVaGpSWLvW\nRHOzwvDhnSgvKhRuVl9ZwI7JU9BSUrGs+s6dcBVKzHfSJBe//30Lzz/fFJ5LPAKoB8tQ6w/jGtZ+\nxF8rFAXHuAmY9pag1FSHdywj4SpAna83DX+8Gy0rm+TH/4y6e5fvjZxO0mdfQvIjD2FdvoTU/7mh\nS0qUlKpDZMycQeLrr2B7922SH7gntPdXVJA67wpMO7dHaYXtCSi+s2bNoqqqitmzZzNv3jzuuece\nVFXG8rorq1cL/98ppzgZMUJcGE891emz1CInR6e5WeFwEDeuVVUKLpfibiF46qku0tN1MjM1KipU\n9uyRDTeOVozvtn9/nX79hAB+9ZX4nYUtvi4X5nWFOIcOQ++R0f51sxnnccdj3vYTic56ILRsZ7MZ\n7r3XzsUXO+mqy5nppx8BcA4dGnBb15Bh4j3bt4V1LLU8cGvJtugZPal/+FGUpiZSb7/Z54CHpL89\ngWXdWpp/cSH1f7oXtbaG5AdDE75IkPS/j2Mu3kLTr36Nc9hwkl78O5bFwTcmSfjPh6KPdidLukIh\n4M/OZrPxxBNP8Pbbb/PBBx9w+umnt3p98eLFUVucJPKsXSsuiscf72LcOGHtnnOO71maoSRdGWVG\nRi3nqFEa27bVc/PNLa2OKzn62LNHJSlJJytLp18/8Zv56isR0RoxIrwsZ9NPP6LWH8Zx3AkdbuM4\ncSoAyfuFIIXSZCMeMG37CQDX0MC9ElxDhECHLb5BtJb0hf2Ci7HPOBPrsiUk/GuB5wVdJ/GFZ0l6\n7M+4cvOo/+tTNP3+JpwjR5P4zpuoB/aHtc5wUPftJfG1l3H160/9o09y+Ln/AyDxtf8Leh+WdcLL\n4hw7Pipr9IU0YY8x1q41kZWl0b+/ztVXO/jtb1v4xS98i68hpBUVgX8mhkC3nVwzaZLLfVxvIjwn\nXNJF6LoQ3/79NRQF+vYVX2xxcecsX0vhGgCck47vcBvHZJEhnLJ7CxBatnM8YN4mLF/X0GEBt3UN\nHgKAaeeOsI6lVpQHVePbDkWh/rH/RU9KIvWOW0j48H2UgwdJ/f08Uu75I1p2DnVvLBDeCbOZpjm/\nBsC66Muw1hkOSU89gdLSQsPtfwCrFefY8TjGT8C66MugS6XM6wrR0tLdNzmxQIrvMcTBgwr79qlM\nnCgulAUFGn/+s73DcWqG+HZk+dbVQWGh2mqbtpNrxozRsFr1VuJ76JDChAnJPPaYrBHv7lRVKdTX\nK/TvL773yZNd7pK17GyNvn3Ds0bNa78H8G/5TpiEbrGQsmMjEFrMNx4wbRNWrHNIEOJ7RBTM4Vi+\nLS2oNTUhxXu90fr2o+6l19AVlbRrriJrzFBs77+LY+Ikar5Y0spabDnjZwBYv/o8rGOFjN1OwsJ/\n48rvg/2Sy9xPN//y8iOlUu8E3IVSU415x3ac4ycSyxiEFN9jiHXrhAAa1mggAonvE08kMHNmMj/9\npLpbCLa1fBMShABv2qTSdCSv5bXXLJSWqnz/fXy7ou12mDkzib/9Td4kdIR3vBegRw/47LNGVq+u\n54svGsO+llnWFaIlp+Aa7icZKSkJ59jx9NothNroWtVdMG37EVefvkF1+NB69UZLTsG0I3TxDaW1\nZEe0nHUONf/9mubLZmM/82c0/PEeaj7+Ei2/T+t1DhiIc/gIrMuX4v6DjyLWJV+j1tViP/8ivOvZ\n7Bddgm6zYXt/gZ93C8w/iPGJjiP11LFCiu8xxNq14uueODE48c3O9i++W7aI/f3wg9ou5uvNxIku\nnE6FjRtNNDbCK6+IuFMo3bO6ghUrTBQWmnjlFYvM1u4A70xnbwYO1OnTJ8yTZrdj2vYTrpGjCFQg\n7Bw1hhNcK3n/8WKuuKLzY/dihXK4DlNZafBuTkXBNWSocDuHOOje090qDLezF64RBRx+5gXq3voX\njTff1mH8uOXMs1GamrB+u7xTxwuGhIX/BsB+wUWtntfTe9Ay7RTMW4sDxp/d8d6Jx0VnkR0gxfcY\nYt06E4qiM2FCsJavkXDl+2eya5d4vqjI5G5Q70t8R43yDFr4978tHDok3hfv4vvf/4qkobIy1d2t\nSdKajsS3M5i2b0NxuXAWjAq4rWvwEBTgjJ6FYfWO7iqMZCvnsOAH07gGD0Gx21H37Q3pWKH0dY4E\nLWecBYBlaZSTcZuaSPj8M1z9B4je2G1wTBfJwZZlS9q95o35iPg6pPhKosWWLSoDB+qkpQW3vSfh\nqr1ItrTAvn3i+c2bhQu5d2/Np/gacb+9exUKC4Ulk5WlceiQEvRNvNMJM2Yk8Yc/xKZdlqZ5el4D\nPofES8R3D7hHUEYC81aRQOUsKAi4rWuISEQy74hdfWYkMG8tBoLLdDZwZzyH6Hr2tJYML+YbKkYs\n3kiaixaWlStQGhuwn3c+7okwXrRMnwGA3+lMmobl+9W4+g1AD7L1ZqSQ4nuMUFcHVVUqAwcGf5FM\nThY9b325nfftU9A08fzKlSYqK1WmTXP5+htwZ8Du3auye7eCouhMnKihaQrV1cFZv2VlCps2mXjl\nFSvPPWdhzpxEvvgieoK4bp1KebnK6aeLTPAlS8LsPXgU09ICixeb6dtXY9CgyPnl3cI0YmTAbd1Z\nwGGW4HQV5o3rAXCOHRf0e8JNulKOWL56rCbQJSbiHDsO88YNUY37Wr8Rbu2WU0/z+bpr6DBceflY\nly3psLzCtLUYtaYGx5SpUVtnR0jxPUYIxz2oKJCV5Vt8DZcz4J7jOm2a75KlvDwdVdXZu1dhzx6V\n/HydvDyxjmBdz8b0GUlWhTYAACAASURBVBBds7780swTT0TPCl6+XIjt5Zc7GDnSxapVJj74wIzz\nyEesrfXZc+CY4rvvTBw+rHD22U6fN13hYireDIBzeBCWb78B6GZzWIlIXYl54wZ0kyko17qBq19/\nANS9IbqdIxTzDQXHcZNRnE4oDH8YRCAs3yxDt1pxnHCi7w0UhZbpp6NWVWHuYCKUe7CFn6lS0UKK\n7zGCIb7GnN5g6dlTp6amY/EdPtzjN542zbcP2WqF3Fz9SM9fUZZiJHMFK76lpeJ448e7GDxYY8gQ\nF+vXmygtjU7c2Njv4MEaV1zhwG5XuPbaRJ54wkpxsUpBQQpvv931vWO7El+jKCOBeWsxWnYOelYQ\n3ZjMZlwDBmLavr373A25XJg3b8I1vCCk4mRXfl8ATCE2sIh1zBfAccKRlqDffReV/SvVQlAdx53g\new6ysQ4j7ttB/NmySqyv5URp+UqixO7drUtCgiUtTbSYtNvb7k/8dM49V1x4+/XT3K0FfdGnj0Zl\npYquKwwYoJGVJbatrAzN8r3pphZWrmzgqqtEZqt3XDaSGOvKzta54goH337bgMWis3ixmaVLTTid\nCsuWCbe3ywXnnZfIb3/b+kJqt8Pvf2/js8+OPpe1rsMXX5hJT9c58cTwulj5Qqk/jKlkD84gXM4G\nriFDUWtrUA4ditg6oolp208oTU04xoXWTUnPzka3WFAP7Avpfe6Yb1aM3M6A8/joiq/l2xUouo7j\n5FP9btdy8nR0RcHqS3x1Hcuq73Dl9EIbOCgq6/SHFN9jhHCzUtPShEjW1bUWScPyvegiJzabzs9+\n5t/68W620L+/HrLle+CAOJ7hrjaO99//mqNi8FRWKqiq7p77OnSoxujRGkVFKitXCtEtKhJr+te/\nzKxZY+bTT82t+mD/979m/vUvC48+evTVCVdUKOzfrzJlijOiw2NMR+K9wSRbGbgGHYn7dpOkq3Di\nvQCoKlpuPuq+UMU3vNaSnUHrnYurbz8hvlH4AzXKmFqm+RdfPTMT57jxWNasgvr6Vq+Zdm7HdLBM\nuJwjGTcJEim+ccgXX5i46iobLS2R26chvkbv3WAxxLftcIXduxUyMnSGDdNYtaqBu++2+3i3B+/j\nDhgQjttZbJeXJ97Xt6/O6NEuli41M2JECosXRzb5qqJCJTNTb9UkYsIEFw6HwqJFwpLdsUOlpgYe\nf1zEnl0uhVWrPOt46y1xsSsuNrFzZ3yXVYWKUVoWqiclEObNRQC4Ro4O+j3uRKRuEvc14o/OMaH3\nEXbl54sYbggXh7BbS3YSx/EnQGUlpl3htcT0h7nwe3SrFef49iVGbWmZPgPF4cC6ckWr5xM+/kis\n87QZEV9fMEjxjUP+/ncrn3xi4ccfI/f17Nmjkp2thVwLaZQl1dZ6xMPlEvsz4sd5eXrA0JW35SvE\nV7w3eLezitmsu93VAA8/bOess5xUVyt89FFk7+orK5VWxwLc9dFOp1izris8/HACe/eq7tdWrBDC\nvGePwrJlZhISxD4++SQy6yssVFm6tOvLnvbtE7/N/PzINuk2bxTC5BgTQhbwEZdhh2Pv4gxL4ffo\nqopzVPA3GAZafh8UXUctKw3uDS0tqNXVXSS+wvVsXrM6sjtubsa8eRPOMWODGmXljvsu8So50nUS\n/v0eekIC9vN+Edn1BYkU3zijocEzhCBSTSicTlFjG46V4svtfOCAQkuLElLZklFuBLRJuAruJ1ha\nqpCbq7dqeDRliot//KOJhATd3W0rEtjt4vO2Fd+JEz2fwbDk33hDiOqzzzZjteqsWCEW+PzzwtX8\npz/ZMZt1PvkkMnHf22+3cfnliW09aDHHiMHn50fY8i3agG6x+G8r2QZXbh4AptIDEV1LNFD3lmBZ\n+z2OqdOCaivZFqOdY7BJV+7WkjGuYQVP3NfyfWTrfc2bNqA4nTgmBNcO0nHcCWjpPbAt/ACamwEw\nFW3C/ONWWs48Gz0tPaLrCxYpvnHGmjUmHA5xYQtmlF8wHDig4HQqYXUhSk9vL75GslUomdN9+mju\n/WVkQEoKJCToQd1guFyizjc3t/3xzGYxOWfrVtVdBtRZDh3yJFt5M3iwRmqqeO7yy0XCl6YpnHyy\nk6FDNY47zkVRkcpzz1l49VUrQ4a4mDvXwckni8zsv/+989ZvRYWC3a50ed1xVCxfhwPzls2i/MYa\nfJxcOyK+QVuDUUApLyf90vPpecI4Um65vsOh9wkfHmmHeNGlYR3HlZcPEHSXq67IdDZwjhwNSUlY\nCiNr+Vp+EDN3g24HabXSPPcq1MoKbO+JQQuJb70OQPPF/y+iawsFKb5xhmE5QfBWYSAMsQxHfA2x\n8RZfI9kqFPHNz9cxm3V3JyRFEeIWjNu5okLB5VLc8d62jBqlYbcr7NgRmfPlnensjaqKqT1Wq86s\nWQ7MZvH6ZZcJIT71VBe6rnDffTaSknRefbWZxER46CE7ubka999v4733whdNXcdd9tXVGdRGzDeS\nlq9p208odrtwJ4ZCYiJajx5dJr7qrp1knCdm3qrl5SS+9U9Sb/ydz0Qj2wf/QrdYwnZ1an2E5Rvs\nvNyuFF/MZpg8GdPWYpTamsjt1t2LOfhBCE1XX4tutZL43NNYP/4I22sv4+o3wN0KsyuQ4htnGDFD\niJzlu3GjUZMbjuUrHuvqPM8Z4jtwYPAX3oQEePnlZv7yl2b3c9nZwvINlAxpuDhzc31vOHKkiLdG\nyvVsWONt3c4ATzzRzMcfN9Krl87YsRrp6bq73Orqq1t4/PFmrr/ezj//2cSIEeJ8Dx2q8cYbotPP\nd9+FH69tbISWFrG2r74yM316Er/7XdcMsd2/X8Vq1dvdoHQGdxZwCPFeA613LmppF4hvYSEZ556J\nafcuGv7nDip37KPl5OkkfP4ZiS/8vdWm5rXfY95SRMuMs8T82zBw5R1xO+8PLuNZLe9C8QWYOhVF\n190jIiOBZW0hWo8euAYODvo9Wq/eNF/2K8w7d5D+m8vBZKLuxVeCihlHi6OvALEbU1KisGGDSt++\nGnv3qhGL+a5ZIy74kyeHXo9pxHy9E66MmuFQYr4AM2e29gtnZenY7QqHD+O333TbMqO2jBolnt+8\nWeXCC0Nakk/8iW9uru6+CXj11Saamz2hu5QUmDvX92SdoUM9LTbDxfs7qKtT2LLFxI8/qjz6qP/z\nFw327VOOdC6L3D7NRWIub8iWL0J8zVuLRdJEGLHUkGlpIWX+nfDP11B0ncN/+SvNv5kHQN1z/0fG\njGkk3z8f5/DhOE4/E5xOUm6/BYCma64L+7Ba/hG3c5DiG/PWkm05+WRAtIJ0nH5mp3enVFdh2rOb\nlumnh1weVP/wo7hGjCBh4Qc0XzoL56TjO72eziAt3zjigQcS0DSF224TZTvhiu+bb1o47TRhMWqa\nEN++fbUO3bb+8JQatXY7JyV13uox5q9WVfn/nG3LjNpiWL6bN0cmC9hwO2dl+b+5yMvTg+5pnJgo\n9rd/f/h/ckYf7JNPdnLccS6mTXPicil8+21s76HtdhESMeL4kcK8cYPIAg6hzMjAnXR1MDbWr+2N\n10j8xyswbBi1Cz5wCy+A3qsXda+/DRYLab+Zi+2VF0m57SYsRRtp+uUcHCedHPZx9fQeYq7v/hDd\nzjEaqtCOU05Bt9mwLl4Ukd2Zi48M3Rgd+g0aNhtNV/+Omk+/ovmK30RkPZ1Bim+csGqVif/8x8Kk\nSS5mzXLSo0dwyUi++OADM0uXwgUXJLFsmYmqKjUsqxfaW766LmLIAwdqna5LT0kR+25s9L8jI7nH\nV8IVQM+e4jVjwk5nqawU+/Fl+XaGPn109u8P7GbvCCPee/zxLj77rJE77hC1nkanrVgRlUxnTcNc\ntEnU7IaTBdy7N0BsXM9OJ0nP/x3dZoPly33WiTonHU/d86+AopD6h9tJfPsNXP0G0HDvg507tqKg\n5ecH3eWqS2O+AImJOKZOw1y8Oeg4tT/cfb9HBt8TO16R4hsnLFggrJe777YfSUbSAsZ86+tFI4e2\nZSclJSqqKkRy7txEIDyXM3iXGon/l5crNDYqIfeI9kVSkth3Q4P/7fbuFefBu1a4LWPHapSVqRw8\n2HlXvcfyjaz45ueLxLBwb6oM8TW6bk2a5CIlRWfZsthavob1HslMZ9Punaj1h8OK9wJovWOX8Zzw\n6X8wleym+bJfgR93bst5v6B65Voa/ucODj/5DFUr1qD3zOz08bW8fNTq6sB/OHRNa8m2tJx+BgDW\nJX5G+wWJecsR8Q1hIEW8IsU3TlixwkyPHrpbJHNydKqqVBy+Q4gAPPFEArfcYuOii5LcF3SnU1gm\nkyfD3LktNDeL58MV39RU8WhkO3uSrTp/4TUMnIYG/2K0d6+Kzab7nBVsYDS5MGqkO4O/mG9n6NNH\n7M/IFA4VQ3x79BD7sVjgpJNc7Nihum9QYoExxzmSlq95kxHvDVd8c4HYWL62N0SZStPvfh9wW61X\nbxrvmk/znLkhDVHwhyuEWl+1/CBaRkZMW0u2pWWGiPVav/6q0/syb9mMbjbjGjqs0/vqaqT4xgF7\n9iiUlKhMnep0N5Ew4qn+SnG+/FJsvH69iT/+UWTtlZWJmt4BA0QHqOnTnQwf7mLYsPDE0myG5GSd\nujoF5/9v77wDoyi3PvzMbE1PgNB7RynSO9IVEUUUREXFa/1s2Hu51y6Wi+XeKzYsiKAgAopSBBUR\novTee09ICCnbZ74/JrNJIGVrdpO8zz/K7szsyZvJ/uac9xQ3rFihfaY/mc6lERene77lia9Ew4Zl\nh7m7dNHEd/364G/pjAyJ2Fg15Hk7+h6pHkb3l6yC0lFdfAF69dKS2DZsqLjQs54AF0rPV+9s5Xe/\n4wKUegXiG+49X5cL05o03G3beXtKVzR6ow1fkq7k9FOR2+8twNO8JZ4mTTH9tpwyvYnyUBSM27dp\nwutHHXi0IsQ3ghw9KvHooxa+/lp7Ku3fv9A71b280kKUBw5I7N5tYPhwN4mJKrt2ab/KQ4d0z1S7\nP2fOtPHbb/lBZaUmJWnie8UVsd4+xu3bBz/JptDzLf2Y3FzIzJTLDDlDoee7bl3wIlRSa8lQoHuK\nuufoL/q+e3Jy4Wt6wtfBgxXn+eqh/Tp1Qun56v2OA0ikoUijjTB7vsZNG5Dy83H1rPgRdDo+e74u\nV8RaSxZDknAOHoqccxZTECVH8qGDSPl5uNv5PvEqmhHiG0HmzTPy+edm3n5bE7Si4qt7vqXt++rN\n/YcNc1O3rrbfCVq5EkDTptpxskzQ5SCJiSonT0qsWWOgXTsPixfn0blzKMLO5Sdc6aU5RdtTlkRS\nErRs6WH9egNKEKY5ndqah1JYdPSfIdCMZz3bWd/zhcLGKfrgjIpAvyfL2gbwC1XFuHkjnsZNUZOS\nyz++BJRaqagGQ9hbTJpWrwLA1at3WD+nLBQfu1x5W0tGqsyoCKEIPXsznavAfi8I8Y0oRb+Ea9dW\nvLWg+r+hdM93yRJNfIcOdVOnjkpWloTdXihWuviGgoQEcDg0OwYP9nDRRaEJNxaGnUt+//RpybuX\nWdasYJ0uXRRycyV27w78tj5wQMbjkWjVKnQzanWC9Xz1PV+95ScU9piuWPGVMRhUatYMjfjKx44i\nnz4dcMgZAIMBpXYd5JMnQmJTaZjStPm0rggMX9fxtcuVfOqkdnykPV/A2XcAqtmMKRjx3aZPvBLi\nKwgSPfHmzjudvPCCo9ieZqHne/6vyOOBtDQDbdt6aNBApW5d7diTJyVv2DmU4lv0y16vqQ0FZSVc\nLVhgpF27eD75RNvb8aWmVN/3/fvvwEPPevi+ZcvQ1rAC1KypEhOjBrHnWzzhCrTGHqmpSoWK78mT\nEqmpoWuwYVy/DsDv4fLnotSrp2U7h2PAM4CiYEpbhadRY+++ayTw1NM83/K6XEW8xrcocXG4evXF\ntGVTwA9IBuH5CkLFsWMyFovKCy84GDOmePcnPaRXkpd04IBW7tOxoyYQdetq/z15UvMUJUmlcePQ\n2amXGwFccEHoRKmshKuPP9b2wfUBAuWFnQEuvlhbQz0RLRD27NH+JAJNUCsLSdKSlM7Nds7Nhbvv\ntvLhh2VnpGZna4lg53bEa9JE5fBhCU/onfXzUFUtGhOykDNg8vbq9bFRfikodeohOZ1ImZmhMOs8\nDLt3IWdl4eoZuZAzALGxKDVqlOv5SnqZURR4vgDOoQWh50U/BXS+cftWlMSkiD74hBIhvhHk6FFt\nTF5JWbytWyukpKj8+KPxvLnZeicn3Qst9HxlDh+WqVv3/C/oYNDF12hUi4XGg6W0hKv9+yVWrSpe\nu+pL2LlFC5U2bTz89puR/PzAbAqn5wtaV6zTp2V9shnZ2TBmTCyzZ2uTkMrizBmpmNer06SJgtst\neZtfhJPcXLDZQiu+xnVrUCXJp8HoZeHNeA7Tvq9pdeRDzjqe+g01z7cML1+OdGvJc3CM1IZJWOZ/\n7//JNhuGvXvwtLvA77aS0YoQ3wiht+grrVzDaoVx41xkZMgsWlRciPQBAnpPY118jxzRvoB98RL9\nQRffVq2UkGb4l+b5zpqleYB6BrPF4nsryxEj3NhsEr/+GljjiT17tIEBgcw+9gU9kUvPGJ4xw+Qt\nEyrPe83KKl18oWL2ffVkqzp1QnSPud2YNqzH06YtakJwDar1Wl/DiaovvkrDhkj5+aWOLoQo6G51\nDkqjxri6dsP0x29IGRl+nWvcvRNJUapMpjMI8Y0Y5fUrBpgwQauJ0we26xR6vtoXoO6FpKUZ8Hgk\nn7xEf9Cb9ocy5AylJ1z99JOR2FiV//3PhiyrNGzo+/7iiBFa6DmQkXuqCrt3y7RooXjrrUNN0S0C\ngI0btQ/q2tWDyyWV2oDD7db6a5ckvnq3MX10ZDg5eVL7jFB5voYd25Hy83AFGXIG8OiNNk6EJ+nK\nlLYKpWbNqGjw4M14LqPHczQlXOk4rhiDpChYfpzv13kGvbNVAH2/oxUhvhFCb1RQViJRmzYKXbt6\n+O03Q7EWktu2ydSurXi9Qf0LXd8f1T3GUKF7vqEW39hY7b/ner4nTmiTnZo3V5kyxc6LL9pLOLtk\nOnVSaNRIYc4co989j48fl8jLk0IaWj+XQs9X+/1v3iyTkKAyYID20FCa91pY41uS51txtb6hLjMK\n1X4vFK31Db3nKx85jOHIYVw9ekdF2NOX0YKGY8dQZTk6Eq4KcFwxGvA/9FyV2krqCPGNELqHU96k\noQsu0Aa062VJ2dlaOVFRIdS/0G027ZrduoVWfIcPd3PppS7GjAmiO00JmExaSLlona/brYVX9TKW\n8ePdDB3q+88jy/Cf/9gxGODmm2Po0SOOd9/1LVYe7v1eKNwiOHFCIjdXC3N36ODxtusszXs9UzCL\nvGiNr44edq4IzzfU4qvPeQ2F56uE0fONppAzgNKoEQDy4YOlHiMfPaI9kBijZ3Ks0qAhro4XYVq9\nEik3x+fzjAUDFTzt2oXLtApHiG+E8LVFn97ZSc963rZN8+b0/V7Q9of1L2WrVS32XiioX1/liy/s\noZ1iU0BcnFos7Hz6tPZzBlND2quXh/fft5OYqGUBf/SRyafqk717td9JxXi+Etu2yaiqRIcOCk2b\naq/rs5LPpbDG9/z36tZVMZsDL2HyBz1cHhLxVVXMf/yOkpSsJdIEiTfhKgx7vtHQXKMonmbNATAc\n2F/yAW438vFjKA0bVaBVvuEcMhTJ5cK04nefzzFu24qnUWPUxBL+ACop5f612mw2Jk2axIQJExg7\ndizLly/n+PHjTJw4kQkTJjBx4kTSC1LaBb7jq+eri7P+xap/OZ/bBEIPPXfq5KlUbU/j4oqHnXXx\nDba94+jRbjZtyuOyy9ycPCmXKmpF0dc41AlrRdETlU6ckNm8WXuQat/eU2rS1L59Ek5n4YNBSYlO\nsqzdRxUxXEGvO9ebwASDfGA/hsOHcPUbQCg22dWERNTYuLC0mDSl/YkaGxvYHNkw4GnaDADD/n0l\nvi+fOI7k8XhbUUYTziGXAGBeutin4+Xjx5DTT+G+sEM4zapwyhXf5cuX0759e6ZPn86UKVN47bXX\nmDJlCuPGjWP69OkMGzaMadOmVYStVQpfPV99Eo7u+Ra2GCx+nO5RdesWPuEIB+HwfIvSq5f2kJKW\nVv6Xu77G5fWRDoainu/mzdo90LGjQp06KlarWix0fOiQRN++cTz3nMXbTnTgwJJD8A0bKpw6VVjC\nFC5CGXY2r/gNAGf/i4O+FgCShKduXQwhHisoZZ7GuHMHrq49IjodqChqcgpKSkqp4msoaD2pNAph\nwX+IcHfthpKSgnnZEp8aohjXrQXA1TX4rYloolzxveyyy7j99tsBOH78OHXq1OH555/nkku0p5eU\nlBTO6BtSAp85ckQiLk71ZhKXxrmTcPTEm3P3/vS9xO7dK6DTQgiJiyve21mf4hRq8V29ulB8MzNh\n8OBY5swpfuyRIzJGY9mjC4MlJkbrGHbypMSmTQasVq12Wpa1vdsDB2Tv99GmTVr2+syZJn75xUij\nRgpt25b8cKU/pIW71vfUKYn4+NBMfDL9/isArgEDg79YAUq9+lpP43OL44PA9FcaED0hZx1Ps+YY\nDh2kpPo0ve9zNHq+GAw4Bw7GcPQIhi2byz3ctF4TX/dFXcJtWYXi8078+PHjOXHiBB988AGxBWmq\nHo+HGTNmcM89Zc+1TEmJxWgMfe1GampCyK9ZEWRkwI4d0Ls31K5d9s+QnKyFFU+dMpGaavJ6Ns2a\nxRab433LLZCTA2PGxHhn8FaG9UlOBrsdUlISMBq1+meA5s2tpKYGP//04ou1Uqm//zaTmqrF43/7\nDbZsga++gquvLlyjY8egUSOoWze869agARw4YMBu1+6BevW0z2vVCnbuBKMxgRo1QI+e6g8nN90k\nlXq/tGmj/Tc3N76s+e5+k5qaQG6ulgiXkKDZVLduCO4tRYGVv0PDhtTo1Tl0GcRNGsFKSPXkQWrw\ng+sB2KRlZMddOpS4En7uiP2dtWkN69aS6siGJk2Kv5el1fgmtG9DQoS/B0pcnwnXw9w51Fg4Fwb3\nLfsCWzYAkDx0ACRH/3ear/gsvjNnzmT79u08+uijzJ8/H0VReOyxx+jVqxe9e5f9RJiVFWC7oTJI\nTU0gPd33bLloYtYsI4oSw+DBDtLTy39Cr1cvjv37IT09jxMnrIAJRcklPb3QQ+vcGaZN04TMbq88\n62MyaT/PwYM5JCbCgQNmwILZnE96emi8+G7dYli2zMiWLbnUqaPyxx/aZ6xfj3eNHA44fjyBvn3d\npKfbQvK5pVGrVgzbtml/ej17Ft4DDRpYADPffGPj6qvdbNigrY1O//6lr0lKihGIYcsWGx07uks8\nxl/0e2jUqBiOHJF58kkHmZkxjBjhJD3dEdS1zT8uIOn0aWwTbiY3I7f8E3wkLiWVWCBryy7cMSnl\nHu8Lyct/xWg0ktHiQjjnbyqSf2ex9RoRB5xZswlXbI1i78Xv3EsMkBlfE08EvwdKXZ8eA6iZkgJf\nfMnph58uPSNbUaj59xqUlq3IchnOW/9op6wHs3LDzlu2bOF4wSN4u3bt8Hg8ZGZm8uSTT9KkSRPu\nvffe0FlaTVi8WLvRLrnEty/JBg0Ujh/XhtmXNNmmMqPX+r78soXrrovxTnEKVdgZtAYWgHePVU90\nOnCgcEC9Hq4NR0b3uRQdV9i3b6GYXnedi9hYlYcesrJunezttnX99U4aN1bo06f0hxE97KxPtQoV\nOTnw118Gjh6VefBBLRJx++1BlpypKrFvvY4qSdjuCu33R2HGc2j2faXcHIwbN+DudFHhzRollJXx\nbDhyCCicgBR1WCw4rroGOf0U5uVLSz3MsHcPcs7ZKhdyBh/Ed82aNXz66acAZGRkkJ+fz8qVKzGZ\nTNx///1hN7AqsXSpgX/9y8Ly5UaaNlV8Lmlp2FDF45E4cUIiO1vCbFaJiQmzsRWE3uXqq6+0fU29\n1WIoxVffJ92xQ9tP3bSp8LbXhVjfU/dlelKw6JnpZrNarCb7ggsUPvrIht0Ozz9v8Xbb+ve/Hfz9\ndx7WMqLw5+YGhIp16wyoqvZg4nJJDBjgDrrZivmnHzFt2YRj9Bg8rduEwkwvnoJGG+UOmvcR8+Kf\nkdxunIOGhuR6ocTTtEB8S0i6ko8eQUlORo2P3jCtffwNAFi/+rLUY/Q6cHfnqie+5Yadx48fz9NP\nP83111+P3W7nueee48MPP8ThcHDjjTcC0KJFC/75z3+G29ZKz5NPWr2lJDfc4PJ5m6voF+uZMxJJ\nSSUPY6iM6Ik7Tqf2A23ZIiNJKjVqhF58d+40cPKkm4wMmdhYrbnH5s0yAwZ4KiTTWacwM91z3kPU\nsGEeevTwkJam/Wm2bKn49LvWS9YCnRVcGmvWaA8nEyc6mT/fyMMPB5fIJGVlEv/kI6hGI/kPPR4K\nE4uh17WWN2jeVyzz5gLguOKqkFwvlHg933PFV1UxHD6Mu3mLCFjlO+5OnXF16IR50ULkY0e9LTOL\nYlmyCABn/4EVbF34KVd8rVYrb731VrHXBg8eHDaDqioHDkgcPCjTubOHnj093H23719iRYewnzkT\nfA1sNKF7vjqqKlGzZmh7KzdrpmA2q+zcKXtDz6NGuZk1y1Tg+bq8HmN5pV+hQBf4/v1LDiOPGuX2\niq+v0RGrVau9DbXnq89GfvRRJ5MnB7fPi6KQ8ND9GI4fI+/JZ/G0aRsCC4vjaaiV1hiOlD3r1hek\n3BzMy5bgbtsOT9vo66ykpqaixMVj2Len2OvSmSyk/LzoDTnrSBL2f9xOwoP3Yv3iU/KfeLb4+w4H\npmVL8TRtFpZ7JdKIDlcVxIoV2pfpuHEuXnjB4S0N8gW96cORIzLZ2VKJXY4qK+eKL4Q25AxaLkfL\nlgq7dsneQQaXX+4iKalwH7giGmzoDBvm5u237dx1V8kPYCNHFuYC+NPqslEjlWPHJLKztWTiorhc\n8PnnpvOGWJSFIgnTxAAAIABJREFUosDatQaaNFF8nipVKqpK/BMPY/lxPs5efci//6Hgrlfax9Sq\nhWq1+u752myYfl2GZdYM5P37kI8ewfzDfKT0dGI++gDJ4cAxanRYbA0aScLTpg2GvXu0X3ABhj27\nAfA0aRYpy3zGftU1KMnJxHzxWWGpQwHmP35DzsvFccllUdFPO9RET9PPKs7vv2tf+vrAd3/QPd8d\nO2Q8npIn21RWSqoXDYdn37atwrZtBu+4wosuUujUCVaskLHZCsO15XUcCwVGY+HEqpJo0EClSxcP\n69YZ/Gp12bChwtq1Blq1SuDSS1188ond2xNi4UIjjz5qxeGAO+7wLWFq506trnzYsOCzp2Pef4eY\nzz7BfUF7zn4+IyQdrUpEkvA0aOhNOCoTm43kkcMwbdlU6iFKfAL2seNDaGBocV/QHtO6tRj27Pa2\n6DTu2F7wXiUYQhAbi/2Gm4n9zztYv52JfcLN3rfMP/8EgHPEyEhZF1aE51sBKAqsWGGgfn2FFi38\n/3LX93z1Ob5VJdMZinu+eiJSqD1f0CZEgda+cehQN3XqqLRpo4W5Dx6UOXRIplYtJWoS2Z55xsFt\ntzlp39538R0wwENsrErDhgo//2zivvsKM7T275eL/dcXtmzR/tupU3AlX4Ytm4l77UU8tetw5tt5\nqCk1yj8pCJSGjZBPnz5/VuU5xD/1qJb4NeJycl9+HefgoTgHDCLvocdwde+J/aqryfp9NUrT6PUg\nvYJbMHgAwLBjm/ZeFIbKS8J2592oZjMx70/xNgyRzmRhmf8dSo0auHr0irCF4UGIbwWwdq1MZqaW\n2BNI9CQhQRPc3bu1X1dV9HyNRtU7izcc4lu0M9T//Z8W7m3VSvv3tm0yhw6Fd5Sgv/Tr5+GVVxx+\nOYg33uhi//5c/vgjj44dPXz3nclbQqX3fT50qOw/+YwMibZt4/jsMxP7CypY9L7TAaGqJDxwD5LL\nRe47/0ENZQeQUvAUJF2VNW7PuH4tMV99gatDJ85O/RTb7f9H9szvyJ49j/wnnuHMj0vImTotKgcT\nFEUfsWfcvs37mnH7NlRJwt26cuyTKnXrYR93HcZ9e7HO0DKfY99+Azkri/x7HoiqqUyhRIhvBfD5\n51pnpauuCrw+skEDBY+nYmt8pZyzJP7jRpLGXH5eUkeo0D3fpk0Vr4cVDvFt1067dvv2Hvr10/5f\nF9/Fi42oqkTr1tEjvoEiSVo56sCBxecD6/W/5Q1f2LVLe1D89VcDBw5orwWTAW5avhTTpg3YR4/B\nOWR4wNfxB18yns2LFgKQ/9BjlFnDFeXo4mso4vkad2xDadK05D2dKCX/3gdQY+NIePh+Em8YS8wn\nU/E0boLt9rsibVrYEOIbZk6flpg3z0jz5goXXxx4+K7oF2BFeL7S6dMkXz4cyw/zMP/xOymD+/vU\nh9VfdPFt2VLh0kvdXH65iyuvDE2HpqI0bary3ns2PvjA7o0+6OL7yy/ak7Uemq4KNG6srevBg7rn\nq/2pHzokl9nLXu+tvW+f7BXfxo0DX5fY998BwHbfgwFfw1+8nm9Z4rt0CarJhOvigRVkVXhQa9bE\nU7uO1/OVTp1CzsjA3Tb4EY0VidK8BVk/LMbTuCmWJYtQrTHkTH67Uj8YlUfV9OejiK+/NuJwSNxy\niwM5iEedoiUwFSG+8Y8/hHH7NmwTb8Xdph0JTz6CdeZ08l56PaSf07SpSmysyoABHmrUgE8/Dd9Y\nnmuvLS7qLVqAJKneYRVVwfPV0QVTF1t9hGV+vsTp01KpSW26+O7fL6Mo2gCPhAD7NBg3bcD8x+84\nBwzC3aFTYBcJAH2ST2mer3zyBKZNG3D2HxjVTSh8xdPuAsy/LUfKOYuxYL/XXQmHznvadyDzzzXI\nJ46jNGgYvqS8KEF4vmFGr9ccMyY4b65o28Nwi69l3ndY58/F1aMXua++if3GiShx8ZgLCt5DSWqq\nyu7dudx6a5AtCwPAai2+rlXL8y0U31OnJOz2wnBzWaFnfaSjwyGxa1dwpVfWr6cDYLujYkOH+iQf\nw+GSM57NvywBwDm0YsLg4ca777txg1d8Pe0qQaZzSZjNKI2bVHnhBSG+Yef4cYmYGDXo8pmiX4Lh\nrPOVTp0i/vGHUGNiyHn3v9ofgdmM6+JBGPfvw7B3d8g/02SKXBlfs2bauiYlhXeUYEXTsKGKJKkc\nPix5y6hiYrSfr6ykK93z1Qk45OxyYZn3HUqtWhXemlGp3wBVlpFLSbgy/foLAM4hwyrSrLDhHKaN\nd7VO/wzzz9petrt9x0iaJPABIb5h5sQJibp1g28HWSFhZ1Ul4bEHkTMzyXv6eTzNW3rf0v/Aw+H9\nRpLmzbV1bd3atzaOlQWzWatZPnRI9u736n2kyxJf3fPVCTTZyvz7cuSMDBxXjqn4AfQmE0r9BqUO\nmjetX4eSkoKnVeuKtStMuPoNwN22HZa5c7Qw/+CheFq2irRZgnIQ4htG3G5IT5e89avBUBEJV+Yf\nF2BZuABnrz7YbiseKtRDdOalS8Ly2ZFCF982bUIzvjCaaNxY4dgxiX37tD9zfYKSL2HnotcIBMuc\nbwGwXz0uoPODxdO6DYaTJ5CyzxR7XcrKxHDwAO5OIZwhHGkkCdttdyEVZNLlPf18hA0S+IIQ3zBy\n6pSEqkp+tZIsjdq1VUwm7TrhKDWScnOIf/oxVLOZ3H+/x7nZYUqdurjbXYBpzV/aU0UVoXNnTVyK\nTheqKjRurKKqEqtXa/tn+kjC8jxfq7Xw/gpIfFUV86+/4KlXH3fX7v6fHwLcbbSEI8OOHcVeN27U\nBrO7qtiUHPs11+Ju2QrbjbdUaHKbIHCE+IaREye0J+tQiK8sQ716KhZLeMYJxr7+Cobjx8i//yE8\nLUoOWbm6dkfKz8NQ0L6uKtCrl4eVK/MYP77qPFDo6HkCf/2lie8FF3hITlbZs6f0cqOMDImGDRVq\n1VIKruH/vWvYtwc5IwNX7z4R8y7dBd2d9AQkHdOGddr7naqW+BIbS9afa8l9651IWyLwESG+YeTE\nCW15QxF2BnjkEQdPPBHkZJkSMGzeRMxH/8PdrHmZDe/dXboBYCqYsVlVaNVKCaoMLFrRvdb8fIkL\nL/SQmKg13zh0SPb2Gi+KokBmpkTNmipt2igYjYFlO5tWrwLA1aN3cD9AEOhTcAw7iz8oGjesB6rm\nfFhB5aIKfuVEhsxM+OQTE2lpBm9U9vjx0Hm+AOPHu7nnnhCX5CgKCY89gKQo5L5edlG7qyCEaFy3\nJrDPstuJf3gSNVs2IvG6qzH/uIAyOz4IgkJvl9mypYevv7YBha01//tfc7FjnU4t5KwoWg3wa685\nWLAgsCZJprQC8e3VJwjrg0NvrWjceW7YeT2e2nVQ6taLhFkCgRfRZCNAXnrJTHq6zJQpdnJzYdy4\nWDZt0ryJq65yMXWqnZMnQyu+4cAydzamtWuwjx6Da2DZc5o9rdugxCcE5vkqCknjRmNe/SdKcjKW\nX5Zg+WUJru49OfPtPK0noiCkdOmi8MUX+fTooTUwAW2Pu1cvN8uXG9m5U6ZNGwWbDbp2jaNjx8LB\nFm3aKKSmQnq6/59rTFuFkpgU2cb+8fF4GjX2TvgBkE8cx3D0CI7hl1adZCtBpUV4vgEwc6aRd9+1\n8PXXJlasMHDbbTFs2mTgyitdtGvnYe5cE7t2ySEPO4cch4O4V19ENZvJe+Zf5R9vMODu3AXjrp3n\nZZGWh+m35ZhX/4lz8FBOb9xJ5so1OAcPxfR3GtZvZwb4A0QHUm4OsW++RsLdtxP3r2eRDx6ItEmA\npi+XXloovDrjx2vRk7Q07WHx+HGJjAyZZcu0Z/FgatKlkycx7t+Hq0fP85L2Khp3m7bI6aeQMk8D\nYF78MwCuAQMjaJVAoCHE108OH5Z4/HErsbHaF9Sdd1pZvtzI4MFu/vc/O48+qoX1pk41eROu6tSJ\nTs/X+tUXGA4dxHbL7VpXGR8oDD2v9euzYj77BIC8x56CmBg8rVqT885/UU0mYj787/nT3ysJhq1b\nSOndlbjJr2CdPYvY/7xDjb7diH3r9aj9mVq21Ozau1f78z9zprgXGMxgC9N67b5wd+8Z8DVChacg\n49m4TRs6YP7pBwAcl1bN+bCCyoUQXz/5+msTNpvESy856NPHzenTMsnJKu+8Y8dohBEj3DRurPDt\nt5r3m5SkRm1E1TrnG1RZJt+Ppvd66YjJj31f+egRzIsW4urUGXfnrt7XlTp1cYwZi3H3Lsy/LPbd\n8DKQss+QeMNYEm67Gezh6xMNQG4uibfdhOHkCfIefpzTf23k7H8+RKmVStzrL5P4jxuRcnPCa0MA\nNG+uiev+/ZroZmUVF99gPF/j5o0AuDtGvtzF1a8/AJZ5c5FyczCv+A33hR18ftAUCMKJEF8/UFWY\nPdtEbKzK6NEuHn3USWysyhtv2L3ercEA997rxG6XOHZMpl696PR+5OPHMP2dhqt3X9TatX0+z1WQ\n8Wz0Y9/X+u1MJEXBPvHW8/babLfeAYBl/vc+X680pOwzJI8eiWXJIqzz55J45z/CWpMc/69nMe7d\nQ/5d95L/+NMoTZvhGDuerF/+wNlvAJaFC0geOQz5wP6w2RAINWuqJCWpXs/3XPENxvM1Fky+iob2\nhs6BQ/DUq4/lu28xL5iH5HTiGCG8XkF0IMTXD9askTlwQGbECDfx8VrHoP37c88bgXfTTS66d9ca\nGkRryNm8cAEAjsuv9Os8NTUVT+OmmufrY6ayedFCVIMBx8hR573n7tAJNTYO48b1ftlREtYvPsO4\ndTO26ybg7H8xlp9+8Db3DzXyieNYv/ocd6vW53UUUmvWJHvWXPJvuxPj9m2kXDIQ04rfwmJHIEiS\n1tnr4EEZjye0YWfjlk0oqbVR6tQN1szgMRiwX3cDcs5ZEh66DwDHqNERNkog0BDi6wfffKP1qB07\ntrDcp6SkSVmGKVPsJCSodOwYnZ2TLAvmAeAsQRDLw9W1K3JmJnIpvXOLIp08iWntGs3DTk45/wCD\nAVfHThh27YTcXL9tKYrl+zmoRiN5/3yJnP98iGo2E/Ofd8AT+t+B9fNPkdxubHfeAxbL+QeYTOS9\n8gY5/34fKTeXpHGjiZn6n6gprWreXMHp1IYuZGZqN/GQIW6aNFFo2jSwaI10JgvD4UO423cIpalB\nYb/uRlRJAkni7PtT8bSrXHNuBVUXIb5FUBR45BELU6aYz3svKwu+/dZEgwYKAwaU/2XeqpXCxo25\nPPOMMxymBoV88gSm1X/i6t4zoHpH776vD6FnyxItw9R5yYjSr9epM5KieEOWgWDYuxvT5o04Bw5G\nTamBUrce9rHjMe7bi3nhDwFft0QcDmI+/xQlORn7NdeWeaj9hps4M3chSs1axD/7JPEP3x+WhwF/\n0Xta790rez3fp5928PffeQHV9kJ0hZx1lCZNOTvjW84sXIpj3HWRNkcg8FKtxdfthunTTWRna//+\n8ksTX3xh5rXXzOzapS3NO++Y6d07jqeespKfL3H77U6MPlZHx8dHZzmhZd532h7smGsCOt/lh/ia\nF2kjzhzDyxDfizpr19u4LiB7ACzff6d9zuirva/Z7pmEKknEfPxBwNctCfOvy5Az0rFfd6NP9cnu\nHj05s/R3XB0vImb65yTecUvEBbhFC0189++XvZ5vSkpwXrlxyyYA3B2iR3wBnEOG475IdLQSRBfV\nWnwXLDDy0ENWHnvMytGjEi+8YMFkUlEUiVdeMTNzppGXX7awd6/MnDkm4uNVJkyo+KHvocby3beo\nsoxj1FUBne9u3xHVbMa4tpyM5/x8zL8tx922HUqz5qVfr0B89dZ/gWBe+AOq2YyzSEKNp2UrXL36\nYFr9J/KJ4wFf+1xMK1cAhWMWfUGpW4/suT/g7NMPy4LviXvZh7rqMFKS5xu8+Eaf5ysQRCvVTnxt\nNrj/fitpaQZvf9u5c01ceWUsOTkSr7/uoEsXDwsXmrj//hji4lReftlOSorKpElOEhMj/AMEibx/\nH6Z1a3H1v9ivLOdiWCy4O3TEuHWztqClYP79VyS7Hecll5V5OU+zFiiJSQEnXUlnsjBu2YSre0/U\nhOK/IMcVo5FUFfMP8wK6dkmYVq1ENZu9EQBfURMSOfv5DNwtWhL7/hTMi34KmU3+oovvvn0yWVlS\nSAZ2GDdvQo2NxVPGg5ZAINCoduL7yy9GZs408fLLZlasMHrHpx06JHPrrU5uuMHFG2/YGTnSxQ03\nOJk1K5/bb3exfXsukyZF3/6tv1i/nwMEP2fV1bU7ktuNcdPGUo/xhpzL2O8FQJZxd+yEcc/ugOpi\nTatXIakqrt59z3vPefmVqJIUklImAOlsNsbNG7V65QDUSk1K5uwnXwIQ+96/Q2JTICQmQq1aCnv3\nauKbkqIGt0Vit2PYvRP3Be21ejuBQFAm1U58dW939Wojhw7JDBrk5rnn7Nx/v4OXXnIgSdChg8K0\naXb+/W8HPXpoHkJFd8qTcnNIGjca6/TPQ3dRVdVCzhYLzssuD+pS5U44UhQsi35CqZXqPbbM6xVk\noRrOaYTvC6Y//wDA1aff+WbUqauFntNWhST0bPprNZKi4OxzvtD7iueCC3EOGoLpr9XephSRoEUL\nhcOHJdLTpeBDzju3I7ndUZXpLBBEM9VQfItnS/Xr5+Hee10884wzqh7YLfO/x/zrMhIeug/rjC9D\nck3Dtq0Yd+7AOWQ4amJSUNcqb8KRce3fyBnpmtfrw5OLp60mvsYAZgWb/vyjzDCwc+QoLfS8NPgu\nWqaVBULf+3yh9wfbbXcCYP14atA2BUrz5lp+Q15eCMRX3+8Vg9wFAp+oFuLr8cCLL5qZOtXEvn0y\n/fu7vb2Z+/WLfNlHSVhmzwJASU4m/uH7Q+IhWefOBsB+9digr6U0boJSK7VUz9fyw3wAnD720dWH\nnxvOGX5eHtLZbG2/t0u3UsPAjqFaYpR5ySK/rl0SpjV/ocoyriB7FzuHDMfTuCnWed+VuW8eTvR9\nX4Dk5CDFV28rKTxfgcAnqoX4Llli4L33LDz7rDardsQINw884OSyy1y0bRt97R/lY0cxrVyBq2dv\nzk6dhuTxEP/Q/cG1SlRVLHNno8Qn4Bzqe5ZuqUgSrq7dMBw9gnz0yPmf9cM87bPKGVOoo4+fM273\nz/M1/Z2GpCi4epc+O1Zp3gJ38xaYfv8VHA6/rl/8QgqGrVvwtGwV2KDbosgyjlFXIuXnY/791+Cu\nFSBFxTcUnq9qMOBuK5pYCAS+UGXFt2izpGnTtKYZSUnaF8yAAR4eeMDJZ5/Zo7MO97vZSKqK/Zpr\ncQ0agn3seEwb12P96ouAr2n8+y8Mhw9pe73BprUW4Bw8DADrrBnFP2vjeu2zLhlRcvenElATEvE0\nauy356tPV3KXk3nsHHYJcl6ud9B7IMiHDiLn5oTMu3MU7Lvr03aCxbB3N4m3TKDmBS2Ie+G5crtp\nhUx89YeSVq1Ddm8JBFWdKim+335rpEWLeDZulNm/X2L5ciM9erhZsiSPadNstG4dfd5uUaxzvkE1\nmXBcofWhzXvuBVSDAevXge/9Wud+CxBwY42ScFwzDiUuHusX04p55Xpmsb99dN1t22E4dRLp9Gmf\nzzEWjLBzdS47qcs5ZDgQXOjZuHULAO4LQiO+7q7d8dSug2XRwuCbbqgqCffeieXH+Uhns4l9fwox\n/32vzFOaNSsadg78ow0H9iHn5eK+UIScBQJfKVd8bTYbkyZNYsKECYwdO5bly5dz/PhxbrzxRq6/\n/nomTZqE0xldJThTp5pRVYm1aw3Mnq31Y5440UXTpiojR4Zvyk0oMGzfhnHrZpxDhqGmaFPQlTp1\ncV08CNO6tRj27Pb/om43lnlzUWrWxNV/YMhsVRMScYy9FsOxo96aVelsNtaZ07WQ86Ahfl3Pm3S1\n08fQs6piWr8WT+MmqKmpZR7q6t0X1WrF/MfvftlUFG8Hp1Dta8oyzktHIp8+HZRHDmD6bTmmtWtw\njLiczLQNeOrWI+6FZzGUkcAWGwv162sCXKNGEMMUCpqjuDtdFPA1BILqRrniu3z5ctq3b8/06dOZ\nMmUKr732Gu+++y7XX389M2bMoEmTJsyePbsibPWJzZtlNm3S0pYPH5bZuVP7Efv2jc7EqnOxzvkG\n4Lyewfq/9UQsfzCt+E3LPL7iKjCZgjeyCLZbbgcg/omHMezbQ+w7byNnZGC7/0G/Q5DepKvtvoWe\n5UMHkU+fxlVkRnCpWCy4uvXAsG0LUlamX3bpeD3fEHp4enTDMufboK4T+/ZkAPIffgylQUNy35iC\npKrEvv16mefpbSaDSbjyiq9o4SgQ+Ey54nvZZZdx++3aF+zx48epU6cOaWlpDBmieTWDBg1i1arg\nntpDhc0GH39cOBTh8GGJAwdkrFY1akf7FUNRsHz3reY1Dru02FuOEZejxsZh/c7/L2lvlvNVwWc5\nn4un3QXkvvQahpMnSBnQi5j/vIOnQUPy77zH72vpyTpGH8XXVBBydvsivmh1wJKqYlr1p9+2ARi3\nbkZJrY1ap05A55doU9/+eOrWwzLvO7DbA7qGYcd2zKv/xDloCO6OmvfpHH4pro4XYZk3t8zaaT30\nHJTnu3G9lgEu2koKBD7j857v+PHjeeSRR3jqqaew2WyYzZrI1axZk/T09LAZ6CtbtshccEE8X39t\nom5dBYtF5dAhbf5ukyZKhTfJCART2ioMRw7jGHXl+V5jXBzOQUMwHNiPfPCA7xe12zH/uABPg4a4\newRXHlMatjvuJmfyv3G3uxBPm7bkTn47oMQbT6vWqLKM0cekK2+yVRcfxbdvfwBMq/7w2zYp+4w2\nLu/C9n6fWyYGA45rrkU+m425YAKUv1j0rmXXXl/4oiSR/9BjSKpKzLSPSj33H/9wMXGik27dAowM\neTyYNm7A07qNNklEIBD4huoH27ZtUy+//HK1Z8+e3tcOHDigXnvttWWe53K5/fmYgHj/fVUFVR03\nTlV37lTV1q1V1WrVXhs1KuwfHxruuEMzeOnSkt9/7z3t/Y8/9v2ac+Zo5zz2WGhsDDdt2qhqUpKq\nKkr5x/btq6qyrKq5ub5d22bTboqLLvLfrl9/Dd86bt6sXfvSS/0/V1G0mz0mRlVzcoq/53KpakqK\nqjZooKoeT2hsPZetWzXbb745PNcXCKoo5Q7H27JlCzVr1qRevXq0a9cOj8dDXFwcdrsdq9XKyZMn\nqV1Og/6srPyQPSzopKYmkJ5e2Ad4xw4zYOHGG/NJSfFQv34Mu3ZpP179+k7S04Oo76wIHA5qzvoG\ntW49Mi/sCunn9zg2dOpBDcD+02Jyrii7N7O+PomffYkFyLzkCjwlXDPaSGzVFsvOnZzetBOlfoPS\nD3S5qLV2LZ52F5KVr0C+bz9bUrcemFau4PSug9Rq3aTYPVQWMSvTiAfONmuNI9TrWKcJSb37Yv75\nZ87M/7nEHtWlYdy8kZRdu7BfOYYcmwq24rYlDLsU6zdfk7X0d5/D8zrn/o2VhGXZChKBnLbtsVeC\n+yvU+LJG1Znqvj6pqQmlvlduMHbNmjV8+umnAGRkZJCfn0+fPn1YtEgr2Vi8eDH9+/cPkan+cfSo\nRKtW8cyZY+ToUe1HadhQ28Nq3LiwjKJp0+guLQIw/7IEOfsMjquuKbUxvadNW62r1MoV5dZwAtpI\nv18W427REk8l6Tzka6cr4/atSHa71tnKD1y9+2r7vqv9y1MwhHlcXt5zLwAQ98+nQfH9frXMmwuA\n48oxJb7vGKHXEv8YpIUlo0+icnfqHJbrCwRVlXLFd/z48WRmZnL99ddzxx138Nxzz3Hffffx/fff\nc/3113PmzBlGj/avnjNUrF9vIDtbYtkyI4cPyxgMKnXraqLUuHGhOFUG8bV+pQ1QsI8dX/pBkoSz\nX38MJ45j2Lun3Guaf12GlJ+Pc+QVRGU3kRLQByyU1+nK3/1eHe++758r/DrPuGUzqsWCp0VLv87z\nFXfX7tivuhrT+nVYvvna5/PMCxegxsTgHDy0xPedAwejWq1YFi4IlanFMG1Yr3W2EjW+AoFflBt2\ntlqtvPXWW+e9Pm3atLAY5A8nTmiCsmePzIkTEvXrq16nsVGjyuP5yocPYV66GFfXbuV6qK6+A7B+\n/x2mP37X2hyWgeVHrb+yY+SokNkabjztLgQoN+nKVDDQwW/Pt0s3VIvFOyDBt5NcGHdux33BhWAs\n908mYPKeexHLop+J/+fTOIddilqzZpnHG3bvwrhnN47LRmlFuyURF4dzwEAsi39GPnIYpWGj0Bns\ndmPcskmrzxadrQQCv6gEOcClc+yYJr67dmniq4ecoVB8ZVmlUaPoLjOyfvkZkqpiu/nWco919R8A\noIWeyzzQhXnxz3jqN6hU9Zeeps1QLZYym0OANk1JiYvXsmz9wWrF1a0Hxq2bIdO3el/D7l1ITmfY\nvTulQUPyHn8aOTOTuFdeKPd4vS2lY0TZwytcFw/Sjv9tefBGFsGwY7sW+u9cee4vgSBaqOTiq5mf\nlyehqhINGhSKrC64DRqomM0lnh4d2GzETP8cJSm51H27oniatcBTrz7mlb+Xve/700/aHvLIUZUm\n5AyA0Yi7dVvN8y2lc5p0NhvD7l24O3cJaHC7Xu/LCt9Cz8at+n5v+EOrttvvwt2yFdavvyy3pMyy\ncAGqwYBz+KVlHuccoImv6ffQiq9J7PcKBAFTqcX3+PHiolLU861dW6V5c4U+faK7s5V1xpfIGenY\nb7rFt9CdJOHq2x85I6Ns7/B//wPAft2NIbK04nB37orkcJQaejauX4ekqrj9DDnr6Pu+LF3q0/H6\nrFpPRexrGo3kP/w4kttN7L/fKPUww57dmNatxdX/Ym8b0tLwtG6Dp249bXqSH8lc5Zrq7Ww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BBEJ5VXfAHHlVchuVxYfjyn1MnpxPr1dJSUFBwjr4iMcQKBQCAQlEKlFl/7mLGoRiMx774NLpf3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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": "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        },
        "outputId": "cbc79b35-b390-42f9-98b2-598216e8bf54"
      },
      "cell_type": "code",
      "source": [
        "signals"
      ],
      "execution_count": 35,
      "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>29.380297</td>\n",
              "      <td>29.380297</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.299324</td>\n",
              "      <td>29.299324</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.482854</td>\n",
              "      <td>29.482854</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.464357</td>\n",
              "      <td>29.464357</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>29.568903</td>\n",
              "      <td>29.568903</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>29.694071</td>\n",
              "      <td>29.641775</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>29.887255</td>\n",
              "      <td>29.719275</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>1.0</td>\n",
              "      <td>29.845150</td>\n",
              "      <td>29.709289</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>1.0</td>\n",
              "      <td>29.991275</td>\n",
              "      <td>29.757090</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.055098</td>\n",
              "      <td>29.812000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.101582</td>\n",
              "      <td>29.850778</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.164069</td>\n",
              "      <td>29.904606</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.298573</td>\n",
              "      <td>29.935937</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.386401</td>\n",
              "      <td>29.981844</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.417075</td>\n",
              "      <td>30.013692</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.476516</td>\n",
              "      <td>30.046322</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.505475</td>\n",
              "      <td>30.081332</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.545103</td>\n",
              "      <td>30.105150</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.532719</td>\n",
              "      <td>30.126811</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.561104</td>\n",
              "      <td>30.150545</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.562057</td>\n",
              "      <td>30.208557</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.572344</td>\n",
              "      <td>30.282286</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.646835</td>\n",
              "      <td>30.333915</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.738281</td>\n",
              "      <td>30.412169</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.783053</td>\n",
              "      <td>30.454083</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.860594</td>\n",
              "      <td>30.500187</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.922702</td>\n",
              "      <td>30.541148</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.943469</td>\n",
              "      <td>30.608495</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.914894</td>\n",
              "      <td>30.643074</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>30.895082</td>\n",
              "      <td>30.664079</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>36.630346</td>\n",
              "      <td>37.759674</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.481554</td>\n",
              "      <td>37.674419</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.297135</td>\n",
              "      <td>37.589020</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.101858</td>\n",
              "      <td>37.413937</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.867142</td>\n",
              "      <td>37.312677</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.528407</td>\n",
              "      <td>37.152596</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.264732</td>\n",
              "      <td>36.956747</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.287214</td>\n",
              "      <td>36.819434</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.367039</td>\n",
              "      <td>36.669021</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.475445</td>\n",
              "      <td>36.600865</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>35.874386</td>\n",
              "      <td>36.555713</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.372202</td>\n",
              "      <td>36.492033</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>36.693981</td>\n",
              "      <td>36.457598</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.032148</td>\n",
              "      <td>36.415781</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.163412</td>\n",
              "      <td>36.334145</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.215421</td>\n",
              "      <td>36.312140</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.290103</td>\n",
              "      <td>36.352148</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.267812</td>\n",
              "      <td>36.386536</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.227040</td>\n",
              "      <td>36.432021</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.057483</td>\n",
              "      <td>36.390871</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>36.834201</td>\n",
              "      <td>36.363103</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>36.594151</td>\n",
              "      <td>36.380297</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>36.410683</td>\n",
              "      <td>36.414923</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>36.546712</td>\n",
              "      <td>36.543235</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>36.856488</td>\n",
              "      <td>36.638207</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.230086</td>\n",
              "      <td>36.788523</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>37.655125</td>\n",
              "      <td>36.977895</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>38.118461</td>\n",
              "      <td>37.122734</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>38.245917</td>\n",
              "      <td>37.262954</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>38.411092</td>\n",
              "      <td>37.372119</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   29.380297  29.380297        NaN\n",
              "2010-11-22     0.0   29.299324  29.299324        0.0\n",
              "2010-11-23     0.0   29.482854  29.482854        0.0\n",
              "2010-11-24     0.0   29.464357  29.464357        0.0\n",
              "2010-11-26     0.0   29.568903  29.568903        0.0\n",
              "2010-11-29     1.0   29.694071  29.641775        1.0\n",
              "2010-11-30     1.0   29.887255  29.719275        0.0\n",
              "2010-12-01     1.0   29.845150  29.709289        0.0\n",
              "2010-12-02     1.0   29.991275  29.757090        0.0\n",
              "2010-12-03     1.0   30.055098  29.812000        0.0\n",
              "2010-12-06     1.0   30.101582  29.850778        0.0\n",
              "2010-12-07     1.0   30.164069  29.904606        0.0\n",
              "2010-12-08     1.0   30.298573  29.935937        0.0\n",
              "2010-12-09     1.0   30.386401  29.981844        0.0\n",
              "2010-12-10     1.0   30.417075  30.013692        0.0\n",
              "2010-12-13     1.0   30.476516  30.046322        0.0\n",
              "2010-12-14     1.0   30.505475  30.081332        0.0\n",
              "2010-12-15     1.0   30.545103  30.105150        0.0\n",
              "2010-12-16     1.0   30.532719  30.126811        0.0\n",
              "2010-12-17     1.0   30.561104  30.150545        0.0\n",
              "2010-12-20     1.0   30.562057  30.208557        0.0\n",
              "2010-12-21     1.0   30.572344  30.282286        0.0\n",
              "2010-12-22     1.0   30.646835  30.333915        0.0\n",
              "2010-12-23     1.0   30.738281  30.412169        0.0\n",
              "2010-12-27     1.0   30.783053  30.454083        0.0\n",
              "2010-12-28     1.0   30.860594  30.500187        0.0\n",
              "2010-12-29     1.0   30.922702  30.541148        0.0\n",
              "2010-12-30     1.0   30.943469  30.608495        0.0\n",
              "2010-12-31     1.0   30.914894  30.643074        0.0\n",
              "2011-01-03     1.0   30.895082  30.664079        0.0\n",
              "...            ...         ...        ...        ...\n",
              "2011-11-17     0.0   36.630346  37.759674        0.0\n",
              "2011-11-18     0.0   36.481554  37.674419        0.0\n",
              "2011-11-21     0.0   36.297135  37.589020        0.0\n",
              "2011-11-22     0.0   36.101858  37.413937        0.0\n",
              "2011-11-23     0.0   35.867142  37.312677        0.0\n",
              "2011-11-25     0.0   35.528407  37.152596        0.0\n",
              "2011-11-28     0.0   35.264732  36.956747        0.0\n",
              "2011-11-29     0.0   35.287214  36.819434        0.0\n",
              "2011-11-30     0.0   35.367039  36.669021        0.0\n",
              "2011-12-01     0.0   35.475445  36.600865        0.0\n",
              "2011-12-02     0.0   35.874386  36.555713        0.0\n",
              "2011-12-05     0.0   36.372202  36.492033        0.0\n",
              "2011-12-06     1.0   36.693981  36.457598        1.0\n",
              "2011-12-07     1.0   37.032148  36.415781        0.0\n",
              "2011-12-08     1.0   37.163412  36.334145        0.0\n",
              "2011-12-09     1.0   37.215421  36.312140        0.0\n",
              "2011-12-12     1.0   37.290103  36.352148        0.0\n",
              "2011-12-13     1.0   37.267812  36.386536        0.0\n",
              "2011-12-14     1.0   37.227040  36.432021        0.0\n",
              "2011-12-15     1.0   37.057483  36.390871        0.0\n",
              "2011-12-16     1.0   36.834201  36.363103        0.0\n",
              "2011-12-19     1.0   36.594151  36.380297        0.0\n",
              "2011-12-20     0.0   36.410683  36.414923       -1.0\n",
              "2011-12-21     1.0   36.546712  36.543235        1.0\n",
              "2011-12-22     1.0   36.856488  36.638207        0.0\n",
              "2011-12-23     1.0   37.230086  36.788523        0.0\n",
              "2011-12-27     1.0   37.655125  36.977895        0.0\n",
              "2011-12-28     1.0   38.118461  37.122734        0.0\n",
              "2011-12-29     1.0   38.245917  37.262954        0.0\n",
              "2011-12-30     1.0   38.411092  37.372119        0.0\n",
              "\n",
              "[281 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "5xs4GhGDmfrH",
        "colab_type": "code",
        "outputId": "e5e90011-7a2f-4ede-edce-edcd70444693",
        "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": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MmzdvZNy4CWzatAGPx80rr7zBd9+tYcWKf578ByQQCLqdUHxbMRpxR5pYPfRcAOZfNJyk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\nFK0Wa1Iva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W/ePGJiYlwd55K8KWsjyewdmcE7c0vmK8t84sQJevfuTW5uLoWFhQwfPpzZ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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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        },
        "outputId": "57612e3f-e51d-4223-babf-3a97bcfc53ef"
      },
      "cell_type": "code",
      "source": [
        "psignals"
      ],
      "execution_count": 41,
      "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>28.019156</td>\n",
              "      <td>28.019156</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.895597</td>\n",
              "      <td>27.895597</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.786540</td>\n",
              "      <td>27.786540</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.700166</td>\n",
              "      <td>27.700166</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.634627</td>\n",
              "      <td>27.634627</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.511057</td>\n",
              "      <td>27.595740</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.461965</td>\n",
              "      <td>27.585860</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.487972</td>\n",
              "      <td>27.599935</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>27.572589</td>\n",
              "      <td>27.629290</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>27.707165</td>\n",
              "      <td>27.670896</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>1.0</td>\n",
              "      <td>27.871759</td>\n",
              "      <td>27.721203</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.043831</td>\n",
              "      <td>27.776681</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.210299</td>\n",
              "      <td>27.834690</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.368588</td>\n",
              "      <td>27.893325</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-10</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.509181</td>\n",
              "      <td>27.950324</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.628318</td>\n",
              "      <td>28.004676</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.723764</td>\n",
              "      <td>28.055235</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.797026</td>\n",
              "      <td>28.102006</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.847389</td>\n",
              "      <td>28.144395</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.876943</td>\n",
              "      <td>28.181979</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.889440</td>\n",
              "      <td>28.225143</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.888756</td>\n",
              "      <td>28.279579</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.876636</td>\n",
              "      <td>28.342983</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.862051</td>\n",
              "      <td>28.412654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.854603</td>\n",
              "      <td>28.486973</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.857663</td>\n",
              "      <td>28.561795</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.873190</td>\n",
              "      <td>28.632385</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.905686</td>\n",
              "      <td>28.697412</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>1.0</td>\n",
              "      <td>28.952447</td>\n",
              "      <td>28.757619</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-03</th>\n",
              "      <td>1.0</td>\n",
              "      <td>29.006049</td>\n",
              "      <td>28.811694</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>33.492917</td>\n",
              "      <td>34.047114</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>33.254845</td>\n",
              "      <td>33.934031</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.991153</td>\n",
              "      <td>33.800945</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.732759</td>\n",
              "      <td>33.661390</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.486651</td>\n",
              "      <td>33.518461</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.264723</td>\n",
              "      <td>33.378219</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.058374</td>\n",
              "      <td>33.243921</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.871172</td>\n",
              "      <td>33.114348</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-11-30</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.713826</td>\n",
              "      <td>32.990797</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-01</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.606490</td>\n",
              "      <td>32.873114</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-02</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.560267</td>\n",
              "      <td>32.767008</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-05</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.591333</td>\n",
              "      <td>32.674809</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-06</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.716945</td>\n",
              "      <td>32.599102</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>0.0</td>\n",
              "      <td>31.931053</td>\n",
              "      <td>32.540345</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>0.0</td>\n",
              "      <td>32.219952</td>\n",
              "      <td>32.497956</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.548837</td>\n",
              "      <td>32.466686</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>32.885783</td>\n",
              "      <td>32.447584</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.197526</td>\n",
              "      <td>32.444199</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.457670</td>\n",
              "      <td>32.458827</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.651411</td>\n",
              "      <td>32.491126</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.763559</td>\n",
              "      <td>32.534347</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.787326</td>\n",
              "      <td>32.580704</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.720760</td>\n",
              "      <td>32.626600</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.578146</td>\n",
              "      <td>32.670174</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-22</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.394818</td>\n",
              "      <td>32.718168</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.223374</td>\n",
              "      <td>32.774009</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.106545</td>\n",
              "      <td>32.842747</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.078368</td>\n",
              "      <td>32.928399</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-29</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.157258</td>\n",
              "      <td>33.031032</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-30</th>\n",
              "      <td>1.0</td>\n",
              "      <td>33.330183</td>\n",
              "      <td>33.149091</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   28.019156  28.019156        NaN\n",
              "2010-11-22     0.0   27.895597  27.895597        0.0\n",
              "2010-11-23     0.0   27.786540  27.786540        0.0\n",
              "2010-11-24     0.0   27.700166  27.700166        0.0\n",
              "2010-11-26     0.0   27.634627  27.634627        0.0\n",
              "2010-11-29     0.0   27.511057  27.595740        0.0\n",
              "2010-11-30     0.0   27.461965  27.585860        0.0\n",
              "2010-12-01     0.0   27.487972  27.599935        0.0\n",
              "2010-12-02     0.0   27.572589  27.629290        0.0\n",
              "2010-12-03     1.0   27.707165  27.670896        1.0\n",
              "2010-12-06     1.0   27.871759  27.721203        0.0\n",
              "2010-12-07     1.0   28.043831  27.776681        0.0\n",
              "2010-12-08     1.0   28.210299  27.834690        0.0\n",
              "2010-12-09     1.0   28.368588  27.893325        0.0\n",
              "2010-12-10     1.0   28.509181  27.950324        0.0\n",
              "2010-12-13     1.0   28.628318  28.004676        0.0\n",
              "2010-12-14     1.0   28.723764  28.055235        0.0\n",
              "2010-12-15     1.0   28.797026  28.102006        0.0\n",
              "2010-12-16     1.0   28.847389  28.144395        0.0\n",
              "2010-12-17     1.0   28.876943  28.181979        0.0\n",
              "2010-12-20     1.0   28.889440  28.225143        0.0\n",
              "2010-12-21     1.0   28.888756  28.279579        0.0\n",
              "2010-12-22     1.0   28.876636  28.342983        0.0\n",
              "2010-12-23     1.0   28.862051  28.412654        0.0\n",
              "2010-12-27     1.0   28.854603  28.486973        0.0\n",
              "2010-12-28     1.0   28.857663  28.561795        0.0\n",
              "2010-12-29     1.0   28.873190  28.632385        0.0\n",
              "2010-12-30     1.0   28.905686  28.697412        0.0\n",
              "2010-12-31     1.0   28.952447  28.757619        0.0\n",
              "2011-01-03     1.0   29.006049  28.811694        0.0\n",
              "...            ...         ...        ...        ...\n",
              "2011-11-17     0.0   33.492917  34.047114        0.0\n",
              "2011-11-18     0.0   33.254845  33.934031        0.0\n",
              "2011-11-21     0.0   32.991153  33.800945        0.0\n",
              "2011-11-22     0.0   32.732759  33.661390        0.0\n",
              "2011-11-23     0.0   32.486651  33.518461        0.0\n",
              "2011-11-25     0.0   32.264723  33.378219        0.0\n",
              "2011-11-28     0.0   32.058374  33.243921        0.0\n",
              "2011-11-29     0.0   31.871172  33.114348        0.0\n",
              "2011-11-30     0.0   31.713826  32.990797        0.0\n",
              "2011-12-01     0.0   31.606490  32.873114        0.0\n",
              "2011-12-02     0.0   31.560267  32.767008        0.0\n",
              "2011-12-05     0.0   31.591333  32.674809        0.0\n",
              "2011-12-06     0.0   31.716945  32.599102        0.0\n",
              "2011-12-07     0.0   31.931053  32.540345        0.0\n",
              "2011-12-08     0.0   32.219952  32.497956        0.0\n",
              "2011-12-09     1.0   32.548837  32.466686        1.0\n",
              "2011-12-12     1.0   32.885783  32.447584        0.0\n",
              "2011-12-13     1.0   33.197526  32.444199        0.0\n",
              "2011-12-14     1.0   33.457670  32.458827        0.0\n",
              "2011-12-15     1.0   33.651411  32.491126        0.0\n",
              "2011-12-16     1.0   33.763559  32.534347        0.0\n",
              "2011-12-19     1.0   33.787326  32.580704        0.0\n",
              "2011-12-20     1.0   33.720760  32.626600        0.0\n",
              "2011-12-21     1.0   33.578146  32.670174        0.0\n",
              "2011-12-22     1.0   33.394818  32.718168        0.0\n",
              "2011-12-23     1.0   33.223374  32.774009        0.0\n",
              "2011-12-27     1.0   33.106545  32.842747        0.0\n",
              "2011-12-28     1.0   33.078368  32.928399        0.0\n",
              "2011-12-29     1.0   33.157258  33.031032        0.0\n",
              "2011-12-30     1.0   33.330183  33.149091        0.0\n",
              "\n",
              "[281 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "WJNyLPAdipED",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "eaafcd66-6a88-4d80-96f4-afd14e542e0b"
      },
      "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": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1rSg4br4VgKhFvyDyV3fyaAivwP33itofvuwcph7YAMD2glo8Xl/wmpyYbAAO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\neMZPAED//RYATs1N5Y4rTsK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cZs6cxYwZp3V47YwZpzFzZvsv8PjwOKamTkZDY23Zui7n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\n0Qlh2LgB6Jiq1dTchmS1ogYFg1kYa0/YPHkQh82dY8Z1/H9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IqqqsmTGM8F+e1eW5pUsf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\nUryFEEIIJyPFWwghhHAy/w8zSC/jNGlHeAAAAABJRU5ErkJggg==\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": "69d97397-f8b9-4898-ba97-80e495d4c5df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        }
      },
      "cell_type": "code",
      "source": [
        "portfolio"
      ],
      "execution_count": 48,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "bddd13b5-5379-470b-d828-ffcde4b3a10a"
      },
      "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": 53,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2000
        },
        "outputId": "3f0a030e-3576-42e5-abbe-1033ad2a3f33"
      },
      "cell_type": "code",
      "source": [
        "pportfolio"
      ],
      "execution_count": 52,
      "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",
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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",
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              "    <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",
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              "    <tr>\n",
              "      <th>2010-11-29</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>2010-11-30</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>2010-12-01</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>2010-12-02</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>2010-12-03</th>\n",
              "      <td>3023.855782</td>\n",
              "      <td>3023.855782</td>\n",
              "      <td>96976.144218</td>\n",
              "      <td>100000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-06</th>\n",
              "      <td>3049.670792</td>\n",
              "      <td>3049.670792</td>\n",
              "      <td>96976.144218</td>\n",
              "      <td>100025.815010</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>96976.144218</td>\n",
              "      <td>100007.334900</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>96976.144218</td>\n",
              "      <td>100034.007454</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>96976.144218</td>\n",
              "      <td>100022.101212</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>96976.144218</td>\n",
              "      <td>100029.720688</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>96976.144218</td>\n",
              "      <td>100040.294456</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>96976.144218</td>\n",
              "      <td>100027.149010</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>96976.144218</td>\n",
              "      <td>100027.815247</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>96976.144218</td>\n",
              "      <td>100036.293793</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>96976.144218</td>\n",
              "      <td>100030.196953</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>96976.144218</td>\n",
              "      <td>100045.438194</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>96976.144218</td>\n",
              "      <td>100064.394188</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>96976.144218</td>\n",
              "      <td>100073.538399</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>96976.144218</td>\n",
              "      <td>100058.679962</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>96976.144218</td>\n",
              "      <td>100068.967247</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>96976.144218</td>\n",
              "      <td>100076.492310</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>96976.144218</td>\n",
              "      <td>100074.777603</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>96976.144218</td>\n",
              "      <td>100059.251213</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>96976.144218</td>\n",
              "      <td>100048.773575</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>96976.144218</td>\n",
              "      <td>100115.548325</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</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>99957.891273</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2011-12-07</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-08</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-09</th>\n",
              "      <td>3749.529266</td>\n",
              "      <td>3749.529266</td>\n",
              "      <td>96208.362007</td>\n",
              "      <td>99957.891273</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-12</th>\n",
              "      <td>3732.572556</td>\n",
              "      <td>3732.572556</td>\n",
              "      <td>96208.362007</td>\n",
              "      <td>99940.934563</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>96208.362007</td>\n",
              "      <td>99912.071419</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>96208.362007</td>\n",
              "      <td>99829.960442</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>96208.362007</td>\n",
              "      <td>99818.052864</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>96208.362007</td>\n",
              "      <td>99837.866020</td>\n",
              "      <td>0.000198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-19</th>\n",
              "      <td>3640.839005</td>\n",
              "      <td>3640.839005</td>\n",
              "      <td>96208.362007</td>\n",
              "      <td>99849.201012</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>96208.362007</td>\n",
              "      <td>99980.085564</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>96208.362007</td>\n",
              "      <td>99984.848595</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>96208.362007</td>\n",
              "      <td>100004.851723</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>96208.362007</td>\n",
              "      <td>100050.385475</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>96208.362007</td>\n",
              "      <td>100080.869102</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>96208.362007</td>\n",
              "      <td>100043.813515</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>96208.362007</td>\n",
              "      <td>100067.436409</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>96208.362007</td>\n",
              "      <td>100066.293526</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     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-11-30     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-12-01     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-12-02     0.000000     0.000000  100000.000000  100000.000000  0.000000\n",
              "2010-12-03  3023.855782  3023.855782   96976.144218  100000.000000  0.000000\n",
              "2010-12-06  3049.670792  3049.670792   96976.144218  100025.815010  0.000258\n",
              "2010-12-07  3031.190681  3031.190681   96976.144218  100007.334900 -0.000185\n",
              "2010-12-08  3057.863235  3057.863235   96976.144218  100034.007454  0.000267\n",
              "2010-12-09  3045.956993  3045.956993   96976.144218  100022.101212 -0.000119\n",
              "2010-12-10  3053.576469  3053.576469   96976.144218  100029.720688  0.000076\n",
              "2010-12-13  3064.150238  3064.150238   96976.144218  100040.294456  0.000106\n",
              "2010-12-14  3051.004791  3051.004791   96976.144218  100027.149010 -0.000131\n",
              "2010-12-15  3051.671028  3051.671028   96976.144218  100027.815247  0.000007\n",
              "2010-12-16  3060.149574  3060.149574   96976.144218  100036.293793  0.000085\n",
              "2010-12-17  3054.052734  3054.052734   96976.144218  100030.196953 -0.000061\n",
              "2010-12-20  3069.293976  3069.293976   96976.144218  100045.438194  0.000152\n",
              "2010-12-21  3088.249969  3088.249969   96976.144218  100064.394188  0.000189\n",
              "2010-12-22  3097.394180  3097.394180   96976.144218  100073.538399  0.000091\n",
              "2010-12-23  3082.535744  3082.535744   96976.144218  100058.679962 -0.000148\n",
              "2010-12-27  3092.823029  3092.823029   96976.144218  100068.967247  0.000103\n",
              "2010-12-28  3100.348091  3100.348091   96976.144218  100076.492310  0.000075\n",
              "2010-12-29  3098.633385  3098.633385   96976.144218  100074.777603 -0.000017\n",
              "2010-12-30  3083.106995  3083.106995   96976.144218  100059.251213 -0.000155\n",
              "2010-12-31  3072.629356  3072.629356   96976.144218  100048.773575 -0.000105\n",
              "2011-01-03  3139.404106  3139.404106   96976.144218  100115.548325  0.000667\n",
              "...                 ...          ...            ...            ...       ...\n",
              "2011-11-17     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-18     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-21     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-22     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-23     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-25     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-28     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-29     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-11-30     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-01     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-02     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-05     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-06     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-07     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-08     0.000000     0.000000   99957.891273   99957.891273  0.000000\n",
              "2011-12-09  3749.529266  3749.529266   96208.362007   99957.891273  0.000000\n",
              "2011-12-12  3732.572556  3732.572556   96208.362007   99940.934563 -0.000170\n",
              "2011-12-13  3703.709412  3703.709412   96208.362007   99912.071419 -0.000289\n",
              "2011-12-14  3621.598434  3621.598434   96208.362007   99829.960442 -0.000822\n",
              "2011-12-15  3609.690857  3609.690857   96208.362007   99818.052864 -0.000119\n",
              "2011-12-16  3629.504013  3629.504013   96208.362007   99837.866020  0.000198\n",
              "2011-12-19  3640.839005  3640.839005   96208.362007   99849.201012  0.000114\n",
              "2011-12-20  3771.723557  3771.723557   96208.362007   99980.085564  0.001311\n",
              "2011-12-21  3776.486588  3776.486588   96208.362007   99984.848595  0.000048\n",
              "2011-12-22  3796.489716  3796.489716   96208.362007  100004.851723  0.000200\n",
              "2011-12-23  3842.023468  3842.023468   96208.362007  100050.385475  0.000455\n",
              "2011-12-27  3872.507095  3872.507095   96208.362007  100080.869102  0.000305\n",
              "2011-12-28  3835.451508  3835.451508   96208.362007  100043.813515 -0.000370\n",
              "2011-12-29  3859.074402  3859.074402   96208.362007  100067.436409  0.000236\n",
              "2011-12-30  3857.931519  3857.931519   96208.362007  100066.293526 -0.000011\n",
              "\n",
              "[281 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "I4N6XBBlm5a7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "7249a8b7-6b72-41b7-9891-90b536d9bc60"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PT2KgUGHT+aThcKmJkJUVMoOhze6G1e5K8ypjxxRnaSHgiwy4ckT4ELER8eo9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\nEEIIoXNSDAghhBA69/8BZjK9475y1YwAAAAASUVORK5CYII=\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cb7030e5-904d-4235-ff32-2198b42f8a48"
      },
      "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": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.03901387936157988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Boy0SlGMBNqI",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "metadata": {
        "id": "MN7SOE0oBeKb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}