Stock_market_using_ARIMA_and_LSTM_.ipynb

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      "name": "Stock market using ARIMA and LSTM .ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mishabz4321/Time-Series-Forecasting-and-Analytics/blob/main/Stock_market_using_ARIMA_and_LSTM_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDEnbzHKLT3I"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import statsmodels as sm\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "import statsmodels.graphics.tsaplots as sgt\n",
        "from scipy.stats.distributions import chi2\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 205
        },
        "id": "rdG7EaKQM2A2",
        "outputId": "00f3132c-c7f6-4ed1-b9ed-9af143d844c4"
      },
      "source": [
        "df= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/013 IndexE8.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07/01/1994</td>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10/01/1994</td>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11/01/1994</td>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12/01/1994</td>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13/01/1994</td>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "         date     spx      dax     ftse    nikkei\n",
              "0  07/01/1994  469.90  2224.95  3445.98  18124.01\n",
              "1  10/01/1994  475.27  2225.00  3440.58  18443.44\n",
              "2  11/01/1994  474.13  2228.10  3413.77  18485.25\n",
              "3  12/01/1994  474.17  2182.06  3372.02  18793.88\n",
              "4  13/01/1994  472.47  2142.37  3360.01  18577.26"
            ]
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      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6269, 5)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
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        "outputId": "d86df9a0-4d42-4681-d0a1-9b9babf04a48"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6269 entries, 0 to 6268\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   date    6269 non-null   object \n",
            " 1   spx     6269 non-null   float64\n",
            " 2   dax     6269 non-null   float64\n",
            " 3   ftse    6269 non-null   float64\n",
            " 4   nikkei  6269 non-null   float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 245.0+ KB\n"
          ],
          "name": "stdout"
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Bpa8wBm5Nq0S"
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      "source": [
        "df['date']= pd.to_datetime(df['date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "height": 299
        },
        "id": "mUMJpDpPOC8x",
        "outputId": "d90231f5-4315-4d24-9da3-9ee667197906"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1288.127542</td>\n",
              "      <td>6080.063363</td>\n",
              "      <td>5422.713545</td>\n",
              "      <td>14597.055700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>487.586473</td>\n",
              "      <td>2754.361032</td>\n",
              "      <td>1145.572428</td>\n",
              "      <td>4043.122953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>438.920000</td>\n",
              "      <td>1911.700000</td>\n",
              "      <td>2876.600000</td>\n",
              "      <td>7054.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>990.671905</td>\n",
              "      <td>4069.350000</td>\n",
              "      <td>4486.100000</td>\n",
              "      <td>10709.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1233.420000</td>\n",
              "      <td>5773.340000</td>\n",
              "      <td>5662.430000</td>\n",
              "      <td>15028.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1459.987747</td>\n",
              "      <td>7443.070000</td>\n",
              "      <td>6304.250000</td>\n",
              "      <td>17860.470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.867839</td>\n",
              "      <td>13559.600000</td>\n",
              "      <td>7778.637689</td>\n",
              "      <td>24124.150000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx           dax         ftse        nikkei\n",
              "count  6269.000000   6269.000000  6269.000000   6269.000000\n",
              "mean   1288.127542   6080.063363  5422.713545  14597.055700\n",
              "std     487.586473   2754.361032  1145.572428   4043.122953\n",
              "min     438.920000   1911.700000  2876.600000   7054.980000\n",
              "25%     990.671905   4069.350000  4486.100000  10709.290000\n",
              "50%    1233.420000   5773.340000  5662.430000  15028.170000\n",
              "75%    1459.987747   7443.070000  6304.250000  17860.470000\n",
              "max    2872.867839  13559.600000  7778.637689  24124.150000"
            ]
          },
          "metadata": {
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yx5MyE0NOEg2"
      },
      "source": [
        "df_copy= df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "m0A5kLZQRmbO",
        "outputId": "9d70c378-1ced-4281-c71f-60cb1f6ac59b"
      },
      "source": [
        "df_copy= df_copy.set_index('date')\n",
        "df_copy.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
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              "    <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>1994-07-01</th>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-10-01</th>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-11-01</th>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-12-01</th>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-13</th>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx      dax     ftse    nikkei\n",
              "date                                          \n",
              "1994-07-01  469.90  2224.95  3445.98  18124.01\n",
              "1994-10-01  475.27  2225.00  3440.58  18443.44\n",
              "1994-11-01  474.13  2228.10  3413.77  18485.25\n",
              "1994-12-01  474.17  2182.06  3372.02  18793.88\n",
              "1994-01-13  472.47  2142.37  3360.01  18577.26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "NrNZESrgRtey",
        "outputId": "5f951020-4469-45ee-8893-cfa460cd37ce"
      },
      "source": [
        "df_copy.plot(subplots=True, title='Change of each Market Index with Time', figsize=(16,8));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SL-XoEWtSlKp"
      },
      "source": [
        "### Here we are going to use spx500"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "HN5-SuVkSe_8",
        "outputId": "a62daa02-26b3-4967-da47-575513a58dc0"
      },
      "source": [
        "df_copy['spx'].plot(figsize=(12,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa88f7048d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQr2EvgHUldC"
      },
      "source": [
        "# setting 'day' frequency and\n",
        "df_copy= df_copy.asfreq('B')\n",
        "df_copy= df_copy.fillna(method='ffill')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHzxnFD7Wkij",
        "outputId": "39c07366-1162-43d3-d646-c15fd438ec3c"
      },
      "source": [
        "df_copy.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 6152 entries, 1994-07-01 to 2018-01-29\n",
            "Freq: B\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   spx     6152 non-null   float64\n",
            " 1   dax     6152 non-null   float64\n",
            " 2   ftse    6152 non-null   float64\n",
            " 3   nikkei  6152 non-null   float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 240.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "tYLx3CE5Wmzt",
        "outputId": "057f0406-7263-41c7-bbd1-f647d509da5f"
      },
      "source": [
        "# computing addictive decomposition\n",
        "s_add= seasonal_decompose(df_copy['spx'])\n",
        "s_add.plot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iG-jdr_oX7vm",
        "outputId": "d158cb3f-becc-40a3-9071-60a45c5605e2"
      },
      "source": [
        "# computing multiplicative decomposition\n",
        "s_add= seasonal_decompose(df_copy['spx'], model='multiplicative')\n",
        "s_add.plot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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57kCxvmw080bKqmqIi4pwe2uBV/WIROJ7rqa9gp2cgPpDTT2TYvjwp9M59+HP2Ooe3hqUkcC0YZktnrPqqDveWNWh+s8YncXmfaVsyG/bxZj3XDCGb08ZUP+4pLKG8qpaMpNi2t2WYPjrpeN5uwPnK+t6uS3YfIAz/jKPtIToTj/8BtA7NY6hWUm88N0pLN1RyOjeKWz+7ez6C129XmV/SSUzhvdkb3EFM4ZnEhMZ/M+DaV2ou5l36mjmwLUA/ft37nhR7VFSWcNvXl/J/1buocydlbJvjzjyDpa3smXXFRsVwX0XjuWkoRkkxUYx95ZTKKuqISrCQ4QIHo9wx3mj2VFQxs9eXs6CzQf49TmjOPfYbHomxXa4c0dHktPqu84gPvrIj8vBUmfitbjoiPru+ClxUURGePB6lV1F5fXn4+okxkQ26NbfXbQlOV0xNYefnzmC2CiP07U7v4RnF2wl0uPhiy0H+NGpQzlYVsWOg+U8/K3j6rfzHbLnhMHpnDA4HaDBKAwej3DjacMCE5TpVGHzKVFVFZGAjmYOPAHOIb5A1Rso//5iO68u2dmgrD3J6d/XTiE2KoL1ew5R41X2Hapkf0kln23cz/E5PXgxN48rT8xh2rBMkmMjuejRBXz6s1MCFUabfXNSwx8LTX3p90uL56FvHcfLi/O4YmpOu07yB1pT7QTqJ5ID6NVo1GePR+jbI75T2xVM//ruZC55clFQnuuO80bX3++XFk+/tHhOGdGz1e3sIvCjS6gTVLcdzbyj1xot+MWpZKcc/mU+rl9qk+vddf4YYiI99R/cUHT9bY/MpBh+MKP5UQzeveHkBr3jvjd9EI9/srnDz9tUr7ru7Ni+KSzLKyIuKoLR2SlcfsIAIiM8zBzRk6lDMli5s4gt+0vZsr+UB+asD3VzzVEm1AnqDeBy4D737+s+5deLyL9xOkkUuUnsPeC3IlI3FMEs4BdBbnO9mlova/cc4oK/fk5GYgzH9kthVHYK1586hLveXEVORgITB6SRlhjNNc/mctf5o3nq0828t2pv65W3YOrgdLKSYltfkdCca+osy34zC0VJjXf2Wkb0SmJiTg+uPHEgESL1Caqu6zvApz87hX5pzl7Mlv2lnPLHuYBzMeVZY7LJTIrho7X5pCdEc9643sRHR7KrsJxbX13B6l3FJMZEMGN467/cj1b/+d4JlFTWkOGOEXfn+Q3nDhrTJ6W+a/VlJwxg3F1zAPjklhlM/8PcZuvdet/ZvLI4j5+6o39PGpjGF1tanJTAdEPB7MX3As7eTwawF7gdeI0uOpq5qnL6n+exsY0nzBvrnxbPJZP789byXazcWcy9XxvD6SOz+MN76+iREE2f1Diqa70kxUZSUe2lqsbLd6cNClAUR5eK6lpioyKoqK6lsKz6iENupvN9umEfER5h6uAMjrvrfQ6WHb604fcXjeXnrzjTTNTtydedV6wbgeK0kVl8a1I/Zo60nnTdSci7mYdSSwnqV/9dgVeVyQPTqayp5aEPN7KzsOG5oGFZiQzNSuL9VXvoER+NV50LGTuqqxxuM6Y9Csuq2FtcSVlVDcOykkiIiWT93kNs3V/KrNG9APhg9V68qvWPTffUrRPUhAkTdfHiphNUR3uGpSdE84MZg0mJi6LGq0RFeBjXL4XMpFi27i/lsU828cvZIymvruXRuZuIifTw3WmD6JkUYxf9GWMM3TxBxWQP1ezL/xLwek8flcWTlx3xPzXGGNMGIb9QN5xER3qoqvHWP85IjCElLpJj+6Xyq9kjeXTuJkQgKzmWCQN6kH+oknnr9zG6tzOY5ptLdxET5eHWs0aGMApjjDm6dYs9qKNtqCNjjDmadOtDfCKyD6eXYGfIANo3I1zXZTF3D90xZuiecYc65gGqmtm4sFskqM4kIrlNZf6jmcXcPXTHmKF7xh2uMdvUpcYYY8KSJShjjDFhyRJUxz0R6gaEgMXcPXTHmKF7xh2WMds5KGOMMWHJ9qCMMcaEJUtQxhhjwpIlqEZE5GkRyReRlT5lx4rIAhFZISJvikiyWx4tIs+45ctEZIbPNtEi8oSIrBeRtSJyUQjC8YuI9BORj0VktYisEpEb3PI0EZkjIhvcvz3cchGRh0Rko4gsF5HxPnVd7q6/QUQuD1VMrQlkzO7yZBHJE5FHQhGPvwL8Wt/v1rHGXScsZwtsR8wj3M97pYjc3Fo94ShQMbvLUkXkZfd7bI2InBC0QFTVbj43YBowHljpU/YlMN29fxVwt3v/h8Az7v2ewGLA4z6+E7jHve8BMkIdWwsxZwPj3ftJwHpgFHA/cKtbfivwe/f+bOBdQIApwCK3PA3Y7P7t4d7vEer4OjNmn/oeBP4FPBLq2IL0Wk8FPgci3NsCYEao4wtQzD2B44F7gZtbqyfU8XVmzO6yZ4Fr3PvRQGqw4rA9qEZUdR7QeOa0YcA89/4coG5vaBTwkbtdPlAI1F3sdhXwO3eZV1XD9sp0Vd2tqkvc+4eANUAf4HycNyfu3wvc++cDz6ljIZAqzozIZwBzVLVAVQ/i/K/ODGIofgtgzIjIBCALeD+IIbRLAONWIBbnCysGiMKZ5y3stDVmVc1X1S+Baj/rCTuBillEUnB+tP/NXa9KVQuDEgR2iM9fq3BeWICvc3g6+mXAeSISKSIDgQlAPxGpm3/9bhFZIiIviUiXmIFNRHKA44BFQJaq7nYX7cH5Egbnjb7DZ7M8t6y58rDWkZhFxAP8CWhwWKQr6EjcqroA+BjY7d7eU9U1QWh2h/gZc1vrCWsdjHkgsA94RkS+EpGnRCShs9ramCUo/1wFXCcii3F2l6vc8qdxPrC5wF+A+UAtzijxfYH5qjoe5/DHH4Pd6LYSkUTgFeBGVS32XabO/v1Rd01CAGK+DnhHVfM6qYmdoqNxi8gQYCTO+7wPcKqInNxJzQ2IQL2/W6on3AQg5kicUx6PqupxQCnOocGgsATlB1Vdq6qzVHUC8AKwyS2vUdWfqOo4VT0fSMU51nsAZ6r6V90qXsJ5kcOWiEThvJGfV9W6du/1OYyVDeS75Ts5vBcJzpfUzhbKw1KAYj4BuF5EtuL8CLlMRO4LQvPbLUBxfw1YqKolqlqCc54qeCfP26iNMbe1nrAUoJjzgDxVrdtTfJkgfpdZgvKDiPR0/3qA24DH3Mfxdbu7InI6UKOqq91fJm8CM9wqZgKrg91uf7m9r/4GrFHVB3wWvQHU9cS7HHjdp/wyt4fXFKDIPWzwHjBLRHq4vYNmuWVhJ1Axq+qlqtpfVXNwDvM9p6pB+4XZVgF8rbcD093D21HAdJzzHGGnHTG3tZ6wE6iYVXUPsENEhrtFwf0u6+xeGF3thrOHtBvnZGEecDVwA86e0XrgPg6PwJEDrMP5YH6AM2R8XT0DcDpWLAc+BPqHOrYWYj4JZ1d/ObDUvc0G0t22b3DjS3PXF+CvOHuSK4CJPnVdBWx0b1eGOrZgxOxT5xWEfy++gMSN03Pvcfe9vxp4INSxBTDmXu5nvxin41MekNxcPaGOrzNjdpeNwzmNsRx4jSD2zLWhjowxxoQlO8RnjDEmLFmCMsYYE5YsQRljjAlLlqCMMcaEJUtQxhhjwpIlKGOMMWHJEpQxxpiwZAnKGGNMWLIEZYwxJixZgjLGGBOWLEEZY4wJS5GhbkAwZGRkaE5OTqibYYwxxketVymuqGbzmhX7VTWz8fJukaBycnLIzc0NdTOMMcb4eOrTzdzz9hpYc862ppbbIT5jjDEhMX/TAQZnNj+DvCUoY4wxQVVV4+WSJxfy0dp8xvZJaXa9bnGIzxhjTOjtLirniy0F3PDvpQCkxEVx+dQcHmxmfUtQxhhj2qW0soY/vLeOm2YNIzk2iv0llSzaXMDynYVcfeJAdhwsZ+HmA5w5phcPfrCBN5btarD9fReO5bj+PZqt3xKUMcaYdnluwTb+Pn8rf5+/lcykGPYdqqxf9vgnm+vv/+G9dU1u3ysltsX6/U5QInJhS8tV9VV/6zLGGNP1FVdU19/3TU7+ykoOUIICznX/9gSmAh+5j08B5gOWoIwxphspraxpdZ1JA9P4YkvBEeUnDcmgZ1JMi9v6naBU9UoAEXkfGKWqu93H2cDf/a3HGGNM1+P1Kh6P1D/+eF0+zy04fPnSBeN6c/Hx/Xlj2U6Kyqs5fVQWZ47OJi46AoCK6lpioyL40/vriPR4uOG0oa0+Z3vOQfWrS06uvUD/ljYQkX7Ac0AWoMATqvqgiKQB/wFygK3AN1T1oIgI8CAwGygDrlDVJW5dlwO3uVXfo6rPtiMGY4w56tUlhdbWOVRRw6tL8rjqpIFERXg4UFLJ8p1FHJ+TxnMLtnL//w6fQzpjdBb7S6pYvO0gAD87czgD0hI4+5hsAE4YnN7k89S146ezhvvdflFVv1cGEJFHgKHAC27RxcBGVf1RC9tkA9mqukREkoDFwAXAFUCBqt4nIrcCPVT15yIyG/gRToKaDDyoqpPdhJYLTMRJdIuBCap6sKU2T5w4UW0kCWPM0Sa/uIKdheU8/flWThqSzuOfbGbz/tJm17/93FFMG5bJF1sKWLajkMKyaj5cu5fq2sN5YFJOGl9sPfKQXHO23nd2h2IAEJHFqjqxcXmb96BU9Xq3w8TJbtETqvrfVrbZDex27x8SkTVAH+B8YIa72rPAXODnbvlz6mTPhSKS6ia5GcAcVS1wg5oDnMnhZGmMMV1aRXUt1bVeEqIj8XiEWq/iEVCF0qoalucVERvlITMxlml/+Lh+uzcbdeFuyp1vrm51nbYkp87Wrm7mbo+9dnWKEJEc4DhgEZDlc7hwD84hQHCS1w6fzfLcsubKm3qea4FrAfr3b/EIpDHGhJTXq8xZs5d56/fx/KLt9eWp8VEUllW3sGXTUuOjuPC4vpw0NJ2/fryp/nCcP4b0TGRjfgkAGYkxjOuXwgdr8gH44KZpvJSbx+PznC7k357Sud+tbU5Q7t7T73F684l7U1VN9mPbROAV4EZVLXZONTlUVUWkbccbW6CqTwBPgHOIL1D1GmNMoM1Zs5fv/WPxEeX+Jqenr5jIKcN74vudWufUEc7v/vdX7eFa9zlG9EritR+eSEykhxdzd/DzV1bUr//f66aybs8hIjxSfxHtPxZuY1zfVIb0TOIXs0dy+qgsRvdOqe8A0Vnaswd1P3Cuqq5py0YiEoWTnJ73uWZqr4hkq+pu9xBevlu+E+jns3lft2wnhw8J1pXPbXMExhgTBipranntq5386f317do+MSaSWq8yaWB6k8nJ16zRvVh2+yw+3bCPYVlJ9Z0WpgxKZ3BmAjnpCfx45lCSYqOYmJPWYNvvTBnQ4HHj5Z2lPZ0kPlfVE9u4jeCcYypQ1Rt9yv8AHPDpJJGmqj8TkbOB6zncSeIhVZ3kdpJYDIx3q1iC00mixYOm1knCGBNq5VW1iDi92eauy6eksobr//XVEetdeFwfpgxO51BFDVMHp9M/LR6A6EgPkR5h5c5ixvZtfoDVrihgnSSAXBH5D/AaUH/pcCsjSZwIfAdYISJL3bJfAvcBL4rI1cA24BvusndwktNGnG7mV7rPUSAidwNfuuvd1VpyMsaYYHh5cR43v7SMk4dmcM4x2eQXV7JiZxGZSTG8uWwXxRUtX9S6+LbT2F1UwZgWRvcGjrrk1JL27EE900SxqupVgWlS4NkelDGms4349btUVHvbvX0gumt3VYHsZn5lYJpkurrqWi9vLN3FGWN6kRjjvJVUleKKGkoraygsq2ZU71b7zphOVFRWTXJcJCJC3sEylu4o5NG5mygsq+aUEZks3VHITacPY/+hKh6ft4lN+0r5x9WT6J0ax+DMxFA3v0tp429944f27EENAx7F6SI+RkSOAc5T1Xs6o4GBYHtQgaGqPPjhBp5bsI1/Xj2ZD9bs5YE564mJ9HDSkAyqar18umF/g23+b0Jfbpg5lOV5RXy5tYBfnzOKCE/LJ3NNYMzftJ9LnlzUoTo2/3Z2g+FtwHkftHZC/mjSVLwllTU88ckmUuKjOWtML7KSYxn8y3eO2DYuKoK3f3wS8dGR3PLyMq6bMYQRvZLYX1LJz15ZzpOXTcQjQlWNt9WRvY9mze1BtSdBfQLcAjyuqse5ZbyO4RwAACAASURBVCtVdUxAWtoJLEE1VFpZg1eVpNgotuwvJSc93jk+rpAUG8ktLy9ncM8EZo/JJifDmY75kY828Md29jTy9eb1J5GWGE2f1LgO19WdNR4Xrc7ibQV8uCaf5xdtp6i8+S7KZ47uxf9W7Wn1eSYM6MFpI7P4+sS+LN1eyDXPOZ+jZbfPIiXOef/8d0kes4/JZkSv8Nxb9nqVwvJq0hKiW113y/5StuwvYemOIuat38fSHYUAXDE1h8GZCTy/aDtr9xxqsY77LhxLQkwk5x7bOyDt7w4CmaC+VNXjReQrnwS1VFXHBaitAWcJqqFLnlzI/E0H2r19cmxk/QnfIT2dw0DVtV5S46P52RnDWbT5ACLC/03oy8n3f9xkHWeO7sVfvjmO2KgICkqr2FNUwcjsJACqar3ERHbu9RVdxcfr8nkpdwd3nDuaXUUV3PXmKpZsL0QEHv7WcVRUe0mOjeTlxXm8v3rvEdv/6NQhTBmUzl8+WM/WA2U8ffnxJMZGMjAjgaoaL5U1tSzbUcRx/VMpKK2iT2ocCzcfoLiihu//88jrclpy2sgsTh/Vk882HuDnZw6nb4/4QP0b/FZQWsUtLy0jKTaSOav3kpUSy+Z9ztA/HnES7pdbDzK+fyqPXDIeBfYWV1BUVs2G/EP89p21HXr+L345k56tTCFhjhTIBPUuThfwl1R1vIj8H3C1qp4VmKYGniWow2q92uShCH+N6JXE/26cxt7iCtbsLmbG8J4trl9QWkVVjZeHP9rQ4Ap5gAHp8VwyqT+/e/fIL4XPbz2Vn7+8nNKqGrJTYvnrJeO77GGlimqne3FBaRUHSqoY0yeFus9dVa2Xr7YXsnJnEaOykykqr2bG8J48M39LgwE626u9J953F5Vzwu8+an3FZozvn8qr17XpapQWNbfH2Nj3/pHLe6uOTNRt5RH4f5eO54RBGXzvn7ks3Hy4s/CF4/vw/emD2XmwnMXbDvLqkjx+cvowzhqbXX8u1rRNIBPUIJwRGqYCB4EtwKWquq2V7Z4GzgHy6w4HBms08+6WoLxe5U9z1nH22N4NOinM37ifn7y4lL3FDScWmzE8k8e+PYHdRRXUepUhPROp9Sq7i8pZvauYpz7bwrh+qVx8fD8yk2JIjo1qV7veXbGbHzy/pN1xPXXZRGaOdK6WX7O7GICR2cE5rFRT62V3UQWVNbUM6ZlUXxYZ4alfp7KmlpjICFSVWq/yyMcb+e9XO9l2oKxDz336qCxG9koiMzmWCf17sK+kkn8u3MYcd48pKTaSM0f3YubInkwamM7mfSWs2XOI2lovV5w4sN3xDvnVu+1u88jsZN694eTWV2yC16vMXZ/PCYMymLdhH79+bSX57mR4X/36dHokRFNd62XTvhJG9Erm9/9by6NzNzVb3znHZPPW8sMTMFxz0kCe+mxLs+t/+NPpDExP8CshmsAISIISkQjg96p6s4gkAB5VbfmA7OFtpwElOIPA1iWo+7HRzNtMVfl/czdx0pAMeqfGcdtrK9hRUM7q3cVHTLt88tAMrj5pYIOLAvulxfHRT2dQ69VWh+IPpAMllUy45wP6pMZx0fg+PPTRxoDUe8G43ozMTmb93hJumjWMrKQYth4oZev+Mk4blcU7K3Yzrl8qvdt53uvt5bv55X9XtHhOxx/j+qXWn9Ooc+6xvTlYWkU/92LMbQdKmb/pAAMzEuiZFMNPZw1n0sCmr9rv7M4Khyqqyd12kCufcS47nDQwjScvm8ixd75fv87wrCTW7T3yK2BYViLv/2S6X8+jqqzYWcTG/BJW7izm6c+bTx6NDcxIYEuj0bvf+tFJDMpMwCOCCMRERvDhmr2MyE4mJS6K2EhPg+Q7a1QWX24t4GBZNf/67mSmDs7w+/lNYHQ4QYlIpKrWiMhCVZ3SzkbkAG/5JKh1wAyfoY7mqupwEXncvf+C73p1N1X9nlveYL3mHG0JavuBsgajGLdVdKSH9feE5ojsU59uZubILHLS4/nN66t4a/kuHvzmcUwamEZpZQ1vr9jNmaN7Mem3HzbYblhWIuv3lvj1HN89eSBPfup8ydV9gQ7OTODDn85g+4EyisqrSYqNJD46gsTYSOat30ffHvGkxkcR4RFeX7qLPUUV/H3+1vo6B2cmMLRnEjsOlrFqV3GD50uKjeRQCxdhvvz9ExoMDbO3uIJ9hyoZ3Tu5Sxy2/Hzjfm56cSn/u2EaPRKiyd1awMuL84iMEG48bRgT7/ngiG0GZSbw/o3TWLSlgIzEGIa65yqLK6p5ftF2rp02iA9W7+WmF5dRXl3bahvG909lyfbCJpcNSI/n5KEZ7D9URVF5Nc9dPYkonz3bpmw7UEpVjZfqWmVEryTbWwqxQCSoJe45p0dxRhB/Caj/6dLKSBJ1deTQMEEVqmqqe1+Ag6qaKiJvAfep6mfusg9xpuGYAcTWdWkXkV8D5ar6x5aeN5wTlO//v7pWiY50PlhFZdVsOVBKYkwE+cWVrN5djCpMHpTGvW+vYVGjKZR7p8QSHxNJpEea7WU0pk8yK3cW8/crj2/13FGo1R22OeeYbH519kiyU+LIufXtDtXZ3K99f9T1WoPDey4V7hdrbJRzWG9HQTkZSdHERzvjo1XXeomJ9HSJJNQR33piIQs2H9npJi0hmoLSqvrHvp1rWrPqzjMorqgmKykWERARtu4vpai8mr98sJ5Zo3shwP6SSq4/tfWZWU14C+RQR7HAAeBUnMNs4v5t1/QbdQI9mnm4T7dRXevl1SV5PDp3E1t9zlFMH5bJV9sP+v1B/uCm6fU96eqs3VNMUVk1E3PS+Neibcwem01aQnSX+qL8wYzBDEiL5+sT+zV53VRKXFSbD7m1NznVPV+duv+j7+FREaF/+uFeaxEeIcLTPXoiPn/NZBZuPsAlTzW85so3OQF+v6cBEmIiSWjU4aDukodnrpzUzpaarqYtCaqniNwErORwYqrT3sTSaaOZh/t0G88t2Mbdbx05edgn6/f5tf0/r57M1MHpTR6a8L0e5Tsn5LS7jaGUHBvFNyc1/GHxnSkDiPAIl50wgEGZiXy8Np/iimqG9kwiLjqCU/44t0PP+f3pg+nbI45dheXsKaogMkJ4MTevQ3V2Bx6PMHVIBsf2S2XZjqYPwzXl5KEZR1zYbYyvtiSoCCCRhompTnsTwBvA5TiDxl4OvO5Tfr2I/Bunk0SRm8TeA34rIj3c9WYBv2jnc4fE/pJKXli0nT/NaftFr5EeocarXDS+LycOaX14/aPN3Rc0vBb8lBEND1NedsIADpZV8+NTh3D6n+cRG+XhvRunMSA9gaLyajbsPcSdb65mf0klfVLjuO+iYxiYkcCBkspmr1351exR1NoYNn55/Ycn8r1/5LKjoJzvzxjMj19oOFL34MwE7v3aWPYUVXDBcc48oy98sZ2C0iq+PXkAsx/6lJ2F5WQkxoSi+SYMtfkcVLufSOQFnL2fDGAvcDvOiOgvAv1xRzN3RywX4BGc6dzLgCtVNdet5yqckdAB7lXVpgavbaCzzkFtO1DKsrwidhSUse9QJSlxUSTFRhId6eE/X+6gssbLTacPo7rWy5/nrG9wKK/ON4/vR1x0BKt2FvPF1gIe/OY4zhjdi7yD5fROjaWovJpHPtrIr88Z1S3OZ5iuzetVvKpERngoq6ph1G/eA+DsY7J58OJxDbrlN2XZjkJ6p8aRmWRJqjsJRCeJ+pEjuppAJKiaWi97iiuoqvGiwD8WbGvQy6s91t1zpo2YYIzp9gLRSWJmANsTNn74/BL2FFcwrl8qMZEe5q7bx+rdxa1v6GNEryTSE6NRheG9khjXL5Xk2CgWbSkgv7iCqUMy2F9SyZzVezlhUDpDsxIZlpVkyckYY1rQ5pEkuqJho4/Vnz32X3YWlpMaF41XlaLyagakx3Pnm0d2VPDXtdMG8cvZIwPYUmOM6X4C2c28y9lWUMY9b69pdb3EmEhOHJLOuj2HGN4riW9M7EdWcizzNuyjR3w0sVEeCsuq+XzjAa4/1Rk23xhjTOfoFntQo445Tj/6dAGxUR7ioiNQha0HSimtrGVYViIJ0ZF2JbkxxoRIwAaL7YpEZB9OL8HOkAF0t4s5LObuoTvGDN0z7lDHPEBVMxsXdosE1ZlEJLepzH80s5i7h+4YM3TPuMM15pYvSjDGGGNCxBKUMcaYsGQJquOeCHUDQsBi7h66Y8zQPeMOy5jtHJQxxpiwZHtQxhhjwpIlKGOMMWHJElQjIvK0iOSLyEqfsmNFZIGIrBCRN0Uk2S2PFpFn3PJlIjLDZ5toEXlCRNaLyFoRuSgE4fhFRPqJyMcislpEVonIDW55mojMEZEN7t8ebrmIyEMislFElovIeJ+6LnfX3yAil4cqptYEMmZ3ebKI5InII6GIx18Bfq3vd+tY464Tlle7tyPmEe7nvVJEbm6tnnAUqJjdZaki8rL7PbZGRE4IWiCqajefGzANGA+s9Cn7Epju3r8KuNu9/0PgGfd+T2Ax4HEf3wnc4973ABmhjq2FmLOB8e79JGA9MAq4H7jVLb8V+L17fzbwLs7cYFOARW55GrDZ/dvDvd8j1PF1Zsw+9T0I/At4JNSxBem1ngp8jjNPXASwAJgR6vgCFHNP4HjgXuDm1uoJdXydGbO77FngGvd+NJAarDhsD6oRVZ0HFDQqHgbMc+/PAer2hkYBH7nb5QOFQN3FblcBv3OXeVU1bK9MV9XdqrrEvX8IWAP0Ac7HeXPi/r3AvX8+8Jw6FgKp4syIfAYwR1ULVPUgzv/qzCCG4rcAxoyITACygPeDGEK7BDBuBWJxvrBigCiced7CTltjVtV8Vf0SqPaznrATqJhFJAXnR/vf3PWqVNX/aZM7yBKUf1bhvLAAX+fwdPTLgPNEJFJEBgITgH4ikuouv1tElojISyKSFdwmt4+I5ADHAYuALFXd7S7ag/MlDM4bfYfPZnluWXPlYa0jMYuIB/gT0OCwSFfQkbhVdQHwMbDbvb2nqq2PyBxifsbc1nrCWgdjHgjsA54Rka9E5CkRSeistjZmCco/VwHXichinN3lKrf8aZwPbC7wF2A+UIszSnxfYL46sxAvAP4Y7Ea3lYgkAq8AN6pqg0mx1Nm/P+quSQhAzNcB76hqXic1sVN0NG4RGQKMxHmf9wFOFZGTO6m5ARGo93dL9YSbAMQciXPK41F1JqwtxTk0GBSWoPygqmtVdZaqTgBeADa55TWq+hNVHaeq5wOpOMd6D+BMVf+qW8VLOC9y2BKRKJw38vOqWtfuvT6HsbKBfLd8J4f3IsH5ktrZQnlYClDMJwDXi8hWnB8hl4nIfUFofrsFKO6vAQtVtURVS3DOUwXv5HkbtTHmttYTlgIUcx6Qp6p1e4ovE8TvMktQfhCRnu5fD3Ab8Jj7OL5ud1dETgdqVHW1+8vkTWCGW8VMoP0zI3Yyt/fV34A1qvqAz6I3gLqeeJcDr/uUX+b28JoCFLmHDd4DZolID7d30Cy3LOwEKmZVvVRV+6tqDs5hvudUNWi/MNsqgK/1dmC6e3g7CpiOc54j7LQj5rbWE3YCFbOq7gF2iMhwtyi432Wd3Qujq91w9pB245wszAOuBm7A2TNaD9zH4RE4coB1OB/MD3CGjK+rZwBOx4rlwIdA/1DH1kLMJ+Hs6i8Hlrq32UC62/YNbnxp7voC/BVnT3IFMNGnrquAje7tylDHFoyYfeq8gvDvxReQuHF67j3uvvdXAw+EOrYAxtzL/ewX43R8ygOSm6sn1PF1ZszusnE4pzGWA68RxJ65NtSRMcaYsGSH+IwxxoQlS1DGGGPCkiUoY4wxYckSlDHGmLBkCcoYY0xYsgRljDEmLFmCMsYYE5YsQRljjAlLlqCMMcaEJUtQxhhjwlJkSwul0bTWjak7IZYxxhgTaC2OxSciH7ewrarqqYFvUuBlZGRoTk5OqJthjDGmCYsXL96vqpmNy1vcg1LVUzqvScFzKKoH+0+7M9TNMMYY05TF52xrqrjFBOVLRMYAo4DYujJVfa7jLTPGGGOO5FeCEpHbcSbfGwW8A5wFfAZYgjLGGNMp/O3F9384MynuUdUrgWOBlE5rlTHGmG7P3wRVrqpeoEZEknHmse/Xec0yxhjT3fl7DipXRFKBJ4HFQAmwoNNaZYwxptvzK0Gp6nXu3cdE5H84c9Uv77xmGWOM6e7a0ouvDzCgbhsRmaaq8zqrYcYYY7o3f3vx/R64GFgN1LrFCliCMsYY0yn83YO6ABiuqpWd2RhjjDGmjr+9+DYDUZ3ZEGOMMcaXv3tQZcBSEfkQqN+LUtUfd0qrjDHGdHv+Jqg33JsxxhgTFH4d4lPVZ4EXcK6BWgz8yy1rkYg8LSL5IrKymeUiIg+JyEYRWe47vYeIXC4iG9zb5T7lE0RkhbvNQyIi/sRgjDGma/ErQYnIDGAD8Ffg/wHrRWSaH5v+HTizheVnAUPd27XAo+7zpQG3A5OBScDtItLD3eZR4Ls+27VUvzHGmC7K304SfwJmqep0VZ0GnAH8ubWN3OukClpY5XzgOXUsBFJFJNutf46qFqjqQWAOcKa7LFlVF6ozkdVzOD0MjTHGHGX8TVBRqrqu7oGqricwvfr6ADt8Hue5ZS2V5zVRfgQRuVZEckUkNwDtNMYYE2T+JqhcEXlKRGa4t6eAsP7iV9UnVHWiqk4MdVuMMca0nb8J6gc4o0j82L2tcss6aicNR0Xv65a1VN63iXJjjDFHGX978VWq6gOqeiFwDfBhgEaVeAO4zO3NNwUoUtXdwHvALBHp4XaOmAW85y4rFpEpbu+9y4DXA9AOY4wxYcbfsfjmAue56y8G8kVkvqr+pJXtXsCZiTdDRPJweuZFAajqYziz884GNuJcDHylu6xARO4GvnSruktV6zpbXIfTOzAOeNe9GWOMOcr4e6FuiqoWi8g1OL3ubheRVqfbUNVvtbJcgR82s+xp4OkmynOBMf412xhjTFfl7zmoSLeL9zeAtzqxPcYYYwzgf4K6C+e80EZV/VJEBuFcuGuMMcZ0Cn9n1H0JeMnn8Wbgos5qlDHGGONvJ4lY4GpgNBBbV66qV3VSu4wxxnRz/h7i+wfQC2cIok9wrj861FmNMsYYY/xNUENU9ddAqTuK+dk4A7kaY4wxncLfBFXt/i0UkTFACtCzc5pkjDHG+H8d1BPuiA6/xhn9IRH4Tae1yhhjTLfnby++p9y7nwCDOq85xhhjjMPfCQuzRORvIvKu+3iUiFztx3Znisg6d/bbW5tYPkBEPnRn050rIn19lv1eRFa6t4t9yk8VkSVu+bMi4u9eoDHGmC7E33NQf8e5ULe3+3g9cGNLG4hIBM4MvGcBo4BvicioRqv9EWfopGNwLgb+nbvt2cB4YBxOZ4ybRSRZRDzAs8A3VXUMsA24HGOMMUcdfxNUhqq+CHgBVLUGqG1lm0k4I09sVtUq4N84M+j6GgV85N7/2Gf5KGCeqtaoaimwHGdq93Sgyp0wEZyZdu2CYWOMOQr5m6BKRSQdUIC6qTFa2aa5WXF9LQMudO9/DUhyn2cZzhTv8SKSAZyCMz/UfpxxAesmIfw/Gs4bVc9m1DXGmK7N3/M3N+H03hssIp8DmTjJoaNuBh4RkSuAeTiTD9aq6vsicjwwH9gHLHDLVUS+CfxZRGKA92lmT05VnwCeAIjJHqoBaKsxxpggajFBuUlih6ouEZHpwPdwDqm9j7NH1JLmZsWtp6q7cPegRCQRuEhVC91l9wL3usv+hXPeC1VdAJzsls8ChrUW5Ng+KeTed3ZrqxljjAkB+X0z5c6UTM0sFFkCnOZOIDgN5zzSj3A6L4xU1Wb3otzedeuBmTiJ6UvgElVd5bNOBlCgql4RuRdnL+k3bgeLVFU9ICLHAP8CxqlqjYj0VNV8dw/qHeBeVf2o8fM3ass+nA4VnSED59Bjd2Ixdw/dMWbonnGHOuYBqprZuLC1Q3wRPjPZXgw8oaqvAK+IyNKWNnSTyfU4vf8igKdVdZWI3AXkquobOLPt/k5EFOcQX93khVHAp86s7hQD33Y7ZgDcIiLn4Jw/e7S15OS25YjAA0VEclV1YutrHj0s5u6hO8YM3TPucI251QQlIpFucpgJXNuGbVHVd3D2cnzLfuNz/2Xg5Sa2q8DpyddUnbcAt7T23MYYY7q21pLMC8AnIrIfKAc+BRCRIbTei88YY4xptxYTlKreKyIfAtnA+3r4hJUH51yUcXsKdjMWc/fQHWOG7hl3WMbcYicJY4wxJlT8vVDXGGOMCSpLUI2IyNMiki8iK33KjhWRBSKyQkTeFJFktzxaRJ5xy5eJyAyfbaJF5AkRWS8ia0UkbIdkEpF+IvKxiKwWkVUicoNbniYic0Rkg/u3h1suIvKQOwjwchEZ71PX5e76G0QkbMdJDGTM7vJkEckTkUdCEY+/Avxa3+/WscZdR0IVV0vaEfMI9/NeKSI3t1ZPOApUzO6yVBF52f0eWyMiJwQtEFW1m88NmIYzUO1Kn7Ivgenu/auAu937PwSece/3BBYDHvfxncA97n0PzniGIY+vmZizgfHu/SSc69dGAfcDt7rltwK/d+/PBt4FBJgCLHLL04DN7t8e7v0eoY6vM2P2qe9BnOv1Hgl1bEF6racCn+NcQhKBM9rLjFDHF6CYewLH4wwUcHNr9YQ6vs6M2V32LHCNez8a5xrVoMRhe1CNqOo8oKBR8TCc67Sg4QC19YPdqmo+UAjUXUtwFe7o7KrqVdWwvfBPVXer6hL3/iFgDc64iefjvDlx/17g3j8fZxR6VdWFQKqIZANnAHNUtUBVD+L8r84MYih+C2DMiMgEIAtnhJWwFsC4FYjF+cKKwbl2cW/QAmmDtsasqvmq+iWHZxJvrZ6wE6iYRSQF50f739z1qtQd7ScYLEH5ZxWHR1r/OoeHcFoGnCcikSIyEJgA9BORVHf53eLMXfWSiGQFt8ntIyI5wHHAIiBLVXe7i/bgfAlD8wMB+zNAcNjpSMziTAHzJ5xxJbuUjsStzpBjHwO73dt7qromCM3uED9jbms9Ya2DMQ/EGQ/1GRH5SkSeEpGEzmprY5ag/HMVcJ2ILMbZXa5yy5/G+cDmAn/BGdy2Fqf7fl9gvqqOxzn88cdgN7qtxBkP8RXgRlUt9l2mzv79UdflMwAxXwe8o6qtjU0ZVjoatzjXQo7EeZ/3AU4VkZM7qbkBEaj3d0v1hJsAxByJc8rjUVU9DijFOTQYFJag/KCqa1V1lqpOwLl4eZNbXqOqP1HVcap6PpCKc6z3AFAGvOpW8RLOixy2RCQK5438vKrWtXuvz2GsbCDfLW9uIOBWBwgOJwGK+QTgehHZivMj5DIRuS8IzW+3AMX9NWChqpaoagnOeargnTxvozbG3NZ6wlKAYs4D8lS1bk/xZYL4XWYJyg8i0tP96wFuAx5zH8fX7e6KyOlAjaqudn+ZvIkz1iA4w0StDna7/eX2vvobsEZVH/BZ9AaHZyy+HHjdp/wyt4fXFKDIPWzwHjBLRHq4vYNmuWVhJ1Axq+qlqtpfVXNwDvM9p6pB+4XZVgF8rbcD093D21HAdJzzHGGnHTG3tZ6wE6iYVXUPsENEhrtFwf0u6+xeGF3thrOHtBvnZGEecDVwA86e0XrgPg5f4JwDrMP5YH6AMyJvXT0DcDpWLAc+BPqHOrYWYj4JZ1d/ObDUvc3GmcH4Q2CDG1+au74Af8XZk1wBTPSp6ypgo3u7MtSxBSNmnzqvIPx78QUkbpyee4+77/3VwAOhji2AMfdyP/vFOB2f8oDk5uoJdXydGbO7bBzOaYzlwGsEsWeujSRhjDEmLNkhPmOMMWHJEpQxxpiwZAnKGGNMWLIEZYwxJixZgjLGGBOWLEEZY4wJS5agjDHGhCVLUMYYY8KSJShjjDFhyRKUMcaYsGQJyhhjTFiKDHUDgiEjI0NzcnJC3QwTJIVl1ewprqC61ktUhIdeybGkxkeFulldUuP/ZVJsJIcqao743xaWVbOzsBxvK2N7ekTokxpnr4dpYPHixftVNbNxebdIUDk5OeTm5h5R/tpXO/nDe+vYVVhO79Q4ThmRycdr97GzsJwIEWpV6ZMaxy1nDOeC45qeGPa1r3ZyxxurKCx3ZkruER/F7eeObnb9xs+9s7Ac4fCsYf5s39Jz+tbbWGuxhJPGMcZHeYiJiqCwrLrBa1X32t1yhjMbwC9eXU5NtZcMn7pqgTOn9GfigLQ2vVaN3x/h9L8LRttue20Fzy/c3uB/Cc787nVqgP3ufX+no81KjePzW08Fjnyd60SI8K3J/bjngrENysP5NanTFdoYbkRkW5Pl3WE084kTJ2rjBPXaVzv5xasrKK+u7ZTnjI4Qqmqd/63vl2tKXBSlldVUezvlaVvlEYiN9FDmNsAj4NXmk5dvwvM3aTfetvEPgF2F5cRGeSgP1T+hFfFRHqprvUe8RnFREfzuwrEh/7Jp7r2bEB3BvV8LTPtue20F/1y4vcP1dDYBLp3S/4hE1lhnJ42WfhiC89qUVdUe8TlIiYtChPrvhqqa2vrPZuMfZZ2R6Dry+fbdvqlY6u7703YRWayqE48o7w4JKiZ7qE684fH6f1JX+fCZo0vdHhvQYK+huR8Jt722ghcW7aC2G3xGTfeVEB3B2j9evKW2rGhQ42VdMkGJyNPAOUC+qo5pbf2Y7KGafflfOr9hxhhj2mz3szdSuXuDNC7vqr34/g6cGepGGGOM6TxdMkGp6jygINTtMMYY03m6ZILyh4hcKyK5InJk9z1jjDFhLyTdzEUkraXlqtrhvSNVfQJ4ApxzUB2tzxhjTHCF6jqoxTiX/gjQHzjo3k8FtgMDQ9QuY4wxYSIkh/hUdaCqDgI+AM5V1QxVTcfpmfd+KNpkjDEmvIT6HNQUVX2n7oGqvgtMbW0jEXkBWAAMF5E8Ebm6E9tojDGmM6m3yav2Q3odgOz+ewAACd1JREFUlIi8B3wK/NMtuhSYpqpnBPJ5mhtJ4paXllHtDUz8cVEe0hJimhw1oe6q6oNl1a1XFAKNLxBtaiilUdlJzN9UUD8kU0J0BF8b36dBjL4jZHgELpnsXOHf2lX2Xcm3fUYtaGp0AiAow9z4M9xV4zb4O5rCa1/t5JaXl1Fd2zVO3WYlRZN/qIpgtbZHfBRnH5PdYKgt32HSGuuTGkdOelyDz09zoiKEi4/vx1vLdjd7ITdwxOgPvqNA5KTHsXDzQWpVESDeZxSLxq95UyNJxEV5qKzx0tavRn9GoGjuffu18X3DbyQJt7PE7cA0t2gecGcgOkn4aipBgf/Dn9SNSdbcf6qtQ+A0N/4YOG/GzMRo9h6qarGOhCbedM4QOMv9GkLI3yFigq25/02Uhw4ND5UaF8Ud541udQSHll7rEwen8fx3T2h/I7qQ177ayY3/WRrSNngEHvjGOKDtSb+jSbozNDX0l28i8nccz1BobkijQP0Pu/VQR80lqPYKxps80M9xtA1g2Vw8TSWY9oyhd7T9v9rqta928pP/LA3aXkljgRxX0IS/sEpQIvL/27vXGDmrOo7j319vCmjZVtJaS0tBA0jCpRe1eGsjtBBetAXUGDVU6+UFkIAGkxJ5o2ioBAmaErVCG2q8JEWjNKmpFagk3GwXpfRCL9QLi4VaAQsSpYW/L85ZO113u7O7M3OeZX6fZDLPc56Zs+e/czlzznOec26LiGslrYX//wxExIJG/r1GV1BWbe1euTTCB5bdN+Qu2b7mHuxLVSbjtdbrq4IqNcz8R/n+lkJ/397AFk2f7C+5IfrbICqnY1UwvXWv9bZkil83q1WkgoqIznz/u+40SeOAKRGxpUSZzOyId3QcV1cLaqTE6xF1VTD+4WADVXTBQkkbgQW5HJ3AfkkPRsSXS5bLrN195aIz+l0vzV1y1mylr4M6MSIOApcBqyPifcCFhctk1vYWTZ/MTZedzeSO4xBppOOnZ089at+VkzVb6SXfR0maBHwc+GrhsphZDXfJWWmlW1BfB9YDT0XEJkmnAbsLl8nMzCqgaAsqItYAa2r29wKXlyuRmZlVRdEWlKTTJd0raWveP0fSDSXLZGZm1VC6i++HwPXAIYA8xPwTRUtkZmaVULqCOj4ift8j7XCRkpiZWaWUrqAOSHonebojSR8F9pUtkpmZVUHpYeZXkZZlP1PSM8CfSEtumJlZmys9im8vcKGkE0ituVdI56D+UrJcZmZWXpEuPkljJV0vabmkeaSKaTGwh3TRrpmZtbmSs5m/QFq2/QukWSQEXBoRZVdJMzOzSihVQZ0WEWcDSLqDNDBiakT8u1B5zMysYkqN4vvfymUR8RrQ5crJzMxqlaqgzpV0MN9eAs7p3pZ0sL8nS7pY0k5JeyQtbUF5zcysxUotWDhysM+VNBK4HZgHdAGbJN0TEdsbVT4zMyuv9HVQg/FeYE8eoo6knwELgT4rqJ07dzJ37tzWlM7MzBqi9EwSgzEZeLpmvyunHUXSFyVtlrT50KFDPQ+bmVnFDccWVF0iYgVplgpmzZoVGzduLFsgMzPrlaRe04djBfUMMKVm/+Sc1qfOzs4Dkpo1O8VJwIEm5V1Vjrk9tGPM0J5xl475lN4SFRGtLsiQSBoF7AIuIFVMm4BPRsS2QuXZHBGzSvztUhxze2jHmKE9465qzMOuBRURhyVdTVoqfiSwslTlZGZmzTPsKiiAiFgHrCtdDjMza57hOIqvalaULkABjrk9tGPM0J5xVzLmYXcOyszM2oNbUGZmVkmuoMzMrJJcQfUgaaWk/ZK21qSdK+lhSU9IWitpbE4fI2lVTn9c0tya54yRtELSLklPSrq8QDh1kTRF0v2StkvaJumanD5e0gZJu/P9uJwuSd/Nk/VukTSjJq/F+fG7JS0uFVN/GhlzPj5WUpek5SXiqVeDX+ubcx478mN6v9qysEHEfGb+vP9H0nX95VNFjYo5H+uQdHf+Htsh6fyWBRIRvtXcgA8DM4CtNWmbgDl5ewlwY96+CliVtycAncCIvP814Bt5ewRwUunYjhHzJGBG3n4r6Tqzs4CbgaU5fSnwrbx9CfBr0iKTs4FHc/p4YG++H5e3x5WOr5kx1+T3HeAnwPLSsbXotX4/8CDpUo+RpMVH55aOr0ExTwDeA3wTuK6/fErH18yY87G7gM/n7TFAR6vicAuqh4h4AHi+R/LpwAN5ewPQ3Ro6C7gvP28/8CLQfbHbEuCmfOz1iKjslekRsS8iHsvbLwE7SPMbLiS9Ocn3i/L2QmB1JI8AHZImARcBGyLi+Yh4gfS/uriFodStgTEjaSYwEfhNC0MYlAbGHcCbSV9YbwJGA8+1LJABGGjMEbE/IjZRs25dP/lUTqNilnQi6Uf7nflxr0bEiy0JAnfx1Wsb6YUF+BhHplp6HFggaZSkU4GZwBRJHfn4jZIek7RG0sTWFnlwJE0DpgOPAhMjYl8+9CzpSxj6nrC3rol8q2YoMUsaAXwbOKpbZDgYStwR8TBwP2k17H3A+ojY0YJiD0mdMQ80n0obYsynAn8HVkn6g6Q7JJ3QrLL25AqqPkuAKyV1kprLr+b0laQP7GbgNuAh4DXSBdAnAw9FxAxS98ctrS70QEl6C/Bz4NqIOGrhyEjt+zfcNQkNiPlKYF1EdDWpiE0x1LglvQt4N+l9Phn4iKQPNam4DdGo9/ex8qmaBsQ8inTK43sRMR34F6lrsCVcQdUhIp6MiPkRMRP4KfBUTj8cEV+KiPMiYiHQQerr/QfwCvCLnMUa0otcWZJGk97IP46I7nI/V9ONNQnYn9P7mrB3wBP5ltSgmM8Hrpb0Z9KPkCskLWtB8QetQXFfCjwSES9HxMuk81StO3k+QAOMeaD5VFKDYu4CuiKiu6V4Ny38LnMFVQdJE/L9COAG4Pt5//ju5q6kecDhiNief5msBebmLC7gGAsqlpZHX90J7IiIW2sO3QN0j8RbDPyqJv2KPMJrNvDP3G2wHpgvaVweHTQ/p1VOo2KOiE9FxNSImEbq5lsdES37hTlQDXyt/wrMyd3bo4E5pPMclTOImAeaT+U0KuaIeBZ4WtIZOam132XNHoUx3G6kFtI+0snCLuBzwDWkltEuYBlHZuCYBuwkfTB/C5xSk88ppIEVW4B7gamlYztGzB8kNfW3AH/Mt0uAt+Wy787xjc+PF3A7qSX5BDCrJq8lwJ58+2zp2FoRc02en6H6o/gaEjdp5N4P8nt/O3Br6dgaGPPb82f/IGngUxcwtq98SsfXzJjzsfNIpzG2AL+khSNzPdWRmZlVkrv4zMysklxBmZlZJbmCMjOzSnIFZWZmleQKyszMKskVlJmZVZIrKDMzq6T/ArWWU3FIWxLuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZWXLHW4ajlJ"
      },
      "source": [
        "### Both seems same"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNkabzUJahQh",
        "outputId": "84158011-6a3b-42cc-9238-8147ebd379d5"
      },
      "source": [
        "# Checking Stationarity of Data\n",
        "# ADFULLER TEST\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(df_copy['spx'])\n",
        "print(f'Test Statistics: {result[0]}')\n",
        "print(f'p-value: {result[1]}')\n",
        "print(f'Critial Values: {result[4]}')\n",
        "\n",
        "if result[1] > 0.05 :\n",
        "  print('Series is not Stationary')\n",
        "else:\n",
        "  print('Series is Stationary')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Statistics: 1.5278847192226328\n",
            "p-value: 0.9976242548159865\n",
            "Critial Values: {'1%': -3.431417565493639, '5%': -2.862011767622901, '10%': -2.567021119188598}\n",
            "Series is not Stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "Mn2xG2E_bAB8",
        "outputId": "3d0a04e8-340d-4a70-d449-d6624474b269"
      },
      "source": [
        "# plotting ACF\n",
        "sgt.plot_acf(df_copy['spx'], lags=40, zero= False)\n",
        "plt.title('ACF OF SPX', size=24);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "U-XwImt6byi2",
        "outputId": "1a37af7f-8974-4ca8-a42b-11a5c8b26259"
      },
      "source": [
        "# plotting PACF\n",
        "sgt.plot_pacf(df_copy['spx'], lags=40, zero= False)\n",
        "plt.title('ACF OF SPX', size=24);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYJRSla7b1Dw"
      },
      "source": [
        "# Splitting into train and test\n",
        "size= int(len(df_copy)*0.8)\n",
        "train= df_copy.iloc[:size]\n",
        "test= df_copy.iloc[size:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeWt5gSycfqG",
        "outputId": "b703485c-31b0-4957-9a4b-22bf3a460135"
      },
      "source": [
        "print(train.shape, test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4921, 4) (1231, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "BSnSUqeQc3VO",
        "outputId": "7cf67feb-2ae8-4a75-cb6e-fcb23d688b1a"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(train['spx'], 'blue', label='training-data')\n",
        "plt.plot(test['spx'], 'green', label='testing-data')\n",
        "plt.xlabel('Stock Index', size=20)\n",
        "plt.ylabel('Date', size=20)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa8857a5610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bjnsxzyaea60",
        "outputId": "536c1bb0-119d-41d6-de50-bd7075cce603"
      },
      "source": [
        "pip install pmdarima"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.7/dist-packages (1.8.2)\n",
            "Requirement already satisfied: numpy~=1.19.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.19.5)\n",
            "Requirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.29.23)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.12.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.2.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RLpXumUdy-z"
      },
      "source": [
        "# Using Auto Arima model to get p, d and q values automatically\n",
        "from pmdarima import auto_arima"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bYZjgl5eZ5U",
        "outputId": "4fde3ef4-df7c-4c3f-a708-b7b73ccfdf20"
      },
      "source": [
        "model_autoarima= auto_arima(train['spx'], start_p=0, start_q=0,\n",
        "                      test='adf',       # use adftest to find optimal 'd'\n",
        "                      max_p=8, max_q=8, # maximum p and q\n",
        "                      m=1,              # frequency of series\n",
        "                      d=None,           # let model determine 'd'\n",
        "                      seasonal=False,   # No Seasonality\n",
        "                      start_P=0, \n",
        "                      D=0, \n",
        "                      trace=True,\n",
        "                      error_action='ignore',  \n",
        "                      suppress_warnings=True, \n",
        "                      stepwise=True)\n",
        "print(model_autoarima.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=52624.768, Time=0.19 sec\n",
            " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=52536.956, Time=0.27 sec\n",
            " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=52514.871, Time=0.62 sec\n",
            " ARIMA(0,1,0)(0,0,0)[0]             : AIC=52622.879, Time=0.10 sec\n",
            " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=52030.022, Time=2.24 sec\n",
            " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=52029.582, Time=3.68 sec\n",
            " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=52496.813, Time=0.53 sec\n",
            " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=52016.779, Time=5.26 sec\n",
            " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=52455.316, Time=0.65 sec\n",
            " ARIMA(4,1,1)(0,0,0)[0] intercept   : AIC=52006.458, Time=7.34 sec\n",
            " ARIMA(4,1,0)(0,0,0)[0] intercept   : AIC=52393.020, Time=0.88 sec\n",
            " ARIMA(5,1,1)(0,0,0)[0] intercept   : AIC=52006.160, Time=10.40 sec\n",
            " ARIMA(5,1,0)(0,0,0)[0] intercept   : AIC=52362.023, Time=1.05 sec\n",
            " ARIMA(6,1,1)(0,0,0)[0] intercept   : AIC=51997.380, Time=12.04 sec\n",
            " ARIMA(6,1,0)(0,0,0)[0] intercept   : AIC=52339.579, Time=1.65 sec\n",
            " ARIMA(7,1,1)(0,0,0)[0] intercept   : AIC=51982.452, Time=13.18 sec\n",
            " ARIMA(7,1,0)(0,0,0)[0] intercept   : AIC=52262.363, Time=1.98 sec\n",
            " ARIMA(8,1,1)(0,0,0)[0] intercept   : AIC=51984.264, Time=17.59 sec\n",
            " ARIMA(7,1,2)(0,0,0)[0] intercept   : AIC=51983.881, Time=17.59 sec\n",
            " ARIMA(6,1,2)(0,0,0)[0] intercept   : AIC=52010.119, Time=11.21 sec\n",
            " ARIMA(8,1,0)(0,0,0)[0] intercept   : AIC=52233.673, Time=2.57 sec\n",
            " ARIMA(8,1,2)(0,0,0)[0] intercept   : AIC=51985.747, Time=14.48 sec\n",
            " ARIMA(7,1,1)(0,0,0)[0]             : AIC=51984.662, Time=4.69 sec\n",
            "\n",
            "Best model:  ARIMA(7,1,1)(0,0,0)[0] intercept\n",
            "Total fit time: 130.261 seconds\n",
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                 4921\n",
            "Model:               SARIMAX(7, 1, 1)   Log Likelihood              -25981.226\n",
            "Date:                Fri, 30 Jul 2021   AIC                          51982.452\n",
            "Time:                        05:17:06   BIC                          52047.462\n",
            "Sample:                             0   HQIC                         52005.255\n",
            "                               - 4921                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.1082      0.059      1.841      0.066      -0.007       0.223\n",
            "ar.L1          0.6679      0.010     70.038      0.000       0.649       0.687\n",
            "ar.L2          0.0079      0.013      0.592      0.554      -0.018       0.034\n",
            "ar.L3         -0.0294      0.015     -2.029      0.042      -0.058      -0.001\n",
            "ar.L4         -0.0409      0.014     -2.839      0.005      -0.069      -0.013\n",
            "ar.L5          0.0063      0.016      0.385      0.700      -0.026       0.039\n",
            "ar.L6         -0.0116      0.017     -0.702      0.482      -0.044       0.021\n",
            "ar.L7         -0.0609      0.014     -4.339      0.000      -0.088      -0.033\n",
            "ma.L1         -0.9234      0.006   -145.800      0.000      -0.936      -0.911\n",
            "sigma2      2260.5649     20.568    109.906      0.000    2220.252    2300.878\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             40774.59\n",
            "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
            "Heteroskedasticity (H):               2.58   Skew:                            -0.37\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                        17.08\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "KJ7XgoBQeURj",
        "outputId": "1d7863dd-1d8d-4bac-c021-0e978d57b7b3"
      },
      "source": [
        "model_autoarima.plot_diagnostics(figsize=(15,8))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Vvrl-OWhfGJ",
        "outputId": "7e45ed2e-24c5-4000-f558-ab278505e200"
      },
      "source": [
        "%time\n",
        "# Builing ARIMA Model\n",
        "#train_ar= train['spx'].values\n",
        "#test_ar= test['spx'].values\n",
        "#history = [x for x in train_ar]\n",
        "#predictions = []\n",
        "#for t in range(len(test)):\n",
        "    #model = ARIMA(history, order=(2,1,5))\n",
        "    #model_fit = model.fit(disp=0)\n",
        "    #output = model_fit.forecast()\n",
        "    #yhat = output[0]\n",
        "    #predictions.append(yhat)\n",
        "    #obs = test_ar[t]\n",
        "    #history.append(obs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 2 µs, total: 5 µs\n",
            "Wall time: 8.58 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7wbzYQbiail"
      },
      "source": [
        "#plt.figure(figsize=(12,7))\n",
        "#plt.plot(train['spx'], color='blue', label='Training Data')\n",
        "#plt.plot(test.index, predictions, color='green', marker='o', linestyle='dashed', label='Predicted Price')\n",
        "#plt.plot(test.index, test['spx'], color='red', label='Actual stock index')\n",
        "#plt.title('Stock index Prediction')\n",
        "#plt.xlabel('Dates')\n",
        "#plt.ylabel('Stock index')\n",
        "#plt.legend()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJbU7HHwmw3n",
        "outputId": "d039b571-8bd2-4be4-d6ec-a72795088989"
      },
      "source": [
        "# Building arima model with p, d and q predicted by auto-arima model\n",
        "model_arima = ARIMA(train['spx'] ,order=(2,1,5))  \n",
        "fitted = model_arima.fit(disp=-1)  \n",
        "print(fitted.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                  D.spx   No. Observations:                 4920\n",
            "Model:                 ARIMA(2, 1, 5)   Log Likelihood              -25928.971\n",
            "Method:                       css-mle   S.D. of innovations             47.040\n",
            "Date:                Fri, 30 Jul 2021   AIC                          51875.943\n",
            "Time:                        05:17:13   BIC                          51934.452\n",
            "Sample:                    07-04-1994   HQIC                         51896.466\n",
            "                         - 05-10-2013                                         \n",
            "===============================================================================\n",
            "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------\n",
            "const           0.2352      0.128      1.841      0.066      -0.015       0.486\n",
            "ar.L1.D.spx     1.8957      0.008    241.537      0.000       1.880       1.911\n",
            "ar.L2.D.spx    -0.9694      0.009   -113.629      0.000      -0.986      -0.953\n",
            "ma.L1.D.spx    -2.2022      0.017   -127.399      0.000      -2.236      -2.168\n",
            "ma.L2.D.spx     1.3372      0.038     35.026      0.000       1.262       1.412\n",
            "ma.L3.D.spx    -0.0816      0.044     -1.856      0.063      -0.168       0.005\n",
            "ma.L4.D.spx     0.1216      0.036      3.423      0.001       0.052       0.191\n",
            "ma.L5.D.spx    -0.1610      0.016    -10.217      0.000      -0.192      -0.130\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1            0.9777           -0.2749j            1.0156           -0.0436\n",
            "AR.2            0.9777           +0.2749j            1.0156            0.0436\n",
            "MA.1            1.0207           -0.2589j            1.0531           -0.0395\n",
            "MA.2            1.0207           +0.2589j            1.0531            0.0395\n",
            "MA.3            1.1558           -0.0000j            1.1558           -0.0000\n",
            "MA.4           -1.2211           -1.8318j            2.2015           -0.3436\n",
            "MA.5           -1.2211           +1.8318j            2.2015            0.3436\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OebKQIf3nTUC",
        "outputId": "b1fa9dd7-a38b-4d3a-c6e8-4168958c1245"
      },
      "source": [
        "test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1231, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDOUuSVim-cp"
      },
      "source": [
        "fc, se, conf= fitted.forecast(1231, alpha=0.05)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "5LF4zwafncwq",
        "outputId": "d8b6a443-7ba9-495c-8613-1108a1d4c868"
      },
      "source": [
        "# Make as pandas series\n",
        "forecast_series = pd.Series(fc, index=test.index)\n",
        "lower_series = pd.Series(conf[:, 0], index=test.index)\n",
        "upper_series = pd.Series(conf[:, 1], index=test.index)\n",
        "# Plot\n",
        "plt.figure(figsize=(10,5), dpi=100)\n",
        "plt.plot(train['spx'],'red',label='training data')\n",
        "plt.plot(test['spx'], color = 'blue', label='Actual Stock index')\n",
        "plt.plot(forecast_series, color = 'green',label='Predicted Stock index')\n",
        "plt.fill_between(lower_series.index, lower_series, upper_series, \n",
        "                 color='k', alpha=.10)\n",
        "plt.title('Stock Index Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Index')\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_xBGYyio7zs"
      },
      "source": [
        "arima_exog= ARIMA(train.spx, exog=train.dax, order=(2,1,5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaKbvtCMpEgA"
      },
      "source": [
        "arima_exog_fit= arima_exog.fit(disp=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "q1lIuJiJpPOd",
        "outputId": "981b5824-f00e-4d9a-80d6-dc416bc30fd4"
      },
      "source": [
        "arima_exog_fit.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>D.spx</td>      <th>  No. Observations:  </th>    <td>4920</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(2, 1, 5)</td>  <th>  Log Likelihood     </th> <td>-25929.076</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>47.041</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Fri, 30 Jul 2021</td> <th>  AIC                </th>  <td>51878.151</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>05:26:18</td>     <th>  BIC                </th>  <td>51943.162</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>07-04-1994</td>    <th>  HQIC               </th>  <td>51900.955</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 05-10-2013</td>   <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>       <td>    0.0565</td> <td>    0.424</td> <td>    0.133</td> <td> 0.894</td> <td>   -0.774</td> <td>    0.887</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>dax</th>         <td> 3.173e-05</td> <td> 7.95e-05</td> <td>    0.399</td> <td> 0.690</td> <td>   -0.000</td> <td>    0.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D.spx</th> <td>    1.8956</td> <td>    0.008</td> <td>  240.077</td> <td> 0.000</td> <td>    1.880</td> <td>    1.911</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2.D.spx</th> <td>   -0.9693</td> <td>    0.009</td> <td> -112.853</td> <td> 0.000</td> <td>   -0.986</td> <td>   -0.952</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.spx</th> <td>   -2.2018</td> <td>    0.017</td> <td> -126.888</td> <td> 0.000</td> <td>   -2.236</td> <td>   -2.168</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2.D.spx</th> <td>    1.3368</td> <td>    0.038</td> <td>   34.983</td> <td> 0.000</td> <td>    1.262</td> <td>    1.412</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3.D.spx</th> <td>   -0.0815</td> <td>    0.044</td> <td>   -1.856</td> <td> 0.063</td> <td>   -0.168</td> <td>    0.005</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L4.D.spx</th> <td>    0.1213</td> <td>    0.036</td> <td>    3.414</td> <td> 0.001</td> <td>    0.052</td> <td>    0.191</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L5.D.spx</th> <td>   -0.1607</td> <td>    0.016</td> <td>  -10.162</td> <td> 0.000</td> <td>   -0.192</td> <td>   -0.130</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>           0.9778</td> <td>          -0.2749j</td> <td>           1.0157</td> <td>          -0.0436</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.2</th> <td>           0.9778</td> <td>          +0.2749j</td> <td>           1.0157</td> <td>           0.0436</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.0208</td> <td>          -0.2587j</td> <td>           1.0531</td> <td>          -0.0395</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.2</th> <td>           1.0208</td> <td>          +0.2587j</td> <td>           1.0531</td> <td>           0.0395</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.3</th> <td>           1.1568</td> <td>          -0.0000j</td> <td>           1.1568</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.4</th> <td>          -1.2217</td> <td>          -1.8329j</td> <td>           2.2027</td> <td>          -0.3436</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.5</th> <td>          -1.2217</td> <td>          +1.8329j</td> <td>           2.2027</td> <td>           0.3436</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:                  D.spx   No. Observations:                 4920\n",
              "Model:                 ARIMA(2, 1, 5)   Log Likelihood              -25929.076\n",
              "Method:                       css-mle   S.D. of innovations             47.041\n",
              "Date:                Fri, 30 Jul 2021   AIC                          51878.151\n",
              "Time:                        05:26:18   BIC                          51943.162\n",
              "Sample:                    07-04-1994   HQIC                         51900.955\n",
              "                         - 05-10-2013                                         \n",
              "===============================================================================\n",
              "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------\n",
              "const           0.0565      0.424      0.133      0.894      -0.774       0.887\n",
              "dax          3.173e-05   7.95e-05      0.399      0.690      -0.000       0.000\n",
              "ar.L1.D.spx     1.8956      0.008    240.077      0.000       1.880       1.911\n",
              "ar.L2.D.spx    -0.9693      0.009   -112.853      0.000      -0.986      -0.952\n",
              "ma.L1.D.spx    -2.2018      0.017   -126.888      0.000      -2.236      -2.168\n",
              "ma.L2.D.spx     1.3368      0.038     34.983      0.000       1.262       1.412\n",
              "ma.L3.D.spx    -0.0815      0.044     -1.856      0.063      -0.168       0.005\n",
              "ma.L4.D.spx     0.1213      0.036      3.414      0.001       0.052       0.191\n",
              "ma.L5.D.spx    -0.1607      0.016    -10.162      0.000      -0.192      -0.130\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1            0.9778           -0.2749j            1.0157           -0.0436\n",
              "AR.2            0.9778           +0.2749j            1.0157            0.0436\n",
              "MA.1            1.0208           -0.2587j            1.0531           -0.0395\n",
              "MA.2            1.0208           +0.2587j            1.0531            0.0395\n",
              "MA.3            1.1568           -0.0000j            1.1568           -0.0000\n",
              "MA.4           -1.2217           -1.8329j            2.2027           -0.3436\n",
              "MA.5           -1.2217           +1.8329j            2.2027            0.3436\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETB4oz2gqII1",
        "outputId": "6c30d558-855b-487b-8531-a21b229d61ac"
      },
      "source": [
        "fc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1645.69321746, 1646.95218238, 1642.05791495, ..., 1901.20047848,\n",
              "       1901.4356767 , 1901.67087493])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rmcm9I9Zp3PU"
      },
      "source": [
        "fc, se, conf= arima_exog_fit.forecast(1231, alpha=0.05, exog=test.dax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "IjL54EJMqLJF",
        "outputId": "5adb69cd-4b92-4b74-b9dc-45229a706045"
      },
      "source": [
        "# Make as pandas series\n",
        "forecast_series = pd.Series(fc, index=test.index)\n",
        "lower_series = pd.Series(conf[:, 0], index=test.index)\n",
        "upper_series = pd.Series(conf[:, 1], index=test.index)\n",
        "# Plot\n",
        "plt.figure(figsize=(10,5), dpi=100)\n",
        "plt.plot(train['spx'],'red',label='training data')\n",
        "plt.plot(test['spx'], color = 'blue', label='Actual Stock index')\n",
        "plt.plot(forecast_series, color = 'green',label='Predicted Stock index')\n",
        "plt.fill_between(lower_series.index, lower_series, upper_series, \n",
        "                 color='k', alpha=.10)\n",
        "plt.title('Stock Index Prediction with Exogenous variable (DAX)')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Index')\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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whgfwodarpxpY7Ah84BUudG+vY/Hdwv+fmc0L72ERJlTPeuOCRToLBVsiXU+oW9huYdOi/QPPA7YAQ6PMG4b/dfX74PVq/C+Qsfg3/lfoR/BjdaLe4LIVfRs87wvU/rM2n7Ew8oJiNZAUSxAUdNO7F78vFfjsWQudc982vuQ2Wx085zZRz/D9jhTtPQ2Xh2/5aeo9bc6vyd8C6WYWFxHkDAub31qW4IOtNcBy51yxmX2CH1dzDL5V74Ym1tEhfik3s3T8+L7Z+G6h/zSzUWFdQ78FJppZckTrVuRx/RZ/ET6Yuq0lkVkD8/Cfy/gYfwyIRagOQ6nfIjuUuu99PtG7Yka2fIZ+RFoALAi6vv4NmG5mNzvnIlvswr0FZAFnmNnb+Hs63RJR5jT898Wp4S2JZtbiH6iC7oo/Am5wzt0UNj3aZzQkMrjeB/8+rm1kmZi/xxrwBUFW1GY6O3heCD5LJn5M4ZP4TISR7sEHY7XBlvlsqScClzvn1pnZb/AB9f1s7RIbLlTPqIl5RDoDjdkS6aTM7MgG0uGG+vqHd+nZjO86VCsYJ/QKcJL5VNih9e6CzxT3tnMu1GXtWWC0RUkrHq0OzrlH8YPoLzazP8e6Ty20CJ9F8dcRdYmWkespfAbHyZEzglTK4T9A/QP/HXkBvrtVFT5bW1unIF6ID4T+YGaJkTODIDI0tm45PqV/atj8o/FZ+RoUBEP/xqdUPiDKNkL7GMq6mBZZJor/4G+AGj5WJgH4Nf7X/TdjWEesluC7WZ0R/B3ap3fxWf0SaXq81pbgOZZ9axPB+/sIftzKZfgEBbsAd4YVC90Y+1cRi1+ODxj/G7xeGDxfElHu1+Evgs/9s8CPzaxesG0Rt1WI0Yf4lqGLzSzUEoyZHYvPIBieEXE1MCx8O2Y2Gp9lMLwedVKlB+9vqMWtO40Iyj4DnIAPEBKI6ELI1ta68NsFjMWPg22peusMNJYd8JcRr0Pv138jC4ZpzvdYNEuBkeHvVVPM32frOnxg+Fgw+RT8uLr7nXPPRD7wSYt+HNqOmSXjA7BlBGPTnHOZwXqPMbOfRNn0/vgfUVqjS7pIu1DLlkjndS/QK0h1/AU+ScSh+AvQtfhfykM+wv86/lv8hd0a59O6X4sfOP62mf0NH1BMx1/M/D5s+b/gfwl+2sweDtbXF/8L5cVEGRvjnLsvSLBxi5kVOufqpQFuDcHYrJn47j8vmtl/8ON4jqV+15O/BHV+0fy9xj7CXyyMCvZvELDBzM7DD+o/1zm3DsDMfo3vevUL6ichaM39KTKzX+CTcnxsZk/gWyP2DOr0DlsvvK/BX8i+HbwvffEXa5/hx1Y05g/48RZvmtlD+F+Od8N3Pz0cP85sOf4C8qogoCsHXndh93EL8xD+3HnEzPbHn4On4S+ifxNlzNG2CAVSQ6mbtOQt/Pteju9W2yDnU69/jm/9+D98Ku+VrvHbKDRHatBVLdq2Qzc7vhbfmvWj4Ph8amY3AX80s2ecc//Bt+wsxn+OBuE/a5PwLQp3OedWB+v8KBir9JsgUAmlfv9BaLNhVbgan2jlfTP7Bz6hSl98i+DE4O+YOZ9y/Sr8d86bZvY4W1O/r6Vu8PgwPiBeaGaz8OOLLsafsylh5f4ZjH96HX8rg73w5/ZyYmvleDIofyOwwjkXucyL+LFcz5vZS/gWlIvxx6Kpz05UwWf3LeD3QSC9Hv9eNdaKNNjM5uO7GR6Cv4fVvPAuj1HE9D3WyPIv4AOcI/A/uEU61syG4a8Rd8G3DB6Nb6E8MSwJylR8l8R3G9jOfOBC/PfWc/hxWbvjWxPDu6beD5wD3GVmL0d8VxyNT+TTIVqiRVqkpWkM9dBDj/Z94LtLzcJfeBTjLzC/wv9yuHNE2aH4loUt1E+H/kP8P/pifEvG68AhUbbXFx/grQu29T3+V/l+wfwJwbpPi1juz8H0XzayL4NoOPX7ARFlQ9uZEDYtDt8VKzPYx8X4+7KsJSLlPf5C6tbgWJXjA5l38APlE/H3DSsA5kep53P4VprBzdmXBspF3b+I/Xw5qEspfpzEbGD/iHKn4i8Qy/AXrKcQkUY7KFcn/XcwbU/8uJ3cYPnV+PEU4Wm+fx5Mrwo/7kRJ4Y2/cH44OKbl+JaIc2M9PtHq2MjxywnK7xw27bBg2ltRykc7JofgW2XKw7cdlC2Jso4ZBMkam6jbGzSc+t0FZcbgW2TviVg2Hn//s/VAWtg5+9dgWgV+TM6VhN3qICjXK3j/NuI/z8/jgy2Hz4QZ+V7dh09+UYHvdrcIuDDKZy3yMx16DyPf29Px6fHLgjrMBQZEOT5Tg3OqHN/KMSny/cFnTFwYvM/l+Av9B4BdYzw/LNg3R0Ta/LD51+C/I8qCeh8XpR6Nna/1jgP+3lfP4btLFuBboXYj4txma+r34fhEOkX4gP9ewtLnB2XX0szvsRiOzyfAPxv4Tgo9yoPz4hV8T4XkiPOnEni0kW30xP9PeQ7fQlWFzxobreyB+B927g6bNiyox49iec/10KOjPkL3RBAREZEuJkgysQw4yzn3WFPlZcdg/sbp9wN7umbecHp7MbO78GMz93e6WJVOTGO2REREugAz6xll8m/wyW7e2s7VkY7tMXzLX+SYsQ4h6Ar7c+BaBVrS2WnMloiISNfw+2C83GJ8l61QyvSHnHPfN7qk7FCcTyISa4bZ7c45t5EWjp0T6WjUjVBERKQLCDJR3oDPRpmEb7mYA9zinOsq92cTEelUFGyJiIiIiIi0AY3ZEhERERERaQMKtkRERERERNqAEmTEwMwMfyO+1rwpp4iIiIiIdE7JQGZTGTMVbMVmd/yNXEVERERERAAG4m843yAFW7EpBvj+++9JSUlp77qIiIiIiEg7KSoqYo899oAYer0p2GqGlJQUBVsiIiIiIhITJcgQERERERFpAwq2RERERERE2oC6EbaC6upqKisr27sasgNKTEwkPj6+vashIiIiIlEo2NpGJSUlrFu3jiayPoq0CTNj4MCBJCUltXdVRERERCSCgq1tUF1dzbp16+jVqxf9+/fH345LZPtwzpGXl8e6devYd9991cIlIiIi0sEo2NoGlZWVOOfo378/PXv2bO/qyA6of//+rF27lsrKSgVbIiIiIh2MEmS0ArVoSXvRuSciIiLScSnY6oJmzJhBWVlZi5bNzMxk3LhxMZWdMmUKX375ZYu2E6uddtqJtWvXNlluW/ZZRERERKQtmBI7NM3MUoDCwsLCOjc1LisrY82aNQwePJgePXq0XwUjmBn5+fmkpaXVm1dVVUVCQufpPbrTTjvx4YcfMmjQoEbLNbbPXVlHPQdFREREuqqioiJSU1MBUp1zRY2VVctWa3EONm9u+0cTwfHFF18MwLhx48jIyCA3N5dzzz2X888/n/HjxzNy5EgApk6dygEHHEB6ejrHHXcc2dnZAKxdu7ZOwGJm3HrrrRx00EEMHjyY2bNn184bNGgQy5cvB2DChAlceeWVjBs3jiFDhtTWAyArK4tJkyYxYsQIJk2axJlnnsmMGTOi1n/+/PkMHz6c9PR0fv/739eZd+WVV3LggQeSkZHB+PHja1vVou3zvHnzGDt2LD/84Q8ZPXo0CxYsaPItFBERERFpVc45PZp4ACmAKywsdOFKS0vd559/7kpLS50rKXHOh0Jt+ygpcU0BXH5+fu3rc845x6Wnp7uioqLaabm5ubV/33bbbW769OnOOefWrFnjUlNT66xr5syZzjnnVq1a5ZKSklxlZaVzzrm99trLLVu2zDnn3BFHHOFOPvlkV1lZ6bZs2eIGDRrk3n33Xeecc6eddpq7/vrrnXPOZWVluV122cXdcMMN9eqdk5Pj+vbt6z777DPnnHMPPvigA9yaNWvq1fnxxx93kydPbnCfN2zY4Gpqamr3aZdddnFlZWVNHrvOps45KCIiIiJtrrCw0AEOSHFNxBGdpz+ZbJOf/OQnJCcn176eN28ec+bMoaysjLKyMnbaaacGl506dSoAw4YNIyEhgezsbAYOHFiv3BlnnEFCQgIJCQlkZGSwevVqDjnkEF577TVmzpwJwK677srxxx8fdTvvvfce6enpjBgxAoALLriAX//617XzX331Ve69916Ki4upqalh06ZNDdZ5zZo1TJ06lXXr1pGQkMCmTZtYs2YNw4YNa+QoiYiIiIi0HgVbraVXLygp2T7baYHwm96+/fbb3HPPPSxdupSdd96Z+fPnc/311ze4bPhYoPj4eKqqqrapXKwZ9MLLfffdd/zqV7/if//7H0OGDOHTTz9l/PjxDS575pln8qc//YnTTjsNgL59+yqBhoiIiIhsVxqz1VrMoHfvtn/EEKgkJydTWFjY4Pz8/HySk5Pp168fFRUVPPjgg615JOo56qijeOSRRwDIycnhxRdfjFrukEMO4dNPP+WLL74A4OGHH6aiogKAwsJCEhMT2W233XDOcd9999VZNnKf8/PzGTx4MABz584lPz+/tXdLRERERLaTmTPhscfauxbNp2CrC7riiis4+uija5NFRDrmmGMYOnQoQ4cOrU0q0ZbuvvtulixZwogRI5g6dSpjx46NmjWwf//+PPzww5xyyimMHj2ar776in79+gEwatQozjzzTPbbbz8OPPBA9txzzzrLRu7z3XffzWmnncYPf/hDli1bVq+8iIiIiHQOq1fD734HZ50FNTXtXZvmUer3GHS21O8dTWlpKYmJiSQkJLBx40YOPvhg5s6dy9ixY9u7ap2ezkERERHp6r74AoYP93/n5UEjqQa2i+akfteYLWlzX331FdOmTcM5R0VFBZdccokCLRERERGJSfgomtzc9g+2mkPBlrS59PT02vtxiYiIiIg0R3jOtZwcCBJXdwoasyUiIiIiIh3WunVb/z7ssParR0so2BIRERERkQ7rgw+2/t2tW/vVoyUUbImIiIiISIcVH++fJ01q33q0hIItERERERHpsEK3Sx01qn3r0RIKtkREREREpMMKBVt9+rRvPVpCwVYXVVxcTFJSEhdccEFM5d944w1efvnlbd7u2rVro96wGKCgoICzzjqLkSNHkp6ezsiRI5k3bx4Ay5cv54knnmizbYfLzMxk3LhxLdrGAQccwBtvvNGiZUVERESk+WbN8s9JSe1bj5ZQsNVFPfnkk+y///4899xzlJSUNFm+tYKtxlx77bX079+fFStW8Omnn7J06VIOPPBAoHWCrVjtvvvuLFmyZLtsS0RERERaxyuvtHcNmk/BVitxDjZvbvuHc7HVZ9asWVx11VWMHz+eJ598snZ6YWEhP//5zxk5ciSjR4/m/PPPZ/ny5TzwwAM89thjZGRkcNNNN9VrJSopKcHC7ig3depUDjjgANLT0znuuOPIzs5usk7r1q1jt912q11PcnIy++67L7m5uVx//fUsXryYjIwMLr74YgAWLlzImDFjSE9P54gjjuDzzz+vXdfs2bPJyMhg9OjRHHDAAaxdu7bOtioqKjjrrLOYPn061dXVdeZF7puZceutt3LQQQcxePBgZs+eXTvv3XffJSMjg5EjR3LeeedRFXajh+zsbE4//XQOOuggRo0axbXXXgvAW2+9xZAhQ9i0aRMAv/rVr7jwwgubPD4iIiIi0rAYO2x1LM45PZp4ACmAKywsdOFKS0vd559/7kpLS11JiXM+FGrbR0mJa9Jnn33mBgwY4KqqqtwLL7zgDjnkkNp55557rvvFL37hqqurnXPO5ebmOuecu+GGG9xll11WW27NmjUuNTW19nVxcbHzp4urs5xzzt12221u+vTpUZcL9/rrr7u+ffu6MWPGuF/+8pduwYIFtfNmz57tTjrppNrXOTk5rm/fvu7TTz91zjk3d+5cN3z4cFdTU+MWL17sBg0a5DIzM51zzm3evNlt3ry5dtv5+fnuyCOPdLfcckvUekTWEXAzZ850zjm3atUql5SU5CorK115ebkbOHCge/XVV51zzi1cuNABbvHixc455yZNmuTeeOMN55xzlZWVbvLkye6pp55yzjl36623uhNOOME9+eSTbvTo0a60tDRqXbZV+DkoIiIi0tVUV2+9Dn799faujVdYWOgAB6S4JuKIhPYL86StzJo1i2nTphEfH8BMpOMAACAASURBVM+UKVOYPn06q1atYvjw4bz44ou8//77xMX5Rs3+/fu3aBvz5s1jzpw5lJWVUVZWxk477dTkMkceeSTfffcdb775Ju+++y7Tp0/n5JNP5v77769X9v3332fUqFGMCtLOTJ06lV/+8pesX7+el156ibPPPpvddtsNgF69etUuV1FRwWGHHcZVV13FtGnTYt6fqVOnAjBs2DASEhLIzs5m06ZNJCQkMHHiRAAmTZrE3nvvDcDmzZt57bXXyMnJqV1HSUkJX375JQBXX301xx57LBdddBEffPABPXr0iLkuIiIiIuJVVm79Oy+v/erRUgq2WkmvXhDD0KhW2U5jKisrmTNnDomJibXJJ7Zs2cKsWbOYOXNmzNtJSEio0/2urKys9u+3336be+65h6VLl7Lzzjszf/58rr/++pjW27t3b6ZMmcKUKVM4/vjjmTRpUtRgq6USExM5/PDDWbBgAT/96U9JTEyMabnwYCg+Pr5Od8FwoS6QLujP+d5770UNpIqLi/nmm2/o3bs3eXl5/OAHP2juroiIiIjs8MIvyYLfvzsVjdlqJWbQu3fbP8KGTUU1f/589t57b9avX8/atWtZu3Yt7733HnPmzKGyspITTzyRmTNnUlNTA0Be8BNBSkoKhYWFtevZddddcc7VjpN69NFHa+fl5+eTnJxMv379qKio4MEHH4zpGL3yyivkh3J3Ah999BFDhgyJuv2DDz6YFStWsHLlSgCeeOIJBgwYwIABAzjhhBOYO3cuWVlZgA8mt2zZErwPxoMPPsgee+zBySefTGlpaUx1i2bYsGFUVVWxePFiABYtWsTq1asBSEpK4sgjj+RPf/pTbfnMzEzWrVsHwAUXXMDUqVN56qmnOPvss9m4cWOL6yEiIiKyowoPtpSNUNrdrFmzarvEhQwfPpwBAwawYMEC7rzzTsrLyxk1ahQZGRn84Q9/AOCUU05h+fLltQkyEhISuPfeezn++OM58MADqQxrwz3mmGMYOnQoQ4cOZdy4cWRkZMRUtxUrVjB+/Pja1O/z589n7ty5APzoRz+ivLyc9PR0Lr74Yvr3789jjz3GtGnTSE9P5+9//ztPP/00Zsb48eO54YYbmDx5MqNHj+aII46oDRpD/vrXvzJmzBimTJkSUzbGaLp168aTTz7J5ZdfzqhRo5g3bx6jR4+unf/YY4/x9ddfM3LkSEaNGsWpp57Kxo0bue+++9i0aRPXXXcdhx12GL/4xS+YNm1abWuYiIiIiMQmPNhK6IR98kwXgE0zsxSgsLCwkJSUlNrpZWVlrFmzhsGDB2tMjrQLnYMiIiLSleXkwK67+r87SthSVFREamoqQKpzrqixsmrZEhERERGRDinUstUZW7VAwZaIiIiIiHRQCrZERERERETagIItERERERGRNrA12HKUlJTUZtTuLBRsiYiIiIhIhxQKtuLiHNnZ2XXu/doZKNgSEREREZEOqazMR1txcdWUl5e3c22aT8FWFzRo0CCGDh1KRkYGI0aM4P7779/mda5cuZJBgwYB/ua948aNa3KZu+66i+zs7BZt78orr2TGjBlR5z333HPsv//+ZGRkMGzYMI466qjaJuVt2WbIueeey1133dVkueuvv57HHnus2et/8cUXmTBhQgtqJiIiIrLjqKioICdnIwCJidbOtWmZTjrUTJry5JNPkpGRwbfffkt6ejrjxo0jPT29dn4oOImLa368vfvuu7NkyZImy911111MmDCBXUM3R2gFWVlZXHTRRXz00UfstddeAHz88ceYWZttsyE33XRTm29DREREZEeUmVnGsccan366C6AEGTs85xybKza3+aO5N6Hea6+9GDp0KP/3f//HjBkz+PGPf8zkyZMZOXIkWVlZLFy4kMMPP5z999+fgw46iMWLF9cuO2PGDPbdd1/2339/nnjiidrpa9euJS0trfb10qVLOfzwwxk9ejTp6em88MIL3HTTTWRmZnLGGWeQkZHB8uXLqays5Oqrr+aggw4iIyOD008/nfz8fMAHUZMnT2bEiBFMnDiRdevWRd2fnJwc4uPj6du3b+20MWPGYGZRt1lSUsL555/PyJEjGTlyJDfeeGPtcuvXr+e0005j1KhRpKenc91119Xb3pIlSxgxYgQffvhhvXnhLWAzZszgjDPO4IQTTmDEiBEcddRRbNq0CYDKykouueQS9t1333rHGGDOnDmMHTuWMWPGMH78eD755BNqamo45phjmDlzJgCrV69m4MCBfPnllw280yIiIiJdw+bNm7n++jI+/bR77bT4+Has0DbopDFix7OlcgtJtyW1+XZKrimhd7feMZdfsWIFX3zxBaNHj2blypUsXbqUZcuWscsuu/DNN98wY8YMFi5cSEpKCl9//TXjxo1j7dq1LFq0iKeffpqPPvqI5ORkzj777Kjr37RpEyeffDLPPPMM48aNo6amhoKCAk466SQefvjh2hY2gFtvvZXevXvzwQcfAHDzzTdz7bXXcv/993PppZdy0EEHsXDhQtavX1/bRTBSeno6hx9+OHvttRdHHHEEhx56KD/72c8YMGAA119/fb1tXnXVVZSXl/Ppp59SWlrK4YcfzrBhwzjjjDM466yzmDRpEs888wwAeXl5dbb15JNPctttt/HSSy8xePDgJo/1+++/z0cffUS/fv0488wzefDBB7nmmmt46KGH+PLLL/nss88AmDx5cu0y77zzDo8//jhvvfUW3bt3Z8mSJfzsZz/js88+Y+7cuRx44IEccMABXHHFFfzlL39h6NChTdZDREREpLMqKioiJyeHb76p20spPr55DQ4dhYKtLuqMM86gZ8+e9OrVi4cffph9990XgClTprDLLr459uWXX+brr79m/PjxtcvFxcXx3Xff8dprr3H66aeTkpICwPTp03n77bfrbWfp0qUMHTq0dgxXXFxcnVancP/+978pLCzk2WefBXw/3NA4sNdee622FWfAgAGceOKJUdcRFxfHs88+yxdffMGbb77Jf//7X2655RY+/PBD9tlnn3rlFy1axB133EFcXBy9e/dm2rRpvPrqqxx33HG8/fbbLFy4sLZs//79a/+eM2cO8fHxLF68mD59+kStS6RjjjmGfv36AXDIIYewYsWK2n2bNm0a3bp1A+D8889n1qxZALzwwgt88sknjB07tnY9mzZtorS0lJ122om5c+dy5JFHcu655/LTn/40pnqIiIiIdDbOOQoKCsjJySEuLo699zbCOwMlJyvY2qH1SuxFyTUl22U7sQhv3QmXlLS19c05x9FHH828efOaXF9oTNS2cM5x7733MmnSpG3e3rBhwxg2bBjTp0/nmGOOYf78+fz2t7/d5vWGpKens2TJElasWFEnGG1Mjx49av+Oj4+nKpSrtJE6OOc455xzuPXWW6OWXbZsGf369WP9+vU451rlfRARERHpSGpqati0aRM5OTn06NGDnj17MmvW1mvWiy4q4aSTtrRjDVtOY7ZaiZnRu1vvNn+05sX25MmTWbRoEZ9++mnttFAXv4kTJ/L0009TXFyMc46HHnoo6joOPfRQvvrqq9qEGaEPC0BKSgqFhYW1ZU8++WTuvPNOtmzxH5YtW7bUdq2bOHEiDz/8MODHb82fPz/q9tavX88777xT+zo/P581a9YwZMiQqNucOHEis2bN8mPqNm9mzpw5TJo0iaSkJMaPH88dd9xRWza8G+Ho0aNZsGAB559/Pi+//HKjx7EpEydOZO7cuVRWVlJRUcHs2bNr55144onMnTuX7777DvDHLzQ+7OOPP2bmzJksW7YMgNtvv32b6iEiIiLS0VRXV5Obm0t2dja9evWiZ8+e9crccEMRo0dXtkPttp2CrR3YPvvsw7x585g+fTqjR49m+PDhtQkfpkyZwmmnncaYMWM44IAD2HPPPaOuo0+fPjz//PNcffXVpKenM2bMmNpg6NJLL+XCCy+sTVZx1VVXceCBBzJ27FjS09M5+OCDWb58OQB333037733HiNGjGDatGkcddRRUbdXVVXFTTfdxA9+8AMyMjIYN24c55xzDieddFLUbV533XUkJiYyatQoxo4dy4knnsjpp58O+K6CH374Ifvttx8ZGRncd999dbY1fPhwFi5cyGWXXVbb9bElLrzwQvbdd19GjBjB4YcfXqfFcdy4cdx+++2ccsopjB49mv32248nnniCoqIizjzzTB5++GF23XVXHn30UR566KE6gaaIiIjI9lRZCUFC61ZaXyXZ2dls2LCB5ORkunf3CTGefnprwJWW1oobbAfW3Ox2OyIzSwEKCwsLa8cwAZSVlbFmzRoGDx5cpwuZyPaic1BERES2h7Iy2HNPGDwY3n+//vzFi+GZZ+DPf4akiJxxzsG778Lw4RAa2l9WVkZOTg7FxcWkpqYSH5ZucMCA3Wv/vv/+fE4+ubR2TNfgwYPp1Su2YTVtpaioiNTUVIBU51xRY2U1ZktERERERBr14YeQl+cf0YQ6JaWkwJlnwt57Q2IifP89fP45nHwy9O8Pubk+tXt2djZlZWWkpaU1et/Xzt6ypWBLREREREQaFWtnuH/8A/70J9hrL9+KtWwZDBzo5+XlbU3tXlVVRVpaWpdP/qVgS0REREREGhXrWK2NG/3zt9/6B8C6dVvnZ2ZmEhcXF+qG16TOHosp2BIRERERkUa1VpqHxMTEqBkHG9LZgy1lI2wFSjIi7UXnnoiIiGwPrZWFsKlAq6td2qhlaxskJiZiZuTl5dG/f/8u3+dUOhbnHHl5eZgZiYmJ7V0dERER6cJaKwjauDGOzMx4Ro2Kft+su+6KSGVI546+FGxtg/j4eAYOHMi6detYu3Zte1dHdkBmxsCBA+ukSxURERFpba0VbKWn7wrAf/6TF/VGxYsW1b2VTWdvy1CwtY2SkpLYd999qazsnHe1ls4tMTFRgZaIiIi0uda8mTHAs8/25Fe/6sOJJ5Zy0EEVvPpqd/bZp4rly7u17obamYKtVhAfH68LXhERERHpslp7LNXs2b2pqTHuuiu50XKdvWVLCTJERERERKRRrd2yVVMTWxSlYEtERERERLq0xlq2lB25YQq2RERERESkUQ3FUzU1NeTl5bXZdtWyJSIiIiIiXVq0boRVVVVkZ2eTm5vbZtvt7MGWEmSIiIiIiEijIlu2KioqyMnJobCwkJSUlPapVCegli0REREREWlUeMvWli2lrF6dxccflzF16g94883e27Tu/v2rG5ynli0REREREenSwlu2fv7zCh5/fK/a19Om9Wu1dUfq7MFWu7Zsmdk1ZvY/Mys2s1wz+7eZDY0o84aZuYjHAxFl9jSzl8xsS7Cev5hZQkSZCWb2sZmVm9nXZnbudthFEREREZFOLzwgevzx1FZd94YNXfd+te3djfAI4H7gYOBoIBF4xcwi2yL/AewW9vh9aIaZxQMvAd2AQ4FzgHOBm8LKDA7KLAYygLuAf5rZ5LbYKRERERGRrsI5R2Fhcbtsu7O3bLVrN0Ln3DHhr4PWplxgf+CtsFlbnHPZDaxmEjACmOicywGWm9l1wJ/NbIZzrgK4GFjjnLsiWGaVmR0OXA4sbLUdEhERERHpQqqrq9mwYQP5+RVA8nbffmcPttq7ZStSqE1yU8T0qWa2wcxWmtltZtYrbN4hwIog0ApZCKQA+4WVWRSxzoXB9HrMrLuZpYQetMeZJSIiIiLSjioqKsjKyiIvL49u3bq3d3U6pQ6TIMPM4vDd+95xzq0MmzUP+BbIBNKBPwNDgVOD+bsC4YEWYa93baJMipn1dM6VRsy7BrihhbsiIiIiItKplZaWkpOTQ0lJCampqcTHt1fY0Ej2jE6gwwRb+LFbI4HDwyc65x4Ke7nCzLKA18xsiHNudRvV5Tbgr2Gvk4F1bbQtEREREZEOo6ioiNzcXCoqKujTpw9mhnPt05+vs3cj7BDBlpndBxwPjHfONRXUvB887wOsBrKBgyLK7BI8Z4c97xKlTFGUVi2cc+VAeVj9mtoFEREREZFOzTlHfn4+OTk5xMfHk5aWVjsv/D5bErv2Tv1uQaB1CnCUc25NDItlBM9ZwfNSYJSZ7RxW5migCPg8rMyPItZzdDBdRERERGSHVl1dTU5ODllZWXTr1o2kpKQ686uq2qdenb3No71btu4HfgacBBSbWWiMVaFzrtTMhgTz/wNsxI/ZuhN4yzn3aVD2FXxQNcfMfo8fn/VH4P6ghQrgAeBXZnY78DBwFHA6cFxb76CIiIiISEdWUVFBbm4uBQUFJCcnk5iYWK/Mli3qRtgS7Z2N8Bf4DIRv4FuqQo8zgvkVwER8QPUFcAfwLHBCaAXOuWp8F8RqfEvVXOBR4PqwMmvwgdXRwCfAFcDPnXNK+y4iIiIiO6wtW7aQmZlJQUEBqampUQMtgM2b2zts6Jza+z5bjcaqzrnv8Tc+bmo93wJTmijzBvDD5tRPRERERKSripYIoyElJWrZaon27kYoIiIiIiLbkXOOTZs2kZeXR1xcXJ1EGA3ZvFnBVkso2BIRERER2UFUV1ezYcMGNmzYQI8ePejZs2dMy7XXmK3Bg9spM0crUbAlIiIiIrIDiCURRkPaa8xWaqpuaiwiIiIiIh3Yli1byM7OprS0lNTUVOLj45u1fEVFG1Wsi1OwJSIiIiLShRUWFpKbm0tlZSVpaWmNJsJoSFVVJx881U4UbImIiIiIdEHOOTZu3EheXh4JCQkxJcJoSE1NK1ZsB6JgS0RERESki6muriYvL48NGzbQq1cvevTosU3rKyjQfbZaQsGWiIiIiEgXsi2JMBry8cfdWqFmOx4FWyIiIiIiXURpaSk5OTmUlJS0KBGGtC4FWyIiIiIiXUBxcTE5OTmUl5fTp0+fFiXCkNalzpciIiIiIp2Yc478/HzWr19PdXV1mwRagwZ17psLtxe1bImIiIiIdFLV1dW1GQd79OhBz54922Q7rnPfW7jdKNgSEREREemEKisrycvLY9OmTSQlJdGtW9slsVDq95ZRsCUiIiIi0smUlZWRk5NDcXHxdkmE0R7Bllnnb07TmC0RERERkU5k8+bNrF+/npKSEtLS0rZLxsGampaPAXvttVwefnhTs5drhYz17U4tWyIiIiIinURRURE5OTm1iTC2l20ZszVsWBXDhjU/wUZCglq2RERERESkjTnn2LRpE5mZmQCkpKRs1+1XVzc879lnN7TJNhO6QLOQgi0RERERkQ7MOcfGjRvJysoiMTGR3r17b/c6NDZm6+CDK/jNb4pbfZtq2RIRERERkTZTU1NDXl4e2dnZ9OrVq81Suzddj8bnX3llMXfckd+q21TLloiIiIiItInq6mqys7PJzc0lKSmJ7t27t1tdnGs8QYYZ7LVXI30NG3DOOZtJS6vh73+vn0BDLVsiIiIiItLqKisrycrKYtOmTaSkpLTpPbRiEUvq98TE5gdHp5yyhZUrszn00Ip687pCy1YX2AURERERka6jvLyc7Ozs7XYPrVjEFmw1f71xcb5VzKI0nHWA3d5matkSEREREekgtmzZwvr16ykuLt5u99CKRSzBVnx84y1b996bT8+eNdx8c2HttLggGokWbLWkpayjUcuWiIiIiEgHUFJSQnZ2NhUVFfTp0weLFoG0k1iCrbgozTjJyVsXPPXUUk46qZRvv40HUoHwIKt+YNVB4sxtopYtEREREZF2VlhYSGZmJtXV1aSlpXWYQGvlygQGDNidsrKmw4bwYMvMB08nnlhap0xkABVaJtr4LLVsiYiIiIhIiznnKCgoIDs7m4SEBJKSkrZ7HWpqGh43NWVK/5jXk5S0NTh67bU8Fi/uztlnb6lXLjypYrdufpmUFMfFF5ewenUCixZ1xzlj4sTy2Heig1KwJSIiIiLSDkI3K87JyaFHjx7tcg+tqio4+uj+9O9fw1NPbaw3v7o69ha2gQOr+f3vi0hKcgwdWsXQoVVRyw0YUM3llxdTVmZ1ylx3XREAmZlxvP12d04+uTTq8p2JOdf5m+fampmlAIWFhYWkpKS0d3VEREREpJMLD7R69uxJjx49tvP2IT8/jvXr4znmGN96tX59Zr1yAwbs3uh6evSoYfXq7DapY7hQC+DgwYPp1atXm2+vMUVFRaSmpgKkOueKGiurMVsiIiIiIttRTU0NeXl5ZGdn06tXr+0aaP3nPz345JNE/vSnZEaN2pXnn9/amhatDWbo0Mo6r3v02Jrw4v33c/jkk5w2q2tXoG6EIiIiIiLbSXV1NXl5eWzYsIGkpKTterPizz5L4MIL+9aZ9uCDW8eIOVd/3NbQoVV8+eXWG2ilpDjKyvzfAwdWt1lduwq1bImIiIiIbAfV1dXk5OSwYcMGkpOTt2ugBfDNN423s0RL714Z1rB10UUlzJ69iSFDKpk9u/74LqlPLVsiIiIiIm2sqqqK3NxcNm3aREpKCgnRcp23saayyVdX10/BXlnpF5o5s4Cf/tRnFnzrrby2qF6XpGBLRERERKQNVVVVkZ2dTUFBAampqcR30Lv1RrZsvfJKdxYt8uPJEhKUVK8l1I1QRERERKSNVFZWtlug9dVXCeTmxn6579zWpq/SUuO88/rVvg7dD0uaRy1bIiIiIiJtIBRoFRYWbvdAa926eCZM2BmIntI9mvCWrdWr69a1HXo9dglq2RIRERERaWXhgVZaWlqrBlorVybw+ecJFBQYjzzSi40b61/Sf/JJYr1psYzZClm3rm50lZiolq2WUIwqIiIiItKKKisrycrKoqioiLS0NOLiWq99Y8sWY/Jk32I1blw5S5Z059lnK1iwYEOdcuGtVM8915P33+/GYYeVN7ru0DJbthgXXFA3RbxatlpGh01EREREpJVUVFSQk5PTJoEWQH7+1uapJUu6A/Dxx/VTyK9fv7Ul7de/7gMQtQUs3HffJdCnTyV33ZVUb15BgTrEtYSOmoiIiIhIK6ioqKjTdbC1Ay3wiSticfPNqfWm5eQ03pVxypT+lJbCqlX1uyBmZ3fMDIodnVq2RERERES2UUVFBVlZWRQXF7dZoAVw773JUaf/4x+9mTixjNWrE1i3LnpgFD4mqyH77LM7vXvXv7txXJzGbLWEgi0RERERkW2wvQItgPfeq99lEGDGjFRmzKjfmhUudIPipmzeXL/+TSXXkOgUbImIiIiItFB5eTnZ2dkUFxfTp08frI2jkm1JVFFU1PK6TZjQeHINiU5jtkREREREWqCsrKy2RWt7BFoACQmxdefr06d+n8H+/et3D4zVgAEx9EGUehRsiYiIiIg0U1lZGdnZ2WzevHm7BVoQe8tWeXnr1qcNe0Z2aTpsIiIiIiLNUFpaSmZmJps3byYtLa3JQGvp0m4MGLA7d9yxNblFQYExa1ZvMjObdzke672RKyrq1ynatFgpQUbLaMyWiIiIiEiMtmzZQlZWFuXl5TEFWs7BaaftBMBf/5rMsmWJLF7cg513riY3N54vvkjgL38pjHn78fGxBT1VVfXrtXlzy4OtWIM8qUvBloiIiIhIDDZv3kxWVhaVlZWkpaWRlxdHnz41nHLKTqSl1dCtm6N3b8dNNxWSluZYsKAHF1/ct846Fi/uAUBuro9e5s3rzeDB1VxySUlMddiWoGft2pZf+ivYahl1IxQRERERaUJxcTGZmZlUVVWRmprKq692JyNjV447bic+/rgbr7/eg5df7smzz/Ziv/12Y9WqhHqBVkNuuSWlzuusrDjWr/etXuef34fPP98aJPXq1T7d+ZT6vWXUsiUiIiIi0oji4mKysrJwzpGS4gOj22/3zytXRr/v1cSJOzdrG3/6UzJXX11MaalxwAG7AmDmcM5YurQ7q1ZlAzB2bAXvvNO9pbvSYgq2WkYtWyIiIiIiDSgsLCQzMxOA5OTksOmtG33ce28yn32WQFbW1stz5/w2ioriKCvz0yoqWnWzncab697kj8v+SGV1ZXtXpVkUbImIiIiIRFFQUEBWVhbx8fEkJSXVTs/OjmP9+tbvIDZp0s4880yvqPOGDNmdAw/cmeLiHevyfXXBas5++WymvjyVJ1Y/wf+y/tfeVWoWdSMUERERkR2Cc1BTE1uyh4KCArKzs0lISKBXr7oB0N//ntTAUtvu7ruTG5yXmZnAv/61Y1y+F5YXcveyu/nnyn9S7apJsAROGXwK/Xr2a++qNcuO8W6JiIiIyA7lyivhs8/gxRe3BlcnnghffgkrVkD3RoY95efnk52dTWJiYp1A6+23u/Gvf/WmoGDHal3anqpqqpj7xVzu/PhONpRuAOCoPY5ixsEz6Ov6Mrjf4HauYfMo2BIRERGRLqGiAs46C44+Gu64w0974w340Y/83y++6J/ffReOPDL6OkItWt26daNnz561052DM87Yqe0qLyxZv4Qb37uRVZtWATAkdQgzDpnBUXschXOOgoKCdq5h8ynYEhEREZEuYfZsePpp/wiprvbPLixjemJi/WU//xxOO62aiy4qYf36/nzySRL/+Mem2rLff68bTbWVNYVrmPHeDBZ9twiAlG4p/G7/33HW8LPoFh8922NnoWBLRERERLqE/Pz600Ipy6uqtk6LFmz99KdVrFqVwOWXD6ydtmBBT049tRSAL77QZXNr21S2ibuX3c0jnz1Clasi3uI5Z8Q5XD7mcvr2iO0eZR2dzhoRERER6RJclPv9xgXDq8JTpkcGWwUFBRQU1M8CWFy8Nb37Qw+1XVKMHU1ZVRmPrnqUu5fdTUG57xp41B5HccPBN7BP2j7tXLvWpWBLRERERLqsUMtWefnWaeHBVigZhtmQestWVW0NtpYu3f43Eu5qnHP8e/W/uf3D2/mu+DsAhvUZxvUHX88RA49o59q1DQVbIiIiItIlRGvZCgVb4S1b69bB734H06dv5p//jCc+fiBm9TMMVnau++d2aO9kvsPN79/Mig0rANi11678Sv1c0QAAIABJREFUdv/fcuYPziQ+ruuOh2vXYMvMrgFOBYYBpcC7wFXOuS/DyvQA7gDOBLoDC4FLnHM5YWX2BP4OHAmUAP8CrnHOVYWVmQD8FdgP+B74o3PukTbcPRERERHZjhrrRhjesnXeeZCTAwsX9q6dlpJSU2/Z8JYtaZlvCr/hj+//kYXfLgSgd2JvLkm/hAtHXUjvxN5NLN35tXfL1hHA/cD/grrcCrxiZiOcc5uDMncCxwE/AQqB+4DngMMAzCweeAnIBg4FdgMeBSqBPwRlBgdlHgCmAj8C/mlmWc65hW2/myIiIiLSHuLiYMuWui1bOTn1y5WW1g+sQkk1brwxpY1q13WFkl/M/mw21a6aOIvj7OFnc8WYKzrdjYm3RbsGW865Y8Jfm9m5QC6wP/CWmaUCFwA/c869HpQ5D1hlZgc7594DJgEjgIlBa9dyM7sO+LOZzXDOVQAXA2ucc1cEm1plZocDl+NbykRERESkk4vWsvXMMzB+PEyf3viylZX1g60HHkjiN78p2ebkGCecUMrLL/eIuo1Yde/uKC/v+C1t5dXlzP5sNvcuv7dO8ovrxl7HD/r8oJ1rt/11tNtfpwbPm4Ln/YFEYFGogHPuC+A74JBg0iHAivBuhfgAKgXfZTBUZhF1LQxbh4iIiIh0UhdfDFOnRg+27rnHPz/4YPPXW1y87ZfKw4dX8sAD+SQn1++mCPD88xtiWs+rr+bGVG7MmAruuSdKDvw25pzjhdUvcOTTR3Lz+zdTUF7A8L7DeeyYx5hzzJwdMtCCDhRsmR+VeBfwjnNuZTB5V6DCORd5u+icYF6oTGRjcE7YvMbKpJhZz4jpmFl3M0sJPYDkZu+QiIiIiLS5igofSM2bB2vXtv76jz9+p21aPpSg45JLSqLOP+igCoYPbzoTx957V3P88aWcd1709fhtFLNgwQYOOaS8wTJt4YPsDzjhhRO45PVL+Lb4W3bptQszx81k4SkLmbDHhO1al46mvcdshbsfGAkc3t4VAa4BbmjvSoiIiIhI48Jbs8LHZbWWZcu6bdPyoWBr+vTN/PGPqVHLxJL10AwefDAf52D27OjdGkPjzuK3U3K/r/K/4tb/3cor374CQM+Envxy9C+5aNRFO0Tyi1h0iJYtM7sPOB440jm3LmxWNtDNzNIiFtklmBcqs0uU+cRQpsg5VxqlSrfhuzSGHgOjlBERERGRdhYebNVE76nXruLiXPDccJmvv05seGYEa2TY1ujRPmpLCGtOueyyYpKTa7jwwoZbxACOO85fEk+ZEu3SuK71Jeu58q0rOerZo3jl21eIt3imDpvK0jOWcvmYyxVohWnv1O8G3AucAkxwzq2JKPIRPqvgj4Bng2WGAnsCS4MyS4H/z959h0dRrQ8c/570RpHeBUSxUAUponhFxYbdiwULXrFjL1hQufYGKCAoiohg73KxN/yBCIpKEayAonQICaSX8/vjZNhJdrbP7iab9/M8++zuzOzMSbLZnXfec95zh1Kqhdba6sx6DJAPrLJtc0KNfR9j20c1WusSYE/+Vfl7VwshhBBCiLipqPA8/rTmCP1awB5kKaXROnrnla1bV1Qd0xOB9upVyk8/7WLHjiSeecZ3oY+uXcuZOHEjmZma9u29RtkAkFucy9RlU3nup+corigGYOjeQ7n9kNvZd699XfxJEke8uxE+CZwLnALsUkpZY6zytNZFWus8pdQMYIJSagcmgJoMLKqqRAjwMSaomq2UugUzPus+4MmqoAlMyffRSqlHgOeAIcBwTEl5IYQQQghRR33xhefxluBqSMSU/Zr9119vYcGCdD75JJ2PP3YOaOySkjSVlcEHZ1ZGq2Y3wuRk/xkx61jZ2dqxyAgpRUxbNo1JP04ivzQfgP6t+nNbv9s4pOUhQbevPop3sHVF1f2XNZZfBDxf9fh6oBKT2dozqbG1oda6Qik1DDOp8SKgADOp8V22bdYqpU7EzNl1LfA3MErm2BJCCCGEqNv++Sc+x23ZsoLNmwMPjrIHOR06VHDuuYV88UV6UMdIS9MUFwcfbCUn66p7zzIrkxaoi6WVgasWlKXthoOfgUGPcN8SMzrngCYHMKbvGI7ucLT0/gpCvOfZCvgX0loXA1dV3Xxt8yfe3QRrbvMl0DvEJgohhBBCCOFl4MAS3nknK+B2TmO1HLNHDoYNK+aNN7Lo3j24yh9WkGUFXeAJsqwJmq31FRXVT8OrtTNjJ/SdBoeOh6ztALTNacuNfW7kzC5nkpwUowocCSDemS0hhBBCCCHqHHtwkpqqfU5YbB8/ZQk22DrjjCLuvTePrKzqL/A19supG6EVbNm7I6akVB/rZtoJWwu3Mn3FdLh+NqTvMit27AMLxrDw3aGkJgVfyEMYEmwJIYQQQog6K1492ewBzcyZO3jkkQY89thOFi9Op1evUk46qbnP9gUTbLVuXcHgwaHNl+XcjdDcp6V5DpqaqikpsTWs8To+SrmPCa+8aApfpAObu8HCMbDiHNDJpCZtCKktwpBgSwghhBBCiBDZM1tHHlnCkUeawOigg8p9bmcJJtg69FDfgZZSzvuwgiz7Ma3tWrSoZOzYPDIyNBMnNmD3bqDZajj8Qej+EkuTKqACejfvzQ9P/Bd+OxF0rZglqk4LOdhSSjXXWm/1sa671npF5M0SQgghhBCi9rJnivxxzmwFTsdt3eo70PFVodCpmIa9MMYVVxQA8MhLq+Go8XDgm3vWdeZfPHziVQxsPZB2V7UN2D4RnHDC1RVVlf2qUUrdBCyJvElCCCGEEEIEp23b4rgct+Y4Kl+sua/sWrb0XlZTKFUILTXHYdlprfnsr884839nkj/8UE+gtfpUmL6EkUnvcGibQ6XCoMvC6UY4AXhTKTUTuAFoArwAdMfMmSWEEEIIIUTUaa0pLc0FWsf82E2b+q+lPnv2dl5+OYu77873WjdmTD47diQxfHihz9eXl4ce9DhlvEorynj1l1d5avlT/LrzV7OwIgVWnAtf3wxbugGQlLQz5OOJwEIOtrTWjyilPgFmA8sxwdZioIfWepPL7RNCCCGEEMKL1ppdu3ZRVBSfzNaxxxaxalUKBxxQ7rh+yJAShgxxHnfVpInmmWdyXW3PQQeV0auXrUR8ej4c/Azjcsez86uNAGSnZnNu13N546bbyV3XsdrrJaEVHeEWyPgdWAmcUfX8VQm0hBBCCCFELFRUVLBx40YKCwtRKrgJgt2Wk6N58snoZYOCLQ9v+eijrShlyrfP+GkGXP8CZOSxsxJaZrVkVLdRnHfAeTRMa8g7BS29Xp8sU2dFRTgFMgYBc4AdQA9gEDBZKXUCcLnW2t0wXQghhBBCCJtdu3axc+dOsrOzycrKjksbMjNDjIaibG3+Gp5e/jRv/PaGKd+eAWzrytntr+SBs08iPdl/UCrBVnSEUyDjc+BVYIDWerXW+lmgN9ABkEqEQgghhBAiaqzug6mpqaSnp4ecAXJLWlp09x/cz6Whw//B8DMY/Npg5vw8h+KKYnq36A2vvA1PrqJf6vkBAy1wnnxZRC6cboRDtdbz7Qu01n9UZbzucKdZQgghhBBCVLdjxw7y8/MpKCigQYMGQPXS5rGUkhKd4GTIkGI+/zyDc87xXTyDpHLo+i4MehTaLQZAA0e1P4ore15J/1b9aXelKd/etm3gyocgY7aiJZwCGfMBlFJdgH2Ar7TWRWaVvtfl9gkhhBBCCEFFRQU7duygpKSEjIwMUlLMaWy8Mlsp4VY+CGDy5FxWrEjlsMNKvdaVVJTw6i+voq5+Fr3XH2ZhWQYsP4/PHziXrk267tn25Ze38/PPKQwa5L0fp8DKafJlEblwxmw1BV4DjsQE0fsCa4AZSqkdWuub3G2iEEIIIYSo7woLCykpKaFRo0Yk2SKDeAVbbmaCLr54NzNm5HDyyUU0bqw5/PDqAVJxeTFzfp7DtGXT2FS4CfYCCpvCd5fDktGwuxVdn9pQ7TWDB5cweLBzNUQnEmxFRzgx+USgDDNGa7Vt+auYObgk2BJCCCGEEK4oKSlh27Zte54n1YgK4hVsuWns2HyGDi2mb9/qQVZReREv/fwSU5dNNUEW0Cq7FfttuZ6vHr+K5MosTjutiGOO2RHS8YLNbN15Zx7LlqVx2WW7Q9q/8AhrzBZwrNb67xozTP8G7O1Kq4QQQgghhMBUHty6dSspKSlkZ3tXHnzxxayotyE5WVNREb1BTWlpVOs2aAVZTy57ks2FmwETZF3X+zqG7zecytJ0XmlUxlFHbaFDh+DGZNmdd14B48c35NBDS/j6a1M8o1Mn7/nC2rev4PLLpdB4JMIJtrIBpxF7TYDgc5VCCCGEEEIEsHv3brKzs8nMzPTKauXnK959N/rBVlISVIQe04Rse9F2Zq6ayfM/PU9uiQly2ua0ZXTP0Zzd9WzSkqtKIGbCRRf5KaARwDXX7KZfv1IOPriMn35K4a+/UujVq8xrOymaEblwgq3/Ay4A7qx6rpVSScAtwBduNUwIIYQQQtRPWmt27NhBcnIyZWVlpKSkeAVaAMuXp7p+7H79SliypHqp9GiPZ9qwewPTV0xn9urZZo4soF1OO0b3Gs1Z+53lCbJckpLiyaQdckgZhxziHWgJd4QTbN0CfKaU6gukAY8AB2EyW4NcbJsQQgghhKiHSktL2bZtG2lpaZSUlJCV5Zy9WrvW96lsVlYlV1+9m4cfbhjweKefXkjnzuUceWQJe+1VySefZLB+fTLPPpsDRC/Ds7FgI0/88ASv/vIqpZUm+OnRrAdX9LiCEzqdQEpSlEoeBiknJwEGxMVZOKXfVyql9gNGA7uAHOAt4Emt9UaX2yeEEEIIIeqZsrKyPbeUlBSUj2hnzRrfp7KffrqVDz7ICOp46ema66/3FIEYNaqARx5psOe5UhpwL+LaXLiZacumMWvVrD1B1oBWA7i619Uc0e4Inz9vrIwbl8eqVakcdpiMEIpUWOGy1joPuN/ltgghhBBCiHqspKSEsrIyiouLUUqhVBNWrUqlf/9Sr+zS5s1JvPqqc8ZrwYLN7L23e4OsevYs45tvPF0L+/TxnrsqGFsKt/DED0/wyi+v7Oku2K9lP2455BYGtBoQ9yDLcsklBfFuQsIIKthSSvUIdoda6+XhN0cIIYQQQiSEtWshPR3atPG5SV4eDBsGw4fD1VfDjh07KCgoID09neTkZIYMacHGjclMnbqDU04p3vO6Y49txsqVnnFMY8bk8957maxebcZwhRqzBNp+ypRcJk1qwMiRBTRvXkGjRqF1r9tauJXJyybz4uoX9wRZfVr04fqDr+df7f5Va4Is4b5gM1s/YiYwVlX3FuudYV+W7EK7hBBCCCFEXZWXB507m8c//wxLlsB550F+PjRosKfixIQJsGCBuY0erSksLKS4uJjk5GRSUlLYuNGcVs6bl7kn2Pr229RqgRZA796lLFuWGnaw5SQ93XN627p1JQ8+mBfyPnKLc5m6bCqzVs+ioMxki3q36M2tfW9lUJtBEmTVA8EGW51sj3sDjwGPAouqlg0EbsQUzxBCCCGEEPXZ2rWex/vvb+7XrIFx42DoUPjoIwB22+bKraiooLKykrKyMgoKCmjUqJHjrm+5pbHXsowMzYcfZu55Hmz1wFatKti0KZnjjy/2Wvef/xTw8ccZjusC2V26m+krpvP0iqfZXWZ+yJ7NenJL31tqxZgsETtBBVta6z+tx0qp14FrtNbv2zZZrpRaD9wLvONuE4UQQgghRJ2iHbrZjRtn7j/+2PElBQUFVFZW0qRJEwCSkz2dpey7+/VX73LvRUXVg5dgY5kvvtjC2rUp9OjhXfq8QQPNvHnbgtuR1Y7yIuasnsOkHyexo3gHAAc2OZCb+97MMR2OkSCrHgqnQEZ3YK3D8rXAgZE1RwghhBBC1HlOwVYAhYWFlJeX+60+WFTk/NrUGvFXo0aVQR2zYUNNz56RzzFVVF7ESz+/xLTl09hYYIpzd2zYkVv63sJJnU8iSUV5oi5Ra4UTbK0GblNKjdJalwIopdKA26rWCSGEEEKI+iyMYKuyshKttd/szzHHtHBcPmBAKRdeWMCsWdmAyUrFQnF5MS/+/CJPLnuSzYWbAWib05Zrel3D2V3Pjvs8WSL+wnkHXA7MBf5WSlmVB3tgimSc5FbDhBBCCCFEHfPZZzBjBlxwQcBNtdZVMZkJrkpK/M/ppLXzJMZt25ajFNx1Vx4dO5Zz9NH+x1idfHIRHTuW069feOXbwRS+mPPzHGasnMHWoq0AtMtpx1U9r2L4fsPJSAlufi+R+MKZ1HiJUqozMAKoGvHIq8BLWmspyi+EEEIIUV8dfbS5//77gJtu2rSJwsKGgMlGFRYWklqzP6DN3LnOAUz79mY+rYwMuPTSwKeiKSmaMWN2BdzOydq8tUxfMZ3Xf3udonLTp7FNdhuu7nU1Z3U9i/Tk9AB7EPVNuJMaFwDTXW6LEEIIIYRIBH/+GXCT8vJyKio8Ew8vWtSchQuzGTcun7Q07+2vuKKJ4378VR4cOrSYe+9tRNu25fzzjznttYKzYGmtWbRxEU8tf4rP13+Orprx6MAmB3JZj8s4ZZ9TSE3yHSSK+i2sYEsptS9wJNACqPYW11rf40K7hBBCCCFEAtNaU1npKWRx6aUtAejSpYL//Cf4zlIpKb7HZ3XuXMHSpZto3LiSr79OZ968DEaP3u1ze7ui8iJe/fVVnv/peX7b+due5UPaD+HyHpdzaOtDpbqgCCjkYEspdQkwDdgGbKL6hMYakGBLCCGEEKI+C7JAhnbYbv36ZIctoWPHctat8z51TXbefI9WrUxAN2RICUOG+B8XBvDP7n+YtWoWL//y8p7y7Zkpmfx7338zqtso9mm8T8B9CGEJJ7M1FrhDa/2w240RQgghhBD1gymQ4R1sVTj08vvyy3SaNnUu5x7sBMaBLN28lOdXPc97f7xHuS4HoEODDlza/VLO2PcMGqY1dOdAol4JJ9jaC3jd7YYIIYQQQogEEURmKykpyTHYKi/37ppXWJhEVpbzPpOTwy/zXlpRyufrP2fWqll89c9Xe5YPbD2QUd1GcXSHo6V8u4hIOO+e14GhwFMut0UIIYQQQiSC0gBl1SsqUEBlpXcaq7zc+SXbtjn3FwzUjdBJfmk+H//5MRO/n8i6/HUApKgUTt/3dEYeOJKezXuGvlMhHIQTbP0O3KuUGgCsAKpNu621nuRGw4QQQgghRILad1+aduxIUofXvFY5dSP0J5Rga1fpLmb+NJNpy6eRX5oPQLPMZpy2z2lc3O1i2jdoH9rBhQggnGDrUmA3cETVzU4DEmwJIYQQQgjf1q4lc+1aki7yHnD19ttZlJYqTjmlKKhdBdONcG3eWh769iE++esTSipMkYyODTtyRpczuLT7peSk5YTWfiGCFM6kxp2i0RAhhBBCCCFKShRvvZXFW29lBbW9r8yW1pp3/3iXqcun8tP2n/Ys36/xflzT+xpO7nwyyUlh9EEUIgQy4k8IIYQQQsSFIvziFpaa1Qh3FO9g+orpfPX3VyzbtmzP8sPaHMbY/mPp1rSbzI8lYiboYEspNSGY7bTWN4TfHCGEEEIIUV/oisiDLWtS4/l/z+e9P95j0cZF/LnrTwCSVTLX9r6WCw+8kGaZzSI+lhChCiWz1TuIbSL/jxFCCCGEEAmtlFT6sJSVL0QYAGXk8mvLyVzx2VLmrpmLrjoVbZnVkpv63MQR7Y6gbU5bF1osRHiCDra01kdGsyFCCCGEEKJ+eJZRrKR7mK/W0PdpaL4Kur7L943/4vs1Zs0xHY7h8LaHc8o+p0gmS9QKMmZLCCGEEELE1BgeDmHrqo5TB70GPeaAToL939uzNlmnM3z/0+nRrAfnHXAeScq7wqEQ8SLBlhBCCCGEiKndzf6BPtPhy7shfReUNDS3xmthZ0fo9Dm0WAkb+8CZZ0NuZ2j9PaQVVN9RfhtOTXuCxwYfGpefQ4hAJNgSQgghhBCxNfoAc9/gH+j6HuR1gF9OgUGPwvf/gZ6zIbnMs33Df6q/ft1gmPUF6CQOuisPqBGECVFLSLAlhBBCCCFiRgNphQ0ozdoF3V4zC5v9Cs0eNY8Pfs73iz+cCH8PgA19THdCID1d6rOJ2ivkTq1KqVQ/62QkohBCCCFEfaSDC3oU0PGLkcHvtyQH3p4FHzwBi68xwVal53Q0I0OCLVF7hTOC8BXlMBOcUqol8GXELRJCCCGEEHVPRUXQm47Inx94o90toDIJPh4Pyy4wgZb2PnXNyAilkULEVjjdCDsAzwIXWwuUUq2AL4CfXGqXEEIIIYSoS0IItoblL+fuQBs9vs4EVxXpfjeTboSiNgsn2DoB+EopNUFrfYNSqg0m0FoGnO1q64QQQgghRN1QWup/vVJ7uhq2y/exyY8j0aXZ8NdhqIoMtPbqTOVFgi1Rm4UcbGmttyqlhgILqnoTDgO+B0ZorStdbp8QQgghhKgLior8r8/MhMJCAJoVQnKloiKpeqCk/zwcfvgPAMkpmvLywIeVYEvUZmFVI9Rar1dKHQP8H/AJcL7WQY6KFEIIIYQQiae42P9626likqZ6oPX2LNhrDaw4x7NNkJUF0tLkFFTUXkEFW0qpXPZM311NFnASsN2qmaG1buJa64QQQgghRN0QKLNV47p8779T+aFdGeS3NQUwagg22JICGaI2CzazdV1UWyGEEEIIIeq2EDJbAA9+kMVxJ3aBL+5x3DwpSWMKxfsnmS1RmwUVbGmtZ0W7IUIIIYRwidamGIEQsRQo2CopqfZ04D+VMP27asuSkjSVlarqcXCHlWBL1GbhTGp8glLqWIflQ5VSx7vTLCGEEEKEJS8PunSBq6+G5cvh1lvNMsvKlTBlCkFVHhAiFIG6EdaQTonXMvs1goEDA1Q3tPYjBTJELRbOpMYPAck+9vVQZM0RQgghhKPt26FHD3j44erLd+yADz80J7rr1sGzz8KaNSag6tnTbH/TTZ7tu3c3gdicOTFtvqgHSryDJ39SKfNaZg+2xo7N47bb8nnqqR1+95PufxouIeIqnGBrX2CVw/KfgS6RNUcIIYSohz7/HJYs8b/NY4/BihUmU2WpqICmTeH44yErCzp1gnfe8X7ts8/CHXdUX/bHH5G3Wwi7ELOlSY611zwaNdKMHr2bLl3871e6EYraLJxgKw/o7LC8C1AQWXOEEEKIembzZjjqKOjf33n9xInQqhX89JP3ugKHr90FC5z388ADJgtmadcu9LYK4U+IwZZ2GJRVUeF5nJJigqhAww9TUyXYErVXOPNsvQs8rpQ6TWv9B4BSqgswHnjPzcYJIYQQCW/rVs/j8nJIsX01FxbCDTeYx3PnVn/NpZfCPvuEdqxS2xgYqZct3GaPlIKRkgI1hmVp7Yms0tLMfeBgK7TDChFL4QRbtwAfAj8rpf6uWtYOM8HxTT5fJYSoX4qKYMwYGDAAzj033q0RovayzijBVHPLyYGyMtOtsG1b59e0aBHescpsY2SSnYZfCxEBf5mtvfeGP/+stkinpPD+O39QXNyI009v5vUSK7MVqCphsFULhYiHkN+eWus84FDgRGAqJqN1lNZ6iNZ6Zyj7UkoNVkrNVUptUEpppdSpNdY/X7XcfvuwxjZNlFIvKqXylVI7lVIzlFI5NbbpoZT6P6VUsVJqvVLqllB/biFEiObPh8mTYcQIyM933kZrc+Xebt06WLo06s0TotawX5ZftAieeMIUtDjsMBg92t1jrVvneSzBlnCbv8xWZqb3suRkOnYspX9/56qD1r9GoMyWzHIgarNwMltorTXwcdUtEtnAMuA54C0f23wIXGR7XrPUzYtAa+AYIBWYCUwHzgVQSjWsauenwOVAd+A5pdROrfX0CNsvhPDFHkRt2AANG3pvc+GFMHs2vPkmnH46/POPGeAP8P330Lt3bNoqRDzZzxSHDq2+bt48d481eLDnsaQDhNv8BVvbtnkvS0mhsrLS50ust6hSMiZL1F1hfdIqpY6oykj9XnV7Tyl1eKj70Vp/oLUeq7V+289mJVrrTbZbrq0dBwDHAaO01ou11guAq4GzlVJtqjYbAaQB/9Fa/6S1fgWYBNwQanuFECGwd1fatQvWroUTToAvvvAsnz3b3J9xhpn7xz5g/88/4fXXYf362LRXiHjxc7IZVVpOYIXL/HUjdAi2KoK8oOaUuWrUqJKnn97BG284BHFC1CLhTGp8HiZLVIgJWiYBRcBnSqloDMz4l1Jqi1LqF6XUNKVUU9u6gcBOrbV9+vFPgUqgv22br7TW9hz1R0BXpdReTgdUSqUrpRpaN6CBez+OEPWEfb6VhQvhvPPggw9gyBDn7Zcvr/58xgwYPhw6OxU/FSKBxCvoef756sU5hIhUsAUyFi6Ea66h6Kmnwj5UcrJm2LDioCc+FiJewsls3QHcorU+S2s9qep2FnArcKe7zeND4ALgKGAMcATwgVLK6mjeCthif4HWuhzYUbXO2mZzjf1utq1zchumxL11+9vHdkIIX+xVz66/Hv62/Ru98ALceGP17UeMqP78w6rhmeXl5mT0hx9M8YBbbzUZsry86LRbiFiLV2bro49g3Lj4HFskpmCDrYMPhieeQLVqhdaaoqIiv5s7ZbZkyKGoK8IJtjoDcx2Wvwd0iqw51WmtX9Fav6e1XqG1fgcYBhwC/MvN4zh4EGhku8lkJEKEqrTG1ca//vI8vvBCmDDB/+vt3VGef958OR99NDz8sMmQvf++a00VIq6+/TZ+x546NX7HFokn2GCrqgJnSkoKycnJ7Nq1y+/mTslfCbZEXRFOsLUek2mq6eiqdVGjtV4DbMNMoAywCahW/1YplQI0qVpnbdOyxq5a2tY5HadEa51v3QD/nwJCCG8lNWvZROA//zH3Cxd6lsUrGyCE22pmdYWoq4Kd1Liq8kVSUhJJSUmkpIRer80qCy/7wejtAAAgAElEQVREbRdONcLxwCSlVC/g66plg4CRwLUutcuRUqod0BTYWLVoEdBYKdVHa23Vih6CCSIX27a5XymVqrW2RuwfA/xiL7YhhAjT3LmwahXcfLOndNTmzTBxYnSPK5c1hRCidglxUuOkpCSUUiQFqIzp1I0wjPhMiLgIZ56tacDZmBLqj1fdugFnaa2fDmVfSqkcpVSvqsANoFPV8w5V6x5VSg1QSnVUSh0FvAv8jilwgdZ6NWZc1zNKqX5KqUHAFOAVrfWGqn2+hJmffIZS6iCl1FmYoDBAHyYhREAbN8LJJ5txVMnJ8McfZvnxx0e/iqBMrCIShdO0CLG0fXt8jy/iY/FiM5Y2Lw9++inw9itXms/d++6DggIzjramMIIt6+ZPq1aVNGpUScuWFQwfbqYVuekm6XQk6oawSr9rrd/WWh+mtW5adTtMa/1uGLvqC/xQdQMTAP0A3ANUAD0wY8F+BWYAS4HDtdb2/kkjgJ+Bz4D3gQXApba25gFDMePJlmIyc/fIHFtCuKDm4Hpr/IfTl3AkcnK8l0k3QpEozjkntO3/9S93j797t7v7E7Xf8uUwYAC0bw9dukC3brBkifd2ZWVmnOzSpfDoo2bZnXdCjx5mHO27NU79rGCrRQu89O8P3bt7uoXjyWyl2ib2btzYO2DLzNR8/fVm5s/fwoQJO/nuu02cdpr/ohoisVRUVAQspFJbhZyEVUqtAQ7RWm+vsbwx8L3WOug6zVrrLwF/l6ePDWIfO6iawNjPNsuBkOcBE0IEULNc+/r1sGWL87Zuk2BLJIoGIc4ukpnp7vFLSuDLL6FvX+cLG6Luy8+HrCxP37uvv/ass+a/mjsX+vWr/rpnnjE9FwDuvtuzfM0acz91KpxyinlcXGyyZQAnnQSnnmqyZvn5cP/9MHMmHHBAtd0rpUhLS0NrzZdfFvDCCwWccUY5J55opkrNzc2lQYMGpKSk0LixZ4xW69by+Z/oKisrKS0tpbS0lPLycpKTk0lPT6d58+akVRVYqSvC6fHaEXAaLJEOtI2oNUKIuqVmEYzNm+H3390/TnGx97IQu6sIUWs99lho27s9N9YDD8CsWSZjZp90XNRtU6aYqq8vvQQDB0KvXp5eB07l/WpewKqsrN698JdfvF9jD8732Qc2VI3gSE6GYcM86y6/3GfX71atWlFZWUnz5pU0b76Npk2b8sEHf5GaupOsrAYUFBTQqFGjIH5gUZdprfcEV2VlZSilyMjIoEGDBmRnZ5ORkUFaWlrALqe1UdDBllLqZNvTY5VS9klukjEVCte51C4hhFu++QYaN4b993d/3zWDoK++gkGD3D+OU4UryWyJ+qRxY9i50zxu2tTct2jhTiZ51ixz/+WXke9L1B5XX23uBw409z/+6FnnFGzZl40eDW++CUOHepa98or3a776CubNgxNP9ARa4F3AyM8YW6sLYXl5OdnZ2WRmZnLQQaXk5kJ6ejoFBQUUFhZSXl5Ow3iPbxSusTJXZWVllFd9x6enp5OVlUVOTg7p6emkp6eTnADFsELJbL1Tda+BWTXWlWECrRqzlAoh4urvvz1ftE5fruHQGtatg44dzSDpeJFgSySCJ58Mbjsr0AJo2dJ0/crKMrdY2LQJPv4YzjgDsrNjc0wRPU7fBxUVcMMN5jvDel++8IL//WzbZjJYL75YfXkYJ8gpKSm0bduWlJQUKioqKC0tJTMzk+TkZEpKSkhNTaW4uJj09HSUFEiqc8rLyykrK6O0tJSKigqSkpJIS0sjOzub7Oxs0tLSSE9PD2sagNou6J9Ia50EoJRaixmztS1qrRJCuMOqDhiMLVvM9lZwVlNJCaSnw8UXm773V19dfaLiWJNgS9R1339vMgihSkryZLci0b598FVDDz/cdBFeuhTuvdd/BcUdO2D6dDN/WPv2kbdTuM/p8/P112Ht2urTdqSnBzdnYs254sLMRlgn2o0aNSIjI4P09HRyc3P3zMW1Y8cOCgsLSU5Olq6FtZjWmrKysj3BldaalJQUUlNTadKkyZ4ugWlpaQmRuQoknNLvnSTQEqKOCOVDbMAAOPRQ+Pxz73UTJ0JGBrz/vgm0ACZPdqeN4ZIxWyKeHnkErrnGZHenTjVZ5FCtXRvescMZs3D55d7L/F1BLiuD//s/KC01z62xmJMmQevWZnzXv//t/NqLL4bbboPDDgu9nSL6PvzQvHdr+vNP72XhTk6fnh7e66okJSWRmZlJUlISrVu3plWrVjRq1Ii0tDT22msvAPLz88nNzd3TBU3Ej1UpcNeuXeTm5rJz505KS0tJTU2lRYsWdOjQgY4dO9KpUydatmxJo0aN9mQt64OgP7GVUgOVUsNqLLtAKbVWKbVFKTVdKRXZf5cQwh0rV0KnTtW7dvjLBJWWek78nn8efvvNdDPJrZr3+4YbzP0FF0SluWGRzJaItWXLYO+9YfZsGDPGXHAYOhSuusqUtZ4ypXrFNn8++sgULnAyapT3soULPY+DDbZatjT3w4fDtGmeqnMWf8HW9dfD4MEmSJs3r/q6wkKYPx/eeKN6d7RRo6BrV3inatRBzcz3li0miBPx88ADZh5EJ25+pkYYbNllZmaSkZFBTk4O7dq123OynpSURMOGDcnLyyM/P5/dMoVBTJSXl1cLrHJzcymoGlKQk5NDmzZt6NixIx07dmTvvfemefPmNGzYkIyMjDpZ3MINoXSMvAv4EvgfgFKqO2buq+eB1cDNwAZgnJsNFEKE4I8/TIB02WVmXNVTT3nWlZX5/gK0Z7Nmzza3vn3hu+/gv//1rIvFB+UJJ5gMWiASbIloKC01VTWdur+de64JIOwXHawS2hs2eAoSnHuuCTp8+eILOO447+VNmpgJhj/8EJ591rN81Kjq3Xs7BznDytlnw4UXeorj1Ox66BRsaW2KGVhjdmbO9GSznVRWmsILe+0FM2b43u7XX83vpHt37ykjhPtWr3ZefscdsTl+FMb1KaXIqap+2LJlS5o0aUJSUhIVFRUopSgvL2fHjh0AZGRkkJWVhdZaxneFSGtNRUVFtVt5eTm66sKK1R0wOzubrKwsUlNTSU1NrbOVAmMhlGCrF3Cn7fnZwGKt9SUASqn1wH+RYEuI+OnSxdw7zZVjBVv5+WY+lZNO8szv89Zb3tt/9525t1+pT0qCAw+EVavcbbfd+PESbIn4GTjQjKX6+mvv8YvBFoTxd4V9507vogMZGSYTbQU1rVtXX9+iRfVqbk4nNIcdBgsWQLNmJpP10UemK5+V3bKsWWOyXNdcYy5s1FRR4T/jVdNvv5kLM4G8/rq5X7Ei+H2L8IUyXtdtbdqYrqRRlJycvKcLWvv27VFKUVxcTF5eHqmpqeTm5u4JvFJTU0lJSSE5ObnOzc/kJq01lZWVXjcroLIopfb8fpOTk8nMzNxTuMIKtFJTUyWwCkEowdZewGbb8yOAD2zPvwVkJKwQtYHTyZ7Vr/2880ywdcoppj/+zp2mPHwwkpI8XQujZe+9zcnuyy/Do4/63k7GbIlo+P57cz97tnewFex77s8/4bnnTFc86wIImIxOz57e259wgimzbenZ02S2rO6ES5YEPubIkaZaYKAJjzt1MuPNAKpKbldTWhpasDVkSHDb1ZOxGbVGPLtrrl8fm14QVaygKysri6yq6pw5OTmUl5dTWVlJbm4uFRUVlJSU7OlqaAUN1qTKdS1wsAdLVhBlX24PrOyZveTkZJKSkkhKSkIpRXp6+p7gyQpIrXt7QCsiE0qwtRnoBKxXSqUBBwP2zukNMCXghRDxsHmz//XWl+/cueb+3XdDP0ZJiak0Fk1JSdC7t8nC+Qu2JLMlosnpJCPYYOuMM8z9vHmmoMTq1XDEEb4rfTqd6I0Y4Qm2zjnH97FWrTIZrZEjQw9onIKt7GyTmQjWxo3BbZeA5ZxrtVgUjVDKuYR8LQhcMjIy9jxuUNWDo6SkhNLSUiorKykoKKCkpAStNfn5+Xu6yAF7Kh9aQUlKSgpKKdcDMisgsgdGWutqN/t6e5dIK/tktUsphVJqT9Bk3ayAyfpZkpKSvAIuEX2hfPq9DzyklBoDnAoUAv9nW98DiGPeWoh6bsoU/+vduNK5a1fk+wjEOmEMNAnzjTeak9mzzjKlqD/4ALp1i377RP3gdBIS6gnsn3+aDHIgTidx9sDp1FN9v/aAA8wtHL66VNknp3WLXCGPrVhktnJyYvOdECEroMjIyNgThDVu3Bhgz3xelZWVlJeX77kVFRXtGatUUlKyJxiy788KVpyCMKeAqWZgYw+UrOAHqBYgWY/twV/N4MlfO0TtEEqwdSfwFjAf2A1cqLUuta3/D/Cxi20TQoTC6Sq1XVlZ5Nkgpy/wQYOqV0rz56yz4NVX/W9jfWEE88Xx+eee4h6XX26u8AvhBqf3X7S6rjodKzUVPvvMBHhNmphlV11lKv1deqk7xw30meEmCbb8+/130/V0xAg46KDI9xeLYKtBgzoRbPljjUlyYi8UYR/bZD2uOd7J6rJn3ezBUs2gqGZGytrGei4SSyiTGm8DBiulGgG7tdY1v3X+jQnChBDxUNVX3afycth3X/ePG8rEkq+8AnPmOJ/k5eRAjx7OGYVglJY6L9+wwZwUWMVAhAhGvIMt8B4PNWWKKTfv1slYLIsFSDdC/847DxYvNheu5s/3LNfafHY7fWauWGGKrdx2mycgt8Qqs1WTiyXf400ptac7nhCRCGdS4zyHQAut9Y4amS4hRCw5ffHZrV9vKpG5zd8Va6fyv76+uLZsMZOo2k8kA3WNtEtLMxW47HMJbdwIbdt6l7wWIhC3g62aFQYDHcsXN696xzKzVVQUu2PVJZWVZtzd4sXm+VdfVV9/1FHmM6yw0LOstNS8F3v0gMce80w5AKaA0UsvQV5e9Nvu9DeVrmxCeJH/CiESxV57+V8/fHh0jmv/cl2/vvq6rVs9j+2ZN6cB/5mZ3l/UoZwMLlxoKr81b+5ZtmiRua95lfeHH+Dvv4Pft6h/nC4iRFJ0wF+wEa8T1Fhmtm66KXbHqm0qKjyfjXl5JjiySuGPG+fdbVApM/Gw1mZOtq1bzYWr7dtN0NWqlSn1b1m61PP41FNNV8Ro/L5r9jyo+XkPEmwJ4UD+K4RIFIFOBO2Bj5vsX64tWlRfZ+8L36qV5/GcOSYL9eOP/vcdqEhGIE5j1H7/HQ4+2Exaq7U5gRGiJvv7evFi+OWXyDJbO3cGd6xYikVm67ff4Pbbqy+rb5VETzsNOnQw41WnTjUZe+vi1733Or/mww/h2murL3v2WTP/W26u7+k6ambG3JKRYS5SBZpjUcbmCeFFgi0hEkW85p2ynyimpED//sG9pnNnM5/QkiW+q58NHgzPPGO6F44eHXrbnMoSW5M1A9x6q5kE9pVXnF+/bJkJzkT9s2KF6b51770wYIAJ/GM9ZivaYpHZ2m8/ePDB6stiUZa8NrGm25gypXrg+a9/+X/d5MnVn996q3NGPi/PFE/5+uuImulXcbF5n9pKqjuSzJYQXuS/QohEURsm+VUKnn8eTjrJMwbB4mvg9CGH+B/PMmqU6TITqJuknRVkOQVbdtbkrtdf771u61bo1Ss6RUVE7TdvnrkIcNddnmXRysgkcmbLSTwn3I01+8/6R43ZceyFMIJ1xRXeyzZtMhmzQYNC31+wrEJIgTJXTuN0hajnJNgSIlHE62qxvQ+/UiYD8N570K+fWfbii9CxI7z8cmTHOf744Le1TnDswdbcuWZgeaACA1u3mgpfP/3kWRYoaBMiEvEq9SzBVvTZC1ukp0derKe4OLLXh2rhQtNb4aOPzHNfwdbjj5ueDXPmxK5tQtQRUs9SiEQRr8xWx47+1597rrlFauDA4LctLTVdpOyZiJNPhptvhj59vLe3n+xefbUZW3HccZ5lvkovi8QSr6A6XpmteI2vqa/BVlFR3Rsjeuih1ceH+XrPXHutybrFsuiKEHWEZLaESBTxymy1bg0rV3p3kYmG3Fy45ZbA21lzbtU8eZ4wIfAJtTXp8ocfeu9PJLZYXJV3mvogXsFWvOYPqq/B1ubN8WtHKM48E8aMcf5MrzlG104CLSEcSbAlRKKI1zw2rVub0sWdO0f/WI0bV59TxhcrOKo5xibc7J8EW/XDPfdE/xj2Cp0WCbYSlz3YqismTICHHnL+TLd/hspE8UIERYItIRLFjh3xOW605u/yJZgTRF+ZLQhvfIwEW/XDn39G/xi7dnkvi1cXYAm2os/tYMspWHebvy7T9mqEVldrCbqE8EvGbAmRKAoK4nPcWI/7CCXYcqt6nARb9UP79rBmTXSPMXKkqdhpF6/31/Ll8TmuBFvha9gwfr0YwPQumDnTfO6feqqp2HrmmfFrjxB1gARbQiSKeH4Bx1IwwZ2/zNY//3gvC5TtkmArsWltSrwHCrQaN/Y/OXEwnKrRxaswxwcfxOe49WmeLbeDLTfGfe29t/kb2D8Lb7jBdB8E39N0WEaO9DwOZgytEPWcdCMUIlEEG2zdfDP06AGPPmqeDx0avTZFQzDB1qZNcPDB8Nhj3utuvNF7ma9JlS0SbMXe77/DffeZCVv9KSkJ/Pfz5557zJip++7zv93558O6dWZKgFNPDf94gU5k6wPJbMWO/ULSAw/AiBHw8cewYIF5z2/fDlu2wPjxJmP1zDOhzWkohAhIaZk/JiClVEMgLy8vj4YNG8a7OUI4O+MMeOutwNtZ//NlZfC//5nSvrNnw19/mfmxao5b6d/fe4Jip/3FSnFx4HELQ4bA55+Htt9du0xmo2dP73U//GBOmg44QE5EYiU72/zOzz/fzHvmy4EHwurVsGIFdOtmli1ZAt9+C1deacpW77cfvP++qTb48svQpInn9cGO4Zs2DS6/3DwO5j3o5LjjYOJE8z7q18+0E0ymYObM0PcXqXjN7/XNN+ZzJdFpHV7xk+zs6HQLf+cdOOUU9/crRD2Un59PIzPZdyOtdb6/bSWzJUSiCLUbYWoqnHYatGwJN90EkyY5nxhcdJHpYlVbZGQEDqScihAEcvDBzoEWmHLwgwaZjKCIDSsjsGCB/+1Wrzb3r7/uWda/P4webYKtQw811TIvuMBc0R8/3rNdMBPEXnEFPPwwXHyxZ1lGBnzyif/X/fQTXHih53luLsybZyb93rKl+s8Vr/GWV1wRn+PWl8zWzz+H9zp7gYoJE8xntBu6dHFnP0KIkEiwJUSi8Jd9CpbTlW6lYPduz/Nu3apnBuLhyCP9rw+n299vv/le99JL5v7vv0Pfr4jMli1w1VXw449wzTVm/h8nTu/dp54y9/ZxLvaLEt9+67yviy7yPJ461YxLqVmh7eijYdYs3+0+8EBPgDZokBnvZV3MaN68+v7ilW047LD4HDcRx2yVlVX/nARP5jJU9t4C558PGzeacVbhePhh8z5/+21z0UEIEXMSbAmRCHJz3Sn9fvbZ5v7AAz3LlDIZgu7dYfp0013LXv63NnJ7jFV9uRJfGxUUmICnd2+YPBkeecRMtjp5cvUMplKwbVvg/dlPgI89tvq6tDT497/N8S66yHnMn90FF8Cll/pef/jhZpzXF1/430+8uqbGqxthItpvP1MC3T7G0F5IIhT2z5vUVPN3CncuthtugL59IxtnKISIiFQjFCIRrFrlzn7uust0lRsyBFq0MMuUgn32iV+ZaF/atfOdaZJgK7ENG2a6aNm74k2aBOPGeSqq+bJwIXToAGPHene9vf12uPtu8/i554JrS6D3RjAZia5dgztWoqgLY8WLi31fVFq2zGRL7cWF1q0z90uWwDHHRHZs+3vKmuoinCk2TjklfnOpCSH2kMyWEInguuuC2y5Q97/0dDjrLNPNyeJ09fuSS8z9EUcEd9xo8JdRKClx91iJ2O2pLrPGwrz2mmeZldm94YbAr1+/Hi67zHu5GewcGnvAtmCByYwFO1bnm29MkZp99gn9uG6oz5mtP/4wAfV335lxdPbiQvPmmQIo9sD9t9/ghBNMsN6rl8mK/vprdNpWM7MFpmhGqOpCUCtEPSDBlhCJIN9PIZwLLzQnFtdfb07uQuV0QjZ2rCkQ8L//hb4/t+Tk+F4nwVbdFq+xcWlpob/m/PPN/YABZmzWa68Fn6nq3x9OPDH0Y7qlPgdbXbqYMXWHHAK//GKquYIJwocNM4/t00ScdpqZl8w+zu2PP8y9PagpKzPb5eWZ7HukrGArnOqXixZFfnwhRMQk2BIiEdQso3z99WYc17Zt8Pzz0LmzuUq7776h73vgQO9lKSmmQIC/gCfasrI8jx94wASV1omQdCOsu1auhPbt43PscLpcnXCCqTwY6lQDtUG8gq14Zlx+/NH3xNS33WbGpTpZu9Z7mTWOyn4x5qmnzHvi8ss91S4nTjRZ04cfDr291t8onN/Z1q2hv0YI4ToJtoRIBFZm64ADzBX2sWNN9bOmTcPf54YN8P33Zp+1kf1E8aKLTFBpXUl2+2ROgq3Y0NoUYomXmhUHg3XggeFlHkRszZ9vCq34yjw+9JDv1zp9pljjqOzB1ty55v6VVzwFXE46yXRztQoQxUo4XQ+FEK6TYEuIumz7dtOdz+p2NW6c6TriRmn21q3NiUltZQ+2rCvM1u/BX7fKcFRWuru/uqSyEn7/3ZxsVlSYW2Ul/POP+8dyu/tnqOpbMYH61o3w7bfN/ZYtob/W6TPA+tyxX4yx/06t93Nmptk2kt93OBeQPv44/OMJIVwjwZYQtc369XD//cGVsW7WzFTEWrrUPO/cObptq03sJx/WFeZAE+CK0N1wg+l++sgjJiNw4IGmy2a7dvDmm8Hvp+bJYmWlmVTXmgtr/XrveYpiTYKt2IhXN8Jwjtu5s/lc8ZfZsgdbTttZVQ2dft8jRpgqsIGqvYbT9kMPDf01QgjXSbAlRG0zZozpBtipkykhPH++83ZO4w7atIlu22oT+5XmcOegEYE98YS5v/VWUxDg119hzhyz7J57/L+2osIEUUVFpuLbeed51n38sQm0rrjCBG0dOlSvghkpf9UqfZFgK7Ft2OB/vVNX0LVrzeewU7DjlNly4i/Yys6G//43cPdZf8HW99/7f60QIq7kDEWIaLj/fjPWaft2+PTT0Lpcvfyyud+927z2X/9y3i4313uZNTdWfWAPtsKZg8ZteXlmnMbEifWn22Ggn/Pss00QddllJkh78UXPOvsk3GeeGfhYAwea7Jo/P/9silU8/jhMmeLpTvvvfwfeP7hfWEXULm+84X99zXnXLMXFwWe2nKSnm3unYCvYjJW/7Vq1Cm4fQoi4kGBLiGgYO9ac+A0ZYq6KulECGMwVzNNPh02b4LHHqq/LyqpfV+ZrS2arqMhUemzcGM45x3S7e/ddz3qtzRX1khJ49ln466/g971iBdx7LxQWut9uN9j/Bt9+C1dfXf0igHVyO3u2/9fuv3/gY339tf85tDIyPN0cr73WFLtYu9aMN5s923TV+u676q+55JLqAZbTBQzhvro4/1OwY7acWEGZ01haN34XteFikxDCp3p0ZiZEHATqhx+sBx80Zc0HDzbPrYHedvEswx4P9pOUWAZb++xjsibHH2+6yT38sOkGZLd+vefxmDHw6KMm62gNzL//fjMXz0cfeUrY5+XBqlWmmqR1BbxHD3O/e3d4ZaOjzToBraiAfv3M48JCmDEj+NeC9yTAzZubCWdPOsk8HzTI3Ps7qXS6oNGwobmB99/ohx/M5LR29S3Yqm/dCCPhrxvhihXB7SMjw1xsUcozvUGwWXB/QVlyspkrLNj53YQQMSWZLSFiZcUKc3Xen2+/dV5+++2eQMuX+lbm16kb4V57Rf+4a9aYeXSmTDHZrEmTvLfZuNHz+NFHzb29Atodd5hB988951l29tlmQPs773jvz9f7It6sv8HkyZ5lq1d7Sl4H81onGRlmYtlFi8w4L/vvpKAA7r7b+zUdOgTX5rVr4auvqgda1pgaX112E9XRR5sMoBUox0pdzGz560Z46qnB76d9++oXBpz2e+KJ5nMl0PeFJSUF9tsPRo70LJs6Nfg2CSGiSjJbQrjN19VxK0uxaRO0bOm9vqAALr44/ONKZgvS0mJ3/Kuv9r3uoYdMcH3UUf73Ye8e+OGH5v7xx83YrxEjPOtq68mpFTDNmuVZtmiRySb5ei+Xl5uTQ3/B1qZN5n7AAHOzy8oy3QRrZqomTAiuzR07mpvdunWmu6GVQasvGjY00ySkpSV+V7Ro/Q89/3xkr3dql9beny+BMltgLgAdeaS5UOHG9B9CCFdIsCWE2xYv9r/+7789wVZ5uTnp/OgjOPnkyI4rma3aVZVw3jxz88fpBOqrr8z9a6+53ya3VVaaTN+PP3qv89WVcK+94MYb/U8CbJ8k1omVibIsW+a5mBGOli2dL4DUBzV/l4kqGnO49e0b+T7snwEHHWQKvDhdpAkm2MrOhgsuiLxNQghXSbAlhNsCDZYGMzbn2Wfh9dfNeBdfVbBC0bhx5PuoS+zBVl0de+LGeI14Kiw0Y9hCsXu3d1aqJn+BWM31KSmRBVoi9uLxfi4oiP0xQ/X666YC7ahR3uuCCbaEELWSBFtCuC1QsKW1uYLptvrWbcR+8mEFW+ecE3x3stqgrpeIt7r7uS2YbMu2bWaurtNOi04bRGLx1+03nuyfYwccYG6BtqupPlWhFaIOqkV9boRIEIH68NuLJ7ipadPo7Le2cgpUrGIUdUVlpbmKfeON/reLV2YrmCxtNNjn4PKlaVMTXNeXbnAifIWFnvkLa5tg/7ft41G7dTPdDa+4wkw0Xpu6TwshvMh/qBBumzvX//rx46Nz3AYNorPf2spXKeZ77/X9mjPPrF1B6Zo1ZmxToGxcvIIt+/xYPXua7KlMoCoipTU88wy8/z789hvcfHN0sqQ332yy3rV5PGuw/9szZ0KzZnDssaaYzoEHmoqDna2E3ucAACAASURBVDtHt31CiIhJ7lmIWJs/Pzr7rW/99n11wbvjDrjzTud1r71mii/EsmqhP/HKHAXLPjnzOefA6NHmpLhLl/i1SdR9K1aYQAhMwZTcXFi61Mwlt99+0KhR+Pv+9FMTjJx1lvfE77VRsMFWt26wdWt02yKEiArJbAmRKOpqkYhw+Qq2lDLl032tS02NXptCFezfLB6ZrZISeO89z/MrrzQZgjZtYt8WkVj+/tvz2Joq44svzHxf3bsHv5/ycti82fx/zJ9vup8ec4zpPRDrucPCVd8KGwlRD0mwJUSikGDL45pr3D1Werq7+7OsWROd/brB/jvcutXTTTUz07ua4OjR5lZTNArBiLrPX2n/9euD389RR5lurbfeaiak7tMnvPbEo5vhnDlmUulAlTmFEHWeBFtCuM0+GW0srVwZn+PGy86dvtcFCjytbnCBSoxbFi82k/Secoq7Qe2KFcFtF4/Mlr1IRbNm1dfddRdMn27Kvq9eDZMnw65d3vuob+9JEZyKCnf2Y81J98gj5n7duvD24/TeDcXee4f+mhEj4JNPatcYUiFEVEiwJYTb4lVA4M0343PceIlkktL33zdjkL75Jrjte/Y086K9847/YCsnJ7R2xKMbob99VVSYALCyEt54wyx77jnnbS+5BH7/Hfbf3zx/6CHn7V580f94QqmkVv+EG2xF66JDpBdQbrvNFOa59lp32iOESCjyLSeE2+r63El1RSS/5333hZdeCm8yXH8nZrH628+bZwb/FxTA55/775ZlV1EBAweaqoxObrzR/E7sk6o2bBjcvlu1MifDWVnVl597Lvzzj/f206ebgG3lStP9KyendleNE+4JNtgqKTFZ1MWLTTDTsSNs2OBuW26/PfJ9ZGTA2LFm6onzzjOVA3v1MuseeCDy/Qsh6jQJtoRwW7yCrXbt4nPceAl0wnbZZaZ7z7ffwqmnmmpn0Rbq3z7Y7Wte0R82zFRza97cjFu55x7PunnzTPCyYoUpOrBsmWfdsmXmxPXNN033v4suMvP0WJ54wtzPnOlZNnBgaD9Thw7ey5wC1EMOMQHXAQfA11+bogn77hvasUTdFGywNX68CbIGDDBB119/mff6l19CUVFkbZgzB9auhfvui2w/4Pn/TE010yWMHAnffWcqd952W+T7F0LUaVL6XQi3xSvYCnb8UaII9Ht+6ilzEqQUvP227+1OOMGcxKWlwfffe5bfeafznF1uZrYi7RZlnXA+/bQZd9KtmycrNWCAmcw1JQU+/hhmzTJX3S1DhpiTwUWLzMmrr/FVoVYffPttU1zDXn6/RQtzUtuokQkQ16/3XPkH87tPSzNdDocPh7vvDu2Yom4JNthyukDy9NPm5is7G4xWrbzH1o4fbzK7M2eaixChcPpMSE6Gli3Db6MQImEoHa/JMusQpVRDIC8vL4+GwXapEfXXVVeZySZjbd994ddfY3/ceLnoInj+efM4ks8xrc1tzZrqmZU1a2DVKhPA2AfAp6b67raXkhJ8lz4wAXIwV+gHDDBBkSXcMSa9e8MPPwS/fUZG5BkEUTfEsprp2WfDK6/4Xm/9P594ohlf6bY2bZy7tlr8/S6uu85krRYs8CwrKPDuPiuESGj5+fk0MnMCNtJa5/vbVjJbQrhNxmzFhlu/Z6XMrUsXaN0aNm40y5OTzcme0/ZutSnYINGti2KrV4e2fYp8RYgwJSX5/n8INrMVrUA/ksBywgTPZwbA5ZdLoCWE8EvGbAnhNrfKGoeqvmWphw4192lp7u3TPp+Wr8mP3Qy2Yh2YFxeHtr0EWyJc/ipQBpv9DfX9GqxIgi3rtdaEytOmudMmIUTCimuwpZQarJSaq5TaoJTSSqlTa6xXSql7lFIblVJFSqlPlVL71timiVLqRaVUvlJqp1JqhlIqp8Y2PZRS/6eUKlZKrVdK3RKLn0/UU/EKtuqbc86BuXPNIHe32E8QYzEGLtwCGbEiwZYIl7+S/vHObLVtG97rOnf2PE5ONmMRhRAigHhntrKBZcBVPtbfAlwDXA70BwqAj5RSGbZtXgQOAo4BhgGDgenWyqrxVh8DfwJ9gJuBcUqpS139SYSwlJXFuwX1Q1KSqcoXagEHf+xBTUaG8zahXhX3lSGD0DJb33xjJlX+/ffQjh8JCbZEuCIJtr76yhRLcTuz9cgjpmjF+PHBbT9lCvTvb6p0Tp7smURZCCFCENdvUq31B8AHAKrGCYwyC64D7tNav1u17AJgM3Aq8IpS6gDgOOAQrfV3VdtcDbyvlLpJa70BGAGkAf/RWpcCPymlegE3YAvKhHBNKAUS3FTfuhFGQ0GB57G9S2Ek2raFdeuc14WS2bJKsIc67ioS/gJFIfyJJNg64gh322K58kozZUIg775rpki44gpT8EgIISIQ78yWP52AVsCn1gKtdR6wGLAmfhkI7LQCrSqfApWYTJi1zVdVgZblI6CrUmqvKLVd1GfxymxJsBW5zZs9j31lsELNbPk76QyW/W/722+R7y9Y9W06AeEe+4WLmuLV1drfODK7k0+G++93539XCFHv1eZPklZV95trLN9sW9cK2GJfqbUuB3bU2MZpH/ZjVKOUSldKNbRuQIPQmy/qrVhltqyy56J2i2VJbbdJlbX67dFHoXFjkxFyU7yCLQmehBBxIJ88zm4D8my3v+PbHFGnxKrr1YUXwjHHeJ4/+GBsjlvfxTuzFUuS2arfbroJtm83c825SYItIUQ9Ups/eTZV3decgr2lbd0moFo5IKVUCtCkxjZO+7Afo6YHgUa2W7tQGi7qoHvvNQOhd+/2LLvxRtNff/duGDUKPvoouH01buy9rH17d9ppsapp2cf8DB/u7jGEs1CDrbqc2ZJgSyQluf8ejtfFg7r8vyiEqLNqc7C1FhMMHWUtqOrS1x9YVLVoEdBYKdXH9rohmJ9rsW2bwUope7rhGOAXrXWu04G11iVa63zrBuxy4wcScbZtmwmc7rsP+vSBXbY/6113wZIl8Mwz8NlnsHWrmbxy6lTThWbGDDjuOHjpJTj1VPPa0lIziLrmVVqnboTz5pl7tyrn/fvf5l4mUK793Mh0SmZLJJJ4fW5JsCWEiIN4z7OVo5TqVVUdEKBT1fMOWmsNPA6MVUqdrJTqDrwAbADeAdBarwY+BJ5RSvVTSg0CpgCvVFUiBHgJKAVmKKUOUkqdBVwLTIjZDyrib/t2aN7cZITuvBO+/x6eesp7u7Fj4eij4bTTPMv++MPzeMQIU6nq9ttNtboBA0zwZp0MT58OM2dW32ffvtC9OyxcaG72uVrS0oIfFzN5sgnubrrJZOJAimLEg5ul34MVr7+zr/L3on6RzJYQQoQt3pOo9AW+sD23AqBZwEjgEcxcXNOBxsAC4DittX3yjRGYAOszTBXCNzFzcwGmgqFSaijwJLAU2Abco7WWsu/1yaKqZGh+vmfZzp3e2xUWmvuFCz3LnPr5T5nieTxuHHz6qcl0LVnive2AAeb+0EPN/fz58OqrcPHFpjrWY4/BPfcE/hkqK6FfP3OzLxPu6doVfvkF9tnHvX3W5bmqJLMlQIItIYSIQLzn2foS8PnpV5Xduqvq5mubHcC5AY6zHDg8vFaKOu3hh+Gtt+CSS7zXbdoE//zjGf/ky4IFgY/jb5vs7OrP27Uz48Est91mslsnnghz55pA7tlnTfbsxx892zl1T5Rgy13z5png96abfG8T6gmbG8GWdCMU8ZQowZYQQsRBHb7kKkQQbr3V3DtlsZ57ztyuucZ7nZucAj27jAwYM8Y87tbNBF8APXuasWJ3VV1rcKrgJSct7tpnH5g2zd191saJgZUK7r0jwZYACbaEECICtblAhhDu+fVX3+smTYrecb/5JvwuaW3amPFlFqdgSzJbsZcIpd+DDaJkzJaIhm++iXcLhBAiZiTYEokr3ldPtTbl5N3iFGydfLK5b+U4P7eIhkQIttLTg9suOdnd44q6ScY6CSFE2KQboUhc8Qy2Bg50f59OY7ZuvNFkzg6XIYm1lhvBltsZplzHWS+8xfuChagdJNgSQoiwSWZLJK54nii+/bZ7+7r0UjNR8uWXe69LTTVzbklmK3biMamxU1ZTiFhxeg/37AkPPFC9cqsQQggvEmyJxBXP8UwtW7q3r6efNpMsu7lP4a4bboAGDTwFWezcyGwtXRr5Puz22svd/YnE5hRsJSWZYj7WlBZCCCEcSbAlElcidYGqy3M1JRqnE8+jjjIVLy+80HudG8GWELWN/f/A+nzq1Ck+bRFCiFpMzgJE4opXZsvNCXFF3ZCUZG6+MgA7d5o53WqL4uLA2whhsb+vR4ww99dd51m2cqWZosI+2bsQQghAgi2RyOKV2XrwwfgcV8SGr4DK1zqloFEjU8q/tigqincLRF1if18/+yx89x2cd55nWdeu8N//mve5xSnLK4QQ9ZAEWyJx2TNbwRYp2HvvyI9bGyexFdHlL9iSboSirrO/rzMyoE8f5/e6fVzpzJnRb5cQQtQBMhBEJC57ZivYLFd2duTHlWCr/vEXbNVliTTuUYQv2Pd1ly4myGrePPH+F4QQIkxyyVUkrnDGbLkxzkuCrcQWavaqsND3fjZv9n+so4+GH34IfhLiQMaNg6ZN4cor3dmfqB8OOST4bUeOhBNPjFpT+PpruOkmaNEiescQQggXSbAlElc4V+WdJg4OlVQOrH/8BVv77ee8PDPT/wnj/Pnwv/9Br17w22/w1ltmXEwk7r4btmyBfv0i249IbGlpMGiQ53nXrvDtt/DXX7FvS4MG1Z8PHAiPPgoNG8a+LUIIEQY5KxSJK5xg6/ffIz+uZLbqjz59YNMm6N7dPHfKjCYnO7/WX4CWlASDB3uet29vbl98EX5b7fs+91z47DM44ggYNcr3ttIVrH7Kz/f+HOvbN/T9ZGREXvly1y7n5fGcR1EIIUIgmS2RuOL1ZSzBVv1hXe23Jgl2CvB9BVX+gq0ZM5yXN2kSWvt8SU2FF16Aiy92Z38isaSnu1PYxVf2aeTI4PfRv7/zcgm2hBB1hARbInHFa3C/BFuJ7dprzf1JJ5nMj/2k1OkE0NdJa36+72P46l54/PHBtVGIUN13n7m/5x739vnEE97LLr0UJk8Ofh++us5KsCWEqCMk2BKJKxZfxk4n0hJsJbY77oAFC+C117zXOQX4vroR+uPrPdS/vyma4cuwYWZ8zeuvh35MUb/dcQesXw9jx7q3z7PPho0bzTgry9NPQ05O8Ps46ijn5VIpUwhRR0iwJRJXLL6MMzK8l0mwldiSk03xAKe/vT3At8Zx2Sd/DZa/k9FPPvG9rls3+PlnOPPM0I/pRE5o65d27dwfp9eqVWTjtnwVHJLMlhCijpBgSySuWHwZS7Al7OzByeLFsGGDqSbopH173/sJZb43e9dCp0zrsGGmW+LEicHvUwg3hRtsff6573XHHOO97PTTzb1bFxuEEMIFEmyJxBXqVfnGjUM/hlOwJaXf6y97gJ+eDq1b+962tNT3uqys4I43bhy8/77p+tW5M1x/vWfdnXdCx44wZYqpmHjddcHtUwi3lZR4L8vMDPy6I4/0ve6JJ2D8ePjmG8+ye++FJUvgqadCb6MQQkSJBFsiccUr2JLMVv1lf88F6o7ldAJqCXQi+umnZoyNNb7m3nvhjz+gWTPPNvfcA2vXwt57Swl3EV9O7/VIu6g2bAg33GDe35bkZDMBs9PnshBCxIkEWyJxhdqNcN260I+Rlua9rOYknKL+cCvYChT4H3WUqR4XTvGNmq66ymTgzjjDs8yqhtinT+T7F8LpvX7NNe7s29511vqfc+P/QgghXCL9nUTiisXgfvuX+n//a577mltGJL4ePczkr23bBt7Waa6ht98276FQxmxFasoUmDTJVI377ju47DIzDuzbbz1jYISIRHm597L77jNzvW3aFNm+7Rc1rMduzBEmhBAuUVqqTQWklGoI5OXl5dFQTqTrjj//NGNWoqlHD1i+3DyW/yUB5n3gK6tlX15YaLoL2pfJe0gkosaNIS/PPLa/x0eMgJde8v06a1t//yPbtkHz5ubxr7/Cvvua4M6pO7f8fwkhXJKfn0+jRo0AGmmt/UycKd0IRSKLxRerXEEVNfnrPjh6tLl/443gCgQIkcjc+Iy2fwZb+5NuhEKIWkTOFEXiCnXM1rRpcMIJ1Su6BSLBlgjFpEmwdWv18VFXXmnuTzklPm0SIl7cCLacsl5SEEYIUYvImaJIXKF+kV98McybBxMmBP8auYIqQqFU9YqBYN5vH3zgvzuVEMKZBFZCiFpOgi0Re5ddZkr2RsPLL5sv3zFjzHFCEU7JdslsiUilp8NxxwU/t5YQdY2vC1/R6kYohBC1iJwpitj66y+YPh0mToSFC0353507TUWqgoLg9/O//0G/fvDzz9WXn3uuuX/kEfjss+D3t3q18/KcHM/jSy4x9xdcYKq0Pf20ZLaEECIQ+1xYdm53I3Qi8x4KIeJMgi0RW/b5Vg47DCZPNsFL69bQpk31bT/4AFaudN7PSSeZ0tTnn+9ZVjPwsjvzTHM/ebK5HzcO3nnHs37//atv/9lncMUVpg2WBx4wgeLj/9/enUfJVdUJHP/+siKQhLAlCJNoWkD2RUEhHiRBRHYcl+EIKgh4BEfngAuCOoKDLAOjjIJ4lIkMKDrRIIhskWHCQUQQZImgIiDIlhCWpBMwSyd3/riv7Orq6nR3ul5Vp/v7OeeeV/Xurfvuq9tV1b+69926GObMgY9/3GBLknrz05/mnxO4447G191bsPWpT+UfOa58WSZJTebvbKm56n2Tef31edtetXLm/Pl5sYqeHlOxeHHn7R12qF9ms81g9mxYtiz/4PAJJ+SV4FasgO22g5126v6YmTNzArj99ryE/Oabd//Anjix57ZJkvL77I03dt9f1gIZ1d75TjjvvPo/QC9JTeDIlpqrrysEPvxw5+05c+CDH4SXXuperrdrpnbbDX75y/yBPG5c3ldZcnvs2Dx98Jpr1l7HfvvBlCn18775zfwjtj/4wdrrkCQ1Xm/XbKVkoCWppRzZUnOtyzeZlSmA224LX/ta17zqaXybbgovv9w1/9RTYY89eq57oAtcTJ2apzNKkvqnGdds9fcnQCSpwRzZUnP19cO1XrnHH+++b+TIPOJ13XWw887d82fM6F/7JEnNUWawVfmJhenTB34MSRoAR7bUHC+/DL/6FWy99drLpdTzh2e9byhHjcrLZt97b/e8yZN7nv4nSWqtesHWEUfAz38Ohx3Wtzp6mkb49NN5hdvNNhtYGyVpgAy21Bwnn5wXqagszd6T1atzANXT3Ptaq1bBAw/Ur6utrf/tlCS1ziWX5EWMKgsU9aanL+c22CAnSWoxpxGqOWbPzturr157uVWres6rF2xVL6RRy7n6kjR4feUreXvssZ37xo/Po1vVv3G4Nr2tRihJLebIlgaXjo68rfeh2d/gqVKXJGnw2XVXWL48rwy7ww55SuCECf2ro7fVCCWpxQy2NLi8//0wd279vP5+kK5ePfD2SJLKM3Zs3p55Zs9l9t8f5s3LwVmt3lYjlKQWcxqhBpeeAi3IwdbixZ1LwffGkS1JWv/Nng3nnw8337z2cpUVCCVpEHFkS6230UZ51ajeLFkCEyf2vd5NNln3NkmSBocttoDTT+85f+5cWLoUttqqeW2SpD4y2FLr9SXQgjyNpD/22qvfTZEkrWcOPLDVLZCkHjmNUOXraWn2slWuBZAkSZJawGBL5Xv66dYc15WpJEmS1EIGWypfX6cJ1rPhht33felL616fJEmS1CQGWyrfsmX9Kz9iBDz4IFxwATz6aPf8M86AW2+F+fPrP/6oo2D0aPjwh/vfVkmSJKlBIjnVqlcRMR5YsmTJEsaPH9/q5jTHmjXw5JMwbRrMmpWX1D3iiDw1rz+/a3LqqXDZZbBiRd8fM2ZM1/JXXAHHH995f+XKHEwtXlx/dcKODmhv79/KhZIkSVIftLe3MyH/CPuElFL72so6sqWuLrooBzcf+xi0teVRpBNOgCOPhMsvhy23hHvv7VtdixfDxRfXD7RmzszbL3yhe97GG3e9/5GP5HZUjBzZdVtr5EgDLUmSJLWcS78PN888k695+vSnYc89u+Y99hh87nNd951/fuftk07K22OOgT/9ae3HWbUKPvSh+nnTpsGNN+ag7W1vy9MGn30WDj8cTjsNfvzjruVHjIBzz82rC44fn+9Dz8GWJEmSNAg4jbAPhtQ0wne8A+68M9+u7ft77snBT2/a2nJg1pM1a9YeCLW3w7hxvR+nNytX1l/e3b9pSZIklcRphOrZ2n7zqq9BSkrw7W/DHXfk0akjj4RHHoFPfAIOPRSuuqr7Yy64oPN2IwItgFFVA7OXXJJHzG67rTF1S5IkSQPkyFYfDKmRrTFj8hQ/yEuyjxyZg6cFC+D552HffRt/zHHj8vVbt98Ou+/e2OupbrgBFi2C445rXJ2SJElSD/ozsuU1W8NNJdCCfP3TFlvkNH8+fPGL5Rxz9Oh8ndWMGY2v+9BDG1+nJEmS1AAGW8PZ6tV5RGvBgnx/1qxyjjN6dDn1SpIkSYOY12ypU39+C6s/RhnTS5Ikafgx2FKn5cvLqddgS5IkScOQwZY6rVlTTr1Ll5ZTryRJkjSIGWypU1kjUK4UKEmSpGHIYEudli0rp96NNiqnXkmSJGkQM9hS+TbcsNUtkCRJkppuUAdbEXFWRKSa9Meq/A0i4tKIeCkilkXEnIiYVFPHlIi4ISJei4gXIuLCiHDFhmYy2JIkSdIwtD4EHQ8D76q631F1+xvAocAHgCXAJcA1wHSAiBgJ3AAsAPYFtgKuBFYBZ5bdcBUMtiRJkjQMrQ/BVkdKaUHtzoiYAJwAfCildFux73jgDxHx9pTSb4B3AzsC70opLQQeiIgvAxdExFkppZXNO41BoKOj9zIDNXEivPJK132ve135x5UkSZIGmUE9jbCwbUQ8FxFPRMQPI2JKsf8twGjg1krBlNIfgb8C+xS79gHmF4FWxS3AeGCnng4YEWMjYnwlAeMaeD6t8+qr5R+jXmDlyJYkSZKGocEebN0NHAe8BzgZeCNwR0SMAyYDK1NKi2ses7DIo9gurJNPVZl6ziBPS6ykZ9ax/YNLWasNVhs7tvs+R7YkSZI0DA3qYCuldFNK6ScppYdSSrcAhwCbAB8s+dDnAROq0jYlH685+jKyNWvWwJZq32CD7vsMtiRJkjQMDepgq1YxivUo8CbyohdjImKTmmKTijyK7aQ6+VSVqXecFSml9koClg648WVKCR56CFauzLd70luwdemlcPzxMGbMurelXqBmsCVJkqRhaL0KtiJiY6ANeB64j7yq4AFV+dsDU4C7il13AbtExJZV1RwItAOPNKPNDffnP8OVV8Ktt8KDD0IETJoEu+2Wp/DtuCMsWZKvk4qAiy6Cs86C7beHv/517XWfckre1gu2PvMZmDoVvvtdmDkz79t44878gw+GN7wBDjig+2O9ZkuSJEnDUKS1jYS0WERcBFwPPAW8Hjgb2B3YMaW0KCIuI08tPI4cQH0LIKW0b/H4kcADwHPA58nXaV0FXJ5S6vPS78UiGUuWLFnC+PHjG3Ny6+rrX8+Bz9ixsGJFY+uu/C1MmQJPP90176GHYJdd8u2ODlizBo46Cm66Ke9bswZWr4ZzzoGzz+762Mceg7a2xrZVkiRJaoH29nYmTJgAMKGYBdejwb70+zbAj4DNgEXAr4C3p5QWFfmnAmuAOcBY8kqDp1QenFJaHRGHAZeRR7leBf4b+NdmnUDDTZ2at40OtKpNntw92Bo9uvP2qOLPpnp6YETeX29UrHoETJIkSRomBnWwlVI6upf85cAni9RTmafIo19Dw1ZbDbyOvfeGe+7Jt485Bn74Q/jylzvzL7wQDjkkTz+cNy9PS9xuu+71nHYa3H47nHhi576jj4af/Qz23Rdeey3vm1R72ZwkSZI09A3qaYSDxaCaRvjii7DFFuv++PPOg89/Pk/5e+mlHAg98QRMm5ZHpyo6OjpHsFLqmidJkiQNU/2ZRrheLZAhYPPN4dhj8+33vrd7/vTp+Xqua67J13cdeGDX/C98AUaMyNMCJ0/OQVRbW/dgalTVoKeBliRJktRvjmz1waAa2aq1fHkepfrFL/Ltj34UVq3qeo3VihVwyy2w//4w2NovSZIkrUf6M7JlsNUHgzrYkiRJktQ0TiOUJEmSpBYz2JIkSZKkEhhsSZIkSVIJDLYkSZIkqQQGW5IkSZJUAoMtSZIkSSqBwZYkSZIklcBgS5IkSZJKYLAlSZIkSSUw2JIkSZKkEhhsSZIkSVIJDLYkSZIkqQQGW5IkSZJUAoMtSZIkSSrBqFY3YH3S3t7e6iZIkiRJaqH+xASRUiqxKUNDRGwNPNPqdkiSJEkaNLZJKT27tgIGW30QEQG8Hlja6rasx8aRA9Zt8Hkcauzboc3+Hbrs26HN/h267NvBYRzwXOolmHIaYR8UT+Jao1atXY5XAViaUnI+5hBi3w5t9u/QZd8Obfbv0GXfDhp9eu5dIEOSJEmSSmCwJUmSJEklMNhSs6wAzi62Glrs26HN/h267Nuhzf4duuzb9YgLZEiSJElSCRzZkiRJkqQSGGxJkiRJUgkMtiRJkiSpBAZbkiRJklQCgy31SUTsFxHXR8RzEZEi4qia/EkRcUWR/1pE3BwR29aUaYuIn0XEoohoj4jZETGpzrEOjYi7I+JvEfFKRFxb9vkNdxFxRkT8NiKWRsQLEXFtRGxfU2aDiLg0Il6KiGURMae2/yJiSkTcUPwNvBARF0bEqJoy+0fE7yJiRUQ8FhHHNeEUh61m9m1V2ekR0RERD5R5bmr6a/eYiHiwKPN8RMyKiM2acZ7DUQP79psRcV/xntvtNVm8J19X9OmrEfFARBxT9vkNR7jU6wAACJ5JREFUZ83q26JMRMRnI+LRotyzEfHFMs9PXRlsqa82Ah4EPlmbEREBXAtMA44E9gCeAm6NiI2KMhsBc4EEzASmA2OA6yNiRFVd7wOuAr4P7FaUu7q0s1LFO4FLgbcDBwKjgbmV/it8Azgc+EBR/vXANZXMiBgJ3EDu132BjwLHAV+tKvPGosz/AbsDFwOXR8RBJZ2XmtS3VWU3Aa4E/rfxp6I6mvXanU7u1/8Cdirq2hv4XjmnJRrQt1VmAf/Tw3H2BR4C3gfsSv78vTIiDmvAOai+ZvUtwH8CJwKfBd4MHAHcM8D2qz9SSiZTvxI5YDqq6v52xb6dqvaNAF4ATizuvxtYDYyvKjMBWAO8q7g/CngGOKHV5zjcE7BF0af7VfXVSuD9VWXeXJR5e3H/4KKPJ1WV+QSwBBhT3L8A+H3NsX4M3Nzqcx4uqay+renPfwPOAh5o9fkOt1Tia/ezwOM1x/oU8Eyrz3m4pHXp25rH9/k1SQ6+Z7X6nIdLKqtvgR2AVcD2rT7H4Zwc2VIjjC22yys7UkpryD+2946qMomuP8C3nBxsVcrsCWwNrImI+4spDTdFxM5lNl51TSi2Lxfbt5C/ebu1UiCl9Efgr8A+xa59gPkppYVV9dwCjCd/E14pcytd3VJVh8pXVt8SEceTR7jPLqXl6ouy+vcu4B8i4pBiWtIk4P3AjaWchepZl74dyLFe7rWUGqWsvj0ceAI4LCL+EhFPRsTlEbFpA9qsPjLYUiNU3gDOi4iJETEmIk4HtgG2Ksr8BngVuCAiNiyGyi8CRlaVmVZszwLOAQ4DXgHm+cbQPMW0zouBO1NKvy92TwZWppQW1xRfWORVyiysk08fyoyPiNcNtO1auzL7NvI1mucDx6aUOhrddvWuzP5NKd0JHEOerrQSWEAe+eo2tVyNN4C+XZdjfRDYizydUCUruW+nAVPJUxE/Qp4e/BbgpwNps/rHYEsDllJaBfwjeTrhy8BrwAzgJvLIFSmlReQX++HAMvKH9CbA7ypl6Px7/FpKaU5K6T7gePKI2AeacjKCPI98Z+DoVjdEDVdK3xbX/FwNfCWl9Ggj61a/lPbajYgdydd+fJX8z9p7gDcA32n0sVRXU96XI2IGOcg6KaX0cJnH0t+V2bcjyDOLPpJSuiOlNA84AZhRuyCHylN3JSmpv4rAaPeImECe478oIu4G7q0qMxdoi4jNgY6U0uKIWEAe4gZ4vtg+UvWYFRHxBDClKScyzEXEJeQRxf1SSs9UZS0AxkTEJjXftE0q8ipl9q6pclJVXmVbuwLlJKA9pfS3gbZfPSu5b8cBbwX2KI4D+UM+IqIDeHdK6bbGnY1qNeG1ewb5m/cLi/sPRcSrwB0R8aWU0vOoFAPs2/4c553A9cCpKaUrB9Jm9U0T+vZ58v9b1V+C/aHYTgH+tA7NVj85sqWGSiktKQKtbcn/fF1Xp8yLRaA1E9gS+HmRdR/5mq6/f9sSEaPJ354+VXbbh7PiGoxLgPcCM1NKf6kpch/5ItsDqh6zPfnN+q5i113ALhGxZdXjDgTa6Qyg76quo6rMXagUTerbdmAX8gqTlfQd8gf57sDdDT4tFZr42t2QzlkIFasrVQ70PNRdg/q2r8fan7woxukppe8OpN3qXRP79k5gVES0Ve3brtj6f1WztHqFDtP6kYCN6fwnKgGnFrenFPkfAPanc/n3J4E5NXUcT17mtA04FngJ+I+aMheTVyR8Nznoupw8R3liq5+DoZyAbwOLycvLTq5Kr6sqcxn5zXkGeRrRr4FfV+WPBOaTL6zfDTiIvCLluVVl3ki+du/fySsrnQJ0AAe1+jkYqqlZfVvnuGfhaoRDpn/J13qsAk4u3uenA78F7m71czBUUyP6tijzJrp/AbI7nStNzijel8+tOc6mrX4OhmpqYt+OIAdut5N/luct5Gvo57b6ORhOqeUNMK0fiRxIpTrpiiL/08DT5AunnyIv/Vy7JPT55OHvlcCjwGlA1JQZTV44YyH5W9VfUrWkvKm0/q3Xtwk4rqrMBuS55S8XH8zXAJNr6plKXp3sNWBR0Zej6vwt3U8exXy8+him9btva8qfhcHWkOpf8lLvDxdlngN+AGzd6udgqKYG9u28Hup5Q5F/RQ/581r9HAzV1Ky+Lcq8HpgDLCX/D/Z9DKSbmqLoCEmSJElSA3nNliRJkiSVwGBLkiRJkkpgsCVJkiRJJTDYkiRJkqQSGGxJkiRJUgkMtiRJkiSpBAZbkiRJklQCgy1JkqpExBURcW2r2yFJWv+NanUDJElqlohIvRQ5G/gXIJrQHEnSEGewJUkaTraquv1PwFeB7av2LUspLWtukyRJQ5XTCCVJw0ZKaUElAUvyrs59KaVltdMII2JeRHwrIi6OiFciYmFEnBQRG0XE9yNiaUQ8FhEHVx8rInaOiJsiYlnxmKsiYvNmn7MkqXUMtiRJ6t1HgReBvYFvAZcBPwF+DewJzAWuiogNASJiE+A24H7grcB7gEnA7Ka3XJLUMgZbkiT17sGU0jkppT8D5wHLgRdTSt8r9n0V2AzYtSj/z8D9KaUzU0p/TCndD3wMmBER27XiBCRJzec1W5Ik9e6hyo2U0uqIeAmYX5W/sNhuWWx3IwdW9a7/agMeLaWVkqRBxWBLkqTeraq5n6r3pZRSREDnjJGNgeuB0+vU9XwZDZQkDT4GW5IkNd7vgPcBT6aUOlrdGElSa3jNliRJjXcpsCnwo4jYKyLaIuKgYvXCka1unCSpOQy2JElqsJTSc8B0YCR5pcL5wMXAYmBNC5smSWqiSCm1ug2SJEmSNOQ4siVJkiRJJTDYkiRJkqQSGGxJkiRJUgkMtiRJkiSpBAZbkiRJklQCgy1JkiRJKoHBliRJkiSVwGBLkiRJkkpgsCVJkiRJJTDYkiRJkqQSGGxJkiRJUgkMtiRJkiSpBP8PqId3+T82+JAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRUhJUbErDVI",
        "outputId": "ec88ee4b-91a9-4ec3-e5e7-4feecde452ca"
      },
      "source": [
        "fc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1646.10069375, 1647.62903279, 1642.93243154, ..., 2091.48778035,\n",
              "       2091.96750673, 2092.44673532])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgvrfD9Tbhxh"
      },
      "source": [
        "## RNN-LSTM APPROACH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vcluT3YhdIl"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3X04-fYbe9G"
      },
      "source": [
        "# Choosing SPX for Forecasting\n",
        "data_train= train['spx']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_fsLa0o44iM"
      },
      "source": [
        "# Normalizing data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler= MinMaxScaler()\n",
        "data_scaled= scaler.fit_transform(data_train.values.reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqC1BNnJe4y3",
        "outputId": "29b85e3f-a1c3-4798-b91f-e69991c685af"
      },
      "source": [
        "data_scaled.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5015, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHDw83xoc2jr"
      },
      "source": [
        "# how many days do i want to base my predictions on ?\n",
        "prediction_days = 60\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for x in range(prediction_days, len(data_scaled)):\n",
        "    x_train.append(data_scaled[x - prediction_days:x, 0])\n",
        "    y_train.append(data_scaled[x, 0])\n",
        "    \n",
        "x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SlGYq8Oemq3",
        "outputId": "549c2d2e-c93c-42b9-a1c6-7e59bbd8382e"
      },
      "source": [
        "print(x_train.shape, y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4955, 60, 1) (4955,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKLDJgnhfMGB"
      },
      "source": [
        "def LSTM_model():\n",
        "  model= Sequential()\n",
        "  model.add(LSTM(units=50, activation='relu', return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(LSTM(units=50, activation='relu', return_sequences=True))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(LSTM(units=50))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Dense(units=1))\n",
        "\n",
        "  return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrtL36Oji06J",
        "outputId": "7c3c8663-07f1-4f57-bbbd-9d1e62f91cf4"
      },
      "source": [
        "model = LSTM_model()\n",
        "model.summary()\n",
        "# Train the ANN\n",
        "es_callback =EarlyStopping(monitor='loss', patience=2, \n",
        "                                               restore_best_weights=True,\n",
        "                                               verbose=1)\n",
        "model.compile(optimizer='adam', \n",
        "              loss='mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Layer lstm_12 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_13 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_12 (LSTM)               (None, 60, 50)            10400     \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_13 (LSTM)               (None, 60, 50)            20200     \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_14 (LSTM)               (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 50,851\n",
            "Trainable params: 50,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N82umax2o_9O",
        "outputId": "6da4630f-27e9-45f6-ca84-3f67cffe6b0e"
      },
      "source": [
        "model.fit(x_train, y_train, epochs = 30, batch_size = 32, callbacks=[es_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "155/155 [==============================] - 45s 266ms/step - loss: 0.0275\n",
            "Epoch 2/30\n",
            "155/155 [==============================] - 41s 262ms/step - loss: 0.0044\n",
            "Epoch 3/30\n",
            "155/155 [==============================] - 41s 266ms/step - loss: 0.0040\n",
            "Epoch 4/30\n",
            "155/155 [==============================] - 41s 266ms/step - loss: 0.0039\n",
            "Epoch 5/30\n",
            "155/155 [==============================] - 42s 269ms/step - loss: 0.0034\n",
            "Epoch 6/30\n",
            "155/155 [==============================] - 41s 263ms/step - loss: 0.0032\n",
            "Epoch 7/30\n",
            "155/155 [==============================] - 40s 261ms/step - loss: 0.0031\n",
            "Epoch 8/30\n",
            "155/155 [==============================] - 40s 257ms/step - loss: 0.0028\n",
            "Epoch 9/30\n",
            "155/155 [==============================] - 40s 259ms/step - loss: 0.0028\n",
            "Epoch 10/30\n",
            "155/155 [==============================] - 40s 257ms/step - loss: 0.0026\n",
            "Epoch 11/30\n",
            "155/155 [==============================] - 41s 265ms/step - loss: 0.0025\n",
            "Epoch 12/30\n",
            "155/155 [==============================] - 41s 262ms/step - loss: 0.0025\n",
            "Epoch 13/30\n",
            "155/155 [==============================] - 41s 263ms/step - loss: 0.0022\n",
            "Epoch 14/30\n",
            "155/155 [==============================] - 41s 265ms/step - loss: 0.0022\n",
            "Epoch 15/30\n",
            "155/155 [==============================] - 40s 261ms/step - loss: 0.0021\n",
            "Epoch 16/30\n",
            "155/155 [==============================] - 41s 263ms/step - loss: 0.0020\n",
            "Epoch 17/30\n",
            "155/155 [==============================] - 40s 261ms/step - loss: 0.0019\n",
            "Epoch 18/30\n",
            "155/155 [==============================] - 41s 264ms/step - loss: 0.0019\n",
            "Epoch 19/30\n",
            "155/155 [==============================] - 41s 265ms/step - loss: 0.0018\n",
            "Epoch 20/30\n",
            "155/155 [==============================] - 41s 261ms/step - loss: 0.0017\n",
            "Epoch 21/30\n",
            "155/155 [==============================] - 41s 264ms/step - loss: 0.0016\n",
            "Epoch 22/30\n",
            "155/155 [==============================] - 40s 261ms/step - loss: 0.0015\n",
            "Epoch 23/30\n",
            "155/155 [==============================] - 40s 260ms/step - loss: 0.0015\n",
            "Epoch 24/30\n",
            "155/155 [==============================] - 40s 255ms/step - loss: 0.0015\n",
            "Epoch 25/30\n",
            "155/155 [==============================] - 40s 257ms/step - loss: 0.0014\n",
            "Epoch 26/30\n",
            "155/155 [==============================] - 40s 257ms/step - loss: 0.0013\n",
            "Epoch 27/30\n",
            "155/155 [==============================] - 40s 254ms/step - loss: 0.0013\n",
            "Epoch 28/30\n",
            "155/155 [==============================] - 40s 258ms/step - loss: 0.0013\n",
            "Epoch 29/30\n",
            "155/155 [==============================] - 39s 253ms/step - loss: 0.0012\n",
            "Epoch 30/30\n",
            "155/155 [==============================] - 40s 258ms/step - loss: 0.0012\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f768bbd5090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVfCT8MezkWz"
      },
      "source": [
        "data_test= test['spx']\n",
        "total_dataset= pd.concat((data_train, data_test), axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EF6VT2iP02rl",
        "outputId": "bb53a1c8-b7e3-4e8b-d2c8-42d1db985163"
      },
      "source": [
        "total_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6269,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWPMlJhn1l8v",
        "outputId": "dddfa1a4-40e7-4c22-b3eb-3055e908b3a1"
      },
      "source": [
        "data_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5015,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yONj00Lc1ADC",
        "outputId": "4aad4015-edcf-4b93-f1ed-90e0a15f7dc2"
      },
      "source": [
        "data_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1254,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlL1Fqk51ZX_",
        "outputId": "783339f9-4478-4fd2-a923-493a95dd4e8f"
      },
      "source": [
        "# Inorder to make prediction of first period of test data, we need last 60 period of train data\n",
        "model_inputs = total_dataset[len(total_dataset) - len(data_test) - prediction_days:].values\n",
        "model_inputs.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1314,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D18acYNQ2oYw"
      },
      "source": [
        "model_inputs= model_inputs.reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6S7jkjb2wqk",
        "outputId": "4e85340b-3b32-4358-fa1b-404cb25195ad"
      },
      "source": [
        "model_inputs.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1314, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cf_mzyP3Hpb"
      },
      "source": [
        "model_inputs = scaler.transform(model_inputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsuaggjLtTAW"
      },
      "source": [
        "predictions= "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8Hvuw9M3Smz"
      },
      "source": [
        "x_test = []\n",
        "for x in range(prediction_days, len(model_inputs)):\n",
        "    x_test.append(model_inputs[x-prediction_days:x, 0])\n",
        "\n",
        "x_test = np.array(x_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1] ,1))\n",
        "\n",
        "predicted_prices = model.predict(x_test)\n",
        "predicted_prices = scaler.inverse_transform(predicted_prices)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrnC-pDT3qBX",
        "outputId": "46edaa1c-6e02-4dae-99e1-35c44f2cde5c"
      },
      "source": [
        "predicted_prices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1486.2025],\n",
              "       [1486.0093],\n",
              "       [1484.9745],\n",
              "       ...,\n",
              "       [2154.5422],\n",
              "       [2156.2183],\n",
              "       [2160.89  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_ZvQ3Wba3tjw",
        "outputId": "c2cbe48f-37c2-494f-d3e8-e9cf946ff84b"
      },
      "source": [
        "plt.plot(data_test.values, color='black', label=\"Actual stock index\")\n",
        "plt.plot(predicted_prices, color= 'green', label=\"predicted stock index\")\n",
        "\n",
        "plt.title(\"Stock Index of SPX\")\n",
        "plt.xlabel(\"time\")\n",
        "plt.ylabel(\"Stock Index of SPX\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5bTkipQk5D8Y"
      },
      "source": [
        "## Predicting for Day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59CmR5H98aDM"
      },
      "source": [
        "# predicting next day\n",
        "real_data = [model_inputs[len(model_inputs)+1 - prediction_days:len(model_inputs+1),0]]\n",
        "real_data = np.array(real_data)\n",
        "real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFPIpMAT8jN9",
        "outputId": "1cadcbe0-df43-4273-d3ac-69013645eb27"
      },
      "source": [
        "prediction = model.predict(real_data)\n",
        "prediction = scaler.inverse_transform(prediction)\n",
        "print(f\"prediction: {prediction}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prediction: [[2162.2178]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skndys8OF2lv"
      },
      "source": [
        "## Predicting for Next 10 Days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bu0wSrqrAQJh"
      },
      "source": [
        "## demonstrate prediction for next 10 days\n",
        "x_input= model_inputs[1255:].reshape(59,)\n",
        "temp_input=list(x_input)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W89PRZLBDX8W",
        "outputId": "8e08a745-1b88-4fe6-f1c8-db9dcadf9aa8"
      },
      "source": [
        "lst_output=[]\n",
        "i=0\n",
        "while(i<10):\n",
        "    \n",
        "    if(len(temp_input)>59):\n",
        "        x_input=np.array(temp_input[1:])\n",
        "        print(\"{} day input {}\".format(i,x_input))\n",
        "        #print(x_input)\n",
        "        x_input = x_input.reshape((1, 59, 1))\n",
        "        #print(x_input)\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_input.append(yhat[0][0])\n",
        "        temp_input=temp_input[1:]\n",
        "        #print(temp_input)\n",
        "        lst_output.append(yhat[0][0])\n",
        "        i=i+1\n",
        "    else:\n",
        "        x_input = x_input.reshape((1, 59, 1))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        print(yhat[0])\n",
        "        temp_input.append(yhat[0][0])\n",
        "        lst_output.append(yhat[0][0])\n",
        "        i=i+1\n",
        "\n",
        "print(lst_output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.5232465]\n",
            "1 day input [1.90523627 1.89661463 1.89456286 1.89681223 1.89153261 1.87893568\n",
            " 1.89751789 1.89151714 1.8944228  1.90934999 1.9076246  1.9076246\n",
            " 1.91235133 1.91146669 1.93411268 1.93324836 1.95226464 1.94752319\n",
            " 1.94506704 1.93634617 1.93607752 1.94289212 1.95573053 1.96323377\n",
            " 1.96687202 1.96575781 1.9561826  1.9772158  1.9899002  1.98222375\n",
            " 1.98025656 1.98495994 1.98387105 1.98387105 1.98136711 1.98323697\n",
            " 1.98758398 1.97527344 1.99489566 2.01014426 2.01980684 2.03673837\n",
            " 2.04077119 2.04393119 2.04122663 2.05831502 2.07483183 2.07483183\n",
            " 2.06614816 2.08924973 2.08524974 2.09609751 2.11613708 2.12157869\n",
            " 2.12017649 2.12168711 2.1513997  2.13430532 1.52324653]\n",
            "1 day output [[1.4586248]]\n",
            "2 day input [1.89661463 1.89456286 1.89681223 1.89153261 1.87893568 1.89751789\n",
            " 1.89151714 1.8944228  1.90934999 1.9076246  1.9076246  1.91235133\n",
            " 1.91146669 1.93411268 1.93324836 1.95226464 1.94752319 1.94506704\n",
            " 1.93634617 1.93607752 1.94289212 1.95573053 1.96323377 1.96687202\n",
            " 1.96575781 1.9561826  1.9772158  1.9899002  1.98222375 1.98025656\n",
            " 1.98495994 1.98387105 1.98387105 1.98136711 1.98323697 1.98758398\n",
            " 1.97527344 1.99489566 2.01014426 2.01980684 2.03673837 2.04077119\n",
            " 2.04393119 2.04122663 2.05831502 2.07483183 2.07483183 2.06614816\n",
            " 2.08924973 2.08524974 2.09609751 2.11613708 2.12157869 2.12017649\n",
            " 2.12168711 2.1513997  2.13430532 1.52324653 1.45862484]\n",
            "2 day output [[1.3843112]]\n",
            "3 day input [1.89456286 1.89681223 1.89153261 1.87893568 1.89751789 1.89151714\n",
            " 1.8944228  1.90934999 1.9076246  1.9076246  1.91235133 1.91146669\n",
            " 1.93411268 1.93324836 1.95226464 1.94752319 1.94506704 1.93634617\n",
            " 1.93607752 1.94289212 1.95573053 1.96323377 1.96687202 1.96575781\n",
            " 1.9561826  1.9772158  1.9899002  1.98222375 1.98025656 1.98495994\n",
            " 1.98387105 1.98387105 1.98136711 1.98323697 1.98758398 1.97527344\n",
            " 1.99489566 2.01014426 2.01980684 2.03673837 2.04077119 2.04393119\n",
            " 2.04122663 2.05831502 2.07483183 2.07483183 2.06614816 2.08924973\n",
            " 2.08524974 2.09609751 2.11613708 2.12157869 2.12017649 2.12168711\n",
            " 2.1513997  2.13430532 1.52324653 1.45862484 1.3843112 ]\n",
            "3 day output [[1.308702]]\n",
            "4 day input [1.89681223 1.89153261 1.87893568 1.89751789 1.89151714 1.8944228\n",
            " 1.90934999 1.9076246  1.9076246  1.91235133 1.91146669 1.93411268\n",
            " 1.93324836 1.95226464 1.94752319 1.94506704 1.93634617 1.93607752\n",
            " 1.94289212 1.95573053 1.96323377 1.96687202 1.96575781 1.9561826\n",
            " 1.9772158  1.9899002  1.98222375 1.98025656 1.98495994 1.98387105\n",
            " 1.98387105 1.98136711 1.98323697 1.98758398 1.97527344 1.99489566\n",
            " 2.01014426 2.01980684 2.03673837 2.04077119 2.04393119 2.04122663\n",
            " 2.05831502 2.07483183 2.07483183 2.06614816 2.08924973 2.08524974\n",
            " 2.09609751 2.11613708 2.12157869 2.12017649 2.12168711 2.1513997\n",
            " 2.13430532 1.52324653 1.45862484 1.3843112  1.30870199]\n",
            "4 day output [[1.2354963]]\n",
            "5 day input [1.89153261 1.87893568 1.89751789 1.89151714 1.8944228  1.90934999\n",
            " 1.9076246  1.9076246  1.91235133 1.91146669 1.93411268 1.93324836\n",
            " 1.95226464 1.94752319 1.94506704 1.93634617 1.93607752 1.94289212\n",
            " 1.95573053 1.96323377 1.96687202 1.96575781 1.9561826  1.9772158\n",
            " 1.9899002  1.98222375 1.98025656 1.98495994 1.98387105 1.98387105\n",
            " 1.98136711 1.98323697 1.98758398 1.97527344 1.99489566 2.01014426\n",
            " 2.01980684 2.03673837 2.04077119 2.04393119 2.04122663 2.05831502\n",
            " 2.07483183 2.07483183 2.06614816 2.08924973 2.08524974 2.09609751\n",
            " 2.11613708 2.12157869 2.12017649 2.12168711 2.1513997  2.13430532\n",
            " 1.52324653 1.45862484 1.3843112  1.30870199 1.23549628]\n",
            "5 day output [[1.1672403]]\n",
            "6 day input [1.87893568 1.89751789 1.89151714 1.8944228  1.90934999 1.9076246\n",
            " 1.9076246  1.91235133 1.91146669 1.93411268 1.93324836 1.95226464\n",
            " 1.94752319 1.94506704 1.93634617 1.93607752 1.94289212 1.95573053\n",
            " 1.96323377 1.96687202 1.96575781 1.9561826  1.9772158  1.9899002\n",
            " 1.98222375 1.98025656 1.98495994 1.98387105 1.98387105 1.98136711\n",
            " 1.98323697 1.98758398 1.97527344 1.99489566 2.01014426 2.01980684\n",
            " 2.03673837 2.04077119 2.04393119 2.04122663 2.05831502 2.07483183\n",
            " 2.07483183 2.06614816 2.08924973 2.08524974 2.09609751 2.11613708\n",
            " 2.12157869 2.12017649 2.12168711 2.1513997  2.13430532 1.52324653\n",
            " 1.45862484 1.3843112  1.30870199 1.23549628 1.16724026]\n",
            "6 day output [[1.1056445]]\n",
            "7 day input [1.89751789 1.89151714 1.8944228  1.90934999 1.9076246  1.9076246\n",
            " 1.91235133 1.91146669 1.93411268 1.93324836 1.95226464 1.94752319\n",
            " 1.94506704 1.93634617 1.93607752 1.94289212 1.95573053 1.96323377\n",
            " 1.96687202 1.96575781 1.9561826  1.9772158  1.9899002  1.98222375\n",
            " 1.98025656 1.98495994 1.98387105 1.98387105 1.98136711 1.98323697\n",
            " 1.98758398 1.97527344 1.99489566 2.01014426 2.01980684 2.03673837\n",
            " 2.04077119 2.04393119 2.04122663 2.05831502 2.07483183 2.07483183\n",
            " 2.06614816 2.08924973 2.08524974 2.09609751 2.11613708 2.12157869\n",
            " 2.12017649 2.12168711 2.1513997  2.13430532 1.52324653 1.45862484\n",
            " 1.3843112  1.30870199 1.23549628 1.16724026 1.10564446]\n",
            "7 day output [[1.051796]]\n",
            "8 day input [1.89151714 1.8944228  1.90934999 1.9076246  1.9076246  1.91235133\n",
            " 1.91146669 1.93411268 1.93324836 1.95226464 1.94752319 1.94506704\n",
            " 1.93634617 1.93607752 1.94289212 1.95573053 1.96323377 1.96687202\n",
            " 1.96575781 1.9561826  1.9772158  1.9899002  1.98222375 1.98025656\n",
            " 1.98495994 1.98387105 1.98387105 1.98136711 1.98323697 1.98758398\n",
            " 1.97527344 1.99489566 2.01014426 2.01980684 2.03673837 2.04077119\n",
            " 2.04393119 2.04122663 2.05831502 2.07483183 2.07483183 2.06614816\n",
            " 2.08924973 2.08524974 2.09609751 2.11613708 2.12157869 2.12017649\n",
            " 2.12168711 2.1513997  2.13430532 1.52324653 1.45862484 1.3843112\n",
            " 1.30870199 1.23549628 1.16724026 1.10564446 1.05179596]\n",
            "8 day output [[1.0059378]]\n",
            "9 day input [1.8944228  1.90934999 1.9076246  1.9076246  1.91235133 1.91146669\n",
            " 1.93411268 1.93324836 1.95226464 1.94752319 1.94506704 1.93634617\n",
            " 1.93607752 1.94289212 1.95573053 1.96323377 1.96687202 1.96575781\n",
            " 1.9561826  1.9772158  1.9899002  1.98222375 1.98025656 1.98495994\n",
            " 1.98387105 1.98387105 1.98136711 1.98323697 1.98758398 1.97527344\n",
            " 1.99489566 2.01014426 2.01980684 2.03673837 2.04077119 2.04393119\n",
            " 2.04122663 2.05831502 2.07483183 2.07483183 2.06614816 2.08924973\n",
            " 2.08524974 2.09609751 2.11613708 2.12157869 2.12017649 2.12168711\n",
            " 2.1513997  2.13430532 1.52324653 1.45862484 1.3843112  1.30870199\n",
            " 1.23549628 1.16724026 1.10564446 1.05179596 1.00593781]\n",
            "9 day output [[0.9684938]]\n",
            "[1.5232465, 1.4586248, 1.3843112, 1.308702, 1.2354963, 1.1672403, 1.1056445, 1.051796, 1.0059378, 0.9684938]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efmKF4B9Ec9u"
      },
      "source": [
        "prediction_next_10_days= scaler.inverse_transform(np.array(np.array(lst_output).reshape(10,1)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arurByU2P5sI"
      },
      "source": [
        "from pandas.tseries.offsets import DateOffset\n",
        "future_dates= [data_test.index[-1] + DateOffset(days=x) for x in range(0,15)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em9DEW6YS04E",
        "outputId": "1f80a490-cf55-468c-9653-a11207ad276a"
      },
      "source": [
        "future_dates"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Timestamp('2018-01-29 00:00:00'),\n",
              " Timestamp('2018-01-30 00:00:00'),\n",
              " Timestamp('2018-01-31 00:00:00'),\n",
              " Timestamp('2018-02-01 00:00:00'),\n",
              " Timestamp('2018-02-02 00:00:00'),\n",
              " Timestamp('2018-02-03 00:00:00'),\n",
              " Timestamp('2018-02-04 00:00:00'),\n",
              " Timestamp('2018-02-05 00:00:00'),\n",
              " Timestamp('2018-02-06 00:00:00'),\n",
              " Timestamp('2018-02-07 00:00:00'),\n",
              " Timestamp('2018-02-08 00:00:00'),\n",
              " Timestamp('2018-02-09 00:00:00'),\n",
              " Timestamp('2018-02-10 00:00:00'),\n",
              " Timestamp('2018-02-11 00:00:00'),\n",
              " Timestamp('2018-02-12 00:00:00')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2M7xPrx9h_a5"
      },
      "source": [
        "data_test= pd.DataFrame(data_test)\n",
        "data_test['prediction']= predicted_prices"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pXLffOGSr8k"
      },
      "source": [
        "future_data= pd.DataFrame(index=future_dates[1:],columns=pd.DataFrame(data_test).columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZpXEUvgTSSm"
      },
      "source": [
        "future_data= future_data.asfreq('B')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "UIKBKNeKTWoQ",
        "outputId": "ad1c096b-d39a-4004-f556-67b5029d0864"
      },
      "source": [
        "future_data"
      ],
      "execution_count": null,
      "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>spx</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-02</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-05</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-06</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-07</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            spx prediction\n",
              "2018-01-30  NaN        NaN\n",
              "2018-01-31  NaN        NaN\n",
              "2018-02-01  NaN        NaN\n",
              "2018-02-02  NaN        NaN\n",
              "2018-02-05  NaN        NaN\n",
              "2018-02-06  NaN        NaN\n",
              "2018-02-07  NaN        NaN\n",
              "2018-02-08  NaN        NaN\n",
              "2018-02-09  NaN        NaN\n",
              "2018-02-12  NaN        NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 365
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiRFHJ-9VrwM"
      },
      "source": [
        "future_data['prediction']=prediction_next_10_days"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOeig7JET1c4"
      },
      "source": [
        "data_concat= pd.concat([pd.DataFrame(data_test), future_data])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "bSnjoeYxUbRW",
        "outputId": "abf4e67e-206d-4b89-8a27-bd8b4313a13b"
      },
      "source": [
        "data_concat"
      ],
      "execution_count": null,
      "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>spx</th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-04-04</th>\n",
              "      <td>1559.979316</td>\n",
              "      <td>1486.202515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-04</th>\n",
              "      <td>1553.278930</td>\n",
              "      <td>1486.009277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-04</th>\n",
              "      <td>1563.071269</td>\n",
              "      <td>1484.974487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-04</th>\n",
              "      <td>1568.607909</td>\n",
              "      <td>1484.122437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>1587.731827</td>\n",
              "      <td>1483.998779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-06</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1759.456543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-07</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1689.771240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1628.850586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1576.969849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-02-12</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1534.608276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1264 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                    spx   prediction\n",
              "2013-04-04  1559.979316  1486.202515\n",
              "2013-05-04  1553.278930  1486.009277\n",
              "2013-08-04  1563.071269  1484.974487\n",
              "2013-09-04  1568.607909  1484.122437\n",
              "2013-10-04  1587.731827  1483.998779\n",
              "...                 ...          ...\n",
              "2018-02-06          NaN  1759.456543\n",
              "2018-02-07          NaN  1689.771240\n",
              "2018-02-08          NaN  1628.850586\n",
              "2018-02-09          NaN  1576.969849\n",
              "2018-02-12          NaN  1534.608276\n",
              "\n",
              "[1264 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 368
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBDz7HliUzUU"
      },
      "source": [
        "data_next_10days= pd.DataFrame(prediction_next_10_days)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JntoL7Cufi0u"
      },
      "source": [
        "data_next_10days.columns= ['predictions']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "WsuLJQDfj2Ch",
        "outputId": "7035fbae-b798-4d0e-cf22-3531b7bc90c6"
      },
      "source": [
        "data_next_10days"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2162.217773</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2089.109375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2005.035889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1919.496704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1836.676758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1759.456543</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1689.771240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1628.850586</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1576.969849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1534.608276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   predictions\n",
              "0  2162.217773\n",
              "1  2089.109375\n",
              "2  2005.035889\n",
              "3  1919.496704\n",
              "4  1836.676758\n",
              "5  1759.456543\n",
              "6  1689.771240\n",
              "7  1628.850586\n",
              "8  1576.969849\n",
              "9  1534.608276"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 372
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHGBKFiBj9Si"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}