ELAN_future_300.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/YanjunLin-Andrie/elan_price_prediction/blob/main/ELAN_future_300.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "source": [
        "pip install yfinance"
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      "id": "173U2GH2BCNy",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
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            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.9.1 requests-2.28.1 yfinance-0.1.74\n"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yahoo-fin\n",
            "  Downloading yahoo_fin-0.8.9.1-py3-none-any.whl (10 kB)\n",
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            "Building wheels for collected packages: fake-useragent, parse, sgmllib3k\n",
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            "  Stored in directory: /root/.cache/pip/wheels/9c/aa/cc/f2228050ccb40f22144b073f15a2c84f11204f29fc0dce028e\n",
            "  Building wheel for sgmllib3k (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/73/ad/a4/0dff4a6ef231fc0dfa12ffbac2a36cebfdddfe059f50e019aa\n",
            "Successfully built fake-useragent parse sgmllib3k\n",
            "Installing collected packages: websockets, urllib3, pyee, cssselect, w3lib, sgmllib3k, pyquery, pyppeteer, parse, fake-useragent, requests-html, feedparser, yahoo-fin\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed cssselect-1.1.0 fake-useragent-0.1.11 feedparser-6.0.10 parse-1.19.0 pyee-8.2.2 pyppeteer-1.0.2 pyquery-1.4.3 requests-html-0.10.0 sgmllib3k-1.0.0 urllib3-1.26.10 w3lib-1.22.0 websockets-10.3 yahoo-fin-0.8.9.1\n"
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    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "f9c9c0be-3375-48f8-98db-ef4a97661c80",
      "metadata": {
        "id": "f9c9c0be-3375-48f8-98db-ef4a97661c80"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import yfinance as yf\n",
        "from yahoo_fin.stock_info import get_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "83b4007f-f095-41c9-93fd-2246ddb410ee",
      "metadata": {
        "id": "83b4007f-f095-41c9-93fd-2246ddb410ee"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c2a529d6-0efe-4160-87ee-db25a527e43b",
      "metadata": {
        "id": "c2a529d6-0efe-4160-87ee-db25a527e43b",
        "colab": {
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          "height": 424
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        "outputId": "b423d14e-410e-4027-ab76-96cbc0fb5ca1"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                 open       high        low      close   adjclose    volume\n",
              "2018-09-20  32.250000  36.009998  32.049999  36.000000  36.000000  40182700\n",
              "2018-09-21  34.560001  35.490002  33.880001  34.099998  34.099998   9269500\n",
              "2018-09-24  34.099998  35.650002  33.700001  34.900002  34.900002   2769100\n",
              "2018-09-25  34.759998  34.990002  34.060001  34.580002  34.580002   3152200\n",
              "2018-09-26  34.349998  35.980000  34.250000  35.500000  35.500000   5835600\n",
              "...               ...        ...        ...        ...        ...       ...\n",
              "2022-07-12  20.049999  20.379999  19.870001  19.990000  19.990000   2555200\n",
              "2022-07-13  19.580000  19.830000  19.350000  19.700001  19.700001   3059500\n",
              "2022-07-14  19.410000  19.639999  19.110001  19.610001  19.610001   3400800\n",
              "2022-07-15  19.740000  20.110001  19.480000  20.080000  20.080000   3504600\n",
              "2022-07-18  20.090000  20.330000  19.930000  20.000000  20.000000   2536900\n",
              "\n",
              "[962 rows x 6 columns]"
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              "      <th></th>\n",
              "      <th>open</th>\n",
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              "      <th>low</th>\n",
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              "    <tr>\n",
              "      <th>2018-09-20</th>\n",
              "      <td>32.250000</td>\n",
              "      <td>36.009998</td>\n",
              "      <td>32.049999</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.000000</td>\n",
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              "    <tr>\n",
              "      <th>2018-09-21</th>\n",
              "      <td>34.560001</td>\n",
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              "      <th>2018-09-25</th>\n",
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              "      <td>19.700001</td>\n",
              "      <td>19.700001</td>\n",
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              "      <th>2022-07-14</th>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-687008d7-0ff0-43e5-bfac-5cfece70d8b3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-687008d7-0ff0-43e5-bfac-5cfece70d8b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#get all ELAN trading history until today from yahoo finance\n",
        "df = get_data('ELAN', index_as_date = True).drop(columns = 'ticker')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4d323399-c906-4d06-b1dc-c0823a13fff6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "4d323399-c906-4d06-b1dc-c0823a13fff6",
        "outputId": "ff719109-de56-4c00-bff5-791e3fa6088f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0db195fad0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['close'].plot()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLE52qYhD1R9",
        "outputId": "e8ca7530-49f0-4664-9c7c-0202fcc1901a"
      },
      "id": "TLE52qYhD1R9",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "962"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "116b7755-d3fe-45aa-a089-29cd49bbfc72",
      "metadata": {
        "id": "116b7755-d3fe-45aa-a089-29cd49bbfc72"
      },
      "outputs": [],
      "source": [
        "def window_data(df, window, feature_col_number, target_col_number):\n",
        "    X = []\n",
        "    y = []\n",
        "    for i in range(len(df) - window - 1): # i in range(962-10-1) # i in range(951)\n",
        "        features = df.iloc[i:(i + window), feature_col_number] # df.iloc[0: 10, 'Close']\n",
        "        target = df.iloc[(i + window), target_col_number] # df.iloc[10, 'Close']\n",
        "        X.append(features)\n",
        "        y.append(target)\n",
        "    return np.array(X), np.array(y).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "27f2354f-48d5-4105-9203-a467d71e060e",
      "metadata": {
        "id": "27f2354f-48d5-4105-9203-a467d71e060e"
      },
      "outputs": [],
      "source": [
        "window_size=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "a292a2e3-c509-49da-865e-4e33c2f50d82",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a292a2e3-c509-49da-865e-4e33c2f50d82",
        "outputId": "f58f296f-7a94-4473-8066-fdcaa3266beb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[36.        , 34.09999847, 34.90000153, ..., 33.72999954,\n",
              "        33.41999817, 34.25      ],\n",
              "       [34.09999847, 34.90000153, 34.58000183, ..., 33.41999817,\n",
              "        34.25      , 34.02000046],\n",
              "       [34.90000153, 34.58000183, 35.5       , ..., 34.25      ,\n",
              "        34.02000046, 34.34000015],\n",
              "       ...,\n",
              "       [20.13999939, 20.        , 19.62999916, ..., 20.45000076,\n",
              "        20.21999931, 19.98999977],\n",
              "       [20.        , 19.62999916, 20.62000084, ..., 20.21999931,\n",
              "        19.98999977, 19.70000076],\n",
              "       [19.62999916, 20.62000084, 20.64999962, ..., 19.98999977,\n",
              "        19.70000076, 19.61000061]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[34.02000046],\n",
              "       [34.34000015],\n",
              "       [34.00999832],\n",
              "       [34.31999969],\n",
              "       [33.52999878],\n",
              "       [32.38999939],\n",
              "       [32.58000183],\n",
              "       [32.45999908],\n",
              "       [32.61000061],\n",
              "       [32.93000031],\n",
              "       [31.79999924],\n",
              "       [31.61000061],\n",
              "       [31.87999916],\n",
              "       [32.43999863],\n",
              "       [31.73999977],\n",
              "       [31.95999908],\n",
              "       [32.43000031],\n",
              "       [30.90999985],\n",
              "       [30.85000038],\n",
              "       [30.47999954],\n",
              "       [31.87999916],\n",
              "       [33.09999847],\n",
              "       [33.24000168],\n",
              "       [33.81000137],\n",
              "       [34.45999908],\n",
              "       [33.81999969],\n",
              "       [34.45999908],\n",
              "       [34.15999985],\n",
              "       [34.5       ],\n",
              "       [34.00999832],\n",
              "       [33.09000015],\n",
              "       [34.        ],\n",
              "       [33.79999924],\n",
              "       [33.13000107],\n",
              "       [33.15999985],\n",
              "       [33.61000061],\n",
              "       [33.        ],\n",
              "       [32.95000076],\n",
              "       [33.47999954],\n",
              "       [33.47999954],\n",
              "       [33.40999985],\n",
              "       [33.75      ],\n",
              "       [33.06999969],\n",
              "       [32.59999847],\n",
              "       [33.56999969],\n",
              "       [33.38000107],\n",
              "       [32.95999908],\n",
              "       [32.86999893],\n",
              "       [32.54000092],\n",
              "       [32.36999893],\n",
              "       [31.76000023],\n",
              "       [31.10000038],\n",
              "       [31.63999939],\n",
              "       [31.32999992],\n",
              "       [30.        ],\n",
              "       [29.62999916],\n",
              "       [30.72999954],\n",
              "       [31.19000053],\n",
              "       [31.31999969],\n",
              "       [31.53000069],\n",
              "       [31.79999924],\n",
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              "       [31.30999947],\n",
              "       [31.11000061],\n",
              "       [31.04999924],\n",
              "       [32.88000107],\n",
              "       [32.24000168],\n",
              "       [32.31000137],\n",
              "       [32.09999847],\n",
              "       [33.95000076],\n",
              "       [34.79999924],\n",
              "       [35.00999832],\n",
              "       [35.29999924],\n",
              "       [35.36999893],\n",
              "       [35.68999863],\n",
              "       [35.75999832],\n",
              "       [35.77999878],\n",
              "       [35.50999832],\n",
              "       [35.70999908],\n",
              "       [35.86999893],\n",
              "       [35.97999954],\n",
              "       [35.5       ],\n",
              "       [35.40999985],\n",
              "       [35.        ],\n",
              "       [34.18000031],\n",
              "       [33.56000137],\n",
              "       [33.36000061],\n",
              "       [33.90000153],\n",
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              "       [34.81000137],\n",
              "       [35.97999954],\n",
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              "       [34.68999863],\n",
              "       [34.86999893],\n",
              "       [35.18999863],\n",
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              "       [34.99000168],\n",
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              "       [35.31999969],\n",
              "       [35.31999969],\n",
              "       [34.68999863],\n",
              "       [35.        ],\n",
              "       [34.93999863],\n",
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              "       [34.56000137],\n",
              "       [34.41999817],\n",
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              "       [35.02000046],\n",
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              "       [34.22999954],\n",
              "       [35.52999878],\n",
              "       [35.66999817],\n",
              "       [36.02999878],\n",
              "       [36.5       ],\n",
              "       [35.81000137],\n",
              "       [36.24000168],\n",
              "       [36.63999939],\n",
              "       [36.61000061],\n",
              "       [36.47000122],\n",
              "       [36.5       ],\n",
              "       [36.72000122],\n",
              "       [36.41999817],\n",
              "       [35.79999924],\n",
              "       [35.36000061],\n",
              "       [29.67000008],\n",
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              "       [30.15999985],\n",
              "       [31.        ],\n",
              "       [31.53000069],\n",
              "       [31.70000076],\n",
              "       [31.48999977],\n",
              "       [31.5       ],\n",
              "       [31.5       ],\n",
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              "       [32.27999878],\n",
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              "       [33.31000137],\n",
              "       [33.43999863],\n",
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              "       [33.52999878],\n",
              "       [33.79999924],\n",
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              "       [34.02000046],\n",
              "       [33.88999939],\n",
              "       [33.54000092],\n",
              "       [33.38000107],\n",
              "       [33.27999878],\n",
              "       [32.91999817],\n",
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              "       [32.40000153],\n",
              "       [31.82999992],\n",
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              "       [31.79999924],\n",
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              "       [32.63000107],\n",
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              "       [32.54999924],\n",
              "       [32.34999847],\n",
              "       [31.88999939],\n",
              "       [32.09000015],\n",
              "       [32.09999847],\n",
              "       [32.13999939],\n",
              "       [32.68999863],\n",
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              "       [33.15000153],\n",
              "       [32.88999939],\n",
              "       [32.18000031],\n",
              "       [32.02999878],\n",
              "       [32.45000076],\n",
              "       [32.54000092],\n",
              "       [32.09999847],\n",
              "       [32.63999939],\n",
              "       [33.65999985],\n",
              "       [33.59999847],\n",
              "       [33.90000153],\n",
              "       [33.97000122],\n",
              "       [33.49000168],\n",
              "       [33.31000137],\n",
              "       [32.90000153],\n",
              "       [32.88000107],\n",
              "       [32.86999893],\n",
              "       [31.72999954],\n",
              "       [32.22000122],\n",
              "       [32.86999893],\n",
              "       [34.09000015],\n",
              "       [33.25      ],\n",
              "       [33.09999847],\n",
              "       [32.97999954],\n",
              "       [32.74000168],\n",
              "       [33.02999878],\n",
              "       [32.15999985],\n",
              "       [32.29000092],\n",
              "       [31.95000076],\n",
              "       [32.34000015],\n",
              "       [32.11000061],\n",
              "       [32.04999924],\n",
              "       [32.06999969],\n",
              "       [30.79000092],\n",
              "       [30.21999931],\n",
              "       [30.19000053],\n",
              "       [28.73999977],\n",
              "       [28.13999939],\n",
              "       [28.78000069],\n",
              "       [28.17000008],\n",
              "       [28.79000092],\n",
              "       [28.96999931],\n",
              "       [29.54000092],\n",
              "       [28.48999977],\n",
              "       [27.96999931],\n",
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              "       [28.11000061],\n",
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              "       [27.97999954],\n",
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              "       [28.20000076],\n",
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              "       [28.95999908],\n",
              "       [28.87999916],\n",
              "       [28.27000046],\n",
              "       [28.70999908],\n",
              "       [28.37999916],\n",
              "       [28.25      ],\n",
              "       [28.40999985],\n",
              "       [27.89999962],\n",
              "       [27.07999992],\n",
              "       [27.04999924],\n",
              "       [28.72999954],\n",
              "       [28.37999916],\n",
              "       [28.09000015],\n",
              "       [27.06999969],\n",
              "       [27.03000069],\n",
              "       [26.80999947],\n",
              "       [26.38999939],\n",
              "       [26.82999992],\n",
              "       [26.77000046],\n",
              "       [27.10000038],\n",
              "       [26.30999947],\n",
              "       [25.77000046],\n",
              "       [24.94000053],\n",
              "       [25.51000023],\n",
              "       [26.04000092],\n",
              "       [25.54000092],\n",
              "       [25.36000061],\n",
              "       [24.64999962],\n",
              "       [24.96999931],\n",
              "       [25.05999947],\n",
              "       [24.86000061],\n",
              "       [25.72999954],\n",
              "       [25.30999947],\n",
              "       [25.48999977],\n",
              "       [25.12999916],\n",
              "       [25.86000061],\n",
              "       [25.88999939],\n",
              "       [25.78000069],\n",
              "       [25.51000023],\n",
              "       [25.97999954],\n",
              "       [25.75      ],\n",
              "       [27.88999939],\n",
              "       [27.87000084],\n",
              "       [28.40999985],\n",
              "       [28.94000053],\n",
              "       [29.        ],\n",
              "       [28.37000084],\n",
              "       [27.95000076],\n",
              "       [25.84000015],\n",
              "       [25.52000046],\n",
              "       [26.62000084],\n",
              "       [26.37999916],\n",
              "       [26.48999977],\n",
              "       [25.20000076],\n",
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              "       [26.40999985],\n",
              "       [26.61000061],\n",
              "       [27.42000008],\n",
              "       [27.20000076],\n",
              "       [27.40999985],\n",
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              "       [27.46999931],\n",
              "       [26.59000015],\n",
              "       [26.71999931],\n",
              "       [27.53000069],\n",
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              "       [26.09000015],\n",
              "       [26.12000084],\n",
              "       [26.15999985],\n",
              "       [25.97999954],\n",
              "       [26.62000084],\n",
              "       [26.54999924],\n",
              "       [26.25      ],\n",
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              "       [25.82999992],\n",
              "       [26.26000023],\n",
              "       [26.25      ],\n",
              "       [25.73999977],\n",
              "       [26.12000084],\n",
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              "       [25.28000069],\n",
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              "       [25.57999992],\n",
              "       [25.64999962],\n",
              "       [25.30999947],\n",
              "       [25.14999962],\n",
              "       [25.28000069],\n",
              "       [25.        ],\n",
              "       [24.18000031],\n",
              "       [23.79000092],\n",
              "       [21.84000015],\n",
              "       [21.89999962],\n",
              "       [20.81999969],\n",
              "       [21.69000053],\n",
              "       [22.27000046],\n",
              "       [23.30999947],\n",
              "       [24.20000076],\n",
              "       [23.35000038],\n",
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              "       [24.27000046],\n",
              "       [23.69000053],\n",
              "       [23.17000008],\n",
              "       [23.32999992],\n",
              "       [23.71999931],\n",
              "       [23.87000084],\n",
              "       [23.70000076],\n",
              "       [24.12999916],\n",
              "       [24.84000015],\n",
              "       [24.19000053],\n",
              "       [23.69000053],\n",
              "       [24.54999924],\n",
              "       [24.13999939],\n",
              "       [23.52000046],\n",
              "       [22.27000046],\n",
              "       [21.64999962],\n",
              "       [20.89999962],\n",
              "       [20.92000008],\n",
              "       [20.28000069],\n",
              "       [20.54999924],\n",
              "       [20.03000069],\n",
              "       [20.01000023],\n",
              "       [19.94000053],\n",
              "       [20.61000061],\n",
              "       [20.47999954],\n",
              "       [20.13999939],\n",
              "       [20.        ],\n",
              "       [19.62999916],\n",
              "       [20.62000084],\n",
              "       [20.64999962],\n",
              "       [20.35000038],\n",
              "       [20.69000053],\n",
              "       [20.45000076],\n",
              "       [20.21999931],\n",
              "       [19.98999977],\n",
              "       [19.70000076],\n",
              "       [19.61000061],\n",
              "       [20.07999992]])"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X, y=window_data(df, window_size, 3, 3)\n",
        "display(X)\n",
        "display(y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qw7qbDvfEXB0",
        "outputId": "ae150dbe-fc7f-4dd8-b61e-29e18c446957"
      },
      "id": "Qw7qbDvfEXB0",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "951"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "GreUcSAngpop",
      "metadata": {
        "id": "GreUcSAngpop"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "X_scaler=MinMaxScaler()\n",
        "X_scaler.fit(X)\n",
        "X_scaled=X_scaler.transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "WUMPL6UQjJWk",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUMPL6UQjJWk",
        "outputId": "8ad00493-4010-4aa5-b077-4c05e4c7cbb3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[36.        , 34.09999847, 34.90000153, ..., 33.72999954,\n",
              "        33.41999817, 34.25      ],\n",
              "       [34.09999847, 34.90000153, 34.58000183, ..., 33.41999817,\n",
              "        34.25      , 34.02000046],\n",
              "       [34.90000153, 34.58000183, 35.5       , ..., 34.25      ,\n",
              "        34.02000046, 34.34000015],\n",
              "       ...,\n",
              "       [20.13999939, 20.        , 19.62999916, ..., 20.45000076,\n",
              "        20.21999931, 19.98999977],\n",
              "       [20.        , 19.62999916, 20.62000084, ..., 20.21999931,\n",
              "        19.98999977, 19.70000076],\n",
              "       [19.62999916, 20.62000084, 20.64999962, ..., 19.98999977,\n",
              "        19.70000076, 19.61000061]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "2732c02c-6188-49aa-99f3-c203b6ae88bb",
      "metadata": {
        "id": "2732c02c-6188-49aa-99f3-c203b6ae88bb"
      },
      "outputs": [],
      "source": [
        "split=int(0.7*len(X_scaled))\n",
        "X_train=X_scaled[:split]\n",
        "X_test=X_scaled[split:]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "2EqW3KfLg7vh",
      "metadata": {
        "id": "2EqW3KfLg7vh"
      },
      "outputs": [],
      "source": [
        "y_scaler=MinMaxScaler()\n",
        "y_scaler.fit(y)\n",
        "y_scaled=y_scaler.transform(y)\n",
        "y_train=y_scaled[:split]\n",
        "y_test=y_scaled[split:]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "cojs-gZjhVB6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "cojs-gZjhVB6",
        "outputId": "c66cc5f6-9c64-46a0-c896-e3bb7f65457a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(665, 10)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(286, 10)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(665, 1)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(286, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "display(X_train.shape)\n",
        "display(X_test.shape)\n",
        "display(y_train.shape)\n",
        "display(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "8fc3e3cc-e86e-48d5-a177-6380182a078c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "8fc3e3cc-e86e-48d5-a177-6380182a078c",
        "outputId": "74cd9e21-d610-4f5d-ae03-61f20fab7111"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(665, 10, 1)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(286, 10, 1)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X_train=X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
        "X_test=X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
        "display(X_train.shape)\n",
        "display(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "16c1c831-8d29-4e17-a6ba-1ea96a37b55f",
      "metadata": {
        "id": "16c1c831-8d29-4e17-a6ba-1ea96a37b55f"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "415d2b5b-0c00-4b33-9033-aed1cca4fc7e",
      "metadata": {
        "id": "415d2b5b-0c00-4b33-9033-aed1cca4fc7e"
      },
      "outputs": [],
      "source": [
        "model=Sequential()\n",
        "first_lstm_layer=LSTM(units=30, return_sequences=True, input_shape=(window_size, 1))\n",
        "model.add(first_lstm_layer)\n",
        "first_dropout_layer=Dropout(0.2)\n",
        "model.add(first_dropout_layer)\n",
        "second_lstm_layer=LSTM(units=30, return_sequences=True)\n",
        "model.add(second_lstm_layer)\n",
        "second_dropout_layer=Dropout(0.2)\n",
        "model.add(second_dropout_layer)\n",
        "third_lstm_layer=LSTM(units=30)\n",
        "model.add(third_lstm_layer)\n",
        "output_layer=Dense(units=1, activation='linear')\n",
        "model.add(output_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "09d56478-8cf5-4c62-ad91-49a73063bc39",
      "metadata": {
        "id": "09d56478-8cf5-4c62-ad91-49a73063bc39"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='mean_squared_error', optimizer='adam', metrics='mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "a6b47ac1-4eb6-4696-b36e-99b42c2e9c48",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6b47ac1-4eb6-4696-b36e-99b42c2e9c48",
        "outputId": "c7c7a7a2-c1fe-4dab-e2fc-47afaf548893"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 10, 30)            3840      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 10, 30)            7320      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 30)                7320      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,511\n",
            "Trainable params: 18,511\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "1424de1d-b9b1-459b-b1f0-783031d3b4cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1424de1d-b9b1-459b-b1f0-783031d3b4cf",
        "outputId": "024d1c9d-e372-406b-9054-df34362bc5ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "21/21 [==============================] - 8s 25ms/step - loss: 0.1554 - mean_squared_error: 0.1554\n",
            "Epoch 2/10\n",
            "21/21 [==============================] - 1s 27ms/step - loss: 0.0168 - mean_squared_error: 0.0168\n",
            "Epoch 3/10\n",
            "21/21 [==============================] - 1s 25ms/step - loss: 0.0125 - mean_squared_error: 0.0125\n",
            "Epoch 4/10\n",
            "21/21 [==============================] - 1s 29ms/step - loss: 0.0104 - mean_squared_error: 0.0104\n",
            "Epoch 5/10\n",
            "21/21 [==============================] - 1s 25ms/step - loss: 0.0093 - mean_squared_error: 0.0093\n",
            "Epoch 6/10\n",
            "21/21 [==============================] - 1s 25ms/step - loss: 0.0087 - mean_squared_error: 0.0087\n",
            "Epoch 7/10\n",
            "21/21 [==============================] - 1s 26ms/step - loss: 0.0079 - mean_squared_error: 0.0079\n",
            "Epoch 8/10\n",
            "21/21 [==============================] - 1s 28ms/step - loss: 0.0083 - mean_squared_error: 0.0083\n",
            "Epoch 9/10\n",
            "21/21 [==============================] - 1s 26ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n",
            "Epoch 10/10\n",
            "21/21 [==============================] - 1s 29ms/step - loss: 0.0080 - mean_squared_error: 0.0080\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0dacc2f790>"
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          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "model.fit(X_train, y_train, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "FD64bWsgfpqQ",
      "metadata": {
        "id": "FD64bWsgfpqQ"
      },
      "outputs": [],
      "source": [
        "prediction=model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "o9wLzWbTgOeH",
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "o9wLzWbTgOeH",
        "outputId": "32d33e52-8879-4b7f-b60a-a7cd1bb803cd"
      },
      "outputs": [
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "GJ3Gyr3Xf0l2",
      "metadata": {
        "id": "GJ3Gyr3Xf0l2"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "0W2puveYf6N4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "0W2puveYf6N4",
        "outputId": "fef1c38f-55c0-495b-9982-22179c5c1bcc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0dac8a7110>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/0o4PPcEautgtt8vej8BogbGX6+vE6eDhS+OBlfz0zW0YhsHzN05l1S/n8ZvFI/kyo5D7lqX3xKdwz7d/g0dSoaVBL87Gjodju9ofX+eQkthNfh5BENpnEAl6mj7uX6EzJcZO1JkSQYtJf6YdH/oJbS7a/b4utRc9Vl97eEPyLCr2fE5eWS3/vm4SC0ZHkxLhz4/mDuUn84axdGsu3+5vp3D2yZK3VR8jhkPSLB0hVJju4mL6ZFc+N7+8mVXb99obRdAFoVsMHkEPjIb4qdBYDZEj4ObP4c7tfT2rrmF2DVsEiAr0wcOkOrfQLS067nz13yFnDUy6ARz84nmJFxDZcIRHRhxgSrJzEes756cRF+zDM99m98hHcaGhClLnwh2b9V9JsROhpVG7hayszSrhjje3sy67hHdWO7hjRNAFoVsMHkEHmH2XPkaN1mFvJjPcsQV+tLZv59UZ7fjQzSZFTHAXQhfXPgbf/AW++StEjNC+agfuOzyWdIZwWcl/tV/aAS8PE9+bmcy67FIyj1Wd9EdxoaZI13K1MeJ8CIjRrhjDoLK+id/8bxepEf588fO5RJkdcsC7S+UgCEK7DC5BH3khTLjWvkUfICINYsb23Zy6QjuCDjpJV4cul5oSWPmg3ql67l/gypfBw75ZKquoii/3lbB9wv2YGqvh9ctd3B3XTEvCy8PEK+sPd33O1cXw/AJdyq+zcf4Ogu7lB2feAzlrMfK28n9Ld5JfXs8jV4wnKdyPq8bo5GoVvgki6ILQTQaXoJtMcOl/Ydj8vp5J92jHhw5WQe/IQj+2S987/VYd2RI92qn7hTWH8fE0sfjchXD+ozr6J2+b05gwfy8umRjHB9vyqKh1DZ90y5F1Oo3ws/OguZ01ioZqaKrRdVwdGXMpALvWfsqK9EJ+s2gk01K0K2h0SAvNeLCmJgnL4XVQXdS1+QiCMMgEfaDSkYUe6kthZT3NLRb39xZZFxGjRrt0NTS38OmufM4fG0t4gDcMP093HF7tMvamWSnUNbWwdKubfDjuKNlvP9/+mvsxNVYx9m9T4CMgkpaQVEr2rmFqcig/nJPa2qVqS1H+4TzFlbQ01sHnv3V9bEMzf/xwD+f8YxUvrjmEYct6KQinOSLo/YEOBD0uxBeLAccq2ylHV5QB/pHg71pIY1VmMZX1zSyZaK2+5Bemc7scchX0MXHBTEkO5c2NR7omkMX7ddriyFGw6133Y6qtkTMBrumJD3iNYryRyV8uHuOc7bKuDLN/OBfPn8sTTRfDnvdg26s6jv21y6ClmfuWpfPGxhx8Pc3c/0kG727p4peQIAxyuiToSqlFSqlMpVSWUuo3bvqTlFIrlVLblVK7lFIDLBtUH9NOHDo4F7pwS9FeiBrltmvZjnzC/b2Y41g1KeUsOLIRmlyfd+30JA6W1LDx0HGXPhdKMnU00fgrdax/2WHXMTYLvY3LpaG5hQ9KEohUFYyq2wb1lfbO2lLwC+fmOal8EXY9GxhH06e/hlcvhuyvufXJD1m6NZefzBvGJz+bw6jYIF5ZlyNWuiDQBUFXSpmBp4DFwGjgWqVU27/v/wC8axjGJOAa4OmenuigphMLHdrJi26xQHGmW3dLVX0TX+0t5MLxsc7VmJJn6rj8wj0u91w4PpYgHw9eXX9YJ/hqb0enxQIlB7Sg2zYw7V/hOq7avctl6ZZcvqwbjoGC1y6BhxMh/UPdWVsKvqF4mk08c9MMPgi+Ec+W2tZ7I43j/HbxSO5akIZSiutmJJFRUMmu3Ar3cxWE04iuWOjTgSzDMA4ahtEIvA1c3GaMAboGMxAMSHhCd+hQ0PX2f7cLoyWZ1rj7kS5dK9ILaWi2sGRivHNHzDh9dCPoPp5mvndGMt/sOULz61fCuzfZqzI5UpkLTbV6s1BIss7q6CZ1cVHBEQDuX1nE+uxSAOqbWnjymyxCk8bAz3fBFS/pItmFe/QXRfkRCEkCICXCnwfvvIW6IHtWzAfnh3H73KF4Wr+kLp4YR6C3Bw8u34vFIla6cHrTFUGPBxydlLnWNkfuA76nlMoFlgM/65HZnS60k5wLwM/Lg1A/T1dBNwz47FfgFWhf7HTgox15JIT6MjmpTQ6b4CR9zzFXQQe4eU4q13mswqO2CMoOuQ9LLM7Ux8gRegNTSDKU5bR255TWcOOLm/hi026OGwG8uSWfa5/bwB1vbuP65zdyrLKeexYOR4UkwdjLwDdMh19WFeh8PGF2ATebTfjOuxtCrQunVc5x9EE+nvzhwlFsPHRc/2UhCKcxPbUoei3wsmEYCcD5wGtKKZdnK6VuU0ptUUptKS7upa3mA5EOfOigI11cXC4HV+nFzXPvg6A4p66jx2tZk1XC5ZMTXDMpmkw6tLHQff6WCM8mfuHzCbstKRgmT9jzvusgm6BHjNDH0BQo14Kenl/B1U9+zb6cAs6MbiQ4Kokd9y7k9rlDWLmviKyiah6/ZiKzhjr49f0joLYEjlt3q4a1yVM/+Qa969fT32VjFMBVUxOZOzyShz/fx+GSGpd+QThd6Iqg5wGOddoSrG2O/BB4F8AwjPWAD+ASdmEYxrOGYUw1DGNqZGRk2+7Tlw7i0AHigt0IevY3utLRhGtdxtuiPq6a1k55veixkLdF+60ba5371vyLwKYSHjHdwlbPyRi73nVdQC3JpNknjN98nsdDy/eyryGUltJD/OnD3fzm6bfYYNzAyrR3SW45gjlqJD6eZn67eBTp9y9ix73ncnFbN5BfhM4SaXPbhA91nbNSEBQLVa7ePKUUD18+Dk+ziV8u3UmLuF6E05SuCPpmIE0plaqU8kIvei5rM+YIMB9AKTUKLehigneVdnK52Iiz7hZ1iuQ49C0kznDJF97cYuHdLUeZOzyyNULGhegxOp/K0pvg+fna3WFj2ysw6iIWL76Yf1bNR9UUwa63W7vrm1rIPbCDbbVRLNuZz0vrDvP2ARPmljpWbE7neb+nAPDP+kRb7W0WbN3mXvcL04uhpdlg9rLXiG1LYGy7u0djg32576IxbMkp46W1h9zfLwiDnE4F3TCMZuAOYAWwFx3Nkq6Uul8ptcQ67B7gVqXUTuAt4PuGxJF1nQ4WRUGn0a1pbKGyzjqmphQKdsGQuS5jv91fTGFlA9dMS2r/fSPOh1EXwYI/6zj2DGuESX0F1BRD/FSunZ6Ib9rZ7LakkPv5v7jr7e3c+dZ2Fv7zW/wrD9ISPpw1vz6Hvfcv4o7LFgDw9dW+RDcesacyhnZDKp1odbkc1L5yk9n9uKA4XcnoyEb9O3j2bB2NY+WyyfEsGBXF31dkcrC4uvP3CsIgo0s+dMMwlhuGMdwwjKGGYTxgbbvXMIxl1vMMwzBmG4YxwTCMiYZhfNGbkx50dOJDb02ja3O7HFkHGJB6lsvYtzYdJSLAm/mjolz6WgmK1UWuZ9+lI1SKrbs+j1st27BUlFI8fcMUihPPI6HpMFlHctmdV8FQ/zpCVTUzZ8wizN8Ls0kRkTgcAP9M6xeDY3Kwrgi6XwTUHtc1Ydv6zx0JjNUZGF9cCKsehPxtuhqTFaUUD146Dh9PM/d9nNH5ewVhkCE7RfsDJjNY2s+h4hKLfmwPoCDGuZzejqPlfLW3kOumJ7aG9XWIUjr0sMS6yFlmE3Qtqt4eZs45eyEAn14RxMqfTeWlmVZPWsRw+3NCkvV8Mj+z+vWv0UVFPHz1gmln+EcAhl5s7ahCkU+Q/XzrK/qYsczJZRQV5MMtc1JZvb9YFkiF0w4R9P5AJy6X1t2iFVZBL0rXouvl1zrGYjG4b1k6kYHe3DbXzaJie0SOtEet2Cx0RxGOnaSPX/0JHkqAj+/SceLxU+xjvPxg3JXQXKcrEnkHQvxk7atvz33iiF+49cRw+ZJyYtyVcMZPdJEMSxOEDdW/t5cv1Fkks1cCejHYbFK8telIl34FgjBYEEHvD9gEvZ1lh3B/L7w8TPY0uoXpWiwd+GB7HjuOlvPrRSO7V1A6criO7a6v0D5s/0gtyDb8w3Xsev52LaRXvaZzzPu2iW8/7wEdT25zA136DFz+XNfm0Cro2Dc+uSMkCRY9BMMX6+sJ18J174BhgayvYN+nAEQH+bBwdDRvbTpCdUP7X5SCMNgQQe8PmKwCbLjPqGgyKeKCfbQPvbFGW9IOgl7b2MzfPt/HhMQQLpvUToRIe9h2mRbv1/lY3Pmw4ybq4zl/hNFLdFm7tgRE6Vjxs3+vr0OTO/aHO2JLLGb2tpcS7IhRF+pomLRz9aaqOzbpd9WVtQ750dyhVNY38/qGnA4eJAiDCxH0/oBN0Dtyu9g2FxXtAwwnQX9p7WGKqhr44wWjnDMXdoVI6+agnLV6UTI01XXM5Bth8k2d55n3DdEhmN3Fzyro0aO7Vsw7Zhz8Lt/+RQPgG+ok6BMSQzgzLYKX1x6WxF3CaYMIen+gI0HP3wEHvrJuLqrXG4KgVdCr6pt45tts5o+MYmpKmOv9nRGaqmt+fnWfdr0MPcd1TNq5sOQJpzqlPYrN5dKR/7wtbb84fMOgzjlL5CUT4zlWWc+evEoE4XRABL0/0J6gN9XDOzfAO99jWEAjhVX1WHa/p3OQWy3p5bsLqKxv5idndxAd0hFKwZJ/awt3+u0w/qqT+CAniIcXLHoYZtx+4s9oY6EDzBsRiVLw1d7Ck5ygIAwMurF6JvQaNkFvaSPoW16ACh2pcU7ZO6xR4ZhyN8H8e1ut5aVbchka6e+ahKs7hCbDPZlOtUhPOWf8+OTu9w2FWmdBDw/wZnJSKN/sK+IX5w5v50ZBGDyIhd4fsIX2tbXQ97wPCdNgyDzS9j/Ha14PY2DS4XvAkdJatuSUccWURPdb6rtDX4p5T+AXBg0VLl+K80dFsTuvgsL2Kj4JwiBCBL0/4M7lYrHoBdD4qbDkSRou/De3NN7D0kkvt+YL/3a/LiCxaGzMKZ5wP8Q3VB/ry52aF4zS5e++3ivFpoXBjwh6f8C2wOco6OU50FSjIz9CEvGeeiPpgbPZUJ/cOuS7AyUkhPqSEu7HaY+vdUG4jR89LSqAxDBfvhY/unAaIILeH3BnoRdZc5E4ZCscEulPtnU7e3OLhfXZpZyZFnny7pbBgM1CbyPoSinmj4xmTVYJDc3t58sRhMGACHp/oNWH7iA4hVZBdygvNyQigIPF1RiGwfaj5VQ1NHNmmkva+dMTP6ug17oWuJ6RGkZDs4V9BVWneFKCcGoRQe8PtFroDgm6itJ10ivvgNamtOgAquqbOVZZz2e7j+HlYeKs4VIoBGjXQge9yQh08jJBGMyIoPcH3LlcSrJcij+PidPZBnfnVvDZngLmDo/sXt6WwUyrD93VQo8N9iEy0JudIujCIEcEvT/gTtArcyE4wWnYqNgglILXNuRQUFHP+eMkuqUV7yBQJrcWulKKiYkh7MgVQRcGNyLo/YFWQbf60BtrtTAFOyfa8vPyYEiEP98dKMHH09Qakiegi1/7hupSdm6YmBjCweIaKmrbzzsvCAMdEfT+QFsLvcpa2d5Nbc2x8cEALBoTQ6DPCSTCGsyEJNsLTbdhQoL2o+/KEytdGLyIoPcH2gp6ZZ4+BsW5DB0bpwX98ikJLn2nPY7FOtowPlH/3nYcEUEXBi+yotYfaM3lYnUH2Crbu7HQr5qaSJCvB7OHSriiC5HDYeebuliHT7BTV5CPJ0Mj/dkpfnRhECMWen+grQ/dZqEHxroMDfbz5OppSd3Pe3464Fisww0TEkPYcbRC8qMLgxYR9P5A2+RcFXl6gc9LtvR3C1vh6uJ9brsnJYZQUt2gKz8JwiBEBL0/4OJDz3frbhE6ITRFl7E7usE1FTH2DUY7j1ac4okJwqlBBL0/0DY5V2We2wVRoRNMZkiYCttfhw9d86uPjAnCy8MkfnRh0CKC3h9o60OvOgaBsmnohPje+5B4BpQecOny8jAxJi5IIl2EQYsIen/A0Yfe0gw1xRAggn5CePrqHbb17t0qExJC2J1XQXOL5RRPTBB6HxH0/oBjcq6aYsCAQNkFesL4BLcr6JOSQqhramF/YfUpnpQg9D4i6P0Bx0XR6mP6XCz0E8cnGOrKwU14om3HqPjRhcGICHp/wNGHXm0tlRYgFvoJ4xsCRgs01rh0JYf7EeLnKTjLLJUAACAASURBVJkXhUGJ7BTtD3h462PuFnvEi7hcThzbLtH6Cqd88qAzL05ICJHc6MKgRCz0/oB3IMz6Gex6G1b/Q7eJhX7itAq6G9FuquP75uVkF5ZT0+Aaqy4IAxkR9P7CuX/RW9crjoBPiN1qF7qPj/aTu10Y3fcpZx/6FxM5IFa6MOgQQe8vKAXJs/W5xKCfHI4ul7ZYszHGe1Ty6e6CUzgpQeh9RND7E8mz9DEgqm/nMdCxCXqdGwvcmudldpzBp7sKaGyWeHRh8CCC3p+wWegSsnhy2ApGu7PQS3QmxmkRzVTUNfHV3sJTODFB6F1E0PsTQbEw4ToYsaivZzKw8dbFtF0EvaUJSrMASPKuITncj2e+zZZ0usKgQQS9v3Hpf2Ds5X09i4GN2QO8AlyjXI4fak2AZqot4Udzh7Izt4K1We7rkA4aGqqhMKOvZyGcAkTQhcGJT4irhV6ww95XXcRlk+OJDvLmyZWuibyc2PQcvLJEfyEMNIoz4aF4+M9MqBL30mCnS4KulFqklMpUSmUppX7TzpirlFIZSql0pdSbPTtNQegmbfO5WCyw9nEIGwJDz4aaYrw9zNx65hA2HDzORzvy2JpznLKaRufnbHsNlv8SDn8HL57ntPu0obmFT3cV8NM3t/H0qiyq+2Nc+9aX7ecl7uutCoOHTneKKqXMwFPAuUAusFkptcwwjAyHMWnAb4HZhmGUKaUkTEPoW2z5XLK/0T9Js6BwD1z6rLbUa74E4LoZSby+IYe73tbWe7i/Fz+aO5TzfdOJ96yCrC8hJAkW3Afv3QxF+yBhCltzyrj9ta2UVDcQ6ufJp7sKePyrA/xufgI3zR4CXv5999kdKcoA/0id9K00G1LP6usZCb1IV7b+TweyDMM4CKCUehu4GHB0yt0KPGUYRhmAYRhFPT1RQegWfmF6AXTHW7D7Xb0gavKAsZdBZS40VkNjLX5efqz4xVmsyiymqcXCk99k8cDyvUz0foBo73w8AqMgZjxEjwWgevfH1L16A7+u/T8CglN49MppnJkWye68Ch77aj+XrTqXlnUemH+f28e/ACtFe2HYAkj/oHVBWBi8dEXQ44GjDte5wIw2Y4YDKKXWAmbgPsMwPm/7IKXUbcBtAElJSScyX0HoGmFD4MCX9h23+1dAaKrOleNv/QOyphi8kvH2MHPeGB0qesG4WHLL6gj494/xaKyA0goYcwmEpmIoM80bnyOSau6O38e0711HZKB+/sTEEP597SQCH66DJqipqcHfv4+t9JpSqC7UX0YFO+H4wb6dj9Dr9NSiqAeQBswDrgWeU0qFtB1kGMazhmFMNQxjamRkZA+9WhDcEJEGLQ1wbLe+LjsE4cP0ub/1315NiX28NXRRKUWidx2hht3/XhE4jLIGyCOKEHQe9fN901vF3Eagj2fr+Rvvv9fDH+gEKLL+ER01Sn/BiYU+6OmKoOcBiQ7XCdY2R3KBZYZhNBmGcQjYjxZ4Qegbwq3//AyHnaARVkEPitXHCusfnhYL/HsKrHlMX7dZPPzzRoPL/7OOAy0OG76ObICGKtj5NlQXu7y+fv83fN3Xm5aK9upj1Gj9ZXb8kL3MoTAo6YqgbwbSlFKpSikv4BpgWZsxH6Ktc5RSEWgXjPx9J/QdEcNd22wWesRwUCa7BVuUDsez4eBKfW1ND0BoChblwYpjAdQ2tjBy7GTdPnS+ri71/i3wwe2w6kHd3mKPclnkvYfbX9vKXz/JwGLpo41LRRk6RDMwRn92SxOUHXYaIqX4Bhed+tANw2hWSt0BrED7x180DCNdKXU/sMUwjGXWvoVKqQygBfg/wzAG+W4NoV/jH65TANSVQXCitsZtgu7pC2FDoTBdXx9arY8Fu+Dz38GGp/T1/Hsx5e8gfeFF+npLjg4FmPUzwID91mUiW3x3Y5X13ZEMr8niB+M8eW7NIUwmxW8Xj0Qp1duf2pmiDG2dKwWx43Vb/nYIHwpASXUDFz6xhutmJHHnfPmDejDQJR+6YRjLDcMYbhjGUMMwHrC23WsVcwzN3YZhjDYMY5xhGG/35qQFoUuEp2lLfOg51uth9r7o0a6CXnccNjytz0NT9Y7dhX+x3zP6Epj3O0iZA5f8B9IWQlCC3dJvsAr6hGsB+N2Qg9xwRjLPrj7I/afaUjcM7XKJGqWvo8aApx/kbm4d8o8vMjlWWc/jXx8gPd99DVZhYCE7RYXBS9IMiJ0Ik74H025xLhoSPVa7H+or4PDa1rBEMGDJk3DdO67P8wuDeb/WkTKBMXD9Uphyk15wbai2C3r8FIgYgdr3CX9eMoabZ6fy0trD3LN056kT9co8aKjUX1yg0yHETW4V9EMlNby9+ShXTEkgxNeTBz7de2rmJfQqUoJOGLws+LNeFDV7QuJ0576o0YABO9/RrpIzfgwf3QFB8foLoKvuEdsXQVGGfQHWOxASp8GBrzCZFH+8cBQhfp7888v9jIkL4pYzh/TYR2wXxwVRGwlTYf1T0FTPC2sO4mky8atFIxgVG8RfPslgfXYpM4eG9/7chF5DLHRh8GIy22u0tiVuoj6u+Zc+pp0HIy+A6bd0XcwBosfoY+Eeu4XuHaT/GqgpBosFpRQ/O2cYC0ZF8fcVmb1foPrYHvuW/8iR9vb4yWBpovroLt7bmsslk+KICvTh+hlJRAZ688KaAZirRnBCBF04PQlOgJQzoSpfW7EBkXDNGzDnF917TkiS9k2XZGkXB2gL3T8KjBbtl0fHtz902Xiigry56aVNPPnNAYqq6nv4Q1lZ+QDs+wRCU7SbyIY1/n7PwSPUN1m4epqORvbxNHPh+FhWHyjun/lohC4jgi6cvkz9gT6eTH4TpSAozuqztlnogfoLAqDangUjMtCb1384g7SoAB79Yj9z/raSDQd7IRisoUov1t68wrnd0w+AzCOFBPl4MCHBvvdv8dhYGpstrNwnWTsGMiLowunLyItg8k3652QIincWdJ8gh/QCzgKZHO7P0h/NYtUv5xHk48mLveHmaKqFyBGutWmtCcMO5hcxe1gEHmb7//5TkkOJCPBmRfqxnp+PcMoQQRdOXzy8YMkT9kiQEyU4ASpsgq7A098eUeNmFylASoQ/l0yMY2VmEcfbpuw9WRprwcvPtd1qoTfUVXNmmnPqDbNJcWZaBBsOlnatglPeVqeNVEL/QARdEE6WoDioPqY3MXkHgslkd7nUtO/CuGxyAk0tBh/taJtJ4yRpqtFfKm2xirw/9ZyZFuHSPT01jJLqRrKLrTnfHYW9uhi++rPOWll+FJ47B7a9wvrsUn7yxlZeXntISvn1AyRsURBOlqB4HbJYsl8LOugt92Yvne2wHUbHBTEhIZjXN+Tw/VkpPbeTtF0LXYt8vL9BYphr/4xUvYC66dBxhu3+F6x/Ei5+CtY+plMP52+HYfNbwzMPb17OtUdi8PE0sXz3MfYdq+L+i8fi5SF2Yl8hv3lBOFmCE/SxONMu6ErpqJJ2XC42bpiZQnZxDeuye3BxtKm21b3iSANmGg0zaaHuvzhSI/yJDPRm46FS+O5RaK6HZXfqjJX52/Wg2lLtXgICCzeycFQk2/+4kJ+dM4y3Nx/lJ29s67vcNYIIuiCcNEHx+lhVYBd0gICoDl0uABeOjyXUz5NX1x/umblYLFrQ3VRM2nyojDq8SQ50L+hKKaanhrHx4HEM24akphrwdQh9rCyACl28I1xV8eg8b3y9zNyzcAT3Xjiar/YW8tx3kpevrxBBF4STJTjefu4o6P5RTmGLThxeAy3N+HiauXpaEl9mFJJfXnfyc2m2PsONhf5FxjHq8CHOr/0UumekhnGssh5LZYFuiJ0AN3zQmp+GqnxqSnJoNMwABBXZc8P8YHYK54+L4ZEVmWzNKTv5zyJ0GxF0QThZvIN0DVNwtoyDYrUbZt2TzuNz1sPLF0DmcgCun6Grd/WIZdtY6zoPwGIx+HzPMfDyx6Ol/S+OGUPC8aEBc30ZzL8Xbl+td9Ve+l+9iaqygOLcbA4YCRjKDJX5rfcqpXj48vHEhfhw51vbKa/t4egdoVNE0AXhZFEKrn0bpnwfJt1ob59zt04Q9sXv7ZkdAQ58oY9lOgY9McyPa6cn8dLaw3x3oGOfe6c0WSNU2ljo24+WU1TVgI9foF303TAsMoCRvtbMi0EJzp2BcRiV+bSU51LrF4fyj9DpDRwI8vHkqesmU1RVz+2vbWVPnmRxPJWIoAtCT5A8Cy56HIYvtLeFJsOVr4CHD2x6TocBHj8IWV/q/gp7Iek/XDCa4dEB/OKdnRRXNZz4PFotdGdBX5F+DE+zIiAwSPvY28FkUsyLaQLACIpz7gyKpbzoCJGWYsJjU/Wir2MZPyvjE0J44NJx7M6r4KIn1/Dg8r0S0niKEEEXhN7ELwzGXQG73oGMj+CJSfY6pw6C7utl5t/XTqaqvunk0uw22ix0u8vFMLS7ZfawCDx8AqCxusNHzInSOWb21gY7tWfW+BNad4QgVUfKkBHgxkK3cdXURDb8bj5XTUnk2dUH+XR3wYl9HqFbiKALQm8z8kJtFa+3+tLDhugsiLaaplZGxATyxwtHs3p/Mc+vOUF/us3l4mCh7y2o4sjxWhaNidG+9Q5cLgBjA6qwGIpPD+vrusYWHv5sH+9n2cvVmUISrBZ6+y6iIB9PHrxsHKNjg3jw071U1Ted2GcSuowIuiD0NvFT9TF3sz6/czskz3ay0G1cPyOJxWNjeOTzTHacSJpdN4uin+8pwKRgwehobbk31ept++/cAEc3uzzCpzafSnMoy9JLaLEY3P9JOs+sziY5KdU+aNh8HcXT1uWy+QX45G6o15knzSbFXy4ZQ2FVAz96fStPfH2Ap1dl8d7WXN7bmtt7GSdPU2SnqCD0NgGRuqRd2SFdzQj0ZqS6Mu0icRBfpRQPXzaeXbnfcfe7O/ji52c5JdHqFJt/3MHl8nn6MaalhBER4K0t98YaqMyFvcv0z0826kReviH6CyHzM+qjJ3H0UB1//jidtzYd5bazhnD9tDR49l9wxUu6Xqt/hHbfOO5MXfWQttqL9sLNnwEwJTmM+5aM4U8f7WFtlvMGqjB/Lx65fLz+shFOGhF0QTgVJExrI+g6FzkVeRA53GlosJ8n9y0Zw62vbuF/2/O4ampi19/T6OxyyS6uZn9hNX+6yLpRyNMq6DUOwvr0DB2hc9HjsOMNqC0l8sp7GPJ+E6+uz2FEdCA/X5AGXh7wu3x7ARBrfnVqS8ArSS/62v5COLJOl/gLTQHghjOSuWRiHD6eZppbDAoq6qioa+L3H+zhlle3cP64GK6cksiUlFCCfNopSiJ0irhcBOFUkDxTHxOs7hdbuoA2fnQbC0ZFMSEhmMe/OkBjs8XtGLe0Wuha0D/arrfpnzfGmkrXyx9aGlxzzFRZ0+amfwjR4zCnzOIvl4zlqqkJvHv7TPy8rLafY74Zm6Db/OjVhdqHP+PH+jrzc6dXBPp44mk24etlZkhkAJOSQvngp7P42TnD+O5ACT94eTMT//wFt7yyhYo68befCCLognAqmHQD3PoNhA/V1yFWq7vssNvhSinuXjiCvPI63tniXvTd0mqh+1Ne28hLaw+zcHQ0cSG+re0AlB/Rx4ue0PHmFmsq3Kp8iEgDpZg9LIJHrphAsF87FnOroFv96KXZ+pi2QC/6Zn5qf9eHP4VvHtDZGmtK4PkFcHgN3h46bcCm3y3gzVtmcPvcoXy7v4jL/7OOI6UdL94KroigC8KpwOxpd7cABMaBV4DeSdoOZ6VFMC0llCe/6YaV3lQLygxmL578JovqxmbuXujg0rFtOCrP0cexl+tiGHXWrfpVha6FMdrDliK45ABsew3SP9DXYUN1ZM/hNdry3/UO7HgdVj8COWt1ibzczfZ6ruiwzVnDIvj1opG8evMMiqsaOO+x1fzxwz3UN7WfqkBwRgRdEPoCk0lbscV77W0tTWCxi5dSip/MG0ZhZQNfZrSfhteJRp2Ya112KS+uPcQ105IYGRNk77dZ6GU54OGrr31DtaA3VGmXSUAXFyj9rDnVv/g9LLsDNj8HKL0+MOEanWZ391LI3aLfAZD5mS5g7R8JWV/rebRh5tBwlt0xmyUT4nhtQw63vrqFhmYR9a4ggi4IfUXUKB0NYuOVJfDZr5yGnDU8kvgQX97Y6Cp8LuTvwDi2i8oWT657fiNxIb787vyRzmMcLXT/SO0Ttwl6lfVLo6sWupefXkyddissfMDaaIDZQ7tt4qfCjjd1daPhi3WY47bXtNBf+owem/GR20cnh/vztyvG88jl4/nuQAl/+GCP7DbtAiLogtBXRI3SC4rVxdDcCLmbtJvCAbNJcd2MJNZll7KxbUHp+grY+zHUHgfAWHoTKmctpY0e/HBOKp/eeSaBbSNGHH3o/uH63C8M6sq1/xy6bqGDjoy54FGY+VMdmjn+anvf1B9AUYb+jAlTIHqM/gvALwKGnqPT8pZ1XFP1qmmJ3HnOMJZuzeWF3qi/OsgQQReEviJqlD4W74Xj2XphsmQ/NDlnQ/zB7BQSw3z57f92U2arP1pfCf8aB+98D964gvrSoyjrAmuqqZA/XDCKYF83i5k2QW+otC9q+oYCht2fHxjb/c+iFPxsq9XytjL+Goiw+u/jp0LMWH2ePEuPD07Q5ew64ecLhrNoTAwPLt/LQ5/tpamlG1E/pxki6ILQV9iKSBTts7teDIuzGwbw8/Lg0YtSmVOxjLMfXcnkv3zJv975HBoqYPw1GHnbOPrfK5zuabecXdhQhwdbfeA2/7btvYEnuMnHZHYOazR7wIWPwehLIHqs/gG9SxZ0Ot6Ko7D5eZe/TJwea1L88+oJXDElgWe+PcjLaw+f2PxOA2RjkSD0FQHROhNjxRG9OcdG4R6In+w0dEbtt8wwv4B/3CTyAsZyOGMNmOHLkCuI8TvEuNqNeuCC+yCgAx94QCTETdIl5fxtgm6tSFS8D8zeuh5qT5EyW/8ADDkbUufCqAv1dXAiZH8Dn/8WYsbDLV9B+v/A5Amjlzg9xs/Lg0eumEBBRT1Pr8rimumJru4kQSx0QegzlIKgOL1btGivTtrlFQDH9riOtdbx/PXYKp64dhK/nKHjyu/5ooyXKqziH5IEc34BE6/t+L02C1lZ//dvtdAztHXeU8Wq2xIYDTcts2+qCk6w5pVphLwt8N4P4L2b4d0bnKJ9HPnVeSMpq23ijY1HemeOAxwRdEHoS4LiddWf4n3aBRM3SRfAaGl2HmdbsMxcDu/eRGL1bgyfYP539/n8+Ve/0pZ17ISuvXPYfH30sYYz2gS9rqxj676nCWmT0iD9Ax0JA1Cw0+0t4xKCOWNIGK9vyKFFilG7IIIuCH1JULyO9CjN1nHpZ/xEX+98y3mcrcbn4e8g40PY9wkqJIlhUQEEBofBNW/COfd27Z1Dz4Ebl8HMO/S1TdDBvpP1VGDLZ+MTAuOuhCk/gB9ZfenZ37R7200zU8gtq2NVZscFuE9HRNAFoS8Jjtc5UIwWHfUyYrH2J2950XmcQ+3OVkKS7edpC1ySfHXIkLng4a3PfR185qMv7vozThaboMdNhMufh4se026Z6HFa0BuqIG+by20LRkcT5OOha6QKToigC0Jf4ljmLXKk9l8nTtdhjI5U5cPwRZC2ENLO022Ogn4ymMz286Hn9Mwzu4J/hA6RTD3LuX3spTpFwAsLdc6XKuddsp5mE2cNj2RlZvGJV3YapIigC0JfYivErMx6dyXolLP1Ffb8Ko21+jpxOly/VLsnQC+C9hTBifrLwma1nwqUgju2wKy7nNtn/FjPpyhD/+Wy7xOXW88ZGUVJdQN78qUItSMStigIfYnNQg8bYhdTm+VdlgMHvrK7XwKtY4fNh8QZ2m3SU/x8t85nfqrxDnBt8/KDK17UVvr2N/SawbQfOg2ZNyIKpWBVZjHjE3owzHKAI4IuCH2JLYQvyiHnSqhV0Nc9AXvet7cHWXdw+oXBD7/o2Xko1XvhiidC4nT901ClszLWV9qjctCVjkbGBLHp0PE+nGT/o0suF6XUIqVUplIqSyn1mw7GXa6UMpRSU3tuioIwiPEN1RZ5ypn2NmuVH/a8D+Fp9vbAOE47osfq3bPlrnHnM1LD2JpTdmKpAMpydHbLQUangq6UMgNPAYuB0cC1SqnRbsYFAncBG3t6koIwaFEK7toJ02+zt/kE23drjroQLvinjs9uG7d9OmD7C6Yyz6VremoYdU0t7Mnrph+9rhyenAbbXu2BCfYvumKhTweyDMM4aBhGI/A24C626S/A3wAp4y0I3cGdu8PmdklbqP3H/3cAPH1P/dz6mqB4fazIdemalqJTFnTb7XI8W5fhK0x36WqxGOwvrOLo8YFZLakrgh4POKZEy7W2taKUmgwkGobxaUcPUkrdppTaopTaUlxc3O3JCsJpQ9hQbaknTO/rmfQtgTE6AsiNhR4Z6M2QSP/uC7qtqEab0NDqhmaufW4DC/+1mrl/X8lDy/cOuBzsJ70oqpQyAf8Evt/ZWMMwngWeBZg6derA+k0Jwqlk/r06x7j5NI9bMJm1qLvbWIX2o3+yq4AWi4HZ1MVFXVv5veMH9fHju7BEjuLH6ZPYmlPGHy4YRVZRNc+sPoiPp5lfnNuNDVt9TFf+teQBjs67BGubjUBgLLDKmrIzBlimlFpiGMaWnpqoIJxWhKXqH0G7Xdy4XED70d/adJTMY1WMjgtyO8YFm4VekUtLYz2mPf+j0BTDd2X38fBl47hmehKGYdDYYuHxrw9wyaR4UiP8e+jD9C5dcblsBtKUUqlKKS/gGmCZrdMwjArDMCIMw0gxDCMF2ACImAuC0DMEx7t1uQBMT9VVlzYdKnXb7xZrIRAMC7c9+iqqoZKo2gPcPC2Ca6brzVpKKX6zaCQeJsVbmwZOZsdOBd0wjGbgDmAFsBd41zCMdKXU/UqpJR3fLQiCcJLYMlK68WfHh/gSH+LLpsMOfvTDa3XRjDbUN7Vw/8cZ5B3eR5mnLuIxrlmnKjYrgz9Ocq4UFRXkw8Ix0SzdcnTAFKnuUhy6YRjLDcMYbhjGUMMwHrC23WsYxjI3Y+eJdS4IQo8RnADN9VDr3gqfkRrGlKynMDY+qxu2vAArfu+SU/2xrw7w8tpsoizFfF6nI69vS7Rb/ip3s8uzL5uUQFltE1sPl/XQh+ldJJeLIAj9m/Bh+ugmzBC0H32RZRX1O97TDdVF+gvA5loBDhRW8dx3B7l1vCeeNLNo4WIMv3D88tfrAV4BkOtqh84YEobZpFiX3Q2XTh8igi4IQv8mYRqg4MgGt93TU0KIopyWcuvCaY01JNqhNuvz3x3C06y4Y2QNAKFDpqCix0KTviZ5NpQccHl2oI8n4xOCWZdd4tLXHxFBFwShf+MbAtFj4Mg6t92pvnV4qhZ86ou0n73aWviiWAt6aXUDH+zI4/JJcQSW7ACzF8SM0z+g0y/EjNMWvZt0ALOGhrMzt4LqhmaXvv6GCLogCP2fpJlwdLNraT5AVelqTh5GE0ZVAdRZF0iL9gHw6e4Cpll28uesK3R+nJhxOrOlTdAD47Rbx2hxctPYmDU0ghaLweYBkAhMBF0QhP5P0hnaPVK8z7Wvyl65KHevQyop69jV6Ud5xPslPGqLdPhjvDV3YPRYfQyKs/vpS7NcHj8lORQvD9OAcLuIoAuC0P8JtBavrnNjJVfZd5EW7rP62cOGQMl+aurqCTu8nHjjmL0wSKI1nULEcO1+CYqz11J1I+g+nmamJIUOiIVREXRBEPo/Xtadmo01rn0OFroq2KlPUs+Clka27djGFDJo8gqGS/6ri2nb6qZ6eMGVL8Psu3SOeb9wt4IO2o+eUVBJWU1jD36onkcEXRCE/o+XtbKRO0GvzAf/SFqUmbi6TN1mrVO6dct6ZnpkYk6eqfPijLwAzJ72e0deYLfOw4dBaZtarlZmDQvHMOj3VroIuiAI/Z9WC73ata/qGATFY/GPIVZZXTLWgiGhhetJogBT8szO3xEQbY+QacOEhBACvT1Yk9W/s8SKoAuC0P/p0OVSAIGxeIbqHIIWFI0+EZR6xnCTx5d6TPKszt/hF+beRw94mE3MGhbO6v0l/Tqlrgi6IAj9H89OfOiBMTD7TgBMGPz6/V2kN0Tp/tAUiJvU+Tt8w6CurN1i2WemRZJXXsehEjdz6CeIoAuC0P8xe4CHjy4a7UhLs87xEhCl/eFL/s3nCXfxwfY8yg2r333hA85+8/bwDQVLMzRUuu2ePSwCgA0H+288+mmePV8QhAGDV4CrhV5bChjgH6mvJ9/IosnwSV4FTceToGm7Fvqu4KdL2lFXpqtFtSEl3I9gX0925ZZz3YykE/8cvYgIuiAIAwMvf1dBt+VtsQm6lbHxwRA/DhjX9ef7WgW99rh207RBKcX4hGB25nazKPUpRFwugiAMDLwCXKNcaqxRKQFRJ//8Vgu9fZfKhIQQ9hdWUdfYP/Oji6ALgjAwcGehV9ss9B4Q9FYLvf3c5xMSQ2ixGKTn908rXQRdEISBgVuXi81Cj3Qd3126ZKFr3/qOo+Un/75eQARdEISBgZe/G5dLsc7H4t3FAtEd4ROij7XtC3pUkA+xwT7s6qd+dBF0QRAGBu586NXF2t2i1Mk/3+yho1s6sNBB+9F35oqFLgiCcOK053LpCXeLDd+wDi10gPGJweSU1lJe2/8SdYmgC4IwMHC7KFrUMwuiNjrY/m9jYoJ2zfTH8EURdEEQBgbegbr4s2PVopoSlxj0k6ILFvrYhGCUgm057UfD9BUi6IIgDAxsCbpshZ2bG6G6UBeo6CkConWyrw4I8vFkbFww6w/2v1S6IuiCIAwMbILeYF0YPZ6t64BGDO+5d4QP0V8SbXPGtGHWsHC2HymjtrF/FY4WQRcEYWDQtshFsbWYRWRPCnqaPrZT6MLGrKERNLUYbDncv9wuIuiCmONf1wAACeRJREFUIAwMbBZ67iawWKBkv762FXjuCTooFu3ItJRQvMwmvt5b2HPv7gFE0AVBGBjYBP2jn0Lmcm2hByfZ23uCsFRAdSrofl4enD8uhve35VHd0H/cLiLogiAMDKJG210i5TnaQu9JdwuApy8EJ3Yq6ADfn51KdUMzS7cc7dk5nAQi6IIgDAz8I+COzWDy0PHnpVk9uyBqI3xolwR9YmIIZwwJ4/GvD1Ba3dDz8zgBRNAFQRg4KKXjzoszoanWbd7ykyZ8KJQe7NLQv1w8lpqGZu58ezsVdU1duqex2UJ9U++k35UCF4IgDCz8I6Bgpz4PjO3554ckQUMF1JWDb0iHQ9OiA3nw0nH89n+7ufTptfz9igmMiQvC28PEnrxKXl53mC8yjpEY6keQrwf55fXkldfx0GXjuGpqYo9PXQRdEISBhX8kHNutz4Pie/75IdbychVHOxV0gCunJpIU5seP39jG5f9Zh5+XmVA/L/LK6/D2MHH+uFjKaxupqm9mfEIwF0+MY3RsD2SHdIMIuiAIAwvHrf5BvWChB1sFvfwoxHSthN2MIeGs+PlZrD9YyvrsUoqr6rlrfhoLx0QT4ufV83NsBxF0QRAGFjZBV6aeTcxlw2ahlx/p1m2Rgd4smRDHkgk9mIqgm8iiqCAIAwv/CH0MiNY5zHvj+R6+2uUywBBBFwRhYGGz0HtjQRR0JE1Ioo51H2CIoAuCMLCwCXpPZllsS0iS9qEPMLok6EqpRUqpTKVUllLqN27671ZKZSildimlvlZKJff8VAVBELC7XHpT0IMToWAHPD1Tp+kdIHQq6EopM/AUsBgYDVyrlBrdZth2YKphGOOB94BHenqigiAIQO+7XADGXaGPRRlQdqj33tPDdMVCnw5kGYZx0DCMRuBt4GLHAYZhrDQMo9Z6uQFI6NlpCoIgWAlKgDl3w5hLe+8dKXPglq/1eSepdN3SUA1F+3p2Tl2gK0vE8YCjMykXmNHB+B8Cn7nrUErdBtwGkJSU1MUpCoIgOGAywYI/9f57wobo43E3gn7oO4gdDz7B9rYjG2Ht49oV5OUP65+CX+yBwJjen6uVHl0UVUp9D5gK/N1dv2EYzxqGMdUwjKmRkT1YB1AQBKGn8QsD31DIWQef3gNNdbq99ji8ciE8MVnnZbex6kHI/BQ2Pw973gdLE+x445ROuSuCngc4Jh1IsLY5oZRaAPweWGIYRv9IPSYIgnAyhA3Vudc3P6+FHeC4NXFXbQnseN0+tqoQQlMBQ8ewKxNsfcVZ9HuZrgj6ZiBNKZWqlPICrgGWOQ5QSk0CnkGLeVHPT1MQBKEPCB9qPy/K0MfjDouk6R/Yz6sLYejZ9tQBM+/QseyHvu39eVrpVNANw2gG7gBWAHuBdw3DSFdK3a+UWmId9ncgAFiqlNqhlFrWzuMEQRAGDmEOgl6Yro+2qJcp39dWe1O9Dm2sOw4BMTD+Kp3W9+zfgW8YbH35lE23S/tmDcNYDixv03avw/mCHp6XIAhC3zP0HNj/uXaf2AT9+CEIjIMRF2ixPrLeXmgjIAom3wRzfwUe3jDhWtj0LFQXQ0DvrxvKTlFBEIT2SJwGt62E5JlQvA9amrUPPSwVUmaDyRMyPtLuFtD5ZUwmLeYAU27Si6M739TXhgGZn2mB7wVE0AVBEDojeiy0NMLKv0L+di3oXv4w+QbY9opeOAUt6I5EjoCkmXpx1DCgMg/eugYyPuyVaYqgC4IgdEbqWRA9Dtb8C1oa7KXvFvxZp/BdbY3UDnCTznfyTTqW/ehGe6Wl2Am9Mk0RdEEQhM4IioMfr4FbvoEh82DE+brdJwhGLLaPcyfoIy8Asxfs/Rjyd2h/fPTYXpmmCLogCEJXSZgCN34E0WPsbSlz7Oc237kjPkGQOhf2LtMJvyJGgJdfr0xPBF0QBOFkcBT09hh1ka6AdOALiJvYa1MRQRcEQTgZupKrZcylOiUv9Jq7Bf6/vbsJjasKwzj+fyy2Qq1oWyihFk2lm27UUKSL0p0fzSa6y8ouhIIo6MJFpJviroKCggiKhSpiNyp2I/iB6KJYrZKmqSVt1IqG2CiCulLR18U9wSHOzcck6ck5PD+4zJ17J+R9eIeXuedOiP+nqJnZ8j18Cq6ZZ5xedwMc+hhOPQe3D69aGR7oZmbL1bmm3mbjFrj7qVUtw0suZmaV8EA3M6uEB7qZWSU80M3MKuGBbmZWCQ90M7NKeKCbmVXCA93MrBKKiDy/WPoJ+K7HH98K/LyC5awVNeZypnLUmKvGTLdERNd/f5RtoC+HpDMRsSd3HSutxlzOVI4ac9WYaT5ecjEzq4QHuplZJUod6C/lLmCV1JjLmcpRY64aM7Uqcg3dzMz+r9RP6GZmNocHuplZJYob6JLukzQhaVLSSO56eiXpsqRzkkYlnUnHNkt6X9Kl9HhT7joXIumYpBlJ4x3HuuZQ4/nUuzFJA/kqb9eS6YikqdSvUUmDHeeeTJkmJN2bp+r5Sdoh6SNJX0k6L+mxdLz0XrXlKrpfPYuIYjZgHfA1sBNYD5wFdueuq8csl4Gtc449DYyk/RHgaO46F5FjPzAAjC+UAxgE3gUE7AVO565/CZmOAE90ee3u9D7cAPSn9+e63Bm61NkHDKT9TcDFVHvpvWrLVXS/et1K+4R+FzAZEd9ExJ/ACWAoc00raQg4nvaPA/dnrGVRIuIT4Jc5h9tyDAGvRuNT4EZJfVen0sVrydRmCDgREX9ExLfAJM37dE2JiOmI+DLt/w5cALZTfq/acrUpol+9Km2gbwe+73j+A/M3by0L4D1JX0g6lI5ti4jptP8jsC1PacvWlqP0/j2alh+OdSyHFZdJ0q3AncBpKurVnFxQSb+WorSBXpN9ETEAHAAekbS/82Q014fFf6e0lhzAi8BtwB3ANPBM3nJ6I+l64E3g8Yj4rfNcyb3qkquKfi1VaQN9CtjR8fzmdKw4ETGVHmeAt2ku+67MXtamx5l8FS5LW45i+xcRVyLi74j4B3iZ/y7Ti8kk6Vqaofd6RLyVDhffq265auhXL0ob6J8DuyT1S1oPDAMnM9e0ZJI2Sto0uw/cA4zTZDmYXnYQeCdPhcvWluMk8GD6BsVe4NeOy/01bc768QM0/YIm07CkDZL6gV3AZ1e7voVIEvAKcCEinu04VXSv2nKV3q+e5b4ru9SN5u77RZq704dz19Njhp00d9rPAudncwBbgA+BS8AHwObctS4iyxs0l7R/0axHPtSWg+YbEy+k3p0D9uSufwmZXks1j9EMhb6O1x9OmSaAA7nrb8m0j2Y5ZQwYTdtgBb1qy1V0v3rd/Kf/ZmaVKG3JxczMWnigm5lVwgPdzKwSHuhmZpXwQDczq4QHuplZJTzQzcwq8S/nVwx4KZkYbAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(prediction)\n",
        "plt.plot(y_test.ravel())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "fs0LOuHGf7ed",
      "metadata": {
        "id": "fs0LOuHGf7ed"
      },
      "outputs": [],
      "source": [
        "y_predicted=y_scaler.inverse_transform(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "PQIpvgdGhkE7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "PQIpvgdGhkE7",
        "outputId": "b5db9e5f-9fd1-4e13-8655-2e427223e221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0dac896490>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(y_predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "YKZw62oXhoS0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKZw62oXhoS0",
        "outputId": "5d110abf-2dd2-4ea2-ca2a-5962a3426f69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21.491463]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# one prediction\n",
        "\n",
        "last_input=df.iloc[-window_size:, :]['close'].values\n",
        "input_scaled=X_scaler.transform([last_input])\n",
        "input_reshaped=input_scaled.reshape(1, window_size, 1)\n",
        "next_prediction=model.predict(input_reshaped)\n",
        "y_scaler.inverse_transform(next_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "u69Lhd5piLjU",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u69Lhd5piLjU",
        "outputId": "329ba2db-683f-4a22-c21e-078486649b52"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.491463]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.434572]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.436792]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.438831]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.497282]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.605846]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.758509]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.95548]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.172756]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.353643]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.55349]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.616829]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.705507]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.811737]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[22.93342]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.062819]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.193438]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.32003]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.437908]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.545261]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.646198]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "array([[23.738935]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[23.837492]], dtype=float32)"
            ]
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ]
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        {
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            ]
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "array([[24.13756]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[24.232786]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[24.324518]], dtype=float32)"
            ]
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
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        },
        {
          "output_type": "display_data",
          "data": {
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            ]
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "array([[24.5843]], dtype=float32)"
            ]
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        {
          "output_type": "display_data",
          "data": {
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            ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[24.751896]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[24.834269]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[24.914946]], dtype=float32)"
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        {
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        {
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        {
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          "data": {
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              "array([[30.798563]], dtype=float32)"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.819544]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.840435]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.861227]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.881926]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.902533]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.92305]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.94347]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.963808]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[30.984049]], dtype=float32)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "last_input=df.iloc[-window_size:, :]['close'].values\n",
        "for i in range(200): \n",
        "  input_array=last_input[-window_size:]\n",
        "  # display(input_array)\n",
        "  input_scaled=X_scaler.transform([input_array])\n",
        "  input_reshaped=input_scaled.reshape(1, window_size, 1)\n",
        "  prediction_scaled=model.predict(input_reshaped)\n",
        "  \n",
        "  next_prediction=y_scaler.inverse_transform(prediction_scaled)\n",
        "  display(next_prediction)\n",
        "  last_input=np.append(last_input, next_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "lu0vF71vkTrE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "lu0vF71vkTrE",
        "outputId": "42ff38eb-2d41-484b-a7ee-199c65c0b07c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0dac7c2d90>]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(last_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "vDJ1qU4-lBXR",
      "metadata": {
        "id": "vDJ1qU4-lBXR"
      },
      "outputs": [],
      "source": [
        "### New Approach ###\n",
        "#### No splitting between train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "175mWZdxqFIm",
      "metadata": {
        "id": "175mWZdxqFIm"
      },
      "outputs": [],
      "source": [
        "def window_data(df, window, feature_col_number, target_col_number):\n",
        "    X = []\n",
        "    y = []\n",
        "    for i in range(len(df) - window - 1): \n",
        "        features = df.iloc[i:(i + window), feature_col_number] \n",
        "        target = df.iloc[(i + window), target_col_number]\n",
        "        X.append(features)\n",
        "        y.append(target)\n",
        "    return np.array(X), np.array(y).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "DebQumzWqFIm",
      "metadata": {
        "id": "DebQumzWqFIm"
      },
      "outputs": [],
      "source": [
        "window_size=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "jjA0K-sEqFIm",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jjA0K-sEqFIm",
        "outputId": "f1c59d39-086b-4508-b0a4-4434b15e1c78"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[36.        , 34.09999847, 34.90000153, ..., 33.72999954,\n",
              "        33.41999817, 34.25      ],\n",
              "       [34.09999847, 34.90000153, 34.58000183, ..., 33.41999817,\n",
              "        34.25      , 34.02000046],\n",
              "       [34.90000153, 34.58000183, 35.5       , ..., 34.25      ,\n",
              "        34.02000046, 34.34000015],\n",
              "       ...,\n",
              "       [20.13999939, 20.        , 19.62999916, ..., 20.45000076,\n",
              "        20.21999931, 19.98999977],\n",
              "       [20.        , 19.62999916, 20.62000084, ..., 20.21999931,\n",
              "        19.98999977, 19.70000076],\n",
              "       [19.62999916, 20.62000084, 20.64999962, ..., 19.98999977,\n",
              "        19.70000076, 19.61000061]])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "       [33.09999847],\n",
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              "       [34.45999908],\n",
              "       [33.81999969],\n",
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              "       [34.5       ],\n",
              "       [34.00999832],\n",
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              "       [31.31999969],\n",
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              "       [32.34000015],\n",
              "       [32.56999969],\n",
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              "       [31.18000031],\n",
              "       [31.11000061],\n",
              "       [31.14999962],\n",
              "       [31.62999916],\n",
              "       [32.06999969],\n",
              "       [32.38999939],\n",
              "       [32.72000122],\n",
              "       [32.27999878],\n",
              "       [31.5       ],\n",
              "       [31.44000053],\n",
              "       [31.36000061],\n",
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              "       [31.12000084],\n",
              "       [31.38999939],\n",
              "       [32.95999908],\n",
              "       [33.29999924],\n",
              "       [32.15000153],\n",
              "       [32.59999847],\n",
              "       [32.75      ],\n",
              "       [33.        ],\n",
              "       [33.04000092],\n",
              "       [32.65000153],\n",
              "       [32.74000168],\n",
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              "       [28.25      ],\n",
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              "       [26.25      ],\n",
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              "       [23.30999947],\n",
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              "       [23.87000084],\n",
              "       [23.70000076],\n",
              "       [24.12999916],\n",
              "       [24.84000015],\n",
              "       [24.19000053],\n",
              "       [23.69000053],\n",
              "       [24.54999924],\n",
              "       [24.13999939],\n",
              "       [23.52000046],\n",
              "       [22.27000046],\n",
              "       [21.64999962],\n",
              "       [20.89999962],\n",
              "       [20.92000008],\n",
              "       [20.28000069],\n",
              "       [20.54999924],\n",
              "       [20.03000069],\n",
              "       [20.01000023],\n",
              "       [19.94000053],\n",
              "       [20.61000061],\n",
              "       [20.47999954],\n",
              "       [20.13999939],\n",
              "       [20.        ],\n",
              "       [19.62999916],\n",
              "       [20.62000084],\n",
              "       [20.64999962],\n",
              "       [20.35000038],\n",
              "       [20.69000053],\n",
              "       [20.45000076],\n",
              "       [20.21999931],\n",
              "       [19.98999977],\n",
              "       [19.70000076],\n",
              "       [19.61000061],\n",
              "       [20.07999992]])"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X, y=window_data(df, window_size, 3, 3)\n",
        "display(X)\n",
        "display(y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZvRWliFEcJ3",
        "outputId": "4b20b8a7-dea9-4c0c-acc8-d6a350e936fc"
      },
      "id": "oZvRWliFEcJ3",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "951"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "q2mvdEAiqFIm",
      "metadata": {
        "id": "q2mvdEAiqFIm"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "X_scaler=MinMaxScaler()\n",
        "X_scaler.fit(X)\n",
        "X_scaled=X_scaler.transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "8QYKvOjQqFIn",
      "metadata": {
        "id": "8QYKvOjQqFIn"
      },
      "outputs": [],
      "source": [
        "y_scaler=MinMaxScaler()\n",
        "y_scaler.fit(y)\n",
        "y_scaled=y_scaler.transform(y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "GZuAtUTQqFIn",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "GZuAtUTQqFIn",
        "outputId": "e5d220c2-9488-4137-906f-43f8b23c99f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(951, 10)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X_scaled=X_scaled.reshape(X_scaled.shape[0], X_scaled.shape[1], 1)\n",
        "display(X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "i6bEcPXqqFIn",
      "metadata": {
        "id": "i6bEcPXqqFIn"
      },
      "outputs": [],
      "source": [
        "model=Sequential()\n",
        "first_lstm_layer=LSTM(units=30, return_sequences=True, input_shape=(window_size, 1))\n",
        "model.add(first_lstm_layer)\n",
        "first_dropout_layer=Dropout(0.2)\n",
        "model.add(first_dropout_layer)\n",
        "second_lstm_layer=LSTM(units=30, return_sequences=True)\n",
        "model.add(second_lstm_layer)\n",
        "second_dropout_layer=Dropout(0.2)\n",
        "model.add(second_dropout_layer)\n",
        "third_lstm_layer=LSTM(units=30)\n",
        "model.add(third_lstm_layer)\n",
        "output_layer=Dense(units=1, activation='linear')\n",
        "model.add(output_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "8qTpRoYpqFIo",
      "metadata": {
        "id": "8qTpRoYpqFIo"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='mean_squared_error', optimizer='adam', metrics='mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "F76x8vPIqFIo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F76x8vPIqFIo",
        "outputId": "92535d07-865b-4f07-8cf4-39bc129baa27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_6 (LSTM)               (None, 10, 30)            3840      \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 10, 30)            7320      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 10, 30)            0         \n",
            "                                                                 \n",
            " lstm_8 (LSTM)               (None, 30)                7320      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,511\n",
            "Trainable params: 18,511\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "2P_Jpt9QqFIo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2P_Jpt9QqFIo",
        "outputId": "0db9078e-8ea8-415f-97b3-36fbb86d8664"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "30/30 [==============================] - 5s 14ms/step - loss: 0.1320 - mean_squared_error: 0.1320\n",
            "Epoch 2/10\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0157 - mean_squared_error: 0.0157\n",
            "Epoch 3/10\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0105 - mean_squared_error: 0.0105\n",
            "Epoch 4/10\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0088 - mean_squared_error: 0.0088\n",
            "Epoch 5/10\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n",
            "Epoch 6/10\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n",
            "Epoch 7/10\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0082 - mean_squared_error: 0.0082\n",
            "Epoch 8/10\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n",
            "Epoch 9/10\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0076 - mean_squared_error: 0.0076\n",
            "Epoch 10/10\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0dac822310>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "model.fit(X_scaled, y_scaled, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "JX5msv2qq1Nq",
      "metadata": {
        "id": "JX5msv2qq1Nq"
      },
      "outputs": [],
      "source": [
        "prediction=model.predict(X_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "202dfd38-204b-4d97-ac8d-f1d141204f11",
      "metadata": {
        "id": "202dfd38-204b-4d97-ac8d-f1d141204f11"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "69DvhtTCqFIo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "69DvhtTCqFIo",
        "outputId": "55f06479-7da9-43cc-d585-2d27dcdb5f83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0da4a1c9d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(prediction)\n",
        "plt.plot(y_scaled.ravel())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "FkCmamxXqFIp",
      "metadata": {
        "id": "FkCmamxXqFIp"
      },
      "outputs": [],
      "source": [
        "y_predicted=y_scaler.inverse_transform(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "6QjNtRIqqFIp",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "6QjNtRIqqFIp",
        "outputId": "13a748f4-9b72-4dfe-936f-8268a6778801"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0da4980c50>]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(y_predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "9pkiFyNRt7ZJ",
      "metadata": {
        "id": "9pkiFyNRt7ZJ"
      },
      "outputs": [],
      "source": [
        "old_X_scaled=X_scaled.copy()\n",
        "old_y_scaled=y_scaled.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "dTuO1I9NuXhR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTuO1I9NuXhR",
        "outputId": "8b218af5-5ade-4029-df88-c509316dcc9e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.9640718 ],\n",
              "        [0.86926135],\n",
              "        [0.90918166],\n",
              "        ...,\n",
              "        [0.85079833],\n",
              "        [0.8353292 ],\n",
              "        [0.87674645]],\n",
              "\n",
              "       [[0.86926135],\n",
              "        [0.90918166],\n",
              "        [0.89321361],\n",
              "        ...,\n",
              "        [0.8353292 ],\n",
              "        [0.87674645],\n",
              "        [0.86526943]],\n",
              "\n",
              "       [[0.90918166],\n",
              "        [0.89321361],\n",
              "        [0.9391217 ],\n",
              "        ...,\n",
              "        [0.87674645],\n",
              "        [0.86526943],\n",
              "        [0.88123748]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.17265464],\n",
              "        [0.16566864],\n",
              "        [0.14720552],\n",
              "        ...,\n",
              "        [0.18812377],\n",
              "        [0.17664665],\n",
              "        [0.16516963]],\n",
              "\n",
              "       [[0.16566864],\n",
              "        [0.14720552],\n",
              "        [0.1966068 ],\n",
              "        ...,\n",
              "        [0.17664665],\n",
              "        [0.16516963],\n",
              "        [0.15069862]],\n",
              "\n",
              "       [[0.14720552],\n",
              "        [0.1966068 ],\n",
              "        [0.19810375],\n",
              "        ...,\n",
              "        [0.16516963],\n",
              "        [0.15069862],\n",
              "        [0.14620759]]])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "X_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "Vrz3QbsiqFIq",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vrz3QbsiqFIq",
        "outputId": "9b7e4cee-6578-4a9b-8cc0-4c14744719a5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.050142]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 1s 27ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 1s 28ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 1s 22ms/step - loss: 0.0070 - mean_squared_error: 0.0070\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 1s 22ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.112051]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 1s 25ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 1s 21ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 1s 21ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 17ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[20.620651]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[20.456434]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[20.616264]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 16ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0047 - mean_squared_error: 0.0047\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 1s 17ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[20.615522]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0045 - mean_squared_error: 0.0045\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0043 - mean_squared_error: 0.0043\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0042 - mean_squared_error: 0.0042\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0039 - mean_squared_error: 0.0039\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[20.574293]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0041 - mean_squared_error: 0.0041\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0040 - mean_squared_error: 0.0040\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0042 - mean_squared_error: 0.0042\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0038 - mean_squared_error: 0.0038\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 15ms/step - loss: 0.0042 - mean_squared_error: 0.0042\n"
          ]
        },
        {
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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            "Epoch 1/5\n",
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          "text": [
            "Epoch 1/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 5.6934e-04 - mean_squared_error: 5.6934e-04\n",
            "Epoch 2/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 6.2471e-04 - mean_squared_error: 6.2471e-04\n",
            "Epoch 3/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 6.4530e-04 - mean_squared_error: 6.4530e-04\n",
            "Epoch 4/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 6.4253e-04 - mean_squared_error: 6.4253e-04\n",
            "Epoch 5/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 6.3194e-04 - mean_squared_error: 6.3194e-04\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.300705]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 6.0580e-04 - mean_squared_error: 6.0580e-04\n",
            "Epoch 2/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 6.0422e-04 - mean_squared_error: 6.0422e-04\n",
            "Epoch 3/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 5.6185e-04 - mean_squared_error: 5.6185e-04\n",
            "Epoch 4/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 5.9198e-04 - mean_squared_error: 5.9198e-04\n",
            "Epoch 5/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 5.9186e-04 - mean_squared_error: 5.9186e-04\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([[21.367378]], dtype=float32)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 5.7817e-04 - mean_squared_error: 5.7817e-04\n",
            "Epoch 2/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 6.3320e-04 - mean_squared_error: 6.3320e-04\n",
            "Epoch 3/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 5.9985e-04 - mean_squared_error: 5.9985e-04\n",
            "Epoch 4/5\n",
            "40/40 [==============================] - 1s 17ms/step - loss: 5.5887e-04 - mean_squared_error: 5.5887e-04\n",
            "Epoch 5/5\n",
            "40/40 [==============================] - 1s 16ms/step - loss: 5.9923e-04 - mean_squared_error: 5.9923e-04\n"
          ]
        }
      ],
      "source": [
        "# last_input=df.iloc[-window_size:, :]['Close'].values\n",
        "\n",
        "for i in range(300): \n",
        "  # input_array=last_input[-window_size:]\n",
        "  # display(input_array)\n",
        "  # input_scaled=X_scaler.transform([input_array])\n",
        "  # input_reshaped=input_scaled.reshape(1, window_size, 1)\n",
        "  # prediction_scaled=model.predict(X_scaled[-1])\n",
        "  prediction_scaled=model.predict(X_scaled[-1].reshape(1, window_size, 1))\n",
        "  \n",
        "  next_prediction=y_scaler.inverse_transform(prediction_scaled)\n",
        "  display(next_prediction)\n",
        "  new_train_sample=np.append(X_scaled[-1], prediction_scaled)[1:].reshape(1, window_size, 1)\n",
        "  X_scaled=np.append(X_scaled, new_train_sample, axis=0)\n",
        "  y_scaled=np.append(y_scaled, prediction_scaled, axis=0)\n",
        "  model.fit(X_scaled, y_scaled, epochs=5)\n",
        "  # last_input=np.append(last_input, next_prediction)\n",
        "  # input_array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "faIQrGZ9qFIq",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "faIQrGZ9qFIq",
        "outputId": "50b37e1a-3619-4fec-a0b5-6123457661e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0da2b9d510>]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(y_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "Ke2ymgDjtyLX",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Ke2ymgDjtyLX",
        "outputId": "75bb16c9-3807-448d-8af4-652083c3261d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0da531fd90>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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2BVlfXc9LS7cDZL0Ox4PzN7C8qpY/XjiFnbaIDCeL3l5jxJ5cMuuPif72wrjoIXsNcYvvPrqI964/mVG2eHtNbrFmku0pPZANLOVufxzwesi0rtjU294EjEzYbBDvo29sDUWteTBCLOtbQoQjiv+u2klVTRONpvHzyeZ9DCwtIOAVDhhYwrK4azISUXiydEPq6WTSeKRQRBaKyBIRWSEit5jbHxORz0RkuYg8KCKJ9YCNcWERWWz+vZDtD5AKe6yvpdxDYZWQvl3g8/Lj0yZEo3UAqhxqcOyq67jv+9aXVvLsJ9sYc+PLPL+4rclWfYtxUi/atJcfPPEp4YiKiXAAmPPe546veclRo2KSWgD62xT9Kz88Pvr4qcrNHZZd034sS76rFX2TbYZoKUy/19OuevQAnizN9T0S23ikpjEYzXAFQ9GD4c787qOLuOXFldHP0NgSJhiO4Pd6ePjyxAZ2vaG5ULbI5OdsAU5RSk0GpgCzRWQm8BhGz9jDgCJsNejjaFJKTTH/zs2G0Jlin35ayjMUiTimbwMMsEVI7Ipb0Fy8pYYZv3qL+80Y5c5gT0d/YoGhgK95cjEvLqli456GGIsHYM57zo0jnJJW7Iq+b3Hbvbc7V+nsjrSEnV03n27ex9zVuSvKFbSZ7pv3NlIc8FJe7G93g+74omUdJd6i/7y6nrEVbaGeltK3FyqzblB1LSH21Lfi93pizusvTh0OwB6dIZsxaX9Nsy+s5Rj2m39KKfWKuU8BCzH6wboKe+KQUopFm/bRGook9T9+edoILpw+gtMmDU5Y1d+w2/gKbn85OyUSrJCxD9fvoaElFD2R1+2qj3HdAMw+dLBj3834mQkQ03jE/jrxlr8mtwSTuG6+eO8HfPOhj3P3vra8kU17GhnRrwivx0N7qyHYXUCdIV7R72tojSlBYhUgfPrjNuPHXmZ8T0NrguETVfS6/0LGZHTbFhGviCwGdmG0Elxg2+cHLgVeS3J4oYhUishHInJ+ive4whxXWV1d3Y6PkBz7lHHe2t18+a8f8N9Vu5Kmbxf6vdx5wWQOGdaHPQ2tMRepFcHQUfqZ1vUhw/oAMNgMGQNYu6s+qqDX7qxLiHn+50ebHV0ATjOTMQPbatTbFb12ZXYtrZbrph114LOBvdBdUzBMeXEAj0N2ajq2t6N8cCriwyvrmkNRdw20zUo/XN8WMBAfVlwUp+itbNnHFmxut0uqt5KRoldKhZVSUzCs9hkicqht973Ae0qpeUkOH62Umg58DbhLRMYleY85SqnpSqnpFRUVTkPazc/PnsRNX5iYYCGnS98e0sdQwvboGMvKmDS0T4dkGVBawFmHDWVo36Loe1jvs2ZHXdRiX7ur3tF6tyys/3fOpOhNw+/wOez+T3uZWXFR5f29Da3c9tLKHl2cqs1Hn73Enm/8YyF/enNNRu9r0bfIn+An70rsPWMjEUV9ayg6mwViOlNZ1MTVson3xVsu1heWVPHWKl2bPhPa5YhTStUAc4HZACLyS6AC+HGKY7aZ/9cD7+DQfzZXDCwt4Lsnjktouefk8rBjhXfZLYvdZsW9jnpAQuEIPq8Q8BkvMKhPAe/feAoFPg+rd9RFm5k/v7iKeWvbYufPPGxIzPsX+r3Rz5NsrWGsadXbe8q6yaL/89vreGD+Bt5d03MvUmsG1p7OTul457Nq/i9NGer49+tb5Deaf6QRY92uev7z6bYYQyEb2F03Da0hlDL6zNr3XzxjVMrX2LYv1sK3J6HZq1tqkpP2VxWRCiColKoRkSLgNOC3IvJt4AzgVKWUo19DRPoBjUqpFhEZCBwL3Jk98TMjvol2uoUmK7TMfoJVmxZ9R6MogmGFz9OWqfvlaSPweoSR/YvZXtsUk0Rl8ccLJ1Mc8PLKsh38x4zUKfB7ovL7ktywnrvqWNZX18f45d3korfS162Io57Imp3Gmk5rF8fRx1v05UXGQmw6181Zd8+jJRRhUFkB9S2hrMXR2xW9FS8f3xhojEPY78FD+7Bq+36AmEqzEGvg3PLiSo4eN4CJQzo20+4tZHL7Hgo8LCJejBnA00qpl0QkBGwCPjQVyrNKqVtFZDrwPaXUt4GDgb+LSMQ89g6l1MqcfJIUxCvEZArSwqrUV2W36M3F2bW76tmwu4EDBpY4HpsMI0xMuO70SVx05CiOPdAoDFVe5Ke2KZiQHSgCX5o2gnBEMXNsf/76jhFiGfC2WfT+JDesvkX+hMQpNy3GWn5Zp8YpPQGlFLVNQUSM5h/hiHPtmFzg5LqpbQqmdd1YNaEaW8N8edoIbj//0JTjM8XjaXMb1ZvnePysYbSDon/me0fzk38t4aqTD3R0l/YvCURdf9trmrWiT0NaRa+UWoqDu0Up5XisUqoSM9RSKfUBRvhlXmmKyz5NZ60U+r0MLC2Ium5qm4Is2LA3uv/fi7Zy7azxSV0nToQiCp9XGFZeFFPytW+Rn+21zQmK3rouvR7hnMnD+Gi98f79iv1UlBWwYXdD2huWnfYuxuUS6/tvCoZRSiEi7K5voV9xoMsUYi6xfru+RX5qGoNs3NPAuIpSlm6tSRjbEgrz2vIdnDt5WFZuxgmum2I/dS2hjKNu6ltClBR4ExZAO4rPI9E1p/3mOV5WGK/oY42mH82aQEmBj79+/Yikr/vC1cfyzKKt3PXftXnNPu4u9OgSCBb745RoJgp6aN9CPt1sXJi3vLAiZt+f567j1pcym5gopdi6r5FgKOIY7dOnyM/ehlaaUoSzDbVF6AwtL4pOdTNZX3v8O0alweIs+147g/X93/naZ5z0+3e48G8fMv32/3Ln66vzLFl2sH6W6aONWdXnZtmKu99K7HX6pzfXcs2Ti3l3TepIs0xnP/HVWvsW+Y2ZRTsWY7N5s7Vq3by7ppob/r0UgH5xhd5G9W+z6DfecRbXzBpPOkb0K2b2ocb6lVPwgiaWXqHoLazzN91iLMCybbV8trOOz3bUsdL0Fdp55MNNGb3nwg17Oe63c6lrCSWsFYAROpYu43ZwnzZFP6RPYTSEssqhumU8h48w6nm7yaK3f/+b9jSycKMxW3l1Wffs3RuP5aqoKDN+tz0NrWzd18iCDYk1h9aa9Vvi673EY1fgO1KEPlpVMy36FvnxiqQNQ5wxpn/0cXx3qc7g9QiRiOIys7l3ScDLpGGxbpaSAh8XzxjFA5dNT/laL1x9LG/+6IToc2utSiv69LjHzMshr/zweHbub+aB+RuYv253u7L+1u6qc+zYNDZDH/0OW4hmn6LEKhFFfm/aafUQm6IvCngZP8go1dCYQVKL5SYJZlrspAtIpnN6gtsG2j6fFe9dXdfCcb+d6zjW6ouwL02o6ccb21yHy7fVRkv3xhMf/94WXplaZvsCqbcdLsF02F03AJNHljvObH/zpfQeXstosbAMhp661pNNeoVFP2lYH06eOCiaYdeeiII1NiU/YXApG+84i7MOH5qxFWH3u8ZPWYGYrL+JQ8oS9kNsWQOAUycO4qYvTOQnpx+U9v0LfB5KAt68traLJ9l3Z5/Cd2csi77Q72FgaYA57yUvm2Ep+h0pegis21XHN/7Rlk1rTy6KJ94FaCVMpWs8Yo8my2a5DPtiLMAt5yZ2ROso9vpVmtT0CkVvYa32t2cR801bQoZ1vh48pIzNexsdQyLjsU+Z+xUnWvT2TlfXn3EQxx5odIqy34ziF+k8HuG7J45LuAE4ISKMGVjC5r3uiTcOx80uRAzrN1tp9/nG+skFY/HdyS1jLcxa1Ut3prgRx/92qfz5LcFITN5I3yJ/tMJjMvdNVU1TTHJgYxo3UnvwikQXiH9x9iTGD3Y2ZjqCNTNw02zVrfQqRV9cYFr07YiWWbMz0W1zsBnu5bQvHrulVO5g0dsjEAb3KeSPF04BjAvUznNXHsNb152YmdBxFAe8CZFH+STeov/uCeOYNqqcfY2t/PCJT/nH+9271rgyl2M9kqikLc798/tAm1LdmcKiL/K3nSPXzhrPul310U5N8TQHwzGZp30KfVEL3cmqf37xNo65423W7qrnwEGlpizZm/3ZywiPGZjdGZtPW/QZ06sUfYnZENypdEA8p08yUrPDERW1xM+bMgyAiaaiX7k9uaJ/9pOtfP+fi2IahfQrSbToT5owiGF9C/nS1OGMH1xK/5IAhwzrwx8unBwzbuqofoyryLzBs51Cv9ex7n2+iFc4Bw8tY2jfIrbta+KFJVXc8mKXp1pkFevjiaRXQlalxlSKXkUXdwuijblXViUGCICh6O1lrn1eT5uLw0HR233/J5mJSQX+7KkF+8w0Poyy069tWfTaR5+WXrEYa2FdAJm413/75cN5Y6XZhKFfEe/feApFpptlWN9CigNeNlQ3JD3+xSVVzP2smnKbu8bJRz9qQDEf3HRqzLaXbXXks0Gh3xstoZBvgmbD54GlAfqXBFiz07Akm1rDNLho1tEZLJ+0R4SBpYGUETWWTz1VJI1V8vivl0xjeLlhFSfz6RuN72MVtTVrrG8JxbgKIXZh/Pypw5k8spwZB/QnW9gX2O1BBdkguhiro27S0qssemtq6hQuGY/dj18c8FEc8EV95SJCaYGPxtbkF7B18tkLNMW7Y7qKQr+XFpf4v8f/7FWeWLgFr0f49RcPY9bBgzhocFnUHdYTiProRbjn4mkx+6yZokWDeRPY3xyKutdOuHNuTN8Dq3JqccAXTWRK5oprbA1TZM5cz5lszECttan6NH2QDx3el3MmD4sJ5+0s9oXd+JtMZ7Gi59ItNGt6mUU/xpw61mTQD9YegulU6Kko4E2Z5ORU1jjbJ3qmFPo8KWXNBz6Ph+lj+nO/Gb89YXCZUU63B1yzlqtFgPGDY91tg/oUxIxrag1HP/euumZGDzAWzm9/eRWXHTOG3766mtdWGPkFAZ8nOqtMtnC9t6GVwX0KWfurL0TdJlFF7zCzsL7u+MJ/2cLuusl2+KzfK/i9kpBVrkmkV1n0fYqME74xAxdBrEWfqKC37mvi+cVVSSMg3KRYSwp8aRNyupr4pLWigLfHhFdaFr1HYgvqbbzjLJpa2wyAUETR0BqOJsDFu29u+PdS7p+/ga1mcb0Cnwe/V/B6xPH8Wr6tlhVV+xlQEsDv9URnoKXRdn2J54AVLOAUEZYNctnTVUQYUFLA3gb3hA67lV6l6K3yqH0K05/UMW0IA4kWvTVdvOzBhdHptx03Kfp+xQHqmkOuWrQa6aDULdcaJA8F7A5EffQeSVB09izoz3bUUdsUjHZcik+Ae/aTbTHPh/YtRMySAn+Zm9hH+Ox75gPE1FKCtvPdKRzY2nfXV3NTPTyLuVeODCgNsMcl609uplcp+r5Ffu684HAe/OaRaceKSHSqaYVlJmNXXaJF4aaY8P5mtM++DFxWucRehsEpgsie7ZnJrMutRKNubNtOOsiIaLG3qLQUs6VsQ2GV9Ab3n6uOTQgLtjeZt0fPjBsU+92mct2EIhHKi/0cPW5Aqo/UYbztCGXuCANKC9jdgxvYZIu0v4KIFIrIQhFZIiIrROQWc/sBIrJARNaJyFMi4pi9IyI3mWM+E5Ezsv0B2suF00dG682nw7LqnSx6Ow9/sJFlW2tjtsUrenstka6mf4lhMea7o5M9OiJeGUFss/P9GSSjuRUrjt5ynSz55enMudSo43Kkw3lgLdKHwpGkC4tTRral/59rLrJe8+Ti6LbXlrfVCYovz1Fa6Kzoq2qa+ODzPRTncO3IqpuTrfr28QwsCbBkS01Cj2dNLJncbluAU5RSk4EpwGwRmQn8FviTUupAYB/wrfgDRWQScBFwCEZXqnvNuvbdAmsK7mTRP3nFzGj/14c+2Mg5f54fs7/Zthg744D+PP29o3MoaWqsDNp8K/pwjEWfGFNtN2bj28d1J5Qtjh4MRW4tdv787ElceVJsN02rBlIwojKqMmlVbbRj/23Hxn23lkUf76M/8+55rNtVT2GWShI7Yd27c+Wrn2nORKx+DRpn0ip6ZWBl/fjNPwWcAjxjbn8YcGr8fR7wpFKqRSm1AVgHzOi01F2ElbrtZNHPHDuA310wOWG7hd1HX5bnEsFuUfQhW6r6gQ4WfYstqau2sfsreqeaMQGfJ9qq0sKy6N9atZP4bP47L6G3BUUAACAASURBVDicX5w9KWbbGIfEoz2237Y47nwt9HsJeD0Jij6+N2su8OTYor9w+kj6lwRctSbmRjJyoImIV0QWA7uAN4HPgRqllHXmbAWGOxw6HNhie55sHCJyhYhUikhldXXq2txdhRUx4RR1A8lD0oJxU/D4RgtdjaXol29Lnz+QS+zfSUVpQcJ+q6wvdHcffVsJBCfibwB9zPPj+cVVCRb9V44YweXHHRCzzakjU1VNE36v8PHPZjm+Z2mhj/qWWMVunZdnHJI4Q8gWVvRaLiuTBryehPpJmlgyUvRKqbBSagowAsMin5htQZRSc5RS05VS0ysqKtIf0AVY7QJLkljk8fXlrcXGeOsivkdmV2Nl5/7t3c+ZcPOreat7Y/nobzvvEMduSleffCA/PNVoOtGdLbRINI7eWbl9adpwfnDKgdHnfW2hjf/7zJKYsU7fU0mBj1kHDwJg2m1vEgxH2LSngW8fPzZaGjme0gJfNGGqcuNextz4MkrB1FHl3DA765dzlFxb9GDcTHR2bGratSSulKoB5gJHA+UiYmmwEcA2h0O2ASNtz5ONcyWWrzNZBE28RX/WPfMJhSPR8ZbFVFqQn4xYC/siZ2sowt48Rd9YFr03ST+AgM/DBdNGAMkzP7sD8T76eAp8Xq6zlZi2h/u+kmHzlUnD+gKGO+7z6nqCYcXIfsnzEIoD3ugs6YmFxiS7viUUTcDKFZYln0uL3ucRXdgsDZlE3VSISLn5uAg4DViFofAvMIddBjzvcPgLwEUiUiAiBwDjgYXZELwruGjGKIBoo4944pN+Vm3fz8zfvE2zmRRjLYLl+mLKhBMntM2S8tXfw7K6Ull3hQHjlMykqYpbsZdAyASnhjRpj7HNErfsNRKqkrkYIdbq7W8rrpfrbG0rpj+XtZa8HiO34PnF21hfXZ/+gF5IJj6FocDDZrSMB3haKfWSiKwEnhSR24FPgQcARORcYLpS6hdKqRUi8jSwEggBVymlus0VfOKEClbfNjvpxeDko99d3xINDbQs6fbUv88VdvdRV81y31+3m32Nrdw3bwMnjB/IV44wJneprLtoin93tuhJ7aOPx6nEBpCy/o/9GKtkcSql7fd6CIYj3PvOOj7bWW/bnttzc9zAjlVcbQ9+r4dQJMI1Ty6myO9l1W2zc/6e3Y20il4ptRRISJtTSq3HIYJGKfUChiVvPf8V8KvOiZk/0l08TvzgiU+BNleFU6/YrsaeiNNV/WMvuX9B9PG6nXV8caqxDp/qxmcp+u7tozf+Z9qpaVjfxLyO33zpMC42Z5RO2G/cVvRMfNVKO36PoejvfO2zmO3TRvXLSMaO0hWBCEZJCGMW3Z3Pm1zSq4qaZZtkCnzDbqN88ZRR5WyracpZ1mF72LavrVFFJA/lBUoLfdEbXyoF6PN6CHjdV4StPUQXY9Po+cqbZ5mtHhMvw3QNuu0WfZuiT2GU+CQmtwPgpR8cx4QsdnxyIpe1bix8HmF/N8676Aryb2p2Y+JP4o13nBXz/PJjx/D+jadw6PC+XSmWI/auQV1d1nVY30LCEWgxC2ilq5RY6Pe4ejH2rv+u4dIHFjjWOILMffQDSwsoKzRa/cXHyqdTkHb3l1UKIZWi93k8CU20xw8uzVnVSjuTR/Tl2lnjc/b6Pq8no7aevRmt6DvJ2l99Iea5vd54n0J/xuUWco29oFlX6PnVO4yY/VkHD+KSmaPZXd8Src5YkEa5FAW8rqoVFM+/Krcyb+1unq7c4rhfpYmjd+Ly4w5gzqVHRJ+n8/bZF16tZKnBfZxDK8HwxbeEYhW9P0n0U7Z5/urjuHbWhJy9vtfjXKp4yZYaNu9pf6/kZz/Zyr3vrMuGaK5Bu246id/r4ckrZkazTu++eCoPzN9AbVPQMfszX9jjjLvCdTP7rnkAfPPYA6K+4/nrdgPpLfoif+pa//mkviXEthrDDbZ4S43jmLaiZu1zW9i/l3T+/SNG9+fW8w7hF8+vAODosQMY6uDrt/B7PTTENcrpCrdKV+DzSExmMMC/Krdw/TNLmTikjNeuPaFdr/fjp41chitPOjDNyO6DtuizwMyxAzjzsKGAMX2+6uQD+emZB2ccXtcVhGIs+jZFX98S4vUVmcVuZ4q989YRo/tx2PByivxeHvpgI5Deoi/0e12bGfv/XlgRffz84irHMe2NurGw91TNJO7860eNjj4emCRRysLn9UTDMHsaextao+7IgaUFBMMRrn9mKQCrd9ShlGLTnga2JGnU3hvQir6XYLfo7T76n/9nOd99dBG/eH551t5rznttbfAK/V4CPg+H2dYpAt7UsduFfve6bjKpF2Rl47f3Pj/GVtogWcilHY9HOHqssdBflKaht9924+hb5I9GQPUEVu+oiz4eWBqgcuO+mP1PfbyFE3/3DsffOberRXMN2nXTS7ArervnZtMeI0LokQ83cet5h2blvZwWKUcPKGahWTO9II1SKvR7XKvo7cleyRLh4ssUZ4qIcM2p42kNRzh+fGZlQCy3WLqkPLtb6N3rT6LcoVF9T8Dv9STMpN5avSvj41tt6xhKKVfNyjuDtuh7Cd8/sa00rt2ib3LobdtZLC+R/YKzZ3+m8z8X+b0JoYBuwZ4D0BQMM+bGl3ly4eaYMamqV6bjR6dN4IbZEzMuGWAtQqYrNWwVthPJrMNad2XZtlq+OuejmG1vrtyZ8fH2aK/4xevujFb0vYSfnHEQ/zA7a9l99CNtJXPje5Z2lGgdf1t8uF1x2TtJOeFm141TnZ6n4qJvIrbm4LlmwhAjDv6cw4elHDfQVi20pyzCdoR0LSrtdaDcHOLbXrSi70VYFqZd0dsTZjaabpzOYrUstGdqWu99/RkHpfU/F/q9NIfceZH5HZRkfI0ZK5S1K6IXbz7rYBb+7NS0uRrWYm03bsXbbiYOSUwGS7fIv8l2DXTnekvxaEXfi/BGFX3bNnuzj1eXbU9oidgRrHK4t5/f5vO34sIzSdYyfPTunDYXOPjC4xt9fLrZCLucOCR5rZpsURzwMags9QwJjEXKnsr00c5lHJ75/jF8dNOpMdvS9U3ebIvMaWp1TojrjmhF34uwjFG7srUvPj384aaElogdYVtNE7MOHsTsQ4dGt00eYfQ8nZSiUJeFm103Q02302vXHs9ss2GHPV+iORjmvbW76V8SiFZudAOZ3Ay6Kw9dPoMXrz6Ob9satFw6czSlBT6G9C2MCed1apBuZ5MtwcqtIb4dQSv6XoTlm7W7blrDkWhjko7yr8ot3PPWWsDwgW7c0xATEw5w+iFDeO/6k5llyxxORqHfS4tLLXoreumgwWX89evTgNjidlc99gnvralOaNCdb4amWRfpzpQW+DhsRN+YG6vVwAZiw1yDodQzygfmb4g+7lU+ehEZKSJzRWSliKwQkWvM7U+JyGLzb6PZatDp+I0isswcV5ntD6DJnKiP3qZDW0IRx6JamVLfEuL6Z5byhzfXsLehleq6FpqDEcd2d6MctjlR6PPSGo7w46cdT6m8Eo5E8HkEEePP75WYZDQrlG+oi6x5MLpSTRxSxm3nZyeE1o2cZjMi7J227N7C1nDmBkRv89GHgOuUUpOAmcBVIjJJKfVVpdQUs8Xgv4FnU7zGyebY6VmQWdNBLMMzxqIPRdJmqqZiny2B6E9vrmFXnVE8bXCfjluQ1iLus5+4qxlZJKLY3xSKiSDyeTzc+87nrKyK7cfr1BM337x27QlcOnN0+oHdlJH9i5l18OCEm5m9LHcwjaL3CMw62Lhh9CqLXim1XSn1ifm4DqO7VDStToyMgguBJ3IlpCY7WMkf9gbULaFIpyoY1trKwz6+cDPV9YaiH1DS8cW/UJI1hHzzhzc/49GPNsXEV1s1eS5/6OOYsYNSFBjT5I77L5uecDMLZajolVJEVFsNfbuPvr4lxJMLN/Py0u1ZlrhraNcVLiJjMJqQLLBtPh7YqZRam+QwBbwhIotE5IoUr32FiFSKSGV1dXV7xNJkiOW6sWKJlVI0tIQSLPpkjUkaW0Nc9fgn/M+DC6PuCqsW+uxDhhCOqGi1wM5kXq6vbgtxS1YhMh+8sMS5tg3Ajv1GDsKpE42m3V87KnnTEE3+SKXordPeCv9tag3RHAxzxSOVHPrL17nx2WVc9fgnXSFm1slY0YtIKYaL5lqllH2eejGprfnjlFLTgC9guH0cS8kppeYopaYrpaZXVGSW/q1pH944H/3db63jg8/3EK/W46scWsxdXc3LS7fz3ppqtpvJVTVNhuvmALORelVN+v6l6bj06DaLzE1h34W+1J9p5/5myosDDC8v6tHZp92ZVDNEKxqt1GbRr9y+nzfakVnrVjJS9CLix1DyjymlnrVt9wFfAp5KdqxSapv5fxfwHA7tBzVdg5XAY01lX1xqWKgbqmMTpZxC0JRSzJnXVqzs/L+8D7RZ9FZ/U6ueTWeaTk8ZWR59XNgFjTEyxarRkyxEdG9DK6FIJOd9WDUdpzWc3HSw1q6iFn1vWow1ffAPAKuUUn+M2z0LWK2U2prk2BIRKbMeA6cD2SuTqGkXlotmRZWRFHWQmRVb1xKK8dNbCU9KKc66ex73vLWW5dv2s8RWf92q/2356I87cCClBb5oslCq/qXtoTM3jGzT2GJc+DMO6O+4/z+Lt/H84qpovXqN+2huDfPrV1bx29dWJ+yzLHqfRyjwGV3O3Brm214yuRqPBS4FTrGFU55p7ruIOLeNiAwTkVfMp4OB+SKyBFgIvKyUei1LsmvaiVUe+J63je459hC0D248hVvPOwRos+hbQhFWVO3nD2+uYd4653WT2qYghX4P/UsCMX7pdG6OdFgLal3R6i5TrByA/519UHTb3RdPjT7++7vGjCeYwmrUdD3XnTaBgM9Dgc/Df1ftZM576/nrO58njLOCFLweoThg9ESwMsd93bw+UCZRN/OVUqKUOtwKp1RKvWLu+4ZS6m9x46uUUmeaj9crpSabf4copX6Vm4+hyYR4pWlFjzx35TEMLC2Iul/mrt7F/fPWM/HnbffkDz/fw+gBxdx81sHRbfubg9Q0tlJeZCy8nnFIWxxzZwtnWTeNdEWouhKPCAGfJ6bkwbmTUxcT0+SfH5w6ntW3zuY7x49N6W+P2JrXF/ktRW9cI5bfvrviHnNJk3PiFX1rKMLw8iKmjjJqhVi+ybvfXsftL6+KGVtV08Qhw/rExMfvqG2mpjEYzaydOtK55khHsCwoN1nHVrJUPBMGu6dlpMYZj0diFvmdsKJuPGL0LW4KhqKKvsym6DOp1+Q2tKLvRcSHUbaEwjHbylJYLU2tYYr8vphyt1U1TdQ0Belr1prPZvlbn5nd5aaLKhxpi1yyc8/F02Ke/+MbR3aVSJp2YDdSBjoktFnnmtcjFAW8vLJsB9vN9RZ7G8Z73l7L+ur6HEubXbSi70U4WfT2bf2TJDn5vUJjMExJgZejxw3g3ksMxbajtpnaxjZFn00syznkKkUfwesQURO/8HyyGUuvcS8tDmWwragbj0dYs9NQ5L951Vi0Pd1WXuGu/67ltD+91wVSZg+t6HsR8W6HvQ2tMfHe8eV2771kGl+dPpJgWFHTGKTIjI0/xVRkexpaqWlq7XRRNCesMgOhdtQmyTVhpRwt+lH9i/nuiWPzIJGmozhF00QtehH62xL+ivxe/vr1I/jpmRMTxnYXtKLvRdj7Xza1hllXXc+Bcf7lsw5vKy185mFDGT2wrRBZsd+4ERT6vZQV+KjcuJed+1tiyvQu/NmpfPLz0zotq9Wyz10WvXJs8Sci3HDGRIcjNG7j/CnG4nlrOJKQAW4pb49HYmZp/UsCeD1CWVwS3FurdvLPjzaxblcdbkcr+l7G/100BYBNexuoaQwmFN/604VTYp4PttUxH9K3beyA0gBzPzNCLk89uG1aO6isMKkLqD34zOwuV1n0SRQ9GMph+uh+fPPYMV0rlKZd3HXRVG6YbdyU43vCWq4br0h0jQjaXJ72ctQA33q4kpv/s5yv3bcAt6MVfS9juFk+94kFRkPreKUc78c/b0pb+OAhw9ra1W239ZdNtYjbUbwu9NE/Xbk15nPH88z3j+GX5xzShRJpOoJlrT/58eaYkgj2xVi7m9PKdE4WarCnIXXXKjegFX0vY0Q/wxXz8IebgNiesU74vB7u/PLhQGwnJbs1lIvsVSsaqD31w3OJm+L5NZ3DOl9veXFltNFISyjMq8t3AEaQgt16t24Ap0wclLbfsVvpnlJrOswgWzbsH74ymaPHDUgY84NTDmREv7bGGRceOZILjxwZM8bKHITEsM1sYL1ms0tqglelsOQ13Qu7/92qZzP7rnls2G3UfNq0tyG6RgRtRk2/kgCf/Pw0/vnRJm59aWXb67koezsZ7pdQk1Xsse7JarZcd/pBfPXI1GV27RZPwJv900jEqDfS7JJ69L+OSyDTdF8KbOU5Al7hpmeXRpU8gFLg97Sd0/bZa8Dn4fLjDuDKk8ZFtzW0hnl1mbvr1GtF34uxW+3txR5lKA4hh9mgKOCeJuHba5uYObY/n90+O9+iaDqJ3aJ/6INNPLEwtufBVScfiN9ns+gdzsH/nT2Rd68/Kfr8+4994uroG+266YXce8k0tu1r6pSC7ooST4U+9yj6UETRx++NsQY13RN7wb3GuN4LPzx1PCUFvpgkwPjoHIvRA0qYPKIvS7Ya1WAXbNjLgYNSr3nlC63oeyFnHjY0/aAM+fPXpqYf1EEK/R6aXVAm9rXlO1i6tZZjHNYzNN2PAptF3xi3BvTt4w8A4PbzD0MpeHX5jpSRX1v2tZVGcHOFy0zq0Y8UkbkislJEVojINeb2/yci2xxKF8cfP1tEPhORdSJyY7Y/gCY/lJjRBzPH5k75FQV8CRdiV/PS0iq+989FAGzZ15hXWTTZIdms7OdnT4pmivcvCXDz2ZPM8cnV5F5baOUN/14W7bDmNjLx0YeA65RSk4CZGO0AJ5n7/hRfutiOiHiBv2C0EZwEXGw7VtONeeTyGVw7a3ynmoCnoyTgTZhadzVXP/5p9LGOsOwZJAsH3rQnttNaqVkSJFlHMYDHv3NUTPmLP725JgsSZp+0rhul1HZgu/m4TkRWAcMzfP0ZwDql1HoAEXkSOA9YmfIojesZW1HKtbMm5PQ9Sgp81DS6PxlF071IFiUW75rrW+xnzqVHMH2Mc3SaccxAjhk3kKPHDuAb//g4K1nhuaBdUTciMgaYClg5v1eLyFIReVBEnIqRDwfsS9pbyfwmoenllBR4acjQdaOU4r731ud06hx0SfKWJvss/OmpzD40ce3q9EOGZKS8TzrISKZyS4JfPBkrehEpxWgQfq1Saj/wV2AcMAXD4v9DZwQRkStEpFJEKqurndvWaXoXJQEfDQ6Nyp2ormvhV6+s4hv/WJhyXGeqDiaLvtB0L4b3K+JL02LtTasya2co9HsdgweeX7yNW1/MrxMjI0UvIn4MJf+YUupZAKXUTqVUWCkVAe7DcNPEsw2wp1SOMLcloJSao5SarpSaXlFR0Z7PoOmhlBT4ov1r02FlOG7cnXzB9NlPtjLup6+wtYOLqj2lUXRvx+sR/mgr3ve7Cw5PqEzZEQp8npjaORbXPLmYB9/f0OnX7wyZRN0I8ACwSin1R9t2+zzni8Byh8M/BsaLyAEiEsBoJv5C50TW9BZKCrzUNYdYZsYpJ0MpxZWPfQKkro3zr8qtAGza0zFF3+zQrELTfbEWWS84YkRWXq/A53FsaGKxqy5/ZTQyseiPBS4FTokLpbxTRJaJyFLgZOBHACIyTESs5uEh4GrgdWAV8LRSakUuPoim52E1Qjnnz/MT9q2s2s81T35KbWOQA256hRVV+9O+nqWoO1qbR0fd9Cwe/85RvPLD47OW2R3weVK692b86q28FcfLJOpmPs6JkAnhlOb4KuBM2/NXko3VaFKRqirmtU99ypqd9RwwsCTj17P8px1dMJtlq7uv6f6UFwcoL85elEwy142dllAkJ9Ve06Fr3WhcS6pMQyth66WlmReTsmqWZOprt3cguv6Mg/jLJbnLAtZ0fwp83qjrZsmWGn7x/PKELlb7m4P5EE0reo178aRQ9FZd8M0O/vbXljsrf6tuTlOK+jn2qbWV+j62ooTvnThO17nRpKTALNlRXdfCeX95n0c+3MT63fUxY+qa85MAqBW9xrWksuiLzXA4JzfM9/75ieMxloJPViht8ZYaDrjpFRZvqQHaQjEvnD4yaQtBjcaiJOCjsTUUk8uxo7YlZky9VvQaTSyplGuJQ6efcRWp/fUNLYaCD4WdF8SWmAr+sY+M7luhiHETcXOxKo176FPkY39TKKaE98rtsRFjqWaTuUQreo1rmTqyPOm+kkCior/smDH4PBLtixuPZf0HI84++j5FxmtaPUDtPUQ1mnT0LfIn+OB//crqmOf5UvS6TLHGtYwfXMa0UeUJDcshtvEJwFEH9Oecw4fx3ppqqmoS45Xti2LBJJERVlTO26t3UVXTFO2ipS16TSaUFvhpbA0TTDJjhPy1xtSKXuNqCv1exxoz8RfTU989GgCfxxN1ucSMt21LVl/c7rs/5o63KTHXAXw5aJWo6XkUBYzzJN6q93qEPoU+9jUGtetGo3HCI+JYnyZZgTGfVxx98PZtySyu+GQXq6CaN0etEjU9iyIzPn5/U6yif/TyGbx27QmA9tFrNI54PIKTXrYnpthdK36vx9EHb1f0oSQ3iWTROFW17mwmoXEXBUkUfZ8if9QNmMxtmGu0ote4Gq/gmDZuV8p+m2vF53G26O3KP5jUdRMh4PVw23mHxGwfVFbYbrk1vQ/Loq9pjFX05cV+fF7DGEnVljCXaEWvcTXJXDfJpsB+n8fRrZOpRV/g9zBpWN/otiNG9+OrR450HK/R2LEU/Y79scEAw/oW4feYFn2KhdpcohdjNa7G43FW9PYyBhGbxe/3iOPFZFf+yfz7LaEIBT5vTNGzCYNLdXilJiOsGjY7ag1F/8oPj2fn/mY8Hmmz6PPUmERb9BpX4xVxrBrZFAwzsr8RL29X9D6vx/Fi+uDz3dHHSRdjg2EK/R4K/W2XhV9H3GgyxIq62bG/mYDPw8FDyzh54iCgbR0pmdsw12RSj36kiMwVkZUiskJErjG3/05EVputBJ8TEcfsFhHZaJYzXiwildn+AJqejccDYQdN3xQM08dsFmG/dnwecRx/w7+XRR87hV+CUca40O+NqWnj82hFr8kM67zZXttMeZE/pvyxiJjrR+616EPAdUqpScBM4CoRmQS8CRyqlDocWAPclOI1TlZKTVFKTe+0xJpehUckoQIgGP70QWUFQGx7QBEhndFk+es37WmIceM0ByMU+j0UxFj02m2jyQyrHeHehlbKixM7Vvm84t7FWKXUdqXUJ+bjOowGIsOVUm+YjUUAPsJoE6jRZBWvR2JcMy8treK2l1bSHAwzrLyIiUPKuPmsg6P7PUmidOwEw4qqmiZO/N07/O71z6Lb61tCFAd8sRa9VvSaDCmy1ZkvL0qsc+/3OAcKdAXtWowVkTHAVGBB3K7LgaeSHKaAN0REAX9XSs1pp4yaXoxSsNFWivjqxz8FjIuqyO+NJqJYeJJY9IcO78PybUYXqmA4wpa9xmt+smlfdMz22iamjeoXsxirXTeaTLE3FOnrYNH7fZ6kBfVyTcZnsYiUYjQIv1Yptd+2/WcY7p3Hkhx6nFJqGvAFDLfPCU6DROQKEakUkcrq6uqMP4CmZ/PCkioA5n62K2Z7UzAcnSrb8Ujs4qzFtFH9ADhwUCmhSIR9ZqxznyLjggxHFDtqmxleXhSj6LXrRpMpsRa9g+vGI0nXh3JNRopeRPwYSv4xpdSztu3fAM4GLlFJ5stKqW3m/13Ac8CMJOPmKKWmK6WmV1RUtOtDaHo+1ftbErY5tWQTM0on/nRsDUUYVFaAzwy/vOu/awAYM8AobVxd10IwrBhWXhSziKbr3GgyxW4g9HVQ9H6vh9aQSy16Mc76B4BVSqk/2rbPBv4XOFcpldjmxxhTIiJl1mPgdGB5NgTX9C4UiReIk6L3mEo63uxoDUUI+DyICBt3N7B6Rx3QdkFuM5tFxJc41pUrNZni8Ui00mqxQ7+EAaUBqusTDZauIBMf/bHApcAyEVlsbvspcDdQALxpWkAfKaW+JyLDgPuVUmcCg4HnzP0+4HGl1GtZ/gyaXkJDS2x3niJHRW/8jyiFx9bTviVsKPpV2/fHjLcWx6yuQMNMRW9Ms5WOo9e0C+v8s+diWAzrW8S66vqE7V1BWkWvlJoPOJk1ryQZXwWcaT5eD0zujIAajUV1Xaw1ZCWo2LH6zMYvyLYEI449X60aOLVmIap+5iKax3D266gbTbuwehoUOpxrw/sV8e6aapRSMe7BrkCbK5puQ/y01+liEptFb6fVtOjjCYUV4YjimUVbjdc0F3it0sR+HXWj6QAFThZ9eRFNwXBC0bOuQJ/Fmm6BUolVAQsdo26S+ejDFDi4YULhCI9+uDHaENy6eVhTcG3Ra9qDVZbD0aIvN6qgbqvp+rLXWtFrugWKxDrfqXz08WUQWkIRCvyemOQqgIc/3BRdmIW2cEqrkJn20Wvaw4jyYsA5UGBQH0PRx7sguwJ9Fmu6BUoltmhLFXVjd93UNQdZWbWfgNfDt48fy7z/PZlfnD0puv/Jj7dEH1u+0zZFry16TeZYBkaBg5vQsvJbQl3fZUorek23IKxUhlE3puvGlpdy0ZyPaAlFaDRbA47sX8zlxx2Q8v0sRa8zYzXtwarL5GSEWJE4zcGuT5rSZ7HG1cy59AjAaMEWX144XXilxYoqI6Tyw/V7Ysb+z9Gjo4+njSrnnounRp8HTJeN9tFr2oN13jmFV1qtBrVFr9HEcdz4gYAR7x7fgMTpYmoLr2wb26fQiCL+4anjY8YeMLAk+vhHp03gnMnDos8rTH9qlmYpygAAEZZJREFUQPvoNe3AskWcmtUU+rRFr9E4Yi2GtoYiCSVenaJuRBLj6JuCYa48aRw/Pm1CzNhZBw+OPj5+fGzZjetOm8D00f2YPNKxzYJG44jlunFS9Pm06HUrQY2riXbmCUcIxxWESuW6sde6CUWUYymDkf2Luf6Mgxxrh58woYITJuiaS5r2Yc0kPQ4JUdYC7T1vreOKE8Z1qVzaote4GhGjfkhrWCX46J1CH60L7J6316GUIhJRKNXm0onnqpMP5JKjRjvu02jay8QhfYDkRc3AqHnT1WhFr3E9Aa/H0UfvhKXPH/1oE7VNwWi4my5OpukKbj//UJ66YiYj+xc77j9hQgXlxQH21LdQ2xjkmUVbGXPjyzy+YHNO5dKuG43r8XuFYDjRR++EvYZIazhCoXlMMoteo8kmRQEvR40dkHT//qYgi7fUcOLv3qHeFi780+eW8bWjRuVMLm3Ra1yPP2rRp49WsPtGW4JtswBt0WvcgFVqoz4uJyTXaEWvcT1+r4cWh6gbJ+z6vCUUjh7jtDim0biJXPaTzaTxyEgRmSsiK0VkhYhcY27vLyJvisha83+/JMdfZo5ZKyKXZfsDaHo+AZ+HYFhl1G/TrtCbg5FouJu26DVuYNbBgxK2Detr5GwsWL83Z++biUUfAq5TSk0CZmL0fZ0E3Ai8pZQaD7xlPo9BRPoDvwSOwmgh+MtkNwSNJhl+rxAMZbYYK0kseqe4Zo2mq7n3kiM4YnSsCnzocqO76g3/Xkpdc25KGKdV9Eqp7UqpT8zHdcAqYDhwHvCwOexh4HyHw88A3lRK7VVK7QPeBGZnQ3BN72F/U4jXVuwgFIkwcUgZz115DOt/fabjWInz0VtxzV5ds0bjAgI+D0cd0D9m28DSAsAoX3zy79+hOZj9hKp2nf0iMgaYCiwABiultpu7dmC0DYxnOLDF9nyruc3pta8QkUoRqayurm6PWJoezo79zcb/2ma8HmHqqH5Jo2jsF4ndr69dNxq3cNGRsdE19oY4g8oKczL7zFjRi0gp8G/gWqVUTONNZaQhdqq9uVJqjlJqulJqekWFzkjUJKJIr7Anj2grWbBlX2O04qUOr9S4hVEDivn4Z7Oiz+2lsP/69Wk56YGQ0SuKiB9DyT+mlHrW3LxTRIaa+4cCuxwO3QaMtD0fYW7TaNpNKKzSWjsHDSnjjR+dAMAvnl/BZQ8uBLRFr3EXFWUF0cf2wnmlBblJbcok6kaAB4BVSqk/2na9AFhRNJcBzzsc/jpwuoj0MxdhTze3aTTtJhiO4MvA2ikrbLtYttcabh9t0Wvcxi/OnsSAkgAiwhUnjAWgtDA3ij6TVz0WuBRYJiKLzW0/Be4AnhaRbwGbgAsBRGQ68D2l1LeVUntF5DbgY/O4W5VSuYsh0vRoguFIRpZ5gUO/Tm3Ra9zG5ccdEG2Ac+Psifxo1gTHczcbpFX0Sqn5QLKr5FSH8ZXAt23PHwQe7KiAGo1FMAPXDTjXqdd15TVuxuMRihzKbmft9XP2yhpNlrDqyLeEMrPoCx2soknD+mRdLo2mu6AVvcb1DO5jLFwFw5GM4uE9HqE4zjoaVl6UE9k0mu6AVvQa12MlQYXCkZhQtFS8ds0JXDrTqDM/1tYyUKPpjWhFr3E9Vv2aTH30YMQqf/VII7L3smPG5Eo0jaZboOvRa1yPpdtbM4y6sTh0eF9W3HIGJTmKTdZougvaote4HntFyvbWrNFKXqPRil7TDbBXpNTx8BpN+9GKXuN67Ba9L8PFWI1G04ZW9BrXE6PotUWv0bQbreg1rseu23VdeY2m/eirRuN6RLtuNJpOoRW9xvV49GKsRtMptKLXuB7to9doOkfaIGMReRA4G9illDrU3PYUcJA5pByoUUpNcTh2I1AHhIGQUmp6luTW9CJE++g1mk6RSTbJQ8CfgUesDUqpr1qPReQPQG2K409WSu3uqIAajQ6v1Gg6Ryb16N8zm4InYHafuhA4JbtiaTRtxFr0WtFrNO2ls/Pg44GdSqm1SfYr4A0RWSQiV6R6IRG5QkQqRaSyurq6k2JpeiraR6/RtJ/OKvqLgSdS7D9OKTUN+AJwlYickGygUmqOUmq6Ump6RUVFJ8XS9CRCYRV9rBW9RtN+OqzoRcQHfAl4KtkYpdQ28/8u4DlgRkffT9N7CYYj0cfNoUiKkRqNxonOWPSzgNVKqa1OO0WkRETKrMfA6cDyTryfppfSalP022ua8iiJRtM9SavoReQJ4EPgIBHZKiLfMnddRJzbRkSGicgr5tPBwHwRWQIsBF5WSr2WPdE1vYWgzXWzvzmUR0k0mu5JJlE3FyfZ/g2HbVXAmebj9cDkTsqn0cS4bm6YPTGPkmg03ROdfaJxPZaiv3TmaIb0LcyzNBpN90Mreo3rOWRYXwCOGz8wz5JoNN0T3WdN43qOGN2PJb88nb5F/nyLotF0S7RFr+kWaCWv0XQcreg1Go2mh6MVvUaj0fRwtKLXaDSaHo5W9BqNRtPD0Ypeo9Foejha0Ws0Gk0PR5RS6Ud1MSJSDWzKtxxxDATc3ClLy9dx3CwbaPk6i5vly6Zso5VSjjXeXano3YiIVLq5562Wr+O4WTbQ8nUWN8vXVbJp141Go9H0cLSi12g0mh6OVvSZMyffAqRBy9dx3CwbaPk6i5vl6xLZtI9eo9FoejjaotdoNJoejlb0Go1G08PRil6j0Wh6OFrRdxNE5GsiMtl8LPmWJxki4spzSkTOFZFx+Zaju9Jdzj834obvzpUXZVcjIueLyG35lsMJEZklIvOAu4CpAMplK+imEv1xvuVwwvz+PgQeAIbmW5543HzugfvPPzd/f2767nptK0HzzuoBvgncCIwWkTeUUvPyK1lUtkLgYWAQcDtwHlBs7vcqpcL5k9BARHzAdcD3gVEi8rZSanG+5TO/vxLgCaAMuBm4FhgNzBcRj1Iqkmf5XHnugfvPPzd/f2797nqtRa8MwsA6jLvtlYArLANTtibgMaXUSUqp14EPgEvN/XlX8gBKqRDwGTAR+DHwd3N7XuUzv7964J/m9/cW8DrGBUc+lbxNPleee+D+88/N359bv7tep+hF5Icicp+IfNvc9K5Sqk4pdR9QIiLfMsd1+Xdjk+07AEqp583tXmADsEJERna1XA4y3iEiF5qbXlZKNSul7gIGicjXzHFd3uTVJttXAJRST5nbPcA+YIuIFHS1XHHyufLci5PPleefm78/t393KKV6zR/wDeAjYDbwLnATMM62/wvACqCfC2T7KTDWtv8w4GOgLE/fnQA/At4HLgBWmTIPso35IrDNRbJV2MYcA6zW5163Pf9c+/25/btTSvU6i/5U4LdKqdcwfMuFwCXWTqXUqxhK4goRKbMswzzJFgC+bpNtGdAMXNSFMkVRxhl7MnCzUuoZDMV6OHCGbcxzwBoR+QkYi1F5lG0yxoVnjfkA2Coi53aFTA64+dxzks9V5x/u/v7c/t31DkVvm8p9CpwNoJSqBD4EhovIsbbhNwC/AdYCQ/Io20embMeZ4wTDz1zY1SFaNhkrgeNNGV/D+I4OEZGDbMO/D9wpIjuA4XmUbY0p20RzXB9gNRDMtUxJ5HPduZdGPlecf27+/tz+3dnpkYre9ItFY1ZV2+Lb+4BHRE4wny8HtgPDzPEHAvcC/wGmKaXuybNsVZghgabVOghoMB/njBQyrgPKROQw8/m7QF+MyBZEZApwH/BvjO/vYRfIVmqO2w+MAAZnWyYHGaPXlZvOvQ7I1+XnX7widNP3107Z8nLtJqNHKXoROVpE7gN+JCJl1pcqRhggGHf6FcBXzTCnrRgX/hhzfy1wtVLqS0qpKhfINsQmG8BPlFIPZlOuOBmPFZGHgZtFpL9NRmthdSEQAk4XEZ9SaiWG1W41TtgDXKmU+koOvr/OygZwkVLqoWzKZZNvhoj8EGKjemxKNW/nXifk67Lzz5TvPuAGEamwbfemkK+rrt2OyNal1246eoyiF5ETgT8Db2Pc5X8qIqdDNAwQoA6YBxQAvzeVRD8MBYVSqloptdaNspljW7Mtm03GsRgW0VyMePPbRORM832D5v91GC6ScRjxywAtmG0flVJbTH+km2TbaL2OUqo527KZ8l0LPIdxE/qCuc1rvqelVPNy7mVLPnNs1s8/EfGKyG8wyvW+D0wDfikig833tMIRu/z7y5Zs5ticXbuZ0GMUPXAE8L5S6gmMJIXBwMXWjyIitwOPY9z5f47xQ8wzn2fdxdCNZLOYAawyLd6fAIuBc0RkqCWjiDwALALuBmaIyCJgL4b/0a2yvZFj2cAInzsbY33iRoiNlxaRW8jv7+t2+bYBF5q/74+AmUCRS+Rzs2yZ05mQnXz+YXzhE2zPzwIeAYaZz+8GHgO+C0zA+DHs4VgechTu5GbZbO9xDnA1MNN8PhbDahllPp8E3IFxch9nynig7fhSoLy3yZZEPq/5Vwi8AvzQ9jse1tW/bzeS70jzeX/zf4H5/z/AdPPx4V187bpWtk59rnwL0IEfohx4GWO6dDNQam4fj+EeeQNjMfA54HoM35j9eE9vlM32HkOBFzGsjp9jhKSdYe77PXCd+diLkc33S6BvF31/rpUtA/msJj6nAkuAgQ7Ha/kS5Ts9bkyZKd+wrpTPzbJl4687um5KMFwFPzAfnwCgDP/cjzHCq/6llPoixgLJSdaBkvsaJ26WzWI6ME8pdbxS6jbg/4ArzH3zgMNE5ChlTO23AScopWq7SEY3y+Yk313A9yCmWNVcjPC6H5hyzTD/i5bPUb4r48bMAFYopapEpFRExneRfG6WrdN0C0UvIv8jIieKSB+l1DaMxZGnMZIQZojIMDAWPJRSc5VST5qHTgNes14nFz+Gm2WLk/EkMdL/3wIete3egxFzDrAAIyb4jyJSChwCbBKR4lzJ6GbZMpBvL4blF41eMeW4HSNCoxaYZiqCnITV9QD5VprjrOipfhilKr6JkU06xZQ76/K5WbZs49rqlSIiGCFKjwMR4HOMrLdrlFK7zTH/BS4ETgH+aTv2OAxrcDeGH7zXyJZGxu8A1yiltouIXxkRK0MxTmCUUjuA/xOR0cCDGBEu/6OUauwtsnVCvoh53DjgHxhrCteq3EQh9UT5rES28zAyXh8GvqqUWtpbZMsp+fYdOf0BXvP/BIwKhGD4Ze8Bno0b+yMMC6UvUGJuGwac2dtka4+MtjEvArPMx4PM/z5yt9jlWtk6KZ+1aDcIOFnL1275Bpr/LwYu6G2y5frPVRa9Gdt7G+AVkVeAPkAYjHAwEbkGqBKRE5VS75qH3YehTN/EqEt9hDISFrKdNOFa2Toqo4gEgGqM+jS/As4WkZOUUvswFpR7hWxZlO9kpdQuYJeWr0PynaCMEOReI1tX4RofvRhJRYv+f3tnrBIxEIThL4IgPsMVtoKKhS8gaGXhA4ggWPsIglZ2lj6AjWIptnbXnmgv+AaCCMIh3FjMRgNqc7lL5jb/BwPJwsEHC5PL7GYWf116xifmE9gs0oKReX3xJEXJDr5o8gSspkTaGbcxHU/Tzxbwznv3+I6CrZRIO+M2Yb9X+dXye+uSW6O0/UpRBt6Qar9yf4F/4HEADNLYHF5fuwGW0tguvvuik241HHv4LoJLYL2rbvLL2y+yW5PRukBlAhbxT4jLGtkecJauH4GjdL0BXMmtluO13OTXBb/Ibk1GmNKNmX2Y2dB+Ps3exutk4GdDLhdFcYefA/oAv7vJddFtTMdBk46R3eSXt19ktyYJtRgL3wsnhveDuU3D7/ipLSvAi/l+dSw9iuU2G46R3eSXt19ktyYI84++wgiYx/eZr6Wn7TEwMrN+ORly+5fIjpHdQH51iewX2W36tF07+ivwpmAjoA8ctu0zK26z4BjZTX55+0V2m3aUjY5CURRFD29adW5mw7Z9qkR2K4nsGNkN5FeXyH6R3aZNyEQvhBBickSs0QshhJggSvRCCJE5SvRCCJE5SvRCCJE5SvRCCJE5SvRCCJE5SvRCCJE5XwHax7jncPKyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['close'].plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "524d5fc7-13a8-49c3-a864-89d4f1711f3b",
      "metadata": {
        "id": "524d5fc7-13a8-49c3-a864-89d4f1711f3b"
      },
      "outputs": [],
      "source": [
        "prediction = model.predict(X_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "ef42091a-16b0-4c74-91cb-3b9822073dee",
      "metadata": {
        "id": "ef42091a-16b0-4c74-91cb-3b9822073dee"
      },
      "outputs": [],
      "source": [
        "y_predicted=y_scaler.inverse_transform(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "k1zcJIHCvGSy",
      "metadata": {
        "id": "k1zcJIHCvGSy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "a65ef3ff-bafc-4afc-8005-14ba83644974"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0da2b6ee90>]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(y_predicted)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "ff9fafa8-4057-4967-b1b1-6602451e3663",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "ff9fafa8-4057-4967-b1b1-6602451e3663",
        "outputId": "a2625ed6-8a1b-4ed9-dcd8-34d440bef930"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0da3416390>]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(df['close'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "49acfba8-d634-4a89-8b7f-1bf2383912f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "49acfba8-d634-4a89-8b7f-1bf2383912f7",
        "outputId": "425c46f3-03d5-434b-e434-43f73c953962"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1151"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1151"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(len(X_scaled))\n",
        "display(len(y_scaled))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "ec8e9773-92de-4b08-84b4-aa5b92a03e25",
      "metadata": {
        "id": "ec8e9773-92de-4b08-84b4-aa5b92a03e25"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ELAN_future_300.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}