moneebullah25 · October 15, 2023 06:41
diff --git a/stock_prediction_pytorch-new.ipynb b/stock_prediction_pytorch-new.ipynb
 {
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        "<a href=\"https://colab.research.google.com/gist/moneebullah25/52615be3313e3e6b334ec8718d306073/stock_prediction_pytorch-new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance numpy pandas matplotlib seaborn truedata_ws"
      ],
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          "name": "stdout",
          "text": [
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        "trusted": true,
        "id": "HgGP8i-p-V6E"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import yfinance as yf\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import time\n",
        "import math, time\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from dateutil.relativedelta import relativedelta\n",
        "from datetime import datetime\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from truedata_ws.websocket.TD import TD\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "td_obj = TD('--', '--', live_port=8086)"
      ],
      "metadata": {
        "id": "yU8VQEDlDnEo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e317d82c-98e5-4a98-f75a-007b55a3d772"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "(2023-10-15 05:48:12,358) WARNING :: Connected successfully to TrueData Historical Data Service...  (PID:239 Thread:133364738707456)\n",
            "(2023-10-15 05:48:12,358) WARNING :: Connected successfully to TrueData Historical Data Service...  (PID:239 Thread:133364738707456)\n",
            "WARNING:truedata_ws.websocket.TD:\u001b[22m\u001b[34mConnected successfully to TrueData Historical Data Service... \u001b[0m\n",
            "(2023-10-15 05:48:13,270) WARNING :: Connected successfully to TrueData Real Time Data Service...  (PID:239 Thread:133359472735808)\n",
            "(2023-10-15 05:48:13,270) WARNING :: Connected successfully to TrueData Real Time Data Service...  (PID:239 Thread:133359472735808)\n",
            "WARNING:truedata_ws.websocket.TD:\u001b[22m\u001b[34mConnected successfully to TrueData Real Time Data Service... \u001b[0m\n"
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    {
      "cell_type": "code",
      "source": [
        "nifty_symbol = \"^NSEI\"\n",
        "banknifty_symbol = \"^NSEBANK\"\n",
        "\n",
        "# Download historical data\n",
        "nifty_data = yf.download(nifty_symbol, start=\"2010-01-01\", end=\"2023-12-12\").reset_index()\n",
        "banknifty_data = yf.download(banknifty_symbol, start=\"2010-01-01\", end=\"2023-12-12\").reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhN8ZXi0qyA2",
        "outputId": "c19f7ed7-8466-4340-b399-6b255bbfbd5b"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%%**********************]  1 of 1 completed\n",
            "[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nifty_data = td_obj.get_historic_data('NIFTY 50', duration='13 Y', bar_size='eod')\n",
        "banknifty_data = td_obj.get_historic_data('NIFTY BANK', duration='13 Y', bar_size='eod')\n",
        "\n",
        "nifty_data = pd.DataFrame(nifty_data)\n",
        "nifty_data.rename(columns={'time': 'Date', 'o': 'Open', 'h': 'High', 'l': 'Low', 'v': 'Volume', 'c': 'Close'}, inplace=True)\n",
        "nifty_data.drop(columns=['oi'], inplace=True)\n",
        "\n",
        "banknifty_data = pd.DataFrame(banknifty_data)\n",
        "banknifty_data.rename(columns={'time': 'Date', 'o': 'Open', 'h': 'High', 'l': 'Low', 'v': 'Volume', 'c': 'Close'}, inplace=True)\n",
        "banknifty_data.drop(columns=['oi'], inplace=True)"
      ],
      "metadata": {
        "id": "tdHqtGfK4uRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = nifty_data\n",
        "SYMBOL = nifty_symbol\n",
        "data['NextOpen'] = data['Open'].shift(-1)"
      ],
      "metadata": {
        "id": "3aPZPcskwCFQ"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.sort_values('Date')"
      ],
      "metadata": {
        "id": "kKpH86fcvbEu"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "1Icd5oFFtkp4",
        "outputId": "216bc2ad-07d3-4501-9712-a53ee2423234"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date          Open          High           Low         Close  \\\n",
              "3378 2023-10-09  19539.449219  19588.949219  19480.500000  19512.349609   \n",
              "3379 2023-10-10  19565.599609  19717.800781  19565.449219  19689.849609   \n",
              "3380 2023-10-11  19767.000000  19839.199219  19756.949219  19811.349609   \n",
              "3381 2023-10-12  19822.699219  19843.300781  19772.650391  19794.000000   \n",
              "3382 2023-10-13  19654.550781  19805.400391  19635.300781  19751.050781   \n",
              "\n",
              "         Adj Close  Volume      NextOpen  \n",
              "3378  19512.349609  165100  19565.599609  \n",
              "3379  19689.849609  216600  19767.000000  \n",
              "3380  19811.349609  213700  19822.699219  \n",
              "3381  19794.000000  217900  19654.550781  \n",
              "3382  19751.050781  255000           NaN  "
            ],
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ba1d908-d019-4a6a-a93d-9f37b60a7303 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "ye0_NCGT-V6Q",
        "outputId": "6f49c9da-0dee-4f80-cccf-e68fb3c0760b"
      },
      "cell_type": "code",
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "plt.figure(figsize = (15,9))\n",
        "plt.plot(data[['Close']])\n",
        "plt.xticks(range(0,data.shape[0],500),data['Date'].loc[::500],rotation=45)\n",
        "plt.title(SYMBOL,fontsize=18, fontweight='bold')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close Price (USD)',fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x900 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize data\n",
        "features = data[['High', 'Low', 'Open', 'Close', 'NextOpen', 'Volume']]"
      ],
      "metadata": {
        "id": "X3gHlLG0vZtI"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UTJs_e_pwFYb",
        "outputId": "5e0a4f37-3836-45aa-efdf-fcda40fefc93"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              High           Low          Open         Close      NextOpen  \\\n",
              "3378  19588.949219  19480.500000  19539.449219  19512.349609  19565.599609   \n",
              "3379  19717.800781  19565.449219  19565.599609  19689.849609  19767.000000   \n",
              "3380  19839.199219  19756.949219  19767.000000  19811.349609  19822.699219   \n",
              "3381  19843.300781  19772.650391  19822.699219  19794.000000  19654.550781   \n",
              "3382  19805.400391  19635.300781  19654.550781  19751.050781           NaN   \n",
              "\n",
              "      Volume  \n",
              "3378  165100  \n",
              "3379  216600  \n",
              "3380  213700  \n",
              "3381  217900  \n",
              "3382  255000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-385edc54-89e1-4a3a-9ba3-ecf33d1fa26c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>NextOpen</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3378</th>\n",
              "      <td>19588.949219</td>\n",
              "      <td>19480.500000</td>\n",
              "      <td>19539.449219</td>\n",
              "      <td>19512.349609</td>\n",
              "      <td>19565.599609</td>\n",
              "      <td>165100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3379</th>\n",
              "      <td>19717.800781</td>\n",
              "      <td>19565.449219</td>\n",
              "      <td>19565.599609</td>\n",
              "      <td>19689.849609</td>\n",
              "      <td>19767.000000</td>\n",
              "      <td>216600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3380</th>\n",
              "      <td>19839.199219</td>\n",
              "      <td>19756.949219</td>\n",
              "      <td>19767.000000</td>\n",
              "      <td>19811.349609</td>\n",
              "      <td>19822.699219</td>\n",
              "      <td>213700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3381</th>\n",
              "      <td>19843.300781</td>\n",
              "      <td>19772.650391</td>\n",
              "      <td>19822.699219</td>\n",
              "      <td>19794.000000</td>\n",
              "      <td>19654.550781</td>\n",
              "      <td>217900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3382</th>\n",
              "      <td>19805.400391</td>\n",
              "      <td>19635.300781</td>\n",
              "      <td>19654.550781</td>\n",
              "      <td>19751.050781</td>\n",
              "      <td>NaN</td>\n",
              "      <td>255000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-385edc54-89e1-4a3a-9ba3-ecf33d1fa26c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-385edc54-89e1-4a3a-9ba3-ecf33d1fa26c 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-385edc54-89e1-4a3a-9ba3-ecf33d1fa26c');\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",
              "\n",
              "\n",
              "<div id=\"df-102ba4b4-fa4c-40ba-9674-637c3ddf1325\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-102ba4b4-fa4c-40ba-9674-637c3ddf1325')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-102ba4b4-fa4c-40ba-9674-637c3ddf1325 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lookback = 30 # choose sequence length"
      ],
      "metadata": {
        "id": "zVbv8o2-ql-H"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = features.shape[1]\n",
        "hidden_dim = 128\n",
        "num_layers = 4\n",
        "output_dim = 1\n",
        "num_epochs = 150"
      ],
      "metadata": {
        "id": "_3R8W35eqqTR"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ecRchAsN-V6O"
      },
      "cell_type": "code",
      "source": [
        "# Combine normalized features with other columns\n",
        "data_scaled = pd.DataFrame(features, columns=['High', 'Low', 'Open', 'NextOpen', 'Volume', 'Close'])\n",
        "data_scaled['Date'] = data['Date']\n",
        "data_scaled = data_scaled.set_index('Date')\n",
        "data_scaled = data_scaled.sort_values('Date')"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_scaled.shape)\n",
        "# Removing last index since we don't have value for NextOpen\n",
        "data_scaled = data_scaled.iloc[:-1 , :]\n",
        "data_scaled.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "QIfwzjLtwmCP",
        "outputId": "90056a3e-65fe-47a8-e139-2af579114e61"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3383, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    High           Low          Open      NextOpen  Volume  \\\n",
              "Date                                                                         \n",
              "2023-10-06  19675.750000  19589.400391  19621.199219  19539.449219  159100   \n",
              "2023-10-09  19588.949219  19480.500000  19539.449219  19565.599609  165100   \n",
              "2023-10-10  19717.800781  19565.449219  19565.599609  19767.000000  216600   \n",
              "2023-10-11  19839.199219  19756.949219  19767.000000  19822.699219  213700   \n",
              "2023-10-12  19843.300781  19772.650391  19822.699219  19654.550781  217900   \n",
              "\n",
              "                   Close  \n",
              "Date                      \n",
              "2023-10-06  19653.500000  \n",
              "2023-10-09  19512.349609  \n",
              "2023-10-10  19689.849609  \n",
              "2023-10-11  19811.349609  \n",
              "2023-10-12  19794.000000  "
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Close</th>\n",
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              "      <th>2023-10-06</th>\n",
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              "      <th>2023-10-12</th>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6efc6906-890c-4ba1-859b-3e64b6be5a8f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6efc6906-890c-4ba1-859b-3e64b6be5a8f 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-6efc6906-890c-4ba1-859b-3e64b6be5a8f');\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",
              "\n",
              "\n",
              "<div id=\"df-2942935f-2a1c-49bf-b913-6f14bd5b7e18\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2942935f-2a1c-49bf-b913-6f14bd5b7e18')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2942935f-2a1c-49bf-b913-6f14bd5b7e18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "78Hwc8Yi-V6R"
      },
      "cell_type": "markdown",
      "source": [
        "## Normalize data"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "VSTwiDZs-V6X"
      },
      "cell_type": "code",
      "source": [
        "def split_data(stock, lookback):\n",
        "    data_raw = stock.to_numpy() # convert to numpy array\n",
        "    data = []\n",
        "\n",
        "    # create all possible sequences of length seq_len\n",
        "    for index in range(len(data_raw) - lookback):\n",
        "        data.append(data_raw[index: index + lookback])\n",
        "\n",
        "    data = np.array(data)\n",
        "    test_set_size = int(np.round(0.01 * data.shape[0]))\n",
        "    train_set_size = data.shape[0] - (test_set_size)\n",
        "\n",
        "    x_train = data[:train_set_size, :-1, :]\n",
        "    y_train = data[:train_set_size, -1, -1]\n",
        "\n",
        "    x_test = data[train_set_size:, :-1, :]\n",
        "    y_test = data[train_set_size:, -1, -1]\n",
        "\n",
        "    return [x_train, y_train.reshape(-1, 1), x_test, y_test.reshape(-1, 1)]\n",
        "\n",
        "def prepare_model_input(data, lookback):\n",
        "    data_raw = data.to_numpy()  # convert to numpy array\n",
        "    data_sequences = []\n",
        "\n",
        "    # create all possible sequences of length lookback\n",
        "    for index in range(len(data_raw) - lookback):\n",
        "        data_sequences.append(data_raw[index: index + lookback])\n",
        "\n",
        "    data_sequences = np.array(data_sequences)\n",
        "\n",
        "    return data_sequences"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pSjpwkV-V6Y",
        "outputId": "82209857-64d0-472d-9545-85300674b812"
      },
      "cell_type": "code",
      "source": [
        "scaler_train = StandardScaler()\n",
        "scaler_test = StandardScaler()\n",
        "\n",
        "x_train, y_train, x_test, y_test = split_data(data_scaled, lookback)\n",
        "x_train = scaler_train.fit_transform(x_train.reshape(x_train.shape[0] * x_train.shape[1], x_train.shape[2])).reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2])\n",
        "y_train = scaler_test.fit_transform(y_train)\n",
        "x_test = scaler_train.fit_transform(x_test.reshape(x_test.shape[0] * x_test.shape[1], x_test.shape[2])).reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2])\n",
        "y_test = scaler_test.fit_transform(y_test)\n",
        "\n",
        "print('x_train.shape = ',x_train.shape)\n",
        "print('y_train.shape = ',y_train.shape)\n",
        "print('x_test.shape = ',x_test.shape)\n",
        "print('y_test.shape = ',y_test.shape)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train.shape =  (3318, 29, 6)\n",
            "y_train.shape =  (3318, 1)\n",
            "x_test.shape =  (34, 29, 6)\n",
            "y_test.shape =  (34, 1)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "MS7nxwk6-V6a"
      },
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "x_train = torch.from_numpy(x_train).type(torch.double).to(device)\n",
        "x_test = torch.from_numpy(x_test).type(torch.double).to(device)\n",
        "y_train_lstm = torch.from_numpy(y_train).type(torch.double).to(device)\n",
        "y_test_lstm = torch.from_numpy(y_test).type(torch.double).to(device)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "jXZYNVoK-V6c"
      },
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        # Use torch.double here\n",
        "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True).to(torch.double)\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim).to(torch.double)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Explicitly set the data type for initial hidden and cell states\n",
        "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim, dtype=torch.double).requires_grad_().to(device)\n",
        "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim, dtype=torch.double).requires_grad_().to(device)\n",
        "        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
        "        out = self.fc(out[:, -1, :])\n",
        "        return out\n"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "5dU0IlR9-V6c"
      },
      "cell_type": "code",
      "source": [
        "model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers).to(device)\n",
        "criterion = torch.nn.MSELoss(reduction='mean')\n",
        "optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLmaWwIk-V6d",
        "outputId": "bd6ed706-9191-4d75-d984-344bfaeee34a"
      },
      "cell_type": "code",
      "source": [
        "hist = np.zeros(num_epochs)\n",
        "start_time = time.time()\n",
        "lstm = []\n",
        "\n",
        "for t in range(num_epochs):\n",
        "    # Declare y_train_pred with torch.double data type\n",
        "    y_train_pred = torch.zeros_like(y_train_lstm, dtype=torch.double).to(device)\n",
        "\n",
        "    # Get predictions from the model\n",
        "    y_train_pred = model(x_train)\n",
        "\n",
        "    loss = criterion(y_train_pred, y_train_lstm)\n",
        "    print(\"Epoch \", t, \"MSE: \", loss.item())\n",
        "    hist[t] = loss.item()\n",
        "\n",
        "    optimiser.zero_grad()\n",
        "    loss.backward()\n",
        "    optimiser.step()\n",
        "\n",
        "training_time = time.time()-start_time\n",
        "print(\"Training time: {}\".format(training_time))\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  0 MSE:  1.0004662374326765\n",
            "Epoch  1 MSE:  0.8099828119271647\n",
            "Epoch  2 MSE:  3.3718586522955785\n",
            "Epoch  3 MSE:  0.27794628381974745\n",
            "Epoch  4 MSE:  0.6675303557719733\n",
            "Epoch  5 MSE:  0.39828495564308486\n",
            "Epoch  6 MSE:  0.15372645476350646\n",
            "Epoch  7 MSE:  0.1255446900357865\n",
            "Epoch  8 MSE:  0.15383234435884485\n",
            "Epoch  9 MSE:  0.1355590363360675\n",
            "Epoch  10 MSE:  0.06827653790434486\n",
            "Epoch  11 MSE:  0.041331619029856284\n",
            "Epoch  12 MSE:  0.021370728631979325\n",
            "Epoch  13 MSE:  0.012778709204198928\n",
            "Epoch  14 MSE:  0.024251842964496242\n",
            "Epoch  15 MSE:  0.04764963545618221\n",
            "Epoch  16 MSE:  0.03318634094106243\n",
            "Epoch  17 MSE:  0.0215641739267155\n",
            "Epoch  18 MSE:  0.041998611296267765\n",
            "Epoch  19 MSE:  0.025089062612428045\n",
            "Epoch  20 MSE:  0.021573077447426207\n",
            "Epoch  21 MSE:  0.027150269115982312\n",
            "Epoch  22 MSE:  0.019486110300891857\n",
            "Epoch  23 MSE:  0.02056293298811144\n",
            "Epoch  24 MSE:  0.0184398072506179\n",
            "Epoch  25 MSE:  0.02397904844312857\n",
            "Epoch  26 MSE:  0.014248046747352307\n",
            "Epoch  27 MSE:  0.009650462460567185\n",
            "Epoch  28 MSE:  0.014737695689270316\n",
            "Epoch  29 MSE:  0.010923311263509058\n",
            "Epoch  30 MSE:  0.008080218078061762\n",
            "Epoch  31 MSE:  0.007499986069431515\n",
            "Epoch  32 MSE:  0.007681084819267518\n",
            "Epoch  33 MSE:  0.0064338351618741145\n",
            "Epoch  34 MSE:  0.00547174005343996\n",
            "Epoch  35 MSE:  0.004957424595885063\n",
            "Epoch  36 MSE:  0.004830491228197163\n",
            "Epoch  37 MSE:  0.004867253766720852\n",
            "Epoch  38 MSE:  0.0046862068389648\n",
            "Epoch  39 MSE:  0.004494320993337324\n",
            "Epoch  40 MSE:  0.003362941171590211\n",
            "Epoch  41 MSE:  0.003833757201033802\n",
            "Epoch  42 MSE:  0.00400271801469481\n",
            "Epoch  43 MSE:  0.0025835819722215395\n",
            "Epoch  44 MSE:  0.0031324030804471214\n",
            "Epoch  45 MSE:  0.0029167424362504285\n",
            "Epoch  46 MSE:  0.0028397386913995037\n",
            "Epoch  47 MSE:  0.002653799346783159\n",
            "Epoch  48 MSE:  0.0021207966512966207\n",
            "Epoch  49 MSE:  0.0023537273098040807\n",
            "Epoch  50 MSE:  0.0023386932417216507\n",
            "Epoch  51 MSE:  0.0022245449241194205\n",
            "Epoch  52 MSE:  0.0019361739370424606\n",
            "Epoch  53 MSE:  0.0021459446884157527\n",
            "Epoch  54 MSE:  0.0018045175931927354\n",
            "Epoch  55 MSE:  0.0020415814291135656\n",
            "Epoch  56 MSE:  0.0016667890281084274\n",
            "Epoch  57 MSE:  0.0017449207796295647\n",
            "Epoch  58 MSE:  0.0017961938727440987\n",
            "Epoch  59 MSE:  0.001721990796383758\n",
            "Epoch  60 MSE:  0.0015339404842776652\n",
            "Epoch  61 MSE:  0.0016833142341877857\n",
            "Epoch  62 MSE:  0.0014439098568659865\n",
            "Epoch  63 MSE:  0.0015951691341545566\n",
            "Epoch  64 MSE:  0.0015236865201710048\n",
            "Epoch  65 MSE:  0.001495541047211238\n",
            "Epoch  66 MSE:  0.001372056128334433\n",
            "Epoch  67 MSE:  0.0015005356958153742\n",
            "Epoch  68 MSE:  0.0014608234121404827\n",
            "Epoch  69 MSE:  0.0013516438150183267\n",
            "Epoch  70 MSE:  0.0013881240901587747\n",
            "Epoch  71 MSE:  0.0013838955602132057\n",
            "Epoch  72 MSE:  0.0013989591742113625\n",
            "Epoch  73 MSE:  0.0012981360711993028\n",
            "Epoch  74 MSE:  0.0013109747042591388\n",
            "Epoch  75 MSE:  0.0013598453787715251\n",
            "Epoch  76 MSE:  0.0013095335293793766\n",
            "Epoch  77 MSE:  0.0012957919361299168\n",
            "Epoch  78 MSE:  0.001268679305613229\n",
            "Epoch  79 MSE:  0.0012826076676077664\n",
            "Epoch  80 MSE:  0.0013003505641061297\n",
            "Epoch  81 MSE:  0.0012504900406891766\n",
            "Epoch  82 MSE:  0.0012381453348591885\n",
            "Epoch  83 MSE:  0.001263490710358195\n",
            "Epoch  84 MSE:  0.0012578522201962502\n",
            "Epoch  85 MSE:  0.00124186245719887\n",
            "Epoch  86 MSE:  0.0012274696057841129\n",
            "Epoch  87 MSE:  0.0012110824858868681\n",
            "Epoch  88 MSE:  0.0012185483863020486\n",
            "Epoch  89 MSE:  0.0012289195205869871\n",
            "Epoch  90 MSE:  0.0012107298293037568\n",
            "Epoch  91 MSE:  0.0011930285876984592\n",
            "Epoch  92 MSE:  0.0011929051608094551\n",
            "Epoch  93 MSE:  0.001190371130701944\n",
            "Epoch  94 MSE:  0.001184338382414717\n",
            "Epoch  95 MSE:  0.0011870802751074829\n",
            "Epoch  96 MSE:  0.0011894410891250058\n",
            "Epoch  97 MSE:  0.001179586368782035\n",
            "Epoch  98 MSE:  0.0011679687197071983\n",
            "Epoch  99 MSE:  0.001163475164722594\n",
            "Epoch  100 MSE:  0.001159495569017117\n",
            "Epoch  101 MSE:  0.0011517452065218537\n",
            "Epoch  102 MSE:  0.0011463982209234498\n",
            "Epoch  103 MSE:  0.0011460613445019197\n",
            "Epoch  104 MSE:  0.0011455391426194463\n",
            "Epoch  105 MSE:  0.0011425915624877967\n",
            "Epoch  106 MSE:  0.001141303825958902\n",
            "Epoch  107 MSE:  0.0011452353877552742\n",
            "Epoch  108 MSE:  0.0011538578418363312\n",
            "Epoch  109 MSE:  0.0011710264297359696\n",
            "Epoch  110 MSE:  0.001208610478302665\n",
            "Epoch  111 MSE:  0.0012983340491653165\n",
            "Epoch  112 MSE:  0.001479313114236104\n",
            "Epoch  113 MSE:  0.001887545125043856\n",
            "Epoch  114 MSE:  0.002590181919222628\n",
            "Epoch  115 MSE:  0.003944471738318074\n",
            "Epoch  116 MSE:  0.004756189194227994\n",
            "Epoch  117 MSE:  0.004392415941812084\n",
            "Epoch  118 MSE:  0.002000921285108879\n",
            "Epoch  119 MSE:  0.001660024731315359\n",
            "Epoch  120 MSE:  0.0029597194442719695\n",
            "Epoch  121 MSE:  0.0020510027459178454\n",
            "Epoch  122 MSE:  0.001319380766672477\n",
            "Epoch  123 MSE:  0.0022687389550834756\n",
            "Epoch  124 MSE:  0.0018153407814466835\n",
            "Epoch  125 MSE:  0.0012181822531928494\n",
            "Epoch  126 MSE:  0.001832826103915618\n",
            "Epoch  127 MSE:  0.0017271452846296786\n",
            "Epoch  128 MSE:  0.0012675979562148734\n",
            "Epoch  129 MSE:  0.0013723022659496969\n",
            "Epoch  130 MSE:  0.001597624930707981\n",
            "Epoch  131 MSE:  0.0013094140691632878\n",
            "Epoch  132 MSE:  0.0011959407838574361\n",
            "Epoch  133 MSE:  0.0014950627181463\n",
            "Epoch  134 MSE:  0.0012579199000838455\n",
            "Epoch  135 MSE:  0.0011873773184073021\n",
            "Epoch  136 MSE:  0.0013729658598922471\n",
            "Epoch  137 MSE:  0.0012068333253172781\n",
            "Epoch  138 MSE:  0.0011455432917284918\n",
            "Epoch  139 MSE:  0.0012322168716765365\n",
            "Epoch  140 MSE:  0.0012077055183664035\n",
            "Epoch  141 MSE:  0.0011189415794849887\n",
            "Epoch  142 MSE:  0.0011517530160330227\n",
            "Epoch  143 MSE:  0.0012053806079490592\n",
            "Epoch  144 MSE:  0.0010938575991991232\n",
            "Epoch  145 MSE:  0.0010937894827086534\n",
            "Epoch  146 MSE:  0.0011642132713363985\n",
            "Epoch  147 MSE:  0.0010858020058315044\n",
            "Epoch  148 MSE:  0.0010611409018807655\n",
            "Epoch  149 MSE:  0.0011208847328800144\n",
            "Training time: 202.2426507472992\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "_DL2eBsd-V6e"
      },
      "cell_type": "code",
      "source": [
        "y_train_pred_cpu = y_train_pred.detach().cpu().numpy()\n",
        "y_train_lstm_cpu = y_train_lstm.detach().cpu().numpy()\n",
        "\n",
        "predict = pd.DataFrame(scaler_test.inverse_transform(y_train_pred_cpu))\n",
        "original = pd.DataFrame(scaler_test.inverse_transform(y_train_lstm_cpu))"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "w00IAORy-V6e",
        "outputId": "9b8bd0b0-8b3c-4c0a-94de-145d5c6392b3"
      },
      "cell_type": "code",
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "fig = plt.figure()\n",
        "fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "ax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\n",
        "ax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\n",
        "ax.set_title('Stock price', size = 14, fontweight='bold')\n",
        "ax.set_xlabel(\"Days\", size = 14)\n",
        "ax.set_ylabel(\"Cost (USD)\", size = 14)\n",
        "ax.set_xticklabels('', size=10)\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "ax = sns.lineplot(data=hist, color='royalblue')\n",
        "ax.set_xlabel(\"Epoch\", size = 14)\n",
        "ax.set_ylabel(\"Loss\", size = 14)\n",
        "ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
        "fig.set_figheight(6)\n",
        "fig.set_figwidth(16)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKGukOh4-V6f",
        "outputId": "a2b3fd8f-6a8a-4392-90c1-92b192884dea"
      },
      "cell_type": "code",
      "source": [
        "# make predictions\n",
        "y_test_pred = model(x_test)\n",
        "\n",
        "# invert predictions\n",
        "y_train_pred = scaler_test.inverse_transform(y_train_pred.detach().cpu().numpy())\n",
        "y_train = scaler_test.inverse_transform(y_train_lstm.detach().cpu().numpy())\n",
        "y_test_pred = scaler_test.inverse_transform(y_test_pred.detach().cpu().numpy())\n",
        "y_test = scaler_test.inverse_transform(y_test_lstm.detach().cpu().numpy())\n",
        "\n",
        "# calculate root mean squared error\n",
        "trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\n",
        "print('Train Score: %.2f RMSE' % (trainScore))\n",
        "testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\n",
        "print('Test Score: %.2f RMSE' % (testScore))\n",
        "lstm.append(trainScore)\n",
        "lstm.append(testScore)\n",
        "lstm.append(training_time)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 8.48 RMSE\n",
            "Test Score: 179.11 RMSE\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "9Wl1XfQz-V6f"
      },
      "cell_type": "code",
      "source": [
        "# shift train predictions for plotting\n",
        "trainPredictPlot = np.empty_like(data_scaled[['Close']])\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[lookback:len(y_train_pred)+lookback, :] = scaler_test.inverse_transform(y_train_pred)\n",
        "\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(data_scaled[['Close']])\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(y_train_pred)+lookback-1:len(data_scaled[['Close']])-1, :] = scaler_test.inverse_transform(y_test_pred)\n",
        "\n",
        "original = scaler_test.inverse_transform(data_scaled['Close'].values.reshape(-1,1))\n",
        "\n",
        "predictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\n",
        "predictions = np.append(predictions, original, axis=1)\n",
        "result = pd.DataFrame(predictions)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "sfo0gwQE-V6g",
        "outputId": "09f8ec84-4d3e-48f0-8886-389b671c0c05"
      },
      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n",
        "                    mode='lines',\n",
        "                    name='Train prediction')))\n",
        "fig.add_trace(go.Scatter(x=result.index, y=result[1],\n",
        "                    mode='lines',\n",
        "                    name='Test prediction'))\n",
        "fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n",
        "                    mode='lines',\n",
        "                    name='Actual Value')))\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        showline=True,\n",
        "        showgrid=True,\n",
        "        showticklabels=False,\n",
        "        linecolor='white',\n",
        "        linewidth=2\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title_text='Close (USD)',\n",
        "        titlefont=dict(\n",
        "            family='Rockwell',\n",
        "            size=12,\n",
        "            color='white',\n",
        "        ),\n",
        "        showline=True,\n",
        "        showgrid=True,\n",
        "        showticklabels=True,\n",
        "        linecolor='white',\n",
        "        linewidth=2,\n",
        "        ticks='outside',\n",
        "        tickfont=dict(\n",
        "            family='Rockwell',\n",
        "            size=12,\n",
        "            color='white',\n",
        "        ),\n",
        "    ),\n",
        "    showlegend=True,\n",
        "    template = 'plotly_dark'\n",
        "\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "annotations = []\n",
        "annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
        "                              xanchor='left', yanchor='bottom',\n",
        "                              text='Results (LSTM)',\n",
        "                              font=dict(family='Rockwell',\n",
        "                                        size=26,\n",
        "                                        color='white'),\n",
        "                              showarrow=False))\n",
        "fig.update_layout(annotations=annotations)\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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(USD)\"},\"tickfont\":{\"family\":\"Rockwell\",\"size\":12,\"color\":\"white\"},\"showline\":true,\"showgrid\":true,\"showticklabels\":true,\"linecolor\":\"white\",\"linewidth\":2,\"ticks\":\"outside\"},\"showlegend\":true,\"annotations\":[{\"font\":{\"color\":\"white\",\"family\":\"Rockwell\",\"size\":26},\"showarrow\":false,\"text\":\"Results (LSTM)\",\"x\":0.0,\"xanchor\":\"left\",\"xref\":\"paper\",\"y\":1.05,\"yanchor\":\"bottom\",\"yref\":\"paper\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "83-5pX4F8c5C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f072cc60-b52f-4528-90a4-5e116bfb07e9"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# './drive/MyDrive/Pretrained_Models/nsei'\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsebank_new\"\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new\"\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank_new_no'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new_no\"\n",
        "torch.save(model.state_dict(), \"./drive/MyDrive/Pretrained_Models/nsei_new_no\")"
      ],
      "metadata": {
        "id": "--SF1rXdioy5"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# './drive/MyDrive/Pretrained_Models/nsei'\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsebank_new\"\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new\"\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank_new_no'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new_no\"\n",
        "model.load_state_dict(torch.load('./drive/MyDrive/Pretrained_Models/nsei_new_no'))\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ML3CCaFKiorI",
        "outputId": "6ae0a091-3469-4926-e7c1-7ec460af7c5a"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LSTM(\n",
              "  (lstm): LSTM(6, 128, num_layers=4, batch_first=True)\n",
              "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# './drive/MyDrive/Pretrained_Models/nsei.pt'\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank.pt'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsebank_new.pt\"\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new.pt\"\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank_new_no.pt'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new_no.pt\"\n",
        "torch.save(model, './drive/MyDrive/Pretrained_Models/nsei_new_no.pt')"
      ],
      "metadata": {
        "id": "_Ws_23l6jGEU"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# './drive/MyDrive/Pretrained_Models/nsei.pt'\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank.pt'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsebank_new.pt\"\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new.pt\"\n",
        "# './drive/MyDrive/Pretrained_Models/nsebank_new_no.pt'\n",
        "# \"./drive/MyDrive/Pretrained_Models/nsei_new_no.pt\"\n",
        "model = torch.load('./drive/MyDrive/Pretrained_Models/nsei_new_no.pt')\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYwpYdxVjF9k",
        "outputId": "7eee1e9a-8897-4c02-86cc-e82a55a112a1"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LSTM(\n",
              "  (lstm): LSTM(6, 128, num_layers=4, batch_first=True)\n",
              "  (fc): Linear(in_features=128, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "td_hist_data = td_obj.get_historic_data('NIFTY BANK', duration='60 D', bar_size='eod') # NIFTY 50 # NIFTY BANK # NIFTY FIN SERVICE"
      ],
      "metadata": {
        "id": "a90lcGZUclEQ"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "td_df = pd.DataFrame(td_hist_data)\n",
        "td_df.rename(columns={'time': 'Date', 'o': 'Open', 'h': 'High', 'l': 'Low', 'v': 'Volume', 'c': 'Close'}, inplace=True)\n",
        "td_df.drop(columns=['oi'], inplace=True)\n",
        "td_df['NextOpen'] = td_df['Open'].shift(-1)"
      ],
      "metadata": {
        "id": "7PPTr---ILPT"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "td_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8Z4JUunaXBDy",
        "outputId": "acb3cadd-3343-45f6-8325-b15cd8439a47"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Open      High       Low     Close  Volume  NextOpen\n",
              "35 2023-10-09  44057.80  44113.20  43796.75  43886.50       0  44027.55\n",
              "36 2023-10-10  44027.55  44487.25  44004.55  44360.15       0  44554.90\n",
              "37 2023-10-11  44554.90  44710.55  44411.40  44516.90       0  44571.55\n",
              "38 2023-10-12  44571.55  44693.05  44530.05  44599.20       0  44322.05\n",
              "39 2023-10-13  44322.05  44563.05  44203.70  44287.95       0       NaN"
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_td = td_df[['High', 'Low', 'Open', 'Volume', 'NextOpen', 'Close']]\n",
        "features_td_scaled = scaler_train.fit_transform(features_td)"
      ],
      "metadata": {
        "id": "VA3YyHb80lcW"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine normalized features_td with other columns\n",
        "data_scaled_td = pd.DataFrame(features_td_scaled, columns=['High', 'Low', 'Open', 'Close', 'NextOpen', 'Volume'])\n",
        "data_scaled_td['Date'] = td_df['Date']\n",
        "data_scaled_td = data_scaled_td.set_index('Date')\n",
        "data_scaled_td = data_scaled_td.sort_values('Date')"
      ],
      "metadata": {
        "id": "7XVXzIuO0wew"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_scaled_td.shape)\n",
        "data_scaled_td.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "_V2S7q3Y1TMv",
        "outputId": "a79e621e-5889-407f-f6eb-9e39d5d47354"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(40, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                High       Low      Open  Close  NextOpen    Volume\n",
              "Date                                                               \n",
              "2023-10-09 -1.184121 -1.079462 -0.961895    0.0 -1.050157 -1.224945\n",
              "2023-10-10 -0.576613 -0.726399 -1.011465    0.0 -0.179975 -0.450382\n",
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              "          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",
              "\n",
              "\n",
              "<div id=\"df-2fc27935-fea7-4e46-8e6b-343aaab2d975\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2fc27935-fea7-4e46-8e6b-343aaab2d975')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2fc27935-fea7-4e46-8e6b-343aaab2d975 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_scaled_td = data_scaled_td.iloc[:-1 , :]"
      ],
      "metadata": {
        "id": "cABkkPh-1TIP"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_scaled_td.shape)\n",
        "data_scaled_td.tail()"
      ],
      "metadata": {
        "id": "iwpQz72mKODf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "ac4c185c-24b9-42b2-8c79-28c5c868c865"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(39, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                High       Low      Open  Close  NextOpen    Volume\n",
              "Date                                                               \n",
              "2023-10-06 -0.554768 -0.321346 -0.408684    0.0 -1.000242 -0.449646\n",
              "2023-10-09 -1.184121 -1.079462 -0.961895    0.0 -1.050157 -1.224945\n",
              "2023-10-10 -0.576613 -0.726399 -1.011465    0.0 -0.179975 -0.450382\n",
              "2023-10-11 -0.213943 -0.035141 -0.147318    0.0 -0.152501 -0.194048\n",
              "2023-10-12 -0.242366  0.166452 -0.120034    0.0 -0.564202 -0.059462"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-792ff64b-5d8d-4485-a773-2d18e540904e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
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              "    <tr>\n",
              "      <th>2023-10-06</th>\n",
              "      <td>-0.554768</td>\n",
              "      <td>-0.321346</td>\n",
              "      <td>-0.408684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.000242</td>\n",
              "      <td>-0.449646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-09</th>\n",
              "      <td>-1.184121</td>\n",
              "      <td>-1.079462</td>\n",
              "      <td>-0.961895</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.050157</td>\n",
              "      <td>-1.224945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-10</th>\n",
              "      <td>-0.576613</td>\n",
              "      <td>-0.726399</td>\n",
              "      <td>-1.011465</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.179975</td>\n",
              "      <td>-0.450382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-11</th>\n",
              "      <td>-0.213943</td>\n",
              "      <td>-0.035141</td>\n",
              "      <td>-0.147318</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.152501</td>\n",
              "      <td>-0.194048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-10-12</th>\n",
              "      <td>-0.242366</td>\n",
              "      <td>0.166452</td>\n",
              "      <td>-0.120034</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.564202</td>\n",
              "      <td>-0.059462</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-792ff64b-5d8d-4485-a773-2d18e540904e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-792ff64b-5d8d-4485-a773-2d18e540904e 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-792ff64b-5d8d-4485-a773-2d18e540904e');\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",
              "\n",
              "\n",
              "<div id=\"df-041b7aa9-ce48-4a9d-b996-5e6067d3d72a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-041b7aa9-ce48-4a9d-b996-5e6067d3d72a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-041b7aa9-ce48-4a9d-b996-5e6067d3d72a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_td = prepare_model_input(data_scaled_td, lookback)"
      ],
      "metadata": {
        "id": "ykcKq52qW8Om"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_td.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bg5JBUFu8XXs",
        "outputId": "52ef52f3-1769-47a9-fc60-9d0bb3c48f82"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9, 30, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_td = torch.from_numpy(x_td).type(torch.double).to(device)\n",
        "x_td = x_td[-1, :, :].unsqueeze(0)"
      ],
      "metadata": {
        "id": "L98hxgk48ZAI"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on TrueData API data\n",
        "y_td_pred = model(x_td)\n",
        "\n",
        "# Invert predictions\n",
        "y_td_pred = scaler_test.inverse_transform(y_td_pred.detach().cpu().numpy())\n",
        "\n",
        "# Print or use predictions\n",
        "print('Predictions for TrueData API:')\n",
        "print(y_td_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bR5ZdYQU9A33",
        "outputId": "a31d852e-9d3f-4ada-939c-91ca9ffcd261"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions for TrueData API:\n",
            "[[19520.28575481]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9JFF885pPEML"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "version": "3.6.4",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "mimetype": "text/x-python"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }
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