Stock_prediction_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PDBPN97cjW-",
        "outputId": "53ab54a7-bfb4-49a0-de33-3f14986bdb1a"
      },
      "execution_count": null,
      "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": [
        "import pandas_datareader as pdr\n",
        "df = pdr.get_data_tiingo('AAPL', api_key = 'your_tiinger_api_key')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXN_4UXwclUU",
        "outputId": "16687adf-775b-4314-d834-b1c089b52b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-54-c593d4b8cbbd>:2: FutureWarning:\n",
            "\n",
            "In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('AAPL.csv')"
      ],
      "metadata": {
        "id": "0MPlu8pqiaML"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('AAPL.csv')"
      ],
      "metadata": {
        "id": "jFvsfvpSet-s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "VAGZDZxYevUu",
        "outputId": "8e055c82-f55b-49fc-b733-cf07a8940890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  symbol                       date   close    high     low    open    volume  \\\n",
              "0   AAPL  2018-08-28 00:00:00+00:00  219.70  220.54  218.92  219.01  22776766   \n",
              "1   AAPL  2018-08-29 00:00:00+00:00  222.98  223.49  219.41  220.15  27254804   \n",
              "2   AAPL  2018-08-30 00:00:00+00:00  225.03  228.26  222.40  223.25  48793824   \n",
              "3   AAPL  2018-08-31 00:00:00+00:00  227.63  228.87  226.00  226.51  43340134   \n",
              "4   AAPL  2018-09-04 00:00:00+00:00  228.36  229.18  226.63  228.41  27390132   \n",
              "\n",
              "    adjClose    adjHigh     adjLow    adjOpen  adjVolume  divCash  splitFactor  \n",
              "0  52.599661  52.800770  52.412917  52.434464   91107064      0.0          1.0  \n",
              "1  53.384945  53.507047  52.530230  52.707398  109019216      0.0          1.0  \n",
              "2  53.875748  54.649061  53.246084  53.449587  195175296      0.0          1.0  \n",
              "3  54.498229  54.795104  54.107981  54.230083  173360536      0.0          1.0  \n",
              "4  54.673002  54.869323  54.258813  54.684973  109560528      0.0          1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec5921a8-8e53-4a08-b37e-06c393fdacf4\" 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>symbol</th>\n",
              "      <th>date</th>\n",
              "      <th>close</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>volume</th>\n",
              "      <th>adjClose</th>\n",
              "      <th>adjHigh</th>\n",
              "      <th>adjLow</th>\n",
              "      <th>adjOpen</th>\n",
              "      <th>adjVolume</th>\n",
              "      <th>divCash</th>\n",
              "      <th>splitFactor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-08-28 00:00:00+00:00</td>\n",
              "      <td>219.70</td>\n",
              "      <td>220.54</td>\n",
              "      <td>218.92</td>\n",
              "      <td>219.01</td>\n",
              "      <td>22776766</td>\n",
              "      <td>52.599661</td>\n",
              "      <td>52.800770</td>\n",
              "      <td>52.412917</td>\n",
              "      <td>52.434464</td>\n",
              "      <td>91107064</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-08-29 00:00:00+00:00</td>\n",
              "      <td>222.98</td>\n",
              "      <td>223.49</td>\n",
              "      <td>219.41</td>\n",
              "      <td>220.15</td>\n",
              "      <td>27254804</td>\n",
              "      <td>53.384945</td>\n",
              "      <td>53.507047</td>\n",
              "      <td>52.530230</td>\n",
              "      <td>52.707398</td>\n",
              "      <td>109019216</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-08-30 00:00:00+00:00</td>\n",
              "      <td>225.03</td>\n",
              "      <td>228.26</td>\n",
              "      <td>222.40</td>\n",
              "      <td>223.25</td>\n",
              "      <td>48793824</td>\n",
              "      <td>53.875748</td>\n",
              "      <td>54.649061</td>\n",
              "      <td>53.246084</td>\n",
              "      <td>53.449587</td>\n",
              "      <td>195175296</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-08-31 00:00:00+00:00</td>\n",
              "      <td>227.63</td>\n",
              "      <td>228.87</td>\n",
              "      <td>226.00</td>\n",
              "      <td>226.51</td>\n",
              "      <td>43340134</td>\n",
              "      <td>54.498229</td>\n",
              "      <td>54.795104</td>\n",
              "      <td>54.107981</td>\n",
              "      <td>54.230083</td>\n",
              "      <td>173360536</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-09-04 00:00:00+00:00</td>\n",
              "      <td>228.36</td>\n",
              "      <td>229.18</td>\n",
              "      <td>226.63</td>\n",
              "      <td>228.41</td>\n",
              "      <td>27390132</td>\n",
              "      <td>54.673002</td>\n",
              "      <td>54.869323</td>\n",
              "      <td>54.258813</td>\n",
              "      <td>54.684973</td>\n",
              "      <td>109560528</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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-ec5921a8-8e53-4a08-b37e-06c393fdacf4')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-ec5921a8-8e53-4a08-b37e-06c393fdacf4 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-ec5921a8-8e53-4a08-b37e-06c393fdacf4');\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-fedd3fbb-38ac-490f-a3b3-c968854c3eab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fedd3fbb-38ac-490f-a3b3-c968854c3eab')\"\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",
              "    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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fedd3fbb-38ac-490f-a3b3-c968854c3eab 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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "maHariIJkB69",
        "outputId": "6f8a3950-b10e-4407-ac2e-f4c0d22a3514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symbol                       date   close     high      low     open  \\\n",
              "1252   AAPL  2023-08-21 00:00:00+00:00  175.84  176.130  173.735  175.070   \n",
              "1253   AAPL  2023-08-22 00:00:00+00:00  177.23  177.680  176.250  177.060   \n",
              "1254   AAPL  2023-08-23 00:00:00+00:00  181.12  181.550  178.325  178.520   \n",
              "1255   AAPL  2023-08-24 00:00:00+00:00  176.38  181.104  176.010  180.674   \n",
              "1256   AAPL  2023-08-25 00:00:00+00:00  178.61  179.150  175.820  177.380   \n",
              "\n",
              "        volume  adjClose  adjHigh   adjLow  adjOpen  adjVolume  divCash  \\\n",
              "1252  46311879    175.84  176.130  173.735  175.070   46311879      0.0   \n",
              "1253  42084245    177.23  177.680  176.250  177.060   42084245      0.0   \n",
              "1254  52722752    181.12  181.550  178.325  178.520   52722752      0.0   \n",
              "1255  54945798    176.38  181.104  176.010  180.674   54945798      0.0   \n",
              "1256  51449594    178.61  179.150  175.820  177.380   51449594      0.0   \n",
              "\n",
              "      splitFactor  \n",
              "1252          1.0  \n",
              "1253          1.0  \n",
              "1254          1.0  \n",
              "1255          1.0  \n",
              "1256          1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b8ea930-b94a-40b2-ad64-5057ba320ed0\" 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>symbol</th>\n",
              "      <th>date</th>\n",
              "      <th>close</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>volume</th>\n",
              "      <th>adjClose</th>\n",
              "      <th>adjHigh</th>\n",
              "      <th>adjLow</th>\n",
              "      <th>adjOpen</th>\n",
              "      <th>adjVolume</th>\n",
              "      <th>divCash</th>\n",
              "      <th>splitFactor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1252</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2023-08-21 00:00:00+00:00</td>\n",
              "      <td>175.84</td>\n",
              "      <td>176.130</td>\n",
              "      <td>173.735</td>\n",
              "      <td>175.070</td>\n",
              "      <td>46311879</td>\n",
              "      <td>175.84</td>\n",
              "      <td>176.130</td>\n",
              "      <td>173.735</td>\n",
              "      <td>175.070</td>\n",
              "      <td>46311879</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1253</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2023-08-22 00:00:00+00:00</td>\n",
              "      <td>177.23</td>\n",
              "      <td>177.680</td>\n",
              "      <td>176.250</td>\n",
              "      <td>177.060</td>\n",
              "      <td>42084245</td>\n",
              "      <td>177.23</td>\n",
              "      <td>177.680</td>\n",
              "      <td>176.250</td>\n",
              "      <td>177.060</td>\n",
              "      <td>42084245</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2023-08-23 00:00:00+00:00</td>\n",
              "      <td>181.12</td>\n",
              "      <td>181.550</td>\n",
              "      <td>178.325</td>\n",
              "      <td>178.520</td>\n",
              "      <td>52722752</td>\n",
              "      <td>181.12</td>\n",
              "      <td>181.550</td>\n",
              "      <td>178.325</td>\n",
              "      <td>178.520</td>\n",
              "      <td>52722752</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2023-08-24 00:00:00+00:00</td>\n",
              "      <td>176.38</td>\n",
              "      <td>181.104</td>\n",
              "      <td>176.010</td>\n",
              "      <td>180.674</td>\n",
              "      <td>54945798</td>\n",
              "      <td>176.38</td>\n",
              "      <td>181.104</td>\n",
              "      <td>176.010</td>\n",
              "      <td>180.674</td>\n",
              "      <td>54945798</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2023-08-25 00:00:00+00:00</td>\n",
              "      <td>178.61</td>\n",
              "      <td>179.150</td>\n",
              "      <td>175.820</td>\n",
              "      <td>177.380</td>\n",
              "      <td>51449594</td>\n",
              "      <td>178.61</td>\n",
              "      <td>179.150</td>\n",
              "      <td>175.820</td>\n",
              "      <td>177.380</td>\n",
              "      <td>51449594</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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-3b8ea930-b94a-40b2-ad64-5057ba320ed0')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-3b8ea930-b94a-40b2-ad64-5057ba320ed0 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-3b8ea930-b94a-40b2-ad64-5057ba320ed0');\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-3f735ef0-3b34-4a20-b65c-9870a5033305\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f735ef0-3b34-4a20-b65c-9870a5033305')\"\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",
              "    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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f735ef0-3b34-4a20-b65c-9870a5033305 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": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'].tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "madTW9-EkB8i",
        "outputId": "1a695208-9e47-44e2-b502-a61faeceaba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1252    2023-08-21 00:00:00+00:00\n",
              "1253    2023-08-22 00:00:00+00:00\n",
              "1254    2023-08-23 00:00:00+00:00\n",
              "1255    2023-08-24 00:00:00+00:00\n",
              "1256    2023-08-25 00:00:00+00:00\n",
              "Name: date, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prices = df['close']\n",
        "prices.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4GhqreXfJBJ",
        "outputId": "f068c2cd-0b79-4416-e9b8-1c0b1a6bd93f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1257,)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(prices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "WbXUaGrtjrsW",
        "outputId": "df47649c-2b55-4265-97f0-56cd7469b31c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78a7bcf65f00>]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# scaling price for lstm\n",
        "\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "prices_scaled = scaler.fit_transform(np.array(prices).reshape(-1, 1))\n"
      ],
      "metadata": {
        "id": "bQyUGq8ij_cq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose an appropriate sequence length(output will depend on seq prev to it we can also say seq as # of features)\n",
        "sequence_length = 100\n",
        "def create_dataset(time_step=1,data = prices_scaled):\n",
        "  sequences = []\n",
        "  next_prices = []\n",
        "  for i in range(len(data) - sequence_length):\n",
        "      sequences.append(data[i:i+sequence_length])\n",
        "      next_prices.append(data[i+sequence_length])\n",
        "  X = np.array(sequences)\n",
        "  y = np.array(next_prices)\n",
        "\n",
        "  return X, y\n",
        "\n"
      ],
      "metadata": {
        "id": "71fsOsZ2pSYe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,y = create_dataset(3, prices_scaled)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "w0Zfw1OCqxMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypgb4gXvrQAH",
        "outputId": "ea2f73dd-bc55-4b27-cd39-0611bb9d2fa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(925, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3syrsK1EsGzb",
        "outputId": "f6f6d9bf-baaf-4657-d926-f7c855fc695c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0.1979211 ]\n",
            "  [0.1756794 ]\n",
            "  [0.17517846]\n",
            "  ...\n",
            "  [0.23070758]\n",
            "  [0.2305072 ]\n",
            "  [0.23140889]]\n",
            "\n",
            " [[0.44440827]\n",
            "  [0.45853475]\n",
            "  [0.45825924]\n",
            "  ...\n",
            "  [0.50770194]\n",
            "  [0.50311835]\n",
            "  [0.5212774 ]]\n",
            "\n",
            " [[0.443732  ]\n",
            "  [0.44440827]\n",
            "  [0.45853475]\n",
            "  ...\n",
            "  [0.50296807]\n",
            "  [0.50770194]\n",
            "  [0.50311835]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.09986224]\n",
            "  [0.10289292]\n",
            "  [0.10161553]\n",
            "  ...\n",
            "  [0.21828428]\n",
            "  [0.21763306]\n",
            "  [0.2210645 ]]\n",
            "\n",
            " [[0.13119599]\n",
            "  [0.15902317]\n",
            "  [0.17016907]\n",
            "  ...\n",
            "  [0.05815905]\n",
            "  [0.06191609]\n",
            "  [0.07271133]]\n",
            "\n",
            " [[0.11448967]\n",
            "  [0.10429555]\n",
            "  [0.10537257]\n",
            "  ...\n",
            "  [0.20348153]\n",
            "  [0.20771446]\n",
            "  [0.20964308]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Dropout\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "model.add(LSTM(units=50, return_sequences=False))\n",
        "model.add(Dense(units=1))\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n"
      ],
      "metadata": {
        "id": "AE5uh5gXsMzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=50, batch_size=32)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lImg1a6ju9eX",
        "outputId": "a6fc3d6d-1b7c-49b4-dca1-f17f88d020c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "29/29 [==============================] - 7s 98ms/step - loss: 0.0133\n",
            "Epoch 2/50\n",
            "29/29 [==============================] - 3s 118ms/step - loss: 0.0044\n",
            "Epoch 3/50\n",
            "29/29 [==============================] - 4s 148ms/step - loss: 0.0032\n",
            "Epoch 4/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 0.0027\n",
            "Epoch 5/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 0.0027\n",
            "Epoch 6/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 0.0019\n",
            "Epoch 7/50\n",
            "29/29 [==============================] - 4s 154ms/step - loss: 0.0020\n",
            "Epoch 8/50\n",
            "29/29 [==============================] - 3s 109ms/step - loss: 0.0015\n",
            "Epoch 9/50\n",
            "29/29 [==============================] - 3s 99ms/step - loss: 0.0020\n",
            "Epoch 10/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 0.0013\n",
            "Epoch 11/50\n",
            "29/29 [==============================] - 4s 147ms/step - loss: 0.0011\n",
            "Epoch 12/50\n",
            "29/29 [==============================] - 4s 136ms/step - loss: 0.0011\n",
            "Epoch 13/50\n",
            "29/29 [==============================] - 3s 95ms/step - loss: 0.0010\n",
            "Epoch 14/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 8.4386e-04\n",
            "Epoch 15/50\n",
            "29/29 [==============================] - 3s 96ms/step - loss: 7.5441e-04\n",
            "Epoch 16/50\n",
            "29/29 [==============================] - 5s 166ms/step - loss: 7.9244e-04\n",
            "Epoch 17/50\n",
            "29/29 [==============================] - 3s 101ms/step - loss: 6.1937e-04\n",
            "Epoch 18/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 5.7102e-04\n",
            "Epoch 19/50\n",
            "29/29 [==============================] - 3s 100ms/step - loss: 5.3689e-04\n",
            "Epoch 20/50\n",
            "29/29 [==============================] - 4s 130ms/step - loss: 5.8811e-04\n",
            "Epoch 21/50\n",
            "29/29 [==============================] - 4s 135ms/step - loss: 6.8845e-04\n",
            "Epoch 22/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 4.6319e-04\n",
            "Epoch 23/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 3.6463e-04\n",
            "Epoch 24/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 3.1280e-04\n",
            "Epoch 25/50\n",
            "29/29 [==============================] - 5s 170ms/step - loss: 3.5670e-04\n",
            "Epoch 26/50\n",
            "29/29 [==============================] - 3s 100ms/step - loss: 2.9623e-04\n",
            "Epoch 27/50\n",
            "29/29 [==============================] - 3s 99ms/step - loss: 2.5863e-04\n",
            "Epoch 28/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 3.1713e-04\n",
            "Epoch 29/50\n",
            "29/29 [==============================] - 4s 140ms/step - loss: 2.4744e-04\n",
            "Epoch 30/50\n",
            "29/29 [==============================] - 4s 127ms/step - loss: 2.4524e-04\n",
            "Epoch 31/50\n",
            "29/29 [==============================] - 3s 95ms/step - loss: 2.2796e-04\n",
            "Epoch 32/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 2.0356e-04\n",
            "Epoch 33/50\n",
            "29/29 [==============================] - 3s 110ms/step - loss: 2.0991e-04\n",
            "Epoch 34/50\n",
            "29/29 [==============================] - 5s 160ms/step - loss: 2.0327e-04\n",
            "Epoch 35/50\n",
            "29/29 [==============================] - 3s 99ms/step - loss: 2.1943e-04\n",
            "Epoch 36/50\n",
            "29/29 [==============================] - 3s 102ms/step - loss: 2.2126e-04\n",
            "Epoch 37/50\n",
            "29/29 [==============================] - 3s 100ms/step - loss: 1.9797e-04\n",
            "Epoch 38/50\n",
            "29/29 [==============================] - 5s 159ms/step - loss: 2.2104e-04\n",
            "Epoch 39/50\n",
            "29/29 [==============================] - 3s 110ms/step - loss: 2.1042e-04\n",
            "Epoch 40/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 2.2047e-04\n",
            "Epoch 41/50\n",
            "29/29 [==============================] - 3s 96ms/step - loss: 1.8464e-04\n",
            "Epoch 42/50\n",
            "29/29 [==============================] - 3s 114ms/step - loss: 2.2568e-04\n",
            "Epoch 43/50\n",
            "29/29 [==============================] - 4s 151ms/step - loss: 2.0511e-04\n",
            "Epoch 44/50\n",
            "29/29 [==============================] - 3s 99ms/step - loss: 1.8140e-04\n",
            "Epoch 45/50\n",
            "29/29 [==============================] - 3s 103ms/step - loss: 2.0682e-04\n",
            "Epoch 46/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 2.3767e-04\n",
            "Epoch 47/50\n",
            "29/29 [==============================] - 5s 162ms/step - loss: 2.0359e-04\n",
            "Epoch 48/50\n",
            "29/29 [==============================] - 3s 103ms/step - loss: 1.8475e-04\n",
            "Epoch 49/50\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 1.8633e-04\n",
            "Epoch 50/50\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 1.6851e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x78a7bcff4430>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_prices = model.predict(X_test)\n",
        "predicted_prices = scaler.inverse_transform(predicted_prices)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30wOxaDBvDzp",
        "outputId": "d20e2fdf-e25d-41e5-9963-abcfc0c081c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 1s 27ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(scaler.inverse_transform(y_test), label='Actual Prices')\n",
        "plt.plot(predicted_prices, label='Predicted Prices')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "NW3Bpq5gvYvo",
        "outputId": "cb30d7ad-c039-4349-a3c0-4b1c3347b6ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta\n",
        "\n",
        "# Generate timestamps for the next 30 days\n",
        "current_date = datetime.now()\n",
        "next_30_days = [current_date + timedelta(days=i) for i in range(30)]\n",
        "\n",
        "# Generate predictions for the next 30 days\n",
        "future_predictions = []\n",
        "last_sequence = X_test[-1]  # Last sequence from the test set\n",
        "\n",
        "for _ in range(30):  # Predict for the next 30 days\n",
        "    prediction = model.predict(last_sequence.reshape(1, sequence_length, 1))\n",
        "    future_predictions.append(prediction)\n",
        "\n",
        "    # Update the last_sequence with the new prediction and remove the first value\n",
        "    last_sequence = np.append(last_sequence[1:], prediction)\n",
        "\n",
        "# Convert the list of future predictions to a numpy array\n",
        "future_predictions = np.array(future_predictions).reshape(-1, 1)\n",
        "\n",
        "# Inverse transform the scaled predictions to get actual stock prices\n",
        "future_predictions = scaler.inverse_transform(future_predictions)\n",
        "\n",
        "# Plot the future predictions with timestamps\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(next_30_days, future_predictions, marker='o', label='Predicted Prices (Next 30 Days)')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.title('Predicted Stock Prices for Next 30 Days')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "id": "6LEn9HRDvZRA",
        "outputId": "6af9416d-4277-480d-892b-36f56b6bc364"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 65ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 62ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly\n",
        "import plotly.express as px\n",
        "\n",
        "# Create a DataFrame for the predicted prices and timestamps\n",
        "future_df = pd.DataFrame({'Date': next_30_days, 'Predicted Price': future_predictions.flatten()})\n",
        "\n",
        "# Create an interactive line chart using Plotly Express\n",
        "fig = px.line(future_df, x='Date', y='Predicted Price', title='Predicted Stock Prices for Next 30 Days')\n",
        "fig.update_xaxes(tickangle=45)  # Rotate x-axis labels for better readability\n",
        "fig.update_layout(showlegend=True)\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "q7jXqIT5wjOZ",
        "outputId": "33ccc803-f3a5-4f24-88a4-c60e1f76bb33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"80295356-b2e4-41be-b652-50fb2f55db98\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"80295356-b2e4-41be-b652-50fb2f55db98\")) {                    Plotly.newPlot(                        \"80295356-b2e4-41be-b652-50fb2f55db98\",                        [{\"hovertemplate\":\"Date=%{x}\\u003cbr\\u003ePredicted Price=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[\"2023-08-27T21:02:39.209986\",\"2023-08-28T21:02:39.209986\",\"2023-08-29T21:02:39.209986\",\"2023-08-30T21:02:39.209986\",\"2023-08-31T21:02:39.209986\",\"2023-09-01T21:02:39.209986\",\"2023-09-02T21:02:39.209986\",\"2023-09-03T21:02:39.209986\",\"2023-09-04T21:02:39.209986\",\"2023-09-05T21:02:39.209986\",\"2023-09-06T21:02:39.209986\",\"2023-09-07T21:02:39.209986\",\"2023-09-08T21:02:39.209986\",\"2023-09-09T21:02:39.209986\",\"2023-09-10T21:02:39.209986\",\"2023-09-11T21:02:39.209986\",\"2023-09-12T21:02:39.209986\",\"2023-09-13T21:02:39.209986\",\"2023-09-14T21:02:39.209986\",\"2023-09-15T21:02:39.209986\",\"2023-09-16T21:02:39.209986\",\"2023-09-17T21:02:39.209986\",\"2023-09-18T21:02:39.209986\",\"2023-09-19T21:02:39.209986\",\"2023-09-20T21:02:39.209986\",\"2023-09-21T21:02:39.209986\",\"2023-09-22T21:02:39.209986\",\"2023-09-23T21:02:39.209986\",\"2023-09-24T21:02:39.209986\",\"2023-09-25T21:02:39.209986\"],\"xaxis\":\"x\",\"y\":[131.3065643310547,131.68850708007812,131.82179260253906,131.8673858642578,131.88624572753906,131.9017333984375,131.9224395751953,131.95083618164062,131.98675537109375,132.02891540527344,132.07569885253906,132.12542724609375,132.1766815185547,132.22825622558594,132.27931213378906,132.32923889160156,132.377685546875,132.4244842529297,132.4696044921875,132.51315307617188,132.55523681640625,132.5960235595703,132.6356964111328,132.67442321777344,132.71231079101562,132.74952697753906,132.78616333007812,132.822265625,132.85792541503906,132.89317321777344],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Date\"},\"tickangle\":45},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Predicted Price\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Predicted Stock Prices for Next 30 Days\"},\"showlegend\":true},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('80295356-b2e4-41be-b652-50fb2f55db98');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        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",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IBxNHVSvySVK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}