8 year Model/Close Feature/125/8model_15_23_125.ipynb

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          "text": [
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (4.9.4)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.1.4)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pandas-datareader) (2.32.3)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pandas-datareader) (2024.7.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.23->pandas-datareader) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas-datareader"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data"
      ],
      "metadata": {
        "id": "vJM1k4sqhXix"
      },
      "execution_count": 43,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "import pandas_datareader.data as web\n",
        "start='2015-01-01'\n",
        "end='2023-01-01'\n",
        "\n",
        "df=web.DataReader('JPM','stooq',start,end)\n",
        "df.head()"
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        "id": "zMRrK-fvhYaE",
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      "execution_count": 44,
      "outputs": [
        {
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              "              Open    High     Low   Close     Volume\n",
              "Date                                                 \n",
              "2022-12-30  132.81  134.33  132.44  134.10  9292519.0\n",
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              "2022-12-28  131.85  133.41  131.01  132.46  8827738.0\n",
              "2022-12-27  131.57  132.22  130.55  131.74  5410991.0\n",
              "2022-12-23  130.58  131.44  129.64  131.28  5091905.0"
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          "metadata": {},
          "execution_count": 44
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        "df.tail()"
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      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Open     High      Low    Close        Volume\n",
              "Date                                                        \n",
              "2015-01-08  48.2753  49.0259  48.2753  48.6122  2.107993e+07\n",
              "2015-01-07  48.2143  48.2143  47.2329  47.5512  2.961840e+07\n",
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              "      <td>47.5512</td>\n",
              "      <td>2.961840e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-06</th>\n",
              "      <td>48.8155</td>\n",
              "      <td>48.9040</td>\n",
              "      <td>46.9735</td>\n",
              "      <td>47.4804</td>\n",
              "      <td>3.611625e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>49.9581</td>\n",
              "      <td>50.1369</td>\n",
              "      <td>48.4845</td>\n",
              "      <td>48.7429</td>\n",
              "      <td>2.496834e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>50.4111</td>\n",
              "      <td>50.6861</td>\n",
              "      <td>49.9670</td>\n",
              "      <td>50.3059</td>\n",
              "      <td>1.564852e+07</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-02 00:00:00\",\n        \"max\": \"2015-01-08 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2015-01-07 00:00:00\",\n          \"2015-01-02 00:00:00\",\n          \"2015-01-06 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.99934698078295,\n        \"min\": 48.2143,\n        \"max\": 50.4111,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          48.2143,\n          50.4111,\n          48.8155\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9985878719471809,\n        \"min\": 48.2143,\n        \"max\": 50.6861,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          48.2143,\n          50.6861,\n          48.904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1882330949775795,\n        \"min\": 46.9735,\n        \"max\": 49.967,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          47.2329,\n          49.967,\n          46.9735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1473067057243231,\n        \"min\": 47.4804,\n        \"max\": 50.3059,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          47.5512,\n          50.3059,\n          47.4804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7850736.525093561,\n        \"min\": 15648522.806486,\n        \"max\": 36116246.853648,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          29618398.02121,\n          15648522.806486,\n          36116246.853648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Date becomes column and are removable.\n",
        "df=df.reset_index()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "3BNr8alAhh9l",
        "outputId": "8bdbb23c-e468-46ea-f401-eab304add362"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date    Open    High     Low   Close     Volume\n",
              "0 2022-12-30  132.81  134.33  132.44  134.10  9292519.0\n",
              "1 2022-12-29  132.93  133.27  132.31  133.22  6585228.0\n",
              "2 2022-12-28  131.85  133.41  131.01  132.46  8827738.0\n",
              "3 2022-12-27  131.57  132.22  130.55  131.74  5410991.0\n",
              "4 2022-12-23  130.58  131.44  129.64  131.28  5091905.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0750fa41-9666-4c9d-bd12-de9fcd84381f\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022-12-30</td>\n",
              "      <td>132.81</td>\n",
              "      <td>134.33</td>\n",
              "      <td>132.44</td>\n",
              "      <td>134.10</td>\n",
              "      <td>9292519.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-12-29</td>\n",
              "      <td>132.93</td>\n",
              "      <td>133.27</td>\n",
              "      <td>132.31</td>\n",
              "      <td>133.22</td>\n",
              "      <td>6585228.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-12-28</td>\n",
              "      <td>131.85</td>\n",
              "      <td>133.41</td>\n",
              "      <td>131.01</td>\n",
              "      <td>132.46</td>\n",
              "      <td>8827738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-12-27</td>\n",
              "      <td>131.57</td>\n",
              "      <td>132.22</td>\n",
              "      <td>130.55</td>\n",
              "      <td>131.74</td>\n",
              "      <td>5410991.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-12-23</td>\n",
              "      <td>130.58</td>\n",
              "      <td>131.44</td>\n",
              "      <td>129.64</td>\n",
              "      <td>131.28</td>\n",
              "      <td>5091905.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0750fa41-9666-4c9d-bd12-de9fcd84381f');\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",
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              "  }\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-5d81ffe5-6414-4582-9a39-0032deb72e69 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2014,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-02 00:00:00\",\n        \"max\": \"2022-12-30 00:00:00\",\n        \"num_unique_values\": 2014,\n        \"samples\": [\n          \"2018-03-29 00:00:00\",\n          \"2020-11-27 00:00:00\",\n          \"2021-06-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.578164349334614,\n        \"min\": 43.8946,\n        \"max\": 167.428,\n        \"num_unique_values\": 1869,\n        \"samples\": [\n          49.4267,\n          54.554,\n          90.9879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.899584424803166,\n        \"min\": 44.5862,\n        \"max\": 167.664,\n        \"num_unique_values\": 1876,\n        \"samples\": [\n          96.3982,\n          73.2843,\n          113.364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.245714264735774,\n        \"min\": 40.8391,\n        \"max\": 165.316,\n        \"num_unique_values\": 1871,\n        \"samples\": [\n          49.2793,\n          87.4157,\n          48.4845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.55604713127469,\n        \"min\": 43.7776,\n        \"max\": 166.524,\n        \"num_unique_values\": 1865,\n        \"samples\": [\n          141.481,\n          91.8888,\n          81.859\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7514946.350022055,\n        \"min\": 3220496.0,\n        \"max\": 66405604.543693,\n        \"num_unique_values\": 2014,\n        \"samples\": [\n          15265637.123071,\n          6327492.7304171,\n          12649756.65376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Information\n",
        "\n",
        "This code deletes \"Date\" column from the dataset as they are not useful in our plotting."
      ],
      "metadata": {
        "id": "2k4qKVRihl9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(['Date'],axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "H280mPXGhq8B",
        "outputId": "32b4a022-4eef-42ee-9313-77258a12b522"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Open    High     Low   Close     Volume\n",
              "0  132.81  134.33  132.44  134.10  9292519.0\n",
              "1  132.93  133.27  132.31  133.22  6585228.0\n",
              "2  131.85  133.41  131.01  132.46  8827738.0\n",
              "3  131.57  132.22  130.55  131.74  5410991.0\n",
              "4  130.58  131.44  129.64  131.28  5091905.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c76743e8-c453-40b7-93e9-9f6204e8ec49\" 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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>132.81</td>\n",
              "      <td>134.33</td>\n",
              "      <td>132.44</td>\n",
              "      <td>134.10</td>\n",
              "      <td>9292519.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.93</td>\n",
              "      <td>133.27</td>\n",
              "      <td>132.31</td>\n",
              "      <td>133.22</td>\n",
              "      <td>6585228.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>131.85</td>\n",
              "      <td>133.41</td>\n",
              "      <td>131.01</td>\n",
              "      <td>132.46</td>\n",
              "      <td>8827738.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131.57</td>\n",
              "      <td>132.22</td>\n",
              "      <td>130.55</td>\n",
              "      <td>131.74</td>\n",
              "      <td>5410991.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130.58</td>\n",
              "      <td>131.44</td>\n",
              "      <td>129.64</td>\n",
              "      <td>131.28</td>\n",
              "      <td>5091905.0</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-c76743e8-c453-40b7-93e9-9f6204e8ec49')\"\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-c76743e8-c453-40b7-93e9-9f6204e8ec49 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-c76743e8-c453-40b7-93e9-9f6204e8ec49');\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-fce5c48e-8c38-4985-ba68-a10c4f5a159d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fce5c48e-8c38-4985-ba68-a10c4f5a159d')\"\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-fce5c48e-8c38-4985-ba68-a10c4f5a159d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2014,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.578164349334614,\n        \"min\": 43.8946,\n        \"max\": 167.428,\n        \"num_unique_values\": 1869,\n        \"samples\": [\n          49.4267,\n          54.554,\n          90.9879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.899584424803166,\n        \"min\": 44.5862,\n        \"max\": 167.664,\n        \"num_unique_values\": 1876,\n        \"samples\": [\n          96.3982,\n          73.2843,\n          113.364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.245714264735774,\n        \"min\": 40.8391,\n        \"max\": 165.316,\n        \"num_unique_values\": 1871,\n        \"samples\": [\n          49.2793,\n          87.4157,\n          48.4845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.55604713127469,\n        \"min\": 43.7776,\n        \"max\": 166.524,\n        \"num_unique_values\": 1865,\n        \"samples\": [\n          141.481,\n          91.8888,\n          81.859\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7514946.350022055,\n        \"min\": 3220496.0,\n        \"max\": 66405604.543693,\n        \"num_unique_values\": 2014,\n        \"samples\": [\n          15265637.123071,\n          6327492.7304171,\n          12649756.65376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "fig=plt.figure(figsize=(10,6))\n",
        "fig.patch.set_facecolor('#42A5F5')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#C7E1F4')\n",
        "\n",
        "plt.plot(df.Close, '#FF8F00', label='Closing Price')\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#BDBDBD')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "m1lS1azghv6g",
        "outputId": "cbae1b96-fa16-45b3-dc72-b651e9aad741"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "F5bO77Fwh0TZ",
        "outputId": "61c8b598-5ce6-4aee-984a-dd2772af699a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Open      High       Low     Close        Volume\n",
              "0     132.8100  134.3300  132.4400  134.1000  9.292519e+06\n",
              "1     132.9300  133.2700  132.3100  133.2200  6.585228e+06\n",
              "2     131.8500  133.4100  131.0100  132.4600  8.827738e+06\n",
              "3     131.5700  132.2200  130.5500  131.7400  5.410991e+06\n",
              "4     130.5800  131.4400  129.6400  131.2800  5.091905e+06\n",
              "...        ...       ...       ...       ...           ...\n",
              "2009   48.2753   49.0259   48.2753   48.6122  2.107993e+07\n",
              "2010   48.2143   48.2143   47.2329   47.5512  2.961840e+07\n",
              "2011   48.8155   48.9040   46.9735   47.4804  3.611625e+07\n",
              "2012   49.9581   50.1369   48.4845   48.7429  2.496834e+07\n",
              "2013   50.4111   50.6861   49.9670   50.3059  1.564852e+07\n",
              "\n",
              "[2014 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dca6e500-7de5-413a-a7f4-c3a182b966be\" 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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>132.8100</td>\n",
              "      <td>134.3300</td>\n",
              "      <td>132.4400</td>\n",
              "      <td>134.1000</td>\n",
              "      <td>9.292519e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.9300</td>\n",
              "      <td>133.2700</td>\n",
              "      <td>132.3100</td>\n",
              "      <td>133.2200</td>\n",
              "      <td>6.585228e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>131.8500</td>\n",
              "      <td>133.4100</td>\n",
              "      <td>131.0100</td>\n",
              "      <td>132.4600</td>\n",
              "      <td>8.827738e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131.5700</td>\n",
              "      <td>132.2200</td>\n",
              "      <td>130.5500</td>\n",
              "      <td>131.7400</td>\n",
              "      <td>5.410991e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>130.5800</td>\n",
              "      <td>131.4400</td>\n",
              "      <td>129.6400</td>\n",
              "      <td>131.2800</td>\n",
              "      <td>5.091905e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>48.2753</td>\n",
              "      <td>49.0259</td>\n",
              "      <td>48.2753</td>\n",
              "      <td>48.6122</td>\n",
              "      <td>2.107993e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>48.2143</td>\n",
              "      <td>48.2143</td>\n",
              "      <td>47.2329</td>\n",
              "      <td>47.5512</td>\n",
              "      <td>2.961840e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>48.8155</td>\n",
              "      <td>48.9040</td>\n",
              "      <td>46.9735</td>\n",
              "      <td>47.4804</td>\n",
              "      <td>3.611625e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>49.9581</td>\n",
              "      <td>50.1369</td>\n",
              "      <td>48.4845</td>\n",
              "      <td>48.7429</td>\n",
              "      <td>2.496834e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>50.4111</td>\n",
              "      <td>50.6861</td>\n",
              "      <td>49.9670</td>\n",
              "      <td>50.3059</td>\n",
              "      <td>1.564852e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2014 rows × 5 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2014,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.578164349334614,\n        \"min\": 43.8946,\n        \"max\": 167.428,\n        \"num_unique_values\": 1869,\n        \"samples\": [\n          49.4267,\n          54.554,\n          90.9879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.899584424803166,\n        \"min\": 44.5862,\n        \"max\": 167.664,\n        \"num_unique_values\": 1876,\n        \"samples\": [\n          96.3982,\n          73.2843,\n          113.364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.245714264735774,\n        \"min\": 40.8391,\n        \"max\": 165.316,\n        \"num_unique_values\": 1871,\n        \"samples\": [\n          49.2793,\n          87.4157,\n          48.4845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.55604713127469,\n        \"min\": 43.7776,\n        \"max\": 166.524,\n        \"num_unique_values\": 1865,\n        \"samples\": [\n          141.481,\n          91.8888,\n          81.859\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7514946.350022055,\n        \"min\": 3220496.0,\n        \"max\": 66405604.543693,\n        \"num_unique_values\": 2014,\n        \"samples\": [\n          15265637.123071,\n          6327492.7304171,\n          12649756.65376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ma100 is created for finding the mean of the 100 values and displaying them.\n",
        "# for that rolling() function is used.\n",
        "\n",
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "faAZdi26h09v",
        "outputId": "c9c8d1ca-e83c-4a51-d074-6f059e207c96"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2009    49.436667\n",
              "2010    49.377171\n",
              "2011    49.316465\n",
              "2012    49.270830\n",
              "2013    49.237437\n",
              "Name: Close, Length: 2014, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>49.436667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>49.377171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>49.316465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>49.270830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>49.237437</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2014 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "fig.patch.set_facecolor('#42A5F5')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#C7E1F4')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#BDBDBD')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.plot(df.Close, '#FF8F00', label='Closing Price')\n",
        "plt.plot(ma100,'g--',label='Mean (100 val)') # this is the mean of 100 values\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "XSqsMweeh3OK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ma200=df.Close.rolling(200).mean()\n",
        "ma200"
      ],
      "metadata": {
        "id": "1VKPi3JDiBxa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "ma100=df.Close.rolling(100).mean()\n",
        "ma100\n",
        "\n",
        "fig=plt.figure(figsize=(12,6))\n",
        "fig.patch.set_facecolor('#A59DDF')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#211970')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#626784')\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.plot(df.Close,'#A720C4',label='Closing Price')\n",
        "plt.plot(ma100,'g',label='Mean (100 val)') # this is the mean of 100 values\n",
        "plt.plot(ma200,'r',label='Mean (200 val)') # this is the mean of 200 values\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Closing Price (USD)')"
      ],
      "metadata": {
        "id": "ZSNViN44iCW_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "dd5b9kBuiFUQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting the data into training and testing\n",
        "import pandas as pd\n",
        "\n",
        "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
        "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])\n",
        "\n",
        "print(\"Used data for training: \", data_training.shape)\n",
        "print(\"Used data for testing: \", data_testing.shape)"
      ],
      "metadata": {
        "id": "Eva3cYA6iLaK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_training.head()"
      ],
      "metadata": {
        "id": "14jPCps1iPhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "id": "EJULLQ2MiR2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "data_training_array = scaler.fit_transform(data_training)\n",
        "data_training_array"
      ],
      "metadata": {
        "id": "lgL43jmNiTyx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# The data is dependant on the previous result. (ma100) and (ma200)\n",
        "# So, to calculate the next day value we train the following array.\n",
        "# 100 days are x_train and 101 day is y_train and it goes on changing.\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "\n",
        "for i in range(100,data_training_array.shape[0]):\n",
        "  x_train.append(data_training_array[i-100: i])\n",
        "  y_train.append(data_training_array[i, 0])\n",
        "\n",
        "\n",
        "x_train\n",
        "\n",
        "# Now, converting x_train into numpy value, so that it can be provided to LSTM.\n",
        "x_train,y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "jHiOANLJiVdx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow\n",
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ],
      "metadata": {
        "id": "87TW9Dk6iYsw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Use the recommended import path for LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "x5jjYVEAiaRp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Use the recommended import path for LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.initializers import Orthogonal\n",
        "\n",
        "# ... rest of your code ...\n",
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(LSTM(units=50,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               input_shape=(x_train.shape[1],1),\n",
        "                recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "# Second Layer\n",
        "model.add(LSTM(units=60,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "\n",
        "model.add(LSTM(units=80,\n",
        "               activation ='relu',\n",
        "               return_sequences = True,\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "\n",
        "\n",
        "model.add(LSTM(units=120,\n",
        "               activation ='relu',\n",
        "               recurrent_initializer=\"orthogonal\"\n",
        "               )\n",
        ")\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "\n",
        "# Dense Layer - to connect all the layers\n",
        "# model.add(Dense(units=1))\n",
        "model.add(tf.keras.layers.Dense(units=1, kernel_initializer=Orthogonal(gain=1.0, seed=None)))\n",
        "\n",
        "# Set Optimizer\n",
        "opt = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n"
      ],
      "metadata": {
        "id": "7ZukFn_RicbD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "ZGbFBc1iifIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-addons==0.17.1\n",
        "\n",
        "!pip install --upgrade tensorflow\n",
        "!pip install --upgrade tensorflow-addons"
      ],
      "metadata": {
        "id": "5iGqW8MKioey"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Try importing AbstractRNNCell from tensorflow.python.keras.layers instead\n",
        "\n",
        "from tensorflow.python.keras.layers import AbstractRNNCell\n",
        "\n",
        "# Remove the incorrect import and keep the original correct one\n",
        "# from tensorflow.python.keras.layers import Dense, Dropout, LSTM, BatchNormalization\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization # This line was already in a previous cell, keep it\n",
        "from tensorflow.python.keras.callbacks import TensorBoard\n",
        "from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "from tensorflow.python.keras.initializers import Orthogonal\n",
        "from tensorflow.python.keras.metrics import MeanSquaredError, MeanAbsoluteError\n",
        "\n",
        "model.compile(optimizer=opt,\n",
        "              loss='mean_squared_error',  # Retain MSE for regression\n",
        "              metrics=['accuracy',  # Include accuracy for reference (optional)\n",
        "                       MeanSquaredError(),\n",
        "                       MeanAbsoluteError()])\n",
        "\n",
        "model.fit(x_train,y_train,epochs=125)"
      ],
      "metadata": {
        "id": "Tk_NCIIGisjQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('keras_model_last.h5')"
      ],
      "metadata": {
        "id": "rCw_U9hTu2-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()\n"
      ],
      "metadata": {
        "id": "MWNX4H6Ku5I0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_training.head()"
      ],
      "metadata": {
        "id": "g425UCsru8zO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We need previous 100 days data and we need to comapre and append the list.\n",
        "past_100_days = data_training.tail(100)\n",
        "final_df = pd.concat([past_100_days, data_testing], ignore_index=True)"
      ],
      "metadata": {
        "id": "PG9BXV51u-gG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
      ],
      "metadata": {
        "id": "3vCo2UmrvEF-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ],
      "metadata": {
        "id": "48ONHDxxvGJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "id": "ZrVGdN1AvHzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "\n",
        "for i in range(100,input_data.shape[0]):\n",
        "  x_test.append(input_data[i-100: i])\n",
        "  y_test.append(input_data[i, 0])\n"
      ],
      "metadata": {
        "id": "VspBlu2ivJ5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test,y_test= np.array(x_test),np.array(y_test)\n",
        "\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "lU5dXvbAvV59"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making Predictions\n",
        "\n",
        "y_predicted = model.predict(x_test)\n"
      ],
      "metadata": {
        "id": "YH_EHl6bve0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted.shape"
      ],
      "metadata": {
        "id": "SEsSMctavgZt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "mXEsCFdoviNX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted"
      ],
      "metadata": {
        "id": "LOaSK0pkvkO9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "id": "LyOnHyA8vnk8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor=1/0.02105662\n",
        "y_predicted= y_predicted* scale_factor\n",
        "y_test = y_test * scale_factor\n"
      ],
      "metadata": {
        "id": "OGPWbyG4vpHc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted\n"
      ],
      "metadata": {
        "id": "MLd3d-Z9q4XI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig=plt.figure(figsize=(12,6))\n",
        "fig.patch.set_facecolor('#A59DDF')\n",
        "\n",
        "ax=plt.axes()\n",
        "ax.set_facecolor('#211970')\n",
        "\n",
        "plt.grid(True,linestyle='--',color='#626784')\n",
        "plt.plot(y_test,'#A720C4',label=\"Original price\")\n",
        "plt.plot(y_predicted,'#DFB729',label=\"Predicted price\")\n",
        "\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Stock Price($)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iwvuV3j4vsfo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}