online_training_pytorch_rnn.ipynb

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        "<a href=\"https://colab.research.google.com/github/erdult/Fine-Tune-LLM/blob/main/online_training_pytorch_rnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# import numpy as np\n",
        "# import torch\n",
        "# import yfinance as yf\n",
        "# tickers = ['MSFT', 'META', 'AAPL', 'AMZN', 'NFLX', 'GOOG']  # FAANG + MSFT\n",
        "\n",
        "# # Download data for all tickers\n",
        "# data = yf.download(tickers, start='2020-01-01', end=pd.to_datetime('today'))\n",
        "# data\n",
        "\n"
      ],
      "metadata": {
        "id": "RYHzEEKPp1d3"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# all_data = data.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)\n",
        "# all_data\n",
        "# # msft_data = all_data[all_data['Ticker'] == 'MSFT']\n",
        "# # msft_dkata\n",
        "# #all_data['MSFT']\n",
        "# close_prices = data['Adj Close']\n",
        "# close_prices.columns = tickers"
      ],
      "metadata": {
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      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# close_prices['MSFT']"
      ],
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      },
      "execution_count": 3,
      "outputs": []
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            "2.5.1+cu121\n",
            "False\n"
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "import yfinance as yf\n",
        "\n",
        "# Check PyTorch version and CUDA availability\n",
        "print(torch.__version__)\n",
        "print(torch.cuda.is_available())\n",
        "\n",
        "# Pull MSFT prices using yfinance\n",
        "msft_data = yf.download('MSFT', start='2020-01-01', end='2024-12-01')\n",
        "msft_data.to_parquet('msft_data.parquet')\n",
        "#msft_data = pd.read_csv('msft_data.csv', index_col='Date', parse_dates=['Date'])\n",
        "# Prepare the data for time series forecasting\n",
        "msft_data['target'] = msft_data['Close'].shift(-1)  # Shift the close price to create a target for the next time step\n",
        "msft_data.dropna(inplace=True)  # Drop the last row with NaN target"
      ]
    },
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              "Price        Adj Close       Close        High         Low        Open  \\\n",
              "Ticker            MSFT        MSFT        MSFT        MSFT        MSFT   \n",
              "Date                                                                     \n",
              "2020-01-02  153.938202  160.619995  160.729996  158.330002  158.779999   \n",
              "2020-01-03  152.021408  158.619995  159.949997  158.059998  158.320007   \n",
              "2020-01-06  152.414352  159.029999  159.100006  156.509995  157.080002   \n",
              "2020-01-07  151.024689  157.580002  159.669998  157.320007  159.320007   \n",
              "2020-01-08  153.430252  160.089996  160.800003  157.949997  158.929993   \n",
              "\n",
              "Price         Volume      target  \n",
              "Ticker          MSFT              \n",
              "Date                              \n",
              "2020-01-02  22622100  158.619995  \n",
              "2020-01-03  21116200  159.029999  \n",
              "2020-01-06  20813700  157.580002  \n",
              "2020-01-07  21634100  160.089996  \n",
              "2020-01-08  27746500  162.089996  "
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            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "msft_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "sMJCetQ0Sjal",
        "outputId": "be5d51e4-231d-4239-b995-70f68f9f279d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price        Date   Adj Close       Close        High         Low        Open  \\\n",
              "Ticker                   MSFT        MSFT        MSFT        MSFT        MSFT   \n",
              "0      2020-01-02  153.938202  160.619995  160.729996  158.330002  158.779999   \n",
              "1      2020-01-03  152.021408  158.619995  159.949997  158.059998  158.320007   \n",
              "2      2020-01-06  152.414352  159.029999  159.100006  156.509995  157.080002   \n",
              "3      2020-01-07  151.024689  157.580002  159.669998  157.320007  159.320007   \n",
              "4      2020-01-08  153.430252  160.089996  160.800003  157.949997  158.929993   \n",
              "...           ...         ...         ...         ...         ...         ...   \n",
              "1231   2024-11-21  412.869995  412.869995  419.779999  410.290009  419.500000   \n",
              "1232   2024-11-22  417.000000  417.000000  417.399994  411.059998  411.369995   \n",
              "1233   2024-11-25  418.790009  418.790009  421.079987  414.850006  418.380005   \n",
              "1234   2024-11-26  427.989990  427.989990  429.040009  418.850006  419.589996   \n",
              "1235   2024-11-27  422.989990  422.989990  427.230011  422.019989  425.109985   \n",
              "\n",
              "Price     Volume      target  \n",
              "Ticker      MSFT              \n",
              "0       22622100  158.619995  \n",
              "1       21116200  159.029999  \n",
              "2       20813700  157.580002  \n",
              "3       21634100  160.089996  \n",
              "4       27746500  162.089996  \n",
              "...          ...         ...  \n",
              "1231    20780200  417.000000  \n",
              "1232    24814600  418.790009  \n",
              "1233    27691100  427.989990  \n",
              "1234    23458900  422.989990  \n",
              "1235    18332400  423.459991  \n",
              "\n",
              "[1236 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-a3668143-b157-4450-9e44-52c4d344d31f\" class=\"colab-df-container\">\n",
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              "      <th>Price</th>\n",
              "      <th>Date</th>\n",
              "      <th>Adj Close</th>\n",
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              "      <th></th>\n",
              "      <th>MSFT</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>MSFT</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>153.938202</td>\n",
              "      <td>160.619995</td>\n",
              "      <td>160.729996</td>\n",
              "      <td>158.330002</td>\n",
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              "      <td>22622100</td>\n",
              "      <td>158.619995</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>152.021408</td>\n",
              "      <td>158.619995</td>\n",
              "      <td>159.949997</td>\n",
              "      <td>158.059998</td>\n",
              "      <td>158.320007</td>\n",
              "      <td>21116200</td>\n",
              "      <td>159.029999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>152.414352</td>\n",
              "      <td>159.029999</td>\n",
              "      <td>159.100006</td>\n",
              "      <td>156.509995</td>\n",
              "      <td>157.080002</td>\n",
              "      <td>20813700</td>\n",
              "      <td>157.580002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>151.024689</td>\n",
              "      <td>157.580002</td>\n",
              "      <td>159.669998</td>\n",
              "      <td>157.320007</td>\n",
              "      <td>159.320007</td>\n",
              "      <td>21634100</td>\n",
              "      <td>160.089996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>153.430252</td>\n",
              "      <td>160.089996</td>\n",
              "      <td>160.800003</td>\n",
              "      <td>157.949997</td>\n",
              "      <td>158.929993</td>\n",
              "      <td>27746500</td>\n",
              "      <td>162.089996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1231</th>\n",
              "      <td>2024-11-21</td>\n",
              "      <td>412.869995</td>\n",
              "      <td>412.869995</td>\n",
              "      <td>419.779999</td>\n",
              "      <td>410.290009</td>\n",
              "      <td>419.500000</td>\n",
              "      <td>20780200</td>\n",
              "      <td>417.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1232</th>\n",
              "      <td>2024-11-22</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>417.399994</td>\n",
              "      <td>411.059998</td>\n",
              "      <td>411.369995</td>\n",
              "      <td>24814600</td>\n",
              "      <td>418.790009</td>\n",
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              "    <tr>\n",
              "      <th>1233</th>\n",
              "      <td>2024-11-25</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>421.079987</td>\n",
              "      <td>414.850006</td>\n",
              "      <td>418.380005</td>\n",
              "      <td>27691100</td>\n",
              "      <td>427.989990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1234</th>\n",
              "      <td>2024-11-26</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>429.040009</td>\n",
              "      <td>418.850006</td>\n",
              "      <td>419.589996</td>\n",
              "      <td>23458900</td>\n",
              "      <td>422.989990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1235</th>\n",
              "      <td>2024-11-27</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>427.230011</td>\n",
              "      <td>422.019989</td>\n",
              "      <td>425.109985</td>\n",
              "      <td>18332400</td>\n",
              "      <td>423.459991</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1236 rows × 8 columns</p>\n",
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            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "msft_data.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Bi5wAKH7Sjam",
        "outputId": "3b3ba9f8-3685-4f83-b380-3d657122efa8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date   Adj Close       Close        High         Low        Open  \\\n",
              "0 2020-01-02  153.938202  160.619995  160.729996  158.330002  158.779999   \n",
              "1 2020-01-03  152.021408  158.619995  159.949997  158.059998  158.320007   \n",
              "2 2020-01-06  152.414352  159.029999  159.100006  156.509995  157.080002   \n",
              "3 2020-01-07  151.024689  157.580002  159.669998  157.320007  159.320007   \n",
              "4 2020-01-08  153.430252  160.089996  160.800003  157.949997  158.929993   \n",
              "\n",
              "     Volume      target  \n",
              "0  22622100  158.619995  \n",
              "1  21116200  159.029999  \n",
              "2  20813700  157.580002  \n",
              "3  21634100  160.089996  \n",
              "4  27746500  162.089996  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
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              "      <th>Volume</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>153.938202</td>\n",
              "      <td>160.619995</td>\n",
              "      <td>160.729996</td>\n",
              "      <td>158.330002</td>\n",
              "      <td>158.779999</td>\n",
              "      <td>22622100</td>\n",
              "      <td>158.619995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>152.021408</td>\n",
              "      <td>158.619995</td>\n",
              "      <td>159.949997</td>\n",
              "      <td>158.059998</td>\n",
              "      <td>158.320007</td>\n",
              "      <td>21116200</td>\n",
              "      <td>159.029999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>152.414352</td>\n",
              "      <td>159.029999</td>\n",
              "      <td>159.100006</td>\n",
              "      <td>156.509995</td>\n",
              "      <td>157.080002</td>\n",
              "      <td>20813700</td>\n",
              "      <td>157.580002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>151.024689</td>\n",
              "      <td>157.580002</td>\n",
              "      <td>159.669998</td>\n",
              "      <td>157.320007</td>\n",
              "      <td>159.320007</td>\n",
              "      <td>21634100</td>\n",
              "      <td>160.089996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>153.430252</td>\n",
              "      <td>160.089996</td>\n",
              "      <td>160.800003</td>\n",
              "      <td>157.949997</td>\n",
              "      <td>158.929993</td>\n",
              "      <td>27746500</td>\n",
              "      <td>162.089996</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-d06fc492-7bd5-48b1-8ed0-74f26891b4d6');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    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",
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              "  }\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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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-2c6be91b-c67a-4f3d-81fc-545a76e66a1a 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": "msft_data",
              "summary": "{\n  \"name\": \"msft_data\",\n  \"rows\": 1236,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-01-02 00:00:00\",\n        \"max\": \"2024-11-27 00:00:00\",\n        \"num_unique_values\": 1236,\n        \"samples\": [\n          \"2021-03-02 00:00:00\",\n          \"2022-07-05 00:00:00\",\n          \"2024-10-09 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.40541242011943,\n        \"min\": 130.1410369873047,\n        \"max\": 466.7187805175781,\n        \"num_unique_values\": 1228,\n        \"samples\": [\n          210.61904907226562,\n          325.329345703125,\n          216.89772033691406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49878530404825,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          421.5299987792969,\n          299.8699951171875,\n          174.5500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.7238187344636,\n        \"min\": 140.57000732421875,\n        \"max\": 468.3500061035156,\n        \"num_unique_values\": 1185,\n        \"samples\": [\n          256.5400085449219,\n          374.4100036621094,\n          417.3999938964844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.18874787330881,\n        \"min\": 132.52000427246094,\n        \"max\": 464.4599914550781,\n        \"num_unique_values\": 1199,\n        \"samples\": [\n          426.29998779296875,\n          342.1700134277344,\n          181.35000610351562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.59479849219558,\n        \"min\": 137.00999450683594,\n        \"max\": 467.0,\n        \"num_unique_values\": 1208,\n        \"samples\": [\n          182.5399932861328,\n          234.47999572753906,\n          238.47000122070312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12727497,\n        \"min\": 9200800,\n        \"max\": 97012700,\n        \"num_unique_values\": 1232,\n        \"samples\": [\n          35204500,\n          22457000,\n          63354900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49923219397779,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          424.79998779296875,\n          294.29998779296875,\n          174.0500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "msft_data.reset_index(inplace=True)\n",
        "msft_data.columns = msft_data.columns.map(lambda x: x[0] if isinstance(x, tuple) else x)\n",
        "\n",
        "msft_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MOKH1MO9Sjan",
        "outputId": "c4894fa7-f400-4756-d1f9-aab644cf5819"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between target and Close: 1.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Autocorrelation between target and Close: 1.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Returns Correlation between target and Close: -0.18\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Autocorrelation between target and Close: -0.18\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# some EDA\n",
        "# Correlation between target and Close\n",
        "# Plot of target vs Close\n",
        "# Autocorrelation between target and Close\n",
        "# Correlation between target and Close\n",
        "msft_data['return'] = msft_data['Close'].pct_change()\n",
        "msft_data['return_target'] = msft_data['return'].shift(-1)\n",
        "# print(msft_data.head())\n",
        "\n",
        "correlation = msft_data['target'].corr(msft_data['Close'])\n",
        "print(f\"Correlation between target and Close: {correlation:.2f}\")\n",
        "\n",
        "# Plot of target vs Close\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(msft_data['Close'], msft_data['target'])\n",
        "plt.xlabel('Close')\n",
        "plt.ylabel('Target')\n",
        "plt.title('Target vs Close')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Calculate autocorrelation\n",
        "autocorrelation = pd.Series(msft_data['Close']).autocorr(lag=1)\n",
        "print(f\"Autocorrelation between target and Close: {autocorrelation:.2f}\")\n",
        "\n",
        "# Plot autocorrelation\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.acorr(msft_data['Close'], maxlags=20)\n",
        "plt.xlabel('Lag')\n",
        "plt.ylabel('Autocorrelation')\n",
        "plt.title('Autocorrelation of Close')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "## Returns\n",
        "correlation = msft_data['return_target'].corr(msft_data['return'])\n",
        "print(f\"Returns Correlation between target and Close: {correlation:.2f}\")\n",
        "\n",
        "# Plot of target vs Close\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(msft_data['return_target'], msft_data['return'])\n",
        "plt.xlabel('return_target')\n",
        "plt.ylabel('return')\n",
        "plt.title('Return Target vs Return')\n",
        "plt.show()\n",
        "\n",
        "# Calculate autocorrelation\n",
        "autocorrelation = pd.Series(msft_data['return']).autocorr(lag=1)\n",
        "print(f\"Autocorrelation between target and Close: {autocorrelation:.2f}\")\n",
        "\n",
        "# Plot autocorrelation\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.acorr(msft_data['return'].dropna(), maxlags=20)\n",
        "plt.xlabel('Lag')\n",
        "plt.ylabel('Autocorrelation')\n",
        "plt.title('Return Autocorrelation')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "A4Kgayg7Sjao"
      },
      "outputs": [],
      "source": [
        "# last day return gives a little info about the future return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "ZIilEhyfSjao",
        "outputId": "e9e90f92-5095-4c4e-9249-e8bdfb360387"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date   Adj Close       Close        High         Low        Open  \\\n",
              "0    2020-01-02  153.938202  160.619995  160.729996  158.330002  158.779999   \n",
              "1    2020-01-03  152.021408  158.619995  159.949997  158.059998  158.320007   \n",
              "2    2020-01-06  152.414352  159.029999  159.100006  156.509995  157.080002   \n",
              "3    2020-01-07  151.024689  157.580002  159.669998  157.320007  159.320007   \n",
              "4    2020-01-08  153.430252  160.089996  160.800003  157.949997  158.929993   \n",
              "...         ...         ...         ...         ...         ...         ...   \n",
              "1231 2024-11-21  412.869995  412.869995  419.779999  410.290009  419.500000   \n",
              "1232 2024-11-22  417.000000  417.000000  417.399994  411.059998  411.369995   \n",
              "1233 2024-11-25  418.790009  418.790009  421.079987  414.850006  418.380005   \n",
              "1234 2024-11-26  427.989990  427.989990  429.040009  418.850006  419.589996   \n",
              "1235 2024-11-27  422.989990  422.989990  427.230011  422.019989  425.109985   \n",
              "\n",
              "        Volume      target    return  return_target  \n",
              "0     22622100  158.619995       NaN      -0.012452  \n",
              "1     21116200  159.029999 -0.012452       0.002585  \n",
              "2     20813700  157.580002  0.002585      -0.009118  \n",
              "3     21634100  160.089996 -0.009118       0.015928  \n",
              "4     27746500  162.089996  0.015928       0.012493  \n",
              "...        ...         ...       ...            ...  \n",
              "1231  20780200  417.000000 -0.006306       0.010003  \n",
              "1232  24814600  418.790009  0.010003       0.004293  \n",
              "1233  27691100  427.989990  0.004293       0.021968  \n",
              "1234  23458900  422.989990  0.021968      -0.011683  \n",
              "1235  18332400  423.459991 -0.011683            NaN  \n",
              "\n",
              "[1236 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3971b3ce-47a1-45f5-99ba-9e38d597ce31\" 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>Date</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>target</th>\n",
              "      <th>return</th>\n",
              "      <th>return_target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>153.938202</td>\n",
              "      <td>160.619995</td>\n",
              "      <td>160.729996</td>\n",
              "      <td>158.330002</td>\n",
              "      <td>158.779999</td>\n",
              "      <td>22622100</td>\n",
              "      <td>158.619995</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.012452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>152.021408</td>\n",
              "      <td>158.619995</td>\n",
              "      <td>159.949997</td>\n",
              "      <td>158.059998</td>\n",
              "      <td>158.320007</td>\n",
              "      <td>21116200</td>\n",
              "      <td>159.029999</td>\n",
              "      <td>-0.012452</td>\n",
              "      <td>0.002585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>152.414352</td>\n",
              "      <td>159.029999</td>\n",
              "      <td>159.100006</td>\n",
              "      <td>156.509995</td>\n",
              "      <td>157.080002</td>\n",
              "      <td>20813700</td>\n",
              "      <td>157.580002</td>\n",
              "      <td>0.002585</td>\n",
              "      <td>-0.009118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-07</td>\n",
              "      <td>151.024689</td>\n",
              "      <td>157.580002</td>\n",
              "      <td>159.669998</td>\n",
              "      <td>157.320007</td>\n",
              "      <td>159.320007</td>\n",
              "      <td>21634100</td>\n",
              "      <td>160.089996</td>\n",
              "      <td>-0.009118</td>\n",
              "      <td>0.015928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-08</td>\n",
              "      <td>153.430252</td>\n",
              "      <td>160.089996</td>\n",
              "      <td>160.800003</td>\n",
              "      <td>157.949997</td>\n",
              "      <td>158.929993</td>\n",
              "      <td>27746500</td>\n",
              "      <td>162.089996</td>\n",
              "      <td>0.015928</td>\n",
              "      <td>0.012493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1231</th>\n",
              "      <td>2024-11-21</td>\n",
              "      <td>412.869995</td>\n",
              "      <td>412.869995</td>\n",
              "      <td>419.779999</td>\n",
              "      <td>410.290009</td>\n",
              "      <td>419.500000</td>\n",
              "      <td>20780200</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>-0.006306</td>\n",
              "      <td>0.010003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1232</th>\n",
              "      <td>2024-11-22</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>417.399994</td>\n",
              "      <td>411.059998</td>\n",
              "      <td>411.369995</td>\n",
              "      <td>24814600</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>0.010003</td>\n",
              "      <td>0.004293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1233</th>\n",
              "      <td>2024-11-25</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>421.079987</td>\n",
              "      <td>414.850006</td>\n",
              "      <td>418.380005</td>\n",
              "      <td>27691100</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>0.004293</td>\n",
              "      <td>0.021968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1234</th>\n",
              "      <td>2024-11-26</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>429.040009</td>\n",
              "      <td>418.850006</td>\n",
              "      <td>419.589996</td>\n",
              "      <td>23458900</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>0.021968</td>\n",
              "      <td>-0.011683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1235</th>\n",
              "      <td>2024-11-27</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>427.230011</td>\n",
              "      <td>422.019989</td>\n",
              "      <td>425.109985</td>\n",
              "      <td>18332400</td>\n",
              "      <td>423.459991</td>\n",
              "      <td>-0.011683</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1236 rows × 10 columns</p>\n",
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              "summary": "{\n  \"name\": \"msft_data\",\n  \"rows\": 1236,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-01-02 00:00:00\",\n        \"max\": \"2024-11-27 00:00:00\",\n        \"num_unique_values\": 1236,\n        \"samples\": [\n          \"2021-03-02 00:00:00\",\n          \"2022-07-05 00:00:00\",\n          \"2024-10-09 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.40541242011943,\n        \"min\": 130.1410369873047,\n        \"max\": 466.7187805175781,\n        \"num_unique_values\": 1228,\n        \"samples\": [\n          210.61904907226562,\n          325.329345703125,\n          216.89772033691406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49878530404825,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          421.5299987792969,\n          299.8699951171875,\n          174.5500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.7238187344636,\n        \"min\": 140.57000732421875,\n        \"max\": 468.3500061035156,\n        \"num_unique_values\": 1185,\n        \"samples\": [\n          256.5400085449219,\n          374.4100036621094,\n          417.3999938964844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.18874787330881,\n        \"min\": 132.52000427246094,\n        \"max\": 464.4599914550781,\n        \"num_unique_values\": 1199,\n        \"samples\": [\n          426.29998779296875,\n          342.1700134277344,\n          181.35000610351562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.59479849219558,\n        \"min\": 137.00999450683594,\n        \"max\": 467.0,\n        \"num_unique_values\": 1208,\n        \"samples\": [\n          182.5399932861328,\n          234.47999572753906,\n          238.47000122070312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12727497,\n        \"min\": 9200800,\n        \"max\": 97012700,\n        \"num_unique_values\": 1232,\n        \"samples\": [\n          35204500,\n          22457000,\n          63354900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49923219397779,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          424.79998779296875,\n          294.29998779296875,\n          174.0500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"return\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01932178353037212,\n        \"min\": -0.1473903128642552,\n        \"max\": 0.14216888119914373,\n        \"num_unique_values\": 1234,\n        \"samples\": [\n          -0.004937501388903698,\n          0.048108948047047795,\n          0.045858233157831885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"return_target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01932178353037212,\n        \"min\": -0.1473903128642552,\n        \"max\": 0.14216888119914373,\n        \"num_unique_values\": 1234,\n        \"samples\": [\n          -0.004937501388903698,\n          0.048108948047047795,\n          0.045858233157831885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "msft_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "v7QLB1o1Sjap",
        "outputId": "6e935b68-d207-44a7-c9c4-bf18e1239e65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Close      target    return\n",
              "0     160.619995  158.619995       NaN\n",
              "1     158.619995  159.029999 -0.012452\n",
              "2     159.029999  157.580002  0.002585\n",
              "3     157.580002  160.089996 -0.009118\n",
              "4     160.089996  162.089996  0.015928\n",
              "...          ...         ...       ...\n",
              "1231  412.869995  417.000000 -0.006306\n",
              "1232  417.000000  418.790009  0.010003\n",
              "1233  418.790009  427.989990  0.004293\n",
              "1234  427.989990  422.989990  0.021968\n",
              "1235  422.989990  423.459991 -0.011683\n",
              "\n",
              "[1236 rows x 3 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
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              "      <th>return</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>160.619995</td>\n",
              "      <td>158.619995</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>158.619995</td>\n",
              "      <td>159.029999</td>\n",
              "      <td>-0.012452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>159.029999</td>\n",
              "      <td>157.580002</td>\n",
              "      <td>0.002585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>157.580002</td>\n",
              "      <td>160.089996</td>\n",
              "      <td>-0.009118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>160.089996</td>\n",
              "      <td>162.089996</td>\n",
              "      <td>0.015928</td>\n",
              "    </tr>\n",
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              "      <th>1231</th>\n",
              "      <td>412.869995</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1232</th>\n",
              "      <td>417.000000</td>\n",
              "      <td>418.790009</td>\n",
              "      <td>0.010003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1233</th>\n",
              "      <td>418.790009</td>\n",
              "      <td>427.989990</td>\n",
              "      <td>0.004293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1234</th>\n",
              "      <td>427.989990</td>\n",
              "      <td>422.989990</td>\n",
              "      <td>0.021968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1235</th>\n",
              "      <td>422.989990</td>\n",
              "      <td>423.459991</td>\n",
              "      <td>-0.011683</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1236 rows × 3 columns</p>\n",
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              "      async function convertToInteractive(key) {\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\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",
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              "      --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",
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              "  }\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",
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              "    60% {\n",
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              "    80% {\n",
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              "      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-f47e76dd-1900-4fc5-9a6d-1f4428a596a0 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",
              "summary": "{\n  \"name\": \"msft_data[['Close','target','return']]\",\n  \"rows\": 1236,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49878530404825,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          421.5299987792969,\n          299.8699951171875,\n          174.5500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.49923219397779,\n        \"min\": 135.4199981689453,\n        \"max\": 467.55999755859375,\n        \"num_unique_values\": 1203,\n        \"samples\": [\n          424.79998779296875,\n          294.29998779296875,\n          174.0500030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"return\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01932178353037212,\n        \"min\": -0.1473903128642552,\n        \"max\": 0.14216888119914373,\n        \"num_unique_values\": 1234,\n        \"samples\": [\n          -0.004937501388903698,\n          0.048108948047047795,\n          0.045858233157831885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "msft_data[['Close','target','return']]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "msft_data[['Close', 'return']].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZKwdaXe2Sah",
        "outputId": "9889f71c-d5fb-470f-ac8e-6b5e9eff25e7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.60619995e+02,             nan],\n",
              "       [ 1.58619995e+02, -1.24517498e-02],\n",
              "       [ 1.59029999e+02,  2.58481701e-03],\n",
              "       ...,\n",
              "       [ 4.18790009e+02,  4.29258644e-03],\n",
              "       [ 4.27989990e+02,  2.19680066e-02],\n",
              "       [ 4.22989990e+02, -1.16825162e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auuVAkd9Sjaq",
        "outputId": "e0b37f6a-111f-4ae4-da99-aa92569f23d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1234\n",
            "1234\n",
            "Epoch 1, Average MSE: 1.0109\n",
            "Epoch 2, Average MSE: 1.0050\n",
            "Epoch 3, Average MSE: 1.0023\n",
            "Epoch 4, Average MSE: 0.9989\n",
            "Epoch 5, Average MSE: 0.9950\n",
            "Epoch 6, Average MSE: 0.9921\n",
            "Epoch 7, Average MSE: 0.9897\n",
            "Epoch 8, Average MSE: 0.9869\n",
            "Epoch 9, Average MSE: 0.9844\n",
            "Epoch 10, Average MSE: 0.9835\n"
          ]
        }
      ],
      "source": [
        "### PYTORCH WAY\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "\n",
        "# Define the RNN model\n",
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(RNN, self).__init__()\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.rnn = nn.RNN(input_dim, hidden_dim, batch_first=True)\n",
        "        # so input should be created in batch_size x sequence_length x input_dim\n",
        "        # hidden_dim is the internal dimension of network\n",
        "        # next layer is the linear output from hidden_dimension\n",
        "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h0 = torch.zeros(1, x.size(0), self.hidden_dim).to(x.device)\n",
        "        out, _ = self.rnn(x, h0)\n",
        "        out = self.fc(out[:, -1, :])\n",
        "        return out\n",
        "\n",
        "# Initialize the model, optimizer, and loss function\n",
        "model = RNN(input_dim=1, hidden_dim=32, output_dim=1)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "# Set the batch size\n",
        "batch_size = 32\n",
        "sequence_length = 16\n",
        "\n",
        "\n",
        "def create_sequences(data_x, data_y, seq_length):\n",
        "       X = []\n",
        "       y = []\n",
        "       for i in range(len(data_x) - seq_length):\n",
        "           X.append(data_x[i:(i + seq_length)])\n",
        "           y.append(data_y[i + seq_length])  # Target is the next value in the sequence\n",
        "       return np.array(X), np.array(y)\n",
        "\n",
        "seq_length = 16  # Choose an appropriate sequence length\n",
        "\n",
        "# .values create an array where each element is [Close, return] array\n",
        "msft_data_no_na = msft_data[['return', 'return_target', 'Date']].dropna()\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# 2. Create a StandardScaler object\n",
        "scaler_x = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "\n",
        "# 3. Fit the scaler and transform the data\n",
        "scaled_data_x = scaler_x.fit_transform(msft_data_no_na[['return']].values)\n",
        "scaled_data_y = scaler_y.fit_transform(msft_data_no_na[['return_target']].values)\n",
        "\n",
        "print(len(scaled_data_x))\n",
        "print(len(scaled_data_y))\n",
        "\n",
        "X, y = create_sequences(data_x= scaled_data_x,\n",
        "                        data_y = scaled_data_y , seq_length=seq_length)\n",
        "\n",
        "# Convert to PyTorch tensors\n",
        "X = torch.tensor(X, dtype=torch.float32)\n",
        "y = torch.tensor(y, dtype=torch.float32)[:, 0].unsqueeze(1) # Keep only 'target' and reshape\n",
        "\n",
        "from torch.utils.data import DataLoader , TensorDataset\n",
        "dataset = TensorDataset(X, y)\n",
        "dl = DataLoader(dataset, batch_size=batch_size)\n",
        "\n",
        "\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "  # Iterate over the data in batches\n",
        "\n",
        "  # Initialize lists to store predictions and MSE, only last epoch will be kept\n",
        "  predictions_scaled = []\n",
        "  mse_values = []\n",
        "  for i, (batch_x, batch_y) in enumerate(dl):\n",
        "      # i is the batch index, (batch_x, batch_y) are the data\n",
        "      # batch_x and batch_y are already tensors\n",
        "      # if i == 1:\n",
        "        # print(batch_x)\n",
        "        # print(batch_x.size())\n",
        "\n",
        "            # Zero the gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # Forward pass\n",
        "      y_pred = model(batch_x)\n",
        "      loss = loss_fn(y_pred, batch_y)\n",
        "\n",
        "      # Store the predictions\n",
        "      predictions_scaled.append(y_pred)\n",
        "\n",
        "      # Compute the MSE\n",
        "      mse = loss.item()\n",
        "      mse_values.append(mse)\n",
        "\n",
        "\n",
        "\n",
        "      # Backward pass\n",
        "      loss.backward()\n",
        "\n",
        "      # Update the model parameters\n",
        "      optimizer.step()\n",
        "\n",
        "      # Print the MSE at each step\n",
        "      # print(f\"Step {i+1}, MSE: {mse:.4f}\") # Use i+1 for batch index\n",
        "\n",
        "  epoch_loss = np.mean(mse_values)\n",
        "  print(f\"Epoch {epoch + 1}, Average MSE: {epoch_loss:.4f}\")\n",
        "\n",
        "\n",
        "# Inverse transform predictions\n",
        "# predictions = scaler_y.inverse_transform(predictions_scaled.cpu().detach().numpy())\n",
        "\n",
        "# Print the final MSE\n",
        "# print(f\"Final MSE: {np.mean(mse_values):.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert predictions array so that you can plot it\n",
        "print(len(predictions_scaled))\n",
        "print(len(predictions_scaled[0]))\n",
        "print(len(predictions_scaled[38]))\n",
        "# print(predictions[37])\n",
        "# print(38*32+18)\n",
        "# print( sum([len(x) for x in predictions]) )\n",
        "print(predictions_scaled[0])\n",
        "\n",
        "predictions_scaled = torch.cat(predictions_scaled, dim=0)\n",
        "\n",
        "# each prediction has size of 32 which is same as batch size, in each batch there is a sequence of length 16 in it and input size of 2\n",
        "# there are 39 iterations/batches\n",
        "# each batch creates 32 output\n",
        "# last batch is length 18\n",
        "\n",
        "\n",
        "\n",
        "# Inverse transform predictions\n",
        "predictions = scaler_y.inverse_transform(predictions_scaled.cpu().detach().numpy())\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#Get the range of target values used for prediction\n",
        "prediction_start_index = seq_length\n",
        "prediction_end_index = len(msft_data_no_na) - 1 # End index adjusted to account for sequence length and shift\n",
        "\n",
        "# Slice target values to match the prediction range\n",
        "target_values = msft_data_no_na['return_target'][prediction_start_index:prediction_end_index + 1].values # +1 to include the last index\n",
        "\n",
        "# X values for plotting (use date or index for a more informative x-axis)\n",
        "x_values = msft_data_no_na['Date'][prediction_start_index:prediction_end_index + 1] # Using the 'Date' column\n",
        "\n",
        "# Assuming predictions is already inverse transformed\n",
        "predictions_aligned = predictions.flatten() # Flatten predictions to a 1D array\n",
        "\n",
        "# Plot the predictions and target values (aligned)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(x_values, predictions_aligned, label='Predictions', linestyle='--')  # Use x_values for aligned x-axis\n",
        "plt.plot(x_values, target_values, label='Actual Returns')  # Use the same x_values\n",
        "plt.legend()\n",
        "plt.xlabel('Date') # Or other relevant label for your x_values\n",
        "plt.ylabel('Returns')\n",
        "plt.title('Aligned Predictions vs Actual Returns')\n",
        "plt.show()#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kiPvyZesrqFO",
        "outputId": "498c5f28-3dc7-4779-e15e-a67e12c06f65"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "39\n",
            "32\n",
            "2\n",
            "tensor([[ 0.0680],\n",
            "        [ 0.0981],\n",
            "        [-0.0990],\n",
            "        [-0.0382],\n",
            "        [ 0.0257],\n",
            "        [ 0.0223],\n",
            "        [-0.0077],\n",
            "        [-0.2507],\n",
            "        [ 0.2104],\n",
            "        [-0.0710],\n",
            "        [-0.0336],\n",
            "        [-0.1081],\n",
            "        [-0.0350],\n",
            "        [ 0.1735],\n",
            "        [-0.2098],\n",
            "        [ 0.1466],\n",
            "        [-0.1647],\n",
            "        [ 0.1855],\n",
            "        [-0.1280],\n",
            "        [-0.0164],\n",
            "        [-0.0445],\n",
            "        [ 0.0215],\n",
            "        [ 0.0074],\n",
            "        [ 0.1457],\n",
            "        [ 0.0077],\n",
            "        [-0.3151],\n",
            "        [ 0.3739],\n",
            "        [-0.0818],\n",
            "        [-0.1696],\n",
            "        [ 0.1349],\n",
            "        [-0.1555],\n",
            "        [ 0.1406]], grad_fn=<AddmmBackward0>)\n"
          ]
        },
        {
          "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": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Create a class to store model-related objects\n",
        "class TimeSeriesModel:\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, dropout_rate=0.2):\n",
        "        # drop out rate might need to be addded to RNN class\n",
        "        self.model = RNN(input_dim, hidden_dim, output_dim)\n",
        "        self.scaler_x = StandardScaler()\n",
        "        self.scaler_y = StandardScaler()\n",
        "        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001, weight_decay=0.001)\n",
        "        self.loss_fn = nn.MSELoss()\n",
        "\n",
        "    def create_sequences(self, data_x, data_y, seq_length):\n",
        "        # ... (your create_sequences function) ...\n",
        "        X = []\n",
        "        y = []\n",
        "        for i in range(len(data_x) - seq_length):\n",
        "          X.append(data_x[i:(i + seq_length)])\n",
        "          y.append(data_y[i + seq_length])  # Target is the next value in the sequence\n",
        "        return np.array(X), np.array(y)\n",
        "\n",
        "    def fit(self, train_data, test_data, val_data, seq_length, batch_size=32, num_epochs=10):\n",
        "        # # 1. Split data into train, validation, and test sets\n",
        "        # train_data, test_data = train_test_split(data, test_size=0.2, shuffle=False)\n",
        "        # train_data, val_data = train_test_split(train_data, test_size=0.2, shuffle=False)\n",
        "\n",
        "        # 2. Scale data\n",
        "        self.scaler_x.fit(train_data[['return']].values)\n",
        "        self.scaler_y.fit(train_data[['return_target']].values)\n",
        "\n",
        "        scaled_data_x_train = self.scaler_x.transform(train_data[['return']].values)\n",
        "        scaled_data_y_train = self.scaler_y.transform(train_data[['return_target']].values)\n",
        "\n",
        "        # 3. Create DataLoaders\n",
        "        X_train, y_train = self.create_sequences(scaled_data_x_train, scaled_data_y_train, seq_length)\n",
        "        X_train = torch.tensor(X_train, dtype=torch.float32)\n",
        "        y_train = torch.tensor(y_train, dtype=torch.float32)[:, 0].unsqueeze(1)\n",
        "        train_dataset = TensorDataset(X_train, y_train)\n",
        "        train_dl = DataLoader(train_dataset, batch_size=batch_size)\n",
        "\n",
        "        # ... (similarly create val_dl and test_dl) ...\n",
        "\n",
        "        # 4. Training loop (with early stopping if needed)\n",
        "        # ... (your training loop with epochs, loss calculation, early stopping, etc.) ...\n",
        "        num_epochs = 10\n",
        "        for epoch in range(num_epochs):\n",
        "          # Iterate over the data in batches\n",
        "\n",
        "          # Initialize lists to store predictions and MSE, only last epoch will be kept\n",
        "          predictions_scaled = []\n",
        "          mse_values = []\n",
        "          for i, (batch_x, batch_y) in enumerate(dl):\n",
        "              # i is the batch index, (batch_x, batch_y) are the data\n",
        "              # batch_x and batch_y are already tensors\n",
        "              # if i == 1:\n",
        "                # print(batch_x)\n",
        "                # print(batch_x.size())\n",
        "\n",
        "              # Zero the gradients\n",
        "              self.optimizer.zero_grad()\n",
        "\n",
        "\n",
        "\n",
        "              # Forward pass\n",
        "              y_pred = self.model(batch_x)\n",
        "              loss = self.loss_fn(y_pred, batch_y)\n",
        "\n",
        "              # Store the predictions\n",
        "              predictions_scaled.append(y_pred)\n",
        "\n",
        "              # Compute the MSE\n",
        "              mse = loss.item()\n",
        "              mse_values.append(mse)\n",
        "\n",
        "\n",
        "\n",
        "              # Backward pass\n",
        "              loss.backward()\n",
        "\n",
        "              # Update the model parameters\n",
        "              self.optimizer.step()\n",
        "\n",
        "              # Print the MSE at each step\n",
        "              # print(f\"Step {i+1}, MSE: {mse:.4f}\") # Use i+1 for batch index\n",
        "\n",
        "          epoch_loss = np.mean(mse_values)\n",
        "          print(f\"Epoch {epoch + 1}, Average MSE: {epoch_loss:.4f}\")\n",
        "\n",
        "\n",
        "        # 5. Return the trained model and other objects\n",
        "        return self  # Return the TimeSeriesModel instance\n",
        "\n",
        "\n",
        "train_data, test_data = train_test_split(msft_data_no_na, test_size=0.2, shuffle=False)\n",
        "train_data, val_data = train_test_split(train_data, test_size=0.2, shuffle=False)\n",
        "\n",
        "fit_model = TimeSeriesModel(input_dim=1, hidden_dim=10, output_dim=1)\n",
        "fit_model.fit(train_data, test_data, val_data, seq_length, batch_size=32, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYOyq9YFmxfL",
        "outputId": "bca4fb59-2e7e-4e2f-cf5c-3f70107c4b0f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Average MSE: 1.0061\n",
            "Epoch 2, Average MSE: 1.0029\n",
            "Epoch 3, Average MSE: 1.0019\n",
            "Epoch 4, Average MSE: 1.0010\n",
            "Epoch 5, Average MSE: 1.0001\n",
            "Epoch 6, Average MSE: 0.9992\n",
            "Epoch 7, Average MSE: 0.9982\n",
            "Epoch 8, Average MSE: 0.9973\n",
            "Epoch 9, Average MSE: 0.9963\n",
            "Epoch 10, Average MSE: 0.9954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.TimeSeriesModel at 0x7c556cc11090>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: generate predictions for test_data, and val_data then concatenate themn\n",
        "\n",
        "def predict(model, data, seq_length, scaler_x, scaler_y):\n",
        "    scaled_data_x = model.scaler_x.transform(data[['return']].values)\n",
        "    X, _ = model.create_sequences(scaled_data_x, scaled_data_x, seq_length) # Dummy y for prediction\n",
        "    X = torch.tensor(X, dtype=torch.float32)\n",
        "    with torch.no_grad():\n",
        "        predictions_scaled = model.model(X)\n",
        "    predictions = model.scaler_y.inverse_transform(predictions_scaled.cpu().detach().numpy())\n",
        "    return predictions\n",
        "\n",
        "# Generate predictions for test and validation data\n",
        "test_predictions = predict(fit_model, test_data, seq_length, fit_model.scaler_x, fit_model.scaler_y)\n",
        "val_predictions = predict(fit_model, val_data, seq_length, fit_model.scaler_x, fit_model.scaler_y)\n",
        "train_predictions = predict(fit_model, train_data, seq_length, fit_model.scaler_x, fit_model.scaler_y)\n",
        "# Concatenate predictions\n",
        "all_predictions = np.concatenate((val_predictions, test_predictions))\n",
        "\n",
        "all_predictions = np.concatenate((train_predictions, all_predictions))"
      ],
      "metadata": {
        "id": "WdD1gK5o8OK0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(all_predictions))\n",
        "print(len(msft_data_no_na))\n",
        "\n",
        "print(len(test_data))\n",
        "print(len(val_data))\n",
        "print(len(train_data))\n",
        "\n",
        "print(len(train_data)+ len(val_data) + len(test_data))\n",
        "\n",
        "print(len(test_predictions))\n",
        "print(len(val_predictions))\n",
        "print(len(train_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvnmmlTx9BI7",
        "outputId": "4f2c16c3-db3b-4c45-f758-d9252d806de4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1186\n",
            "1234\n",
            "247\n",
            "198\n",
            "789\n",
            "1234\n",
            "231\n",
            "182\n",
            "773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Prepare data for plotting ---\n",
        "train_target = train_data['return_target'][seq_length:].values  # Slice target values to match predictions\n",
        "val_target = val_data['return_target'][seq_length:].values\n",
        "test_target = test_data['return_target'][seq_length:].values\n",
        "\n",
        "train_dates = train_data['Date'][seq_length:]  # Slice dates to match predictions\n",
        "val_dates = val_data['Date'][seq_length:]\n",
        "test_dates = test_data['Date'][seq_length:]\n",
        "\n",
        "# --- Plotting ---\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "plt.plot(train_dates, train_target, label='Train Target', color='blue', alpha=0.5)  # Train target\n",
        "plt.plot(train_dates, train_predictions, label='Train Predictions', color='blue', linestyle='--')  # Train predictions\n",
        "\n",
        "plt.plot(val_dates, val_target, label='Validation Target', color='green', alpha=0.5)  # Validation target\n",
        "plt.plot(val_dates, val_predictions, label='Validation Predictions', color='green', linestyle='--')  # Validation predictions\n",
        "\n",
        "plt.plot(test_dates, test_target, label='Test Target', color='red', alpha=0.5)  # Test target\n",
        "plt.plot(test_dates, test_predictions, label='Test Predictions', color='red', linestyle='--')  # Test predictions\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Returns')\n",
        "plt.title('Predictions vs Target Values (Train, Validation, Test)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "Vm-0tHkEFe6T",
        "outputId": "1475bdc4-099c-4b72-c092-5d449a95b0f9"
      },
      "execution_count": 18,
      "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": [
        "# If over fit... now redo the problem by creating train, valid, and test data"
      ],
      "metadata": {
        "id": "NZ4UoX82cIRg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming msft_data_no_na is your DataFrame with 'Close', 'return', and 'target' columns\n",
        "\n",
        "# 1. Split into train and test sets (e.g., 80% train, 20% test)\n",
        "train_data, test_data = train_test_split(msft_data_no_na, test_size=0.2, shuffle=False)\n",
        "\n",
        "# 2. Split train set into train and validation sets (e.g., 80% train, 20% validation)\n",
        "train_data, val_data = train_test_split(train_data, test_size=0.2, shuffle=False)\n",
        "\n",
        "# Now you have:\n",
        "# - train_data: For training your model\n",
        "# - val_data: For evaluating performance during training (and hyperparameter tuning)\n",
        "# - test_data: For final evaluation of your model\n",
        "\n",
        "# --- Create DataLoaders for each set ---\n",
        "# (using the same logic as before, but with the respective datasets)\n",
        "\n",
        "# --- Train DataLoader ---\n",
        "# ... (your existing code to create scaled_data_x, scaled_data_y, X, y, and dl) ...\n",
        "# Use train_data instead of msft_data_no_na\n",
        "\n",
        "# --- Validation DataLoader ---\n",
        "# ... (Similar to train DataLoader, but use val_data) ...\n",
        "\n",
        "# --- Test DataLoader ---\n",
        "# ... (Similar to train DataLoader, but use test_data) ..."
      ],
      "cell_type": "code",
      "metadata": {
        "id": "r8O2tkKdtv8L"
      },
      "execution_count": 20,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}