Stock_Market_predictor.ipynb

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      "source": [
        "import yfinance as yf\n"
      ]
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        "sp500 = yf.Ticker(\"^GSPC\")"
      ],
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        "sp500 = sp500.history(period = \"max\")"
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
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              "DatetimeIndex(['1927-12-30 00:00:00-05:00', '1928-01-03 00:00:00-05:00',\n",
              "               '1928-01-04 00:00:00-05:00', '1928-01-05 00:00:00-05:00',\n",
              "               '1928-01-06 00:00:00-05:00', '1928-01-09 00:00:00-05:00',\n",
              "               '1928-01-10 00:00:00-05:00', '1928-01-11 00:00:00-05:00',\n",
              "               '1928-01-12 00:00:00-05:00', '1928-01-13 00:00:00-05:00',\n",
              "               ...\n",
              "               '2023-07-26 00:00:00-04:00', '2023-07-27 00:00:00-04:00',\n",
              "               '2023-07-28 00:00:00-04:00', '2023-07-31 00:00:00-04:00',\n",
              "               '2023-08-01 00:00:00-04:00', '2023-08-02 00:00:00-04:00',\n",
              "               '2023-08-03 00:00:00-04:00', '2023-08-04 00:00:00-04:00',\n",
              "               '2023-08-07 00:00:00-04:00', '2023-08-08 00:00:00-04:00'],\n",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del sp500[\"Dividends\"]\n",
        "del sp500[\"Stock Splits\"]"
      ],
      "metadata": {
        "id": "gc437CEgUbUk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Tomorrow\"] = sp500[\"Close\"].shift(-1)\n",
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZXWfBhnvW4dk",
        "outputId": "03d87672-c84f-4017-b9a1-5038c9bb62a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
              "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-08-02 00:00:00-04:00  4550.930176  4550.930176  4505.750000  4513.390137   \n",
              "2023-08-03 00:00:00-04:00  4494.270020  4519.490234  4485.540039  4501.890137   \n",
              "2023-08-04 00:00:00-04:00  4513.959961  4540.339844  4474.549805  4478.029785   \n",
              "2023-08-07 00:00:00-04:00  4491.580078  4519.839844  4491.149902  4518.439941   \n",
              "2023-08-08 00:00:00-04:00  4498.029785  4503.310059  4464.390137  4499.379883   \n",
              "\n",
              "                               Volume     Tomorrow  \n",
              "Date                                                \n",
              "1927-12-30 00:00:00-05:00           0    17.760000  \n",
              "1928-01-03 00:00:00-05:00           0    17.719999  \n",
              "1928-01-04 00:00:00-05:00           0    17.549999  \n",
              "1928-01-05 00:00:00-05:00           0    17.660000  \n",
              "1928-01-06 00:00:00-05:00           0    17.500000  \n",
              "...                               ...          ...  \n",
              "2023-08-02 00:00:00-04:00  4270710000  4501.890137  \n",
              "2023-08-03 00:00:00-04:00  4149120000  4478.029785  \n",
              "2023-08-04 00:00:00-04:00  4143310000  4518.439941  \n",
              "2023-08-07 00:00:00-04:00  3493920000  4499.379883  \n",
              "2023-08-08 00:00:00-04:00  3884910000          NaN  \n",
              "\n",
              "[24015 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-96a4c883-58fb-434f-8eed-b505aa024f28\">\n",
              "    <div 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",
              "      <th>Tomorrow</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1927-12-30 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.760000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-03 00:00:00-05:00</th>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>17.760000</td>\n",
              "      <td>0</td>\n",
              "      <td>17.719999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-04 00:00:00-05:00</th>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.549999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-05 00:00:00-05:00</th>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>0</td>\n",
              "      <td>17.660000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-06 00:00:00-05:00</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
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              "      <td>17.500000</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-08-02 00:00:00-04:00</th>\n",
              "      <td>4550.930176</td>\n",
              "      <td>4550.930176</td>\n",
              "      <td>4505.750000</td>\n",
              "      <td>4513.390137</td>\n",
              "      <td>4270710000</td>\n",
              "      <td>4501.890137</td>\n",
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              "    <tr>\n",
              "      <th>2023-08-03 00:00:00-04:00</th>\n",
              "      <td>4494.270020</td>\n",
              "      <td>4519.490234</td>\n",
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              "      <td>4149120000</td>\n",
              "      <td>4478.029785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-08-04 00:00:00-04:00</th>\n",
              "      <td>4513.959961</td>\n",
              "      <td>4540.339844</td>\n",
              "      <td>4474.549805</td>\n",
              "      <td>4478.029785</td>\n",
              "      <td>4143310000</td>\n",
              "      <td>4518.439941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-08-07 00:00:00-04:00</th>\n",
              "      <td>4491.580078</td>\n",
              "      <td>4519.839844</td>\n",
              "      <td>4491.149902</td>\n",
              "      <td>4518.439941</td>\n",
              "      <td>3493920000</td>\n",
              "      <td>4499.379883</td>\n",
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              "    <tr>\n",
              "      <th>2023-08-08 00:00:00-04:00</th>\n",
              "      <td>4498.029785</td>\n",
              "      <td>4503.310059</td>\n",
              "      <td>4464.390137</td>\n",
              "      <td>4499.379883</td>\n",
              "      <td>3884910000</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>24015 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96a4c883-58fb-434f-8eed-b505aa024f28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
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              "\n",
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              "      <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96a4c883-58fb-434f-8eed-b505aa024f28 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-96a4c883-58fb-434f-8eed-b505aa024f28');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Target\"] = (sp500[\"Tomorrow\"] > sp500[\"Close\"]).astype(int)\n",
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b71DZvDXUt0U",
        "outputId": "2247dd26-7761-484d-fe8e-74a0f9658491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
              "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-08-02 00:00:00-04:00  4550.930176  4550.930176  4505.750000  4513.390137   \n",
              "2023-08-03 00:00:00-04:00  4494.270020  4519.490234  4485.540039  4501.890137   \n",
              "2023-08-04 00:00:00-04:00  4513.959961  4540.339844  4474.549805  4478.029785   \n",
              "2023-08-07 00:00:00-04:00  4491.580078  4519.839844  4491.149902  4518.439941   \n",
              "2023-08-08 00:00:00-04:00  4498.029785  4503.310059  4464.390137  4499.379883   \n",
              "\n",
              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
              "1927-12-30 00:00:00-05:00           0    17.760000       1  \n",
              "1928-01-03 00:00:00-05:00           0    17.719999       0  \n",
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              "1928-01-06 00:00:00-05:00           0    17.500000       0  \n",
              "...                               ...          ...     ...  \n",
              "2023-08-02 00:00:00-04:00  4270710000  4501.890137       0  \n",
              "2023-08-03 00:00:00-04:00  4149120000  4478.029785       0  \n",
              "2023-08-04 00:00:00-04:00  4143310000  4518.439941       1  \n",
              "2023-08-07 00:00:00-04:00  3493920000  4499.379883       0  \n",
              "2023-08-08 00:00:00-04:00  3884910000          NaN       0  \n",
              "\n",
              "[24015 rows x 7 columns]"
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              "      <th>2023-08-02 00:00:00-04:00</th>\n",
              "      <td>4550.930176</td>\n",
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              "      <th>2023-08-03 00:00:00-04:00</th>\n",
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              "      <th>2023-08-07 00:00:00-04:00</th>\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d0451676-6364-409c-8cb3-18189baba98d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-49b0710c-0162-4056-b31e-e3f97ae4c63b 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-49b0710c-0162-4056-b31e-e3f97ae4c63b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.loc[\"1990-01-01\":].copy()\n",
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PXs2eD9pWH5k",
        "outputId": "cacf154b-610c-4c5f-e646-f08815248534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1990-01-02 00:00:00-05:00   353.399994   359.690002   351.980011   359.690002   \n",
              "1990-01-03 00:00:00-05:00   359.690002   360.589996   357.890015   358.760010   \n",
              "1990-01-04 00:00:00-05:00   358.760010   358.760010   352.890015   355.670013   \n",
              "1990-01-05 00:00:00-05:00   355.670013   355.670013   351.350006   352.200012   \n",
              "1990-01-08 00:00:00-05:00   352.200012   354.239990   350.540009   353.790009   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-08-02 00:00:00-04:00  4550.930176  4550.930176  4505.750000  4513.390137   \n",
              "2023-08-03 00:00:00-04:00  4494.270020  4519.490234  4485.540039  4501.890137   \n",
              "2023-08-04 00:00:00-04:00  4513.959961  4540.339844  4474.549805  4478.029785   \n",
              "2023-08-07 00:00:00-04:00  4491.580078  4519.839844  4491.149902  4518.439941   \n",
              "2023-08-08 00:00:00-04:00  4498.029785  4503.310059  4464.390137  4499.379883   \n",
              "\n",
              "                               Volume     Tomorrow  Target  \n",
              "Date                                                        \n",
              "1990-01-02 00:00:00-05:00   162070000   358.760010       0  \n",
              "1990-01-03 00:00:00-05:00   192330000   355.670013       0  \n",
              "1990-01-04 00:00:00-05:00   177000000   352.200012       0  \n",
              "1990-01-05 00:00:00-05:00   158530000   353.790009       1  \n",
              "1990-01-08 00:00:00-05:00   140110000   349.619995       0  \n",
              "...                               ...          ...     ...  \n",
              "2023-08-02 00:00:00-04:00  4270710000  4501.890137       0  \n",
              "2023-08-03 00:00:00-04:00  4149120000  4478.029785       0  \n",
              "2023-08-04 00:00:00-04:00  4143310000  4518.439941       1  \n",
              "2023-08-07 00:00:00-04:00  3493920000  4499.379883       0  \n",
              "2023-08-08 00:00:00-04:00  3884910000          NaN       0  \n",
              "\n",
              "[8465 rows x 7 columns]"
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              "      <th>1990-01-02 00:00:00-05:00</th>\n",
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              "      <td>4501.890137</td>\n",
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              "      <th>2023-08-03 00:00:00-04:00</th>\n",
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              "<p>8465 rows × 7 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57220c48-7da4-48a3-9acd-e4351ebb99c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "              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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-12bf529c-5830-46d5-bc78-10237aa78a08 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57220c48-7da4-48a3-9acd-e4351ebb99c2 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-57220c48-7da4-48a3-9acd-e4351ebb99c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "6quWkPIRbucv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=100, min_samples_split= 100, random_state=1)\n",
        "\n",
        "train = sp500.iloc[:-100]\n",
        "test = sp500.iloc[-100:]\n",
        "\n",
        "predictors = [\"Close\", \"Volume\", \"Open\", \"High\", \"Low\"]\n",
        "model.fit(train[predictors], train[\"Target\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "CPc_I1JtccDV",
        "outputId": "b5222a26-6c1f-4923-d6d1-2fc6b0f25afe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_split=100, random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "\n",
        "preds = model.predict(test[predictors])"
      ],
      "metadata": {
        "id": "kddikntAupbf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "preds = pd.Series(preds, index= test.index)"
      ],
      "metadata": {
        "id": "FyTO5dy0y1lD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(test[\"Target\"], preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GB0i_vf8zQs2",
        "outputId": "755a51f5-4e7f-4c0b-cd2e-384086a52483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5689655172413793"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined = pd.concat([test[\"Target\"], preds], axis = 1)"
      ],
      "metadata": {
        "id": "zKDenFgLzaLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "JPnog0r61jDu",
        "outputId": "90fdf4e8-3f72-4954-9185-e9df3ee12a26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(train, test, predictors, model):\n",
        "  model.fit(train[predictors], train[\"Target\"])\n",
        "  preds = model.predict(test[predictors])\n",
        "  preds = pd.Series(preds, index = test.index, name = \"Predictions\")\n",
        "  combined = pd.concat([test[\"Target\"], preds], axis = 1)\n",
        "  return combined"
      ],
      "metadata": {
        "id": "0KI6hFKs1k7b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def backtest(data, model, predictors, start = 2500, step = 250):\n",
        "  all_predictions = []\n",
        "\n",
        "  for i in range (start, data.shape[0], step):\n",
        "    train = data.iloc[0:i].copy()\n",
        "    test = data.iloc[i:(i+step)].copy()\n",
        "    predictions = predict(train, test, predictors, model)\n",
        "    all_predictions.append(predictions)\n",
        "\n",
        "  return pd.concat(all_predictions)"
      ],
      "metadata": {
        "id": "DLjC0Y9eUImf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp500, model, predictors )"
      ],
      "metadata": {
        "id": "4X63GKuuVd49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYTsP4EJVpWj",
        "outputId": "9bf086c0-76f9-4678-ad4a-5373676cd5e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3433\n",
              "1    2532\n",
              "Name: Predictions, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wt0cFD36YeYC",
        "outputId": "9884df40-6a89-4aa9-afb2-5501ad8b2d5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5296208530805687"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Target\"].value_counts()/predictions.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5xHvKgOoUgd",
        "outputId": "44f6ec69-0726-415e-c008-ef1075b4c0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.533948\n",
              "0    0.466052\n",
              "Name: Target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "horizons = [2, 5, 60, 250, 1000]\n",
        "new_predictors = []\n",
        "\n",
        "for horizon in horizons:\n",
        "  rolling_averages = sp500.rolling(horizon).mean()\n",
        "\n",
        "  ratio_column = f\"Close_ratio_{horizon}\"\n",
        "  sp500[ratio_column] = sp500[\"Close\"]/rolling_averages[\"Close\"]\n",
        "\n",
        "  trend_column = f\"Trend_{horizon}\"\n",
        "  sp500[trend_column] = sp500.shift(1).rolling(horizon).sum()[\"Target\"]\n",
        "\n",
        "  new_predictors += [ratio_column, trend_column]\n"
      ],
      "metadata": {
        "id": "t45K9WqrogWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500=sp500.dropna()"
      ],
      "metadata": {
        "id": "dqlx6X0G1Vr7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mxkt81Qi8fQI",
        "outputId": "6cf2a2de-b153-4efe-af19-488b436fb08b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                  Open         High          Low        Close  \\\n",
              "Date                                                                            \n",
              "1993-12-14 00:00:00-05:00   465.730011   466.119995   462.459991   463.059998   \n",
              "1993-12-15 00:00:00-05:00   463.059998   463.690002   461.839996   461.839996   \n",
              "1993-12-16 00:00:00-05:00   461.859985   463.980011   461.859985   463.339996   \n",
              "1993-12-17 00:00:00-05:00   463.339996   466.380005   463.339996   466.380005   \n",
              "1993-12-20 00:00:00-05:00   466.380005   466.899994   465.529999   465.850006   \n",
              "...                                ...          ...          ...          ...   \n",
              "2023-08-01 00:00:00-04:00  4578.830078  4584.620117  4567.529785  4576.729980   \n",
              "2023-08-02 00:00:00-04:00  4550.930176  4550.930176  4505.750000  4513.390137   \n",
              "2023-08-03 00:00:00-04:00  4494.270020  4519.490234  4485.540039  4501.890137   \n",
              "2023-08-04 00:00:00-04:00  4513.959961  4540.339844  4474.549805  4478.029785   \n",
              "2023-08-07 00:00:00-04:00  4491.580078  4519.839844  4491.149902  4518.439941   \n",
              "\n",
              "                               Volume     Tomorrow  Target  Close_ratio_2  \\\n",
              "Date                                                                        \n",
              "1993-12-14 00:00:00-05:00   275050000   461.839996       0       0.997157   \n",
              "1993-12-15 00:00:00-05:00   331770000   463.339996       1       0.998681   \n",
              "1993-12-16 00:00:00-05:00   284620000   466.380005       1       1.001621   \n",
              "1993-12-17 00:00:00-05:00   363750000   465.850006       0       1.003270   \n",
              "1993-12-20 00:00:00-05:00   255900000   465.299988       0       0.999431   \n",
              "...                               ...          ...     ...            ...   \n",
              "2023-08-01 00:00:00-04:00  4042370000  4513.390137       0       0.998666   \n",
              "2023-08-02 00:00:00-04:00  4270710000  4501.890137       0       0.993032   \n",
              "2023-08-03 00:00:00-04:00  4149120000  4478.029785       0       0.998724   \n",
              "2023-08-04 00:00:00-04:00  4143310000  4518.439941       1       0.997343   \n",
              "2023-08-07 00:00:00-04:00  3493920000  4499.379883       0       1.004492   \n",
              "\n",
              "                           Trend_2  Close_ratio_5  Trend_5  Close_ratio_60  \\\n",
              "Date                                                                         \n",
              "1993-12-14 00:00:00-05:00      1.0       0.996617      1.0        1.000283   \n",
              "1993-12-15 00:00:00-05:00      0.0       0.995899      1.0        0.997329   \n",
              "1993-12-16 00:00:00-05:00      1.0       0.999495      2.0        1.000311   \n",
              "1993-12-17 00:00:00-05:00      2.0       1.004991      3.0        1.006561   \n",
              "1993-12-20 00:00:00-05:00      1.0       1.003784      2.0        1.005120   \n",
              "...                            ...            ...      ...             ...   \n",
              "2023-08-01 00:00:00-04:00      1.0       1.001381      2.0        1.053060   \n",
              "2023-08-02 00:00:00-04:00      0.0       0.989834      2.0        1.036986   \n",
              "2023-08-03 00:00:00-04:00      0.0       0.988853      2.0        1.032905   \n",
              "2023-08-04 00:00:00-04:00      0.0       0.988135      1.0        1.026023   \n",
              "2023-08-07 00:00:00-04:00      1.0       1.000165      1.0        1.033779   \n",
              "\n",
              "                           Trend_60  Close_ratio_250  Trend_250  \\\n",
              "Date                                                              \n",
              "1993-12-14 00:00:00-05:00      32.0         1.028047      127.0   \n",
              "1993-12-15 00:00:00-05:00      32.0         1.025151      126.0   \n",
              "1993-12-16 00:00:00-05:00      32.0         1.028274      127.0   \n",
              "1993-12-17 00:00:00-05:00      32.0         1.034781      128.0   \n",
              "1993-12-20 00:00:00-05:00      32.0         1.033359      128.0   \n",
              "...                             ...              ...        ...   \n",
              "2023-08-01 00:00:00-04:00      36.0         1.127839      121.0   \n",
              "2023-08-02 00:00:00-04:00      35.0         1.111837      120.0   \n",
              "2023-08-03 00:00:00-04:00      34.0         1.108622      120.0   \n",
              "2023-08-04 00:00:00-04:00      34.0         1.102385      120.0   \n",
              "2023-08-07 00:00:00-04:00      34.0         1.111919      121.0   \n",
              "\n",
              "                           Close_ratio_1000  Trend_1000  \n",
              "Date                                                     \n",
              "1993-12-14 00:00:00-05:00          1.176082       512.0  \n",
              "1993-12-15 00:00:00-05:00          1.172676       512.0  \n",
              "1993-12-16 00:00:00-05:00          1.176163       513.0  \n",
              "1993-12-17 00:00:00-05:00          1.183537       514.0  \n",
              "1993-12-20 00:00:00-05:00          1.181856       513.0  \n",
              "...                                     ...         ...  \n",
              "2023-08-01 00:00:00-04:00          1.196973       536.0  \n",
              "2023-08-02 00:00:00-04:00          1.179904       536.0  \n",
              "2023-08-03 00:00:00-04:00          1.176413       535.0  \n",
              "2023-08-04 00:00:00-04:00          1.169678       535.0  \n",
              "2023-08-07 00:00:00-04:00          1.179718       535.0  \n",
              "\n",
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              "      <th>Date</th>\n",
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              "      <th>1993-12-14 00:00:00-05:00</th>\n",
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              "      <td>1.176082</td>\n",
              "      <td>512.0</td>\n",
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              "      <th>1993-12-15 00:00:00-05:00</th>\n",
              "      <td>463.059998</td>\n",
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              "      <td>1.025151</td>\n",
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              "      <td>1.172676</td>\n",
              "      <td>512.0</td>\n",
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              "    <tr>\n",
              "      <th>1993-12-16 00:00:00-05:00</th>\n",
              "      <td>461.859985</td>\n",
              "      <td>463.980011</td>\n",
              "      <td>461.859985</td>\n",
              "      <td>463.339996</td>\n",
              "      <td>284620000</td>\n",
              "      <td>466.380005</td>\n",
              "      <td>1</td>\n",
              "      <td>1.001621</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.999495</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.000311</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.028274</td>\n",
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              "      <td>1.176163</td>\n",
              "      <td>513.0</td>\n",
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              "    <tr>\n",
              "      <th>1993-12-17 00:00:00-05:00</th>\n",
              "      <td>463.339996</td>\n",
              "      <td>466.380005</td>\n",
              "      <td>463.339996</td>\n",
              "      <td>466.380005</td>\n",
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              "      <td>1.006561</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.034781</td>\n",
              "      <td>128.0</td>\n",
              "      <td>1.183537</td>\n",
              "      <td>514.0</td>\n",
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              "    <tr>\n",
              "      <th>1993-12-20 00:00:00-05:00</th>\n",
              "      <td>466.380005</td>\n",
              "      <td>466.899994</td>\n",
              "      <td>465.529999</td>\n",
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              "      <td>1.033359</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8cc058c1-ff6d-40a5-9e7b-64e9faf75c95 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-977d2a5c-e591-4ae4-9999-6dadc390bed5 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-977d2a5c-e591-4ae4-9999-6dadc390bed5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=200, min_samples_split=50, random_state = 1)"
      ],
      "metadata": {
        "id": "KJ56PVuEqWWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(train, test, predictors, model):\n",
        "  model.fit(train[predictors], train[\"Target\"])\n",
        "  preds = model.predict_proba(test[predictors])[:,1]\n",
        "  preds[preds >= 0.6]= 1\n",
        "  preds[preds < 0.6]= 0\n",
        "  preds = pd.Series(preds, index = test.index, name = \"Predictions\")\n",
        "  combined = pd.concat([test[\"Target\"], preds], axis = 1)\n",
        "  return combined"
      ],
      "metadata": {
        "id": "ff3_MMxKsSPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp500, model, new_predictors)"
      ],
      "metadata": {
        "id": "EtgQEy9rssdL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9X3cojKvx2b",
        "outputId": "54a75365-9bac-4348-e0a2-e94f2ff25829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    4145\n",
              "1.0     819\n",
              "Name: Predictions, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCvE6SfB-2sg",
        "outputId": "08f1a3d9-8726-4fea-8a62-1d2710205515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.568986568986569"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WGbI-cZg_mZx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}