{
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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM8aRetLugGAuJTszQyJcdv"
    },
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      "name": "python3",
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ypqzlIOtPD26"
      },
      "outputs": [],
      "source": [
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = yf.Ticker(\"^GSPC\")"
      ],
      "metadata": {
        "id": "43Wje7uIPJVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.history(period=\"max\")"
      ],
      "metadata": {
        "id": "ywC_nz-pPOS_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "MrtkJJLePVbA",
        "outputId": "54e14aef-4dce-4023-f29f-182a677ac4ba"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                  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",
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              "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",
              "2024-09-09 00:00:00-04:00  5442.069824  5484.200195  5434.490234  5471.049805   \n",
              "2024-09-10 00:00:00-04:00  5490.509766  5497.910156  5441.720215  5495.520020   \n",
              "2024-09-11 00:00:00-04:00  5496.419922  5560.410156  5406.959961  5554.129883   \n",
              "2024-09-12 00:00:00-04:00  5557.479980  5600.709961  5535.500000  5595.759766   \n",
              "2024-09-13 00:00:00-04:00  5603.339844  5636.270020  5601.649902  5626.020020   \n",
              "\n",
              "                               Volume  Dividends  Stock Splits  \n",
              "Date                                                            \n",
              "1927-12-30 00:00:00-05:00           0        0.0           0.0  \n",
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              "2024-09-11 00:00:00-04:00  3839450000        0.0           0.0  \n",
              "2024-09-12 00:00:00-04:00  3655070000        0.0           0.0  \n",
              "2024-09-13 00:00:00-04:00  3500790000        0.0           0.0  \n",
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              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_371fda52-c964-4e38-b98d-67d1c84533f2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sp500');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24292,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-09-13 00:00:00-04:00\",\n        \"num_unique_values\": 24292,\n        \"samples\": [\n          \"1942-12-10 00:00:00-04:00\",\n          \"1973-06-29 00:00:00-04:00\",\n          \"2021-04-08 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1058.35028929706,\n        \"min\": 0.0,\n        \"max\": 5644.08984375,\n        \"num_unique_values\": 13660,\n        \"samples\": [\n          1448.81005859375,\n          353.8299865722656,\n          1133.8399658203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1053.1360389200468,\n        \"min\": 4.400000095367432,\n        \"max\": 5669.669921875,\n        \"num_unique_values\": 16773,\n        \"samples\": [\n          291.4599914550781,\n          337.6300048828125,\n          26.1200008392334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1041.2658931775507,\n        \"min\": 4.400000095367432,\n        \"max\": 5639.02001953125,\n        \"num_unique_values\": 16770,\n        \"samples\": [\n          1298.9000244140625,\n          2099.139892578125,\n          3074.8701171875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047.5810291936132,\n        \"min\": 4.400000095367432,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 16771,\n        \"samples\": [\n          55.45000076293945,\n          181.2899932861328,\n          103.7300033569336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1620351534,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 13017,\n        \"samples\": [\n          9410000,\n          356380000,\n          4760000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dividends\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Splits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEC_TSx3PXwX",
        "outputId": "63dcc036-f994-48c9-ca4c-75082d28380d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "               '2024-08-30 00:00:00-04:00', '2024-09-03 00:00:00-04:00',\n",
              "               '2024-09-04 00:00:00-04:00', '2024-09-05 00:00:00-04:00',\n",
              "               '2024-09-06 00:00:00-04:00', '2024-09-09 00:00:00-04:00',\n",
              "               '2024-09-10 00:00:00-04:00', '2024-09-11 00:00:00-04:00',\n",
              "               '2024-09-12 00:00:00-04:00', '2024-09-13 00:00:00-04:00'],\n",
              "              dtype='datetime64[ns, America/New_York]', name='Date', length=24292, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CLEANING AND VISUALIZING OUR STOCK MARKET DATA:"
      ],
      "metadata": {
        "id": "cLedwVapPcM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500.plot.line(y=\"Close\", use_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "L2PwehEqParX",
        "outputId": "cbb1c42c-663a-4412-89c8-47c28082ab6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "del sp500 [\"Dividends\"]\n",
        "del sp500 [\"Stock Splits\"]"
      ],
      "metadata": {
        "id": "o2iH0jBCPwH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PREDICTING DIRECTIONALITY**"
      ],
      "metadata": {
        "id": "ieBa12lLQ06K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "creating tommorow column and shifting close minus 1 day"
      ],
      "metadata": {
        "id": "dZgSKYyERHHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Tomorrow\"] = sp500[\"Close\"].shift(-1)"
      ],
      "metadata": {
        "id": "5RDNBaXcP4_T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "M-z3SXtBREto",
        "outputId": "3fd08ebc-74bc-4d94-bda6-9ed40ba155a1"
      },
      "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",
              "2024-09-09 00:00:00-04:00  5442.069824  5484.200195  5434.490234  5471.049805   \n",
              "2024-09-10 00:00:00-04:00  5490.509766  5497.910156  5441.720215  5495.520020   \n",
              "2024-09-11 00:00:00-04:00  5496.419922  5560.410156  5406.959961  5554.129883   \n",
              "2024-09-12 00:00:00-04:00  5557.479980  5600.709961  5535.500000  5595.759766   \n",
              "2024-09-13 00:00:00-04:00  5603.339844  5636.270020  5601.649902  5626.020020   \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",
              "2024-09-09 00:00:00-04:00  3825940000  5495.520020  \n",
              "2024-09-10 00:00:00-04:00  3848180000  5554.129883  \n",
              "2024-09-11 00:00:00-04:00  3839450000  5595.759766  \n",
              "2024-09-12 00:00:00-04:00  3655070000  5626.020020  \n",
              "2024-09-13 00:00:00-04:00  3500790000          NaN  \n",
              "\n",
              "[24292 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f60ff9dc-088c-4ffd-ac23-df36c58a1da6\" 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",
              "      <td>0</td>\n",
              "      <td>17.500000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-09 00:00:00-04:00</th>\n",
              "      <td>5442.069824</td>\n",
              "      <td>5484.200195</td>\n",
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              "      <td>5495.520020</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-10 00:00:00-04:00</th>\n",
              "      <td>5490.509766</td>\n",
              "      <td>5497.910156</td>\n",
              "      <td>5441.720215</td>\n",
              "      <td>5495.520020</td>\n",
              "      <td>3848180000</td>\n",
              "      <td>5554.129883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-11 00:00:00-04:00</th>\n",
              "      <td>5496.419922</td>\n",
              "      <td>5560.410156</td>\n",
              "      <td>5406.959961</td>\n",
              "      <td>5554.129883</td>\n",
              "      <td>3839450000</td>\n",
              "      <td>5595.759766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-12 00:00:00-04:00</th>\n",
              "      <td>5557.479980</td>\n",
              "      <td>5600.709961</td>\n",
              "      <td>5535.500000</td>\n",
              "      <td>5595.759766</td>\n",
              "      <td>3655070000</td>\n",
              "      <td>5626.020020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-13 00:00:00-04:00</th>\n",
              "      <td>5603.339844</td>\n",
              "      <td>5636.270020</td>\n",
              "      <td>5601.649902</td>\n",
              "      <td>5626.020020</td>\n",
              "      <td>3500790000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sp500')\"\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24292,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-09-13 00:00:00-04:00\",\n        \"num_unique_values\": 24292,\n        \"samples\": [\n          \"1942-12-10 00:00:00-04:00\",\n          \"1973-06-29 00:00:00-04:00\",\n          \"2021-04-08 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1058.35028929706,\n        \"min\": 0.0,\n        \"max\": 5644.08984375,\n        \"num_unique_values\": 13660,\n        \"samples\": [\n          1448.81005859375,\n          353.8299865722656,\n          1133.8399658203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1053.1360389200468,\n        \"min\": 4.400000095367432,\n        \"max\": 5669.669921875,\n        \"num_unique_values\": 16773,\n        \"samples\": [\n          291.4599914550781,\n          337.6300048828125,\n          26.1200008392334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1041.2658931775507,\n        \"min\": 4.400000095367432,\n        \"max\": 5639.02001953125,\n        \"num_unique_values\": 16770,\n        \"samples\": [\n          1298.9000244140625,\n          2099.139892578125,\n          3074.8701171875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047.5810291936132,\n        \"min\": 4.400000095367432,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 16771,\n        \"samples\": [\n          55.45000076293945,\n          181.2899932861328,\n          103.7300033569336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1620351534,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 13017,\n        \"samples\": [\n          9410000,\n          356380000,\n          4760000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047.5949265248084,\n        \"min\": 4.400000095367432,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 16771,\n        \"samples\": [\n          55.45000076293945,\n          181.2899932861328,\n          103.7300033569336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500[\"Target\"] = (sp500[\"Tomorrow\"] > sp500[\"Close\"]).astype(int)"
      ],
      "metadata": {
        "id": "637wtDteRKvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "g7JRA9CDRYvr",
        "outputId": "7e89437f-f897-4b47-f3bc-0921983f7ba5"
      },
      "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",
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              "2024-09-11 00:00:00-04:00  5496.419922  5560.410156  5406.959961  5554.129883   \n",
              "2024-09-12 00:00:00-04:00  5557.479980  5600.709961  5535.500000  5595.759766   \n",
              "2024-09-13 00:00:00-04:00  5603.339844  5636.270020  5601.649902  5626.020020   \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",
              "1928-01-04 00:00:00-05:00           0    17.549999       0  \n",
              "1928-01-05 00:00:00-05:00           0    17.660000       1  \n",
              "1928-01-06 00:00:00-05:00           0    17.500000       0  \n",
              "...                               ...          ...     ...  \n",
              "2024-09-09 00:00:00-04:00  3825940000  5495.520020       1  \n",
              "2024-09-10 00:00:00-04:00  3848180000  5554.129883       1  \n",
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              "2024-09-12 00:00:00-04:00  3655070000  5626.020020       1  \n",
              "2024-09-13 00:00:00-04:00  3500790000          NaN       0  \n",
              "\n",
              "[24292 rows x 7 columns]"
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              "      <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",
              "      <td>1</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",
              "      <td>0</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",
              "      <td>0</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",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-09 00:00:00-04:00</th>\n",
              "      <td>5442.069824</td>\n",
              "      <td>5484.200195</td>\n",
              "      <td>5434.490234</td>\n",
              "      <td>5471.049805</td>\n",
              "      <td>3825940000</td>\n",
              "      <td>5495.520020</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-10 00:00:00-04:00</th>\n",
              "      <td>5490.509766</td>\n",
              "      <td>5497.910156</td>\n",
              "      <td>5441.720215</td>\n",
              "      <td>5495.520020</td>\n",
              "      <td>3848180000</td>\n",
              "      <td>5554.129883</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-11 00:00:00-04:00</th>\n",
              "      <td>5496.419922</td>\n",
              "      <td>5560.410156</td>\n",
              "      <td>5406.959961</td>\n",
              "      <td>5554.129883</td>\n",
              "      <td>3839450000</td>\n",
              "      <td>5595.759766</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-12 00:00:00-04:00</th>\n",
              "      <td>5557.479980</td>\n",
              "      <td>5600.709961</td>\n",
              "      <td>5535.500000</td>\n",
              "      <td>5595.759766</td>\n",
              "      <td>3655070000</td>\n",
              "      <td>5626.020020</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-13 00:00:00-04:00</th>\n",
              "      <td>5603.339844</td>\n",
              "      <td>5636.270020</td>\n",
              "      <td>5601.649902</td>\n",
              "      <td>5626.020020</td>\n",
              "      <td>3500790000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24292 rows × 7 columns</p>\n",
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              "\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_24fafdda-8afa-4ec3-be50-df213d04b08b\">\n",
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              "        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-generate: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-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sp500')\"\n",
              "            title=\"Generate code using this dataframe.\"\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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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sp500');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 24292,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1927-12-30 00:00:00-05:00\",\n        \"max\": \"2024-09-13 00:00:00-04:00\",\n        \"num_unique_values\": 24292,\n        \"samples\": [\n          \"1942-12-10 00:00:00-04:00\",\n          \"1973-06-29 00:00:00-04:00\",\n          \"2021-04-08 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1058.35028929706,\n        \"min\": 0.0,\n        \"max\": 5644.08984375,\n        \"num_unique_values\": 13660,\n        \"samples\": [\n          1448.81005859375,\n          353.8299865722656,\n          1133.8399658203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1053.1360389200468,\n        \"min\": 4.400000095367432,\n        \"max\": 5669.669921875,\n        \"num_unique_values\": 16773,\n        \"samples\": [\n          291.4599914550781,\n          337.6300048828125,\n          26.1200008392334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1041.2658931775507,\n        \"min\": 4.400000095367432,\n        \"max\": 5639.02001953125,\n        \"num_unique_values\": 16770,\n        \"samples\": [\n          1298.9000244140625,\n          2099.139892578125,\n          3074.8701171875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047.5810291936132,\n        \"min\": 4.400000095367432,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 16771,\n        \"samples\": [\n          55.45000076293945,\n          181.2899932861328,\n          103.7300033569336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1620351534,\n        \"min\": 0,\n        \"max\": 11456230000,\n        \"num_unique_values\": 13017,\n        \"samples\": [\n          9410000,\n          356380000,\n          4760000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047.5949265248084,\n        \"min\": 4.400000095367432,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 16771,\n        \"samples\": [\n          55.45000076293945,\n          181.2899932861328,\n          103.7300033569336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.loc[\"1990-01-01\":].copy()"
      ],
      "metadata": {
        "id": "gn_97wMnRfAk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "^^working from 1990+"
      ],
      "metadata": {
        "id": "XUIkmd_jR99H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "EALfOph5R6v_",
        "outputId": "8eebf03b-9e4d-499f-92c8-e833451faba4"
      },
      "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",
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              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 8742,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1990-01-02 00:00:00-05:00\",\n        \"max\": \"2024-09-13 00:00:00-04:00\",\n        \"num_unique_values\": 8742,\n        \"samples\": [\n          \"2004-09-08 00:00:00-04:00\",\n          \"2019-03-07 00:00:00-05:00\",\n          \"2014-12-30 00:00:00-05:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1195.299412233824,\n        \"min\": 295.45001220703125,\n        \"max\": 5644.08984375,\n        \"num_unique_values\": 8531,\n        \"samples\": [\n          1401.68994140625,\n          3875.22998046875,\n          2888.2900390625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1201.4121839886575,\n        \"min\": 301.45001220703125,\n        \"max\": 5669.669921875,\n        \"num_unique_values\": 8483,\n        \"samples\": [\n          442.5199890136719,\n          3098.06005859375,\n          1229.719970703125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1188.839089062555,\n        \"min\": 294.510009765625,\n        \"max\": 5639.02001953125,\n        \"num_unique_values\": 8538,\n        \"samples\": [\n          935.6599731445312,\n          1089.550048828125,\n          5331.52001953125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1195.4949954690178,\n        \"min\": 295.4599914550781,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 8525,\n        \"samples\": [\n          3431.280029296875,\n          1119.31005859375,\n          2925.510009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1844088422,\n        \"min\": 14990000,\n        \"max\": 11456230000,\n        \"num_unique_values\": 8505,\n        \"samples\": [\n          4685500000,\n          3930910000,\n          1738100000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1195.4827308257697,\n        \"min\": 295.4599914550781,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 8524,\n        \"samples\": [\n          3443.6201171875,\n          1128.1800537109375,\n          2901.610107421875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TRANING AN INITIAL MACHINE LEARNING MODEL**"
      ],
      "metadata": {
        "id": "q1M3f1bhSDbt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "using random forest to train decision trees (this is resistant to overfitting)"
      ],
      "metadata": {
        "id": "NZCrgNA2Skm6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***need to experiment with higher estimator values.\n",
        "min sample split values help avoid overfitting - higher = more accurate but likely to overfit.\n",
        "random state means if we run same model twice, the random numbers generated will be in a predicatable sequence (with setting 1)..."
      ],
      "metadata": {
        "id": "nl91rgwwS3zL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data without shuffling to maintain temporal order\n",
        "train, test = train_test_split(sp500, test_size=0.1, shuffle=False)\n",
        "\n",
        "# Define your predictors and train the Random Forest\n",
        "predictors = ['Close', 'Volume', 'Open', 'High', 'Low']\n",
        "model = RandomForestClassifier(n_estimators=100, min_samples_split=100, random_state=1)\n",
        "model.fit(train[predictors], train[\"Target\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "X03kZNlYSGen",
        "outputId": "eb2d52bb-6b86-4d9f-e29c-8703ea896ccf"
      },
      "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-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "\n",
        "preds = model.predict(test[predictors])"
      ],
      "metadata": {
        "id": "AKbiGtHQUMZ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "preds = pd.Series(preds, index=test.index)"
      ],
      "metadata": {
        "id": "jNnQoX0VUg75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VHhvPE1lVQi0",
        "outputId": "3c2f792f-9b4f-4fe2-ec2f-77857bd9d001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2021-03-24 00:00:00-04:00    1\n",
              "2021-03-25 00:00:00-04:00    0\n",
              "2021-03-26 00:00:00-04:00    0\n",
              "2021-03-29 00:00:00-04:00    0\n",
              "2021-03-30 00:00:00-04:00    0\n",
              "                            ..\n",
              "2024-09-09 00:00:00-04:00    0\n",
              "2024-09-10 00:00:00-04:00    0\n",
              "2024-09-11 00:00:00-04:00    0\n",
              "2024-09-12 00:00:00-04:00    0\n",
              "2024-09-13 00:00:00-04:00    0\n",
              "Length: 875, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-03-24 00:00:00-04:00</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-25 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-26 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-29 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-03-30 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-09 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-10 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-11 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-12 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-13 00:00:00-04:00</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>875 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(test[\"Target\"], preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H98CEqhzUoty",
        "outputId": "5f0bb331-95dd-41ed-e6a1-fc0784cf56c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4714285714285714"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined = pd.concat([test[\"Target\"], preds], axis=1)"
      ],
      "metadata": {
        "id": "t6lQZ9n6ZBt2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "predictions vs actual"
      ],
      "metadata": {
        "id": "ToHUSB-OZL0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "PSD8jlwwZIVo",
        "outputId": "25809be1-b746-4010-88fe-d3bcf0e6d000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BUILDING A BACKTESTING SYSTEM**"
      ],
      "metadata": {
        "id": "EBbktWgUZV-Q"
      }
    },
    {
      "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": "9I-bRY4bZbf7"
      },
      "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": "KzUklTEMZwOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp500, model, predictors)"
      ],
      "metadata": {
        "id": "DUbPCYtLaUDf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "BUQjksnHaZKH",
        "outputId": "c61e369f-c170-4cf0-d16b-5e15aa1e98b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Predictions\n",
              "0    3646\n",
              "1    2596\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Predictions</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbTYrAy3ad9O",
        "outputId": "63626e9a-e5c4-493d-f024-2fd7b728643c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5288906009244992"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Target\"].value_counts() / predictions.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "1SfRbRSfamp4",
        "outputId": "bfb34bac-9a00-4fc4-9855-7e47890290f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Target\n",
              "1    0.535566\n",
              "0    0.464434\n",
              "Name: count, dtype: float64"
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              "      <th>Target</th>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "^^ S&P went up ~53% of days - the algorithm performed a litle worse than the natural progression"
      ],
      "metadata": {
        "id": "UDAq-KXEbL4y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ADDING ADDITIONAL PREDICTORS TO THE MODEL**"
      ],
      "metadata": {
        "id": "AHmmDn_jbVNd"
      }
    },
    {
      "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]"
      ],
      "metadata": {
        "id": "qdrFTc9fbpIw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "^^^ these are the horizons that we want to view rolling means"
      ],
      "metadata": {
        "id": "U3y2aZ8ebq1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sp500 = sp500.dropna()"
      ],
      "metadata": {
        "id": "yCdCwZg5bvl_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sp500"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "WSWryorvcvpX",
        "outputId": "bc8ef26c-f9b3-48da-b92a-8fb1c6e92956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  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",
              "2024-09-06 00:00:00-04:00  5507.330078  5522.470215  5402.620117  5408.419922   \n",
              "2024-09-09 00:00:00-04:00  5442.069824  5484.200195  5434.490234  5471.049805   \n",
              "2024-09-10 00:00:00-04:00  5490.509766  5497.910156  5441.720215  5495.520020   \n",
              "2024-09-11 00:00:00-04:00  5496.419922  5560.410156  5406.959961  5554.129883   \n",
              "2024-09-12 00:00:00-04:00  5557.479980  5600.709961  5535.500000  5595.759766   \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",
              "2024-09-06 00:00:00-04:00  3822800000  5471.049805       1       0.991295   \n",
              "2024-09-09 00:00:00-04:00  3825940000  5495.520020       1       1.005757   \n",
              "2024-09-10 00:00:00-04:00  3848180000  5554.129883       1       1.002231   \n",
              "2024-09-11 00:00:00-04:00  3839450000  5595.759766       1       1.005304   \n",
              "2024-09-12 00:00:00-04:00  3655070000  5626.020020       1       1.003734   \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",
              "2024-09-06 00:00:00-04:00      0.0       0.979459      1.0        0.983825   \n",
              "2024-09-09 00:00:00-04:00      1.0       0.997207      1.0        0.995066   \n",
              "2024-09-10 00:00:00-04:00      2.0       1.002888      2.0        0.999330   \n",
              "2024-09-11 00:00:00-04:00      2.0       1.012325      3.0        1.009613   \n",
              "2024-09-12 00:00:00-04:00      2.0       1.016491      4.0        1.016803   \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",
              "2024-09-06 00:00:00-04:00      34.0         1.084735      143.0   \n",
              "2024-09-09 00:00:00-04:00      34.0         1.096431      143.0   \n",
              "2024-09-10 00:00:00-04:00      34.0         1.100423      144.0   \n",
              "2024-09-11 00:00:00-04:00      35.0         1.111192      144.0   \n",
              "2024-09-12 00:00:00-04:00      35.0         1.118544      144.0   \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",
              "2024-09-06 00:00:00-04:00          1.246199       524.0  \n",
              "2024-09-09 00:00:00-04:00          1.260025       525.0  \n",
              "2024-09-10 00:00:00-04:00          1.265038       526.0  \n",
              "2024-09-11 00:00:00-04:00          1.277872       527.0  \n",
              "2024-09-12 00:00:00-04:00          1.286765       528.0  \n",
              "\n",
              "[7741 rows x 17 columns]"
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              "      <th>Open</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1993-12-14 00:00:00-05:00</th>\n",
              "      <td>465.730011</td>\n",
              "      <td>466.119995</td>\n",
              "      <td>462.459991</td>\n",
              "      <td>463.059998</td>\n",
              "      <td>275050000</td>\n",
              "      <td>461.839996</td>\n",
              "      <td>0</td>\n",
              "      <td>0.997157</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.996617</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000283</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.028047</td>\n",
              "      <td>127.0</td>\n",
              "      <td>1.176082</td>\n",
              "      <td>512.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993-12-15 00:00:00-05:00</th>\n",
              "      <td>463.059998</td>\n",
              "      <td>463.690002</td>\n",
              "      <td>461.839996</td>\n",
              "      <td>461.839996</td>\n",
              "      <td>331770000</td>\n",
              "      <td>463.339996</td>\n",
              "      <td>1</td>\n",
              "      <td>0.998681</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.995899</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.997329</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.025151</td>\n",
              "      <td>126.0</td>\n",
              "      <td>1.172676</td>\n",
              "      <td>512.0</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>127.0</td>\n",
              "      <td>1.176163</td>\n",
              "      <td>513.0</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>363750000</td>\n",
              "      <td>465.850006</td>\n",
              "      <td>0</td>\n",
              "      <td>1.003270</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.004991</td>\n",
              "      <td>3.0</td>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
              "      <td>465.850006</td>\n",
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              "      <td>465.299988</td>\n",
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              "      <td>0.999431</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.003784</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.005120</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.033359</td>\n",
              "      <td>128.0</td>\n",
              "      <td>1.181856</td>\n",
              "      <td>513.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-06 00:00:00-04:00</th>\n",
              "      <td>5507.330078</td>\n",
              "      <td>5522.470215</td>\n",
              "      <td>5402.620117</td>\n",
              "      <td>5408.419922</td>\n",
              "      <td>3822800000</td>\n",
              "      <td>5471.049805</td>\n",
              "      <td>1</td>\n",
              "      <td>0.991295</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.979459</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.983825</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.084735</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1.246199</td>\n",
              "      <td>524.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2024-09-09 00:00:00-04:00</th>\n",
              "      <td>5442.069824</td>\n",
              "      <td>5484.200195</td>\n",
              "      <td>5434.490234</td>\n",
              "      <td>5471.049805</td>\n",
              "      <td>3825940000</td>\n",
              "      <td>5495.520020</td>\n",
              "      <td>1</td>\n",
              "      <td>1.005757</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.997207</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.995066</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.096431</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1.260025</td>\n",
              "      <td>525.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-10 00:00:00-04:00</th>\n",
              "      <td>5490.509766</td>\n",
              "      <td>5497.910156</td>\n",
              "      <td>5441.720215</td>\n",
              "      <td>5495.520020</td>\n",
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              "      <td>1.002888</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.999330</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1.100423</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1.265038</td>\n",
              "      <td>526.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-11 00:00:00-04:00</th>\n",
              "      <td>5496.419922</td>\n",
              "      <td>5560.410156</td>\n",
              "      <td>5406.959961</td>\n",
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              "      <td>1.111192</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1.277872</td>\n",
              "      <td>527.0</td>\n",
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              "    <tr>\n",
              "      <th>2024-09-12 00:00:00-04:00</th>\n",
              "      <td>5557.479980</td>\n",
              "      <td>5600.709961</td>\n",
              "      <td>5535.500000</td>\n",
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              "      <td>5626.020020</td>\n",
              "      <td>1</td>\n",
              "      <td>1.003734</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.016491</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.016803</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1.118544</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1.286765</td>\n",
              "      <td>528.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7741 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa75ca5c-6616-4652-8621-fdf36e04fa44')\"\n",
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              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aa75ca5c-6616-4652-8621-fdf36e04fa44 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-aa75ca5c-6616-4652-8621-fdf36e04fa44');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "            title=\"Suggest charts\"\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\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",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2e36cb9a-e129-4f26-9520-f2b7284d2ed7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_de6973b4-c729-4af3-8657-2d485cbe08f8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sp500')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_de6973b4-c729-4af3-8657-2d485cbe08f8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sp500');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sp500",
              "summary": "{\n  \"name\": \"sp500\",\n  \"rows\": 7741,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1993-12-14 00:00:00-05:00\",\n        \"max\": \"2024-09-12 00:00:00-04:00\",\n        \"num_unique_values\": 7741,\n        \"samples\": [\n          \"2014-11-26 00:00:00-05:00\",\n          \"1996-11-27 00:00:00-05:00\",\n          \"2000-04-27 00:00:00-04:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1174.0138397862747,\n        \"min\": 439.1400146484375,\n        \"max\": 5644.08984375,\n        \"num_unique_values\": 7583,\n        \"samples\": [\n          772.5,\n          633.7899780273438,\n          904.030029296875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1179.801416387707,\n        \"min\": 444.7200012207031,\n        \"max\": 5669.669921875,\n        \"num_unique_values\": 7549,\n        \"samples\": [\n          4234.1201171875,\n          558.7999877929688,\n          3829.820068359375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1167.8878241081109,\n        \"min\": 435.8599853515625,\n        \"max\": 5639.02001953125,\n        \"num_unique_values\": 7574,\n        \"samples\": [\n          2069.280029296875,\n          1120.3399658203125,\n          2904.510009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1174.192723370253,\n        \"min\": 438.9200134277344,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 7576,\n        \"samples\": [\n          1056.75,\n          1895.5799560546875,\n          2073.070068359375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1753928694,\n        \"min\": 14990000,\n        \"max\": 11456230000,\n        \"num_unique_values\": 7534,\n        \"samples\": [\n          3323430000,\n          935900000,\n          4309570000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tomorrow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1174.8875858489534,\n        \"min\": 438.9200134277344,\n        \"max\": 5667.2001953125,\n        \"num_unique_values\": 7576,\n        \"samples\": 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     \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6913330422311094,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close_Ratio_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.012058461062039397,\n        \"min\": 0.897275411607735,\n        \"max\": 1.087229920261824,\n        \"num_unique_values\": 7741,\n        \"samples\": [\n          1.003741251871158,\n          1.0041308929531587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trend_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.058966592101219,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close_Ratio_60\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.042788023467816565,\n        \"min\": 0.7175361421200196,\n        \"max\": 1.1580538106135483,\n        \"num_unique_values\": 7741,\n        \"samples\": [\n          1.043446389554839,\n          1.0714990208720971\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trend_60\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.850350580136147,\n        \"min\": 21.0,\n        \"max\": 43.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          39.0,\n          37.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close_Ratio_250\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09112007952781451,\n        \"min\": 0.5869198849444063,\n        \"max\": 1.2334124132219988,\n        \"num_unique_values\": 7741,\n        \"samples\": [\n          1.0854590315863555,\n          1.1438617486363925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trend_250\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.701911167925516,\n        \"min\": 107.0,\n        \"max\": 157.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          114.0,\n          117.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close_Ratio_1000\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2028958156719407,\n        \"min\": 0.5255676898963808,\n        \"max\": 1.6640838563193754,\n        \"num_unique_values\": 7741,\n        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            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPROVING THE MODEL**"
      ],
      "metadata": {
        "id": "Cb145yqGc94y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(n_estimators=200, min_samples_split=50, random_state=1)"
      ],
      "metadata": {
        "id": "w8Aog_Mvcy4Q"
      },
      "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 >=.6] = 1\n",
        "    preds[preds <.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": "7rE-wi3ldFuD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "^^^ predict-proba method returns a probability of stock price going up/down. the 60% threshold means the model has to be more confident, which reducses number of trading days but increases chances"
      ],
      "metadata": {
        "id": "oFukD65OdKvG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = backtest(sp500, model, new_predictors)"
      ],
      "metadata": {
        "id": "sSCEw6L3dm0_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[\"Predictions\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "iavkWct5dtc0",
        "outputId": "271ef336-f3bc-4fee-8071-65e3d51c3971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Predictions\n",
              "0.0    4395\n",
              "1.0     846\n",
              "Name: count, dtype: int64"
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              "      <th>count</th>\n",
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              "      <th>Predictions</th>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision_score(predictions[\"Target\"], predictions[\"Predictions\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNbdYH0Pd7vd",
        "outputId": "96e57835-5a17-431b-afc3-0daff3ec56ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.574468085106383"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PAPER TRADE:**"
      ],
      "metadata": {
        "id": "OlwWn4ZtJ3K1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "adding RSI to dataframe"
      ],
      "metadata": {
        "id": "wDk5uVQPKz51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to calculate RSI\n",
        "def calculate_rsi(data, window=14):\n",
        "    delta = data['Close'].diff()\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window).mean()\n",
        "    avg_loss = loss.rolling(window=window).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Add RSI to your DataFrame using .loc to avoid SettingWithCopyWarning\n",
        "sp500.loc[:, 'RSI'] = calculate_rsi(sp500)\n",
        "\n",
        "# Verify that RSI is added correctly\n",
        "print(sp500[['Close', 'RSI']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw8pKiMSK1hp",
        "outputId": "5352c39c-2371-4940-f2f6-93e78726c425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                Close  RSI\n",
            "Date                                      \n",
            "1993-12-14 00:00:00-05:00  463.059998  NaN\n",
            "1993-12-15 00:00:00-05:00  461.839996  NaN\n",
            "1993-12-16 00:00:00-05:00  463.339996  NaN\n",
            "1993-12-17 00:00:00-05:00  466.380005  NaN\n",
            "1993-12-20 00:00:00-05:00  465.850006  NaN\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-174ad213062f>:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sp500.loc[:, 'RSI'] = calculate_rsi(sp500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nan_rsi_count = sp500['RSI'].isna().sum()\n",
        "print(nan_rsi_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWmeN7bALmFC",
        "outputId": "173c1fd5-ce68-433c-8bad-6b11d5d24cc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp500['RSI'] = sp500['RSI'].fillna(method='bfill')\n",
        "nan_rsi_count = sp500['RSI'].isna().sum()\n",
        "print(nan_rsi_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YgFdbIoMFjS",
        "outputId": "8acb600c-051f-463d-eb3f-d5e438c8f750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-37-bf28431096e5>:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
            "  sp500['RSI'] = sp500['RSI'].fillna(method='bfill')\n",
            "<ipython-input-37-bf28431096e5>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sp500['RSI'] = sp500['RSI'].fillna(method='bfill')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 1: Filter buy/sell signals based on RSI"
      ],
      "metadata": {
        "id": "QxuJsviFJ7KT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the 'RSI' column from predictions if it already exists\n",
        "if 'RSI' in predictions.columns:\n",
        "    predictions = predictions.drop(columns=['RSI'])\n",
        "\n",
        "# Merge the RSI column into the predictions DataFrame\n",
        "predictions = predictions.merge(sp500[['RSI']], left_index=True, right_index=True)\n",
        "\n",
        "# Define the conditions for buy/sell based on model prediction and RSI\n",
        "buy_condition = (predictions['Predictions'] == 1) & (predictions['RSI'] < 10)\n",
        "sell_condition = (predictions['Predictions'] == 0) & (predictions['RSI'] > 90)\n",
        "\n",
        "# Create new columns for filtered buy/sell signals\n",
        "predictions['Filtered_Signal'] = 0  # Default to 'hold' (no action)\n",
        "predictions.loc[buy_condition, 'Filtered_Signal'] = 1  # Buy signal\n",
        "predictions.loc[sell_condition, 'Filtered_Signal'] = -1  # Sell signal\n",
        "\n",
        "# Display the filtered signals for reference\n",
        "print(predictions[['Predictions', 'Filtered_Signal', 'RSI']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcDVpbRdH4q4",
        "outputId": "3272437a-476b-47b3-ffd8-40a66add451c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Predictions  Filtered_Signal        RSI\n",
            "Date                                                              \n",
            "2003-11-14 00:00:00-05:00          0.0                0  62.762215\n",
            "2003-11-17 00:00:00-05:00          1.0                0  47.619030\n",
            "2003-11-18 00:00:00-05:00          1.0                0  40.633446\n",
            "2003-11-19 00:00:00-05:00          0.0                0  47.244003\n",
            "2003-11-20 00:00:00-05:00          1.0                0  40.156976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_buy = (predictions['Filtered_Signal'] == 1).sum()\n",
        "count_sell = (predictions['Filtered_Signal'] == -1).sum()\n",
        "print(f\"Number of 'Buy' signals: {count_buy}\")\n",
        "print(f\"Number of 'Sell' signals: {count_sell}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_sWb6LbP6w5",
        "outputId": "09373804-8871-46f8-a64d-32f6a265b2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of 'Buy' signals: 5\n",
            "Number of 'Sell' signals: 51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MACD CROSSOVER + ONLY SELL OVERBOUGHT RSI SIMULATION (89% ACCURATE):**"
      ],
      "metadata": {
        "id": "Va8jePG5lyUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the MACD (Moving Average Convergence Divergence)\n",
        "short_window = 12  # Short-term EMA (e.g., 12-period EMA)\n",
        "long_window = 26  # Long-term EMA (e.g., 26-period EMA)\n",
        "signal_window = 9  # Signal line (e.g., 9-period EMA of MACD line)\n",
        "\n",
        "# Calculate MACD and Signal Line\n",
        "sp500['MACD'] = sp500['Close'].ewm(span=short_window, adjust=False).mean() - sp500['Close'].ewm(span=long_window, adjust=False).mean()\n",
        "sp500['Signal_Line'] = sp500['MACD'].ewm(span=signal_window, adjust=False).mean()"
      ],
      "metadata": {
        "id": "aw3yDGSXjvod"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize values\n",
        "initial_balance = 20000  # Starting balance of $20,000\n",
        "balance = initial_balance\n",
        "shares = 0\n",
        "balance_over_time = []\n",
        "\n",
        "# Trade log to track each trade\n",
        "trade_log = pd.DataFrame(columns=['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss'])\n",
        "\n",
        "# Use sp500 DataFrame for this\n",
        "sp500 = sp500.copy()\n",
        "sp500['Buy_Signal'] = (sp500['MACD'] > sp500['Signal_Line'])  # MACD Cross up\n",
        "sp500['Sell_Signal'] = (sp500['MACD'] < sp500['Signal_Line']) & (sp500['RSI'] > 70)  # MACD Cross down + RSI > 70\n",
        "\n",
        "# Initialize columns for tracking\n",
        "sp500['Portfolio_Value'] = np.nan\n",
        "sp500['Balance'] = np.nan\n",
        "sp500['Shares_Held'] = np.nan\n",
        "\n",
        "# Simulate the paper trading\n",
        "for i, row in sp500.iterrows():\n",
        "    close_price = row['Close']\n",
        "    trade_date = row.name\n",
        "    amount_to_invest = 0.1 * balance  # Recalculate 10% of the balance each time\n",
        "\n",
        "    if row['Buy_Signal'] and shares == 0:  # Buy signal - invest 10% of balance\n",
        "        shares_to_buy = amount_to_invest // close_price  # Calculate the number of shares to buy\n",
        "        shares_bought = shares_to_buy  # Whole shares only\n",
        "\n",
        "        if shares_bought > 0:  # Only proceed if we actually bought shares\n",
        "            shares += shares_bought\n",
        "            balance -= shares_bought * close_price  # Update balance based on actual share purchase\n",
        "\n",
        "            # Log the trade\n",
        "            new_trade = pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares_bought],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares * close_price)],\n",
        "                'Profit/Loss': [0]  # No profit or loss on buy\n",
        "            })\n",
        "            trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "    elif row['Sell_Signal'] and shares > 0:  # Sell signal - sell all shares if RSI > 70\n",
        "        sell_value = shares * close_price\n",
        "        balance += sell_value\n",
        "\n",
        "        # Calculate the profit/loss\n",
        "        buy_price = trade_log.iloc[-1]['Price']  # Get the last buy price\n",
        "        profit_loss = (close_price - buy_price) * shares\n",
        "\n",
        "        # Log the trade\n",
        "        new_trade = pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance],\n",
        "            'Profit/Loss': [profit_loss]\n",
        "        })\n",
        "        trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "        shares = 0  # Sell all shares\n",
        "\n",
        "    # Record portfolio value (balance + value of held shares)\n",
        "    portfolio_value = balance + (shares * close_price)\n",
        "    balance_over_time.append(portfolio_value)\n",
        "\n",
        "    # Update the DataFrame with current values\n",
        "    sp500.at[i, 'Portfolio_Value'] = portfolio_value\n",
        "    sp500.at[i, 'Balance'] = balance\n",
        "    sp500.at[i, 'Shares_Held'] = shares\n",
        "\n",
        "# Final balance after all trades\n",
        "final_balance = balance + (shares * sp500['Close'].iloc[-1])  # If holding shares, sell at last close price\n",
        "profit = final_balance - initial_balance\n",
        "\n",
        "# Print final results\n",
        "print(f\"Final Balance: ${final_balance:.2f}\")\n",
        "print(f\"Total Profit: ${profit:.2f}\")\n",
        "\n",
        "# Convert balance over time to pandas series for plotting\n",
        "balance_over_time = pd.Series(balance_over_time, index=sp500.index[:len(balance_over_time)])\n",
        "\n",
        "# Plotting Portfolio Value vs S&P 500 Close Price\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(sp500.index, sp500['Portfolio_Value'], label='Portfolio Value', color='cyan')\n",
        "plt.plot(sp500.index, sp500['Close'], label='S&P 500 Close Price', color='magenta', alpha=0.5)\n",
        "plt.title('Portfolio Value vs S&P 500 Close Price')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value ($)')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Trade summary\n",
        "total_trades = len(trade_log[trade_log['Shares'] > 0])  # Only count trades with shares > 0\n",
        "winning_trades = trade_log[trade_log['Profit/Loss'] > 0]\n",
        "losing_trades = trade_log[trade_log['Profit/Loss'] < 0]\n",
        "print(f\"Total Trades: {total_trades}\")\n",
        "print(f\"Winning Trades: {len(winning_trades)}\")\n",
        "print(f\"Losing Trades: {len(losing_trades)}\")\n",
        "\n",
        "# Plotting Profit/Loss of each trade\n",
        "plt.figure(figsize=(12, 6))\n",
        "trade_log['Profit/Loss'].plot(kind='bar', color=['green' if x > 0 else 'red' for x in trade_log['Profit/Loss']])\n",
        "plt.title('Profit/Loss of Each Trade')\n",
        "plt.ylabel('Profit/Loss ($)')\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Display the trade log with Profit/Loss\n",
        "print(trade_log[['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RBbBmh5JH5CF",
        "outputId": "07d1e111-cc6b-4454-8767-6b5ce5585e4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-7b09048e26ca>:48: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Balance: $22555.64\n",
            "Total Profit: $2555.64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Trades: 70\n",
            "Winning Trades: 31\n",
            "Losing Trades: 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Date Action  Shares        Price       Balance  \\\n",
            "0  1993-12-17 00:00:00-05:00    Buy     4.0   466.380005  18134.479980   \n",
            "1  1995-04-11 00:00:00-04:00   Sell     4.0   505.529999  20156.599976   \n",
            "2  1995-04-27 00:00:00-04:00    Buy     3.0   513.549988  18615.950012   \n",
            "3  1995-06-29 00:00:00-04:00   Sell     3.0   543.869995  20247.559998   \n",
            "4  1995-07-06 00:00:00-04:00    Buy     3.0   553.989990  18585.590027   \n",
            "..                       ...    ...     ...          ...           ...   \n",
            "65 2016-04-27 00:00:00-04:00   Sell     1.0  2095.149902  22388.800171   \n",
            "66 2016-05-25 00:00:00-04:00    Buy     1.0  2090.540039  20298.260132   \n",
            "67 2016-07-29 00:00:00-04:00   Sell     1.0  2173.600098  22471.860229   \n",
            "68 2016-09-22 00:00:00-04:00    Buy     1.0  2177.179932  20294.680298   \n",
            "69 2016-12-22 00:00:00-05:00   Sell     1.0  2260.959961  22555.640259   \n",
            "\n",
            "    Portfolio Value Profit/Loss  \n",
            "0      20000.000000           0  \n",
            "1      20156.599976  156.599976  \n",
            "2      20156.599976           0  \n",
            "3      20247.559998   90.960022  \n",
            "4      20247.559998           0  \n",
            "..              ...         ...  \n",
            "65     22388.800171   16.609863  \n",
            "66     22388.800171           0  \n",
            "67     22471.860229   83.060059  \n",
            "68     22471.860229           0  \n",
            "69     22555.640259   83.780029  \n",
            "\n",
            "[70 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MACD Histogram + ONLY SELL RSI OVERBOUGHT Simulation/Strategy (88%):**"
      ],
      "metadata": {
        "id": "e3Ca7jw8sXMh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate MACD Histogram (MACD - Signal Line)\n",
        "sp500['MACD_Histogram'] = sp500['MACD'] - sp500['Signal_Line']\n",
        "\n",
        "# Now you can rerun the MACD histogram strategy simulation"
      ],
      "metadata": {
        "id": "RRY2rJuXtX4F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize values\n",
        "initial_balance = 20000  # Starting balance of $20,000\n",
        "balance = initial_balance\n",
        "shares = 0\n",
        "amount_to_invest = 0.1 * balance  # Invest 10% of balance on each trade\n",
        "balance_over_time = []\n",
        "\n",
        "# Trade log to track each trade\n",
        "trade_log = pd.DataFrame(columns=['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss'])\n",
        "\n",
        "# Calculate the MACD (Moving Average Convergence Divergence)\n",
        "short_window = 12  # Short-term EMA (e.g., 12-period EMA)\n",
        "long_window = 26  # Long-term EMA (e.g., 26-period EMA)\n",
        "signal_window = 9  # Signal line (e.g., 9-period EMA of MACD line)\n",
        "\n",
        "# Calculate MACD and Signal Line\n",
        "sp500['MACD'] = sp500['Close'].ewm(span=short_window, adjust=False).mean() - sp500['Close'].ewm(span=long_window, adjust=False).mean()\n",
        "sp500['Signal_Line'] = sp500['MACD'].ewm(span=signal_window, adjust=False).mean()\n",
        "\n",
        "# Calculate MACD Histogram (MACD - Signal Line)\n",
        "sp500['MACD_Histogram'] = sp500['MACD'] - sp500['Signal_Line']\n",
        "\n",
        "# Initialize columns for tracking\n",
        "sp500['Portfolio_Value'] = np.nan\n",
        "sp500['Balance'] = np.nan\n",
        "sp500['Shares_Held'] = np.nan\n",
        "\n",
        "# Simulate the paper trading based on MACD Histogram signals\n",
        "for i, row in sp500.iterrows():\n",
        "    close_price = row['Close']\n",
        "    trade_date = row.name\n",
        "\n",
        "    # Calculate amount to invest (10% of the current balance)\n",
        "    amount_to_invest = 0.1 * balance\n",
        "\n",
        "    # Buy when MACD histogram crosses above 0\n",
        "    if row['MACD_Histogram'] > 0 and shares == 0:\n",
        "        shares_to_buy = amount_to_invest // close_price  # Buy whole shares only\n",
        "\n",
        "        # Only execute the trade if shares_to_buy is greater than 0\n",
        "        if shares_to_buy > 0:\n",
        "            shares += shares_to_buy\n",
        "            balance -= shares_to_buy * close_price\n",
        "\n",
        "            # Log the trade\n",
        "            new_trade = pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares * close_price)],\n",
        "                'Profit/Loss': [0]  # Set profit/loss to 0 for now; we'll calculate it after sell\n",
        "            })\n",
        "            trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "    # Sell when MACD histogram crosses below 0 and RSI is greater than 70 (overbought)\n",
        "    elif row['MACD_Histogram'] < 0 and shares > 0 and row['RSI'] > 70:\n",
        "        balance += shares * close_price\n",
        "\n",
        "        # Calculate profit/loss for the trade\n",
        "        buy_price = trade_log.loc[trade_log['Action'] == 'Buy', 'Price'].iloc[-1] if len(trade_log) > 0 else 0\n",
        "        profit_loss = (close_price - buy_price) * shares\n",
        "\n",
        "        # Log the trade\n",
        "        new_trade = pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "                'Shares': [shares],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance],\n",
        "                'Profit/Loss': [profit_loss]\n",
        "            })\n",
        "        trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "        shares = 0  # Reset shares to 0 after selling\n",
        "\n",
        "    # Record portfolio value (balance + value of held shares)\n",
        "    portfolio_value = balance + (shares * close_price)\n",
        "    balance_over_time.append(portfolio_value)\n",
        "\n",
        "    # Update the DataFrame with current values\n",
        "    sp500.at[i, 'Portfolio_Value'] = portfolio_value\n",
        "    sp500.at[i, 'Balance'] = balance\n",
        "    sp500.at[i, 'Shares_Held'] = shares\n",
        "\n",
        "# Final balance after all trades\n",
        "final_balance = balance + (shares * sp500['Close'].iloc[-1])  # If holding shares, sell at last close price\n",
        "profit = final_balance - initial_balance\n",
        "\n",
        "# Print final results\n",
        "print(f\"Final Balance: ${final_balance:.2f}\")\n",
        "print(f\"Total Profit: ${profit:.2f}\")\n",
        "\n",
        "# Convert balance over time to pandas series for plotting\n",
        "balance_over_time = pd.Series(balance_over_time, index=sp500.index[:len(balance_over_time)])\n",
        "\n",
        "# Plotting Portfolio Value and P/L\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(sp500.index, sp500['Portfolio_Value'], label='Portfolio Value', color='cyan')\n",
        "plt.plot(sp500.index, sp500['Close'], label='S&P 500 Close Price', color='magenta', alpha=0.5)\n",
        "plt.title('Portfolio Value vs S&P 500 Close Price')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value ($)')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Display the profit/loss per trade\n",
        "trade_log['Profit/Loss'] = trade_log['Profit/Loss'].fillna(0)  # Ensure no NaN values in P/L\n",
        "trade_log.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Plot Profit/Loss for each trade\n",
        "plt.figure(figsize=(12, 6))\n",
        "trade_log['Profit/Loss'].plot(kind='bar', color=['green' if x > 0 else 'red' for x in trade_log['Profit/Loss']])\n",
        "plt.title('Profit/Loss of Each Trade')\n",
        "plt.ylabel('Profit/Loss ($)')\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Print trade summary\n",
        "total_trades = len(trade_log) // 2  # Buy/Sell count as one trade\n",
        "winning_trades = len(trade_log[trade_log['Profit/Loss'] > 0]) // 2\n",
        "losing_trades = len(trade_log[trade_log['Profit/Loss'] < 0]) // 2\n",
        "\n",
        "print(f\"Total Trades: {total_trades}\")\n",
        "print(f\"Winning Trades: {winning_trades}\")\n",
        "print(f\"Losing Trades: {losing_trades}\")\n",
        "\n",
        "# Display trade log\n",
        "print(trade_log[['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yrHvti64l5KC",
        "outputId": "41af8841-5afb-491f-cf4c-e0eecfdc97bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-a169aeb4ae19>:59: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Balance: $22555.64\n",
            "Total Profit: $2555.64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Trades: 35\n",
            "Winning Trades: 15\n",
            "Losing Trades: 2\n",
            "                        Date Action  Shares        Price       Balance  \\\n",
            "0  1993-12-17 00:00:00-05:00    Buy     4.0   466.380005  18134.479980   \n",
            "1  1995-04-11 00:00:00-04:00   Sell     4.0   505.529999  20156.599976   \n",
            "2  1995-04-27 00:00:00-04:00    Buy     3.0   513.549988  18615.950012   \n",
            "3  1995-06-29 00:00:00-04:00   Sell     3.0   543.869995  20247.559998   \n",
            "4  1995-07-06 00:00:00-04:00    Buy     3.0   553.989990  18585.590027   \n",
            "..                       ...    ...     ...          ...           ...   \n",
            "65 2016-04-27 00:00:00-04:00   Sell     1.0  2095.149902  22388.800171   \n",
            "66 2016-05-25 00:00:00-04:00    Buy     1.0  2090.540039  20298.260132   \n",
            "67 2016-07-29 00:00:00-04:00   Sell     1.0  2173.600098  22471.860229   \n",
            "68 2016-09-22 00:00:00-04:00    Buy     1.0  2177.179932  20294.680298   \n",
            "69 2016-12-22 00:00:00-05:00   Sell     1.0  2260.959961  22555.640259   \n",
            "\n",
            "    Portfolio Value  Profit/Loss  \n",
            "0      20000.000000     0.000000  \n",
            "1      20156.599976   156.599976  \n",
            "2      20156.599976     0.000000  \n",
            "3      20247.559998    90.960022  \n",
            "4      20247.559998     0.000000  \n",
            "..              ...          ...  \n",
            "65     22388.800171    16.609863  \n",
            "66     22388.800171     0.000000  \n",
            "67     22471.860229    83.060059  \n",
            "68     22471.860229     0.000000  \n",
            "69     22555.640259    83.780029  \n",
            "\n",
            "[70 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BOLLINGER BANDS (89% ACCURATE):**"
      ],
      "metadata": {
        "id": "6Hy7mjw1wTie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Bollinger Bands Parameters\n",
        "window = 20  # Typical period for Bollinger Bands\n",
        "std_dev = 2  # Standard deviation multiplier\n",
        "\n",
        "# Calculate Bollinger Bands\n",
        "sp500['Rolling_Mean'] = sp500['Close'].rolling(window).mean()\n",
        "sp500['Rolling_Std'] = sp500['Close'].rolling(window).std()\n",
        "sp500['Bollinger_Upper'] = sp500['Rolling_Mean'] + (std_dev * sp500['Rolling_Std'])\n",
        "sp500['Bollinger_Lower'] = sp500['Rolling_Mean'] - (std_dev * sp500['Rolling_Std'])\n",
        "\n",
        "# Initialize variables for simulation\n",
        "initial_balance = 20000\n",
        "balance = initial_balance\n",
        "shares = 0\n",
        "amount_to_invest = 0.1 * balance  # 10% of current balance\n",
        "balance_over_time = []\n",
        "\n",
        "# Trade log to track each trade\n",
        "trade_log = pd.DataFrame(columns=['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value'])\n",
        "\n",
        "# Simulate the Bollinger Bands Strategy\n",
        "for i, row in sp500.iterrows():\n",
        "    close_price = row['Close']\n",
        "    trade_date = row.name\n",
        "\n",
        "    # Buy signal - price crosses below the lower band and we are not holding shares\n",
        "    if close_price < row['Bollinger_Lower'] and shares == 0:\n",
        "        shares_to_buy = (amount_to_invest // close_price) * close_price\n",
        "        shares += shares_to_buy // close_price\n",
        "        balance -= shares_to_buy\n",
        "\n",
        "        # Log the trade\n",
        "        new_trade = pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Buy'],\n",
        "            'Shares': [shares],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance + (shares * close_price)]\n",
        "        })\n",
        "        trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "    # Sell signal - price crosses above the upper band and we are holding shares\n",
        "    elif close_price > row['Bollinger_Upper'] and shares > 0:\n",
        "        balance += shares * close_price\n",
        "\n",
        "        # Log the trade\n",
        "        new_trade = pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance]\n",
        "        })\n",
        "        trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n",
        "\n",
        "        shares = 0  # Reset shares after selling\n",
        "\n",
        "    # Record portfolio value (balance + value of held shares)\n",
        "    portfolio_value = balance + (shares * close_price)\n",
        "    balance_over_time.append(portfolio_value)\n",
        "\n",
        "    # Update the DataFrame with current values\n",
        "    sp500.at[i, 'Portfolio_Value'] = portfolio_value\n",
        "    sp500.at[i, 'Balance'] = balance\n",
        "    sp500.at[i, 'Shares_Held'] = shares\n",
        "\n",
        "# Final balance after all trades\n",
        "final_balance = balance + (shares * sp500['Close'].iloc[-1])  # If holding shares, sell at last close price\n",
        "profit = final_balance - initial_balance\n",
        "\n",
        "# Print final results\n",
        "print(f\"Final Balance: ${final_balance:.2f}\")\n",
        "print(f\"Total Profit: ${profit:.2f}\")\n",
        "\n",
        "# Convert balance over time to pandas series for plotting\n",
        "balance_over_time = pd.Series(balance_over_time, index=sp500.index[:len(balance_over_time)])\n",
        "\n",
        "# Plotting Portfolio Value vs S&P 500 Close Price\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(sp500.index, sp500['Portfolio_Value'], label='Portfolio Value', color='cyan')\n",
        "plt.plot(sp500.index, sp500['Close'], label='S&P 500 Close Price', color='magenta', alpha=0.5)\n",
        "plt.plot(sp500.index, sp500['Bollinger_Upper'], label='Upper Bollinger Band', color='green', linestyle='--', alpha=0.6)\n",
        "plt.plot(sp500.index, sp500['Bollinger_Lower'], label='Lower Bollinger Band', color='red', linestyle='--', alpha=0.6)\n",
        "plt.title('Portfolio Value vs S&P 500 Close Price with Bollinger Bands')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value ($)')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Display Profit/Loss of each trade\n",
        "trade_log['Profit/Loss'] = 0\n",
        "for i in range(1, len(trade_log), 2):\n",
        "    buy_price = trade_log.loc[i-1, 'Price']\n",
        "    sell_price = trade_log.loc[i, 'Price']\n",
        "    shares = trade_log.loc[i-1, 'Shares']\n",
        "    profit_loss = (sell_price - buy_price) * shares\n",
        "    trade_log.at[i, 'Profit/Loss'] = profit_loss\n",
        "\n",
        "# Plotting Profit/Loss for each trade\n",
        "plt.figure(figsize=(12, 6))\n",
        "trade_log['Profit/Loss'].plot(kind='bar', color=['green' if x > 0 else 'red' for x in trade_log['Profit/Loss']])\n",
        "plt.title('Profit/Loss of Each Trade')\n",
        "plt.ylabel('Profit/Loss ($)')\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Summary of trades\n",
        "total_trades = len(trade_log) // 2  # Each trade consists of a Buy and Sell\n",
        "winning_trades = len(trade_log[trade_log['Profit/Loss'] > 0]) // 2\n",
        "losing_trades = len(trade_log[trade_log['Profit/Loss'] < 0]) // 2\n",
        "print(f\"Total Trades: {total_trades}\")\n",
        "print(f\"Winning Trades: {winning_trades}\")\n",
        "print(f\"Losing Trades: {losing_trades}\")\n",
        "\n",
        "# Display trade log\n",
        "print(trade_log[['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lL0bzaGql5MW",
        "outputId": "acae2139-9619-4d6e-aeeb-ce36b9a6d58e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-4da79694ac3f>:45: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  trade_log = pd.concat([trade_log, new_trade], ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Balance: $22193.58\n",
            "Total Profit: $2193.58\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-4da79694ac3f>:104: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '1.7200927734375' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  trade_log.at[i, 'Profit/Loss'] = profit_loss\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Trades: 100\n",
            "Winning Trades: 24\n",
            "Losing Trades: 3\n",
            "                         Date Action  Shares        Price       Balance  \\\n",
            "0   1994-03-25 00:00:00-05:00    Buy     4.0   460.579987  18157.680054   \n",
            "1   1994-08-01 00:00:00-04:00   Sell     4.0   461.010010  20001.720093   \n",
            "2   1994-09-20 00:00:00-04:00    Buy     4.0   463.359985  18148.280151   \n",
            "3   1995-01-13 00:00:00-05:00   Sell     4.0   465.970001  20012.160156   \n",
            "4   1995-10-10 00:00:00-04:00    Buy     3.0   577.520020  18279.600098   \n",
            "..                        ...    ...     ...          ...           ...   \n",
            "196 2024-04-18 00:00:00-04:00    Buy     0.0  5011.120117  22193.580505   \n",
            "197 2024-04-19 00:00:00-04:00    Buy     0.0  4967.229980  22193.580505   \n",
            "198 2024-07-25 00:00:00-04:00    Buy     0.0  5399.220215  22193.580505   \n",
            "199 2024-08-02 00:00:00-04:00    Buy     0.0  5346.560059  22193.580505   \n",
            "200 2024-08-05 00:00:00-04:00    Buy     0.0  5186.330078  22193.580505   \n",
            "\n",
            "     Portfolio Value  Profit/Loss  \n",
            "0       20000.000000     0.000000  \n",
            "1       20001.720093     1.720093  \n",
            "2       20001.720093     0.000000  \n",
            "3       20012.160156    10.440063  \n",
            "4       20012.160156     0.000000  \n",
            "..               ...          ...  \n",
            "196     22193.580505     0.000000  \n",
            "197     22193.580505    -0.000000  \n",
            "198     22193.580505     0.000000  \n",
            "199     22193.580505    -0.000000  \n",
            "200     22193.580505     0.000000  \n",
            "\n",
            "[201 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**COMBINING THE FOUR MODELS:**"
      ],
      "metadata": {
        "id": "C0nu4NXPp2KF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize values\n",
        "initial_balance = 500  # Starting balance of $20,000\n",
        "balance = initial_balance\n",
        "balance_over_time = []\n",
        "\n",
        "# Track open trades for each strategy\n",
        "open_trades = {'MACD Crossover': False, 'MACD Histogram': False, 'Bollinger Bands': False, 'Golden Cross/Death Cross': False}\n",
        "shares_held = {'MACD Crossover': 0, 'MACD Histogram': 0, 'Bollinger Bands': 0, 'Golden Cross/Death Cross': 0}\n",
        "\n",
        "# Trade log to track each trade\n",
        "trade_log = pd.DataFrame(columns=['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss', 'Strategy'])\n",
        "\n",
        "# Simulate the paper trading for each strategy (example: MACD Crossover, Bollinger Bands, etc.)\n",
        "for i, row in sp500.iterrows():\n",
        "    close_price = row['Close']\n",
        "    trade_date = row.name\n",
        "    amount_to_invest = 1 * balance  # Recalculate 10% of the balance each time\n",
        "\n",
        "    ### MACD Crossover Strategy ###\n",
        "    if row['MACD'] > row['Signal_Line'] and not open_trades['MACD Crossover']:  # Buy signal\n",
        "        shares_to_buy = amount_to_invest // close_price\n",
        "        if shares_to_buy > 0:\n",
        "            open_trades['MACD Crossover'] = True\n",
        "            shares_held['MACD Crossover'] = shares_to_buy\n",
        "            balance -= shares_to_buy * close_price\n",
        "\n",
        "            # Log the buy trade\n",
        "            trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares_to_buy],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares_to_buy * close_price)],\n",
        "                'Profit/Loss': [0],  # No profit/loss for a buy\n",
        "                'Strategy': ['MACD Crossover']\n",
        "            })], ignore_index=True)\n",
        "\n",
        "    elif row['MACD'] < row['Signal_Line'] and row['RSI'] > 70 and open_trades['MACD Crossover']:  # Sell signal\n",
        "        sell_value = shares_held['MACD Crossover'] * close_price\n",
        "        balance += sell_value\n",
        "\n",
        "        # Calculate the profit/loss\n",
        "        buy_price = trade_log.loc[trade_log['Strategy'] == 'MACD Crossover', 'Price'].iloc[-1]\n",
        "        profit_loss = (close_price - buy_price) * shares_held['MACD Crossover']\n",
        "\n",
        "        # Log the sell trade\n",
        "        trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares_held['MACD Crossover']],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance],\n",
        "            'Profit/Loss': [profit_loss],\n",
        "            'Strategy': ['MACD Crossover']\n",
        "        })], ignore_index=True)\n",
        "\n",
        "        open_trades['MACD Crossover'] = False  # Reset to allow future trades\n",
        "        shares_held['MACD Crossover'] = 0  # Reset shares after selling\n",
        "\n",
        "    ### Bollinger Bands Strategy ###\n",
        "    if close_price < row['Bollinger_Lower'] and not open_trades['Bollinger Bands']:  # Buy signal\n",
        "        shares_to_buy = amount_to_invest // close_price\n",
        "        if shares_to_buy > 0:\n",
        "            open_trades['Bollinger Bands'] = True\n",
        "            shares_held['Bollinger Bands'] = shares_to_buy\n",
        "            balance -= shares_to_buy * close_price\n",
        "\n",
        "            # Log the buy trade\n",
        "            trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares_to_buy],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares_to_buy * close_price)],\n",
        "                'Profit/Loss': [0],  # No profit/loss for a buy\n",
        "                'Strategy': ['Bollinger Bands']\n",
        "            })], ignore_index=True)\n",
        "\n",
        "    elif close_price > row['Bollinger_Upper'] and open_trades['Bollinger Bands']:  # Sell signal\n",
        "        sell_value = shares_held['Bollinger Bands'] * close_price\n",
        "        balance += sell_value\n",
        "\n",
        "        # Calculate the profit/loss\n",
        "        buy_price = trade_log.loc[trade_log['Strategy'] == 'Bollinger Bands', 'Price'].iloc[-1]\n",
        "        profit_loss = (close_price - buy_price) * shares_held['Bollinger Bands']\n",
        "\n",
        "        # Log the sell trade\n",
        "        trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares_held['Bollinger Bands']],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance],\n",
        "            'Profit/Loss': [profit_loss],\n",
        "            'Strategy': ['Bollinger Bands']\n",
        "        })], ignore_index=True)\n",
        "\n",
        "        open_trades['Bollinger Bands'] = False  # Reset to allow future trades\n",
        "        shares_held['Bollinger Bands'] = 0  # Reset shares after selling\n",
        "\n",
        "    # Record portfolio value (balance + value of held shares)\n",
        "    portfolio_value = balance + (shares_held['MACD Crossover'] * close_price) + (shares_held['Bollinger Bands'] * close_price)\n",
        "    balance_over_time.append(portfolio_value)\n",
        "\n",
        "# Final balance after all trades\n",
        "final_balance = balance + (shares_held['MACD Crossover'] * sp500['Close'].iloc[-1]) + (shares_held['Bollinger Bands'] * sp500['Close'].iloc[-1])\n",
        "profit = final_balance - initial_balance\n",
        "\n",
        "# Print final results\n",
        "print(f\"Final Balance: ${final_balance:.2f}\")\n",
        "print(f\"Total Profit: ${profit:.2f}\")\n",
        "\n",
        "# Convert balance over time to pandas series for plotting\n",
        "balance_over_time = pd.Series(balance_over_time, index=sp500.index[:len(balance_over_time)])\n",
        "\n",
        "# Plotting Portfolio Value vs S&P 500 Close Price\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(sp500.index, balance_over_time, label='Portfolio Value', color='cyan')\n",
        "plt.plot(sp500.index, sp500['Close'], label='S&P 500 Close Price', color='magenta', alpha=0.5)\n",
        "plt.title('Portfolio Value vs S&P 500 Close Price')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Value ($)')\n",
        "plt.legend()\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Trade summary\n",
        "total_trades = len(trade_log[trade_log['Shares'] > 0])  # Only count trades with shares > 0\n",
        "winning_trades = trade_log[trade_log['Profit/Loss'] > 0]\n",
        "losing_trades = trade_log[trade_log['Profit/Loss'] < 0]\n",
        "print(f\"Total Trades: {total_trades}\")\n",
        "print(f\"Winning Trades: {len(winning_trades)}\")\n",
        "print(f\"Losing Trades: {len(losing_trades)}\")\n",
        "\n",
        "# Plotting Profit/Loss of each trade\n",
        "plt.figure(figsize=(12, 6))\n",
        "trade_log['Profit/Loss'].plot(kind='bar', color=['green' if x > 0 else 'red' for x in trade_log['Profit/Loss']])\n",
        "plt.title('Profit/Loss of Each Trade')\n",
        "plt.ylabel('Profit/Loss ($)')\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "plt.show()\n",
        "\n",
        "# Display the trade log with Profit/Loss\n",
        "print(trade_log[['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss', 'Strategy']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jl8yDZCKrZOq",
        "outputId": "97b2a484-d5ab-4b72-a130-6e9d303303ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-24db981f89a6>:32: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  trade_log = pd.concat([trade_log, pd.DataFrame({\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Balance: $5813.37\n",
            "Total Profit: $5313.37\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Trades: 129\n",
            "Winning Trades: 57\n",
            "Losing Trades: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         Date Action  Shares        Price      Balance  \\\n",
            "0   1993-12-17 00:00:00-05:00    Buy     1.0   466.380005    33.619995   \n",
            "1   1995-04-11 00:00:00-04:00   Sell     1.0   505.529999   539.149994   \n",
            "2   1995-04-27 00:00:00-04:00    Buy     1.0   513.549988    25.600006   \n",
            "3   1995-06-29 00:00:00-04:00   Sell     1.0   543.869995   569.470001   \n",
            "4   1995-07-06 00:00:00-04:00    Buy     1.0   553.989990    15.480011   \n",
            "..                        ...    ...     ...          ...          ...   \n",
            "124 2024-01-22 00:00:00-05:00    Buy     1.0  4850.430176   390.580170   \n",
            "125 2024-03-12 00:00:00-04:00   Sell     1.0  5175.270020  5565.850189   \n",
            "126 2024-03-21 00:00:00-04:00    Buy     1.0  5241.529785   324.320404   \n",
            "127 2024-06-28 00:00:00-04:00   Sell     1.0  5460.479980  5784.800385   \n",
            "128 2024-07-05 00:00:00-04:00    Buy     1.0  5567.189941   217.610443   \n",
            "\n",
            "     Portfolio Value Profit/Loss        Strategy  \n",
            "0         500.000000           0  MACD Crossover  \n",
            "1         539.149994   39.149994  MACD Crossover  \n",
            "2         539.149994           0  MACD Crossover  \n",
            "3         569.470001   30.320007  MACD Crossover  \n",
            "4         569.470001           0  MACD Crossover  \n",
            "..               ...         ...             ...  \n",
            "124      5241.010345           0  MACD Crossover  \n",
            "125      5565.850189  324.839844  MACD Crossover  \n",
            "126      5565.850189           0  MACD Crossover  \n",
            "127      5784.800385  218.950195  MACD Crossover  \n",
            "128      5784.800385           0  MACD Crossover  \n",
            "\n",
            "[129 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.gridspec import GridSpec\n",
        "\n",
        "# Initialize values\n",
        "initial_balance = 500  # Starting balance\n",
        "balance = initial_balance\n",
        "balance_over_time = []\n",
        "holding_periods = []  # To track when we were holding stock\n",
        "\n",
        "# Track open trades for each strategy\n",
        "open_trades = {'MACD Crossover': False, 'MACD Histogram': False, 'Bollinger Bands': False, 'Golden Cross/Death Cross': False}\n",
        "shares_held = {'MACD Crossover': 0, 'MACD Histogram': 0, 'Bollinger Bands': 0, 'Golden Cross/Death Cross': 0}\n",
        "\n",
        "# Trade log to track each trade\n",
        "trade_log = pd.DataFrame(columns=['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss', 'Strategy'])\n",
        "\n",
        "# Simulate the paper trading for each strategy\n",
        "for i, row in sp500.iterrows():\n",
        "    close_price = row['Close']\n",
        "    trade_date = row.name\n",
        "    amount_to_invest = 1 * balance  # Recalculate 100% of the balance each time\n",
        "\n",
        "    ### MACD Crossover Strategy ###\n",
        "    if row['MACD'] > row['Signal_Line'] and not open_trades['MACD Crossover']:  # Buy signal\n",
        "        shares_to_buy = amount_to_invest // close_price\n",
        "        if shares_to_buy > 0:\n",
        "            open_trades['MACD Crossover'] = True\n",
        "            shares_held['MACD Crossover'] = shares_to_buy\n",
        "            balance -= shares_to_buy * close_price\n",
        "            holding_periods.append([trade_date, None])  # Start of holding period\n",
        "\n",
        "            # Log the buy trade\n",
        "            trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares_to_buy],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares_to_buy * close_price)],\n",
        "                'Profit/Loss': [0],  # No profit/loss for a buy\n",
        "                'Strategy': ['MACD Crossover']\n",
        "            })], ignore_index=True)\n",
        "\n",
        "    elif row['MACD'] < row['Signal_Line'] and row['RSI'] > 70 and open_trades['MACD Crossover']:  # Sell signal\n",
        "        sell_value = shares_held['MACD Crossover'] * close_price\n",
        "        balance += sell_value\n",
        "\n",
        "        # Calculate the profit/loss\n",
        "        buy_price = trade_log.loc[trade_log['Strategy'] == 'MACD Crossover', 'Price'].iloc[-1]\n",
        "        profit_loss = (close_price - buy_price) * shares_held['MACD Crossover']\n",
        "\n",
        "        # Log the sell trade\n",
        "        trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares_held['MACD Crossover']],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance],\n",
        "            'Profit/Loss': [profit_loss],\n",
        "            'Strategy': ['MACD Crossover']\n",
        "        })], ignore_index=True)\n",
        "\n",
        "        open_trades['MACD Crossover'] = False  # Reset to allow future trades\n",
        "        shares_held['MACD Crossover'] = 0  # Reset shares after selling\n",
        "        holding_periods[-1][1] = trade_date  # End of holding period\n",
        "\n",
        "    ### Bollinger Bands Strategy ###\n",
        "    if close_price < row['Bollinger_Lower'] and not open_trades['Bollinger Bands']:  # Buy signal\n",
        "        shares_to_buy = amount_to_invest // close_price\n",
        "        if shares_to_buy > 0:\n",
        "            open_trades['Bollinger Bands'] = True\n",
        "            shares_held['Bollinger Bands'] = shares_to_buy\n",
        "            balance -= shares_to_buy * close_price\n",
        "            holding_periods.append([trade_date, None])  # Start of holding period\n",
        "\n",
        "            # Log the buy trade\n",
        "            trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "                'Date': [trade_date],\n",
        "                'Action': ['Buy'],\n",
        "                'Shares': [shares_to_buy],\n",
        "                'Price': [close_price],\n",
        "                'Balance': [balance],\n",
        "                'Portfolio Value': [balance + (shares_to_buy * close_price)],\n",
        "                'Profit/Loss': [0],  # No profit/loss for a buy\n",
        "                'Strategy': ['Bollinger Bands']\n",
        "            })], ignore_index=True)\n",
        "\n",
        "    elif close_price > row['Bollinger_Upper'] and open_trades['Bollinger Bands']:  # Sell signal\n",
        "        sell_value = shares_held['Bollinger Bands'] * close_price\n",
        "        balance += sell_value\n",
        "\n",
        "        # Calculate the profit/loss\n",
        "        buy_price = trade_log.loc[trade_log['Strategy'] == 'Bollinger Bands', 'Price'].iloc[-1]\n",
        "        profit_loss = (close_price - buy_price) * shares_held['Bollinger Bands']\n",
        "\n",
        "        # Log the sell trade\n",
        "        trade_log = pd.concat([trade_log, pd.DataFrame({\n",
        "            'Date': [trade_date],\n",
        "            'Action': ['Sell'],\n",
        "            'Shares': [shares_held['Bollinger Bands']],\n",
        "            'Price': [close_price],\n",
        "            'Balance': [balance],\n",
        "            'Portfolio Value': [balance],\n",
        "            'Profit/Loss': [profit_loss],\n",
        "            'Strategy': ['Bollinger Bands']\n",
        "        })], ignore_index=True)\n",
        "\n",
        "        open_trades['Bollinger Bands'] = False  # Reset to allow future trades\n",
        "        shares_held['Bollinger Bands'] = 0  # Reset shares after selling\n",
        "        holding_periods[-1][1] = trade_date  # End of holding period\n",
        "\n",
        "    # Record portfolio value (balance + value of held shares)\n",
        "    portfolio_value = balance + (shares_held['MACD Crossover'] * close_price) + (shares_held['Bollinger Bands'] * close_price)\n",
        "    balance_over_time.append(portfolio_value)\n",
        "\n",
        "# Final balance after all trades\n",
        "final_balance = balance + (shares_held['MACD Crossover'] * sp500['Close'].iloc[-1]) + (shares_held['Bollinger Bands'] * sp500['Close'].iloc[-1])\n",
        "profit = final_balance - initial_balance\n",
        "\n",
        "# Convert balance over time to pandas series for plotting\n",
        "balance_over_time = pd.Series(balance_over_time, index=sp500.index[:len(balance_over_time)])\n",
        "\n",
        "# Set up the dashboard layout using GridSpec\n",
        "fig = plt.figure(figsize=(10, 7))\n",
        "fig.patch.set_facecolor('black')  # Set figure background to black\n",
        "gs = GridSpec(3, 3, figure=fig)\n",
        "\n",
        "# Plot 1: Portfolio Value vs S&P 500 Close Price with shading for holding periods\n",
        "ax1 = fig.add_subplot(gs[0, :])\n",
        "ax1.plot(sp500.index, balance_over_time, label='Portfolio Value', color='cyan')\n",
        "ax1.plot(sp500.index, sp500['Close'], label='S&P 500 Close Price', color='magenta', alpha=0.5)\n",
        "ax1.set_title('Portfolio Value vs S&P 500 Close Price', color='white')\n",
        "ax1.set_xlabel('Date', color='white')\n",
        "ax1.set_ylabel('Value ($)', color='white')\n",
        "ax1.legend()\n",
        "ax1.grid(True, linestyle='--', alpha=0.3)\n",
        "ax1.set_facecolor('black')  # Set axes background to black\n",
        "ax1.tick_params(colors='white')  # Set tick colors to white\n",
        "\n",
        "# Add shading for holding periods\n",
        "for period in holding_periods:\n",
        "    if period[1]:  # Ensure the holding period has an end date\n",
        "        ax1.axvspan(period[0], period[1], color='orange', alpha=0.2)\n",
        "\n",
        "# Plot 2: Profit/Loss of each trade without x-axis labels\n",
        "ax2 = fig.add_subplot(gs[1, :2])\n",
        "trade_log['Profit/Loss'].plot(kind='bar', ax=ax2, color=['green' if x > 0 else 'red' for x in trade_log['Profit/Loss']])\n",
        "ax2.set_title('Profit/Loss of Each Trade', color='white')\n",
        "ax2.set_ylabel('Profit/Loss ($)', color='white')\n",
        "ax2.grid(True, linestyle='--', alpha=0.3)\n",
        "ax2.set_facecolor('black')  # Set axes background to black\n",
        "ax2.set_xticks([])\n",
        "ax2.tick_params(colors='white')  # Set tick colors to white\n",
        "\n",
        "# Plot 3: Trade summary table with directionality percentage (Moved table to a higher position)\n",
        "ax3 = fig.add_subplot(gs[1:, 2])\n",
        "ax3.axis('off')\n",
        "\n",
        "# Create a trade summary table (Rounded Win/Loss Ratio as percentage, moved table higher)\n",
        "win_trades = len(trade_log[trade_log['Profit/Loss'] > 0])\n",
        "loss_trades = len(trade_log[trade_log['Profit/Loss'] < 0])\n",
        "\n",
        "summary_data = pd.DataFrame({\n",
        "    'Winning Trades': [win_trades],\n",
        "    'Losing Trades': [loss_trades],\n",
        "})\n",
        "\n",
        "# Increase column width to avoid text truncation\n",
        "col_widths = [0.35, 0.35]\n",
        "\n",
        "# Display the summary table\n",
        "table = ax3.table(cellText=summary_data.values, colLabels=summary_data.columns, cellLoc='center', loc='upper center', colWidths=col_widths)\n",
        "\n",
        "# Set table background color to black and text to white for visibility\n",
        "table.auto_set_font_size(False)\n",
        "table.set_fontsize(10)\n",
        "table.scale(1.5, 1.5)\n",
        "\n",
        "# Set the background and font color for the table cells\n",
        "for key, cell in table.get_celld().items():\n",
        "    cell.set_edgecolor('white')  # White borders\n",
        "    cell.set_text_props(color='white')  # White text\n",
        "    cell.set_facecolor('black')  # Black background for table cells\n",
        "\n",
        "# Add space at the top and move everything down\n",
        "plt.tight_layout()\n",
        "\n",
        "# Adjust the top to move everything down and add space at the top\n",
        "plt.subplots_adjust(top=0.85, bottom=0.1, hspace=0.5)\n",
        "\n",
        "# Show the dashboard\n",
        "plt.show()\n",
        "\n",
        "# Display the trade log with Profit/Loss\n",
        "print(trade_log[['Date', 'Action', 'Shares', 'Price', 'Balance', 'Portfolio Value', 'Profit/Loss', 'Strategy']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s5dJbkclViDl",
        "outputId": "c79a79d5-4dbd-4c6c-fd66-09b53dc703b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-50-aefd7ecb95c8>:35: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  trade_log = pd.concat([trade_log, pd.DataFrame({\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         Date Action  Shares        Price      Balance  \\\n",
            "0   1993-12-17 00:00:00-05:00    Buy     1.0   466.380005    33.619995   \n",
            "1   1995-04-11 00:00:00-04:00   Sell     1.0   505.529999   539.149994   \n",
            "2   1995-04-27 00:00:00-04:00    Buy     1.0   513.549988    25.600006   \n",
            "3   1995-06-29 00:00:00-04:00   Sell     1.0   543.869995   569.470001   \n",
            "4   1995-07-06 00:00:00-04:00    Buy     1.0   553.989990    15.480011   \n",
            "..                        ...    ...     ...          ...          ...   \n",
            "124 2024-01-22 00:00:00-05:00    Buy     1.0  4850.430176   390.580170   \n",
            "125 2024-03-12 00:00:00-04:00   Sell     1.0  5175.270020  5565.850189   \n",
            "126 2024-03-21 00:00:00-04:00    Buy     1.0  5241.529785   324.320404   \n",
            "127 2024-06-28 00:00:00-04:00   Sell     1.0  5460.479980  5784.800385   \n",
            "128 2024-07-05 00:00:00-04:00    Buy     1.0  5567.189941   217.610443   \n",
            "\n",
            "     Portfolio Value Profit/Loss        Strategy  \n",
            "0         500.000000           0  MACD Crossover  \n",
            "1         539.149994   39.149994  MACD Crossover  \n",
            "2         539.149994           0  MACD Crossover  \n",
            "3         569.470001   30.320007  MACD Crossover  \n",
            "4         569.470001           0  MACD Crossover  \n",
            "..               ...         ...             ...  \n",
            "124      5241.010345           0  MACD Crossover  \n",
            "125      5565.850189  324.839844  MACD Crossover  \n",
            "126      5565.850189           0  MACD Crossover  \n",
            "127      5784.800385  218.950195  MACD Crossover  \n",
            "128      5784.800385           0  MACD Crossover  \n",
            "\n",
            "[129 rows x 8 columns]\n"
          ]
        }
      ]
    }
  ]
}