BTC_algo_model_strategy_2.ipynb

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      "metadata": {
        "id": "oDJv9hHjvHtS"
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      "source": [
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, r2_score\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "# Ignore warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "zUfqbVyT7C9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "372af816-f05e-4641-f1ed-083b8ac4e4b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/btc_18_22_4h.csv\")\n",
        "test_df = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/BTC_23_4h.csv\")"
      ],
      "metadata": {
        "id": "_7KSQEqD7FrN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.drop(columns = ['Unnamed: 0'])"
      ],
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          "height": 424
        },
        "id": "-lwQBKF-m2PB",
        "outputId": "d5384996-2488-4542-dbed-9c66b09a7ba7"
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              "                 datetime      open      high       low     close       volume\n",
              "0     2023-01-01 05:30:00  16541.77  16559.77  16508.39  16533.04  15515.82327\n",
              "1     2023-01-01 09:30:00  16533.04  16550.00  16499.01  16526.19  16532.24115\n",
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              "3     2023-01-01 17:30:00  16556.66  16572.94  16533.68  16558.73  15046.09096\n",
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              "...                   ...       ...       ...       ...       ...          ...\n",
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              "2189  2024-01-01 01:30:00  42619.04  42680.36  42056.00  42283.58   5460.30980\n",
              "2190  2024-01-01 05:30:00  42283.58  42775.00  42230.08  42330.49   3948.08335\n",
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            "text/plain": [
              "                  datetime      open      high       low     close  \\\n",
              "0      2018-01-01 05:30:00  13715.65  13715.65  13155.38  13410.03   \n",
              "1      2018-01-01 09:30:00  13434.98  13818.55  13322.15  13570.35   \n",
              "2      2018-01-01 13:30:00  13569.98  13735.24  13001.13  13220.56   \n",
              "3      2018-01-01 17:30:00  13220.56  13330.00  12750.00  13247.00   \n",
              "4      2018-01-01 21:30:00  13247.00  13290.65  12940.00  13240.37   \n",
              "...                    ...       ...       ...       ...       ...   \n",
              "10930  2022-12-30 13:30:00  16475.10  16531.10  16458.25  16496.27   \n",
              "10931  2022-12-30 17:30:00  16495.96  16580.70  16333.00  16555.26   \n",
              "10932  2022-12-30 21:30:00  16555.26  16577.00  16515.45  16525.37   \n",
              "10933  2022-12-31 01:30:00  16525.91  16677.35  16523.91  16607.48   \n",
              "10934  2022-12-31 05:30:00  16607.48  16616.37  16550.01  16552.46   \n",
              "\n",
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              "10930  20505.906850  \n",
              "10931  44689.949880  \n",
              "10932  25853.486930  \n",
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              "[10935 rows x 6 columns]"
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              "      <td>2018-01-01 13:30:00</td>\n",
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              "      <td>13001.13</td>\n",
              "      <td>13220.56</td>\n",
              "      <td>1319.755931</td>\n",
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              "      <td>2018-01-01 17:30:00</td>\n",
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              "      <td>12750.00</td>\n",
              "      <td>13247.00</td>\n",
              "      <td>1831.933153</td>\n",
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              "      <th>4</th>\n",
              "      <td>2018-01-01 21:30:00</td>\n",
              "      <td>13247.00</td>\n",
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              "      <td>16555.26</td>\n",
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              "      <td>16515.45</td>\n",
              "      <td>16525.37</td>\n",
              "      <td>25853.486930</td>\n",
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              "      <th>10933</th>\n",
              "      <td>2022-12-31 01:30:00</td>\n",
              "      <td>16525.91</td>\n",
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              "      <td>16607.48</td>\n",
              "      <td>24063.452570</td>\n",
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              "    <tr>\n",
              "      <th>10934</th>\n",
              "      <td>2022-12-31 05:30:00</td>\n",
              "      <td>16607.48</td>\n",
              "      <td>16616.37</td>\n",
              "      <td>16550.01</td>\n",
              "      <td>16552.46</td>\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_val_df",
              "summary": "{\n  \"name\": \"train_val_df\",\n  \"rows\": 10935,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10935,\n        \"samples\": [\n          \"2018-04-01 05:30:00\",\n          \"2019-06-10 17:30:00\",\n          \"2018-06-06 17:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16982.529855833684,\n        \"min\": 3172.62,\n        \"max\": 68490.0,\n        \"num_unique_values\": 10853,\n        \"samples\": [\n          6294.03,\n          37722.91,\n          8257.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17180.547209410142,\n        \"min\": 3210.0,\n        \"max\": 69000.0,\n        \"num_unique_values\": 10176,\n        \"samples\": [\n          62506.05,\n          10788.0,\n          41136.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16768.21174046623,\n        \"min\": 3156.26,\n        \"max\": 67627.14,\n        \"num_unique_values\": 10189,\n        \"samples\": [\n          27902.95,\n          9738.0,\n          66357.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16982.48273189442,\n        \"min\": 3172.05,\n        \"max\": 68490.0,\n        \"num_unique_values\": 10852,\n        \"samples\": [\n          6329.26,\n          12967.74,\n          8257.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13796.06062009829,\n        \"min\": 5.887034,\n        \"max\": 284711.694213,\n        \"num_unique_values\": 10935,\n        \"samples\": [\n          3229.309898,\n          8775.516177,\n          4769.845146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Moving Average**"
      ],
      "metadata": {
        "id": "79rMLq6zj8-w"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mmlKzOH2mz9e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df2 = train_val_df.copy()"
      ],
      "metadata": {
        "id": "FEn8MWGIkESE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df2['10_MA_Open'] = train_val_df2['open'].rolling(window=10).mean()"
      ],
      "metadata": {
        "id": "jjfytKaRk4H6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df2['10_MA_Open'] = train_val_df2['10_MA_Open'].fillna(train_val_df2['open'])"
      ],
      "metadata": {
        "id": "F227BLujlzIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df2.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "ENKTveQslxvQ",
        "outputId": "616cbbb1-5f1a-496f-bafd-7afd398cb241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               datetime      open      high       low     close       volume  \\\n",
              "0   2018-01-01 05:30:00  13715.65  13715.65  13155.38  13410.03  1676.204807   \n",
              "1   2018-01-01 09:30:00  13434.98  13818.55  13322.15  13570.35  1302.214836   \n",
              "2   2018-01-01 13:30:00  13569.98  13735.24  13001.13  13220.56  1319.755931   \n",
              "3   2018-01-01 17:30:00  13220.56  13330.00  12750.00  13247.00  1831.933153   \n",
              "4   2018-01-01 21:30:00  13247.00  13290.65  12940.00  13240.37  1092.337234   \n",
              "5   2018-01-02 01:30:00  13222.03  13599.70  13222.03  13380.00  1387.469883   \n",
              "6   2018-01-02 05:30:00  13382.16  13850.00  13231.96  13353.78  2365.532926   \n",
              "7   2018-01-02 09:30:00  13353.78  13480.84  12890.02  13343.00  2980.316053   \n",
              "8   2018-01-02 13:30:00  13343.01  13617.28  13302.59  13490.00  2596.182674   \n",
              "9   2018-01-02 17:30:00  13490.00  13894.86  13450.46  13690.03  3365.879508   \n",
              "10  2018-01-02 21:30:00  13690.02  15473.49  13503.00  14732.75  5090.273483   \n",
              "11  2018-01-03 01:30:00  14710.02  15160.31  14544.00  14675.11  3679.907467   \n",
              "12  2018-01-03 05:30:00  14690.00  15299.99  14660.00  14818.49  3303.833590   \n",
              "13  2018-01-03 09:30:00  14805.81  15307.56  14795.05  15170.00  3211.702067   \n",
              "14  2018-01-03 13:30:00  15170.00  15172.99  14150.00  14730.05  4202.802942   \n",
              "\n",
              "    10_MA_Open  \n",
              "0    13715.650  \n",
              "1    13434.980  \n",
              "2    13569.980  \n",
              "3    13220.560  \n",
              "4    13247.000  \n",
              "5    13222.030  \n",
              "6    13382.160  \n",
              "7    13353.780  \n",
              "8    13343.010  \n",
              "9    13397.915  \n",
              "10   13395.352  \n",
              "11   13522.856  \n",
              "12   13634.858  \n",
              "13   13793.383  \n",
              "14   13985.683  "
            ],
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              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
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              "      <th>10_MA_Open</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01 05:30:00</td>\n",
              "      <td>13715.65</td>\n",
              "      <td>13715.65</td>\n",
              "      <td>13155.38</td>\n",
              "      <td>13410.03</td>\n",
              "      <td>1676.204807</td>\n",
              "      <td>13715.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-01 09:30:00</td>\n",
              "      <td>13434.98</td>\n",
              "      <td>13818.55</td>\n",
              "      <td>13322.15</td>\n",
              "      <td>13570.35</td>\n",
              "      <td>1302.214836</td>\n",
              "      <td>13434.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-01 13:30:00</td>\n",
              "      <td>13569.98</td>\n",
              "      <td>13735.24</td>\n",
              "      <td>13001.13</td>\n",
              "      <td>13220.56</td>\n",
              "      <td>1319.755931</td>\n",
              "      <td>13569.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-01 17:30:00</td>\n",
              "      <td>13220.56</td>\n",
              "      <td>13330.00</td>\n",
              "      <td>12750.00</td>\n",
              "      <td>13247.00</td>\n",
              "      <td>1831.933153</td>\n",
              "      <td>13220.560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-01 21:30:00</td>\n",
              "      <td>13247.00</td>\n",
              "      <td>13290.65</td>\n",
              "      <td>12940.00</td>\n",
              "      <td>13240.37</td>\n",
              "      <td>1092.337234</td>\n",
              "      <td>13247.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2018-01-02 01:30:00</td>\n",
              "      <td>13222.03</td>\n",
              "      <td>13599.70</td>\n",
              "      <td>13222.03</td>\n",
              "      <td>13380.00</td>\n",
              "      <td>1387.469883</td>\n",
              "      <td>13222.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2018-01-02 05:30:00</td>\n",
              "      <td>13382.16</td>\n",
              "      <td>13850.00</td>\n",
              "      <td>13231.96</td>\n",
              "      <td>13353.78</td>\n",
              "      <td>2365.532926</td>\n",
              "      <td>13382.160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2018-01-02 09:30:00</td>\n",
              "      <td>13353.78</td>\n",
              "      <td>13480.84</td>\n",
              "      <td>12890.02</td>\n",
              "      <td>13343.00</td>\n",
              "      <td>2980.316053</td>\n",
              "      <td>13353.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2018-01-02 13:30:00</td>\n",
              "      <td>13343.01</td>\n",
              "      <td>13617.28</td>\n",
              "      <td>13302.59</td>\n",
              "      <td>13490.00</td>\n",
              "      <td>2596.182674</td>\n",
              "      <td>13343.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2018-01-02 17:30:00</td>\n",
              "      <td>13490.00</td>\n",
              "      <td>13894.86</td>\n",
              "      <td>13450.46</td>\n",
              "      <td>13690.03</td>\n",
              "      <td>3365.879508</td>\n",
              "      <td>13397.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2018-01-02 21:30:00</td>\n",
              "      <td>13690.02</td>\n",
              "      <td>15473.49</td>\n",
              "      <td>13503.00</td>\n",
              "      <td>14732.75</td>\n",
              "      <td>5090.273483</td>\n",
              "      <td>13395.352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2018-01-03 01:30:00</td>\n",
              "      <td>14710.02</td>\n",
              "      <td>15160.31</td>\n",
              "      <td>14544.00</td>\n",
              "      <td>14675.11</td>\n",
              "      <td>3679.907467</td>\n",
              "      <td>13522.856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2018-01-03 05:30:00</td>\n",
              "      <td>14690.00</td>\n",
              "      <td>15299.99</td>\n",
              "      <td>14660.00</td>\n",
              "      <td>14818.49</td>\n",
              "      <td>3303.833590</td>\n",
              "      <td>13634.858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2018-01-03 09:30:00</td>\n",
              "      <td>14805.81</td>\n",
              "      <td>15307.56</td>\n",
              "      <td>14795.05</td>\n",
              "      <td>15170.00</td>\n",
              "      <td>3211.702067</td>\n",
              "      <td>13793.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2018-01-03 13:30:00</td>\n",
              "      <td>15170.00</td>\n",
              "      <td>15172.99</td>\n",
              "      <td>14150.00</td>\n",
              "      <td>14730.05</td>\n",
              "      <td>4202.802942</td>\n",
              "      <td>13985.683</td>\n",
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              "summary": "{\n  \"name\": \"train_val_df2\",\n  \"rows\": 10935,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10935,\n        \"samples\": [\n          \"2018-04-01 05:30:00\",\n          \"2019-06-10 17:30:00\",\n          \"2018-06-06 17:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16982.529855833684,\n        \"min\": 3172.62,\n        \"max\": 68490.0,\n        \"num_unique_values\": 10853,\n        \"samples\": [\n          6294.03,\n          37722.91,\n          8257.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17180.547209410142,\n        \"min\": 3210.0,\n        \"max\": 69000.0,\n        \"num_unique_values\": 10176,\n        \"samples\": [\n          62506.05,\n          10788.0,\n          41136.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16768.21174046623,\n        \"min\": 3156.26,\n        \"max\": 67627.14,\n        \"num_unique_values\": 10189,\n        \"samples\": [\n          27902.95,\n          9738.0,\n          66357.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16982.48273189442,\n        \"min\": 3172.05,\n        \"max\": 68490.0,\n        \"num_unique_values\": 10852,\n        \"samples\": [\n          6329.26,\n          12967.74,\n          8257.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13796.06062009829,\n        \"min\": 5.887034,\n        \"max\": 284711.694213,\n        \"num_unique_values\": 10935,\n        \"samples\": [\n          3229.309898,\n          8775.516177,\n          4769.845146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"10_MA_Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16974.31138745294,\n        \"min\": 3224.488,\n        \"max\": 67226.64099999999,\n        \"num_unique_values\": 10931,\n        \"samples\": [\n          6940.911999999999,\n          10310.143,\n          7539.138000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_val_df.iloc[:, 1].values\n",
        "def create_regression_sequences(data, lookback, forecast_size):\n",
        "    sequences, targets = [], []\n",
        "    for i in range(len(data) - lookback):\n",
        "        seq = data[i:i + lookback]\n",
        "        label = data[i + lookback:i + lookback + forecast_size]\n",
        "        sequences.append(seq)\n",
        "        targets.append(label)\n",
        "\n",
        "    return np.array(sequences), np.array(targets)\n",
        "\n",
        "# Define the lookback window and forecast size\n",
        "lookback_window = 10\n",
        "forecast_size = 1\n",
        "\n",
        "# Create regression sequences\n",
        "X_reg, y_reg = create_regression_sequences(X, lookback_window, forecast_size)\n",
        "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42, shuffle=False)"
      ],
      "metadata": {
        "id": "hU-JWDEkmhl5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_val_df.tail(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "lb-96NzSIvHY",
        "outputId": "a7575cd0-c12c-4860-accd-bddc66840e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                  datetime      open      high       low     close  \\\n",
              "10925  2022-12-29 17:30:00  16598.70  16664.41  16590.64  16616.60   \n",
              "10926  2022-12-29 21:30:00  16616.60  16654.54  16590.82  16612.40   \n",
              "10927  2022-12-30 01:30:00  16612.40  16654.51  16555.57  16633.47   \n",
              "10928  2022-12-30 05:30:00  16633.47  16648.77  16579.75  16599.08   \n",
              "10929  2022-12-30 09:30:00  16598.79  16615.35  16429.00  16474.93   \n",
              "10930  2022-12-30 13:30:00  16475.10  16531.10  16458.25  16496.27   \n",
              "10931  2022-12-30 17:30:00  16495.96  16580.70  16333.00  16555.26   \n",
              "10932  2022-12-30 21:30:00  16555.26  16577.00  16515.45  16525.37   \n",
              "10933  2022-12-31 01:30:00  16525.91  16677.35  16523.91  16607.48   \n",
              "10934  2022-12-31 05:30:00  16607.48  16616.37  16550.01  16552.46   \n",
              "\n",
              "            volume  \n",
              "10925  34295.37736  \n",
              "10926  26230.77683  \n",
              "10927  27491.95801  \n",
              "10928  20857.62793  \n",
              "10929  28945.88758  \n",
              "10930  20505.90685  \n",
              "10931  44689.94988  \n",
              "10932  25853.48693  \n",
              "10933  24063.45257  \n",
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              "      <th>10926</th>\n",
              "      <td>2022-12-29 21:30:00</td>\n",
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              "      <td>16474.93</td>\n",
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              "      <th>10930</th>\n",
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              "      <th>10933</th>\n",
              "      <td>2022-12-31 01:30:00</td>\n",
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              "      <td>16607.48</td>\n",
              "      <td>24063.45257</td>\n",
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              "      <th>10934</th>\n",
              "      <td>2022-12-31 05:30:00</td>\n",
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              "\n",
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              "        document.querySelector('#df-f4c05fd6-63cf-47a8-aeed-a784a0656758 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f4c05fd6-63cf-47a8-aeed-a784a0656758');\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",
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              "\n",
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              "    border-color: transparent;\n",
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              "    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",
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              "  }\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-6b17cd71-4800-4d27-986e-bb74efae47e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_reg[-10:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AymgQAV8rFmF",
        "outputId": "a233ea97-da3e-40d6-da1d-08736ffb0511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16690.75 16706.06 16650.65 16647.16 16678.52 16578.02 16595.43 16547.32\n",
            "  16558.33 16556.37]\n",
            " [16706.06 16650.65 16647.16 16678.52 16578.02 16595.43 16547.32 16558.33\n",
            "  16556.37 16598.7 ]\n",
            " [16650.65 16647.16 16678.52 16578.02 16595.43 16547.32 16558.33 16556.37\n",
            "  16598.7  16616.6 ]\n",
            " [16647.16 16678.52 16578.02 16595.43 16547.32 16558.33 16556.37 16598.7\n",
            "  16616.6  16612.4 ]\n",
            " [16678.52 16578.02 16595.43 16547.32 16558.33 16556.37 16598.7  16616.6\n",
            "  16612.4  16633.47]\n",
            " [16578.02 16595.43 16547.32 16558.33 16556.37 16598.7  16616.6  16612.4\n",
            "  16633.47 16598.79]\n",
            " [16595.43 16547.32 16558.33 16556.37 16598.7  16616.6  16612.4  16633.47\n",
            "  16598.79 16475.1 ]\n",
            " [16547.32 16558.33 16556.37 16598.7  16616.6  16612.4  16633.47 16598.79\n",
            "  16475.1  16495.96]\n",
            " [16558.33 16556.37 16598.7  16616.6  16612.4  16633.47 16598.79 16475.1\n",
            "  16495.96 16555.26]\n",
            " [16556.37 16598.7  16616.6  16612.4  16633.47 16598.79 16475.1  16495.96\n",
            "  16555.26 16525.91]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(X_train_reg) ,np.shape(X_test_reg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWepD30lpTJI",
        "outputId": "0e93be04-2979-44bd-e4d4-cff0c5b7772f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8740, 10), (2185, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def metrics(text, y, y_pred, reg=False):\n",
        "    print(f\"{text} Set Metrics:\")\n",
        "    r2_score_ = r2_score(y, y_pred)\n",
        "    print(f\"R2 Score: {r2_score_}\")\n",
        "\n",
        "    if reg:\n",
        "        # Regression metrics\n",
        "        mse = mean_squared_error(y, y_pred)\n",
        "        mae = mean_absolute_error(y, y_pred)\n",
        "        print(f\"Mean Squared Error: {mse}\")\n",
        "        print(f\"Mean Absolute Error: {mae}\")\n",
        "    else:\n",
        "        # Classification metrics\n",
        "        accuracy = accuracy_score(y, y_pred)\n",
        "        precision = precision_score(y, y_pred, average='micro')\n",
        "        recall = recall_score(y, y_pred, average='micro')\n",
        "        f1_score_ = f1_score(y, y_pred, average='micro')\n",
        "\n",
        "        # Print the classification metrics\n",
        "        print(f\"Accuracy: {accuracy}\")\n",
        "        print(f\"Precision: {precision}\")\n",
        "        print(f\"Recall: {recall}\")\n",
        "        print(f\"F1 Score: {f1_score_}\")\n",
        "    print()"
      ],
      "metadata": {
        "id": "8SN_U9nQpLFB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the RandomForestRegressor with the best parameters\n",
        "regressor = RandomForestRegressor(n_estimators=100, max_depth=None, min_samples_split=2, criterion=\"squared_error\", min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=\"auto\",\n",
        "        max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None,\n",
        "        random_state=12)\n",
        "\n",
        "# Train the regressor on the training set\n",
        "regressor.fit(X_train_reg, y_train_reg)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_train_rf = regressor.predict(X_train_reg)\n",
        "y_pred_test_rf = regressor.predict(X_test_reg)\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "metrics('Train', y_pred_train_rf, y_train_reg, True)\n",
        "metrics('Test', y_pred_test_rf, y_test_reg, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2oWZPm7pVgW",
        "outputId": "9fe5cb30-cafd-4cbb-fe0b-a155eb097484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Set Metrics:\n",
            "R2 Score: 0.9999008099988282\n",
            "Mean Squared Error: 31249.718007414307\n",
            "Mean Absolute Error: 86.29419756292934\n",
            "\n",
            "Test Set Metrics:\n",
            "R2 Score: 0.9938996002441327\n",
            "Mean Squared Error: 608500.568550981\n",
            "Mean Absolute Error: 569.955623066361\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Reshape arrays for plotting\n",
        "y_test_reg_array = np.array(y_test_reg).reshape(-1)\n",
        "y_train_reg_array = np.array(y_train_reg).reshape(-1)\n",
        "y_pred_test_rf_array = np.array(y_pred_test_rf).reshape(-1)\n",
        "y_pred_train_rf_array = np.array(y_pred_train_rf).reshape(-1)\n",
        "\n",
        "# Plotting the results using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add true values and predicted values traces for the test set\n",
        "fig.add_trace(go.Scatter(x=list(range(len(y_train_reg), len(y_test_reg) + len(y_train_reg))),\n",
        "                         y=y_test_reg_array,\n",
        "                         mode='lines',\n",
        "                         name='True Values (Test)'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=list(range(len(y_train_reg), len(y_test_reg) + len(y_train_reg))),\n",
        "                         y=y_pred_test_rf_array,\n",
        "                         mode='lines',\n",
        "                         name='Predicted Values (Test)'))\n",
        "\n",
        "# Add true values and predicted values traces for the train set\n",
        "fig.add_trace(go.Scatter(x=list(range(len(y_train_reg))),\n",
        "                         y=y_train_reg_array,\n",
        "                         mode='lines',\n",
        "                         name='True Values (Train)'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=list(range(len(y_train_reg))),\n",
        "                         y=y_pred_train_rf_array,\n",
        "                         mode='lines',\n",
        "                         name='Predicted Values (Train)'))\n",
        "\n",
        "# Update layout\n",
        "fig.update_layout(title='Random Forest Regressor Prediction',\n",
        "                  xaxis_title='Time',\n",
        "                  yaxis_title='Values')\n",
        "\n",
        "# Show the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QwT0P-ZTriMd",
        "outputId": "effb61b1-eeb6-4722-ed83-e593e5e3bdb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Function to implement a simple gap trading strategy\n",
        "def gap_trading_strategy(prices, gap_threshold_percent=1.15):\n",
        "    # Create a DataFrame with 'Close' prices\n",
        "    data = pd.DataFrame(prices, columns=['Close'])\n",
        "\n",
        "    # Calculate price change and identify gaps\n",
        "    data['PriceChange'] = data['Close'].diff()\n",
        "    data['Gap'] = np.where((data['PriceChange'].shift(-1) / data['Close']) * 100 > gap_threshold_percent, 1.0, 0.0)\n",
        "\n",
        "    # Determine position based on gap\n",
        "    data['Position'] = data['Gap'].diff()\n",
        "\n",
        "    return data\n"
      ],
      "metadata": {
        "id": "2_yJKjTq10ip"
      },
      "execution_count": null,
      "outputs": []
    },
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      "metadata": {
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      "execution_count": null,
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    },
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      "source": [
        "def gap_trading_strategy(prices, gap_threshold_percent=2.5, short_window=10, long_window=200):\n",
        "    # Create a DataFrame with 'Close' prices\n",
        "    # data = pd.DataFrame(prices, columns=['datetime', 'Close'])\n",
        "    # data['datetime'] = pd.to_datetime(data['datetime'])\n",
        "    # data.set_index('datetime', inplace=True)\n",
        "\n",
        "    data = pd.DataFrame(prices, columns=['Close'])\n",
        "    # data['datetime'] = pd.to_datetime(data['datetime'])\n",
        "    # data.set_index('datetime', inplace=True)\n",
        "\n",
        "    # Calculate price change and identify gaps\n",
        "    data['PriceChange'] = data['Close'].diff()\n",
        "    data['Gap'] = np.where((data['PriceChange'].shift(-1) / data['Close']) * 100 > gap_threshold_percent, 1.0, 0.0)\n",
        "\n",
        "    # Calculate moving averages\n",
        "    data['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()\n",
        "    data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()\n",
        "\n",
        "    # Determine position based on gap and moving averages\n",
        "    data['Position'] = np.where((data['Gap'].diff() == 1) & (data['Short_MA'] > data['Long_MA']), 1.0, 0.0)\n",
        "    data['Position'] = np.where((data['Gap'].diff() == -1) & (data['Short_MA'] < data['Long_MA']), -1.0, data['Position'])\n",
        "\n",
        "    return data\n",
        "\n",
        "# # Apply the strategy\n",
        "# result = gap_trading_strategy(data)\n",
        "# print(result)\n"
      ],
      "metadata": {
        "id": "MnvTmEWyoB4A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# import numpy as np\n",
        "\n",
        "# def calculate_rsi(data, window=10):\n",
        "#     delta = data['Close'].diff()\n",
        "#     gain = (delta.where(delta > 0, 0)).rolling(window=window, min_periods=1).mean()\n",
        "#     loss = (-delta.where(delta < 0, 0)).rolling(window=window, min_periods=1).mean()\n",
        "#     rs = gain / loss\n",
        "#     rsi = 100 - (100 / (1 + rs))\n",
        "#     return rsi\n",
        "\n",
        "# def calculate_macd(data, short_window=10, long_window=50, signal_window=10):\n",
        "#     data['Short_EMA'] = data['Close'].ewm(span=short_window, min_periods=1).mean()\n",
        "#     data['Long_EMA'] = data['Close'].ewm(span=long_window, min_periods=1).mean()\n",
        "#     data['MACD'] = data['Short_EMA'] - data['Long_EMA']\n",
        "#     data['Signal_Line'] = data['MACD'].ewm(span=signal_window, min_periods=1).mean()\n",
        "#     return data['MACD'], data['Signal_Line']\n",
        "\n",
        "# def gap_trading_strategy(prices, gap_threshold_percent=3.5, short_window=10, long_window=200, rsi_window=10, rsi_threshold=50, macd_short_window=10, macd_long_window=50, macd_signal_window=10, take_profit=0.04):\n",
        "#     # Create a DataFrame with 'Close' prices\n",
        "#     data = pd.DataFrame(prices, columns=['Close'])\n",
        "\n",
        "#     # Calculate price change and identify gaps\n",
        "#     data['PriceChange'] = data['Close'].diff()\n",
        "#     data['Gap'] = np.where((data['PriceChange'].shift(-1) / data['Close']) * 100 > gap_threshold_percent, 1.0, 0.0)\n",
        "\n",
        "#     # Calculate moving averages\n",
        "#     data['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()\n",
        "#     data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()\n",
        "\n",
        "#     # Calculate RSI\n",
        "#     data['RSI'] = calculate_rsi(data, window=rsi_window)\n",
        "\n",
        "#     # Calculate MACD and Signal Line\n",
        "#     data['MACD'], data['Signal_Line'] = calculate_macd(data, short_window=macd_short_window, long_window=macd_long_window, signal_window=macd_signal_window)\n",
        "\n",
        "#     # Determine position based on gap, moving averages, RSI, and MACD\n",
        "#     data['Position'] = np.where(\n",
        "#         (data['Gap'].diff() == 1) &\n",
        "#         (data['Short_MA'] > data['Long_MA']) &\n",
        "#         (data['RSI'] < rsi_threshold) &\n",
        "#         (data['MACD'] > data['Signal_Line']),\n",
        "#         1.0, 0.0)\n",
        "\n",
        "#     data['Position'] = np.where(\n",
        "#         (data['Gap'].diff() == -1) &\n",
        "#         (data['Short_MA'] < data['Long_MA']) &\n",
        "#         (data['RSI'] > 100 - rsi_threshold) &\n",
        "#         (data['MACD'] < data['Signal_Line']),\n",
        "#         -1.0, data['Position'])\n",
        "\n",
        "#     # Calculate daily returns\n",
        "#     data['Daily_Return'] = data['Close'].pct_change()\n",
        "\n",
        "#     # Initialize strategy return and cumulative return\n",
        "#     data['Strategy_Return'] = data['Daily_Return'] * data['Position'].shift(1)\n",
        "#     data['Cumulative_Return'] = (1 + data['Strategy_Return']).cumprod()\n",
        "\n",
        "#     # Implement take-profit level\n",
        "#     take_profit_reached = (data['Cumulative_Return'] - data['Cumulative_Return'].cummax().shift(1)) / data['Cumulative_Return'].cummax().shift(1)\n",
        "#     data.loc[take_profit_reached > take_profit, 'Position'] = 0\n",
        "\n",
        "#     # Recalculate strategy returns with take-profit logic\n",
        "#     data['Strategy_Return'] = data['Daily_Return'] * data['Position'].shift(1)\n",
        "#     data['Cumulative_Return'] = (1 + data['Strategy_Return']).cumprod()\n",
        "\n",
        "#     return data\n"
      ],
      "metadata": {
        "id": "quZA99nHE11N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def gap_trading_strategy(prices, gap_threshold_percent=3.5, short_window=10, long_window=200, take_profit=0.04):\n",
        "#     # Create a DataFrame with 'Close' prices\n",
        "#     data = pd.DataFrame(prices, columns=['Close'])\n",
        "\n",
        "#     # Calculate price change and identify gaps\n",
        "#     data['PriceChange'] = data['Close'].diff()\n",
        "#     data['Gap'] = np.where((data['PriceChange'].shift(-1) / data['Close']) * 100 > gap_threshold_percent, 1.0, 0.0)\n",
        "\n",
        "#     # Calculate moving averages\n",
        "#     data['Short_MA'] = data['Close'].rolling(window=short_window, min_periods=1).mean()\n",
        "#     data['Long_MA'] = data['Close'].rolling(window=long_window, min_periods=1).mean()\n",
        "\n",
        "#     # Determine initial position based on gap and moving averages\n",
        "#     data['Position'] = np.where((data['Gap'].diff() == 1) & (data['Short_MA'] > data['Long_MA']), 1.0, 0.0)\n",
        "#     data['Position'] = np.where((data['Gap'].diff() == -1) & (data['Short_MA'] < data['Long_MA']), -1.0, data['Position'])\n",
        "\n",
        "#     # Calculate daily returns\n",
        "#     data['Daily_Return'] = data['Close'].pct_change()\n",
        "\n",
        "#     # Initialize strategy return and cumulative return\n",
        "#     data['Strategy_Return'] = data['Daily_Return'] * data['Position'].shift(1)\n",
        "#     data['Cumulative_Return'] = (1 + data['Strategy_Return']).cumprod()\n",
        "\n",
        "#     # Implement take-profit level\n",
        "#     take_profit_reached = (data['Cumulative_Return'] - data['Cumulative_Return'].cummax().shift(1)) / data['Cumulative_Return'].cummax().shift(1)\n",
        "#     data.loc[take_profit_reached > take_profit, 'Position'] = 0\n",
        "\n",
        "#     # Recalculate strategy returns with take-profit logic\n",
        "#     data['Strategy_Return'] = data['Daily_Return'] * data['Position'].shift(1)\n",
        "#     data['Cumulative_Return'] = (1 + data['Strategy_Return']).cumprod()\n",
        "\n",
        "#     return data"
      ],
      "metadata": {
        "id": "1IG-_KuzBk-R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prices = [100, 102, 105, 107, 115, 120, 119, 121]\n",
        "result = gap_trading_strategy(prices)\n",
        "print(result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYvRztsi12-j",
        "outputId": "fcdf70e3-5de3-47f0-83d5-a70e691b187a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Close  PriceChange  Gap    Short_MA     Long_MA  Position\n",
            "0    100          NaN  1.0  100.000000  100.000000       0.0\n",
            "1    102          2.0  1.0  101.000000  101.000000       0.0\n",
            "2    105          3.0  1.0  102.333333  102.333333       0.0\n",
            "3    107          2.0  1.0  103.500000  103.500000       0.0\n",
            "4    115          8.0  1.0  105.800000  105.800000       0.0\n",
            "5    120          5.0  0.0  108.166667  108.166667       0.0\n",
            "6    119         -1.0  1.0  109.714286  109.714286       0.0\n",
            "7    121          2.0  0.0  111.125000  111.125000       0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Em7pVXed14GR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "def merge_and_calculate_profit(X_test_reg, regressor, initial_balance=100000,gap_threshold_percent=3.5,stop_loss_percent=0.005):\n",
        "    # Initialize variables and parameters\n",
        "    history = []  # List to store historical prices and forecasts\n",
        "    history.extend(X_test_reg[0])  # Initialize history with the first observation\n",
        "    history.pop()  # Remove the last element to maintain the desired lookback window\n",
        "    actions = []  # List to store trading actions\n",
        "    position = 0  # Current trading position (0: neutral, 1: long, -1: short)\n",
        "    total_profit = 0  # Total profit accumulated during trading\n",
        "    balance = initial_balance  # Initial balance for trading\n",
        "    stop_loss = 700  # Initial stop-loss value\n",
        "    shares_bought = 0 # Initial number of shares bought\n",
        "    shares_sold_short = 0\n",
        "    buy_price =0\n",
        "    price = 0\n",
        "    # Create an empty DataFrame to store the log\n",
        "    log_df = pd.DataFrame(columns=['open', 'signal'])\n",
        "\n",
        "    # Loop through each observation in the test set\n",
        "    for idx, i in enumerate(X_test_reg):\n",
        "        price = i[-1]\n",
        "        history.append(price)\n",
        "        forecast = regressor.predict([i])[0]\n",
        "        history.append(forecast)\n",
        "\n",
        "        # Apply the gap trading strategy to get the current trading action\n",
        "        gap_strategy_data = gap_trading_strategy(history, gap_threshold_percent)\n",
        "        history.pop()\n",
        "\n",
        "        current_action = gap_strategy_data.iloc[-2, gap_strategy_data.columns.get_loc('Position')]\n",
        "\n",
        "        # Execute trading actions based on the current position and trading strategy\n",
        "        if position == 1:\n",
        "            if buy_price - price > stop_loss:\n",
        "                # Sell\n",
        "                position = 0\n",
        "                profit_loss = shares_bought * (price - buy_price)\n",
        "                total_profit += profit_loss\n",
        "                balance = shares_bought * price\n",
        "                shares_bought = 0\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [-1],'Stop_loss':[stop_loss],  'Model_Action':['Stop Loss'], 'Forecast': [forecast], 'Action': ['Sell'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "                                       ignore_index=True)\n",
        "            elif current_action == 1:\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Buy'],  'Forecast': [forecast] , 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "            elif current_action == -1:\n",
        "                # Sell\n",
        "                position = 0\n",
        "                profit_loss = shares_bought * (price - buy_price)\n",
        "                total_profit += profit_loss\n",
        "                balance = shares_bought * price\n",
        "                shares_bought = 0\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [-1],'Stop_loss':[stop_loss],  'Model_Action':['Sell'], 'Forecast': [forecast], 'Action': ['Sell'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "                                       ignore_index=True)\n",
        "                # Short Sell\n",
        "                position = -1\n",
        "                share_price_short = price\n",
        "                shares_sold_short = balance / price\n",
        "                short_sell_price = price * shares_sold_short\n",
        "                shares_bought = -shares_sold_short\n",
        "                balance = 0\n",
        "                stop_loss = share_price_short*stop_loss_percent\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [-1],'Stop_loss':[stop_loss],  'Model_Action':'Sell Short Sell', 'Forecast': [forecast], 'Action': ['Sell'], 'Current Position':[shares_bought] , 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                                       ignore_index=True)\n",
        "            else:\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Hold'], 'Forecast': [forecast], 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "\n",
        "        elif position == 0:\n",
        "            if current_action == 1:\n",
        "                # Buy\n",
        "                position = 1\n",
        "                buy_price = price\n",
        "                shares_bought = balance / buy_price\n",
        "                balance = 0\n",
        "                stop_loss = buy_price * stop_loss_percent\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss':[stop_loss],  'Model_Action':['Buy'], 'Forecast': [forecast], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                                       ignore_index=True)\n",
        "            elif current_action == -1:\n",
        "\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Sell'], 'Forecast': [forecast], 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "            else:\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Hold'], 'Forecast': [forecast], 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "        else:\n",
        "            # print(idx,price - share_price_short , stop_loss)\n",
        "            if price - share_price_short > stop_loss:\n",
        "                # Close short sell\n",
        "                position = 0\n",
        "                profit_loss = shares_sold_short * (share_price_short - price)\n",
        "                total_profit += profit_loss\n",
        "                balance = short_sell_price + profit_loss\n",
        "                shares_bought=0\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price], 'signal': [1],'Stop_loss':[stop_loss],  'Model_Action':['Buy Close short sell'], 'Forecast': [forecast], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "                                       ignore_index=True)\n",
        "                # Buy\n",
        "                position = 1\n",
        "                buy_price = price\n",
        "                shares_bought = balance / buy_price\n",
        "                balance = 0\n",
        "                stop_loss = buy_price * stop_loss_percent\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss':[stop_loss],  'Model_Action':['Buy'], 'Forecast': [forecast], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                                       ignore_index=True)\n",
        "\n",
        "            elif current_action == 1:\n",
        "                # Close short sell\n",
        "                position = 0\n",
        "                profit_loss = shares_sold_short * (share_price_short - price)\n",
        "                total_profit += profit_loss\n",
        "                balance = short_sell_price + profit_loss\n",
        "                shares_bought = 0\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price], 'signal': [1],'Stop_loss':[stop_loss],  'Model_Action':['Close short sell stop loss'], 'Forecast': [forecast], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "                                       ignore_index=True)\n",
        "                # Buy\n",
        "                position = 1\n",
        "                buy_price = price\n",
        "                shares_bought = balance / buy_price\n",
        "                balance = 0\n",
        "                stop_loss = buy_price * stop_loss_percent\n",
        "                log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss':[stop_loss],  'Model_Action':['Buy'], 'Forecast': [forecast], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                                       ignore_index=True)\n",
        "\n",
        "            elif current_action == -1:\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Sell'], 'Forecast': [forecast], 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "            else:\n",
        "                # Hold\n",
        "                log_df = pd.concat([log_df,\n",
        "                    pd.DataFrame({'open': [price],'signal': [0],'Stop_loss':[stop_loss],  'Model_Action':['Hold'], 'Forecast': [forecast], 'Action': ['Hold'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [0]})],\n",
        "                    ignore_index=True)\n",
        "    # If there's an open position at the end, close it at the last price\n",
        "    if position == 1:\n",
        "        # Sell\n",
        "        profit_loss = shares_bought * (price - buy_price)\n",
        "        total_profit += profit_loss\n",
        "        log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        log_df = pd.concat([log_df,\n",
        "            pd.DataFrame({'open': [price],'signal': [-1],'Model_Action': ['Sell'], 'Action': ['Sell'], 'Current Position': [0],\n",
        "             'Balance': [shares_bought * price], 'Profit/Loss': [profit_loss]})], ignore_index=True)\n",
        "\n",
        "    elif position == -1:\n",
        "        # Close short sell\n",
        "        position = 0\n",
        "        profit_loss = shares_sold_short * (share_price_short - price)\n",
        "        total_profit += profit_loss\n",
        "        balance = short_sell_price + profit_loss\n",
        "        shares_bought=0+6\n",
        "        log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss': [stop_loss],  'Model_Action':['Close short sell'], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "                                       ignore_index=True)\n",
        "    return log_df, total_profit, history,gap_strategy_data, position\n"
      ],
      "metadata": {
        "id": "-VqiGPxPWhlw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df, total_profit, history,gap_strategy_data, position = merge_and_calculate_profit(X_test_reg, regressor,gap_threshold_percent=2.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCxavrecoPAf",
        "outputId": "eefb271f-8af4-4659-ea04-6bfba1181b52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: -15968.34996999398\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "diGvMe9uGtix",
        "outputId": "ceaf9641-d8b6-4dbb-a894-e645e5a35e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          open signal  Stop_loss Model_Action    Forecast Action  \\\n",
              "0     45728.28      0   700.0000         Hold  45629.4491   Hold   \n",
              "1     46216.93      0   700.0000         Hold  45819.6190   Hold   \n",
              "2     46813.21      0   700.0000         Hold  46920.9891   Hold   \n",
              "3     47194.73      0   700.0000         Hold  47266.4951   Hold   \n",
              "4     46758.87      0   700.0000         Hold  47018.2255   Hold   \n",
              "...        ...    ...        ...          ...         ...    ...   \n",
              "2180  16598.79      0    91.2762         Hold  16698.3849   Hold   \n",
              "2181  16475.10      0    91.2762         Hold  16660.8788   Hold   \n",
              "2182  16495.96      0    91.2762         Hold  16694.5578   Hold   \n",
              "2183  16555.26      0    91.2762         Hold  16766.6860   Hold   \n",
              "2184  16525.91      0    91.2762         Hold  16766.6473   Hold   \n",
              "\n",
              "      Current Position       Balance  Profit/Loss  \n",
              "0                  0.0  100000.00000          0.0  \n",
              "1                  0.0  100000.00000          0.0  \n",
              "2                  0.0  100000.00000          0.0  \n",
              "3                  0.0  100000.00000          0.0  \n",
              "4                  0.0  100000.00000          0.0  \n",
              "...                ...           ...          ...  \n",
              "2180               0.0   84031.65003          0.0  \n",
              "2181               0.0   84031.65003          0.0  \n",
              "2182               0.0   84031.65003          0.0  \n",
              "2183               0.0   84031.65003          0.0  \n",
              "2184               0.0   84031.65003          0.0  \n",
              "\n",
              "[2185 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-076c4fff-e956-4506-b8a7-5172c18e2000\" 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",
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              "    }\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>signal</th>\n",
              "      <th>Stop_loss</th>\n",
              "      <th>Model_Action</th>\n",
              "      <th>Forecast</th>\n",
              "      <th>Action</th>\n",
              "      <th>Current Position</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Profit/Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45728.28</td>\n",
              "      <td>0</td>\n",
              "      <td>700.0000</td>\n",
              "      <td>Hold</td>\n",
              "      <td>45629.4491</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100000.00000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>46216.93</td>\n",
              "      <td>0</td>\n",
              "      <td>700.0000</td>\n",
              "      <td>Hold</td>\n",
              "      <td>45819.6190</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100000.00000</td>\n",
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              "      <th>2</th>\n",
              "      <td>46813.21</td>\n",
              "      <td>0</td>\n",
              "      <td>700.0000</td>\n",
              "      <td>Hold</td>\n",
              "      <td>46920.9891</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100000.00000</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47194.73</td>\n",
              "      <td>0</td>\n",
              "      <td>700.0000</td>\n",
              "      <td>Hold</td>\n",
              "      <td>47266.4951</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100000.00000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46758.87</td>\n",
              "      <td>0</td>\n",
              "      <td>700.0000</td>\n",
              "      <td>Hold</td>\n",
              "      <td>47018.2255</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100000.00000</td>\n",
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              "      <th>2180</th>\n",
              "      <td>16598.79</td>\n",
              "      <td>0</td>\n",
              "      <td>91.2762</td>\n",
              "      <td>Hold</td>\n",
              "      <td>16698.3849</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>84031.65003</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2181</th>\n",
              "      <td>16475.10</td>\n",
              "      <td>0</td>\n",
              "      <td>91.2762</td>\n",
              "      <td>Hold</td>\n",
              "      <td>16660.8788</td>\n",
              "      <td>Hold</td>\n",
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              "      <td>16495.96</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>84031.65003</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2183</th>\n",
              "      <td>16555.26</td>\n",
              "      <td>0</td>\n",
              "      <td>91.2762</td>\n",
              "      <td>Hold</td>\n",
              "      <td>16766.6860</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84031.65003</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2184</th>\n",
              "      <td>16525.91</td>\n",
              "      <td>0</td>\n",
              "      <td>91.2762</td>\n",
              "      <td>Hold</td>\n",
              "      <td>16766.6473</td>\n",
              "      <td>Hold</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84031.65003</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>2185 rows × 9 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('log_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "log_df",
              "summary": "{\n  \"name\": \"log_df\",\n  \"rows\": 2185,\n  \"fields\": [\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10193.420768721826,\n        \"min\": 15721.51,\n        \"max\": 47956.28,\n        \"num_unique_values\": 2182,\n        \"samples\": [\n          44219.99,\n          41180.54,\n          24166.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"signal\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stop_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 294.42630018911785,\n        \"min\": 91.2762,\n        \"max\": 700.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          98.7424,\n          100.00850000000001,\n          700.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model_Action\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sell\",\n          \"Stop Loss\",\n          \"Hold\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Forecast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9989.66099179911,\n        \"min\": 15651.1748,\n        \"max\": 47876.21469999997,\n        \"num_unique_values\": 2163,\n        \"samples\": [\n          30036.807000000026,\n          20636.148100000002,\n          23702.186899999986\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Action\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Hold\",\n          \"Buy\",\n          \"Sell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Current Position\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7291368997216061,\n        \"min\": 0.0,\n        \"max\": 4.717102721642685,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          4.4103173784603635,\n          4.448871222657262,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36259.330292724284,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          86098.21586230322,\n          88984.98753422376,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Profit/Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119.7888951019278,\n        \"min\": -3469.058908741944,\n        \"max\": 0.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -998.8486798737013,\n          -1887.9229920468383,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = train_val_df.iloc[len(X_test_reg)*(-1)-2:]\n",
        "df2= log_df.iloc[:,:2]\n",
        "merged_df = pd.merge(new_df, df2, on='open', how='left')\n",
        "merged_df['signal']=merged_df['signal'].fillna(-1)"
      ],
      "metadata": {
        "id": "mxIr_Iv_oXqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If there's an open position at the end, close it at the last price\n",
        "if position == 1:\n",
        "        merged_df.loc[-1, \"signal\"] = -1\n",
        "        # log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        # log_df = pd.concat([log_df,\n",
        "        #     pd.DataFrame({'open': [price],'signal': [-1],'Model_Action': ['Sell'], 'Action': ['Sell'], 'Current Position': [0],\n",
        "        #      'Balance': [shares_bought * price], 'Profit/Loss': [profit_loss]})], ignore_index=True)\n",
        "\n",
        "elif position == -1:\n",
        "        # Close short sell\n",
        "        position = 0\n",
        "        merged_df.loc[-1, \"signal\"] = 1\n",
        "        # shares_bought=0+6\n",
        "        # log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        # log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss': [stop_loss],  'Model_Action':['Close short sell'], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "        #                                ignore_index=True)"
      ],
      "metadata": {
        "id": "2I7ew-l3dKqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DGMU7KEHZ1dL",
        "outputId": "352b3a5c-13b2-4443-9c3c-170743c95a6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  datetime      open      high       low     close  \\\n",
              "8748   2021-12-31 21:30:00  48005.37  48118.45  45678.96  45728.28   \n",
              "8749   2022-01-01 01:30:00  45728.28  46569.97  45678.00  46216.93   \n",
              "8750   2022-01-01 05:30:00  46216.93  46949.99  46208.37  46813.20   \n",
              "8751   2022-01-01 09:30:00  46813.21  47555.55  46591.23  47194.73   \n",
              "8752   2022-01-01 13:30:00  47194.73  47344.69  46715.39  46758.87   \n",
              "...                    ...       ...       ...       ...       ...   \n",
              "10930  2022-12-30 13:30:00  16475.10  16531.10  16458.25  16496.27   \n",
              "10931  2022-12-30 17:30:00  16495.96  16580.70  16333.00  16555.26   \n",
              "10932  2022-12-30 21:30:00  16555.26  16577.00  16515.45  16525.37   \n",
              "10933  2022-12-31 01:30:00  16525.91  16677.35  16523.91  16607.48   \n",
              "10934  2022-12-31 05:30:00  16607.48  16616.37  16550.01  16552.46   \n",
              "\n",
              "            volume  \n",
              "8748    9090.79287  \n",
              "8749    5543.95691  \n",
              "8750    3495.20465  \n",
              "8751    3522.79888  \n",
              "8752    2729.67811  \n",
              "...            ...  \n",
              "10930  20505.90685  \n",
              "10931  44689.94988  \n",
              "10932  25853.48693  \n",
              "10933  24063.45257  \n",
              "10934  15656.04768  \n",
              "\n",
              "[2187 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>8748</th>\n",
              "      <td>2021-12-31 21:30:00</td>\n",
              "      <td>48005.37</td>\n",
              "      <td>48118.45</td>\n",
              "      <td>45678.96</td>\n",
              "      <td>45728.28</td>\n",
              "      <td>9090.79287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8749</th>\n",
              "      <td>2022-01-01 01:30:00</td>\n",
              "      <td>45728.28</td>\n",
              "      <td>46569.97</td>\n",
              "      <td>45678.00</td>\n",
              "      <td>46216.93</td>\n",
              "      <td>5543.95691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8750</th>\n",
              "      <td>2022-01-01 05:30:00</td>\n",
              "      <td>46216.93</td>\n",
              "      <td>46949.99</td>\n",
              "      <td>46208.37</td>\n",
              "      <td>46813.20</td>\n",
              "      <td>3495.20465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8751</th>\n",
              "      <td>2022-01-01 09:30:00</td>\n",
              "      <td>46813.21</td>\n",
              "      <td>47555.55</td>\n",
              "      <td>46591.23</td>\n",
              "      <td>47194.73</td>\n",
              "      <td>3522.79888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8752</th>\n",
              "      <td>2022-01-01 13:30:00</td>\n",
              "      <td>47194.73</td>\n",
              "      <td>47344.69</td>\n",
              "      <td>46715.39</td>\n",
              "      <td>46758.87</td>\n",
              "      <td>2729.67811</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>10930</th>\n",
              "      <td>2022-12-30 13:30:00</td>\n",
              "      <td>16475.10</td>\n",
              "      <td>16531.10</td>\n",
              "      <td>16458.25</td>\n",
              "      <td>16496.27</td>\n",
              "      <td>20505.90685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10931</th>\n",
              "      <td>2022-12-30 17:30:00</td>\n",
              "      <td>16495.96</td>\n",
              "      <td>16580.70</td>\n",
              "      <td>16333.00</td>\n",
              "      <td>16555.26</td>\n",
              "      <td>44689.94988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10932</th>\n",
              "      <td>2022-12-30 21:30:00</td>\n",
              "      <td>16555.26</td>\n",
              "      <td>16577.00</td>\n",
              "      <td>16515.45</td>\n",
              "      <td>16525.37</td>\n",
              "      <td>25853.48693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10933</th>\n",
              "      <td>2022-12-31 01:30:00</td>\n",
              "      <td>16525.91</td>\n",
              "      <td>16677.35</td>\n",
              "      <td>16523.91</td>\n",
              "      <td>16607.48</td>\n",
              "      <td>24063.45257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10934</th>\n",
              "      <td>2022-12-31 05:30:00</td>\n",
              "      <td>16607.48</td>\n",
              "      <td>16616.37</td>\n",
              "      <td>16550.01</td>\n",
              "      <td>16552.46</td>\n",
              "      <td>15656.04768</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2187 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8fd6fea6-3ce6-40cd-b0f9-c615bfa23fc4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_04346738-3a7c-4e11-814e-2c81636c1e43\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df[merged_df.isna().any(axis=1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "WEbQepnziejg",
        "outputId": "7b353c9f-792f-41ee-f87f-abd041404d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [datetime, open, high, low, close, volume, signal]\n",
              "Index: []"
            ],
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              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>datetime</th>\n",
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              "      <th>volume</th>\n",
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              "      display:flex;\n",
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              "\n",
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              "        const element = document.querySelector('#df-21f6bc36-66e9-431c-9a93-bdc56c736bd1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZKleuHcGiuLj",
        "outputId": "7383f8fc-cef5-4fde-be91-7fc25ac7224d"
      },
      "execution_count": null,
      "outputs": [
        {
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              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 2193,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2187,\n        \"samples\": [\n          \"2022-11-04 13:30:00\",\n          \"2022-08-08 05:30:00\",\n          \"2022-05-08 05:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10198.52455046135,\n        \"min\": 15721.51,\n        \"max\": 48005.37,\n        \"num_unique_values\": 2184,\n        \"samples\": [\n          43598.62,\n          43704.41,\n          44569.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10299.602812542124,\n        \"min\": 15824.16,\n        \"max\": 48189.84,\n        \"num_unique_values\": 2162,\n        \"samples\": [\n          21373.48,\n          39686.64,\n          28794.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10091.656057507664,\n        \"min\": 15476.0,\n        \"max\": 47345.0,\n        \"num_unique_values\": 2157,\n        \"samples\": [\n          35722.24,\n          18860.0,\n          17374.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10193.268585417962,\n        \"min\": 15722.98,\n        \"max\": 47956.28,\n        \"num_unique_values\": 2186,\n        \"samples\": [\n          39002.3,\n          23306.63,\n          17251.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23464.70089829225,\n        \"min\": 1312.93102,\n        \"max\": 284711.694213,\n        \"num_unique_values\": 2187,\n        \"samples\": [\n          61029.30462,\n          18056.13149,\n          21158.18057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"signal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -1,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.to_csv(\"log_year_22\", index=False)"
      ],
      "metadata": {
        "id": "Hbdm3vZn6aTl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['signal'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jXcf2RWiw0_",
        "outputId": "d1629569-7d26-461f-94ca-05ab957f56b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1,  0,  1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def map_actions_to_colors(log_df):\n",
        "    # Map actions to colors\n",
        "    log_df_copy = log_df.copy()\n",
        "    # log_df_copy.loc[(log_df_copy['Action'] == 'Sell') & (log_df_copy['Profit/Loss'] < 0), 'Action'] = 'Lost_sell'\n",
        "    action_colors = {'Buy': 'green', 'Sell': 'red', 'Hold': 'Blue'}\n",
        "    log_df_copy['Color'] = log_df_copy['Action'].map(action_colors)\n",
        "    return log_df_copy\n",
        "\n",
        "def plot_open_vs_action(log_df):\n",
        "    # Create line plot for opens\n",
        "    fig = go.Figure()\n",
        "\n",
        "    # Plot opens as lines\n",
        "    fig.add_trace(go.Scatter(x=log_df.index, y=log_df['open'], mode='lines', name='open'))\n",
        "\n",
        "    # Plot Buy and Sell actions as markers\n",
        "    action_colors = {'Buy': 'green', 'Sell': 'red'}\n",
        "    for action, color in action_colors.items():\n",
        "        if action in log_df['Action'].unique():\n",
        "            subset = log_df[log_df['Action'] == action]\n",
        "            fig.add_trace(go.Scatter(x=subset.index, y=subset['open'],\n",
        "                                     mode='markers', marker=dict(color=color),\n",
        "                                     name=action))\n",
        "\n",
        "    # Customize the layout\n",
        "    fig.update_layout(title='open vs. Action (Buy and Sell)',\n",
        "                      xaxis_title='Index',\n",
        "                      yaxis_title='open_price',\n",
        "                      showlegend=True)\n",
        "\n",
        "    # Show the plot\n",
        "    fig.show()\n",
        "log_df_mapped = map_actions_to_colors(log_df)\n",
        "plot_open_vs_action(log_df_mapped)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "5j8SCLbyi4cz",
        "outputId": "c72a3946-bf3b-4877-f441-be67609e08ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      Unnamed: 0             datetime      open      high       low     close  \\\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "        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",
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              "    (() => {\n",
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              "        document.querySelector('#df-037664a6-7fc4-4572-9a7b-05eedb36d203 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\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",
              "        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('test_df')\"\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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              "\n",
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              "        google.colab.notebook.generateWithVariable('test_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 2191,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 632,\n        \"min\": 0,\n        \"max\": 2190,\n        \"num_unique_values\": 2191,\n        \"samples\": [\n          382,\n          632,\n          1670\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2191,\n        \"samples\": [\n          \"2023-03-05 21:30:00\",\n          \"2023-04-16 13:30:00\",\n          \"2023-10-06 13:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5886.24342456178,\n        \"min\": 16525.7,\n        \"max\": 44180.21,\n        \"num_unique_values\": 2185,\n        \"samples\": [\n          23517.72,\n          22319.69,\n          30729.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5925.036055931744,\n        \"min\": 16550.0,\n        \"max\": 44700.0,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          28573.55,\n          37500.2,\n          22938.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5846.817226815542,\n        \"min\": 16499.01,\n        \"max\": 44093.82,\n        \"num_unique_values\": 2159,\n        \"samples\": [\n          41146.83,\n          27531.85,\n          41683.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5887.49415104537,\n        \"min\": 16526.19,\n        \"max\": 44180.22,\n        \"num_unique_values\": 2187,\n        \"samples\": [\n          35085.98,\n          29535.39,\n          27934.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23960.402086021528,\n        \"min\": 757.14626,\n        \"max\": 216519.77954,\n        \"num_unique_values\": 2191,\n        \"samples\": [\n          16269.59179,\n          3645.14709,\n          5204.72994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_23 = test_df.loc[:, 'open'].values"
      ],
      "metadata": {
        "id": "vdeXGJKkl2sn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_23)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXjaRSUwoI_T",
        "outputId": "c1a62dac-2b9f-46e8-984f-d2f19929a5bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16541.77 16533.04 16525.7  ... 42457.17 42619.04 42283.58]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(X_23))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ay60n6HNo8w",
        "outputId": "6a42fa93-46f1-4683-c0d6-27528ca63cb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2191\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_23_reg, y_test_23_reg = create_regression_sequences(X_23, lookback_window, forecast_size)"
      ],
      "metadata": {
        "id": "jwOv23ABl23E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test_23_reg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVaA9tfbMb3-",
        "outputId": "3c683a89-fb60-4576-e489-443bfd6d4716"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16541.77 16533.04 16525.7  ... 16662.38 16721.27 16735.51]\n",
            " [16533.04 16525.7  16556.66 ... 16721.27 16735.51 16734.66]\n",
            " [16525.7  16556.66 16558.73 ... 16735.51 16734.66 16737.56]\n",
            " ...\n",
            " [42066.94 41972.81 41947.16 ... 42175.99 42537.1  42518.36]\n",
            " [41972.81 41947.16 41893.96 ... 42537.1  42518.36 42457.17]\n",
            " [41947.16 41893.96 42374.97 ... 42518.36 42457.17 42619.04]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_23, total_profit_23, history_23,gap_strategy_data_23, position = merge_and_calculate_profit(X_test_23_reg, regressor,gap_threshold_percent=2.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_23)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vyO0cnvmEa8",
        "outputId": "42a30225-8be4-4a7e-e7c0-b17fa3330dcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: 91774.49102235842\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = test_df.copy()\n",
        "df2= log_df_23.iloc[:,:2]\n",
        "merged_df = pd.merge(new_df, df2, on='open', how='left')\n",
        "merged_df['signal']=merged_df['signal'].fillna(-1)\n",
        "# position = -1"
      ],
      "metadata": {
        "id": "o28IOqmkM5-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# If there's an open position at the end, close it at the last price\n",
        "if position == 1:\n",
        "        merged_df['signal'][-1] = -1\n",
        "        # log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        # log_df = pd.concat([log_df,\n",
        "        #     pd.DataFrame({'open': [price],'signal': [-1],'Model_Action': ['Sell'], 'Action': ['Sell'], 'Current Position': [0],\n",
        "        #      'Balance': [shares_bought * price], 'Profit/Loss': [profit_loss]})], ignore_index=True)\n",
        "\n",
        "elif position == -1:\n",
        "        # Close short sell\n",
        "        position = 0\n",
        "        merged_df['signal'][-1] = 1\n",
        "        # shares_bought=0+6\n",
        "        # log_df.drop(log_df.tail(1).index, inplace=True)\n",
        "        # log_df = pd.concat([log_df, pd.DataFrame({'open': [price],'signal': [1],'Stop_loss': [stop_loss],  'Model_Action':['Close short sell'], 'Action': ['Buy'], 'Current Position': [shares_bought], 'Balance': [balance], 'Profit/Loss': [profit_loss]})],\n",
        "        #                                ignore_index=True)"
      ],
      "metadata": {
        "id": "K-DIdg1Rd40k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "sdw42JzTafr0",
        "outputId": "d1573329-89ec-46f7-a75d-ee7820f08c27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0             datetime      open      high       low     close  \\\n",
              "0              0  2023-01-01 05:30:00  16541.77  16559.77  16508.39  16533.04   \n",
              "1              1  2023-01-01 09:30:00  16533.04  16550.00  16499.01  16526.19   \n",
              "2              2  2023-01-01 13:30:00  16525.70  16557.00  16505.20  16556.66   \n",
              "3              3  2023-01-01 17:30:00  16556.66  16572.94  16533.68  16558.73   \n",
              "4              4  2023-01-01 21:30:00  16558.73  16623.65  16558.00  16603.08   \n",
              "...          ...                  ...       ...       ...       ...       ...   \n",
              "2200        2186  2023-12-31 13:30:00  42537.10  42899.00  42369.15  42518.36   \n",
              "2201        2187  2023-12-31 17:30:00  42518.36  42646.27  42352.76  42457.17   \n",
              "2202        2188  2023-12-31 21:30:00  42457.17  42719.80  42436.47  42619.04   \n",
              "2203        2189  2024-01-01 01:30:00  42619.04  42680.36  42056.00  42283.58   \n",
              "2204        2190  2024-01-01 05:30:00  42283.58  42775.00  42230.08  42330.49   \n",
              "\n",
              "           volume  signal  \n",
              "0     15515.82327      -1  \n",
              "1     16532.24115      -1  \n",
              "2     15915.96701      -1  \n",
              "3     15046.09096      -1  \n",
              "4     18532.64857      -1  \n",
              "...           ...     ...  \n",
              "2200   4626.12098       0  \n",
              "2201   3403.65176       0  \n",
              "2202   3036.19166       0  \n",
              "2203   5460.30980      -1  \n",
              "2204   3948.08335      -1  \n",
              "\n",
              "[2205 rows x 8 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>datetime</th>\n",
              "      <th>open</th>\n",
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              "      <th>low</th>\n",
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              "      <th>signal</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>2023-01-01 05:30:00</td>\n",
              "      <td>16541.77</td>\n",
              "      <td>16559.77</td>\n",
              "      <td>16508.39</td>\n",
              "      <td>16533.04</td>\n",
              "      <td>15515.82327</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>2023-01-01 09:30:00</td>\n",
              "      <td>16533.04</td>\n",
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              "      <td>16499.01</td>\n",
              "      <td>16526.19</td>\n",
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              "      <th>2</th>\n",
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              "      <td>16525.70</td>\n",
              "      <td>16557.00</td>\n",
              "      <td>16505.20</td>\n",
              "      <td>16556.66</td>\n",
              "      <td>15915.96701</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2023-01-01 17:30:00</td>\n",
              "      <td>16556.66</td>\n",
              "      <td>16572.94</td>\n",
              "      <td>16533.68</td>\n",
              "      <td>16558.73</td>\n",
              "      <td>15046.09096</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2023-01-01 21:30:00</td>\n",
              "      <td>16558.73</td>\n",
              "      <td>16623.65</td>\n",
              "      <td>16558.00</td>\n",
              "      <td>16603.08</td>\n",
              "      <td>18532.64857</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2200</th>\n",
              "      <td>2186</td>\n",
              "      <td>2023-12-31 13:30:00</td>\n",
              "      <td>42537.10</td>\n",
              "      <td>42899.00</td>\n",
              "      <td>42369.15</td>\n",
              "      <td>42518.36</td>\n",
              "      <td>4626.12098</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2201</th>\n",
              "      <td>2187</td>\n",
              "      <td>2023-12-31 17:30:00</td>\n",
              "      <td>42518.36</td>\n",
              "      <td>42646.27</td>\n",
              "      <td>42352.76</td>\n",
              "      <td>42457.17</td>\n",
              "      <td>3403.65176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2202</th>\n",
              "      <td>2188</td>\n",
              "      <td>2023-12-31 21:30:00</td>\n",
              "      <td>42457.17</td>\n",
              "      <td>42719.80</td>\n",
              "      <td>42436.47</td>\n",
              "      <td>42619.04</td>\n",
              "      <td>3036.19166</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2203</th>\n",
              "      <td>2189</td>\n",
              "      <td>2024-01-01 01:30:00</td>\n",
              "      <td>42619.04</td>\n",
              "      <td>42680.36</td>\n",
              "      <td>42056.00</td>\n",
              "      <td>42283.58</td>\n",
              "      <td>5460.30980</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2204</th>\n",
              "      <td>2190</td>\n",
              "      <td>2024-01-01 05:30:00</td>\n",
              "      <td>42283.58</td>\n",
              "      <td>42775.00</td>\n",
              "      <td>42230.08</td>\n",
              "      <td>42330.49</td>\n",
              "      <td>3948.08335</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2205 rows × 8 columns</p>\n",
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            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df[merged_df.isna().any(axis=1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "6_IwGZNTadct",
        "outputId": "7741c5ac-8478-40c9-e09a-6b056060cf15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Unnamed: 0, datetime, open, high, low, close, volume, signal]\n",
              "Index: []"
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              "  <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",
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              "      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-4d024a9d-cbcb-47f2-85be-ac5435fc75b5 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-4d024a9d-cbcb-47f2-85be-ac5435fc75b5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.to_csv(\"log_year_23\", index=False)"
      ],
      "metadata": {
        "id": "rcRbO1hia2rd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log_df_mapped_23 = map_actions_to_colors(log_df_23)\n",
        "plot_open_vs_action(log_df_mapped_23)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rHU8GdSOmRVu",
        "outputId": "1d7fa026-4007-472d-a886-712175f94398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "test_df_18_19 = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/4h_data/btc_18_19_4h.csv\")"
      ],
      "metadata": {
        "id": "lX8lcQf7y_bB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_18_19"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hy4hlHfr9XUJ",
        "outputId": "d355cbe4-6680-4f9f-c46f-d427716ee793"
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      "execution_count": null,
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              "      Unnamed: 0             datetime      open      high       low     close  \\\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f3c1a5ed-6a09-4d0d-bec9-9d9aff7e8c5b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3c1a5ed-6a09-4d0d-bec9-9d9aff7e8c5b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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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",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      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-f3c1a5ed-6a09-4d0d-bec9-9d9aff7e8c5b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7001ecd9-c30c-42d6-9b9a-0a4d9be3a259\">\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('test_df_18_19')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7001ecd9-c30c-42d6-9b9a-0a4d9be3a259 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_df_18_19');\n",
              "      }\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df_18_19",
              "summary": "{\n  \"name\": \"test_df_18_19\",\n  \"rows\": 4364,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1259,\n        \"min\": 0,\n        \"max\": 4363,\n        \"num_unique_values\": 4364,\n        \"samples\": [\n          1760,\n          51,\n          4308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 4364,\n        \"samples\": [\n          \"2018-10-23 05:30:00\",\n          \"2018-01-09 17:30:00\",\n          \"2019-12-22 17:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2522.886406827821,\n        \"min\": 3172.62,\n        \"max\": 17069.79,\n        \"num_unique_values\": 4310,\n        \"samples\": [\n          11507.36,\n          7964.85,\n          11830.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2573.58055383439,\n        \"min\": 3210.0,\n        \"max\": 17176.24,\n        \"num_unique_values\": 4036,\n        \"samples\": [\n          10616.69,\n          11643.0,\n          3726.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2459.6831181802127,\n        \"min\": 3156.26,\n        \"max\": 16612.18,\n        \"num_unique_values\": 3996,\n        \"samples\": [\n          5634.0,\n          11801.05,\n          5588.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2521.1111327395397,\n        \"min\": 3172.05,\n        \"max\": 17069.79,\n        \"num_unique_values\": 4307,\n        \"samples\": [\n          11010.0,\n          8064.81,\n          10579.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4649.519089572529,\n        \"min\": 520.107646,\n        \"max\": 63695.782267,\n        \"num_unique_values\": 4364,\n        \"samples\": [\n          2058.821522,\n          3506.537695,\n          3098.613582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_18 = test_df_18_19.iloc[:, 2].values"
      ],
      "metadata": {
        "id": "CEe6cAoTzRc9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtj6vU5yzUZo",
        "outputId": "c633b727-cc02-438b-df16-1c89ed84f7fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13715.65 13434.98 13569.98 ...  7250.3   7243.64  7195.  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_18_reg, y_test_18_reg = create_regression_sequences(X_18, lookback_window, forecast_size)"
      ],
      "metadata": {
        "id": "cAthqV-wzcqy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_18, total_profit_18, history_18,gap_strategy_data_18, position = merge_and_calculate_profit(X_test_18_reg, regressor,gap_threshold_percent=2.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "3POxqRT3zi_3",
        "outputId": "6b5f0f64-1d7e-4f32-f4c8-907d923fa51e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "too many values to unpack (expected 4)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-fe6c37099829>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Use the trained regression model to merge and calculate profits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlog_df_18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_profit_18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory_18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgap_strategy_data_18\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_and_calculate_profit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_18_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgap_threshold_percent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstop_loss_percent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.005\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Print the total profit obtained during the trading period\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Total Profit:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_profit_18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 4)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_df_mapped_23 = map_actions_to_colors(log_df_23)\n",
        "plot_open_vs_action(log_df_mapped_23)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "k5cDh7z0zr1y",
        "outputId": "a1d2539b-6ff3-4861-8f0b-be04c85d0027"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "test_df_18_20 = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/4h_data/btc_18_20_4h.csv\")"
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      "metadata": {
        "id": "rrYzhRya2Pjl"
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      "outputs": []
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        "test_df_18_20"
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              "      Unnamed: 0             datetime      open      high       low     close  \\\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-514f16c1-b5a3-446b-909a-e7d6cb56449e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-514f16c1-b5a3-446b-909a-e7d6cb56449e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-514f16c1-b5a3-446b-909a-e7d6cb56449e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fa1bb62b-f646-4057-b764-5c49a769d187\">\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('test_df_18_20')\"\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_fa1bb62b-f646-4057-b764-5c49a769d187 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_df_18_20');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df_18_20",
              "summary": "{\n  \"name\": \"test_df_18_20\",\n  \"rows\": 6559,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1893,\n        \"min\": 0,\n        \"max\": 6558,\n        \"num_unique_values\": 6559,\n        \"samples\": [\n          2481,\n          4701,\n          1504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 6559,\n        \"samples\": [\n          \"2019-02-20 13:30:00\",\n          \"2020-02-26 09:30:00\",\n          \"2018-09-10 13:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3620.262458645533,\n        \"min\": 3172.62,\n        \"max\": 29155.24,\n        \"num_unique_values\": 6484,\n        \"samples\": [\n          10200.0,\n          7421.09,\n          3567.89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3669.8123019219156,\n        \"min\": 3210.0,\n        \"max\": 29300.0,\n        \"num_unique_values\": 5951,\n        \"samples\": [\n          7237.73,\n          9862.18,\n          3845.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3566.842148501657,\n        \"min\": 3156.26,\n        \"max\": 28738.1,\n        \"num_unique_values\": 5951,\n        \"samples\": [\n          7896.0,\n          9720.0,\n          3922.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3628.3268807539803,\n        \"min\": 3172.05,\n        \"max\": 29155.25,\n        \"num_unique_values\": 6481,\n        \"samples\": [\n          10180.65,\n          7436.82,\n          3695.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6804.121362668676,\n        \"min\": 520.107646,\n        \"max\": 118131.08094,\n        \"num_unique_values\": 6559,\n        \"samples\": [\n          8779.674187,\n          8808.674449,\n          5248.058099\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_18_20= test_df_18_20.iloc[:, 2].values"
      ],
      "metadata": {
        "id": "-2EYrI472ada"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_18_20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbwtUuEw2e0O",
        "outputId": "0339ecb7-bec3-41d1-b778-256495e6725d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13715.65 13434.98 13569.98 ... 29155.24 28910.29 28782.01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_18_20_reg, y_test_18_20_reg = create_regression_sequences(X_18_20, lookback_window, forecast_size)"
      ],
      "metadata": {
        "id": "46T94tZc2ird"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_18_20, total_profit_18_20, history_18_20,gap_strategy_data_18_20 = merge_and_calculate_profit(X_test_18_20_reg, regressor,gap_threshold_percent=1.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_18_20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmwh7dt_2pg2",
        "outputId": "5723def0-5822-4932-c246-400bf3e0bb6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: 666596.0966412202\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_df_mapped_23 = map_actions_to_colors(log_df_18_20)\n",
        "plot_open_vs_action(log_df_mapped_18_20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "Apbzca272xoZ",
        "outputId": "d365e1e7-0789-4636-eed3-f3a31277bd6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'log_df_mapped_18_20' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-de04442c4eee>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlog_df_mapped_23\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_actions_to_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_df_18_20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot_open_vs_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_df_mapped_18_20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'log_df_mapped_18_20' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ycjXkUos33WD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_18_21 = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/4h_data/btc_18_21_4h.csv\")\n",
        "X_18_21= test_df_18_21.iloc[:, 2].values\n",
        "print(X_18_21)\n",
        "X_test_18_21_reg, y_test_18_21_reg = create_regression_sequences(X_18_21, lookback_window, forecast_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfWyDEAY6PJ0",
        "outputId": "fba6225a-120f-4d09-c847-b3eff7fced5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13715.65 13434.98 13569.98 ... 47191.08 47975.76 48005.37]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_18_21, total_profit_18_21, history_18_21,gap_strategy_data_18_21 = merge_and_calculate_profit(X_test_18_21_reg, regressor,gap_threshold_percent=3.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_18_21)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ob18Ep166pl1",
        "outputId": "a1934f42-cf34-40a7-f283-9fcf0ee1051a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: 267700.4485144951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BhDht-LZ9fwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_18_22 = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/4h_data/btc_18_22_4h.csv\")\n",
        "X_18_22= test_df_18_22.iloc[:, 2].values\n",
        "print(X_18_22)\n",
        "X_test_18_22_reg, y_test_18_22_reg = create_regression_sequences(X_18_22, lookback_window, forecast_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_gQ3RHG6xhd",
        "outputId": "c8dd2201-7424-4e61-9971-758375582935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13715.65 13818.55 13735.24 ... 16577.   16677.35 16616.37]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_18_22, total_profit_18_22, history_18_22,gap_strategy_data_18_22 = merge_and_calculate_profit(X_test_18_22_reg, regressor,gap_threshold_percent=1.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_18_22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DH3xOgRw9ekQ",
        "outputId": "3555b4e0-ea0d-4325-cb1e-ae20e802758a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: 257630.38202322667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HTrGnvAW9-tT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_18_23 = pd.read_csv(\"/content/drive/Shareddrives/Hackathon/btc_18_23_4h.csv\")\n",
        "X_18_23= test_df_18_23.iloc[:, 2].values\n",
        "print(X_18_23)\n",
        "X_test_18_23_reg, y_test_18_23_reg = create_regression_sequences(X_18_23, lookback_window, forecast_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzti6rPVAZF1",
        "outputId": "b37a027a-84fa-4146-c3be-1bb702d1b6f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13715.65 13434.98 13569.98 ... 42457.17 42619.04 42283.58]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained regression model to merge and calculate profits\n",
        "log_df_18_23, total_profit_18_23, history_18_23,gap_strategy_data_18_23 = merge_and_calculate_profit(X_test_18_23_reg, regressor,gap_threshold_percent=3.5,stop_loss_percent=0.005)\n",
        "\n",
        "# Print the total profit obtained during the trading period\n",
        "print(\"Total Profit:\", total_profit_18_23)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnuDONpuAjOO",
        "outputId": "ab9ed218-fa11-4877-a789-46c558ef1caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Profit: 238279.3078662512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RMXhv7LRAp_o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}