Final_Backtesting.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install kiteconnect"
      ],
      "metadata": {
        "id": "1UzdgsfHRpd3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1494ca72-8ba1-42d0-902b-573bc79032ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kiteconnect\n",
            "  Downloading kiteconnect-4.2.0-py3-none-any.whl (24 kB)\n",
            "Collecting service-identity>=18.1.0 (from kiteconnect)\n",
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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/59.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.0/59.0 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading autobahn-19.11.2-py2.py3-none-any.whl (771 kB)\n",
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            "\u001b[?25hCollecting txaio>=18.8.1 (from autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading txaio-23.1.1-py2.py3-none-any.whl (30 kB)\n",
            "Collecting cryptography>=2.7 (from autobahn[twisted]==19.11.2->kiteconnect)\n",
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            "\u001b[?25hCollecting zope.interface>=3.6.0 (from autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading zope.interface-6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (246 kB)\n",
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            "Collecting constantly>=15.1 (from Twisted>=12.1.0->autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading constantly-15.1.0-py2.py3-none-any.whl (7.9 kB)\n",
            "Collecting incremental>=21.3.0 (from Twisted>=12.1.0->autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading incremental-22.10.0-py2.py3-none-any.whl (16 kB)\n",
            "Collecting Automat>=0.8.0 (from Twisted>=12.1.0->autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading Automat-22.10.0-py2.py3-none-any.whl (26 kB)\n",
            "Collecting hyperlink>=17.1.1 (from Twisted>=12.1.0->autobahn[twisted]==19.11.2->kiteconnect)\n",
            "  Downloading hyperlink-21.0.0-py2.py3-none-any.whl (74 kB)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from zope.interface>=3.6.0->autobahn[twisted]==19.11.2->kiteconnect) (67.7.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=2.7->autobahn[twisted]==19.11.2->kiteconnect) (2.21)\n",
            "Installing collected packages: incremental, constantly, zope.interface, txaio, hyperlink, Automat, Twisted, cryptography, service-identity, pyOpenSSL, autobahn, kiteconnect\n",
            "Successfully installed Automat-22.10.0 Twisted-22.10.0 autobahn-19.11.2 constantly-15.1.0 cryptography-41.0.1 hyperlink-21.0.0 incremental-22.10.0 kiteconnect-4.2.0 pyOpenSSL-23.2.0 service-identity-23.1.0 txaio-23.1.1 zope.interface-6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from kiteconnect import KiteConnect\n",
        "\n",
        "# logging.basicConfig(level=logging.DEBUG)\n",
        "print('here')\n",
        "kite = KiteConnect(api_key=\"aygbpvrrcsaybucb\")\n",
        "\n",
        "# Step 1 - Open the below URL and login and collect the request token\n",
        "# https://kite.zerodha.com/connect/login?v=4&api_key=aygbpvrrcsaybucb\n",
        "# Step 2 - Run this program and provide the request token\n",
        "# Step 3 - You would get access token\n",
        "# Step 4 - Update the access token in KiteSettings.py\n",
        "\n",
        "# request_token = input(\"Request Token: \")\n",
        "# data = kite.generate_session(request_token, api_secret=\"ant7m5o74v4x0sptg18gx18kzbkl6i5x\")\n",
        "kite.set_access_token(\"0ktv2vzfBlzqukwQ9M6EqcJx4zZEs5xe\")\n",
        "\n",
        "# print(\"====================\")\n",
        "# print(\"Access Token: \",data[\"access_token\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1P8lMIbFwrXE",
        "outputId": "928083df-1e6b-4354-83b3-5cf90cd87133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "here\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "nse = kite.instruments('NSE')\n",
        "nse_data = pd.DataFrame(nse)\n",
        "nse_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Se8nM_UYkxFw",
        "outputId": "4f1c31ad-21a1-4103-9ff4-c0ac40217533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      instrument_token exchange_token      tradingsymbol  \\\n",
              "0               264713           1034  HANGSENG BEES-NAV   \n",
              "1               264969           1035          INDIA VIX   \n",
              "2               260617           1018          NIFTY 100   \n",
              "3               264457           1033          NIFTY 200   \n",
              "4               256265           1001           NIFTY 50   \n",
              "...                ...            ...                ...   \n",
              "5509           2916865          11394               ZOTA   \n",
              "5510           7436801          29050              ZUARI   \n",
              "5511            979713           3827           ZUARIIND   \n",
              "5512           2029825           7929          ZYDUSLIFE   \n",
              "5513           4514561          17635          ZYDUSWELL   \n",
              "\n",
              "                      name  last_price expiry  strike  tick_size  lot_size  \\\n",
              "0        HANGSENG BEES-NAV         0.0            0.0       0.00         0   \n",
              "1                INDIA VIX         0.0            0.0       0.00         0   \n",
              "2                NIFTY 100         0.0            0.0       0.00         0   \n",
              "3                NIFTY 200         0.0            0.0       0.00         0   \n",
              "4                 NIFTY 50         0.0            0.0       0.00         0   \n",
              "...                    ...         ...    ...     ...        ...       ...   \n",
              "5509      ZOTA HEALTH CARE         0.0            0.0       0.05         1   \n",
              "5510  ZUARI AGRO CHEMICALS         0.0            0.0       0.05         1   \n",
              "5511      ZUARI INDUSTRIES         0.0            0.0       0.05         1   \n",
              "5512    ZYDUS LIFESCIENCES         0.0            0.0       0.05         1   \n",
              "5513        ZYDUS WELLNESS         0.0            0.0       0.05         1   \n",
              "\n",
              "     instrument_type  segment exchange  \n",
              "0                 EQ  INDICES      NSE  \n",
              "1                 EQ  INDICES      NSE  \n",
              "2                 EQ  INDICES      NSE  \n",
              "3                 EQ  INDICES      NSE  \n",
              "4                 EQ  INDICES      NSE  \n",
              "...              ...      ...      ...  \n",
              "5509              EQ      NSE      NSE  \n",
              "5510              EQ      NSE      NSE  \n",
              "5511              EQ      NSE      NSE  \n",
              "5512              EQ      NSE      NSE  \n",
              "5513              EQ      NSE      NSE  \n",
              "\n",
              "[5514 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b158d19c-724f-4a5a-a31d-2a566d8ba3d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instrument_token</th>\n",
              "      <th>exchange_token</th>\n",
              "      <th>tradingsymbol</th>\n",
              "      <th>name</th>\n",
              "      <th>last_price</th>\n",
              "      <th>expiry</th>\n",
              "      <th>strike</th>\n",
              "      <th>tick_size</th>\n",
              "      <th>lot_size</th>\n",
              "      <th>instrument_type</th>\n",
              "      <th>segment</th>\n",
              "      <th>exchange</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>264713</td>\n",
              "      <td>1034</td>\n",
              "      <td>HANGSENG BEES-NAV</td>\n",
              "      <td>HANGSENG BEES-NAV</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>EQ</td>\n",
              "      <td>INDICES</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>264969</td>\n",
              "      <td>1035</td>\n",
              "      <td>INDIA VIX</td>\n",
              "      <td>INDIA VIX</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>EQ</td>\n",
              "      <td>INDICES</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>260617</td>\n",
              "      <td>1018</td>\n",
              "      <td>NIFTY 100</td>\n",
              "      <td>NIFTY 100</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>EQ</td>\n",
              "      <td>INDICES</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>264457</td>\n",
              "      <td>1033</td>\n",
              "      <td>NIFTY 200</td>\n",
              "      <td>NIFTY 200</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>EQ</td>\n",
              "      <td>INDICES</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>256265</td>\n",
              "      <td>1001</td>\n",
              "      <td>NIFTY 50</td>\n",
              "      <td>NIFTY 50</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>EQ</td>\n",
              "      <td>INDICES</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5509</th>\n",
              "      <td>2916865</td>\n",
              "      <td>11394</td>\n",
              "      <td>ZOTA</td>\n",
              "      <td>ZOTA HEALTH CARE</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>EQ</td>\n",
              "      <td>NSE</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5510</th>\n",
              "      <td>7436801</td>\n",
              "      <td>29050</td>\n",
              "      <td>ZUARI</td>\n",
              "      <td>ZUARI AGRO CHEMICALS</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>EQ</td>\n",
              "      <td>NSE</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5511</th>\n",
              "      <td>979713</td>\n",
              "      <td>3827</td>\n",
              "      <td>ZUARIIND</td>\n",
              "      <td>ZUARI INDUSTRIES</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>EQ</td>\n",
              "      <td>NSE</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5512</th>\n",
              "      <td>2029825</td>\n",
              "      <td>7929</td>\n",
              "      <td>ZYDUSLIFE</td>\n",
              "      <td>ZYDUS LIFESCIENCES</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>EQ</td>\n",
              "      <td>NSE</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5513</th>\n",
              "      <td>4514561</td>\n",
              "      <td>17635</td>\n",
              "      <td>ZYDUSWELL</td>\n",
              "      <td>ZYDUS WELLNESS</td>\n",
              "      <td>0.0</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>EQ</td>\n",
              "      <td>NSE</td>\n",
              "      <td>NSE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5514 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b158d19c-724f-4a5a-a31d-2a566d8ba3d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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",
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              "\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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b158d19c-724f-4a5a-a31d-2a566d8ba3d0 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-b158d19c-724f-4a5a-a31d-2a566d8ba3d0');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_transaction_charge_buy(quantity, price):\n",
        "    total_transaction_charge = 0\n",
        "    total_amount = quantity * price\n",
        "    brokerage = min(20, 0.0003 * total_amount)\n",
        "    transaction_charge = 0.0000325 * total_amount\n",
        "    sebi_charges = 0.000001 * total_amount\n",
        "    stamp_duty = 0.00003*total_amount\n",
        "    gst = 0.18 * (brokerage + sebi_charges + transaction_charge)\n",
        "    total_transaction_charge += brokerage + stamp_duty + transaction_charge + gst + sebi_charges\n",
        "    return total_transaction_charge\n",
        "\n",
        "def calculate_transaction_charge_sell(quantity, price):\n",
        "    total_transaction_charge = 0\n",
        "    total_amount = quantity * price\n",
        "    brokerage = min(20, 0.0003 * total_amount)\n",
        "    stt = 0.00025 * (total_amount)\n",
        "    transaction_charge = 0.0000325 * total_amount\n",
        "    sebi_charges = 0.000001 * total_amount\n",
        "    gst = 0.18 * (brokerage + sebi_charges + transaction_charge)\n",
        "    total_transaction_charge += brokerage + stt + transaction_charge + gst + sebi_charges\n",
        "    return total_transaction_charge"
      ],
      "metadata": {
        "id": "rjLMK47vVxOG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import yfinance as yf"
      ],
      "metadata": {
        "id": "sVTBQZbOVyZu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings"
      ],
      "metadata": {
        "id": "mE1wlTyqjAK-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BhyejrvOlLCU"
      },
      "outputs": [],
      "source": [
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def final_backtest(IA_buy, IA_sell, profit_margin,stop_loss, stock_data_file):\n",
        "\n",
        "    result_df = pd.DataFrame(columns=['Date','Buy_stocks','Sell_stocks' , 'Status_L1', 'TP_L1', 'SL_L1', 'Status_L2', 'TP_L2', 'SL_L2', 'Status_L3', 'TP_L3', 'SL_L3', 'Status_S1', 'TP_S1', 'SL_S1', 'Status_S2', 'TP_S2', 'SL_S2', 'Status_S3', 'TP_S3', 'SL_S3', 'IA_buy','IA_sell','buy_profit','sell_profit','Transaction_Charges','Profit_after_Transaction'])\n",
        "\n",
        "    # Read stock data from CSV file\n",
        "    stock_data = pd.read_csv(stock_data_file)\n",
        "    profit_margin=profit_margin/5\n",
        "    stop_loss=stop_loss/5\n",
        "    # # Add \".NS\" to stock names\n",
        "    stock_data['Buy'] = stock_data['Buy'].apply(lambda x: [stock + '' for stock in eval(x)])\n",
        "    stock_data['Sell'] = stock_data['Sell'].apply(lambda x: [stock + '' for stock in eval(x)])\n",
        "\n",
        "    # Iterate over each date\n",
        "    for i in range(len(stock_data)):\n",
        "        n=3\n",
        "        date = pd.to_datetime(stock_data.loc[i, 'Date'], dayfirst=True)\n",
        "        buy_amount=IA_buy\n",
        "        sell_amount=IA_sell\n",
        "        buy_amount_per_stock=buy_amount/3\n",
        "        sell_amount_per_stock=sell_amount/3\n",
        "        buy_list = stock_data.loc[i, 'Buy']\n",
        "        sell_list = stock_data.loc[i, 'Sell']\n",
        "\n",
        "        tp = profit_margin\n",
        "        ls = stop_loss\n",
        "        status_long=[]\n",
        "        status_short=[]\n",
        "        TP_long =[]\n",
        "        TP_short=[]\n",
        "        SL_long =[]\n",
        "        SL_short=[]\n",
        "        buy_profit=0\n",
        "        sell_profit=0\n",
        "        total_transaction=0\n",
        "        # print(buy_list)\n",
        "        for stock in buy_list:\n",
        "            # print(stock)\n",
        "            token = nse_data[nse_data.tradingsymbol == stock].instrument_token.values[0]\n",
        "            #print(date)\n",
        "            df = kite.historical_data(token, str(date)[:10] + \" 09:15:00\", str(date)[:10] + \" 15:14:00\", \"minute\", False, False)\n",
        "            df = pd.DataFrame(df)\n",
        "            if(df.size == 0):\n",
        "                continue\n",
        "            traded_close = df['open'][0]\n",
        "            tp_key=0\n",
        "            sl_key=0\n",
        "            status_key=-2 #Initial Stage\n",
        "            total_transaction+=calculate_transaction_charge_buy(((buy_amount_per_stock*5)/traded_close),traded_close)\n",
        "            for j in range(len(df)):\n",
        "                if(j<60):\n",
        "                  ls=0.02\n",
        "                else:\n",
        "                  ls=0.003\n",
        "                if((df.loc[j, 'high']-traded_close)/traded_close >= tp and tp_key==0):\n",
        "                    if(status_key==-2):\n",
        "                        ltp=df.loc[j,'high']\n",
        "                        status_long.append(1)\n",
        "                        status_key=1\n",
        "                        total_transaction+=calculate_transaction_charge_sell(((buy_amount_per_stock*5)/traded_close),ltp)\n",
        "                        buy_profit+=tp*buy_amount_per_stock*5\n",
        "                        # buy_profit+=(ltp-traded_close)*((buy_amount_per_stock*5)/traded_close)\n",
        "                    TP_long.append(df.loc[j, 'date'])\n",
        "                    tp_key=1\n",
        "                if((traded_close-df.loc[j, 'low'])/traded_close >=ls and sl_key==0):\n",
        "                    if(status_key==-2):\n",
        "                        ltp=df.loc[j,'low']\n",
        "                        status_long.append(0)\n",
        "                        status_key=0\n",
        "                        total_transaction+=calculate_transaction_charge_sell(((buy_amount_per_stock*5)/traded_close),ltp)\n",
        "                        # buy_profit-=ls*buy_amount_per_stock*5\n",
        "                        buy_profit-=(traded_close-ltp)*((buy_amount_per_stock*5)/traded_close)\n",
        "                    SL_long.append(df.loc[j, 'date'])\n",
        "                    sl_key=1\n",
        "            if(tp_key==0 and sl_key==0):\n",
        "                status_long.append(-1)\n",
        "                total_transaction+=calculate_transaction_charge_sell(((buy_amount_per_stock*5)/traded_close),traded_close)\n",
        "                ltp = df.loc[-1,'close']\n",
        "                buy_profit+=(ltp-traded_close)*((buy_amount_per_stock*5)/traded_close)\n",
        "                TP_long.append(-1)\n",
        "                SL_long.append(-1)\n",
        "            elif(tp_key==0):\n",
        "                TP_long.append(-1)\n",
        "            elif(sl_key==0):\n",
        "                SL_long.append(-1)\n",
        "\n",
        "        for stock in sell_list:\n",
        "            token = nse_data[nse_data.tradingsymbol == stock].instrument_token.values[0]\n",
        "            df = kite.historical_data(token, str(date)[:10] + \" 09:15:00\", str(date)[:10] + \" 15:14:00\", \"minute\", False, False)\n",
        "            df = pd.DataFrame(df)\n",
        "            if(df.size == 0):\n",
        "                continue\n",
        "            traded_close = df['open'][0]\n",
        "            tp_key=0\n",
        "            sl_key=0\n",
        "            status_key=-2 #Initial Stage\n",
        "            total_transaction+=calculate_transaction_charge_sell(((sell_amount_per_stock*5)/traded_close),traded_close)\n",
        "            for j in range(len(df)):\n",
        "                if(j<60):\n",
        "                  ls=0.02\n",
        "                else:\n",
        "                  ls=0.003\n",
        "                if((traded_close-df.loc[j, 'low'])/traded_close >= tp and tp_key==0):\n",
        "                    if(status_key==-2):\n",
        "                        ltp=df.loc[j,'low']\n",
        "                        status_short.append(1)\n",
        "                        status_key=1\n",
        "                        total_transaction+=calculate_transaction_charge_buy(((sell_amount_per_stock*5)/traded_close),ltp)\n",
        "                        # sell_profit+=(traded_close-ltp)*((sell_amount_per_stock*5)/traded_close)\n",
        "                        sell_profit+=tp*sell_amount_per_stock*5\n",
        "                    TP_short.append(df.loc[j, 'date'])\n",
        "                    tp_key=1\n",
        "                if((df.loc[j, 'high']-traded_close)/traded_close >=ls and sl_key==0):\n",
        "                    if(status_key==-2):\n",
        "                        ltp=df.loc[j,'high']\n",
        "                        status_short.append(0)\n",
        "                        status_key=0\n",
        "                        total_transaction+=calculate_transaction_charge_buy(((sell_amount_per_stock*5)/traded_close),ltp)\n",
        "                        # sell_profit-=ls*sell_amount_per_stock*5\n",
        "                        sell_profit-=(ltp-traded_close)*((sell_amount_per_stock*5)/traded_close)\n",
        "                    SL_short.append(df.loc[j, 'date'])\n",
        "                    sl_key=1\n",
        "            if(tp_key==0 and sl_key==0):\n",
        "                status_short.append(-1)\n",
        "                total_transaction+=calculate_transaction_charge_buy(((sell_amount_per_stock*5)/traded_close),traded_close)\n",
        "                ltp = df.loc[-1,'close']\n",
        "                sell_profit+=(traded_close-ltp)*((sell_amount_per_stock*5)/traded_close)\n",
        "                TP_short.append(-1)\n",
        "                SL_short.append(-1)\n",
        "            elif(tp_key==0):\n",
        "                TP_short.append(-1)\n",
        "            elif(sl_key==0):\n",
        "                SL_short.append(-1)\n",
        "        #print(\"YESS\")\n",
        "        status_long_df = pd.DataFrame(status_long)\n",
        "        if(status_long_df.size == 0):\n",
        "            continue\n",
        "        total_profit=buy_profit+sell_profit\n",
        "        Pro_after_Transaction=total_profit-total_transaction\n",
        "        IA_buy+=Pro_after_Transaction/2\n",
        "        IA_sell+=Pro_after_Transaction/2\n",
        "        result_df = result_df.append({'Date': date,'Buy_stocks': buy_list, 'Sell_stocks': sell_list,\n",
        "                                      'Status_L1': status_long[0], 'TP_L1':TP_long[0], 'SL_L1':SL_long[0],\n",
        "                                      'Status_L2': status_long[1], 'TP_L2':TP_long[1], 'SL_L2':SL_long[1],\n",
        "                                      'Status_L3': status_long[2], 'TP_L3':TP_long[2], 'SL_L3':SL_long[2],\n",
        "                                      'Status_S1': status_short[0], 'TP_S1':TP_short[0], 'SL_S1':SL_short[0],\n",
        "                                      'Status_S2': status_short[1], 'TP_S2':TP_short[1], 'SL_S2':SL_short[1],\n",
        "                                      'Status_S3': status_short[2], 'TP_S3':TP_short[2], 'SL_S3':SL_short[2],\n",
        "                                      'IA_buy':buy_amount, 'IA_sell': sell_amount, 'buy_profit': buy_profit, 'sell_profit': sell_profit, 'Transaction_Charges':total_transaction, 'Profit_after_Transaction': Pro_after_Transaction},  ignore_index=True)\n",
        "        print(date)\n",
        "\n",
        "    return result_df\n",
        "\n"
      ],
      "metadata": {
        "id": "s-m7KrbRV5Jo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data_file = '/content/nifty200_stock_recommendation.csv'\n",
        "\n",
        "result = final_backtest(60000,60000,0.01313,0.015,stock_data_file)\n",
        "\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lsPhumH_o789",
        "outputId": "cb23e7a5-b1b7-47e9-aa00-48eca5a8c7ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date                       Buy_stocks  \\\n",
              "0   2022-07-01     [BANKBARODA, M&MFIN, CONCOR]   \n",
              "1   2022-07-04     [M&MFIN, BANKBARODA, CONCOR]   \n",
              "2   2022-07-05      [BANKBARODA, M&MFIN, NYKAA]   \n",
              "3   2022-07-06     [BANKBARODA, M&MFIN, CONCOR]   \n",
              "4   2022-07-07     [M&MFIN, BANKBARODA, CONCOR]   \n",
              "..         ...                              ...   \n",
              "241 2023-06-23   [DIXON, JSWENERGY, OBEROIRLTY]   \n",
              "242 2023-06-26  [DIXON, OBEROIRLTY, METROPOLIS]   \n",
              "243 2023-06-27  [DIXON, OBEROIRLTY, METROPOLIS]   \n",
              "244 2023-06-28   [DIXON, METROPOLIS, POLICYBZR]   \n",
              "245 2023-06-30          [DIXON, JSWENERGY, PFC]   \n",
              "\n",
              "                       Sell_stocks Status_L1                      TP_L1  \\\n",
              "0         [IDFCFIRSTB, DMART, DLF]         0                         -1   \n",
              "1        [IDFCFIRSTB, DMART, LTTS]         1  2022-07-04 09:15:00+05:30   \n",
              "2        [IDFCFIRSTB, DMART, LTTS]         1  2022-07-05 09:15:00+05:30   \n",
              "3       [IDFCFIRSTB, LTTS, ZOMATO]         1  2022-07-06 09:15:00+05:30   \n",
              "4       [IDFCFIRSTB, ZOMATO, LTTS]         0                         -1   \n",
              "..                             ...       ...                        ...   \n",
              "241  [ADANIENT, NYKAA, PERSISTENT]         1  2023-06-23 09:15:00+05:30   \n",
              "242  [ADANIENT, PERSISTENT, NYKAA]         1  2023-06-26 09:16:00+05:30   \n",
              "243  [ADANIENT, NYKAA, BANDHANBNK]         1  2023-06-27 09:15:00+05:30   \n",
              "244  [ADANIENT, NYKAA, BANDHANBNK]         0                         -1   \n",
              "245  [ADANIENT, NYKAA, BANDHANBNK]         1  2023-06-30 09:15:00+05:30   \n",
              "\n",
              "                         SL_L1 Status_L2                      TP_L2  \\\n",
              "0    2022-07-01 10:15:00+05:30         0  2022-07-01 13:03:00+05:30   \n",
              "1                           -1         1  2022-07-04 09:15:00+05:30   \n",
              "2                           -1         1  2022-07-05 09:15:00+05:30   \n",
              "3                           -1         1  2022-07-06 09:15:00+05:30   \n",
              "4    2022-07-07 09:28:00+05:30         1  2022-07-07 09:15:00+05:30   \n",
              "..                         ...       ...                        ...   \n",
              "241  2023-06-23 09:31:00+05:30         0                         -1   \n",
              "242                         -1         1  2023-06-26 09:15:00+05:30   \n",
              "243  2023-06-27 09:47:00+05:30         1  2023-06-27 09:15:00+05:30   \n",
              "244  2023-06-28 10:15:00+05:30         0                         -1   \n",
              "245  2023-06-30 13:59:00+05:30         1  2023-06-30 09:33:00+05:30   \n",
              "\n",
              "                         SL_L2 Status_L3  ...                      SL_S2  \\\n",
              "0    2022-07-01 09:20:00+05:30         0  ...                         -1   \n",
              "1                           -1         1  ...                         -1   \n",
              "2                           -1         1  ...  2022-07-05 10:15:00+05:30   \n",
              "3                           -1         1  ...  2022-07-06 10:15:00+05:30   \n",
              "4                           -1         1  ...  2022-07-07 10:23:00+05:30   \n",
              "..                         ...       ...  ...                        ...   \n",
              "241  2023-06-23 09:22:00+05:30         1  ...                         -1   \n",
              "242  2023-06-26 10:15:00+05:30         1  ...  2023-06-26 10:33:00+05:30   \n",
              "243  2023-06-27 12:30:00+05:30         1  ...  2023-06-27 12:06:00+05:30   \n",
              "244  2023-06-28 09:33:00+05:30         0  ...                         -1   \n",
              "245                         -1         0  ...  2023-06-30 09:26:00+05:30   \n",
              "\n",
              "    Status_S3                      TP_S3                      SL_S3  \\\n",
              "0           1  2022-07-01 09:16:00+05:30  2022-07-01 10:15:00+05:30   \n",
              "1           1  2022-07-04 09:17:00+05:30                         -1   \n",
              "2           1  2022-07-05 09:15:00+05:30  2022-07-05 10:15:00+05:30   \n",
              "3           1  2022-07-06 09:15:00+05:30                         -1   \n",
              "4           1  2022-07-07 09:15:00+05:30                         -1   \n",
              "..        ...                        ...                        ...   \n",
              "241         1  2023-06-23 09:15:00+05:30                         -1   \n",
              "242         1  2023-06-26 09:15:00+05:30  2023-06-26 10:24:00+05:30   \n",
              "243         0  2023-06-27 14:36:00+05:30  2023-06-27 10:15:00+05:30   \n",
              "244         1  2023-06-28 09:15:00+05:30                         -1   \n",
              "245         1  2023-06-30 09:15:00+05:30  2023-06-30 10:15:00+05:30   \n",
              "\n",
              "           IA_buy       IA_sell   buy_profit  sell_profit Transaction_Charges  \\\n",
              "0           60000         60000 -5767.948220   787.800000          496.777030   \n",
              "1    57261.537375  57261.537375   751.843986 -1687.667365          489.636611   \n",
              "2     56548.80738   56548.80738   742.485841  -655.555625          486.865602   \n",
              "3    56348.839687  56348.839687   739.860265   739.860265          485.874877   \n",
              "4    56845.762513  56845.762513 -1532.969652   746.384862          487.017335   \n",
              "..            ...           ...          ...          ...                 ...   \n",
              "241  10797.967139  10797.967139  -295.276252   141.777309          114.811169   \n",
              "242  10663.812083  10663.812083   140.015853   140.015853          113.751022   \n",
              "243  10746.952425  10746.952425   141.107485   -64.171227          114.876218   \n",
              "244  10727.982445  10727.982445  -849.465243  -283.517254          113.996073   \n",
              "245   10104.49316   10104.49316  -248.368442  -248.368442          107.754045   \n",
              "\n",
              "    Profit_after_Transaction  \n",
              "0               -5476.925250  \n",
              "1               -1425.459990  \n",
              "2                -399.935386  \n",
              "3                 993.845653  \n",
              "4               -1273.602125  \n",
              "..                       ...  \n",
              "241              -268.310112  \n",
              "242               166.280683  \n",
              "243               -37.939959  \n",
              "244             -1246.978570  \n",
              "245              -604.490929  \n",
              "\n",
              "[246 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-06f30095-a713-4ef4-a1d8-a739b202098e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Buy_stocks</th>\n",
              "      <th>Sell_stocks</th>\n",
              "      <th>Status_L1</th>\n",
              "      <th>TP_L1</th>\n",
              "      <th>SL_L1</th>\n",
              "      <th>Status_L2</th>\n",
              "      <th>TP_L2</th>\n",
              "      <th>SL_L2</th>\n",
              "      <th>Status_L3</th>\n",
              "      <th>...</th>\n",
              "      <th>SL_S2</th>\n",
              "      <th>Status_S3</th>\n",
              "      <th>TP_S3</th>\n",
              "      <th>SL_S3</th>\n",
              "      <th>IA_buy</th>\n",
              "      <th>IA_sell</th>\n",
              "      <th>buy_profit</th>\n",
              "      <th>sell_profit</th>\n",
              "      <th>Transaction_Charges</th>\n",
              "      <th>Profit_after_Transaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-07-01</td>\n",
              "      <td>[BANKBARODA, M&amp;MFIN, CONCOR]</td>\n",
              "      <td>[IDFCFIRSTB, DMART, DLF]</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2022-07-01 10:15:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>2022-07-01 13:03:00+05:30</td>\n",
              "      <td>2022-07-01 09:20:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-01 09:16:00+05:30</td>\n",
              "      <td>2022-07-01 10:15:00+05:30</td>\n",
              "      <td>60000</td>\n",
              "      <td>60000</td>\n",
              "      <td>-5767.948220</td>\n",
              "      <td>787.800000</td>\n",
              "      <td>496.777030</td>\n",
              "      <td>-5476.925250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-07-04</td>\n",
              "      <td>[M&amp;MFIN, BANKBARODA, CONCOR]</td>\n",
              "      <td>[IDFCFIRSTB, DMART, LTTS]</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-04 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-04 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-04 09:17:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>57261.537375</td>\n",
              "      <td>57261.537375</td>\n",
              "      <td>751.843986</td>\n",
              "      <td>-1687.667365</td>\n",
              "      <td>489.636611</td>\n",
              "      <td>-1425.459990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-07-05</td>\n",
              "      <td>[BANKBARODA, M&amp;MFIN, NYKAA]</td>\n",
              "      <td>[IDFCFIRSTB, DMART, LTTS]</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-05 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-05 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2022-07-05 10:15:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-05 09:15:00+05:30</td>\n",
              "      <td>2022-07-05 10:15:00+05:30</td>\n",
              "      <td>56548.80738</td>\n",
              "      <td>56548.80738</td>\n",
              "      <td>742.485841</td>\n",
              "      <td>-655.555625</td>\n",
              "      <td>486.865602</td>\n",
              "      <td>-399.935386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-07-06</td>\n",
              "      <td>[BANKBARODA, M&amp;MFIN, CONCOR]</td>\n",
              "      <td>[IDFCFIRSTB, LTTS, ZOMATO]</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-06 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-06 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2022-07-06 10:15:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-06 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>56348.839687</td>\n",
              "      <td>56348.839687</td>\n",
              "      <td>739.860265</td>\n",
              "      <td>739.860265</td>\n",
              "      <td>485.874877</td>\n",
              "      <td>993.845653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-07-07</td>\n",
              "      <td>[M&amp;MFIN, BANKBARODA, CONCOR]</td>\n",
              "      <td>[IDFCFIRSTB, ZOMATO, LTTS]</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2022-07-07 09:28:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-07 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2022-07-07 10:23:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-07-07 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>56845.762513</td>\n",
              "      <td>56845.762513</td>\n",
              "      <td>-1532.969652</td>\n",
              "      <td>746.384862</td>\n",
              "      <td>487.017335</td>\n",
              "      <td>-1273.602125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>2023-06-23</td>\n",
              "      <td>[DIXON, JSWENERGY, OBEROIRLTY]</td>\n",
              "      <td>[ADANIENT, NYKAA, PERSISTENT]</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-23 09:15:00+05:30</td>\n",
              "      <td>2023-06-23 09:31:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2023-06-23 09:22:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-23 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>10797.967139</td>\n",
              "      <td>10797.967139</td>\n",
              "      <td>-295.276252</td>\n",
              "      <td>141.777309</td>\n",
              "      <td>114.811169</td>\n",
              "      <td>-268.310112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2023-06-26</td>\n",
              "      <td>[DIXON, OBEROIRLTY, METROPOLIS]</td>\n",
              "      <td>[ADANIENT, PERSISTENT, NYKAA]</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-26 09:16:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-26 09:15:00+05:30</td>\n",
              "      <td>2023-06-26 10:15:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2023-06-26 10:33:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-26 09:15:00+05:30</td>\n",
              "      <td>2023-06-26 10:24:00+05:30</td>\n",
              "      <td>10663.812083</td>\n",
              "      <td>10663.812083</td>\n",
              "      <td>140.015853</td>\n",
              "      <td>140.015853</td>\n",
              "      <td>113.751022</td>\n",
              "      <td>166.280683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>2023-06-27</td>\n",
              "      <td>[DIXON, OBEROIRLTY, METROPOLIS]</td>\n",
              "      <td>[ADANIENT, NYKAA, BANDHANBNK]</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-27 09:15:00+05:30</td>\n",
              "      <td>2023-06-27 09:47:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-27 09:15:00+05:30</td>\n",
              "      <td>2023-06-27 12:30:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2023-06-27 12:06:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>2023-06-27 14:36:00+05:30</td>\n",
              "      <td>2023-06-27 10:15:00+05:30</td>\n",
              "      <td>10746.952425</td>\n",
              "      <td>10746.952425</td>\n",
              "      <td>141.107485</td>\n",
              "      <td>-64.171227</td>\n",
              "      <td>114.876218</td>\n",
              "      <td>-37.939959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>2023-06-28</td>\n",
              "      <td>[DIXON, METROPOLIS, POLICYBZR]</td>\n",
              "      <td>[ADANIENT, NYKAA, BANDHANBNK]</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2023-06-28 10:15:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2023-06-28 09:33:00+05:30</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-28 09:15:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>10727.982445</td>\n",
              "      <td>10727.982445</td>\n",
              "      <td>-849.465243</td>\n",
              "      <td>-283.517254</td>\n",
              "      <td>113.996073</td>\n",
              "      <td>-1246.978570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>2023-06-30</td>\n",
              "      <td>[DIXON, JSWENERGY, PFC]</td>\n",
              "      <td>[ADANIENT, NYKAA, BANDHANBNK]</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-30 09:15:00+05:30</td>\n",
              "      <td>2023-06-30 13:59:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-30 09:33:00+05:30</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2023-06-30 09:26:00+05:30</td>\n",
              "      <td>1</td>\n",
              "      <td>2023-06-30 09:15:00+05:30</td>\n",
              "      <td>2023-06-30 10:15:00+05:30</td>\n",
              "      <td>10104.49316</td>\n",
              "      <td>10104.49316</td>\n",
              "      <td>-248.368442</td>\n",
              "      <td>-248.368442</td>\n",
              "      <td>107.754045</td>\n",
              "      <td>-604.490929</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>246 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06f30095-a713-4ef4-a1d8-a739b202098e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-06f30095-a713-4ef4-a1d8-a739b202098e 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-06f30095-a713-4ef4-a1d8-a739b202098e');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result.to_csv('/content/nifty200_v4.csv', index=False)"
      ],
      "metadata": {
        "id": "85PdOPAZNJcZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fD65mnUvRON9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}