Final_Stock_Recommendation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUhzSg6XFX5p",
        "outputId": "1fe332f2-79d6-476a-f465-0d99ffc01c66"
      },
      "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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            "  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",
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            "  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",
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            "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"
          ]
        }
      ],
      "source": [
        "!pip install kiteconnect"
      ],
      "id": "EUhzSg6XFX5p"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VV_knESDFXrl",
        "outputId": "53724bc5-6f80-4275-f26c-3bac7e1da817"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "here\n"
          ]
        }
      ],
      "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(data[\"access_token\"])\n",
        "kite.set_access_token(\"dwICUtJsEQT1OOQQ0nNkc0TxcboFU2Rb\")\n",
        "\n",
        "# print(\"====================\")\n",
        "# print(\"Access Token: \",data[\"access_token\"])"
      ],
      "id": "VV_knESDFXrl"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "wV_MCUykFaK7",
        "outputId": "bd78c724-a12a-4977-ae51-c7eed8ec3bdf"
      },
      "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",
              "5494           2916865          11394               ZOTA   \n",
              "5495           7436801          29050              ZUARI   \n",
              "5496            979713           3827           ZUARIIND   \n",
              "5497           2029825           7929          ZYDUSLIFE   \n",
              "5498           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",
              "5494      ZOTA HEALTH CARE         0.0            0.0       0.05         1   \n",
              "5495  ZUARI AGRO CHEMICALS         0.0            0.0       0.05         1   \n",
              "5496      ZUARI INDUSTRIES         0.0            0.0       0.05         1   \n",
              "5497    ZYDUS LIFESCIENCES         0.0            0.0       0.05         1   \n",
              "5498        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",
              "5494              EQ      NSE      NSE  \n",
              "5495              EQ      NSE      NSE  \n",
              "5496              EQ      NSE      NSE  \n",
              "5497              EQ      NSE      NSE  \n",
              "5498              EQ      NSE      NSE  \n",
              "\n",
              "[5499 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c020814f-78de-4f82-a167-d014f196e87c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>5494</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>5495</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>5496</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>5497</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>5498</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>5499 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c020814f-78de-4f82-a167-d014f196e87c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "  <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",
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              "\n",
              "    .colab-df-convert: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",
              "\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-c020814f-78de-4f82-a167-d014f196e87c 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-c020814f-78de-4f82-a167-d014f196e87c');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "nse = kite.instruments('NSE')\n",
        "nse_data = pd.DataFrame(nse)\n",
        "nse_data"
      ],
      "id": "wV_MCUykFaK7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "109b4e41"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import time as Time\n",
        "from datetime import datetime, timedelta, time\n",
        "import yfinance as yf\n",
        "import pytz\n",
        "import warnings\n",
        "import sys\n",
        "from IPython.display import clear_output"
      ],
      "id": "109b4e41"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BhyejrvOlLCU"
      },
      "outputs": [],
      "source": [
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ],
      "id": "BhyejrvOlLCU"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4FDoXJkwSi_b"
      },
      "outputs": [],
      "source": [
        "def find_missing_elements(list1, list2):\n",
        "    missing_elements = [element for element in list1 if element not in list2]\n",
        "    return missing_elements\n",
        "\n",
        "def extract_strings(input_list):\n",
        "    output_list = []\n",
        "    for input_string in input_list:\n",
        "        split_string = input_string.split('.')\n",
        "        output_list.append(split_string[0])\n",
        "    return output_list"
      ],
      "id": "4FDoXJkwSi_b"
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta\n",
        "import pandas as pd\n",
        "\n",
        "def calculate_P(df, k, date):\n",
        "    days = [14, 28, 56, 112, 224, 336, 448, 896]\n",
        "    P = 0\n",
        "\n",
        "    for i in range(len(days)):\n",
        "        mask = (df.index >= (date - timedelta(days=days[i]))) & (df.index <= date)\n",
        "        data = df.loc[mask]\n",
        "        value = ((data['high'] - data['open']) * 100 / data['open'] > k).sum()\n",
        "        P += (2 ** (len(days) - i - 1)) * value\n",
        "\n",
        "    return P\n",
        "\n",
        "\n",
        "def calculate_R(df, k, date):\n",
        "    days = [14, 28, 56, 112, 224, 336, 448, 896]\n",
        "    R = 0\n",
        "\n",
        "    for i in range(len(days)):\n",
        "        mask = (df.index >= (date - timedelta(days=days[i]))) & (df.index <= date)\n",
        "        data = df.loc[mask]\n",
        "        value = ((data['high'] - data['open']) * 100 / data['open'] < k).sum()\n",
        "        R += (2 ** (len(days) - i - 1)) * value\n",
        "\n",
        "    return R\n",
        "\n",
        "\n",
        "def calculate_P2(df, k, date):\n",
        "    days = [14, 28, 56, 112, 224, 336, 448, 896]\n",
        "    P = 0\n",
        "\n",
        "    for i in range(len(days)):\n",
        "        mask = (df.index >= (date - timedelta(days=days[i]))) & (df.index <= date)\n",
        "        data = df.loc[mask]\n",
        "        value = ((data['open'] - data['low']) * 100 / data['open'] > k).sum()\n",
        "        P += (2 ** (len(days) - i - 1)) * value\n",
        "\n",
        "    return P\n",
        "\n",
        "\n",
        "def calculate_R2(df, k, date):\n",
        "    days = [14, 28, 56, 112, 224, 336, 448, 896]\n",
        "    R = 0\n",
        "\n",
        "    for i in range(len(days)):\n",
        "        mask = (df.index >= (date - timedelta(days=days[i]))) & (df.index <= date)\n",
        "        data = df.loc[mask]\n",
        "        value = ((data['open'] - data['low']) * 100 / data['open'] < k).sum()\n",
        "        R += (2 ** (len(days) - i - 1)) * value\n",
        "\n",
        "    return R"
      ],
      "metadata": {
        "id": "BJTGCM2epQ_R"
      },
      "id": "BJTGCM2epQ_R",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_intraday_margin_sell(symbol):\n",
        "    order_param_single = [{\n",
        "            \"exchange\": \"NSE\",\n",
        "            \"tradingsymbol\": symbol,\n",
        "            \"transaction_type\": \"SELL\",\n",
        "            \"variety\": \"regular\",\n",
        "            \"product\": \"MIS\",\n",
        "            \"order_type\": \"MARKET\",\n",
        "            \"quantity\": 1\n",
        "        }]\n",
        "    return kite.order_margins(order_param_single)[0]['leverage']\n",
        "\n",
        "def get_intraday_margin(symbol):\n",
        "\n",
        "    order_param_single = [{\n",
        "            \"exchange\": \"NSE\",\n",
        "            \"tradingsymbol\": symbol,\n",
        "            \"transaction_type\": \"BUY\",\n",
        "            \"variety\": \"regular\",\n",
        "            \"product\": \"MIS\",\n",
        "            \"order_type\": \"MARKET\",\n",
        "            \"quantity\": 1\n",
        "        }]\n",
        "    return kite.order_margins(order_param_single)[0]['leverage']\n",
        "\n",
        "def filter_by_margin(input_list, type):\n",
        "    if(type == 'buy'):\n",
        "        output = [element for element in input_list if get_intraday_margin(element)==5]\n",
        "    if(type == 'sell'):\n",
        "        output = [element for element in input_list if get_intraday_margin_sell(element)==5]\n",
        "    return output"
      ],
      "metadata": {
        "id": "r1DXKwfdH9UX"
      },
      "id": "r1DXKwfdH9UX",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta\n",
        "import pandas as pd\n",
        "\n",
        "def archive_stock_data(symbol, stock_data, date):\n",
        "    # print(date)\n",
        "    # stock_data.head()\n",
        "    try:\n",
        "        # Define the timeframes\n",
        "        timeframes = {\"2y\": \"2y\", \"1y\": \"1y\", \"6m\": \"6mo\", \"16w\": \"16wk\", \"8w\": \"8wk\", \"4w\": \"4wk\", \"2w\": \"2wk\", \"1w\": \"1wk\"}\n",
        "\n",
        "        # Calculate the P values for each k\n",
        "        k_values = [0.25, 0.35, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5]\n",
        "        l_values = [0, 0.1, 0.2, 0.3, 0.5]\n",
        "        p_values = []\n",
        "        r_values = []\n",
        "        p_values2 = []\n",
        "        r_values2 = []\n",
        "\n",
        "        for k in k_values:\n",
        "            p_value = calculate_P(stock_data, k, date)\n",
        "            p_values.append(p_value)\n",
        "            p_value2 = calculate_P2(stock_data, k, date)\n",
        "            p_values2.append(p_value2)\n",
        "\n",
        "        for l in l_values:\n",
        "            r_value = calculate_R(stock_data, l, date)\n",
        "            r_values.append(r_value)\n",
        "            r_value2 = calculate_R2(stock_data, l, date)\n",
        "            r_values2.append(r_value2)\n",
        "\n",
        "        return symbol, stock_data, p_values, r_values, p_values2, r_values2\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error fetching data for {symbol}: {e}\")\n",
        "        return symbol, None, None, None, None, None\n"
      ],
      "metadata": {
        "id": "_CsqhH_Rx3yv"
      },
      "id": "_CsqhH_Rx3yv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_stock_data(stock_archive, start_date, end_date):\n",
        "    # Find the start and end date locations\n",
        "    strt = None\n",
        "    end = None\n",
        "    for i, date in enumerate(stock_archive):\n",
        "        if date == start_date:\n",
        "            strt = i\n",
        "        if date == end_date:\n",
        "            end = i\n",
        "            break\n",
        "\n",
        "    # Check if both start and end date locations are found\n",
        "    if strt is None or end is None:\n",
        "        print(\"Start or end date not found in the stock archive.\")\n",
        "        return None\n",
        "\n",
        "    # Retrieve the sublist from stock_archive\n",
        "    sublist = stock_archive[strt:end+1]\n",
        "\n",
        "    return sublist"
      ],
      "metadata": {
        "id": "yu9oMDNXPk6b"
      },
      "id": "yu9oMDNXPk6b",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#SUJAL\n",
        "column_names = [\"Code\"]\n",
        "# nifty500 = pd.read_csv(\"ind_nifty500list.csv\", names=column_names)\n",
        "# nifty500 = pd.read_excel(\"/content/temp_testing.xlsx\", names=column_names)\n",
        "nifty500 = pd.read_csv(\"/content/harshad.csv\", names=column_names)\n",
        "\n",
        "symbol = nifty500.Code.to_list()\n",
        "\n",
        "columns = ['Symbol'] + [f'P{k:.2f}' for k in [0.25, 0.35, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5]] + [f'R{k:.2f}' for k in [0, 0.1, 0.2, 0.3, 0.5]]\n",
        "\n",
        "output_df = pd.DataFrame(columns=columns)\n",
        "output_df2 = pd.DataFrame(columns=columns)\n",
        "\n",
        "k_values = [0.25, 0.35, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5]\n",
        "l_values = [0, 0.1, 0.2, 0.3, 0.5]\n",
        "start_date = datetime(2023, 7, 3)\n",
        "end_date = datetime(2023, 7, 5)\n",
        "date_range = pd.date_range(start=start_date, end=end_date)\n",
        "results=[]\n",
        "\n",
        "stock_archive = {}\n",
        "diff = (end_date - start_date).days\n",
        "# print(diff)\n",
        "for i in symbol[:3]:\n",
        "    print(\"== downloading for symbol:   \", i);\n",
        "    # a = nse_data[nse_data.tradingsymbol == i].instrument_token.values\n",
        "    a = nse_data[nse_data.tradingsymbol == i[:-3]].instrument_token.values\n",
        "    try:\n",
        "      token=a[0]\n",
        "    except Exception as e:\n",
        "      print(e)\n",
        "      continue\n",
        "    # df=kite.historical_data(token, str(end_date - timedelta(days=365 * 2 + (end_date - start_date).days)), str(end_date), \"day\", False, False)\n",
        "    # df=kite.historical_data(7929, str(end_date - timedelta(days=365 * 2 + (end_date - start_date).days)), str(end_date), \"day\", False, False)\n",
        "    df=kite.historical_data(token, str(start_date - timedelta(days=365 * 2)), str(end_date), \"day\", False, False)\n",
        "    df = pd.DataFrame(df)\n",
        "    if(len(df)==0):\n",
        "      print(\"no data\")\n",
        "      continue\n",
        "    df['date'] = pd.to_datetime(df['date']).dt.date\n",
        "    df = df.set_index('date')\n",
        "    # print(df)\n",
        "    stock_archive[i] = df\n",
        "\n",
        "for date_i in range(diff):\n",
        "    symbol = nifty500.Code.to_list()\n",
        "    columns = ['Symbol'] + [f'P{k:.2f}' for k in [0.25, 0.35, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5]] + [f'R{k:.2f}' for k in [0, 0.1, 0.2, 0.3, 0.5]]\n",
        "    output_df = pd.DataFrame(columns=columns)\n",
        "    output_df2 = pd.DataFrame(columns=columns)\n",
        "    k_values = [0.25, 0.35, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5]\n",
        "    l_values = [0, 0.1, 0.2, 0.3, 0.5]\n",
        "\n",
        "\n",
        "    for i in symbol[:3]:\n",
        "        strt = date_i\n",
        "        strt_date=(start_date+timedelta(days=strt)).strftime('%Y-%m-%d')\n",
        "        strt_date = datetime.strptime(strt_date, '%Y-%m-%d').date()\n",
        "        # print(strt_date)\n",
        "        try:\n",
        "          end = len(stock_archive[i]) - (end_date - start_date).days + date_i\n",
        "          # end = date_i\n",
        "          # end = (stock_archive['days'][-1]-stock_archive['days'][0]).days - (end_date - start_date).days + date_i\n",
        "          # end_dat=(start_date+timedelta(days=end)).strftime('%Y-%m-%d')\n",
        "          # print(end_dat)\n",
        "        except Exception as e:\n",
        "          continue\n",
        "        date = start_date + timedelta(days=date_i)\n",
        "        # print(date)\n",
        "\n",
        "        # symbol, stock_data, p_values, r_values, p_values2, r_values2 = archive_stock_data(i, stock_archive[i][strt:end], date.date())\n",
        "        # symbol, stock_data, p_values, r_values, p_values2, r_values2 = archive_stock_data(i, stock_archive[i][stock_archive[i]['date'].find(start_date):stock_archive[i]['date'].find(end_date)], date.date())\n",
        "        # symbol, stock_data, p_values, r_values, p_values2, r_values2 = archive_stock_data(i, stock_archive[i].loc[(stock_archive[i].index >= strt_date - pd.DateOffset(years=2)) & (stock_archive[i].index < strt_date)], date.date())\n",
        "        # stock_archive[i].loc[(stock_archive[i]['date'] >= strt_date - pd.DateOffset(years=2)) & (stock_archive[i]['date'] < strt_date)]\n",
        "        # selected_data = stock_archive[i].loc[(stock_archive[i].index >= strt_date - pd.DateOffset(years=2)) & (stock_archive[i].index < strt_date)]\n",
        "        symbol, stock_data, p_values, r_values, p_values2, r_values2 = archive_stock_data(i, stock_archive[i].loc[(stock_archive[i].index >= pd.Timestamp(strt_date) - pd.DateOffset(years=2)) & (stock_archive[i].index < pd.Timestamp(strt_date))], pd.Timestamp(strt_date).date())\n",
        "        print(stock_archive[i].loc[(stock_archive[i].index >= pd.Timestamp(strt_date) - pd.DateOffset(years=2)) & (stock_archive[i].index < pd.Timestamp(strt_date))])\n",
        "        # print(symbol)\n",
        "        # print(stock_data)\n",
        "\n",
        "        if stock_data is None:\n",
        "            continue\n",
        "\n",
        "        output_data = {'Symbol': symbol}\n",
        "        output_data2 = {'Symbol': symbol}\n",
        "\n",
        "        for i, p_value in enumerate(p_values):\n",
        "            output_data[f'P{k_values[i]:.2f}'] = p_value\n",
        "\n",
        "        for i, p_value in enumerate(p_values2):\n",
        "            output_data2[f'P{k_values[i]:.2f}'] = p_value\n",
        "\n",
        "        for i, r_value in enumerate(r_values):\n",
        "            output_data[f'R{l_values[i]:.2f}'] = r_value\n",
        "\n",
        "        for i, r_value in enumerate(r_values2):\n",
        "            output_data2[f'R{l_values[i]:.2f}'] = r_value\n",
        "\n",
        "        output_df.loc[len(output_df)] = output_data\n",
        "        output_df2.loc[len(output_df2)] = output_data2\n",
        "\n",
        "    output_df['Pro'] = 8 * output_df['P2.50'] + 4 * output_df['P3.00'] + 4 * output_df['P1.00'] + 8 * output_df['P5.00']\n",
        "    output_df['Ris'] = 2 * output_df['R0.50'] + 4 * output_df['R0.30'] + 8 * output_df['R0.10']\n",
        "    output_df['Val'] = output_df['Pro'] * output_df['Pro'] / (output_df['Ris'] + 1)\n",
        "\n",
        "    output_df2['Pro'] = 8 * output_df2['P2.50'] + 4 * output_df2['P3.00'] + 4 * output_df2['P1.00'] + 8 * output_df2['P5.00']\n",
        "    output_df2['Ris'] = 2 * output_df2['R0.50'] + 4 * output_df2['R0.30'] + 8 * output_df2['R0.10']\n",
        "    output_df2['Val'] = output_df2['Pro'] * output_df2['Pro'] / (output_df2['Ris'] + 1)\n",
        "\n",
        "    df = output_df.sort_values(output_df.columns[-1], ascending=False)\n",
        "    df_dash = output_df2.sort_values(output_df2.columns[-1], ascending=False)\n",
        "\n",
        "    output_df['Val2'] = output_df['Pro'] / (output_df['Ris'] * output_df['Ris'] + 1)\n",
        "    output_df2['Val2'] = output_df2['Pro'] / (output_df2['Ris'] * output_df2['Ris'] + 1)\n",
        "    # print(output_df)\n",
        "    # print(output_df2)\n",
        "\n",
        "    df2 = output_df.sort_values(output_df.columns[-1], ascending=False)\n",
        "    df2_dash = output_df2.sort_values(output_df2.columns[-1], ascending=False)\n",
        "\n",
        "    n = 10  # Assumed no. of stocks to be invested\n",
        "    symbol1 = df2.iloc[:n, 0].tolist()\n",
        "    symbol2 = df2_dash.iloc[:n, 0].tolist()\n",
        "\n",
        "\n",
        "    buy_symbols = find_missing_elements(symbol1, symbol2)\n",
        "    sell_symbols = find_missing_elements(symbol2, symbol1)\n",
        "    buy_symbols = extract_strings(buy_symbols)\n",
        "    sell_symbols = extract_strings(sell_symbols)\n",
        "    BUY_SYMBOLS = filter_by_margin(buy_symbols, 'buy')\n",
        "    SELL_SYMBOLS = filter_by_margin(sell_symbols, 'sell')\n",
        "\n",
        "    new_df = {'Date': date.strftime('%Y-%m-%d'), 'Buy': BUY_SYMBOLS[:3], 'SeLl': SELL_SYMBOLS[:3]}\n",
        "\n",
        "    results.append(new_df)\n",
        "    print(date.strftime('%Y-%m-%d'))\n",
        "# Create a dataframe from the results list\n",
        "out = pd.DataFrame(results)\n",
        "out"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "00281970-3db0-44f6-b4cb-c62c4e19f4b1",
        "id": "9D-SP1LlH_Df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== downloading for symbol:    ABB.NS\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TokenException",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTokenException\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-78b1918545b1>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;31m# df=kite.historical_data(token, str(end_date - timedelta(days=365 * 2 + (end_date - start_date).days)), str(end_date), \"day\", False, False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;31m# df=kite.historical_data(7929, str(end_date - timedelta(days=365 * 2 + (end_date - start_date).days)), str(end_date), \"day\", False, False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistorical_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_date\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m365\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"day\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\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     36\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/kiteconnect/connect.py\u001b[0m in \u001b[0;36mhistorical_data\u001b[0;34m(self, instrument_token, from_date, to_date, interval, continuous, oi)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0mto_date_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_date\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdate_string_format\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_date\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mto_date\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m         data = self._get(\"market.historical\",\n\u001b[0m\u001b[1;32m    632\u001b[0m                          \u001b[0murl_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"instrument_token\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minstrument_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"interval\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minterval\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m                          params={\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/kiteconnect/connect.py\u001b[0m in \u001b[0;36m_get\u001b[0;34m(self, route, url_args, params, is_json)\u001b[0m\n\u001b[1;32m    849\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroute\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_json\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m         \u001b[0;34m\"\"\"Alias for sending a GET request.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 851\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"GET\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_json\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_json\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    852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_post\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroute\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_json\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/kiteconnect/connect.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, route, method, url_args, params, is_json, query_params)\u001b[0m\n\u001b[1;32m    925\u001b[0m                 \u001b[0;31m# native Kite errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m                 \u001b[0mexp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"error_type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeneralException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 927\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"message\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\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    928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTokenException\u001b[0m: Incorrect `api_key` or `access_token`."
          ]
        }
      ],
      "id": "9D-SP1LlH_Df"
    },
    {
      "cell_type": "code",
      "source": [
        "out.to_csv('/content/nifty200_3.csv', index=False)"
      ],
      "metadata": {
        "id": "X65oeuLgQHi_"
      },
      "id": "X65oeuLgQHi_",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}