submit_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "83715e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlsxwriter \n",
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import yfinance as yf\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e32b7838",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"D:\\quant PS\\put_option_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5c764673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>Option type</th>\n",
       "      <th>Strike Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>No. of contracts</th>\n",
       "      <th>Open Int</th>\n",
       "      <th>Change in OI</th>\n",
       "      <th>Underlying Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>PE</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>5.60</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.70</td>\n",
       "      <td>323130.00</td>\n",
       "      <td>1285750.00</td>\n",
       "      <td>276300.00</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>PE</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>3.65</td>\n",
       "      <td>5.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>4.50</td>\n",
       "      <td>138440.00</td>\n",
       "      <td>287375.00</td>\n",
       "      <td>151975.00</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>PE</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>4.75</td>\n",
       "      <td>6.55</td>\n",
       "      <td>3.20</td>\n",
       "      <td>5.45</td>\n",
       "      <td>91321.00</td>\n",
       "      <td>476550.00</td>\n",
       "      <td>470175.00</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>PE</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>4.95</td>\n",
       "      <td>7.25</td>\n",
       "      <td>4.40</td>\n",
       "      <td>5.85</td>\n",
       "      <td>147668.00</td>\n",
       "      <td>1091575.00</td>\n",
       "      <td>384375.00</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>PE</td>\n",
       "      <td>39500.0</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7.60</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5.85</td>\n",
       "      <td>127925.00</td>\n",
       "      <td>911475.00</td>\n",
       "      <td>304850.00</td>\n",
       "      <td>45158.10</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>79071</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>PE</td>\n",
       "      <td>49500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2461.35</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79072</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>PE</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>3101.75</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79073</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>PE</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>3828.25</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79074</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>PE</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4638.15</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79075</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>PE</td>\n",
       "      <td>55500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>5526.60</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79076 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Expiry   Option type    Strike Price   Open   High    \\\n",
       "0      03-Jul-2023  06-Jul-2023            PE         37500.0   4.45   5.60   \n",
       "1      03-Jul-2023  06-Jul-2023            PE         38000.0   3.65   5.95   \n",
       "2      03-Jul-2023  06-Jul-2023            PE         38500.0   4.75   6.55   \n",
       "3      03-Jul-2023  06-Jul-2023            PE         39000.0   4.95   7.25   \n",
       "4      03-Jul-2023  06-Jul-2023            PE         39500.0   5.30   7.60   \n",
       "...            ...          ...           ...             ...    ...    ...   \n",
       "79071  29-Dec-2023  26-Dec-2024            PE         49500.0      -      -   \n",
       "79072  29-Dec-2023  26-Dec-2024            PE         51000.0      -      -   \n",
       "79073  29-Dec-2023  26-Dec-2024            PE         52500.0      -      -   \n",
       "79074  29-Dec-2023  26-Dec-2024            PE         54000.0      -      -   \n",
       "79075  29-Dec-2023  26-Dec-2024            PE         55500.0      -      -   \n",
       "\n",
       "      Low    Close   No. of contracts    Open Int   Change in OI    \\\n",
       "0      2.85     3.70          323130.00  1285750.00      276300.00   \n",
       "1      3.40     4.50          138440.00   287375.00      151975.00   \n",
       "2      3.20     5.45           91321.00   476550.00      470175.00   \n",
       "3      4.40     5.85          147668.00  1091575.00      384375.00   \n",
       "4      4.15     5.85          127925.00   911475.00      304850.00   \n",
       "...     ...      ...                ...         ...            ...   \n",
       "79071     -  2461.35                  -           -              -   \n",
       "79072     -  3101.75                  -           -              -   \n",
       "79073     -  3828.25                  -           -              -   \n",
       "79074     -  4638.15                  -           -              -   \n",
       "79075     -  5526.60                  -           -              -   \n",
       "\n",
       "       Underlying Value    \n",
       "0                45158.10  \n",
       "1                45158.10  \n",
       "2                45158.10  \n",
       "3                45158.10  \n",
       "4                45158.10  \n",
       "...                   ...  \n",
       "79071            48292.25  \n",
       "79072            48292.25  \n",
       "79073            48292.25  \n",
       "79074            48292.25  \n",
       "79075            48292.25  \n",
       "\n",
       "[79076 rows x 12 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e6f446d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final=pd.read_csv(\"D:\\quant PS\\index_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ebfa700d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-07-03</td>\n",
       "      <td>44957.949219</td>\n",
       "      <td>45353.199219</td>\n",
       "      <td>44882.101562</td>\n",
       "      <td>45158.101562</td>\n",
       "      <td>228200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-07-04</td>\n",
       "      <td>45310.550781</td>\n",
       "      <td>45655.500000</td>\n",
       "      <td>45000.250000</td>\n",
       "      <td>45301.449219</td>\n",
       "      <td>463000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-07-05</td>\n",
       "      <td>45169.699219</td>\n",
       "      <td>45418.898438</td>\n",
       "      <td>45073.398438</td>\n",
       "      <td>45151.800781</td>\n",
       "      <td>366300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-07-06</td>\n",
       "      <td>45060.550781</td>\n",
       "      <td>45417.500000</td>\n",
       "      <td>45042.601562</td>\n",
       "      <td>45339.898438</td>\n",
       "      <td>244900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-07-07</td>\n",
       "      <td>45118.050781</td>\n",
       "      <td>45227.250000</td>\n",
       "      <td>44844.800781</td>\n",
       "      <td>44925.000000</td>\n",
       "      <td>220900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>47837.750000</td>\n",
       "      <td>48071.398438</td>\n",
       "      <td>47415.851562</td>\n",
       "      <td>47491.851562</td>\n",
       "      <td>165800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>47576.398438</td>\n",
       "      <td>47838.449219</td>\n",
       "      <td>47411.648438</td>\n",
       "      <td>47724.851562</td>\n",
       "      <td>118700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>47818.500000</td>\n",
       "      <td>48347.648438</td>\n",
       "      <td>47806.699219</td>\n",
       "      <td>48282.199219</td>\n",
       "      <td>184900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>48479.050781</td>\n",
       "      <td>48636.449219</td>\n",
       "      <td>48343.148438</td>\n",
       "      <td>48508.550781</td>\n",
       "      <td>391100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>48374.851562</td>\n",
       "      <td>48477.699219</td>\n",
       "      <td>48091.851562</td>\n",
       "      <td>48292.250000</td>\n",
       "      <td>174000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Open          High           Low         Close  \\\n",
       "0    2023-07-03  44957.949219  45353.199219  44882.101562  45158.101562   \n",
       "1    2023-07-04  45310.550781  45655.500000  45000.250000  45301.449219   \n",
       "2    2023-07-05  45169.699219  45418.898438  45073.398438  45151.800781   \n",
       "3    2023-07-06  45060.550781  45417.500000  45042.601562  45339.898438   \n",
       "4    2023-07-07  45118.050781  45227.250000  44844.800781  44925.000000   \n",
       "..          ...           ...           ...           ...           ...   \n",
       "118  2023-12-22  47837.750000  48071.398438  47415.851562  47491.851562   \n",
       "119  2023-12-26  47576.398438  47838.449219  47411.648438  47724.851562   \n",
       "120  2023-12-27  47818.500000  48347.648438  47806.699219  48282.199219   \n",
       "121  2023-12-28  48479.050781  48636.449219  48343.148438  48508.550781   \n",
       "122  2023-12-29  48374.851562  48477.699219  48091.851562  48292.250000   \n",
       "\n",
       "     Volume  \n",
       "0    228200  \n",
       "1    463000  \n",
       "2    366300  \n",
       "3    244900  \n",
       "4    220900  \n",
       "..      ...  \n",
       "118  165800  \n",
       "119  118700  \n",
       "120  184900  \n",
       "121  391100  \n",
       "122  174000  \n",
       "\n",
       "[123 rows x 6 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "46bb3cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# i am using weighted moving average instead of simple moving average \n",
    "def xday_MA(final,x):\n",
    "    Seven_MA=np.zeros(123)\n",
    "    den=0\n",
    "    add=0\n",
    "    for i in range(x-1,123):\n",
    "        den=0\n",
    "        add=0\n",
    "        for j in range(x):\n",
    "            den=den+(j+1)\n",
    "            add=add+(final['Close'][i-j])*(x-j)\n",
    "        Seven_MA[i]=add/den\n",
    "    return Seven_MA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "79e7b807",
   "metadata": {},
   "outputs": [],
   "source": [
    "sevendays=xday_MA(final,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "37c74ce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tw1days=xday_MA(final,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7b87a815",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['Seven_MA']=sevendays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a2bc5090",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['tw1_MA']=tw1days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d00d044c",
   "metadata": {},
   "outputs": [],
   "source": [
    "MA=np.zeros(123)\n",
    "add2=0\n",
    "for i in range(20,123):\n",
    "    p=0\n",
    "    add2=0\n",
    "    while p<21:\n",
    "        add2=add2+(final['Volume'][(i-p)])/21\n",
    "        p=p+1\n",
    "    MA[i]=add2\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "98314c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['Volume_MA']=MA#21 days moving average of volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5f44aec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "for i in range(123):\n",
    "    final.Date[i]=datetime.strptime(final.Date[i], '%Y-%m-%d').date()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ffe33ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(df.shape[0]):#converting string to datetime\n",
    "    df[df.columns[0]][i]=datetime.strptime(df[df.columns[0]][i], '%d-%b-%Y').date()\n",
    "    df[df.columns[1]][i]=datetime.strptime(df[df.columns[1]][i], '%d-%b-%Y').date()\n",
    "#hold on it will take time.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a102e95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Seven_MA</th>\n",
       "      <th>tw1_MA</th>\n",
       "      <th>Volume_MA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-07-03</td>\n",
       "      <td>44957.949219</td>\n",
       "      <td>45353.199219</td>\n",
       "      <td>44882.101562</td>\n",
       "      <td>45158.101562</td>\n",
       "      <td>228200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-07-04</td>\n",
       "      <td>45310.550781</td>\n",
       "      <td>45655.500000</td>\n",
       "      <td>45000.250000</td>\n",
       "      <td>45301.449219</td>\n",
       "      <td>463000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-07-05</td>\n",
       "      <td>45169.699219</td>\n",
       "      <td>45418.898438</td>\n",
       "      <td>45073.398438</td>\n",
       "      <td>45151.800781</td>\n",
       "      <td>366300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-07-06</td>\n",
       "      <td>45060.550781</td>\n",
       "      <td>45417.500000</td>\n",
       "      <td>45042.601562</td>\n",
       "      <td>45339.898438</td>\n",
       "      <td>244900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-07-07</td>\n",
       "      <td>45118.050781</td>\n",
       "      <td>45227.250000</td>\n",
       "      <td>44844.800781</td>\n",
       "      <td>44925.000000</td>\n",
       "      <td>220900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>47837.750000</td>\n",
       "      <td>48071.398438</td>\n",
       "      <td>47415.851562</td>\n",
       "      <td>47491.851562</td>\n",
       "      <td>165800</td>\n",
       "      <td>47707.730329</td>\n",
       "      <td>47185.604471</td>\n",
       "      <td>207895.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>47576.398438</td>\n",
       "      <td>47838.449219</td>\n",
       "      <td>47411.648438</td>\n",
       "      <td>47724.851562</td>\n",
       "      <td>118700</td>\n",
       "      <td>47696.380720</td>\n",
       "      <td>47301.857870</td>\n",
       "      <td>210104.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>47818.500000</td>\n",
       "      <td>48347.648438</td>\n",
       "      <td>47806.699219</td>\n",
       "      <td>48282.199219</td>\n",
       "      <td>184900</td>\n",
       "      <td>47824.634068</td>\n",
       "      <td>47450.825335</td>\n",
       "      <td>214300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>48479.050781</td>\n",
       "      <td>48636.449219</td>\n",
       "      <td>48343.148438</td>\n",
       "      <td>48508.550781</td>\n",
       "      <td>391100</td>\n",
       "      <td>48004.523577</td>\n",
       "      <td>47600.832995</td>\n",
       "      <td>225723.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>48374.851562</td>\n",
       "      <td>48477.699219</td>\n",
       "      <td>48091.851562</td>\n",
       "      <td>48292.250000</td>\n",
       "      <td>174000</td>\n",
       "      <td>48107.450335</td>\n",
       "      <td>47711.144041</td>\n",
       "      <td>226757.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Open          High           Low         Close  \\\n",
       "0    2023-07-03  44957.949219  45353.199219  44882.101562  45158.101562   \n",
       "1    2023-07-04  45310.550781  45655.500000  45000.250000  45301.449219   \n",
       "2    2023-07-05  45169.699219  45418.898438  45073.398438  45151.800781   \n",
       "3    2023-07-06  45060.550781  45417.500000  45042.601562  45339.898438   \n",
       "4    2023-07-07  45118.050781  45227.250000  44844.800781  44925.000000   \n",
       "..          ...           ...           ...           ...           ...   \n",
       "118  2023-12-22  47837.750000  48071.398438  47415.851562  47491.851562   \n",
       "119  2023-12-26  47576.398438  47838.449219  47411.648438  47724.851562   \n",
       "120  2023-12-27  47818.500000  48347.648438  47806.699219  48282.199219   \n",
       "121  2023-12-28  48479.050781  48636.449219  48343.148438  48508.550781   \n",
       "122  2023-12-29  48374.851562  48477.699219  48091.851562  48292.250000   \n",
       "\n",
       "     Volume      Seven_MA        tw1_MA      Volume_MA  \n",
       "0    228200      0.000000      0.000000       0.000000  \n",
       "1    463000      0.000000      0.000000       0.000000  \n",
       "2    366300      0.000000      0.000000       0.000000  \n",
       "3    244900      0.000000      0.000000       0.000000  \n",
       "4    220900      0.000000      0.000000       0.000000  \n",
       "..      ...           ...           ...            ...  \n",
       "118  165800  47707.730329  47185.604471  207895.238095  \n",
       "119  118700  47696.380720  47301.857870  210104.761905  \n",
       "120  184900  47824.634068  47450.825335  214300.000000  \n",
       "121  391100  48004.523577  47600.832995  225723.809524  \n",
       "122  174000  48107.450335  47711.144041  226757.142857  \n",
       "\n",
       "[123 rows x 9 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def day_diff(s1,s2):\n",
    "    days_difference = (s1- s2).days\n",
    "    return days_difference\n",
    "#final=final[20:]\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3eea7e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12467"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans=0\n",
    "for i in range(df.shape[0]):\n",
    "    if(df[df.columns[0]][i]==final.Date[20]):\n",
    "        ans=i\n",
    "        break\n",
    "diff=int(float(df[df.columns[3]][1])-float(df[df.columns[3]][0]))\n",
    "ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "856967d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39994480., 40255650., 36499920., 49335350., 31968650., 37793855.,\n",
       "       45550520., 47730400., 52812190., 22742840., 35554755., 45116105.,\n",
       "       46595080., 24557785., 38880600., 36033090., 67506695., 51370250.,\n",
       "       26242110., 39287520., 43097930., 38020245., 55160615., 38900850.,\n",
       "       50014450., 54296235., 60419935., 41883645., 48915585., 61127880.,\n",
       "       61399765., 84873940., 34822630., 43105675., 43002945., 49534765.,\n",
       "       21955545., 26411995., 33845455., 38789035., 47504295., 48725965.,\n",
       "       28728030., 41840200., 55848720., 24941655., 33630810., 27978840.,\n",
       "       54405375., 68662335., 27853560., 28174950., 35444505., 45668220.,\n",
       "       42659370., 24258035., 27324335., 23792255., 31256110., 44195460.,\n",
       "       28765350., 47219485., 46452100., 66129150., 28625860., 36634940.,\n",
       "       45948395., 61619060., 65065465., 30272315., 37459655., 39186635.,\n",
       "       50413115., 58772970., 19611915., 33202475., 39190310., 36717980.,\n",
       "       61227920., 27263105., 35038460., 50422455., 65810235., 62871450.,\n",
       "       29359020., 59612025., 64081800., 67723935., 35082435., 45642135.,\n",
       "       44588745., 50708475., 78584115., 34769445., 36703395., 40136925.,\n",
       "       38964315., 51804930., 27021700., 28566520., 46910800., 60492340.,\n",
       "       50361505.])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here we have calculated open interest of put option which is at the money and out of the money to the extent it matter most . In the money and far out of the money gives wrong sentiment  \n",
    "new_col=np.zeros(103)\n",
    "j=0\n",
    "put_oi=0\n",
    "date=final.Date[20]\n",
    "for i in range(ans,df.shape[0]):\n",
    "    if(j<103):\n",
    "        if df[df.columns[0]][i]==date and (float(df[df.columns[3]][i])<=final['Close'][j+20]) and (8*diff+float(df[df.columns[3]][i])>=final['Close'][j+20]):\n",
    "            if df[df.columns[9]][i]!='-':#at the money and out of the money datas are important \n",
    "                put_oi+=float(df[df.columns[9]][i])\n",
    "        elif df[df.columns[0]][i]!=date:\n",
    "            date=df[df.columns[0]][i]\n",
    "            new_col[j]=put_oi\n",
    "            put_oi=0\n",
    "            if df[df.columns[9]][i]!='-':\n",
    "                put_oi+=float(df[df.columns[9]][i])\n",
    "            j=j+1\n",
    "new_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ca08f303",
   "metadata": {},
   "outputs": [],
   "source": [
    "call=pd.read_csv(\"D:\\quant PS\\call_option_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e4d96558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Expiry</th>\n",
       "      <th>Option type</th>\n",
       "      <th>Strike Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>No. of contracts</th>\n",
       "      <th>Open Int</th>\n",
       "      <th>Change in OI</th>\n",
       "      <th>Underlying Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>CE</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>6556.85</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>CE</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>6067.95</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>CE</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>5650.00</td>\n",
       "      <td>-</td>\n",
       "      <td>100.00</td>\n",
       "      <td>-</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>CE</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>5325.00</td>\n",
       "      <td>-</td>\n",
       "      <td>125.00</td>\n",
       "      <td>-</td>\n",
       "      <td>45158.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-Jul-2023</td>\n",
       "      <td>06-Jul-2023</td>\n",
       "      <td>CE</td>\n",
       "      <td>39500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>5230.10</td>\n",
       "      <td>-</td>\n",
       "      <td>1025.00</td>\n",
       "      <td>-</td>\n",
       "      <td>45158.10</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>78958</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>CE</td>\n",
       "      <td>49500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4936.25</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78959</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>CE</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>4181.70</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78960</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>CE</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>3513.25</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78961</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>CE</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2928.20</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78962</th>\n",
       "      <td>29-Dec-2023</td>\n",
       "      <td>26-Dec-2024</td>\n",
       "      <td>CE</td>\n",
       "      <td>55500.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>2421.65</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>48292.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78963 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Expiry   Option type    Strike Price   Open   High    \\\n",
       "0      03-Jul-2023  06-Jul-2023            CE         37500.0      -      -   \n",
       "1      03-Jul-2023  06-Jul-2023            CE         38000.0      -      -   \n",
       "2      03-Jul-2023  06-Jul-2023            CE         38500.0      -      -   \n",
       "3      03-Jul-2023  06-Jul-2023            CE         39000.0      -      -   \n",
       "4      03-Jul-2023  06-Jul-2023            CE         39500.0      -      -   \n",
       "...            ...          ...           ...             ...    ...    ...   \n",
       "78958  29-Dec-2023  26-Dec-2024            CE         49500.0      -      -   \n",
       "78959  29-Dec-2023  26-Dec-2024            CE         51000.0      -      -   \n",
       "78960  29-Dec-2023  26-Dec-2024            CE         52500.0      -      -   \n",
       "78961  29-Dec-2023  26-Dec-2024            CE         54000.0      -      -   \n",
       "78962  29-Dec-2023  26-Dec-2024            CE         55500.0      -      -   \n",
       "\n",
       "      Low    Close   No. of contracts   Open Int   Change in OI    \\\n",
       "0         -  6556.85                  -          -              -   \n",
       "1         -  6067.95                  -          -              -   \n",
       "2         -  5650.00                  -     100.00              -   \n",
       "3         -  5325.00                  -     125.00              -   \n",
       "4         -  5230.10                  -    1025.00              -   \n",
       "...     ...      ...                ...        ...            ...   \n",
       "78958     -  4936.25                  -          -              -   \n",
       "78959     -  4181.70                  -          -              -   \n",
       "78960     -  3513.25                  -          -              -   \n",
       "78961     -  2928.20                  -          -              -   \n",
       "78962     -  2421.65                  -          -              -   \n",
       "\n",
       "       Underlying Value    \n",
       "0                45158.10  \n",
       "1                45158.10  \n",
       "2                45158.10  \n",
       "3                45158.10  \n",
       "4                45158.10  \n",
       "...                   ...  \n",
       "78958            48292.25  \n",
       "78959            48292.25  \n",
       "78960            48292.25  \n",
       "78961            48292.25  \n",
       "78962            48292.25  \n",
       "\n",
       "[78963 rows x 12 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bc64fac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(call.shape[0]):\n",
    "    call[call.columns[0]][i]=datetime.strptime(call[call.columns[0]][i], '%d-%b-%Y').date()\n",
    "    call[call.columns[1]][i]=datetime.strptime(call[call.columns[1]][i], '%d-%b-%Y').date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "caba675a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12447"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans1=0\n",
    "for i in range(call.shape[0]):\n",
    "    if(call[call.columns[0]][i]==final.Date[20]):\n",
    "        ans1=i\n",
    "        break\n",
    "ans1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0db8a767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([39002045., 49176635., 69869325., 68217605., 39022520., 51054420.,\n",
       "       52002420., 60862560., 72716355., 42331635., 48325225., 55728370.,\n",
       "       74231845., 34303720., 35033695., 49328650., 45040360., 70436145.,\n",
       "       34076320., 37066065., 49210985., 73844545., 82742285., 30216690.,\n",
       "       36698510., 54522235., 68656170., 27041545., 33386030., 32764835.,\n",
       "       47788535., 49586650., 28483610., 32169410., 59641385., 87338825.,\n",
       "       44904930., 51847245., 50330685., 60402800., 65324385., 75758180.,\n",
       "       33287415., 60576375., 81437145., 30970785., 38378355., 48576360.,\n",
       "       49555250., 68164670., 30471590., 40658120., 46735865., 62238440.,\n",
       "       90502490., 38325610., 40882150., 62565925., 74497630., 77244195.,\n",
       "       31480815., 33829215., 58043235., 69655035., 32393460., 32691015.,\n",
       "       34183620., 41732520., 81391605., 33602840., 32732660., 37672250.,\n",
       "       34781240., 86070705., 34926020., 65973065., 62816630., 78624035.,\n",
       "       73056920., 25808925., 29293950., 21901045., 39406705., 38795170.,\n",
       "       11469495., 18060720., 53111250., 75274650., 25230035., 28915685.,\n",
       "       46543280., 65971355., 38047715., 24262470., 32805380., 45686420.,\n",
       "       59613930., 63644450., 27141960., 34688355., 34595165., 33616920.,\n",
       "       57895215.])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here we have calculated open interest of put option which is at the money and out of the money to the extent it matter most . In the money and far out of the money gives wrong sentiment  \n",
    "old_col=np.zeros(103)\n",
    "k=0\n",
    "call_oi=0\n",
    "date1=final.Date[20]\n",
    "for i in range(ans1 , call.shape[0]):\n",
    "    if(k<103):\n",
    "        if call[call.columns[0]][i]==date1 and (float(call[call.columns[3]][i])>=final['Close'][k+20]) and (float(call[call.columns[3]][i])<=final['Close'][k+20]+8*diff):\n",
    "            if call[call.columns[9]][i]!='-':#at the money and out of the money datas are important \n",
    "                call_oi+=float(call[call.columns[9]][i])\n",
    "        elif call[call.columns[0]][i]!=date1:\n",
    "            date1=call[call.columns[0]][i]\n",
    "            old_col[k]=call_oi\n",
    "            call_oi=0\n",
    "            if call[call.columns[9]][i]!='-':\n",
    "                call_oi+=float(call[call.columns[9]][i])\n",
    "            k=k+1\n",
    "old_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "90c2b9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "np=1\n",
    "if np==1:\n",
    "    final=final[20:]\n",
    "    np=np+1\n",
    "final[\"call_oi\"]=old_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2edf41d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "final[\"put_oi\"]=new_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "47466ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Seven_MA</th>\n",
       "      <th>tw1_MA</th>\n",
       "      <th>Volume_MA</th>\n",
       "      <th>call_oi</th>\n",
       "      <th>put_oi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>45546.351562</td>\n",
       "      <td>45694.898438</td>\n",
       "      <td>45359.750000</td>\n",
       "      <td>45651.101562</td>\n",
       "      <td>247900</td>\n",
       "      <td>45731.018973</td>\n",
       "      <td>45551.499932</td>\n",
       "      <td>266657.142857</td>\n",
       "      <td>39002045.0</td>\n",
       "      <td>39994480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>45740.000000</td>\n",
       "      <td>45782.750000</td>\n",
       "      <td>45471.050781</td>\n",
       "      <td>45592.500000</td>\n",
       "      <td>175200</td>\n",
       "      <td>45675.425921</td>\n",
       "      <td>45570.615260</td>\n",
       "      <td>264133.333333</td>\n",
       "      <td>49176635.0</td>\n",
       "      <td>40255650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>45234.648438</td>\n",
       "      <td>45404.898438</td>\n",
       "      <td>44720.898438</td>\n",
       "      <td>44995.699219</td>\n",
       "      <td>233600</td>\n",
       "      <td>45487.871931</td>\n",
       "      <td>45533.595458</td>\n",
       "      <td>253209.523810</td>\n",
       "      <td>69869325.0</td>\n",
       "      <td>36499920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023-08-03</td>\n",
       "      <td>44862.851562</td>\n",
       "      <td>45038.449219</td>\n",
       "      <td>44279.398438</td>\n",
       "      <td>44513.449219</td>\n",
       "      <td>245800</td>\n",
       "      <td>45212.875140</td>\n",
       "      <td>45454.058340</td>\n",
       "      <td>247471.428571</td>\n",
       "      <td>68217605.0</td>\n",
       "      <td>49335350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023-08-04</td>\n",
       "      <td>44754.750000</td>\n",
       "      <td>45118.050781</td>\n",
       "      <td>44520.550781</td>\n",
       "      <td>44879.500000</td>\n",
       "      <td>245200</td>\n",
       "      <td>45076.946429</td>\n",
       "      <td>45410.561993</td>\n",
       "      <td>247485.714286</td>\n",
       "      <td>39022520.0</td>\n",
       "      <td>31968650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>47837.750000</td>\n",
       "      <td>48071.398438</td>\n",
       "      <td>47415.851562</td>\n",
       "      <td>47491.851562</td>\n",
       "      <td>165800</td>\n",
       "      <td>47707.730329</td>\n",
       "      <td>47185.604471</td>\n",
       "      <td>207895.238095</td>\n",
       "      <td>27141960.0</td>\n",
       "      <td>27021700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>47576.398438</td>\n",
       "      <td>47838.449219</td>\n",
       "      <td>47411.648438</td>\n",
       "      <td>47724.851562</td>\n",
       "      <td>118700</td>\n",
       "      <td>47696.380720</td>\n",
       "      <td>47301.857870</td>\n",
       "      <td>210104.761905</td>\n",
       "      <td>34688355.0</td>\n",
       "      <td>28566520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>47818.500000</td>\n",
       "      <td>48347.648438</td>\n",
       "      <td>47806.699219</td>\n",
       "      <td>48282.199219</td>\n",
       "      <td>184900</td>\n",
       "      <td>47824.634068</td>\n",
       "      <td>47450.825335</td>\n",
       "      <td>214300.000000</td>\n",
       "      <td>34595165.0</td>\n",
       "      <td>46910800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>48479.050781</td>\n",
       "      <td>48636.449219</td>\n",
       "      <td>48343.148438</td>\n",
       "      <td>48508.550781</td>\n",
       "      <td>391100</td>\n",
       "      <td>48004.523577</td>\n",
       "      <td>47600.832995</td>\n",
       "      <td>225723.809524</td>\n",
       "      <td>33616920.0</td>\n",
       "      <td>60492340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>48374.851562</td>\n",
       "      <td>48477.699219</td>\n",
       "      <td>48091.851562</td>\n",
       "      <td>48292.250000</td>\n",
       "      <td>174000</td>\n",
       "      <td>48107.450335</td>\n",
       "      <td>47711.144041</td>\n",
       "      <td>226757.142857</td>\n",
       "      <td>57895215.0</td>\n",
       "      <td>50361505.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date          Open          High           Low         Close  \\\n",
       "20   2023-07-31  45546.351562  45694.898438  45359.750000  45651.101562   \n",
       "21   2023-08-01  45740.000000  45782.750000  45471.050781  45592.500000   \n",
       "22   2023-08-02  45234.648438  45404.898438  44720.898438  44995.699219   \n",
       "23   2023-08-03  44862.851562  45038.449219  44279.398438  44513.449219   \n",
       "24   2023-08-04  44754.750000  45118.050781  44520.550781  44879.500000   \n",
       "..          ...           ...           ...           ...           ...   \n",
       "118  2023-12-22  47837.750000  48071.398438  47415.851562  47491.851562   \n",
       "119  2023-12-26  47576.398438  47838.449219  47411.648438  47724.851562   \n",
       "120  2023-12-27  47818.500000  48347.648438  47806.699219  48282.199219   \n",
       "121  2023-12-28  48479.050781  48636.449219  48343.148438  48508.550781   \n",
       "122  2023-12-29  48374.851562  48477.699219  48091.851562  48292.250000   \n",
       "\n",
       "     Volume      Seven_MA        tw1_MA      Volume_MA     call_oi      put_oi  \n",
       "20   247900  45731.018973  45551.499932  266657.142857  39002045.0  39994480.0  \n",
       "21   175200  45675.425921  45570.615260  264133.333333  49176635.0  40255650.0  \n",
       "22   233600  45487.871931  45533.595458  253209.523810  69869325.0  36499920.0  \n",
       "23   245800  45212.875140  45454.058340  247471.428571  68217605.0  49335350.0  \n",
       "24   245200  45076.946429  45410.561993  247485.714286  39022520.0  31968650.0  \n",
       "..      ...           ...           ...            ...         ...         ...  \n",
       "118  165800  47707.730329  47185.604471  207895.238095  27141960.0  27021700.0  \n",
       "119  118700  47696.380720  47301.857870  210104.761905  34688355.0  28566520.0  \n",
       "120  184900  47824.634068  47450.825335  214300.000000  34595165.0  46910800.0  \n",
       "121  391100  48004.523577  47600.832995  225723.809524  33616920.0  60492340.0  \n",
       "122  174000  48107.450335  47711.144041  226757.142857  57895215.0  50361505.0  \n",
       "\n",
       "[103 rows x 11 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e81779f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22d2bca3310>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.zeros(122)\n",
    "x[0]=1\n",
    "for i in range(1,122):\n",
    "    x[i]=x[i-1]+1\n",
    "plt.plot(x[0:103],final.Seven_MA ,color='grey')\n",
    "plt.plot(x[0:103],final.tw1_MA)\n",
    "plt.plot(x[0:103],final.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "13a73773",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PcR(a,b):#put to call ratio \n",
    "    return a/b\n",
    "long_short_none={\n",
    "    1:\"long\",\n",
    "    0:\"no position\",\n",
    "    -1:\"short\"\n",
    "}\n",
    "Vol_std=np.std(MA)\n",
    "mean=np.mean(MA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "9de880be",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio=100000\n",
    "res_port=np.zeros(122,dtype=float)\n",
    "for g in range(21):\n",
    "    res_port[g]=portfolio # as i had to use 21 days weighted moving average i do not take any position for fisrt  21 days \n",
    "pos=[]\n",
    "for y in range(21):\n",
    "    pos.append(long_short_none[0])\n",
    "position=0\n",
    "long=0 #long is the no of shares bought\n",
    "#analysing previous day data i take position during todays close \n",
    "for i in range(21,122):\n",
    "    res_port[i]=res_port[i-1]+long*(final.Close[i]-final.Close[i-1])\n",
    "    if(res_port[i]<0.99*res_port[i-1] and position==1):\n",
    "        position=0\n",
    "        long=0 #if my portfolio value drops bhy 1% I will loose my long position if any  \n",
    "    else:\n",
    "        if(final.Close[i-1]>=final.tw1_MA[i-1] and position==0):\n",
    "            position=1\n",
    "            long=res_port[i]/final.Close[i]\n",
    "        elif(final.Close[i-1]<final.tw1_MA[i-1] and position==1) and (PcR(final.put_oi[i-1],final.call_oi[i-1])<1):\n",
    "            position=0\n",
    "            long=0\n",
    "        elif(final.Close[i-1]>final.tw1_MA[i-1] and position==1) and (PcR(final.put_oi[i-1],final.call_oi[i-1])<0.3) and final.Volume[i-1]< 0.8*final.Volume_MA[i-1]:\n",
    "            postion=0\n",
    "            long=0\n",
    "        elif(final.Close[i-1]<final.tw1_MA[i-1] and position==0) and (PcR(final.put_oi[i-1],final.call_oi[i-1])>2) and final.Volume[i-1]< final.Volume_MA[i-1]:\n",
    "            position=1\n",
    "            long=res_port[i]/final.Close[i]\n",
    "    pos.append(long_short_none[position])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "4e7f7ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'no position',\n",
       " 'no position',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long',\n",
       " 'long']"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "89e0628f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.zeros(122) #Normal buy on the first date and then hold  \n",
    "for lp in range(20):\n",
    "    y[lp]=portfolio\n",
    "for i in range(20,122):\n",
    "    y[i]=(100000/final.Close[20])*final.Close[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "26b36764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22d325f8e90>"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,res_port,label=\"Using_Moving_Average_As_Well_As_Open_Interest_Data\")\n",
    "plt.plot(x,y,label=\"Buy_Hold_Strategy\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "0142cf8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio=100000\n",
    "MA_strategy=np.zeros(122,dtype=float)\n",
    "for g in range(21):\n",
    "    MA_strategy[g]=portfolio \n",
    "mpos=[0]\n",
    "mposition=0\n",
    "mlong=0\n",
    "for i in range(21,122):\n",
    "    MA_strategy[i]=MA_strategy[i-1]+mlong*(final.Close[i]-final.Close[i-1])\n",
    "    if(final.Close[i]>=final.Seven_MA[i] and mposition==0):\n",
    "        mposition=1\n",
    "        mlong=MA_strategy[i]/final.Close[i]\n",
    "    elif(final.Close[i]<final.Seven_MA[i] and mposition==1):\n",
    "        mposition=0\n",
    "        mlong=0\n",
    "    mpos.append(position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "2e8dc356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22d3271f810>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,MA_strategy,label=\"using moving avergae tsrategy many people use\")\n",
    "plt.plot(x,y,label=\"buy_hold\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "7e5a9d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#performence metric \n",
    "#1 sharp ratio \n",
    "#2 drawdown\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "8bbfefa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sharpe \n",
    "def annualized_sharpe(res_port,n):\n",
    "    daily_ret=np.zeros(n-1)\n",
    "    for i in range(n-1):\n",
    "        daily_ret[i]=(res_port[i+1]-res_port[i])/res_port[i]\n",
    "    daily_ret\n",
    "    mean=np.mean(daily_ret)\n",
    "    risk=np.std(daily_ret)\n",
    "    risk_free_rate_yearly=7.074/100\n",
    "    rfr_daily=risk_free_rate_yearly/252\n",
    "    sharpe=(mean-rfr_daily)/risk\n",
    "    annualized_sharp=(np.sqrt(252)*sharpe)\n",
    "    return annualized_sharp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "c7626190",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drawdown\n",
    "def drawdown(res_port,n):\n",
    "    daily_ret=np.zeros(n-1)\n",
    "    for i in range(n-1):\n",
    "        daily_ret[i]=(res_port[i+1]-res_port[i])/res_port[i]\n",
    "    daily_ret\n",
    "    drawdown=np.min(daily_ret)\n",
    "    drawdown=100*drawdown\n",
    "    return str(-drawdown)+'%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "6ee46af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sharpe ratio of our algo is  1.3313702759308812\n",
      "drwadown is  1.3089889373252226%\n",
      "percent_return is 9.179371403853452 instead of buy and hold which would have given 6.259321508020846\n"
     ]
    }
   ],
   "source": [
    "print(\"sharpe ratio of our algo is \",annualized_sharpe(res_port,122))\n",
    "print(\"drwadown is \",drawdown(res_port,122))\n",
    "print(\"percent_return is\",(res_port[121]-portfolio)/portfolio*100 ,\"instead of buy and hold which would have given\",(y[121]-portfolio)/portfolio*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b8ebc5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e2d7543",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99478932",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}