Python Code (defined as Class) to get NSE options data (PCR, MaxPain, High OI, Change in OI) for all scripts like Nifty, Banknifty and Stocks.

import pandas as pd
import requests
from datetime import datetime

# Colunn at which strike price is listed in NSE option chain table
strike_price_column_index = 11

# Encapuslate NSE option data and function
class OptionChain:

    # static variable to hold current running expiry date
    expiry = ''

    # Common Utility to find maxinum value in the column, its index and then return the strike price with respect to index value
    def find_max_strike_price(self, df, option_type, column_name):

        # Covert "-" as 0 so that all data can be treated as integer
        temp_df = df[option_type].replace("-", "0")
        # Delete the last row which will have summation of all the data
        temp_df = temp_df[:-1]
        # Set the specific column as integer (by default it is string)
        # Dind the index value where the max value exists
        max_at_index = temp_df[column_name].idxmax()
        # Return the strike price which is available in the index value
        return(int(df.iloc[max_at_index, strike_price_column_index]))

    # Get strike prices where max OI /change in OI for CE and PE
    def get_max_OI_data(self, symbol, expiry):

        url ="" + symbol + "&date" + expiry
        header = {
          "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",
          "X-Requested-With": "XMLHttpRequest"

        # Pull NSE option chain
        r = requests.get(url, headers=header)
        # Convert html page as Table and read the first table which has option data
        df = pd.read_html(r.text)[1]

        # Get all max OI data and store to local object variables
        self.max_high_oi_ce = self.find_max_strike_price(df, "CALLS", "OI")
        self.max_change_oi_ce = self.find_max_strike_price(df, "CALLS", "Chng in OI")
        self.max_high_oi_pe = self.find_max_strike_price(df, "PUTS", "OI")
        self.max_change_oi_pe = self.find_max_strike_price(df, "PUTS", "Chng in OI")

    # Constructor for OptionChain which will run for every script like Nifty and Banknifty
    def __init__(self, symbol):

        # Store the symbol
        self.symbol = symbol
        # Find out the expiry for which we need to pull the details

        if not OptionChain.expiry: # If expiry is not yet found
            # List the expiry details and read the first expiry
            base_url = '' + symbol
            page_output = str(requests.get(base_url).content)
            expiry_list = page_output.split(",")
            OptionChain.expiry = expiry_list[0].split("\"")[1]
            expirydate = datetime.strptime(OptionChain.expiry, '%d%b%Y').date()
            todaydate =

            # If today is greater than expiry, then it is expired. Choose the next expiry in the list
            if todaydate > expirydate:
                NSEOption.expiry = expiry_list[1].split("\"")[1]

        # Form the actual URL from which we can pull PCR and max pain
        base_url = '' + symbol + "&expiry=" + OptionChain.expiry

        # Load the page and sent to HTML parse
        page_output = str(requests.get(base_url).content)
        # Locate the string where max pain string is available
        loc = page_output.find("\"max_pain\"")
        data = page_output[loc:].split("\"")
        # Get max pain and convert from float to integer
        self.max_pain = int(float(data[3]))
        # Get max pain and convert from string to float
        self.pcr = float(data[11])
        self.get_max_OI_data(symbol, OptionChain.expiry)

    # Tracing utility to display elements of this class if needed (For debugging purpose)
    def display_all(self):

        print(self.max_pain, self.pcr);

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