सत्यनारायण लाल - किसान (Kisan - Satyanarayan Lal)

नहीं हुआ है अभी सवेरा
पूरब की लाली पहचान
चिड़ियों के जगने से पहले
खाट छोड़ उठ गया किसान ।

खिला-पिलाकर बैलों को ले
करने चला खेत पर काम
नहीं कोई त्योहार न छुट्टी
उसको नहीं काबी आराम।

गरम-गरम लू चलती सन-सन
धरती जलती तवा समान
तब भी करता काम खेत पर
बिना किए आराम किसान।

बादल गरज रहे गड़-गड़-गड़
बिजली चमक रही चम-चम
मुसलाधार बरसता पानी
ज़रा न रुकता लेता दम।

हाथ पांव ठिठुरते जाते हैं
घर से बाहर निकले कौन
फिर भी आग जला, खेतों की
रखवाली करता है वह मौन।

है किसान को चैन कहाँ, वह
करता रहता हरदम काम
सोचा नहीं कभी भी उसने
घर पर रह करना आराम।

- सत्यनारायण लाल

Read Option chain data from Nifty using Python

Read Option chain data from Nifty using Python


Option chain data is very crucial for Future and Options(FnO) trader analysis, many use it for positional while many use it to find day trend and it's strength as well as option strike selection for trading.

In this blog post we will try to figure out how to read options data from NSE APIs using Python.

Code:

   
  #!/usr/bin/env python3
 
import requests
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta, TH
 
# Find next thursday to figure out weekly expiry.
def next_thu_expiry_date():
    todayte = datetime.today()
 
    next_thursday_expiry = todayte + relativedelta(weekday=TH(1))
 
    str_next_thursday_expiry = str(next_thursday_expiry.strftime("%d")) + "-" + next_thursday_expiry.strftime(
        "%b") + "-" + next_thursday_expiry.strftime("%Y")
    print(str_next_thursday_expiry)
    return str_next_thursday_expiry
 
 
def find_ce_pe(spot_price, ce_values, pe_values):
    spot_price = round(spot_price, -2)
    otm_calls = []
    otm_puts = []
 
    ce_dt = pd.DataFrame(ce_values).sort_values(['strikePrice'])
    pe_dt = pd.DataFrame(pe_values).sort_values(['strikePrice'], ascending=False)
 
    for ce_value in ce_dt['strikePrice']:
        if ce_value >= spot_price:
            otm_calls.append(ce_value)
            if len(otm_calls) > 10:
                break
 
    for ce_value in pe_dt['strikePrice']:
        if ce_value <= spot_price:
            otm_puts.append(ce_value)
            if len(otm_puts) > 10:
                break
 
    return otm_calls, otm_puts
 
 
def read_oi(spot_price, ce_values, pe_values, symbol):
    sheet_name = str(datetime.today().strftime("%d-%m-%Y")) + symbol
 
    strike_oi_ce_pe = find_ce_pe(float(spot_price), ce_values, pe_values)
 
    ce_dt = pd.DataFrame(ce_values).sort_values(['strikePrice'])
    pe_dt = pd.DataFrame(pe_values).sort_values(['strikePrice'], ascending=False)
 
    drop_col = ['pchangeinOpenInterest', 'askQty', 'askPrice', 'underlyingValue', 'totalBuyQuantity',
                'totalSellQuantity', 'bidQty', 'bidprice', 'change', 'pChange', 'impliedVolatility',
                'totalTradedVolume']
    ce_dt.drop(drop_col, inplace=True, axis=1)
    pe_dt.drop(drop_col, inplace=True, axis=1)
 
    ce_dt = ce_dt.loc[ce_dt['strikePrice'].isin(strike_oi_ce_pe[0])]
    pe_dt = pe_dt.loc[pe_dt['strikePrice'].isin(strike_oi_ce_pe[1])]
 
    # print(ce_dt[['strikePrice', 'lastPrice']])
    # print(pe_dt[['strikePrice', 'changeinOpenInterest']])
 
    print(ce_dt.head(1))
    print(pe_dt.head(1))
 
    print(str([spot_price, (ce_dt['changeinOpenInterest'].sum()),
               (pe_dt['changeinOpenInterest'].sum())]))
 
 
def main():
    symbols = ["BANKNIFTY", "NIFTY"]
    base_url = 'https://www.nseindia.com/api/option-chain-indices?symbol='
    url_oc = "https://www.nseindia.com/option-chain"
 
    for symbol in symbols:
        headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, ''like Gecko) '
                                 'Chrome/80.0.3987.149 Safari/537.36',
                   'accept-language': 'en,gu;q=0.9,hi;q=0.8', 'accept-encoding': 'gzip, deflate, br'}
        session = requests.Session()
        request = session.get(url_oc, headers=headers, timeout=5)
        cookies = dict(request.cookies)
        response = session.get(base_url + symbol, headers=headers, timeout=5, cookies=cookies)
        print(response.status_code)
        dajs = response.json()
        expiry = next_thu_expiry_date()
 
        spot_price = dajs['records']['underlyingValue']
        ce_values = [data['CE'] for data in dajs['records']['data'] if "CE" in data and data['expiryDate']
                     == expiry]
        pe_values = [data['PE'] for data in dajs['records']['data'] if "PE" in data and data['expiryDate']
                     == expiry]
 
        read_oi(spot_price, ce_values, pe_values, symbol)
 
        print(len(dajs))
 
 
if __name__ == '__main__':
    main()
  
  

In case of any doubt leave a comment I will try to answer.

Note: This post is for educational purpose only.