In a previous article, we discussed how to fetch Nifty and Bank Nifty data using Python. The response to that article was great, so due to popular demand, here’s an extended version. In this article, we'll learn how to fetch option chain data from the NSE website every 30 seconds. This is for learning purposes only.
In Python, we'll use asyncio to make an API request to NSE data every 30 seconds.
Install required libraries in Python
pip install aiohttp asyncio
Code
import aiohttp
import asyncio
import requests
import json
import math
import time
def strRed(skk): return "\033[91m {}\033[00m".format(skk)
def strGreen(skk): return "\033[92m {}\033[00m".format(skk)
def strYellow(skk): return "\033[93m {}\033[00m".format(skk)
def strLightPurple(skk): return "\033[94m {}\033[00m".format(skk)
def strPurple(skk): return "\033[95m {}\033[00m".format(skk)
def strCyan(skk): return "\033[96m {}\033[00m".format(skk)
def strLightGray(skk): return "\033[97m {}\033[00m".format(skk)
def strBlack(skk): return "\033[98m {}\033[00m".format(skk)
def strBold(skk): return "\033[1m {}\033[00m".format(skk)
def round_nearest(x, num=50): return int(math.ceil(float(x)/num)*num)
def nearest_strike_bnf(x): return round_nearest(x, 100)
def nearest_strike_nf(x): return round_nearest(x, 50)
url_oc = "https://www.nseindia.com/option-chain"
url_bnf = 'https://www.nseindia.com/api/option-chain-indices?symbol=BANKNIFTY'
url_nf = 'https://www.nseindia.com/api/option-chain-indices?symbol=NIFTY'
url_indices = "https://www.nseindia.com/api/allIndices"
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'}
cookies = dict()
def set_cookie():
sess = requests.Session()
request = sess.get(url_oc, headers=headers, timeout=5)
return dict(request.cookies)
async def get_data(url, session):
global cookies
async with session.get(url, headers=headers, timeout=5, cookies=cookies) as response:
if response.status == 401:
cookies = set_cookie()
async with session.get(url, headers=headers, timeout=5, cookies=cookies) as response:
return await response.text()
elif response.status == 200:
return await response.text()
return ""
async def fetch_all_data():
async with aiohttp.ClientSession() as session:
indices_data = await get_data(url_indices, session)
bnf_data = await get_data(url_bnf, session)
nf_data = await get_data(url_nf, session)
return indices_data, bnf_data, nf_data
# Process the fetched data
def process_indices_data(data):
global bnf_ul, nf_ul, bnf_nearest, nf_nearest
data = json.loads(data)
for index in data["data"]:
if index["index"] == "NIFTY 50":
nf_ul = index["last"]
if index["index"] == "NIFTY BANK":
bnf_ul = index["last"]
bnf_nearest = nearest_strike_bnf(bnf_ul)
nf_nearest = nearest_strike_nf(nf_ul)
def process_oi_data(data, nearest, step, num):
data = json.loads(data)
currExpiryDate = data["records"]["expiryDates"][0]
oi_data = []
for item in data['records']['data']:
if item["expiryDate"] == currExpiryDate:
if nearest - step*num <= item["strikePrice"] <= nearest + step*num:
oi_data.append((item["strikePrice"], item["CE"]["openInterest"], item["PE"]["openInterest"]))
return oi_data
def print_oi_data(nifty_data, bank_nifty_data, prev_nifty_data, prev_bank_nifty_data):
print(strBold(strLightPurple("Nifty Open Interest:")))
for i, (strike, ce_oi, pe_oi) in enumerate(nifty_data):
ce_change = ce_oi - prev_nifty_data[i][1] if prev_nifty_data else 0
pe_change = pe_oi - prev_nifty_data[i][2] if prev_nifty_data else 0
ce_color = strGreen(ce_oi) if ce_change > 0 else strRed(ce_oi)
pe_color = strGreen(pe_oi) if pe_change > 0 else strRed(pe_oi)
print(f"Strike Price: {strike}, Call OI: {ce_color} ({strBold(f'+{ce_change}') if ce_change > 0 else strBold(ce_change) if ce_change < 0 else ce_change}), Put OI: {pe_color} ({strBold(f'+{pe_change}') if pe_change > 0 else strBold(pe_change) if pe_change < 0 else pe_change})")
print(strBold(strLightPurple("\nBank Nifty Open Interest:")))
for i, (strike, ce_oi, pe_oi) in enumerate(bank_nifty_data):
ce_change = ce_oi - prev_bank_nifty_data[i][1] if prev_bank_nifty_data else 0
pe_change = pe_oi - prev_bank_nifty_data[i][2] if prev_bank_nifty_data else 0
ce_color = strGreen(ce_oi) if ce_change > 0 else strRed(ce_oi)
pe_color = strGreen(pe_oi) if pe_change > 0 else strRed(pe_oi)
print(f"Strike Price: {strike}, Call OI: {ce_color} ({strBold(f'+{ce_change}') if ce_change > 0 else strBold(ce_change) if ce_change < 0 else ce_change}), Put OI: {pe_color} ({strBold(f'+{pe_change}') if pe_change > 0 else strBold(pe_change) if pe_change < 0 else pe_change})")
def calculate_support_resistance(oi_data):
highest_oi_ce = max(oi_data, key=lambda x: x[1])
highest_oi_pe = max(oi_data, key=lambda x: x[2])
return highest_oi_ce[0], highest_oi_pe[0]
async def update_data():
global cookies
prev_nifty_data = prev_bank_nifty_data = None
while True:
cookies = set_cookie()
indices_data, bnf_data, nf_data = await fetch_all_data()
process_indices_data(indices_data)
nifty_oi_data = process_oi_data(nf_data, nf_nearest, 50, 10)
bank_nifty_oi_data = process_oi_data(bnf_data, bnf_nearest, 100, 10)
support_nifty, resistance_nifty = calculate_support_resistance(nifty_oi_data)
support_bank_nifty, resistance_bank_nifty = calculate_support_resistance(bank_nifty_oi_data)
print(strBold(strCyan(f"\nMajor Support and Resistance Levels:")))
print(f"Nifty Support: {strYellow(support_nifty)}, Nifty Resistance: {strYellow(resistance_nifty)}")
print(f"Bank Nifty Support: {strYellow(support_bank_nifty)}, Bank Nifty Resistance: {strYellow(resistance_bank_nifty)}")
print_oi_data(nifty_oi_data, bank_nifty_oi_data, prev_nifty_data, prev_bank_nifty_data)
prev_nifty_data = nifty_oi_data
prev_bank_nifty_data = bank_nifty_oi_data
for i in range(30, 0, -1):
print(strBold(strLightGray(f"\rFetching data in {i} seconds...")), end="")
time.sleep(1)
print(strBold(strCyan("\nFetching new data... Please wait.")))
await asyncio.sleep(1)
async def main():
await update_data()
asyncio.run(main())
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Output:
You can even watch the demo video following this link
Thank you!!
See you in the next insightful blog.
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