Stock Market Trend

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# Import necessary libraries
import requests
import pandas as pd
import xlwings as xw
from datetime import datetime, timedelta
import time
import traceback

def fetch_and_update_data():
# Define the URL and headers
url = “https://www.nseindia.com/api/option-chain-indices?symbol=NIFTY”
headers = {
‘user-agent’: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.5060.53 Safari/537.36 Edg/103.0.1264.37’,
‘accept-encoding’: ‘gzip, deflate, br’,
‘accept-language’: ‘en-GB,en;q=0.9,en-US;q=0.8’
}

# Create a session and get the response
session = requests.Session()
response = session.get(url, headers=headers)
cookies = response.cookies # Get cookies from the response

# Extract the data from the response
data = response.json()[“records”][“data”]
ocdata = []

# Calculate the upcoming thursday
current_date = datetime.now()
upcoming_thursday = current_date + timedelta((3 – current_date.weekday() + 7) % 7)
upcoming_thursday_str = upcoming_thursday.strftime(‘%d-%b-%Y’)

# Get the current market price of NIFTY
market_price = response.json()[“records”][“underlyingValue”]

# Round the market price to the nearest 50
market_price_rounded = round(market_price / 50) * 50

# Define the specific strike prices for CE and PE based on the rounded market price
ce_strikes = [market_price_rounded – i*50 for i in range(2, -6, -1)] # 2 ITM and 5 OTM for CE
pe_strikes = [market_price_rounded + i*50 for i in range(2, -6, -1)] # 2 ITM and 5 OTM for PE

# Extract the required data
for i in data:
for j, k in i.items():
if j == “CE” and k[‘expiryDate’] == upcoming_thursday_str and k[‘strikePrice’] in ce_strikes:
info = k
info[“instrumentType”] = j
ocdata.append(info)
elif j == “PE” and k[‘expiryDate’] == upcoming_thursday_str and k[‘strikePrice’] in pe_strikes:
info = k
info[“instrumentType”] = j
ocdata.append(info)

# Create a DataFrame from the data
df = pd.DataFrame(ocdata)

# Filter and sort the data
if ‘instrumentType’ in df.columns:
df_ce = df[(df[‘instrumentType’] == ‘CE’) & (df[‘strikePrice’] >= market_price)].sort_values(by=’strikePrice’)
df_ce2 = df[(df[‘instrumentType’] == ‘CE’) & (df[‘strikePrice’] <= market_price)].sort_values(by='strikePrice') df_pe = df[(df['instrumentType'] == 'PE') & (df['strikePrice'] <= market_price)].sort_values(by='strikePrice', ascending=False) df_pe2 = df[(df['instrumentType'] == 'PE') & (df['strikePrice'] >= market_price)].sort_values(by=’strikePrice’, ascending=False)

# Concatenate and sort the final DataFrame by instrument type and strike price
df_final = pd.concat([df_ce, df_ce2, df_pe, df_pe2])
df_final_ce = df_final[df_final[‘instrumentType’] == ‘CE’].sort_values(by=’strikePrice’, ascending=True)
df_final_pe = df_final[df_final[‘instrumentType’] == ‘PE’].sort_values(by=’strikePrice’, ascending=False)
df_final_sorted = pd.concat([df_final_ce, df_final_pe])
else:
print(“No data found for the given conditions.”)
df_final_sorted = pd.DataFrame() # Create an empty DataFrame

# Open the workbook and update the data
wb = xw.Book(“oi1.xlsm”)
st = wb.sheets(“nifty”)
st.range(“A1″).value = df_final_sorted

# Save the workbook
#wb.save()

# Pause the script
time.sleep(1)

# Run the function in a loop
while True:
try:
fetch_and_update_data()
except Exception as e:
print(f”An error occurred: {e}”)
traceback.print_exc() # This will print the stack trace
time.sleep(1)