blog / 2026-10-31

NIFTY weekly options historical data download: building a week-by-week archive

How to download NIFTY weekly options historical data and organise it by weekly series: separating weeklies from monthlies, tracking days to expiry, and choosing the pack window.

A NIFTY weekly option lives for a week or two of active trading. Research on weeklies means following each series from listing to expiry, not looking at one day in isolation. This guide covers how to download NIFTY weekly options historical data so that each weekly series can be rebuilt from first trade to last.

How the files map to weekly series

Each trading day holds a file per listed expiry: the current weekly, the next weeklies and the monthly. To follow one weekly series, collect every file that shares its expiry:

import re
from pathlib import Path
import pandas as pd

pat = re.compile(r"NIFTY_OPT_1min_(\d{4}-\d{2}-\d{2})_exp(\d{4}-\d{2}-\d{2})")
by_expiry = {}
for f in Path("NIFTY").rglob("NIFTY_OPT_1min_*.parquet"):
    if m := pat.search(f.name):
        by_expiry.setdefault(m[2], []).append((m[1], f))

series = by_expiry["2025-10-14"]          # one weekly, every day it traded
week = pd.concat(pd.read_parquet(f).assign(day=d) for d, f in sorted(series))
week["dte"] = (pd.to_datetime("2025-10-14") - pd.to_datetime(week["day"])).dt.days

Weekly or monthly?

The monthly contract is simply the last expiry of the month. Mark it from the expiry dates themselves: for each calendar month, the latest expiry date is the monthly, the rest are weeklies. That rule keeps working across the Thursday-to-Tuesday change and holiday shifts.

Picking a window

• Testing a single weekly setup: one year, for example 2025 or 2026.
• Comparing the Thursday and Tuesday regimes: last 3 years.
• Every weekly expiry second by second: the full NIFTY 1-second archive.