Guides / 31 October 2026

Historical options data for research in India: a guide for students and academics

Indian index options data for theses and papers: what intraday option history is available, how to describe it in a data section, and how to keep results reproducible.

Research on Indian derivatives often stops at daily data because intraday option history is hard to obtain. Minute-level NIFTY, SENSEX and MCX history makes questions about intraday volatility, expiry effects and market microstructure practical for a thesis or paper.

Questions this data supports

• Intraday implied volatility patterns and their change around expiry.
• Effects of exchange rule changes, such as the move of expiry weekdays.
• Put-call parity deviations and their persistence by minute.
• Comparing NSE and BSE index options on the same days.

Writing the data section

Describe the sample precisely: instruments, date range, bar size, the timestamp convention (IST, bar start), and any filters you applied. Keep a manifest of the files you used:

import hashlib
from pathlib import Path
import pandas as pd

rows = [(f.name, f.stat().st_size, hashlib.md5(f.read_bytes()).hexdigest())
        for f in sorted(Path("NIFTY").rglob("*.parquet"))]
pd.DataFrame(rows, columns=["file", "bytes", "md5"]).to_csv("data_manifest.csv", index=False)

A manifest lets a reviewer, or you a year later, confirm the exact inputs.

Practical points

• Parquet loads directly into Python, R (arrow) and Stata via conversion.
• Start with the free samples to write and test your cleaning code.
• Pick the shortest window that answers the question; you can add years later.

Common choices: NIFTY options, 3 years, NIFTY + SENSEX, 3 years.