Options backtesting
Backtest options strategies on NSE data
Last updated: · Reviewed by the INDfolio AI research desk
In short
INDfolio AI backtests NSE options strategies as options, not as spot-price proxies: strike selection by ATM/OTM points, percentage or premium; NSE lot sizes and weekly/monthly expiry calendars; per-leg entries, stop-losses and re-entries; and the full Indian cost stack — brokerage, STT on premium, exchange charges, GST, stamp duty and slippage — charged per leg. Results carry the institutional metric set plus Monte Carlo drawdown distributions, and the tested strategy paper trades and deploys unchanged to seven brokers. Covers NIFTY, BANKNIFTY, FINNIFTY and stock options, intraday to positional.
Options strategies live or die on details a stock backtester cannot see: which strike you enter, how premiums decay into expiry, what slippage does to a four-leg position. INDfolio AI backtests options as options — strike selection rules, lot sizes, expiry handling and the full Indian cost stack included.
Describe the strategy in plain English ("sell a BANKNIFTY strangle at 9:20, 1% OTM strikes, exit at 25% stop-loss per leg or 3:15 square-off") or build it visually, then replay it across years of NSE options history.
Strike selection logic
ATM, OTM by percentage or points, premium-based selection — the rules that define real options strategies are first-class.
Index and stock options
NIFTY, BANKNIFTY and stock options with correct lot sizes and expiry mechanics.
Costs that options traders actually pay
STT on premium, brokerage per leg, exchange charges and slippage — deducted per trade, per leg.
Risk seen clearly
Drawdown, win rate and expectancy per strategy, plus Monte Carlo simulation to see how bad a losing streak can plausibly get.
Specification
Options backtesting on INDfolio AI — specification at a glance
| Dimension | Options backtesting on INDfolio AI |
|---|---|
| Instruments | NIFTY, BANKNIFTY, FINNIFTY index options; NSE stock options |
| Strike selection | ATM; OTM/ITM by points or percentage; premium-based (e.g. nearest ₹50 premium); dynamic re-selection on re-entry |
| Contract mechanics | NSE lot sizes, weekly and monthly expiry calendars, expiry-day handling, auto square-off time |
| Legs | Single and multi-leg structures (straddles, strangles, spreads, condors); per-leg stop-loss, target, trailing and re-entry rules |
| Pricing | Historical option premiums for the selected strikes — not spot-price approximations |
| Costs | Brokerage per leg, STT on premium (sell side), exchange charges, SEBI fee, GST, stamp duty, slippage — per leg |
| Sizing | Lot-based; margin-aware |
| Metrics | CAGR, Sharpe, Sortino, Calmar, max drawdown & duration, win rate, profit factor, expectancy, per-leg trade log |
| Validation | Parameter optimisation, walk-forward analysis, Monte Carlo (1,000 paths), live-data paper trading |
| Plans | Free: 3 years of data; Trader (₹999/month): full history + cost model, optimisation, Monte Carlo; Pro: walk-forward |
| Coding required | None |
Why options backtesting needs its own engine
An equity backtest asks one question: what did the price do? An options backtest asks several more: what was the premium at your selected strike, how did it decay, what happened at expiry, and what did each leg cost to enter and exit? Tools that approximate options with spot-price logic produce results that are not just imprecise but directionally wrong — especially for selling strategies, where the edge is the decay itself.
INDfolio AI simulates the option legs your rules would actually have traded, with lot sizes and expiry behaviour matching NSE contract specifications.
From popular setups to your own system
The Indian options community iterates on a familiar family of systematic setups: time-based straddles and strangles, directional option buying on breakouts, expiry-day strategies with tight stops. All of these are expressible in INDfolio AI in plain English, then adjustable rule by rule — entry time, strike distance, per-leg stop-loss, re-entry, square-off.
Once a backtest looks robust, the same strategy paper trades on live NSE data and deploys to your broker with position limits and automatic square-off enforced in the cloud.
Methodology & assumptions
- Option legs are priced from historical premiums at the selected strikes; where a strike did not trade in a bar, the engine uses the nearest traded value and flags it in the log.
- Signals are evaluated on closed bars and filled at the next bar with slippage; per-leg charges are applied at NSE/segment rates and printed on the report.
- Lot sizes and expiry calendars follow NSE contract specifications for the period tested.
- Backtested performance is hypothetical and does not guarantee future results.