NSE backtesting is the process of testing a trading strategy against historical data from the National Stock Exchange of India (and BSE) before risking capital. Done well, it tells you whether a strategy had an edge after Indian transaction costs and microstructure. Done poorly, it produces numbers that never survive contact with the live market.

This page covers how to backtest NSE/BSE strategies correctly, and how alphabench does it automatically with AI agents on the open-source RaptorBT Rust engine.

What you can backtest on NSE/BSE

  • Individual equities: any NSE-listed stock.
  • Entire universes: Nifty 50, Nifty Next 50, Nifty Bank, Nifty Midcap 150, and others, swept and ranked in one run.
  • Indices and futures: NIFTY, BANKNIFTY, and single-stock futures.
  • Options: spreads, straddles, strangles, and condors (see Indian options backtesting).

Indicators and strategy logic

Behind the scenes strategies are expressed in a compact rule language covering SMA, EMA, RSI, MACD, Bollinger Bands, ATR, ADX, Supertrend, VWAP, and more. On alphabench you don't write this by hand: you describe the idea in plain English and AI agents translate, backtest, and report. See Equity Strategies and Strategy Chat.

Realistic Indian assumptions

A credible NSE backtest must include:

  • Transaction costs: STT, exchange charges, SEBI fees, GST, stamp duty, brokerage.
  • Slippage and spread: especially for mid/small-caps and thin options.
  • Corporate-action adjustment: splits, bonuses, dividends.
  • Point-in-time universes to avoid survivorship bias.

Validate before you trust it

Run out-of-sample and walk-forward tests, then Monte Carlo resampling to see the distribution of drawdowns and returns rather than a single backtest path. The Walk-Forward & Monte Carlo guide walks through it, and Best Practices covers overfitting hygiene.

The RaptorBT engine

alphabench's backtests run on RaptorBT, an open-source Rust engine with Python bindings, with sub-millisecond per-backtest performance and 30+ performance metrics, which is what makes universe-wide sweeps and rapid agent iteration practical.

How alphabench backtests NSE strategies for you

Describe a strategy in Strategy Chat. AI agents pick instruments or a universe, write the logic, run the backtest on adjusted NSE/BSE data with realistic costs, validate robustness, and surface the metrics and trade log, then help you diagnose losing trades and move to paper trading when ready.

Key takeaways

  • A trustworthy NSE backtest models Indian costs and uses point-in-time universes.
  • Validate with walk-forward and Monte Carlo, not a single backtest.
  • alphabench runs NSE/BSE backtests automatically with AI agents on RaptorBT, for free.