Backtesting Moving Average Crossover Signals for Nifty Options

Summary

This guide describes a trend-following method that calculates short- and long-window moving averages on the Nifty index, then uses their crossovers to signal long call option trades. It outlines a Python workflow: obtain index and option data, combine the relevant fields, calculate current and lagged averages, generate entry and exit signals, and plot cumulative profit and loss. The underlying index supplies the signals, while the option’s last traded price is used to estimate the trade value.

The document offers a procedural backtest outline rather than reported performance evidence: it does not provide specific results, transaction costs, or a comparison across parameter choices. It suggests trying different average windows and notes that the strategy relies only on average price behavior while seeking exposure through option delta. Option pricing, expiry effects, execution assumptions, and broader risk controls are not developed, so the described backtest alone cannot establish live-trading profitability.

Key ideas

• The method classifies moving average crossovers as trend-following signals.
• A short average crossing above a long average triggers a call purchase in the described rules.
• The moving averages are calculated from the Nifty index, while option prices are used to value trades.
• The workflow calculates cumulative profit and loss but reports no specific backtest results.
• Look-back periods can be varied, and the signal rules do not model option pricing in depth.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.