Building a Mean-Reversion Backtest with Quantstrat in R

Summary

The article outlines a four-stage workflow—form a hypothesis, test it, refine it, and prepare for production—using a mean-reversion strategy on an Indian equity ETF. It describes how quantstrat represents a strategy through market instruments, indicators, signals, and order rules, and explains the intended logic: update a reference threshold when price moves sufficiently, then buy below a lower band or sell above an upper band. The article also introduces portfolio setup, trade statistics, and parameter optimization as parts of the backtest workflow.

The example is presented as support for the mean-reversion hypothesis, but it gives no detailed performance figures or validation results. The code excerpts are incomplete, and the article acknowledges that results depend on parameter choices and would benefit from more data, stricter entry conditions, stop losses, and volatility-aware thresholds. Backtest success alone does not establish live profitability; execution and production concerns are outside its scope.

Key ideas

• A basic systematic strategy moves from hypothesis formation through testing and refinement before production.
• Quantstrat organizes signal-based strategies around instruments, indicators, signals, and rules.
• The example buys below a lower price band and sells above an upper band.
• Threshold parameters can be varied during backtesting, but results need broader data and further validation.
• The article does not provide detailed performance evidence or cover live execution.

Tags

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