Statistical Mean Reversion with Confidence Intervals and Risk Controls

Summary

The article outlines an MQL5 mean-reversion strategy that calculates rolling price statistics, including mean, variance, skewness, kurtosis, and the Jarque-Bera statistic. It looks for price moves beyond confidence intervals, using skewness thresholds and a non-normality filter to identify possible reversals. Optional higher-timeframe confirmation adds trend context to entries, while an on-chart dashboard displays the computed statistics and trade state.

Trade management can use equity-based or fixed position sizing, base stop-loss and take-profit distances, trailing stops, partial exits, and a maximum holding period. The article describes the implementation and includes backtesting as a section, but the supplied text shows no performance figures or report details. Its proposed interpretation of distribution shape as evidence for reversion therefore remains a strategy hypothesis rather than demonstrated profitability. Results will depend on instrument, lookback and threshold settings, and the author advises careful testing and risk management before live deployment.

Key ideas

• The strategy calculates rolling moments and Jarque-Bera statistics to characterize recent price behavior.
• Confidence-interval breaches, skewness, and non-normality filters generate potential reversal signals.
• Optional higher-timeframe confirmation provides additional trend context.
• Trade management combines position sizing with stops, partial exits, trailing adjustments, and time limits.
• The provided text gives no backtest metrics, so profitability cannot be assessed from the reported evidence.

Tags

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