Rolling Sharpe Ratios with Confidence Bands and Recalculation-Safe MQL5 Design

Summary

This article explains how to interpret rolling Sharpe estimates alongside uncertainty bands. Using Lo’s asymptotic standard error under independent, identically distributed returns, it scales both the Sharpe and its standard error to an annualized measure and plots bands around the estimate. If the interval includes zero, the article treats the observed Sharpe as statistically indistinguishable from zero at the selected confidence level. Its examples show how uncertainty falls as the sample window grows, while longer windows respond more slowly to changing conditions.

For implementation, it describes a circular buffer with running sums and squared sums, return calculation from prices, and an MQL5 indicator designed to recalculate bars directly from close data when platform history or viewport behavior changes. Window length, periods per year, and return convention must match the instrument and timeframe. The statistical interpretation has substantial limits: the formula assumes IID returns and uses an asymptotic approximation; serial dependence, non-normality, and repeated testing can undermine the stated significance. Confidence bands alone do not prove a persistent or tradable edge.

Key ideas

• A short-window Sharpe estimate can have substantial sampling uncertainty, so it should not be read as proof of alpha.
• The article uses Lo’s asymptotic standard error under IID return assumptions to construct confidence bands.
• Annualize the Sharpe estimate and its uncertainty using the same periods-per-year setting.
• Longer windows reduce sampling noise but can make the measure slower to reflect changing conditions.
• Return dependence, distributional departures, and repeated evaluation can weaken the confidence-band interpretation.

Tags

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