Statistical Market Regime Detection with MQL5
Summary
The article outlines an MQL5 system for classifying market behavior as trending, ranging, or volatile. It motivates regime awareness by explaining that strategies built for one condition may struggle when the market changes. The proposed foundation combines return autocorrelation, which can indicate persistence or reversal, with volatility and trend-strength measures. A statistics class is described for calculating summary statistics, autocorrelation, trend and mean-reversion strength, and percentiles; a detector and chart indicator then turn those measurements into regime labels and visual changes.
The evidence presented is a conceptual explanation and implementation outline, including sample statistical-class code and a list of system components. The supplied text does not show quantitative tests, out-of-sample results, or classification accuracy. It also does not establish universal thresholds for regime boundaries, and the discussion treats three broad regimes as a practical framework rather than an exhaustive market taxonomy. The adaptive Expert Advisor and practical optimization details are deferred to a later installment, so this part is best read as a design and implementation introduction rather than proof of trading performance.
Key ideas
- Return autocorrelation can help distinguish persistent price movement from mean-reverting behavior.
- Volatility and trend strength provide complementary information for classifying market conditions.
- The proposed system combines statistical measures in an MQL5 detector and visualizes regime changes with a custom indicator.
- A regime label can provide context for choosing strategies, but the article does not demonstrate that its classifications improve returns.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.