Discussing the article: "Market Microstructure in MQL5: Measuring long memory in MQL5 with Hurst estimators (Part 2)"
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Check out the new article: Market Microstructure in MQL5: Measuring long memory in MQL5 with Hurst estimators (Part 2).
Part 2 focuses on practical long-memory detection for intraday data. Three complementary Hurst estimators are implemented and combined into a confidence‑weighted composite, with confidence tied to valid regression scales. The final H and confidence populate the shared analysis struct, enabling indicators to act only when H departs from the neutral 0.40–0.60 band and to select trend‑following above 0.60 or mean‑reversion below 0.40.
The Hurst exponent H characterizes the long-range dependence of a time series. Harold Edwin Hurst introduced the concept in 1951 while studying the long-term storage capacity of the Nile River. The range of H is between 0 and 1 and the interpretation divides into three regimes.
When H > 0.5, the series is persistent. Large moves tend to be followed by large moves in the same direction. Autocorrelation between returns is positive at long lags. This is the regime where trend-following strategies have a statistical foundation.
When H = 0.5, increments are uncorrelated. The series behaves as a random walk. No directional bias exists at any lag. Trend-following and mean-reversion strategies both lack a statistical edge.
When H < 0.5, the series is anti-persistent. Large up-moves tend to be followed by down-moves and vice versa. Autocorrelation is negative. This is the regime where mean-reversion strategies have a statistical foundation.
The H boundary value at 0.5 is not a "safe neutral" value—it is the decision threshold. A trader who applies a trend-following framework when H is below 0.5 is fighting the statistical structure of the data. The practical question is therefore not whether H exists as a concept but whether we can estimate it accurately enough and quickly enough to make a useful regime call before the opportunity passes.
Author: Max Brown