Discussing the article: "Testing for Residual Autocorrelation with the Ljung-Box Portmanteau Test in MQL5"

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Check out the new article: Testing for Residual Autocorrelation with the Ljung-Box Portmanteau Test in MQL5.

A complete MQL5 implementation of the Ljung-Box test helps verify independence in trading data and fitted-model residuals. It computes sample autocorrelations, the Q statistic over selected horizons, degrees of freedom with user-controlled adjustments, and right-tail p-values via the regularized incomplete gamma function. Run it on returns, deal outcomes, or external residuals and review decisions directly in the Experts tab.

You've built a strategy. The equity curve looks nice, the return distribution seems reasonable, and when you eyeball a plot of returns over time nothing obviously screams "pattern." But "doesn't obviously scream pattern" doesn't mean there's no serial autocorrelation hiding in the data. A small lag-1 correlation tells you nothing about dependence at lag 2, 5, or 10. If you fit a model to returns (ARIMA, GARCH, or another time-series model), the residuals should show no significant autocorrelation. If they do, your model may still be missing some of the serial structure it was supposed to capture.

The tool for this job is the Ljung-Box portmanteau test. Instead of checking one autocorrelation at a time, it checks a whole block of lags together and gives you a single p-value: is there evidence of autocorrelation somewhere in this range of lags, or not?

This article presents an MQL5 implementation (LjungBoxToolkit.mq5) with no external dependencies. It computes sample autocorrelations, the Ljung-Box Q statistic, degrees of freedom, and a chi-square-approximation p-value, then prints a plain-language report to the Experts tab. It works on raw price returns, on your closed-trade history, or on an external residual series you export from any model you've already fitted.

Author: Kayode Michael Oyetunde