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ArfimaPro – Real‑Time Market Regime Detection Most strategies fail because they trend‑follow during mean‑reverting markets and mean‑revert during trending markets. ArfimaPro solves this by measuring the market's long‑memory structure in real time using the Geweke‑Porter‑Hudak (GPH) estimator of the fractional differencing parameter d – the key ARFIMA statistic. Key Features GPH d(t) estimation – Plots the fractional differencing parameter d (blue line). d > 0 indicates long memory
A GPH‑based estimator for d, the key ARFIMA parameter, is added to MicroStructure_Foundation.mqh. GPHEstimator() computes d via log‑periodogram regression, while PopulateARFIMAAnalysis() stores d with an R² confidence score and validates the theoretical relationship H = d + 0.5. An empirical study on 72 US100 M1 sessions confirms pooled d = −0.006, consistent with the random walk boundary established in 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.
NAS100 Compression Cross. H1 MACD with Regime and Volatility Filters A systematic trend-following Expert Advisor for NAS100 and US_TECH100 CFD. Entries are triggered by MACD histogram zero-line crossovers on the H1 timeframe. Three structural filters determine whether each signal is taken. All results presented in this listing are simulated. Past performance is not indicative of future results. Figures represent walk-forward optimisation out-of-sample periods on historical data, not real money
This article builds the foundation layer of a twelve-part MQL5 market microstructure toolkit. It implements guarded math helpers (SafeDivide, SafeLog, SafeSqrt, SafeExp, SafeTanh), robust data validation (ValidateSymbolV2, SafeCopyClose), trimmed statistical estimators (robust mean var), a linear regression slope, shared structs, and an FFT. You compile a single include file that hardens indicators and expert advisors against silent numerical failures and standardizes data flow for later parts.
GoertzelBrain combines Goertzel spectral analysis with an online‑trained neural network ensemble to convert cycle features into a directional confirmation signal. The indicator builds a compact feature vector from the dominant period, amplitude, confidence and their dynamics, plus local volatility, and outputs +1, −1 or 0. The article provides the full MQL5 implementation, explains the architecture and feature engineering, and shows how to use it as a directional filter.