Discussing the article: "Feature Engineering for ML (Part 10): Structural Break Tests in MQL5"
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Check out the new article: Feature Engineering for ML (Part 10): Structural Break Tests in MQL5.
We port AFML Chapter 17 structural break tests to MQL5 as a single include, CStructuralBreaks, delivering six bar-indexed features for EAs: CSW statistic and critical value, Chow-Type DFC, SADF with a rolling lookback (default 252), SM-Exp, and SM-Power. SADF uses O(L²) rolling windows for real-time viability. A companion StructuralBreaksViewer indicator plots all series with per‑series visibility and optional z‑score normalization. SB_EMPTY marks invalid values for safe integration.
Part 9 of this series implemented the structural break test suite from AFML Chapter 17 in Python, covering the Chu-Stinchcombe-White CUSUM test, the Chow-Type Dickey-Fuller test, SADF across six regression models, and two robustifying variants (QADF and CADF). This article ports the three core tests — CSW, Chow, and SADF — to MQL5 as a single include file, CStructuralBreaks.mqh, placed in MQL5/Include/StructuralBreaks/. The sub- and super-martingale tests (SM-Exp and SM-Power) are included as well, rounding the suite to five statistics per bar.
The MQL5 implementation departs from the Python version in one structural decision that is worth stating immediately: SADF uses a rolling lookback window rather than a full expanding window. In Python, the expanding window is viable because the computation runs once offline on a fixed dataset. In an EA that recalculates on each new bar, an expanding window scales as O(T²) with the number of completed bars. At T = 1,000 bars — a modest two-year daily series — the expanding window performs 15× more inner-loop operations than a 252-bar rolling window. At T = 5,000 bars it performs 400× more. The rolling window is not a compromise; it changes the interpretation of the statistic slightly, and that change is documented in Section 6.
Author: Patrick Murimi Njoroge