MetaTrader 5 Python User Group - the summary - page 38
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Feature Engineering for ML (Part 9): Structural Break Tests in Python
The preceding articles in this Feature Engineering for ML series built features from the structure of time itself: Part 1 established fractional differentiation as a way to preserve long memory in a stationary series; Part 3 embedded the trading calendar into Fourier coordinates; and Part 5 compressed tick-level order flow into bar-indexed microstructural statistics. This article builds features from a qualitatively different question: not what the current bar looks like, but whether the data-generating process that produced it has recently changed.
Feature Engineering for ML (Part 10): Structural Break Tests in MQL5
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.
Feature Engineering for ML (Part 11): Fractal Features in Python
The preceding articles in this Feature Engineering for ML series derived features from price geometry (Part 1), from the trading calendar (Part 3), from the bid-ask spread and price impact (Parts 5–6), from the information content of the trade-direction sequence (Part 7), and, most recently, from the timing of regime changes themselves (Part 10). This eleventh installment returns to price geometry, but at a coarser structural level: the swing highs and swing lows that define support, resistance, and trend.