Martin Alejandro Bamonte / Профиль

Presentamos una clase reutilizable de dimensionamiento adaptativo, CRiskEngine, que calcula el lote por riesgo y stop y lo reduce según tres factores medibles: racha de pérdidas, volatilidad actual frente a su promedio y caída de capital. Se incluye un asesor de prueba con modos conmutables para comparar seis esquemas (lote fijo, riesgo fijo, cada factor y combinados) sobre 842 operaciones de EURUSD, con métricas y curvas. El lector obtiene un motor listo para integrar y una medición clara del efecto de cada factor.

A Sharpe ratio read off the best of many optimization runs is not the number it looks like. This article ships a reusable native CDeflatedSharpe class that turns a raw Sharpe into an honest confidence statement. The Probabilistic Sharpe Ratio corrects it for sample length and for skew and kurtosis; the Deflated Sharpe Ratio adds the correction almost nobody applies, for the number of variants you tried before keeping the best. Everything is from scratch, the sample moments, the normal CDF and its inverse included, so there is no Python, no DLL and no library. On a real sweep of 56 moving-average variants on XAUUSD the winner looked significant at 98.5 percent by PSR, then fell to 90.6 percent once the 56 trials were admitted, below the usual bar. That gap is the selection bias, made measurable.

The series develops state persistence for MQL5 Expert Advisors. Part 1 delivers a crash-safe key-value store: a CStateStore class that saves through a temporary file and a rename, carries a versioned header with a checksum, and stores integers, doubles, strings, booleans, and double arrays, plus a demo advisor that resumes a counter and a rolling window after a restart. Readers get a compact include file and a pattern that protects the live state file if the process dies mid-save.

This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.

The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.