Adewumi Babatunde Gbadebo / Profil

The article delivers a practical PSR/MinTRL workflow in MQL5: construct returns from deal history, compute SR/skew/kurtosis, and evaluate normal CDF/quantile via rational approximations with runtime self-tests. We apply it in an EA and a script and validate across 12 Strategy Tester runs on three symbols. This lets you verify a Sharpe ratio against a benchmark and quantify how many trades your strategy still needs.

This article shows how to implement a native MQL5 portfolio optimizer for multi-symbol baskets. It derives a covariance matrix from bar log-returns, inverts it via Gauss–Jordan, and solves the global minimum-variance, tangency, and target-return portfolios without external libraries. You will build an EA that rebalances positions long-only with clip-and-renormalize and a read‑only dashboard that tracks drift versus targets, plus learn Strategy Tester setup and common pitfalls.

We implement a native Weibull maximum-likelihood fit in MQL5 and use it to model strategy wear-out from trade history. Failures are defined as the rolling profit factor dropping below a floor, producing lifetimes in trades. The article wires a survival-based governor into an EA and a companion indicator, so you can estimate conditional survival over the next N trades and apply explicit retirement thresholds with cooldown.

Build Constant Proportion Portfolio Insurance natively in MQL5. The article details the floor and cushion formulas, a rebalance engine that operates safely on both netting and hedging accounts, and a price‑only reference indicator that reuses the same math. A real Strategy Tester run on XAUUSD M5 demonstrates configuration, order sizing, and stability under a rebalance band.

The article rebuilds a Gaussian Process forecaster in native MQL5 around market-state inputs and direct horizon log-return targets. It employs a Matern 3/2 kernel, symmetric Cholesky with adaptive jitter, standardized targets, and a log‑space grid search that avoids state side effects. The EA trades only when az‑score over observed variance clears a threshold and the edge beats the live spread. Calibration is verified in Strategy Tester.

Standard HMMs assume geometric, memoryless state durations, which poorly match real market phases. This piece implements a duration‑explicit Hidden Semi‑Markov Model natively in MQL5, with per‑state sojourn distributions and a residual‑time forward filter. Parameters are fit offline via EM and loaded through a compact JSON manifest. The EA for XAUUSD M5 uses expected remaining duration to gate entries and exits, helping hold trends while avoiding late entries near regime exhaustion.

Fixed exits ignore state changes while a trade is open. We implement a discrete-time competing-risks model entirely in MQL5, estimate cause-specific hazards for take-profit and stop-loss via Newton–Raphson on a person-period dataset with time-varying features, and turn cumulative incidence into a bar-by-bar hold-or-close rule, then test it against fixed take-profit/stop-loss with identical entry logic.