MQL5 Programming Articles

We will create an indicator written entirely in MQL5 to display the activation function and its derivative on the chart, using ReLU as an example. We will explain how derivatives affect the choice of activation function and what precautions are needed at nondifferentiable points. Readers will be able to interactively visualize the curves and gain a practical foundation for deciding when to use certain activation functions and when not to.

The EA now computes an equal-weight score at M1 confirmation from normalized body fraction, breakout relative to C's range, and elapsed time, while excursions are measured only afterward. We refine processing of closed minutes and export a typed CSV that distinguishes completed observations from snapshots. The result is a clearer, reproducible basis for analyzing confirmed-signal behavior.

This article implements a James–Stein and empirical Bayes shrinkage library in MQL5, validates it against hand-checked arithmetic and a Python reference, and applies it to ten years of H1 data across eight symbols. It covers fixed-target and grand-mean forms, unequal variances with a fitted between-group spread, and a proportions wrapper. You finish with a reusable header, a reproducible test script, a walk-forward study, and a chart indicator that shrinks an hour-of-day table.

The article describes TradeMux as a bridge between a Python pipeline and the MetaTrader 5 terminal for the seamless transfer of trading decisions without duplicating logic. The article examines a four-layer production architecture and a complete Python execution service: connection setup, reading account and position data, signal generation (including CatBoost), pre-trade risk control, kill_switch, and a supervisor. Practical benefits include cross-broker normalization (RoboForex, IC Markets, Alpari, OANDA) and scaling from a single account to multi-account broadcasting without changing the trading logic.

This article implements a multi‑timeframe Granger causality monitor for MetaTrader 5. A Jupyter notebook tests predefined timeframe pairs, selects lags, applies multiple‑testing control, and exports a calibration table. An MQL5 Expert Advisor reloads the setup, recomputes the F‑test live on rolling windows, and marks pairs VALID/INVALID to gate its vote‑based BUY/SELL generator. The workflow shows alignment rules, live/offline parity, and a filtered vs unfiltered comparison on EURUSD.

This article implements a PSI-based drift watchdog for MQL5 EAs. It compares a feature and the model's predicted probability against a quantile-based reference using a rolling window at a configurable cadence, applies hysteresis to avoid flapping, and raises a webhook on state transitions. You get tested math, an audit CSV and a plotting indicator, plus tuning guidance for thresholds, window size, and cooldown.

A published daily history of option-implied variance is combined with a broker symbol's daily bars to fit a HAR model of realized volatility in MQL5; the implied term enters the regression as a predictor, not as a ready-made forecast. Out-of-sample tests on four funds paired with their Cboe indices, ten US equities, and two broker symbols show where it improves the forecast. A background service refits on the broker's bars and draws a five-session price range on the chart, and a self-test matches independent C++ and Python implementations.

We build a neuron-level view for MQL5 models: count neurons per row, compute ranks, and render one line per stored weight from dense layers. The implementation reads matrix orientation from attributes, colors connections by sign and relative strength, and keeps activations on the same row. You can toggle between the layer graph and neurons, or see why the neuron view is unavailable, to analyze how weights connect inputs to outputs.

An EA that passes CodeBase validation can still fail under execution delay and a freeze level. Using a tick scalper, the article reproduces four such failures in the Strategy Tester and shows each fix with its limits. An ablation under a fixed delay measures each change on its own. The burst-based stop distance removes most rejected modifications, while the weekend case behind the session buffer did not recur in those runs.

We describe and implement CVO: infection as candidate generation, coordinate-wise normal perturbation, and a dynamic population. The algorithm is integrated into C_AO and has been tested on a standard benchmark. The analysis identifies a scaling-related cause of stagnation and provides a practical solution — a shift to a relative step based on the range width. The code is ready for use.