MQL4 and MQL5 Programming Articles

The article builds an MQL5‑native weak‑labeling engine that infers per‑indicator trust from vote agreement, then fuses five noisy signals into P(Buy) using Expectation‑Maximization. We describe the EA architecture, rolling‑window refits, abstain handling, diagnostics, and a majority‑vote baseline used for comparison. Readers can reuse the EM library, swap in their own labeling functions, and obtain a transparent, probability‑based entry filter.

This article builds a native MQL5 LOF engine without DLLs, ALGLIB or ONNX. Bars are mapped to five standardized features; k‑NN reachability and density produce a scale‑free LOF score, and the alarm level is a moving quantile of the reference cloud. The score gates only new entries in an EMA‑cross EA, with an indicator for charting and a script for independent checks on XAUUSD, EURUSD and GBPUSD.

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 takes a detailed look at using subwindows in MQL5 indicators: creating them, detecting instances, and preventing duplication. It also covers INDICATOR_SHORTNAME, querying information about chart windows and indicators, and the difference between displaying indicators in the main chart window and in a separate window. The article also shows how to set the height and boundaries of a subwindow to standardize the interface and simplify the placement of objects.

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.

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.

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.

A reusable MQL5 component measures floating P/L with swap, gross loss, two stop‑loss quantities, equity gap, margin level, and stop coverage, then classifies results for selected positions and the whole account. The snapshot records validity and readable reasons, supporting a quote‑age policy and missing‑stop handling. Integrate it into an Expert Advisor and block new exposure when thresholds or data checks fail.

We estimate the probability, at broker time 08:00, that EURUSD's 08:00–16:00 range exceeds 1.25 times the mean of the previous 20 complete, valid target ranges. A two-feature SPSS logistic regression model employs the completed 00:00–08:00 H1 window. Coefficients from 2020–2023 sessions are fixed for the 2024 holdout comparison against constant and 60-outcome rolling frequency baselines, using Brier scores and log losses. MQL5 scripts allow readers to extract broker data and recreate the probabilities in MetaTrader 5.

The article implements a MetaTrader 5 Expert Advisor that provides spread-only pre-trade estimates in points for a planned Buy or Sell. It compares a current-spread result with a historical scenario that uses the maximum of the live spread and a selected percentile from recent Bid/Ask ticks, and shows both on a collapsible panel. Manual market execution is available without SL/TP, with readiness checks kept separate from the estimates.