MQL4 and MQL5 Programming Articles

This article shows how to build a fully native MQL5 metrics pipeline for a live EA without DLLs or sidecar processes. It implements counters, gauges, and histograms, safe text/label encoding, and an HTTP client that pushes one exposition payload to a Prometheus Pushgateway on a timer. You will wire metrics into OnTick/OnTimer, export equity, drawdown, trade P&L, and latency, and learn configuration, testing, and limitations for reliable observability.

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.

A candidate for our ranking table is Beluga Whale Optimization, a metaheuristic based on three behavioral patterns of the beluga whale: pair swimming, hunting using Lévy flight, and population renewal through whale fall. During implementation, we discovered that the algorithm does not so much optimize as read the geometry of the test bench; we examine how this works and develop an honest, promising modification called BWOm.

The Session Analyzer is upgraded to define sessions by local market time and to translate those windows into broker/server time with date-aware DST logic. We describe the session model, broker offset treatment, and efficient bar selection, then add a relationship layer that measures temporal overlap, shared price range, range breaks, and size ratios. This provides a clear, reusable basis for session-based research and chart inspection

The article presents an adaptation of the SDformerFlow spiking architecture for the task of dense analysis of price micro-movements. The spatio-temporal structure provides a high level of detail, while the spiking logic ensures computational efficiency and the ability to work with sparse, pulse-like data. As a result, traders gain access to a tool that detects even the slightest shifts in liquidity and provides the foundation for more accurate and consistent real-time decisions.

A calendar release is not a category but a number against a consensus. In MQL5 we build a robust, standardized surprise (actual minus forecast scaled by each indicator's own dispersion), recover the broker's daylight saving schedule from price to anchor each release on the bar that moved, and regress price response by symbol and horizon. The result is a measurable profile in pips per sigma showing which events actually move your market.

We extend an MQL5 AI assistant from read-only tools to safe trade execution with a human-in-the-loop gate. The article defines action tools for placing, closing, modifying, and deleting trades and for drawing on the chart, validates each request, sizes volume to broker limits, and scopes ownership. We pause the tool loop behind an approval card, queue actions, and resume execution only after approval, which holds while auto-approval stays off, as it is by default.

This article presents a high-volume pivot indicator in MQL5 that identifies confirmed pivot highs and lows using surrounding price bars and normalized volume strength. It calculates rolling volume sums, determines a 95th-percentile volume reference, and filters pivot points based on their relative volume. The indicator uses ATR(500) to scale concentric rings around qualifying pivots and draws reaction levels, pivot markers, and formatted volume labels on the chart, helping you study high-volume pivot areas and their interaction with price structure and market volatility.

The article delivers AdaptivePSARTrail.mqh: a reusable header that lets an EA trail positions (its own or external, by symbol/magic) with an adaptive Parabolic SAR. The utility adapts the step from divergence, guards flips with a side check, and orders broker and improvement gates (stops/freeze, tick grid, break‑even, start and step thresholds). A calculation‑only build serves indicators, result codes aid logging, and a demo EA verifies the integration.

We introduce the permutation process for backtest validation: permute bars, rerun the complete development process, and compare the observed Sharpe to the null distribution it produces. Outputs include one-sided p-values, robust z, and quantile confidence intervals, plus a spurious performance curve that quantifies selection versus trial budget. A cache-isolation check prevents false passes. The EURUSD H1 case shows how to set a tolerance and assess whether performance is distinguishable from selection.

This article implements the Garman–Klass volatility estimator as an MQL5 indicator that uses OHLC data for higher statistical efficiency than close-to-close measures. A percentile-based squeeze/expansion detector produces breakout signals, and a companion Expert Advisor executes them via iCustom. Implementation details include series-aware indexing, recursive squeeze-state handling in indicator buffers, and a baseline-on-attach rule for the EA, giving a ready-to-use toolkit for volatility-regime trading.

The article explores the practical application of the L1 Trend Filter for noise reduction and the construction of structural features suitable for live trading. The full workflow is presented: H1 data for 29 instruments from MetaTrader 5, causal filtering, CatBoost with a horizon of three L1 bars, rigorous walk-forward testing, and VaR-based allocation of lots. The reader is provided with a reproducible code pipeline and a portfolio evaluation methodology.

We implement an adaptive trend stop indicator in MQL5 that reseeds on every flip, then accelerates toward price, with speed boosted by distance from VWAP and all distances scaled by ATR. The article covers the acceleration engine, the VWAP agreement filter that grades each flip, the anti-aliased stop line with its cloud, and persistent flip levels and arrows. The result highlights where the trend turned and whether VWAP agreed.

The article shows why a fixed posterior threshold, checked trade by trade, lacks error control and retires healthy strategies far too often on a real EURUSD case. It introduces WinRateSPRT (with Page's CUSUM for continuous use) and a permutation-tested profit factor trend to cover distinct failure modes. LiveStrategyMonitor integrates both and reports a normalized retirement-progress measure for operational decisions.

The implementation of the EV-MGRFlowNet framework demonstrates its key advantages: modularity, resilience to market fluctuations, and the ability to develop strategies independently. These features make the framework a powerful tool for analysis, forecasting, and the development of autonomous trading strategies.

This article presents a single MQL5 include file that adds runtime text-to-speech through Windows SAPI. The library starts a hidden PowerShell process through WinAPI and exposes one function, Speak(), with control of voice, speed, volume, XML tags, and a timeout. It also covers the limits: Windows only, DLL imports required, a blocking call, and refusal in indicators and the tester.

Oscillator thresholds are often treated as direct reversal instructions, causing traders to sell strong bullish markets or buy persistent bearish declines too early. We develop an analytical Expert Advisor that interprets Relative Strength Index events through confirmed swing structure. The model distinguishes continuation, reversal, pending, and expired conditions by combining closed-bar RSI crossings with higher highs, higher lows, lower highs, lower lows, and breaks of structure. It displays each validated interpretation without placing trades.

A simple grid trading strategy becomes a resilient system thanks to three components: an entry filter based on RSI divergence, a strict limit on the number of grid levels, and an independent risk manager that monitors equity on every tick and blocks trading when the daily limit or maximum drawdown is reached. The article examines the mathematics behind the classic martingale trap, the implementation of pivot point and RSI divergence detection in MQL5, the complete code for the CRiskManager class with protection against floating losses, and the architecture of the grid itself, with TP based on the weighted average entry price.

The article takes readers deep into the heart of the EV-MGRFlowNet framework, showing how its architecture is applied in a practical implementation for financial forecasting tasks. Step by step, we build a carefully designed combination of modules capable of detecting subtle temporal patterns and translating them into meaningful market signals.

This article examines the use of subwindows in MetaTrader 5 in detail and shows how the calculation direction in OnCalculate affects indicator buffers and the plotting of averages. Practical examples explain how indicators are linked using FIRST INDICATOR’S DATA and PREVIOUS INDICATOR’S DATA, the effect of cascading removal of dependent indicators, and how indicator_separate_window behaves. Readers will learn how to diagnose display issues and create indicators that work correctly in subwindows.

Implements the Lo–MacKinlay Variance Ratio Test in MQL5 to assess serial dependence via random‑walk variance scaling. Provides a reusable CVarianceRatio class with classical (homoskedasticity) and heteroskedasticity‑robust statistics, plus a script that scans multiple horizons and logs VR and Z‑values. Shows how to embed the test in an Expert Advisor as a statistical gate for mean‑reversion logic.

An Expert Advisor that takes signals from an external process must know which messages were delivered, which were acknowledged, and which orders reached the broker. This article builds that confirmation protocol on Redis Streams: MQL5 publishes market events, Python suggests trades, and MQL5 alone decides what is executed. Live tests on a demo account show duplicate detection, guard refusals, and dead-lettering; crash recovery is verified with unit tests.

An evidence-first workflow for Range Breakout in MQL5: a non-trading logger captures session/time windows and records 61 fields per breakout to CSV, with a strict split between entry features and hindsight labels. The MT5 AI Assistant processes the dataset in nine phases, helping validate data, quantify baselines, and locate stable, entry-usable thresholds for subsequent EA coding.

The article implements the Andean Condor Algorithm (ACA) for MQL5 — a compact optimizer with a multiscale intensification operator. A significant improvement in solution quality was observed with a small population: a single adjustment to the settings propels it into the top 45 — and behind this lies a distinctive feature of the algorithm worth knowing about. This resource provides ready-to-use code and practical guidelines for its application.

Rolling lookback windows implicitly fix a probability vector over past bars. We implement entropy pooling in MQL5 to keep all historical scenarios, reweight them by conditioning on today's volatility under an effective‑scenarios budget, and add views on volatility, tails and correlation. A walk‑forward test, a chart indicator and an ES‑based sizing EA demonstrate how to calibrate risk forecasts and position size to the current regime.