History of MQL5.community development

The article describes the development of a multi-agent machine learning system for algorithmic trading on MetaTrader 5 based on reinforcement learning. The system has a three-tier architecture: memory neurons store experience, agents make independent decisions, and the collective mind combines them through weighted voting. The system is continuously improved through Q-learning, pruning of ineffective neurons, and evolutionary reduction of exploration.

A technical history of MQL evolution: from the limited MQL and MQL II languages, through procedural MQL4, to object-oriented MQL5 with native compilation, rich APIs, and a full-fledged engineering environment. We show here the key capabilities of the language and its integrations with Python, OpenCL, ONNX, OpenBLAS, databases, DirectX, the agentic AI Assistant, and the Model Context Protocol (MCP), which connects AI systems with the terminal, MetaEditor, market data, trading operations, and development tools. This article examines archival materials on the origins of MetaQuotes and MetaTrader, the launch of MQL4.COM and MQL5.COM, the championships, Algo Forge, and their impact on the ecosystem.

In this article, we build the core of the TimeFound intelligent model step by step, adapting it to real-world time series forecasting tasks. If you are interested in the practical implementation of neural network patching algorithms in MQL5, you have come to the right place.

We build a CSV exporter for MQL5 custom indicators that preserves the exact values seen on the chart. The script creates the indicator handle with iCustom, waits for BarsCalculated, aligns buffers to CopyRates, and writes a locale-safe CSV that pandas loads with parsed dates and NaN for warm-up bars. It addresses compile-time argument limits, jagged-array workarounds, and EMPTY_VALUE handling, enabling reliable Python backtests without re-coding the indicator.

This article implements an MQL5 Liquidity Heatmap that infers likely liquidation zones from price and volume. It qualifies bars with a rolling volume SMA, computes leverage-based liquidation levels from candle extremes, ranks signals across two volume modes, and manages chart objects (lines and bubbles) that extend until price crosses them, allowing you to highlight potential stop-hunt areas and strengthen structural analysis.

This article implements BOSS from scratch in MQL5 and applies it to regime classification: SFA turns windows into words, bags record word frequencies, and an ensemble over window lengths votes on labels. We cover the encoding steps, the BOSS distance, training with auto-generated regime labels, and practical parameters. A BTCUSD benchmark versus DTW shows higher macro accuracy on clean data and markedly faster inference.

This article presents two versions of the Crystal Structure Algorithm: the original and the modified version. The Crystal Structure Algorithm (CryStAl), published in 2021 and inspired by the physics of crystal structures, was positioned as a parameter-free metaheuristic for global optimization. However, testing revealed a critical problem with the algorithm. A modified version, CryStAlm, is also presented; it addresses the original's key shortcomings.

The article implements a self-sufficient Adaptive SuperTrend EA with internal calculations on a selectable timeframe, avoiding external buffers and indicator files. It includes risk-based lot sizing, ATR stops, stepwise RR trailing, optional anti-repainting confirmation, and session control. Practitioners can reuse the structure for consistent new‑bar signal handling and broker‑compliant order validation.

An MQL5 implementation sends trade lifecycle events to a local HTTP service through WinINet with a reusable session and per-request handles. The trade callback only enqueues JSON and returns, while a 500 ms timer drains the queue and retries failed posts, preserving order. A three-stage log policy keeps the Experts tab clear during downtime and summarizes recovery.

In this article, we will examine the application of mathematics to grid strategies. We will consider the basic principles of the strategy, as well as its advantages and disadvantages. You will learn how to build a trading grid, set optimal parameters, and manage risks effectively.

This article introduces a modular Fair Value Gap (FVG) detection engine for MQL5 packaged as a reusable include class, it evaluates imbalance zones on closed bars, applies a Simple True Range average filter to eliminate low-volatility noise, and supports wick-touch and close-through mitigation. A companion diagnostic indicator plots active gaps, and an Expert Advisor template demonstrates automated pullback entries with new-bar execution controls.

We build a session-based reversal program in MQL5 using the Bread and Butter Judas Swing model. It derives a higher-timeframe daily bias, defines New York kill zones, maps each session's premium and discount from the live range, and requires a sweep before a market structure shift confirms entry. Readers get a ready approach to arm setups only during active sessions and execute in the bias direction with clear, testable rules.

The Metropolis-Hastings algorithm is a fundamental Markov chain Monte Carlo (MCMC) method that is widely used to approximate posterior distributions in Bayesian inference. This article describes the theoretical foundations of the algorithm, the implementation of the MHSampler class in MQL5, and examples of its application, including an analysis of the resulting samples.

The Mantis framework transforms complex time series into informative tokens and serves as a reliable foundation for an intelligent trading agent capable of operating in real time.

Kohonen's self-organizing maps transform the chaos of market data into an ordered two-dimensional map, where similar patterns are grouped together. The article demonstrates a complete implementation of a SOM in an MQL5 Expert Advisor with 400 neurons and continuous learning. We break down the Best Matching Unit search algorithm, weight updates using a Gaussian neighborhood function, integration with quantum effects, and the generation of trading signals. The code is open-source, the math is clear, and the results are verifiable.

A metaheuristic algorithm that simulates the growth of coronary arteries in the human heart for optimization problems. It uses the principles of angiogenesis (the growth of new blood vessels), bifurcation (branching), and pruning of weak branches to find optimal solutions in a multidimensional space. Testing its effectiveness across a wide range of tasks yielded unexpected results.

We implement a Hierarchical Risk Parity allocator in MQL5 as a single class, validate each stage against an independent Python reference, and package it in a rebalancing Expert Advisor. The pipeline covers returns, covariance/correlation, clustering, quasi-diagonalization, and recursive bisection, and contrasts HRP with Markowitz on stressed data. You finish with a verified allocator and an EA ready for basket-level testing.