History of MQL5.community development

Manual population of MqlTradeRequest leaves cross-field rules unchecked, creating silent misconfigurations at execution time. A fluent COrderBuilder for MQL5 adds pointer-based method chaining, per-field validation, and directional SL/TP checks against broker stop‑level constraints. Its Send() method runs a four-stage gate—flag completeness, cross-field consistency, OrderCheck(), then OrderSend()—so configuration errors are caught early and order code stays clear and reusable.

Build a restart-aware virtual protection layer on top of the SQLite persistence from Part 1. The EA reconstructs hidden stop-loss and take-profit after restart, verifies current price against recovered exits, and closes or continues positions accordingly. The result is a consistent recovery path that detects managed positions and sustains safe runtime management.

A typed publish-subscribe event bus in MQL5 replaces global variables and direct cross-references. Using an abstract listener interface and an enum-indexed subscription table, a signal engine, order manager, and drawdown monitor communicate only through the bus, with no shared state. The article analyzes dispatch overhead, pointer validation, and recursive publish risks, helping you design decoupled, testable EAs.

The article discusses the Extremal Optimization (EO) algorithm, an optimization method inspired by the Bak-Sneppen self-organized criticality model, where evolution occurs through the elimination of the worst-case components of the system. The modified population version of the algorithm demonstrates a shift away from theoretical principles in favor of practical efficiency, leading to the creation of powerful computational tools.

We invite you to explore the Actor-Director-Critic framework, which combines hierarchical learning and a multi-component architecture for creating adaptive trading strategies. In this article, we take a detailed look at how using the Director to classify the Actor's actions helps to effectively optimize trading decisions and improve the robustness of models in financial market conditions.

This article develops a class for managing partial position closing in MQL5 and then integrates it into an Order Blocks Expert Advisor. It also presents test results comparing the strategy with and without partial position closing, and analyzes the conditions under which this approach can help provide and maximize profit. In conclusion, partial position closing can be highly beneficial in trading strategies, especially those focused on wider price movements.

The article discusses the practical implementation of the HiSSD framework in algorithmic trading tasks. It explains how the skill hierarchy and adaptive architecture can be used to build sustainable trading strategies.

This article adds a regression gate to the MQL5 debugging and profiling workflow. It keeps the Part II profiler, TestLite runner, and trading math helper as contracts, then compares current profiler evidence with an accepted baseline. The workflow also adds symbol-aware assertions, compact status files, and report tables so performance drift, missing tests, and broker-assumption problems are visible before a build is accepted.

The article presents a new approach to creating trading systems based on quantum principles and artificial intelligence. The author describes the development of a unique neural network that goes beyond classical machine learning by combining quantum mechanics with modern AI architectures.

For this article we rotate to a custom MQL5 Wizard class implementation that explores Trailing Stops. Our custom class is ‘CTrailingReservoirLinReg’ that we derive by combining the Reservoir Sampling algorithm with a Linear Regression network. As has been the case throughout these series, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and money management classes.

This article shows how to build a local stop-loss system in an MQL5 Expert Advisor that keeps stop levels on the terminal side. It walks through the execution logic, event handlers, inputs, and an OOP design using CTrade, CPositionInfo, CHashMap/CHashSet, and chart objects. You will implement multi-position tracking, draggable stops, visual spacers and labels, plus cleanup and disconnection behavior to create a practical risk-control utility.

MQL5's file system operates within a strict sandbox. Understanding its access flags and path resolution rules is the foundation of any reliable export pipeline. This article builds a CCSVExporter class that handles file creation, safe appending, and error recovery. It also covers CSV parsing, field tokenization, concurrent access conflicts, and write-buffering strategies for high-frequency optimization runs.

The article provides a detailed guide to creating the innovative 3DBarCustomSymbol.mq5 indicator, which generates custom symbols in MetaTrader 5 that combine price, time, volume, and volatility into a single three-dimensional representation. The mathematical foundations, system architecture, practical aspects of implementation and application in trading strategies are considered.

This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.

Multi-core optimization in MetaTrader 5 can silently drop results when parallel agents contend for the same CSV file. A reusable MQL5 export engine applies an iteration-based spin-lock to acquire the file handle reliably and append rows without loss. It persists custom metrics such as the Sortino Ratio, average trade duration, and signal-quality measures (lag and whipsaws) into a consolidated CSV for downstream analysis.

The article explores the possibility of using regression models in algorithmic trading. Regression models, unlike binary classification, allow for the creation of more flexible trading strategies by quantifying predicted price changes.

The article extends the MQL5 recurrence library to Recurrence Network Analysis (RNA) by treating recurrence matrices as adjacency matrices of undirected graphs. It implements core network metrics—clustering, transitivity, average path length, betweenness, assortativity, and density—and applies them in rolling windows for single-series RNA and Joint RNA (JRNA). A modular metrics engine and two indicators visualize the evolving network structure on MetaTrader 5 charts for practical time-series analysis.

Manually drawn analytical object tools like Fibonacci tools, and Andrews Pitchforks are invisible to automated trading logic. This article extends a base detector to extract anchor points, level arrays, and geometric offsets from complex objects. You will implement a reusable collector that normalizes the raw chart data into structured memory arrays, ready for strategy decisions.

We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.

This article implements the Chapter 19 microstructure suite in afml.features.microstructure and explains a two-layer design for OHLCV-only and tick-augmented workflows. We cover Roll and Corwin–Schultz spread/volatility, Kyle's, Amihud's, and Hasbrouck's lambdas, VPIN, and bar‑level imbalance features, all in Numba‑accelerated kernels. A single np.searchsorted pass resolves bar boundaries, enabling prange parallelization and producing a bar‑indexed feature matrix ready for downstream ML models.

Automated trading systems often focus heavily on signal generation while neglecting the mechanisms required to protect capital during periods of stress. This article presents an Equity Governance Framework in MQL5 that monitors drawdown conditions, evaluates equity pressure, and dynamically controls trading activity through a state-driven risk management model. By combining drawdown analysis, cooldown logic, trade authorization, and execution restrictions, the framework demonstrates how trading discipline can be engineered directly into code using a modular and extensible architecture.

In this article, we explore the HiSSD framework, which combines hierarchical learning and multi-agent approaches to create adaptive systems. We examine in detail how this innovative methodology helps uncover hidden patterns in financial markets and optimize trading strategies in decentralized environments.

This article builds the Spectrum Fitter: from tau(q) we compute f(alpha) with a discrete Legendre transform, then fit Normal, Binomial, Poisson, and Gamma spectra under box constraints using BLEIC. The best model by SSE is selected, and its parameters (eg, alpha min, alpha max or alpha_0, gamma) become the cascade inputs for multifractal simulation.

Most retail traders ignore overnight swap rates, but for long-term positions, these interest payments can make or break your strategy. This article shows you how to build a dynamic MQL5 module that retrieves real-time swap data and converts it into actual profit or loss in your account currency. You will learn how to program an Expert Advisor that automatically calculates if a trade is worth holding based on carry income and adjusts your position size to account for expected interest. It is a practical guide to turning a hidden cost into a mathematical advantage for your trading systems.

The article presents a detailed analysis of the Exchange Market Algorithm (EMA) inspired by the behavior of stock market traders. The algorithm simulates stock trading, where market participants with varying levels of success employ different strategies to maximize profits.