History of MQL5.community development

This article builds a complete Recurrence Quantification Analysis (RQA) toolkit for MetaTrader 5 in pure MQL5. We cover phase-space reconstruction, time-delay embedding, distance and recurrence matrix construction, RQA metric extraction, automatic epsilon selection, and rolling-window computation through a modular library design. The article concludes by applying the library in a practical indicator that plots RR, DET, LAM, ENTR, and TREND directly on the chart, providing a solid foundation for nonlinear time-series analysis in MQL5.

Build a Liquidity Spectrum Volume Profile in MQL5 that allocates volume to equal price bins over a chosen lookback using candle close prices. The guide covers data retrieval with copy functions, binning and normalization, and drawing rectangles and POC lines with chart objects and time offsets to reveal high-activity liquidity zones on the chart.

Mining central bank balance sheet data provides a picture of global liquidity in the Forex market and key currencies. We combine data from the Fed, ECB, BOJ and PBoC into a composite index and use machine learning to uncover hidden patterns. This approach turns raw data into real trading signals by combining fundamental and technical analysis.

This article presents a custom MQL5 money management class that adapts position sizing to real-time volatility using a monotonic queue for O(N) sliding-window extremes. The class applies inverse volatility scaling and optionally validates risk with an RBF network. We show implementation details in the Optimize method and compare results with the inbuilt Size-Optimized class to assess latency and risk control benefits.

If there is a need to display text on a chart, we can use the Comment() function. But its capabilities are quite limited. Therefore, in this article, we will create our own component - a full-screen dialog window capable of displaying multi-line text with flexible font settings and scrolling support.

This article implements a complete MQL5 Expert Advisor that monitors manually drawn support and resistance levels in real time. It synchronizes horizontal lines, detects approaches, touches, breakouts, reversals, and retests, and adds optional candlestick pattern checks. Alerts and on‑chart markers provide clear, repeatable feedback, allowing you to keep manual analysis while automating the surveillance of key price levels.

The article implements GJR-GARCH and TARCH in an MQL5 volatility library and explains why asymmetry improves on standard ARCH/GARCH. It covers model formulation, parameterization, and usage through derived classes and scripts. Readers get code examples for calibration and one-step-ahead forecasting on real data to support risk and diagnostics.

In this article, we expand our butterfly animation program with a four-stage animation pipeline: sequential curve drawing, smooth wing fill fading, detailed body rendering, and continuous flight. We implement a timer-driven state machine, four oscillators for wing flapping, vertical bobbing, horizontal sway, and tilt, as well as a neon glow around the wing outlines and a cyclical color change based on hue. You will learn how to structure these effects on the MetaTrader 5 canvas for clean and controlled playback.

This article shows how to connect AI agents directly to MetaTrader 5 by building a complete MCP (Model Context Protocol) server in Python. It details the architecture, MetaTrader 5 client wrapper, market data and order handlers, and tool registration over stdio, with testing via MCP Inspector and connections to clients like Claude Desktop or OpenClaw. The result is a standardized bridge for natural-language queries, live data retrieval, and safe order execution in MetaTrader 5.

Adaptation of the classical CAPM model for the Forex currency market in MQL5. The indicator calculates expected return and risk premium based on historical volatility. The indicators rise at peaks and bottoms, reflecting the fundamental principles of pricing. Practical application for counter-trend and trend-following strategies, taking into account the dynamics of the risk-reward ratio in real time. The article includes mathematical apparatus and technical implementation.

The article describes a variant of options emulation through an underlying asset implemented in the MQL5 programming language. The pros and cons of the chosen approach are compared with real exchange options using the example of the FORTS futures market of the MOEX Moscow exchange and the Bybit crypto exchange.

This article explains how to implement parameter versioning in MQL5 using binary files and packed structures. It shows how to write and read fixed-size records with FileWriteStruct and FileReadStruct in FILE_BIN mode, including version numbers, timestamps, and a checksum. You will also see how to detect changes via checksums, append records safely, and load the latest configuration without overwriting prior settings.

We build a lightweight bridge that captures closed trades in MetaTrader 5 and sends them to an external backend over HTTP as JSON. It uses OnTradeTransaction for event detection, reads details from deal history, assembles a JSON payload, and posts it via WebRequest. A local Flask API is used to test the flow, delivering a working path to move trade data outside the terminal.

We implement an MQL5 expert advisor that detects order blocks formed after consolidation breakouts and confirms them with fair value gaps. Each zone is validated by a break of structure and a preceding inducement, then filtered by the higher-timeframe trend. The program adds mitigation tracking, risk-based lot sizing, and two trailing stop modes, providing clear on-chart visuals and backtest-ready trade execution logic.

This article implements a static, CSV-based news source for the Strategy Tester, so historical economic news events can be preloaded and queried during backtesting. It replaces live calendar calls in tester mode with a fast in-memory search, preserves the live logic for trading, and delivers deterministic, repeatable results with explicit control over included events, enabling reliable validation of news-aware filters, stop suspension, and trade-blocking rules.

The CATCH framework combines Fourier transform and frequency patching to accurately identify market anomalies beyond the reach of traditional methods. Let us examine how this approach reveals hidden patterns in financial data.