History of MQL5.community development

The article presents a minimal working set for maintaining a trading journal in MQL5 using SQLite: a table structure for trades, signals, and events, indices, prepared statements and trades, as well as standard analytical SQL queries. Integration with the statistics dashboard in MetaTrader 5 and working with the database via MetaEditor are demonstrated. The approach allows automating the journal, accelerating calculations, and performing analysis without complicating the EA code.

The article demonstrates how Python and MetaTrader 5 integration combines research flexibility and trade execution into a single workflow. Python is used for data analysis, feature selection and model training, while MetaTrader 5 is used for testing and trading automation. This approach simplifies the transfer of solutions into practice, increases reproducibility, and makes the development of trading systems faster and more structured.

A backtest shows only one path among many possible outcomes. This MQL5 script performs 1000 bootstrap Monte Carlo resamples of a trade P&L series, draws a percentile fan chart on the chart via CCanvas, and reports probability of ruin, value at risk, and 95th‑percentile worst drawdown. The result is a practical view of path risk and drawdown exposure beyond a single equity curve.

Let's try mining CFTC data, downloading COT and TFF reports via Python, connecting all this with MetaTrader 5 quotes and an AI model, and get forecasts. What are COT reports in the Forex market? How to use COT and TFF reports for forecasting?

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.