History of MQL5.community development

This article explores an accessibility-focused enhancement that goes beyond default terminal alerts by leveraging MQL5 resource management to deliver contextual voice feedback. Instead of generic tones, the indicator communicates what has occurred and why, allowing traders to understand market events without relying solely on visual observation. This approach is especially valuable for visually impaired traders, but it also benefits busy or multitasking users who prefer hands-free interaction.

This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.

Learn how to build a Supertrend-driven Expert Advisor in MQL5 from the ground up. The article covers embedding the indicator as a resource, reading buffer values on closed bars, detecting confirmed flips, aligning and switching positions, and configuring stop-loss modes and position sizing. It concludes with Strategy Tester setup and reproducible tests, leaving you with a configurable EA and a clear framework for further research and extensions.

In this article, we will examine how to implement a moving average calculation and what precautions should be taken when performing this calculation. We will also discuss overloading the OnCalculate function to know when and how to work with one model or another.

We determine the overbought and oversold condition of the market according to chaos theory: integrating the principles of chaos theory, fractal geometry and neural networks to forecast financial markets. The study demonstrates the use of the Lyapunov exponent as a measure of market randomness and the dynamic adaptation of trading signals. The methodology includes an algorithm for generating fractal noise, hyperbolic tangent activation, and moment optimization.

While studying a wide range of breakout setups, I noticed that failed breakouts were rarely caused by a lack of volatility, but more often by weak internal structure. That observation led to the framework presented in this article. The approach identifies patterns where the final price leg shows superior length, steepness, and speed—clear signs of momentum accumulation ahead of directional expansion. By detecting these subtle geometric imbalances within consolidation, traders can anticipate higher-probability breakouts before price exits the range. Continue reading to see how this fractal-based, geometric framework translates structural imbalance into precise breakout signals.

In this article, we will create our first fully practical and functional indicator. The goal is not to show how to create an application, but to help you understand how you can develop your own ideas and give you the opportunity to apply them in a safe, simple, and practical way.

In this article, we explore how to build a position information visualization tool using the MQL5 Standard Library’s CCanvas. This project strengthens your skills in working with library modules while providing traders with a practical tool to visualize and interact with open positions directly on a live chart. Join the discussion to learn more.

We implement Larry Williams’ Smash Day reversal patterns in MQL5 by building a rule-based Expert Advisor with dynamic risk management, breakout confirmation logic, and one trade at a time execution. Readers can backtest, reproduce, and study parameter effects using the MetaTrader 5 Strategy Tester and the provided source.

The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images", their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.

We revisit the Ilan grid Expert Advisor and integrate Q-learning in MQL5 to build an adaptive version for MetaTrader 5. The article shows how to define state features, discretize them for a Q-table, select actions with ε-greedy, and shape rewards for averaging and exits. You will implement saving/loading the Q-table, tune learning parameters, and test on EURUSD/AUDUSD in the Strategy Tester to evaluate stability and drawdown risks.

This article discusses an approach to trading only in the chosen direction (buy or sell). For this purpose, the technique of causal inference and machine learning are used.

In this article, we will see that not everything always needs to be implemented in a certain specific way. There are alternative approaches to problem-solving. To properly understand this article, it is necessary to grasp the concepts described in the previous articles. The materials presented here are for educational purposes only. Do not regard it as a finished application. Its purpose is to study the concepts presented here.