MQL4 and MQL5 Programming Articles

This article implements a regime-adaptive grid trading EA based on the PhD research of Aldo Taranto. It presents a regime‑adaptive grid trading EA that constrains risk through restartable cycles and equity‑based safeguards. We explain why naive grids fail (variance growth and almost‑sure ruin), derive the loss formula for real‑time exposure, and implement regime‑aware gating, ATR‑dynamic spacing, and a live kill switch. Readers get the mathematical tools and production patterns needed to build, test, and operate a constrained grid safely.

This topic focuses on incorporating a trained AI model (such as a reinforcement learning model like LSTM or a machine learning-based predictive model) into an existing MQL5 trading strategy.

While some concepts may seem straightforward at first glance, bringing them to life in practice can be quite challenging. In the article below, we'll take you on a journey through our innovative approach to automating an Expert Advisor (EA) that skillfully analyzes the market using a mean reversion strategy. Join us as we unravel the intricacies of this exciting automation process.

This article presents Fractal Reaction System, a compact MQL5 system that converts fractal pivots into actionable market-structure signals. Using closed-bar logic to avoid repainting, the EA detects Change-of-Character (ChoCH) warnings and confirms Breaks-of-Structure (BOS), draws persistent chart objects, and logs/alerts every confirmed event (desktop, mobile and sound). Read on for the algorithm design, implementation notes, testing results and the full EA code so you can compile, test and deploy the detector yourself.

Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.

This article teaches you how to build an MQL5 Expert Advisor that automatically detects support and resistance zones and executes trades based on them. You’ll learn how to program your EA to identify these key market levels, monitor price reactions, and make trading decisions without manual intervention.

We have previously considered various types of neural networks along with their implementations. In all cases, the neural networks were trained using the gradient decent method, for which we need to choose a learning rate. In this article, I want to show the importance of a correctly selected rate and its impact on the neural network training, using examples.

In my previous articles, I have explained how to create an indicator with multiple subwindows, which becomes interesting when using custom indicators. This time we will see how to add multiple windows to an Expert Advisor.

In this article, I decided to conduct a study related to the possibility of reducing multiple states to double-state systems. The main purpose of the article is to analyze and to come to useful conclusions that may help in the further development of scalable trading algorithms based on the probability theory. Of course, this topic involves mathematics. However, given the experience of previous articles, I see that generalized information is more useful than details.

We are going to develop a modular trading system that combines Python for data analysis with MQL5 for trade execution. Four independent modules monitor different market aspects in parallel: volumes, arbitrage, economics and risks, and use RandomForest with 400 trees for analysis. Particular emphasis is placed on risk management, since even the most advanced trading algorithms are useless without proper risk management.

Global market sessions shape the rhythm of the trading day, and understanding their overlap is vital to timing entries and exits. In this article, we’ll build an interactive trading sessions  EA that brings those global hours to life directly on your chart. The EA automatically plots color‑coded rectangles for the Asia, Tokyo, London, and New York sessions, updating in real time as each market opens or closes. It features on‑chart toggle buttons, a dynamic information panel, and a scrolling ticker headline that streams live status and breakout messages. Tested on different brokers, this EA combines precision with style—helping traders see volatility transitions, identify cross‑session breakouts, and stay visually connected to the global market’s pulse.

In Part 38, we build a production-grade MT5 monitoring panel that converts raw ticks into actionable signals. The EA buffers tick data to compute tick-level VWAP, a short-window imbalance (flow) metric, and ATR-based position sizing. It then visualizes spread, ATR, and flow with low-flicker bars. The system calculates a suggested lot size and a 1R stop, and issues configurable alerts for tight spreads, strong flow, and edge conditions. Auto-trading is intentionally disabled; the focus remains on robust signal generation and a clean user experience.

This project aims to leverage the MQL5 algorithm to develop a comprehensive set of analysis tools for MetaTrader 5. These tools—ranging from scripts and indicators to AI models and expert advisors—will automate the market analysis process. At times, this development will yield tools capable of performing advanced analyses with no human involvement and forecasting outcomes to appropriate platforms. No opportunity will ever be missed. Join me as we explore the process of building a robust market analysis custom tools' chest. We will begin by developing a simple MQL5 program that I have named, Chart Projector.

In this article, we build a multi-timeframe scanner dashboard in MQL5 to display real-time trading signals. We plan an interactive grid interface, implement signal calculations with multiple indicators, and add a close button. The article concludes with backtesting and strategic trading benefits

In this article, we will provide a simple and easy guide to anyone who needs to create one of the most valuable and helpful tools in trading which is the graphical panel to simplify and ease doing tasks around trading which helps to save time and focus more on your trading process itself without any distractions.

We all know the importance of the Moving Average indicator for a lot of traders. There are other Moving average types that can be useful in trading, we will identify these types in this article and make a simple comparison between each one of them and the most popular simple Moving average type to see which one can show the best results.

Price action is a fundamental approach for identifying profitable trading setups. However, manually monitoring price movements and patterns can be challenging and time-consuming. To address this, we are developing tools that analyze price action automatically, providing timely signals whenever potential opportunities are detected. This article introduces a robust tool that leverages fractal breakouts alongside EMA 14 and EMA 200 to generate reliable trading signals, helping traders make informed decisions with greater confidence.

The article considers handling trade request structures, namely creating a request, its preliminary verification before sending it to the server, the server's response to a trade request and the structure of trade transactions. We will create simple and convenient functions for sending trading orders to the server and, based on everything discussed, create an EA informing of trade transactions.

This article demonstrates how to automate Larry Williams’ volatility breakout strategy in MQL5 using a practical, step-by-step approach. You will learn how to calculate daily range expansions, derive buy and sell levels, manage risk with range-based stops and reward-based targets, and structure a professional Expert Advisor for MetaTrader 5. Designed for traders and developers looking to transform Larry Williams’ market concepts into a fully testable and deployable automated trading system.