History of MQL5.community development

This article develops a market state classification module for MQL5 that interprets price behavior using completed price data. By examining volatility contraction, expansion, and structural consistency, the tool classifies market conditions as compression, transition, expansion, or trend, providing a clear contextual framework for price action analysis.

In this article, we enhance the canvas-based price dashboard in MQL5 by adding a pixel-perfect scrollable text panel for usage guides, overcoming native scrolling limitations through custom antialiasing and a rounded scrollbar design with hover-expand functionality. The text panel supports themed backgrounds with opacity, dynamic line wrapping for content like instructions and contacts, and interactive navigation via up/down buttons, slider dragging, and mouse wheel scrolling within the body area.

An in-depth walkthrough of building a Larry Williams inspired volatility breakout Expert Advisor in MQL5, combining swing structure, volatility-based entries, trade day of the week filtering, time filters, and flexible risk management, with a complete implementation and reproducible test setup.

In this article, we build a versatile RSI indicator in MQL5 supporting multiple variants, data sources, and smoothing methods for improved analysis. We add hue shifts for color visuals, dynamic boundaries for overbought/oversold zones, and notifications for trend alerts. It includes multi-timeframe support with interpolation, offering us a customizable RSI tool for diverse strategies.

In this article, we show how to send authenticated requests to the Binance API using MQL5 to retrieve your account balance for all assets. Learn how to use your API key, server time, and signature to securely access account data, and how to save the response to a file for future use.

In this discussion, we follow up on the previously developed multi-signal Expert Advisor with the objective of exploring and applying available optimization methods. The aim is to determine whether the trading performance of the EA can be meaningfully improved through systematic optimization based on historical data.

From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.

In this article, we develop a canvas-based price dashboard in MQL5 using the CCanvas class to create interactive panels for visualizing recent price graphs and account statistics, with support for background images, fog effects, and gradient fills. The system includes draggable and resizable features via mouse event handling, theme toggling between dark and light modes with dynamic color adjustments, and minimize/maximize controls for efficient chart space management.

In this article, we will begin creating a core risk management class that will be key to controlling risks in the system. We will focus on building the foundations, defining the basic structures, variables and functions. In addition, we will implement the necessary methods for setting maximum profit and loss values, thereby laying the foundation for risk management.

This article presents a session-based analytical framework that combines time-defined market sessions with the Candle Pressure Index (CPI) to classify acceptance and rejection behavior at session boundaries using closed-candle data and clearly defined rules.

An empirical study of Larry Williams’ Trade Day of the Week concept, showing how time-based market bias can be measured, tested, and applied using MQL5. This article presents a practical framework for analyzing win rates and performance across trading days to improve short-term trading systems.

This study introduces a novel methodology for the development of trend-following trading strategies. This section describes the process of annotating training data and using it to train classifiers. This process yields fully operational trading systems designed to run on MetaTrader 5.