History of MQL5.community development

Successfully employing algorithmic trading requires continuous, interdisciplinary learning. However, the infinite range of possibilities can consume years of effort without yielding tangible results. To address this, we propose a framework that gradually introduces complexity, allowing traders to refine their strategies iteratively rather than committing indefinite time to uncertain outcomes.

An innovative approach to collecting indicator information in MQL5 enables more flexible and streamlined data analysis by allowing developers to pass custom inputs to indicators for immediate calculations. This approach is particularly useful for algorithmic trading, as it provides enhanced control over the information processed by indicators, moving beyond traditional constraints.

This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.

Many beginning programmers have a hard time understanding why their code doesn't work as they expect. There are many things that make code truly functional. It's not just a bunch of different functions and operations that make the code work. Today I invite you to learn how to properly create real code, rather than copy and paste fragments of it. The materials presented here are for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

In this article, we will look at creating a service app for sending notifications to a smartphone about trading results. We will learn how to handle lists of Standard Library objects to organize a selection of objects by required properties.

This is the third article in a series describing the main aspects of MQL5 programming. This article covers complex data types that were not discussed in the previous article. These include structures, unions, classes, and the 'function' data type. It also explains how to add modularity to your program using the #include preprocessor directive.

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

As one of the most powerful Price Action analysis toolkits, the Metrics Board is designed to streamline market analysis by instantly providing essential market metrics with just a click of a button. Each button serves a specific function, whether it’s analyzing high/low trends, volume, or other key indicators. This tool delivers accurate, real-time data when you need it most. Let’s dive deeper into its features in this article.

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.

Discover how to develop an Expert Advisor (EA) in MQL5 using multiple indicators like RSI, MA, and Stochastic Oscillator to detect hidden bullish and bearish divergences. Learn to implement effective risk management and automate trades with detailed examples and fully commented source code for educational purposes!