History of MQL5.community development

Depth of Market is undoubtedly a very important element for executing fast trades, especially in High Frequency Trading (HFT) algorithms. In this series of articles, we will look at this type of trading events that can be obtained through a broker on many tradable symbols. We will start with an indicator, where you can customize the color palette, position and size of the histogram displayed directly on the chart. We will also look at how to generate BookEvent events to test the indicator under certain conditions. Other possible topics for future articles include how to store price distribution data and how to use it in a strategy tester.

This article describes the fundamentals of portfolio-level statistical arbitrage. Its goal is to facilitate the understanding of the principles of statistical arbitrage to readers without deep math knowledge and propose a starting point conceptual framework. The article includes a working Expert Advisor, some notes about its one-year backtest, and the respective backtest configuration settings (.ini file) for the reproduction of the experiment.

In today's article, we will discuss a compilation directive that is widely used in various codes that can be found in MQL5. Although this directive will be explained rather superficially here, it is important that you begin to understand how to use it, as it will soon become indispensable as you move to higher levels of programming. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

The article is devoted to the metaheuristic Atmosphere Clouds Model Optimization (ACMO) algorithm, which simulates the behavior of clouds to solve optimization problems. The algorithm uses the principles of cloud generation, movement and propagation, adapting to the "weather conditions" in the solution space. The article reveals how the algorithm's meteorological simulation finds optimal solutions in a complex possibility space and describes in detail the stages of ACMO operation, including "sky" preparation, cloud birth, cloud movement, and rain concentration.

Direct point cloud analysis avoids unnecessary data growth and improves the performance of models in classification and segmentation tasks. Such approaches demonstrate high performance and robustness to perturbations in the original data.

This article explores the potential of the Value at Risk (VaR) model for multi-currency portfolio optimization. Using the power of Python and the functionality of MetaTrader 5, we demonstrate how to implement VaR analysis for efficient capital allocation and position management. From theoretical foundations to practical implementation, the article covers all aspects of applying one of the most robust risk calculation systems – VaR – in algorithmic trading.

Every price action trader manually uses trendlines to confirm trends and spot potential turning or continuation levels. In this series on developing a price action analysis toolkit, we introduce a tool focused on drawing slanted trendlines for easy market analysis. This tool simplifies the process for traders by clearly outlining key trends and levels essential for effective price action evaluation.

This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.

We continue studying the Hierarchical Vector Transformer method. In this article, we will complete the construction of the model. We will also train and test it on real historical data.

We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.

In this article, we will look at how to use the RETURN, BREAK, and CONTINUE statements in a loop. Understanding what each of these statements does in the loop execution flow is very important for working with more complex applications. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.