History of MQL5.community development

In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.

Learn how to import and use EX5 libraries in your MQL5 code or projects. In this continuation article, we will expand the EX5 library by adding more position management functions to the existing library and creating two Expert Advisors. The first example will use the Variable Index Dynamic Average Technical Indicator to develop a trailing stop trading strategy expert advisor, while the second example will utilize a trade panel to monitor, open, close, and modify positions. These two examples will demonstrate how to use and implement the upgraded EX5 position management library.

In this article, I will get acquainted with the GTGAN algorithm, which was introduced in January 2024 to solve complex problems of generation architectural layouts with graph constraints.

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.

Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.

In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.

Convolutional Neural Networks (CNNs) are renowned for their prowess in detecting patterns in images and videos, with applications spanning diverse fields. In this article, we explore the potential of CNNs to identify valuable patterns in financial markets and generate effective trading signals for MetaTrader 5 trading bots. Let us discover how this deep machine learning technique can be leveraged for smarter trading decisions.

The Learning Rate, is a step size towards a training target in many machine learning algorithms’ training processes. We examine the impact its many schedules and formats can have on the performance of a Generative Adversarial Network, a type of neural network that we had examined in an earlier article.

One major challenge is managing multiple chart windows of the same pair running the same program with different features. Let's discuss how to consolidate several integrations into one main program. Additionally, we will share insights on configuring the program to print to a journal and commenting on the successful signal broadcast on the chart interface. Find more information in this article as we progress the article series.

We continue our experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Research results are provided.

In this article, I will introduce a very important tool for developers. If you are not familiar with GIT, read this article to get an idea of what it is and how to use it with MQL5.