Articles with MQL5 programming examples

Today we will begin to study structures in a simpler, more practical, and comfortable way. Structures are among the foundations of programming, whether they are structured or not. I know many people think of structures as just collections of data, but I assure you that they are much more than just structures. And here we will begin to explore this new universe in the most didactic way.

Tired of wasting time searching for that very file that you application needs in order to work? How about including everything in the executable? This way you won't have to search for the things. I know that many people use this form of distribution and storage, but there is a much more suitable way. At least as far as the distribution of executable files and their storage is concerned. The method that will be presented here can be very useful, since you can use MetaTrader 5 itself as an excellent assistant, as well as MQL5. Furthermore, it is not that difficult to understand.

Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.

A custom forward simulation engine detects fast/slow EMA crossovers and immediately projects synthetic candles ahead of the signal bar. It generates bodies and wicks using controlled logic, draws them with chart objects, and refreshes on every new signal or anchor change. You get a clear forward-looking view to test timing, visualize scenarios, and manage invalidation on the chart.

This article shows how to build a local stop-loss system in an MQL5 Expert Advisor that keeps stop levels on the terminal side. It walks through the execution logic, event handlers, inputs, and an OOP design using CTrade, CPositionInfo, CHashMap/CHashSet, and chart objects. You will implement multi-position tracking, draggable stops, visual spacers and labels, plus cleanup and disconnection behavior to create a practical risk-control utility.

In this article, we explore the File Operations classes of the MQL5 Standard Library to build a robust reporting module that automatically generates Excel-ready CSV files. Along the way, we clearly distinguish between manually executed trades and algorithmically executed orders, laying the groundwork for reliable, auditable trade reporting.

In this article, we'll look at how you can do something very similar to what's implemented in languages like C, C++, and Java. I am talking about passing a virtually infinite number of parameters inside a function or procedure. While this may seem like a fairly advanced topic, in my opinion, what will be shown here can be easily implemented by anyone who has understood the previous concepts. Provided that they were really properly understood.

In this article, we present an MQL5 library for modeling volatility, designed to function similarly to Python's arch package. The library currently supports the specification of common conditional mean (HAR, AR, Constant Mean, Zero Mean) and conditional volatility (Constant Variance, ARCH, GARCH) models.

Traditional machine learning teaches practitioners to be vigilant not to overfit their models. However, this ideology is being challenged by new insights published by diligent researches from Harvard, who have discovered that what appears to be overfitting may in some circumstances be the results of terminating your training procedures prematurely. We will demonstrate how we can use the ideas published in the research paper, to improve our use of AI in forecasting market returns.

Whale Optimization Algorithm (WOA) is a metaheuristic algorithm inspired by the behavior and hunting strategies of humpback whales. The main idea of WOA is to mimic the so-called "bubble-net" feeding method, in which whales create bubbles around prey and then attack it in a spiral motion.

ROC curves are graphical representations used to evaluate the performance of classifiers. Despite ROC graphs being relatively straightforward, there exist common misconceptions and pitfalls when using them in practice. This article aims to provide an introduction to ROC graphs as a tool for practitioners seeking to understand classifier performance evaluation.

The article explores why trading results can differ significantly between brokers, even when using the same strategy and financial symbol, due to decentralized pricing and data discrepancies. The piece helps MQL5 developers understand why their products may receive mixed reviews on the MQL5 Marketplace, and urges developers to tailor their approaches to specific brokers to ensure transparent and reproducible outcomes. This could grow to become an important domain-bound best practice that will serve our community well if the practice were to be widely adopted.

Here we will consider the evolution of the ACS algorithm: three modifications aimed at improving the convergence characteristics and the algorithm efficiency. Transformation of one of the leading optimization algorithms. From matrix modifications to revolutionary approaches regarding population formation.

In this article, we will discuss the first part of the topic, which is not so easy for beginners to understand. In order not to get even more confused and to explain this topic correctly, we will divide the explanation into stages. We will devote this article to the first stage. However, although at the end of the article it may seem that we have reached the deadlock, in fact we will take a step towards another situation, which will be better understood in the next article.

Today we will look at why we need the iSpread feature. At the same time, we will understand how the system informs us about the remaining time of the bar when there is not a single tick available for it. 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.

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.

Today we will continue to develop the order system. As you will see, we will be massively reusing what has already been shown in other articles. Nevertheless, you will receive a small reward in this article. First, we will develop a system that can be used with a real trading server, both from a demo account or from a real one. We will make extensive use of the MetaTrader 5 platform, which will provide us with all the necessary support from the beginning.

In this article, we discuss and demonstrate how to convert nominal predictors into numerical formats that are suitable for machine learning algorithms, using both Python and MQL5.

Today's article is a continuation of the previous one. We will look at the implementation of an Expert Advisor, focusing mainly on how the server code is executed. The code given in the previous article is not enough to make everything work as expected, so we need to dig a little deeper into it. Therefore, it is necessary to read both articles to better understand what will happen.

This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference.

The article provides a detailed discussion of the key components and innovations of the ATA optimization algorithm, which is an evolutionary method with a unique dual behavior system that adapts depending on the situation. ATA combines individual and social learning while using crossover for explorations and migration to find solutions when stuck in local optima.

Today we will continue working on getting the mouse pointer to tell us how much time is left on a bar during periods of low liquidity. Although at first glance it seems simple, in reality this task is much more difficult. This involves some obstacles that we will have to overcome. Therefore, it is important that you have a good understanding of the material in this first part of this subseries in order to understand the following parts.

How about creating something practical using sockets? In today's article, we'll start creating a mini-chat. Let's look together at how this is done - it will be very interesting. Please note that the code provided here is for educational purposes only. It should not be used for commercial purposes or in ready-made applications, as it does not provide data transfer security and the content transmitted over the socket can be accessed.

This is definitely the most difficult question to be explained purely theoretically. That is why you need to practice everything that we're going to discuss here. While this may seem simple at first, the topic of operators can only be understood in practice combined with constant education.

This is the first part of an article series presenting the implementation of bivariate copulae in MQL5. This article presents code implementing Gaussian and Student's t-copulae. It also delves into the fundamentals of statistical copulae and related topics. The code is based on the Arbitragelab Python package by Hudson and Thames.

Perhaps this article will be the most confusing for novice programmers. As a matter of fact, here I will show that it is not always that all functions and procedures have unique names in the same code. Yes, we can easily use functions and procedures with the same name — and this is called overload.

Sockets. Do you know what they are for or how to use them in MetaTrader 5? If the answer is no, let's start by studying them. In today's article, we'll cover the basics. Since there are several ways to do the same thing, and we are always interested in the result, I want to show that there is indeed a simple way to transfer data from MetaTrader 5 to other programs, such as Excel. However, the main idea is not to transfer data from MetaTrader 5 to Excel, but the opposite, that is, to transfer data from Excel or any other program to MetaTrader 5.