Articles with MQL5 programming examples

A complete MQL5 implementation of the Ljung-Box test helps verify independence in trading data and fitted-model residuals. It computes sample autocorrelations, the Q statistic over selected horizons, degrees of freedom with user-controlled adjustments, and right-tail p-values via the regularized incomplete gamma function. Run it on returns, deal outcomes, or external residuals and review decisions directly in the Experts tab.

In this article, we will look at how to modify the code to implement a solution entirely unlike what many consider possible in MQL5. Important note: To fully understand this material, you must have a solid grasp of the concepts covered in the previous articles.

The article examines an example of a multipurpose trading robot template that is suitable both for creating your own strategies and as a codebase for freelance work. A key feature of the solution is bar-based trading; the code already includes built-in modes for averaging, martingale, and holding positions for extended periods. This material will be most useful to beginners who want to develop their own simple strategies or learn about common trading techniques.

The article examines the practical application of finite differences in trading: types of differences, their relationship to price dynamics, and the binomial transform for noise filtering. The rules for encoding patterns based on difference levels and the application of these patterns to forecasting are described. This section presents naive, adaptive, and probabilistic approaches that help smooth time series, identify recurring patterns, and estimate future movements.

In this article, we will examine how to implement overloading for both logical operators and comparison operators. This requires a certain amount of caution and a fair amount of attention. Even a minor oversight when implementing the overloading of these operators can render the entire code completely unusable. If any problems arise in the overloading, the entire database created from the results generated by the code will have to be either discarded completely or, at the very least, reviewed in full.

The article describes a practical approach to synchronizing bars between instruments in a portfolio in MQL5. Classes are provided for loading, storing, and aligning OHLCV data, with options to use an empty bar or carry over values from the previous bar, select a synchronization symbol, and process new bars asynchronously. Examples of use in multi-chart and basket indicators are shown. Readers receive a ready-to-use API for reliable portfolio calculations.

This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.

Debugging is an integral part of the programming cycle. This article discusses common techniques for debugging any application running in the MetaTrader 5 environment.

In this article, we explain what a class is and why this concept came about. Although the topic is interesting, we will focus here on the principles underlying MQL5 programming. This article is just an introduction.

In this article, we implemented the first components of a tree structure. Since I realize that this structure can be very complex at the beginning of the learning process, we will introduce it gradually, step by step. This way, everyone will be able to understand how a tree works and when it is best to use one.

In this article, we will conclude the section on the implementation and explanation of the linked list. However, the implementation presented here omits one detail that can be implemented in a linked list. We will discuss this later, in another article.

The article shows how to tune ICA hyperparameters with a supervised evaluation pipeline and apply spectral clustering to time-lagged indicators. Cross-validation identifies the optimal number of clusters, which are translated into expected return and risk measures. These signals drive dynamic position sizing and stop-loss control, with surrogate models converted to ONNX and integrated into an MQL5 Expert Advisor.

In this article, we will take the next step in understanding what a linked list is and how it works. Although the content of this article may seem rather complex and confusing to beginners, I will try to explain it in the simplest terms possible. This will help you understand why and when to use linked lists.

This is an article that you, dear reader, should study carefully. That is due to the nature of the material presented here. Although we have tried to present the material as simply and informatively as possible, the information provided here can certainly seem quite complex to those who are just beginning to learn programming. Nevertheless, this is no reason to lose heart or ignore what is explained here, as this article will establish a link between two completely different, though closely related, topics.

In this article, we'll look at two ways to inspect the contents of the sandbox and even interact with it, using MetaTrader 5 as the base platform. Understanding the material in this article is essential to understanding what will be covered in subsequent articles.