Articles with MQL5 programming examples

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.

Net profit and win rate do not tell you if a strategy's edge is statistically real. This MQL5 toolkit analyzes return series built from price data or deal history and reports t‑statistics, p‑values, and confidence intervals using one-sample and Welch t‑tests, the Mann–Whitney U test, and volatility‑regime analysis to support evidence‑based trading decisions.

In this article, we refine a moving average crossover strategy with a momentum candle filter and an immediate retracement bar confirmation. When both conditions are met, a pending stop order is placed using a pivot-based stop loss and a 2R take profit. The complete MQL5 Expert Advisor code, finite-state-machine logic, and chart annotations are detailed.

This article presents CSwingEngine, a reusable MQL5 class that detects H4 swing highs and lows, labels them HH, LH, HL, or LL, and classifies market structure as trend or range. Swings are always computed on H4, regardless of the attached chart, and each point draws correctly on lower timeframes via native datetime anchoring. The engine exposes a clean interface to query the current trend and retrieve the swing array for context-aware pattern logic.