MQL5 Programming Articles

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.

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.

The article considers methods of encoding initial data in hyperbolic latent space through anisotropic diffusion processes. This helps to more accurately preserve the topological characteristics of the current market situation and improves the quality of its analysis.

We continue dwelling on the topic of social behavior of living organisms and its impact on the development of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will dive into the two-phase evolution, test the algorithm and draw conclusions. Just as in nature a group of living organisms join their efforts to survive, ASBO uses principles of collective behavior to solve complex optimization problems.

This article shows how to drastically simplify the use of the Logify library with the Builder pattern and automatic default configurations. It explains the structure of the specialized builders, how to use them with smart auto-completion, and how to ensure a functional log even without manual configuration. It also covers tweaks for MetaTrader 5 build 5100.

In this article, we will continue diving into the implementation of the ACMO (Atmospheric Cloud Model Optimization) algorithm. In particular, we will discuss two key aspects: the movement of clouds into low-pressure regions and the rain simulation, including the initialization of droplets and their distribution among clouds. We will also look at other methods that play an important role in managing the state of clouds and ensuring their interaction with the environment.

We created a log suppression system in the Logify library. It details how the CLogifySuppression class reduces console noise by applying configurable rules to avoid repetitive or irrelevant messages. We also cover the external configuration framework, validation mechanisms, and comprehensive testing to ensure robustness and flexibility in log capture during bot or indicator development.

This article is the fifth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we describe the structure of PUBLISH packets, how we are setting their Publish Flags, encoding Topic Name(s) strings, and setting Packet Identifier(s) when required.

Learn how to read a CSV file in MQL5 and organize its trading data into dynamic arrays. This article shows step by step how to count file elements, store all data in a single array, and separate each column into dedicated arrays, laying the foundation for advanced analysis and trading performance visualization.

In this article, we create a canvas-based graphing tool in MQL5 for statistical correlation and linear regression analysis between two symbols, with draggable and resizable features. We incorporate ALGLIB for regression calculations, dynamic tick labels, data points, and a stats panel displaying slope, intercept, correlation, and R-squared. This interactive visualization aids in pair trading insights, supporting customizable themes, borders, and real-time updates on new bars

In this article, we will look at how to solve certain problems and issues that arise when using Python code within other programs. More specifically, we will demonstrate a common issue encountered when using Excel in conjunction with MetaTrader 5, although we will be using Python to facilitate this interaction. However, this implementation has a minor drawback. It does not occur in all cases, but only in certain specific situations. When it does happen, it is necessary to understand the cause. In today’s article, we will begin explaining how to resolve this issue.

This article introduces Jardine's Gate, a six-gate orthogonal signal filter for MetaTrader 5 that validates LSTM predictions across entropy, expert interference, confidence, regime-adjusted probability, trend direction, and consecutive-loss kill switch dimensions. Out of 43,200 raw signals per month, only 127 pass all six gates. Readers get the complete QuantumEdgeFilter MQL5 class, threshold calibration logic, and gate performance analytics.

We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.