MQL4 and MQL5 Programming Articles

In this article, we will talk about algorithms for using attention methods in solving problems of detecting objects in a point cloud. Object detection in point clouds is important for many real-world applications.

In this article, we will practically understand the difference between passing by value and passing by reference. Although this seems like something simple and common and not causing any problems, many experienced programmers often face real failures in working on the code precisely because of this small detail. Knowing when, how, and why to use pass by value or pass by reference will make a huge difference in our lives as programmers. 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.

The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.

How to use World Bank economic data for forecasts? What happens when you combine AI models and economics?

After improving the C_Mouse class, we can focus on creating a class designed to create a completely new framework fr our analysis. We will not use inheritance or polymorphism to create this new class. Instead, we will change, or better said, add new objects to the price line. That's what we will do in this article. In the next one, we will look at how to change the analysis. All this will be done without changing the code of the C_Mouse class. Well, actually, it would be easier to achieve this using inheritance or polymorphism. However, there are other methods to achieve the same result.

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.

The article discusses the Tabu Search algorithm, one of the first and most well-known metaheuristic methods. We will go through the algorithm operation in detail, starting with choosing an initial solution and exploring neighboring options, with an emphasis on using a tabu list. The article covers the key aspects of the algorithm and its features.

We will review the basics of Gaussian processes (GP) as a probabilistic machine learning model and demonstrate its application to regression problems using synthetic data.

This article explores the use of databases to store logs in a structured and scalable way. It covers fundamental concepts, essential operations, configuration and implementation of a database handler in MQL5. Finally, it validates the results and highlights the benefits of this approach for optimization and efficient monitoring.

In this part, we focus on how to merge real-time market feedback—such as live trade outcomes, volatility changes, and liquidity shifts—with adaptive model learning to maintain a responsive and self-improving trading system.

This article presents CPyramidEngine, a reusable MQL5 class that adds disciplined pyramiding to any Expert Advisor with about six lines of integration. The engine enforces three constraints: strictly decreasing lot sizes, a single unified stop that advances after each add-on, and broker-level validation of every modification. It explains common failure modes in naive implementations and shows how to keep total account risk quantifiable and controlled as positions are added.

We follow up our last article, where we introduced the indicator pair of the SAR and the RVI, by considering how this indicator pairing could be extended with Machine Learning. SAR and RVI are a trend and momentum complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we will perform Enhanced Data Visualization by going beyond basic charts by incorporating features like interactivity, layered data, and dynamic elements, enabling traders to explore trends, patterns, and correlations more effectively.

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.

We will build a biologically correct system of neurons for time series forecasting. The introduction of a plasma-like environment into the neural network architecture creates a kind of "collective intelligence," where each neuron influences the system's operation not only through direct connections, but also through long-range electromagnetic interactions. Let's see how the neural brain modeling system will perform in the market.

In this article, we take our second attempt to convert the changes in price levels on any market, into a corresponding change in angle. This time around, we selected a more mathematically sophisticated approach than we selected in our first attempt, and the results we obtained suggest that our change in approach may have been the right decision. Join us today, as we discuss how we can use Polar coordinates to calculate the angle formed by changes in price levels, in a meaningful way, regardless of which market you are analyzing.

High probability Setups are well known in our trading community, but regrettably they are not well-defined. In this article, we will aim to find an empirical and algorithmic way of defining exactly what is a high probability setup, identifying and exploiting them. By using Gradient Boosting Trees, we demonstrated how the reader can improve the performance of an arbitrary trading strategy and better communicate the exact job to be done to our computer in a more meaningful and explicit manner.

In this article, we enhance the canvas-based price dashboard in MQL5 by adding a pixel-perfect scrollable text panel for usage guides, overcoming native scrolling limitations through custom antialiasing and a rounded scrollbar design with hover-expand functionality. The text panel supports themed backgrounds with opacity, dynamic line wrapping for content like instructions and contacts, and interactive navigation via up/down buttons, slider dragging, and mouse wheel scrolling within the body area.

We implement an EquiVolume indicator in MQL5 that converts standard candlesticks into volume-weighted boxes. The workflow includes selecting volume type, detecting the maximum volume within a lookback range, normalizing all values against it, and mapping them into proportional box widths. The result is a chart-based structure that visualizes trading activity intensity alongside price movement in MetaTrader 5.

In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.

Computer vision for trading: how it works and how to develop it step by step. We create an algorithm for recognition of RGB images of price charts using the attention mechanism and a bidirectional LSTM layer. As a result, we obtain a working model for forecasting the EURUSD price with the accuracy of up to 55% in the validation section.

By studying the FEDformer method, we opened the door to the frequency domain of time series representation. In this new article, we will continue the topic we started. We will consider a method with which we can not only conduct an analysis, but also predict subsequent states in a particular area.

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.

While analyzing the market situation, we divide it into separate segments, identifying key trends. However, traditional analysis methods often focus on one aspect and thus limit the proper perception. In this article, we will learn about a method that enables the selection of multiple objects to ensure a more comprehensive and multi-layered understanding of the situation.

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.

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.