MQL4 and MQL5 Programming Articles

Machine Learning is a complex and rewarding field for anyone of any experience. In this article we dive deep into the inner mechanisms powering the models you build, we explore the intricate world of features,predictions and impactful decisions unravelling the complexities and gaining a firm grasp of model interpretation. Learn the art of navigating tradeoffs , enhancing predictions, ranking feature importance all while ensuring robust decision making. This essential read helps you clock more performance from your machine learning models and extract more value for employing machine learning methodologies.

Since these articles are educational in nature and are not intended to show the implementation of specific functionality, we will do things a little differently in this article. Instead of showing how to apply factorization to obtain the inverse of a matrix, we will focus on factorization of the pseudoinverse. The reason is that there is no point in showing how to get the general coefficient if we can do it in a special way. Even better, the reader can gain a deeper understanding of why things happen the way they do. So, let's now figure out why hardware is replacing software over time.

This article shows how to build an MQL5 Expert Advisor around the UT Bot Alerts indicator. The EA reads custom indicator signals via iCustom() and CopyBuffer(), evaluates entries only on new bars, using the last closed candle at index 1, and enforces a one-direction-at-a-time model by closing opposite positions before taking new entries. It also adds optional ATR-based stop-losses, reward-to-risk take-profits, dedicated buy/sell execution functions, magic-number tracking, and basic backtesting for repeatable evaluation.

In this article, we'll look at what you need to do to start using Excel to manage MetaTrader 5, but in a very interesting way. To do this, we will use an Excel add-in to avoid using built-in VBA. If you don't know what add-in is meant, read this article and learn how to program in Python directly in Excel.

The Camel Algorithm, developed in 2016, simulates the behavior of camels in the desert to solve optimization problems, taking into account temperature, supply, and endurance. This article also presents a modified version of the algorithm (CAm) with key improvements: the use of a Gaussian distribution in generating solutions and the optimization of the oasis effect parameters.

This article demonstrates how to integrate the Google Generative AI API with MetaTrader 5 using MQL5. You will learn how to structure API requests, handle server responses, extract AI-generated content, manage rate limits, and save the results to a text file for easy access.

In this article, we'll look at what can be achieved with a little code refinement. This refinement is aimed at simplifying our code, making more use of MQL5 library calls and, above all, making it much more stable, secure and easy to use in other projects that we may develop in the future.

The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.

In this article I will show that although we are still at a very basic stage of programming, we can already implement some interesting applications. In this case, we will create a fairly simple password generator. This way we will be able to apply some of the concepts that have been explained so far. In addition, we will look at how solutions can be developed for some specific problems.

In this sixth article of the Connexus library series, we will focus on a complete HTTP request, covering each component that makes up a request. We will create a class that represents the request as a whole, which will help us bring together the previously created classes.

In this article, we present an MQL5 implementation of Stepwise Feature Selection based on the mutual information between an optimal predictor set and a target variable.

In this article, we will look at an innovative optimization method called BSO (Brain Storm Optimization) inspired by a natural phenomenon called "brainstorming". We will also discuss a new approach to solving multimodal optimization problems the BSO method applies. It allows finding multiple optimal solutions without the need to pre-determine the number of subpopulations. We will also consider the K-Means and K-Means++ clustering methods.

In this article, we'll look at a few ideas, including how mathematical formulas are more complex in appearance than when implemented in code. In addition, we will consider how to set up a chart quadrant, as well as one interesting problem that may arise in your MQL5 code. Although, to be honest, I still don't quite understand how to explain it. Anyway, I'll show you how to fix it in code.