MQL4 and MQL5 Programming Articles

The moving averages are by far the best indicators for our AI models to predict. However, we can improve our accuracy even further by carefully transforming our data. This article will demonstrate, how you can build AI Models capable of forecasting further into the future than you may currently be practicing without significant drops to your accuracy levels. It is truly remarkable, how useful the moving averages are.

You must admit that it sounds alluringly - you become a fortunate possessor of a program that can develop for you a profitable automated trading system (ATC) within a few minutes. All you need is to enter desirable inputs and press Enter. And - here you are, take your ATC tested and having positive expected payoff. Where thousands of people spend thousands of hours on developing that very unique ATC, which will "wine and dine", these statements soundб to put it mildly, very hollow. On the one hand, this really looks a little larger than life... However, to my mind, this problem can be solved.

This is the third article in a series describing the main aspects of MQL5 programming. This article covers complex data types that were not discussed in the previous article. These include structures, unions, classes, and the 'function' data type. It also explains how to add modularity to your program using the #include preprocessor directive.

There are ten basic errors of a newcomer intrading: trading at market opening, undue hurry in taking profit, adding of lots in a losing position, closing positions starting with the best one, revenge, the most preferable positions, trading by the principle of 'bought for ever', closing of a profitable strategic position on the first day, closing of a position when alerted to open an opposite position, doubts.

In the previous article, we introduced the DIAYN method, which offers the algorithm for learning a variety of skills. The acquired skills can be used for various tasks. But such skills can be quite unpredictable, which can make them difficult to use. In this article, we will look at an algorithm for learning predictable skills.

We continue our work on creating the FinMem framework, which uses layered memory approaches that mimic human cognitive processes. This allows the model not only to effectively process complex financial data but also to adapt to new signals, significantly improving the accuracy and effectiveness of investment decisions in dynamically changing markets.

25 to 30% of all crashlogs received from users appear due to errors occurring when functions imported from custom dlls are executed.

This article explores the practical application of L1 trend filtering in MetaTrader 5, covering both its mathematical foundations and usage in MQL5 programs. The L1 filter enables extraction of piecewise-linear trends that preserve essential market structure while reducing price noise. The study analyzes parameter scaling, trend estimation behavior, and integration of the method into algorithmic trading strategies. Experimental results demonstrate how L1 trend filtering can enhance signal stability, trade timing, and overall robustness of trading systems.

Today we interview Francisco García García (chuliweb) from Spain. A week ago his Expert Advisor reached the 8th place, but the unfortunate logic error in programming threw it from the first page of the Championship leaders. As confirmed by statistics, such an error is not uncommon for many participants.

In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.

Can we benefit from seasonality when creating models for Deep Learning with Python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.

What is common between skydiving, Futures, Hawaii, translations and spies? We didn't know it until we've managed to communicate with disqualified participant Atsushi Yamanaka (alohafx). His has a creed "Life is Good!", and one can hardly doubt that. It was interesting to know that distances between the continents are not an obstacle for communication among our Championship's participants.

This article will cover angle-based operations. We will look at methods for constructing angles and using them in trading.

The article dwells on the notion of "market intuition" and ways of developing it. The method described in the article is based on the modeling of financial betting in the form of a simple game.

In this article, we expand the range of tasks of our agent. The training process will include some aspects of money and risk management, which are an integral part of any trading strategy.

Algorithmic trading becomes more popular and needed, which naturally led to a demand for exotic algorithms and unusual tasks. To some extent, such complex applications are available in the Code Base or in the Market. Although traders have simple access to those apps in a couple of clicks, these apps may not satisfy all needs in full. In this case, traders look for developers who can write a desired application in the MQL5 Freelance section and assign an order.