MQL4 and MQL5 Programming Articles

The main purpose of the Championship is to popularize automated trading and accumulate practical information in this field of knowledge. As the Organizer of the Championship, we are doing our best to provide a fair competition and suppress all attempts to “play booty”. It is this reasoning that sets the strict Rules of the Championship.

We explored the advanced use of #define for metaprogramming in MQL5, creating entities that represent tables and column metadata (type, primary key, auto-increment, nullability, etc.). We centralized these definitions in TickORM.mqh, automating the generation of metadata classes and paving the way for efficient data manipulation by the ORM, without having to write SQL manually.

An indicator for interactive working with trend lines, Fibo levels, icons manually imposed on a chart. It allows you to draw the colored zones of Fibo levels, shows the moments of the price crossing the trend line, manages the "Price label" object.

The article shows how to pair SQLite (OLTP) with DuckDB (OLAP) for statistical arbitrage data processing. DuckDB’s columnar engine, ASOF JOIN, and array functions accelerate core tasks such as quote–trade alignment and RWEC, with measured speedups from 2x to 23x versus SQLite on larger inputs. You get simpler queries and faster analytics while keeping trade execution in SQLite.

In this article, we will look at what a dynamic array and a static array are. Is there a difference between using one or the other? Or are they always the same? When should you use one and when the other type? And what about constant arrays? We will try to understand what they are designed for and consider the risks of not initializing all the values in the array.

In this article we continue our exploration of the Group Method of Data Handling family of algorithms, with the implementation of the Combinatorial Algorithm along with its refined incarnation, the Combinatorial Selective Algorithm in MQL5.

Unlike MQL5, Python programming language offers control and flexibility when it comes to dealing with and manipulating time. In this article, we will implement similar modules for better handling of dates and time in MQL5 as in Python.

We continue our look at natural transformations by considering naturality square induction. Slight restraints on multicurrency implementation for experts assembled with the MQL5 wizard mean we are showcasing our data classification abilities with a script. Principle applications considered are price change classification and thus its forecasting.

Self-supervised learning is a powerful paradigm of statistical learning that searches for supervisory signals generated from the observations themselves. This approach reframes challenging unsupervised learning problems into more familiar supervised ones. This technology has overlooked applications for our objective as a community of algorithmic traders. Our discussion, therefore, aims to give the reader an approachable bridge into the open research area of self-supervised learning and offers practical applications that provide robust and reliable statistical models of financial markets without overfitting to small datasets.

Find out how to build a practical CPU-to-GPU migration path in MQL5 using OpenCL. We will focus on context initialization, buffer organization, large batches, kernel startup, and minimizing data exchanges. Typical errors and ways to eliminate them will be considered as well. An example with candlestick patterns illustrates the practical benefit of the approach.

In the article, an attempt is made to build a trading EA for predicting exchange rate quotes. The algorithm is based on classical classification models - logistic and probit regression. The likelihood ratio criterion is used as a filter for trading signals.