MQL5 Programming Articles

In this article, I will fix and optimize handling the appearance of WinForms objects after moving the mouse cursor away from the object, as well as start the development of the TabControl WinForms object.

Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.

The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.

MQL5 provides programmers with a very complete set of functions and object-oriented API thanks to which they can do everything they want within the MetaTrader environment. However, Web Technology is an extremely versatile tool nowadays that may come to the rescue in some situations when you need to do something very specific, want to marvel your customers with something different or simply you do not have enough time to master a specific part of MT5 Standard Library. Today's exercise walks you through a practical example about how you can manage your development time at the same time as you also create an amazing tech cocktail.

We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.

This article covers the use of assertions in MQL5 language. It provides two examples of the assertion mechanism and some general guidance for implementing assertions.

In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.

From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.

This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.

This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.

In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.

Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.

Here I will show how to create screens with price tickers which are usually used to display quotes on the exchange. I will do it by only using MQL5, without using complex external programming.