Articles on the MQL5 programming and use of trading robots

This article presents an analysis of currency data to better understand why expert advisors can have good performance in some regions of time and poor performance in other regions of time.

By using special data types 'matrix' and 'vector', it is possible to create code which is very close to mathematical notation. With these methods, you can avoid the need to create nested loops or to mind correct indexing of arrays in calculations. Therefore, the use of matrix and vector methods increases the reliability and speed in developing complex programs.

This article shows the basics of MQL for beginners to design their Algorithmic trading system (Expert Advisor) through designing a simple algorithmic trading system after mentioning some basics of MQL5

Stop-loss is a major tool when it comes to money management in trading. Effective use of stop-loss, take profit and lot size can make a trader more consistent in trading and overall more profitable. Although stop-loss is a great tool, there are challenges that are encountered when being used. The major one being stop-loss hunt. This article looks on how to reduce stop-loss hunt in trade and compare with the classical stop-loss usage to determine its profitability.

This article aims to teach the reader how to make a Deep Neural Network from scratch using the MQL4/5 language.

In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.

As the next step in studying neural networks, I suggest considering the methods of increasing convergence during neural network training. There are several such methods. In this article we will consider one of them entitled Dropout.

In this article I will demonstrate some very interesting and useful techniques for automated trading. Some of them may be familiar to you. I will try to cover the most interesting methods and will explain why they are worth using. Furthermore, I will show what these techniques are apt to in practice. We will create Expert Advisors and test all the described techniques using historic quotes.

The use of computer vision allows training neural networks on the visual representation of the price chart and indicators. This method enables wider operations with the whole complex of technical indicators, since there is no need to feed them digitally into the neural network.

We have previously considered the mechanism of self-attention in neural networks. In practice, modern neural network architectures use several parallel self-attention threads to find various dependencies between the elements of a sequence. Let us consider the implementation of such an approach and evaluate its impact on the overall network performance.

This article provides a continuation to the brute force topic, and it introduces new opportunities for market analysis into the program algorithm, thereby accelerating the speed of analysis and improving the quality of results. New additions enable the highest-quality view of global patterns within this approach.