Having already studied the fundamentals of MQL5, you are now prepared to take on one of the most important tasks associated with algorithmic trading: creating a working Expert Advisor. As I indicated in the previous article, We will use a project-based approach for this series. This method helps in both comprehending abstract ideas and recognizing how they are used in practical situations. You will have a firm grasp of how to automate trading decisions based on candlestick patterns and predetermined ...
In the previous article "Matrix Factorization: The Basics", I talked a little about how you, my dear readers, can use matrices in your general calculations. However, at that time I wanted you to understand how the calculations were done, so I didn't pay much attention to creating the correct model of the matrices. more...
In my previous article we looked at the main programs that are used by MQL5 programmers (and came to the conclusion that the MetaEditor IDE is well suited for beginner needs). In addition, we took a quick look at the concept of a function and created a simple script that prints a message in the system log. These messages can be viewed at the bottom of the terminal window in the Experts tab. Now we come to the point that this data must be stored somewhere. Today we will ...
In this article series, we'll explore how MQL5 indicators are created, customized, and utilized to enhance trading strategies in MetaTrader 5. From basic indicator logic to advanced customization options, we'll cover the basics and gradually move deeper into the more advanced concepts of indicator development as we progress with the article series. The main aim of this article series is to empower you to create your own MQL5 custom indicators tailored to your trading preferences and goals. ...
Forecasting long-term timeseries is of specifically great importance for trading. The Transformer architecture, which was introduced in 2017, has demonstrated impressive performance in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The use of Self-Attention mechanisms allows the effective capturing of dependencies over long time intervals, extracting key information from the context. Naturally, quite quickly a large number of different algorithms based on this mechanism ...
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