These series of articles, on the MQL5 Wizard, are a segue on how often abstract ideas in Mathematics of other fields of life can be enlivened as trading systems and tested or validated before any serious commitments is made on their premise. This ability to take simple and not fully implemented or envisaged ideas and explore their potential as trading systems is one of the gems presented by the MQL5 wizard assembly for expert advisers. The expert classes of the wizard furnish a lot of the mundane ...
Welcome back, fellow traders and aspiring algorithmic enthusiasts! As we step into the third chapter of our MQL5 journey, we stand at the crossroads of theory and practice, poised to unravel the secrets behind arrays, custom functions, preprocessors, and event handling. Our mission is to empower every reader, regardless of their programming background, with a profound understanding of these fundamental MQL5 elements. more...
I sometimes receive private messages from those who want to learn how to create their own Expert Advisors or indicators. Although there is a lot of material on this site and on the Internet in general, including very good resources with examples, beginners still need help. Some users seek more consistency in presentation, others require clarity or something else. Sometimes users ask: "Add comments to the code of a working Expert Advisor, I will understand everything and make the same one myself!" ...
The article considers the theoretical application of quantization in the construction of tree models No complex mathematical equations are used. While writing the article, I discovered the absence of established unified terminology in the scientific works of different authors, so I will choose the terminology options that, in my opinion, best reflect the meaning. Besides, I will use the terms of my own in the matters left unattended by other researchers. This article will use terms and concepts ...
We continue the theme of environmental exploration in reinforcement learning. In previous articles within this series, we have already looked at algorithms for exploring the environment through curiosity and disagreement in an ensemble of models. Both approaches exploited intrinsic rewards to motivate the agent to perform different actions in similar situations while exploring new areas. But the problem is that the intrinsic reward decreases as the environment gets better explored. In complex cases ...
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