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mql5

  1. Introduction to MQL5 (Part 3): Mastering the Core Elements of MQL5

    by , 03-24-2024 at 05:32 PM
    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.
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  2. Master MQL5 from beginner to pro (Part I): Getting started with programming

    by , 03-23-2024 at 05:27 AM
    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!"
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  3. Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost

    by , 03-21-2024 at 03:35 PM
    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
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  4. Neural networks made easy (Part 39): Go-Explore, a different approach to exploration

    by , 03-21-2024 at 02:24 PM
    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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  5. Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

    by , 03-09-2024 at 03:07 PM
    This is the continuation of Deep Learning Forecast and Order Placement using Python, the MetaTrader5 Python package and an ONNX model file, but you continue this one without the previous one. All will be explained. Everything we will use is included in this article. In this section, we will guide you through the entire process, culminating in the creation of an Expert Advisor (EA) for trading and subsequent testing.

    Machine learning is a subset of artificial intelligence (AI)
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