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  1. Presenting the book "MQL5 Programming for Traders"

    by , 12-18-2023 at 05:01 PM
    We have released the most comprehensive guide to MQL5 programming, authored by experienced algorithmic trader Stanislav Korotky with MetaQuotes' support.
    The book is intended for programmers of all levels. Beginners will learn the fundamentals as the book introduces key development tools and basic programming concepts. With this material, you can create, compile, and run your first application in the MetaTrader 5 trading platform. Users with experience in other programming languages can
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  2. Presenting the book "MQL5 Programming for Traders"

    by , 12-18-2023 at 02:43 PM
    We have released the most comprehensive guide to MQL5 programming, authored by experienced algorithmic trader Stanislav Korotky with MetaQuotes' support.

    The book is freely available online, under the "Book" section of the MQL5.community website.
    read more here
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  3. Neural networks made easy (Part 54): Using random encoder for efficient research (RE3)

    by , 12-17-2023 at 06:51 AM
    The issue of efficient exploration of the environment is one of the main problems of reinforcement learning methods. We have discussed this issue more than once. Each time, a proposed solution led to additional complication of the algorithm. In most cases, we resorted to using additional internal reward mechanisms to encourage the model to explore new actions and search for unexplored paths.
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  4. Evaluating ONNX models using regression metrics

    by , 12-17-2023 at 05:49 AM
    Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc.

    The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions.

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  5. Wrapping ONNX models in classes

    by , 12-14-2023 at 01:03 AM
    In the previous article, we used two ONNX models to arrange the voting classifier. The entire source text was organized as a single MQ5 file. The entire code was divided into functions. But what if we try to swap models? Or add another model? The original text will become even bigger. Let's try the object-oriented approach.
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