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  1. ALGLIB numerical analysis library in MQL5

    by , 01-27-2024 at 04:40 PM
    Financial markets generate data with a huge amount of complex relationships. To analyze them, we need to use the most modern methods of applied mathematics. Successfully combining the high complexity of financial data with the simplicity and efficiency of analysis is a challenging task. ALGLIB is a high-performance library designed specifically for working with numerical methods and data analysis algorithms. It is a reliable assistant in the analysis of financial markets.
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  2. Wrapping ONNX models in classes

    by , 01-26-2024 at 01:49 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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  3. Category Theory in MQL5 (Part 17): Functors and Monoids

    by , 01-12-2024 at 01:49 AM
    We continue our look at category theory with one more take on functors. So far, we have seen applications of category theory in implementing custom instances of the Expert trailing class, and the Expert Signal class so we will consider applications in using the Expert Money class for this article. All these classes come with the Meta Editor IDE and are used with the MQL5 wizard in assembling expert advisors with minimal coding.

    In this article as we sum up our look at functors
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  4. Category Theory in MQL5 (Part 11): Graphs

    by , 01-05-2024 at 01:49 AM
    In our previous article, we delved into monoid groups, by exploring concept of symmetry within typical monoids. In introducing an additional axiom that all members of a monoid group must possess an inverse and restricting binary operations between mirror elements to yield the identity element, we extended applicability of monoids at crucial trade decision points. Building upon this, we now continue our study of category theory and its practical applications in trade system development by examining
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  5. Neural networks made easy (Part 53): Reward decomposition

    by , 12-31-2023 at 06:48 AM
    We continue to explore reinforcement learning methods. As you know, all algorithms for training models in this area of machine learning are based on the paradigm of maximizing rewards from the environment. The reward function plays a key role in the model training process. Its signals are usually pretty ambiguous.
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