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  1. Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

    by , 05-20-2024 at 06:39 PM
    Welcome to the third installment of our "Optimizing a Simple Hedging Strategy" series. In this segment, we'll begin with a brief review of our progress to date. So far, we have developed two key components: the Simple Hedge Expert Advisor (EA) and the Simple Grid EA. This article will focus on further refining the Simple Hedge EA. Our goal is to improve its performance through a combination of mathematical analysis and a brute force approach to find the most effective way to implement this
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  2. Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

    by , 05-19-2024 at 03:27 PM
    In the previous articles, I have described various ways to use machine learning algorithms to create trading systems. Some turned out to be quite successful, others (mostly from early publications) were greatly overtrained. Thus, the sequence of my articles reflects the evolution of understanding: what machine learning is actually capable of. We are, of course, talking about the classification of time series.

    The current article is a development of the previous topic and the next
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  3. MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors

    by , 05-17-2024 at 08:06 PM
    We continue the series on MQL5 wizard implementation by looking into Neural Architecture Search while specifically dwelling on the role Eigen Vectors can play in making this process, of expediting network training, more efficient. Neural networks are arguably the fitting of a curve to a set of data in that they help come up with a formulaic expression that, when applied to input data (x), provides a target value (y) just like a quadratic equation does with a curve. The x and y data points
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  4. Neural networks made easy (Part 60): Online Decision Transformer (ODT)

    by , 05-17-2024 at 12:58 PM
    The last two articles were devoted to the Decision Transformer method, which models action sequences in the context of an autoregressive model of desired rewards. As you might remember, according to the results of practical tests of two articles, the beginning of the testing period saw a fairly good increase in the profitability of the trained model results. Further on, the performance of the model decreases and a number of unprofitable transactions are observed, which leads to losses. The amount
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  5. Master MQL5 from beginner to pro (Part I): Getting started with programming

    by , 05-17-2024 at 06:25 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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