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  1. Experiments with neural networks (Part 4): Templates

    by , Yesterday at 01:45 AM
    In the previous articles (Part 1, Part 2, Part 3), we experimented with shapes and angles whose values were passed to the perceptron and the neural network built on the basis of the DeepNeuralNetwork.mqh library. We also conducted experiments on optimization methods in the strategy tester.
    An important task in the current experiments was to track the influence of the amount of transmitted data and the depth of history we take this data from. In addition, we needed to reveal patterns,
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  2. Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

    by , 05-20-2024 at 05:39 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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  3. Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

    by , 05-19-2024 at 02:27 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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  4. MQL5 Wizard Techniques you should know (Part 18): Neural Architecture Search with Eigen Vectors

    by , 05-17-2024 at 07:06 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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  5. Neural networks made easy (Part 60): Online Decision Transformer (ODT)

    by , 05-17-2024 at 11:58 AM
    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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