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  1. Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators

    by , 02-10-2024 at 07:10 AM
    The article continues the topic of ready-made templates for using indicators in EAs. Here we will look at connecting to EAs and using volume and Bill Williams' indicators. We will display the data received from indicators on the dashboard created in the first article of this series. The panel has also been improved. At the end of the article, we will briefly look at its changes and improvements.
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  2. Wrapping ONNX models in classes

    by , 02-10-2024 at 04:19 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 , 02-03-2024 at 04:19 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. Modified Grid-Hedge EA in MQL5 (Part II): Making a Simple Grid EA

    by , 02-01-2024 at 06:37 AM
    Welcome to the second installment of our article series, "Modified Grid-Hedge EA in MQL5." Let's begin by recapping what we covered in the first part. In Part I, we explored the classic hedge strategy, automated it using an Expert Advisor (EA), and conducted tests in the strategy tester, analyzing some initial results. This marked the first step in our journey toward creating a Modified Grid-Hedge EA—a blend of classic hedge and grid strategies.
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  5. Building Your First Glass-box Model Using Python And MQL5

    by , 01-20-2024 at 02:40 PM
    Glass-box algorithms are machine learning algorithms that are fully transparent and inherently intelligible. They defy conventional wisdom that there is a tradeoff between prediction accuracy and interpretability in Machine Learning because they offer an unparalleled level of accuracy and transparency. This means they are exponentially easier to debug, maintain, and improve upon iteration when compared to their black-box alternatives that we are more familiar with. Black-box models are all machine
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