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  1. Python, ONNX and MetaTrader 5: Creating a RandomForest model with RobustScaler and PolynomialFeatures data preprocessing

    by , 05-24-2024 at 04:30 PM
    Random Forest is widely used in a variety of fields, and its flexibility makes it suitable for both classification and regression problems. In a classification task, the model decides which of the predefined classes the current state belongs to. For example, in the financial market, this could mean a decision to buy (class 1) or sell (class 0) an asset based on a variety of indicators.

    However, in this article, we will focus on regression problems. Regression in machine learning
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  2. 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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  3. Seasonality Filtering and time period for Deep Learning ONNX models with python for EA

    by , 05-14-2024 at 11:09 AM
    [QUOTE]When I read the article: Benefiting from Forex market seasonality, I thought of this to make this what I think is an interesting article. I could start comparing an EA with and without seasonality's and with to see if it can benefit.

    I all ready new that markets depend on seasonality since I read that the Mark Zuckerberg got the money for Facebook, from an investor that got his money investing the one that they gave him in the Bar Mitzvah in stocks of petrol when the ...
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  4. Introduction to MQL5 (Part 7): Beginner's Guide to Building Expert Advisors and Utilizing AI-Generated Code in MQL5

    by , 05-12-2024 at 05:42 PM
    The rapid advancement of artificial intelligence in today's technological landscape has sparked discussions regarding the future of coding. Some argue that code produced by AI could replace the need for conventional coding knowledge. But nothing could be further from reality than this. This article seeks to dispel this myth and highlight the importance of knowing how to code. We're going to reveal ways you can incorporate AI-generated code into our programming projects in a seamless way so
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  5. Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal

    by , 05-08-2024 at 10:58 AM
    In this article, we will see how we can use an autoencoder neural network in the financial space to help us remove noise in the market so that we can discover trading opportunities.

    This article is an easy read if you have a basic understanding of ONNX, PCA, and Neural Networks in general.

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