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  1. Build Self Optmising Expert Advisors in MQL5

    by , 05-30-2024 at 06:39 AM
    Opting for MQL5's matrix and vector capabilities over alternatives like ONNX and Python has considerable benefits. Using an ONNX model would require separate model instances for each trading symbol and new models for any minor parameter changes, such as time frame adjustments. MQL5, however, offers adaptability without the need to manage numerous models for varying conditions.
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  2. Triangular arbitrage with predictions

    by , 05-30-2024 at 05:05 AM
    This article is about the triangular arbitrage strategy, it has an example of two triangular arbitrages, and this is done with deep learning models, which are added to the downloads, and a EA which is also added to the downloads. Triangular arbitrage leverages discrepancies in exchange rates to generate risk-free profits.
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  3. 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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  4. 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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  5. 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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