mql5

Having already studied the fundamentals of MQL5, you are now prepared to take on one of the most important tasks associated with algorithmic trading: creating a working Expert Advisor. As I indicated in the previous article, We will use a project-based approach for this series. This method helps in both comprehending abstract ideas and recognizing how they are used in practical situations. You will have a firm grasp of how to automate trading decisions based on candlestick patterns and predetermined

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In the previous article "Matrix Factorization: The Basics", I talked a little about how you, my dear readers, can use matrices in your general calculations. However, at that time I wanted you to understand how the calculations were done, so I didn't pay much attention to creating the correct model of the matrices.

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Forecasting long-term timeseries is of specifically great importance for trading. The Transformer architecture, which was introduced in 2017, has demonstrated impressive performance in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The use of Self-Attention mechanisms allows the effective capturing of dependencies over long time intervals, extracting key information from the context. Naturally, quite quickly a large number of different algorithms based on this mechanism

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CatBoost is an open-source software library with gradient-boosting algorithms on decision trees, it was designed specifically to address the challenges of handling categorical features and data in machine learning.

It was developed by Yandex and was made open-source in the year of 2017, read more.
Despite being introduced recently compared to machine learning techniques such as Linear regression or SVM's, CatBoost gained massive popularity among AI communities and rose to

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