mql5

Gradient boosting is a powerful machine learning algorithm. The method produces an ensemble of weak models (for example, decision trees), in which (in contrast to bagging) models are built sequentially, rather than independently (in parallel). This means that the next tree learns from the mistakes of the previous one, then this process is repeated, increasing the number of weak models. This builds a strong model which can generalize using heterogeneous data. In this experiment, I used the CatBoost library developed by Yandex. It is one of the most popular libraries, along with XGboost and LightGBM. The purpose of the article is to demonstrate the creation of a model based on machine learning. The creation process consists of the following steps:

• receive and preprocess data
• train the model using the prepared data
• test the model in a custom strategy tester
• port the model to MetaTrader 5

The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.

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