Abstract: The research concentrates on recognizing stock markets long-term upward and downward trends. The key results are obtained with the use of gradient boosting algorithms, XGBoost in particular. The raw data is represented by time series with basic stock market quotes with periods labelled by experts as Trend or Flat. The features are then obtained via various data transformations, aiming to catch implicit factors resulting in change of stock direction. Modelling is done in two stages: stage one aims to detect endpoints of tendencies (i.e. “sliding windows”), stage two recognizes the tendency itself inside the window. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need of specific quality metrics to keep up with practical applicability. The model can be used to design an investment strategy though further research in feature engineering and fine calibration is required.