Qlib provides a comprehensive model training framework that supports a variety of machine learning algorithms including LightGBM. The platform uses YAML configuration files to define the training parameters, and users can flexibly set the time intervals for the training set, validation set and test set. Taking the Alpha158 feature set as an example, Qlib can automatically complete the whole process of feature engineering, model training and evaluation.
In terms of model application, Qlib not only supports traditional supervised learning methods, but also integrates cutting-edge technologies such as reinforcement learning. The trained models are automatically saved in a designated directory for subsequent use in backtesting and real-time forecasting. This modularized design allows researchers to quickly compare the forecasting effects of different models, so as to choose the optimal investment signal generation scheme.
This answer comes from the articleQlib: an AI quantitative investment research tool developed by MicrosoftThe
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