Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft that automates the end-to-end machine learning workflow for financial forecasting. It provides researchers and developers with a unified framework to handle data processing, model training, and strategy backtesting within a single ecosystem.

• Provides a high-performance data infrastructure optimized for the storage and retrieval of financial time-series data.

• Implements an ExpressionEngine for automated feature engineering and formulaic alpha generation.

• Supports a full-stack pipeline including data preparation, model training, and automated strategy evaluation.

• Enables benchmarking against baselines like LightGBM, GRU, and Transformer-based architectures.

• Built primarily in Python with performance-critical components implemented in C++.

• Integrates with deep learning frameworks including PyTorch and LightGBM.

• Compatible with Linux and Windows environments running Python 3.8+.

• Developing and evaluating alpha-seeking signals in global equity markets.

• Benchmarking deep learning models for high-frequency financial time-series prediction.

Install the package via pip and explore the provided example notebooks to initialize your first quantitative trading pipeline.