FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow
