FinRL Meta

FinRL-Meta is an open-source library designed to facilitate research and development in financial reinforcement learning (FinRL), addressing the challenges of applying deep reinforcement learning to the dynamic and noisy nature of financial markets. Current research focuses on creating robust and realistic market simulation environments, systematically evaluating FinRL agent performance across multiple criteria (including profitability and risk), and developing advanced algorithms like mixture-of-experts models for improved trading strategies. This work contributes significantly to the field by providing standardized tools and datasets, fostering collaboration, and accelerating the development and validation of more reliable and effective AI-driven trading strategies.