Welcome to FinRL Library!ΒΆ


Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.

AI4Finance community provides this demonstrative and educational resource, in order to efficiently automate trading. FinRL is the first open source framework for financial reinforcement learning.

Reinforcement learning (RL) trains an agent to solve tasks by trial and error, while DRL uses deep neural networks as function approximators. DRL balances exploration (of uncharted territory) and exploitation (of current knowledge), and has been recognized as a competitive edge for automated trading. DRL framework is powerful in solving dynamic decision making problems by learning through interactions with an unknown environment, thus exhibiting two major advantages: portfolio scalability and market model independence. Automated trading is essentially making dynamic decisions, namely to decide where to trade, at what price, and what quantity, over a highly stochastic and complex stock market. Taking many complex financial factors into account, DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders.

FinRL provides a framework that supports various markets, SOTA DRL algorithms, benchmarks of many quant finance tasks, live trading, etc.

Join or discuss FinRL with us: AI4Finance mailing list.

Feel free to leave us feedback: report bugs using Github issues or discuss FinRL development in the Slack Channel.