FinRL is an open-source framework to help practitioners establish the development pipeline of trading strategies using deep reinforcement learning (DRL). An agent learns by continuously interacting with an environment in a trial-and-error manner, making sequential decisions under uncertainty, and achieving a balance between exploration and exploitation. The open-source community AI4Finance (efficiently automating trading) provides resources about DRL in quantitative finance. It aims to accelerate the paradigm shift from the conventional machine learning approach to RLOps in finance.
Roadmaps of FinRL:
- FinRL 1.0: entry-level for beginners, with a demonstrative and educational purpose.
- FinRL 2.0: intermediate-level for full-stack developers and professionals. Check out ElegantRL.
- FinRL 3.0: advanced-level for investment banks and hedge funds. Check out our cloud-native solution GPU-podracer.
- FinRL 0.0: tens of training/testing/trading environments in NeoFinRL.
FinRL provides a unified DRL framework for various markets, SOTA DRL algorithms, benchmark finance tasks (portfolio allocation, cryptocurrency trading, high-frequency trading), live trading, etc.
We published papers in FinTech at Google Scholar and now arrive at this project:
- Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach, ACM International Conference on AI in Finance, ICAIF 2021.
- FinRL: A Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance, ACM International Conference on AI in Finance, ICAIF 2021.
- FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, Deep RL Workshop, NeurIPS 2020.
- Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy, paper and codes, ACM International Conference on AI in Finance, ICAIF 2020.
- Multi-agent Reinforcement Learning for Liquidation Strategy Analysis, paper and codes. Workshop on Applications and Infrastructure for Multi-Agent Learning, ICML 2019.
- Practical Deep Reinforcement Learning Approach for Stock Trading, paper and codes, Workshop on Challenges and Opportunities for AI in Financial Services, NeurIPS 2018.
Installation (Recommend using cloud service - Google Colab or AWS EC2)
Clone the repository:
git clone https://github.com/AI4Finance-Foundation/FinRL.git
Install the unstable development version of FinRL using pip:
pip install git+https://github.com/AI4Finance-Foundation/FinRL.git