AthenaraArchitectures / finrl
FinRL
A deep reinforcement learning library that packages market environments, DRL algorithms, and backtesting into a pipeline for training automated trading agents.
- reinforcement-learning
- drl
- portfolio-allocation
- backtesting
- gym
added 2026-08-15 · MIT · external
Use this
$ git clone https://github.com/AI4Finance-Foundation/FinRLPrerequisites
Details
| authors | AI4Finance Foundation, Xiao-Yang Liu, Hongyang Yang, Christina Dan Wang |
|---|---|
| origin | external |
| license | MIT |
| components | market environments, DRL agents, backtesting |
| markets | equities |
FinRL supplies gym-style environments built from market data for indices including NASDAQ-100, S&P 500, HSI, and SSE 50, together with implementations of DQN, DDPG, PPO, SAC, A2C, and TD3 and a backtesting module. Environments model practical frictions such as transaction costs and liquidity constraints — the fidelity issues that make or break the RL-policy pattern (see related).
The library ships tutorials for single-stock trading, multi-stock trading, and portfolio allocation. Maintained by the AI4Finance Foundation; introduced at the Deep RL Workshop, NeurIPS 2020 (arXiv:2011.09607).
Connections
Implements: rl-policy