Financial reinforcement learning (FinRL®) is the first open-source framework for financial reinforcement learning.
| Dev Roadmap | Stage | Users | Project | Description |
|---|---|---|---|---|
| 0.0 (Preparation) | Entry | Practitioners | FinRL-Meta | gym-style market environments |
| 1.0 (Proof-of-Concept) | End-to-End | Developers | this repo | automatic pipeline |
| 2.0 (Professional) | Advanced | Researchers and Experts | ElegantRL | algorithms |
| 3.0 (Production) | Production | Institutions and Hedge Funds | FinRL-X | An AI-Native deployment |
FinGPT: an open-source project for financial large language models, designed for research and real-world FinTech applications.
- Overview
- File Structure
- Supported Data Sources
- Installation
- Status Update
- Tutorials
- Publications
- News
- Citing FinRL
- Join and Contribute
- LICENSE
FinRL® is an open-source financial reinforcement learning framework developed by contributors from the AI4Finance community and maintained by the AI4Finance Foundation.
Key contributors include:
- Hongyang (Bruce) Yang – research and development on financial reinforcement learning frameworks, market environments, and quantitative trading applications
- [other contributors…]
FinRL has three layers: market environments, agents, and applications. For a trading task (on the top), an agent (in the middle) interacts with a market environment (at the bottom), making sequential decisions.
Videos FinRL at AI4Finance Youtube Channel.
git clone https://github.com/AI4Finance-Foundation/FinRL.git
cd FinRLpython3 -m venv venv
source venv/bin/activatepip install -e .1. Data Download & Preprocessing
python examples/FinRL_StockTrading_2026_1_data.pyThis script downloads DOW 30 stock data from Yahoo Finance, adds technical indicators (MACD, RSI, etc.), VIX, and turbulence index, then splits the data into training set (2014–2025) and trading set (2026-01-01 to 2026-03-20), saving them as train_data.csv and trade_data.csv.
2. Train DRL Agents
python examples/FinRL_StockTrading_2026_2_train.pyThis script trains 5 DRL agents (A2C, DDPG, PPO, TD3, SAC) using Stable Baselines 3 on the training data. Trained models are saved to the trained_models/ directory.
3. Backtest
python examples/FinRL_StockTrading_2026_3_Backtest.pyThis script loads the trained agents, runs them on the trading data, and compares their performance against two baselines: Mean Variance Optimization (MVO) and the DJIA index. Results are printed to the console and a plot is saved as backtest_result.png.
The main folder finrl has three subfolders applications, agents, meta. We employ a train-test-trade pipeline with three files: train.py, test.py, and trade.py.
FinRL
├── finrl (main folder)
│ ├── applications
│ ├── Stock_NeurIPS2018
│ ├── imitation_learning
│ ├── cryptocurrency_trading
│ ├── high_frequency_trading
│ ├── portfolio_allocation
│ └── stock_trading
│ ├── agents
│ ├── elegantrl
│ ├── rllib
│ └── stablebaseline3
│ ├── meta
│ ├── data_processors
│ ├── env_cryptocurrency_trading
│ ├── env_portfolio_allocation
│ ├── env_stock_trading
│ ├── preprocessor
│ ├── data_processor.py
│ ├── meta_config_tickers.py
│ └── meta_config.py
│ ├── config.py
│ ├── config_tickers.py
│ ├── main.py
│ ├── plot.py
│ ├── train.py
│ ├── test.py
│ └── trade.py
│
├── examples
├── unit_tests (unit tests to verify codes on env & data)
│ ├── environments
│ └── test_env_cashpenalty.py
│ └── downloaders
│ ├── test_yahoodownload.py
│ └── test_alpaca_downloader.py
├── setup.py
├── requirements.txt
└── README.md
| Data Source | Type | Range and Frequency | Request Limits | Raw Data | Preprocessed Data |
|---|---|---|---|---|---|
| Akshare | CN Securities | 2015-now, 1day | Account-specific | OHLCV | Prices&Indicators |
| Alpaca | US Stocks, ETFs | 2015-now, 1min | Account-specific | OHLCV | Prices&Indicators |
| Baostock | CN Securities | 1990-12-19-now, 5min | Account-specific | OHLCV | Prices&Indicators |
| Binance | Cryptocurrency | API-specific, 1s, 1min | API-specific | Tick-level daily aggregated trades, OHLCV | Prices&Indicators |
| CCXT | Cryptocurrency | API-specific, 1min | API-specific | OHLCV | Prices&Indicators |
| EODhistoricaldata | US Securities | Frequency-specific, 1min | API-specific | OHLCV | Prices&Indicators |
| IEXCloud | NMS US securities | 1970-now, 1 day | 100 per second per IP | OHLCV | Prices&Indicators |
| JoinQuant | CN Securities | 2005-now, 1min | 3 requests each time | OHLCV | Prices&Indicators |
| QuantConnect | US Securities | 1998-now, 1s | NA | OHLCV | Prices&Indicators |
| RiceQuant | CN Securities | 2005-now, 1ms | Account-specific | OHLCV | Prices&Indicators |
| Sinopac | Taiwan securities | 2023-04-13~now, 1min | Account-specific | OHLCV | Prices&Indicators |
| Tushare | CN Securities, A-share | -now, 1 min | Account-specific | OHLCV | Prices&Indicators |
| WRDS | US Securities | 2003-now, 1ms | 5 requests each time | Intraday Trades | Prices&Indicators |
| YahooFinance | US Securities | Frequency-specific, 1min | 2,000/hour | OHLCV | Prices&Indicators |
OHLCV: open, high, low, and close prices; volume. adjusted_close: adjusted close price
Technical indicators: 'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'dx_30', 'close_30_sma', 'close_60_sma'. Users also can add new features.
- Install description for all operating systems (MAC OS, Ubuntu, Windows 10)
- FinRL for Quantitative Finance: Install and Setup Tutorial for Beginners
Version History [click to expand]
- 2022-06-25 0.3.5: Formal release of FinRL, neo_finrl is changed to FinRL-Meta with related files in directory: meta.
- 2021-08-25 0.3.1: pytorch version with a three-layer architecture, apps (financial tasks), drl_agents (drl algorithms), neo_finrl (gym env)
- 2020-12-14 Upgraded to Pytorch with stable-baselines3; Removed TensorFlow 1.x support; TensorFlow 2.0 support was under development at the time.
- 2020-11-27 0.1: Beta version with tensorflow 1.5
- [Towards Data Science] Deep Reinforcement Learning for Automated Stock Trading
| Title | Conference/Journal | Link | Citations | Year |
|---|---|---|---|---|
| Dynamic Datasets and Market Environments for Financial Reinforcement Learning | Machine Learning - Springer Nature | paper code | 51 | 2024 |
| FinRL-Meta: FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning | NeurIPS 2022 | paper code | 136 | 2022 |
| FinRL: Deep reinforcement learning framework to automate trading in quantitative finance | ACM International Conference on AI in Finance (ICAIF) | paper | 212 | 2021 |
| FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance | NeurIPS 2020 Deep RL Workshop | paper | 275 | 2020 |
| Deep reinforcement learning for automated stock trading: An ensemble strategy | ACM International Conference on AI in Finance (ICAIF) | paper code | 426 | 2020 |
| Practical deep reinforcement learning approach for stock trading | NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services | paper code | 303 | 2018 |
- [央广网] 2021 IDEA大会于福田圆满落幕：群英荟萃论道AI 多项目发布亮点纷呈
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- [IDEA新闻] 2021 IDEA大会发布产品FinRL-Meta——基于数据驱动的强化学习金融风险模拟系统
- [知乎] FinRL-Meta基于数据驱动的强化学习金融元宇宙
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- [商业新知] 金融科技讲座回顾|AI4Finance: 从AlphaGo到FinRL
- [Kaggle] Jane Street Market Prediction
- [矩池云Matpool] 在矩池云上如何运行FinRL股票交易策略框架
- [财智无界] 金融学会常务理事陈学彬: 深度强化学习在金融资产管理中的应用
- [Neurohive] FinRL: глубокое обучение с подкреплением для трейдинга
- [ICHI.PRO] 양적 금융을위한 FinRL: 단일 주식 거래를위한 튜토리얼
- [知乎] 基于深度强化学习的金融交易策略（FinRL+Stable baselines3，以道琼斯30股票为例）
- [知乎] 动态数据驱动的金融强化学习
- [知乎] FinRL的W&B化+超参数搜索和模型优化(基于Stable Baselines 3）
- [知乎] FinRL-Meta: 未来金融强化学习的元宇宙
@article{finrl2020,
author = {Liu, Xiao-Yang and Yang, Hongyang and Chen, Qian and Zhang, Runjia and Yang, Liuqing and Xiao, Bowen and Wang, Christina Dan},
title = {{FinRL}: A deep reinforcement learning library for automated stock trading in quantitative finance},
journal = {Deep RL Workshop, NeurIPS 2020},
year = {2020}
}
@article{liu2021finrl,
author = {Liu, Xiao-Yang and Yang, Hongyang and Gao, Jiechao and Wang, Christina Dan},
title = {{FinRL}: Deep reinforcement learning framework to automate trading in quantitative finance},
journal = {ACM International Conference on AI in Finance (ICAIF)},
year = {2021}
}
@article{dynamic_datasets,
author = {Liu, Xiao-Yang and Xia, Ziyi and Yang, Hongyang and Gao, Jiechao and Zha, Daochen and Zhu, Ming and Wang, Christina Dan and Wang, Zhaoran and Guo, Jian},
title = {Dynamic Datasets and Market Environments for Financial Reinforcement Learning},
journal = {Machine Learning - Springer Nature},
year = {2024}
}
@article{liu2022finrl_meta,
title={FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning},
author={Liu, Xiao-Yang and Xia, Ziyi and Rui, Jingyang and Gao, Jiechao and Yang, Hongyang and Zhu, Ming and Wang, Christina Dan and Wang, Zhaoran and Guo, Jian},
journal={NeurIPS},
year={2022}
}
@article{liu2018practical,
title={Practical deep reinforcement learning approach for stock trading},
author={Liu, Xiao-Yang and Xiong, Zhuoran and Zhong, Shan and Yang, Hongyang and Walid, Anwar},
journal={NeurIPS Workshop on Deep Reinforcement Learning},
year={2018}
}
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Trademark Notice
FinRL and the FinRL logo are trademarks of FinRL LLC. Use of these marks by the AI4Finance Foundation is permitted under license. The open-source license for this repository does not grant any right to use the FinRL name, logo, or related trademarks without prior written permission from FinRL LLC, except as permitted by applicable law.
Disclaimer: We are sharing codes for academic purposes under the MIT license. Nothing herein constitutes financial advice or a recommendation to trade real money. Users are solely responsible for any financial decisions made using this software. Consult a qualified professional before deploying capital.
