FinRL: Financial Reinforcement Learning → FinRL-X

[!IMPORTANT] FinRL-X is the next-generation evolution of FinRL, designed for AI-native, modular, and production-oriented quantitative trading.

• This repository (FinRL) preserves the original end-to-end educational and research framework.
• For the latest architecture, live trading deployment, and production-focused development, please use FinRL-X / FinRL-Trading.

FinRL® is widely recognized as the first open-source framework for financial reinforcement learning. This repository contains the original FinRL library for education, benchmarking, and research prototyping.

For the next-generation AI-native and production-oriented trading stack, please visit FinRL-X / FinRL-Trading.

FinRL Ecosystem Roadmap

Recommended for new users: Start with FinRL-X / FinRL-Trading if you are building modern or production-oriented trading systems.

🔄 FinRL-X vs. FinRL: What Changed

FinGPT: an open-source project for financial large language models, designed for research and real-world FinTech applications.

Outline

• Overview
• File Structure
• Supported Data Sources
• Installation
• Status Update
• Tutorials
• Publications
• News
• Citing FinRL
• Join and Contribute
  • Contributors
  • Sponsorship
• LICENSE

Project Contributors

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…]

Overview

FinRL is the original open-source framework for financial reinforcement learning, organized around three core layers:

• Market Environments
• DRL Agents
• Financial Applications

For a trading task, an agent interacts with a market environment and learns sequential decision-making policies.

This repository focuses on the classic FinRL workflow for education, experimentation, and research prototyping.

For the next-generation production-oriented stack, including modular deployment and AI-native trading infrastructure, please visit FinRL-X / FinRL-Trading.

Videos FinRL at AI4Finance Youtube Channel.

FinRL Stock Trading 2026 Tutorial

This tutorial demonstrates the original FinRL workflow for educational and research purposes. For the latest production-oriented pipeline, please use FinRL-X / FinRL-Trading.

Step 1: Clone the Repository

git clone https://github.com/AI4Finance-Foundation/FinRL.git
cd FinRL

Step 2: Create and Activate Virtual Environment

python3 -m venv venv
source venv/bin/activate

Step 3: Install FinRL

pip install -e .

Step 4: Run the Scripts

1. Data Download & Preprocessing

python examples/FinRL_StockTrading_2026_1_data.py

This 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.py

This 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.py

This 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.

File Structure

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

Supported Data Sources

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.

Installation

• Install description for all operating systems (MAC OS, Ubuntu, Windows 10)
• FinRL for Quantitative Finance: Install and Setup Tutorial for Beginners

Status Update

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

Tutorials

• [Towards Data Science] Deep Reinforcement Learning for Automated Stock Trading

Publications

News

• [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
• [央广网] 2021 IDEA大会开启AI思想盛宴 沈向洋理事长发布六大前沿产品
• [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
• [量化投资与机器学习] 基于深度强化学习的股票交易策略框架（代码+文档)
• [运筹OR帷幄] 领读计划NO.10 | 基于深度增强学习的量化交易机器人：从AlphaGo到FinRL的演变过程
• [深度强化实验室] 【重磅推荐】哥大开源“FinRL”: 一个用于量化金融自动交易的深度强化学习库
• [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

Citing FinRL

For the next-generation AI-native modular trading infrastructure, see FinRL-X / FinRL-Trading.

@inproceedings{yang2026finrlx,
  title     = {FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading},
  author    = {Yang, Hongyang and Zhang, Boyu and She, Yang and Liao, Xinyu and Zhang, Xiaoli},
  booktitle = {Proceedings of the 2nd International Workshop on Decision Making and Optimization in Financial Technologies (DMO-FinTech)},
  year      = {2026},
  note      = {Workshop at PAKDD 2026}
}

If you use the original FinRL framework, please cite the FinRL papers:

@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}
}

Join and Contribute

Welcome to AI4Finance community!

Please check Contributing Guidelines.

Contributors

Thanks to all contributors who have helped build FinRL.

LICENSE

MIT License

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.