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https://github.com/AI4Finance-Foundation/FinRL

FinRL: Financial Reinforcement Learning. 🔥
https://github.com/AI4Finance-Foundation/FinRL

algorithmic-trading deep-reinforcement-learning drl-algorithms drl-framework drl-trading-agents finance fintech multi-agent-learning openai-gym pythorch stock-markets stock-trading tensorflow2 trading-tasks

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FinRL: Financial Reinforcement Learning. 🔥

Host: GitHub
URL: https://github.com/AI4Finance-Foundation/FinRL
Owner: AI4Finance-Foundation
License: mit
Created: 2020-07-26T13:18:16.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-08-06T01:15:06.000Z (6 months ago)
Last Synced: 2024-08-19T13:40:42.428Z (5 months ago)
Topics: algorithmic-trading, deep-reinforcement-learning, drl-algorithms, drl-framework, drl-trading-agents, finance, fintech, multi-agent-learning, openai-gym, pythorch, stock-markets, stock-trading, tensorflow2, trading-tasks
Language: Jupyter Notebook
Homepage: https://ai4finance.org
Size: 80.3 MB
Stars: 9,546
Watchers: 199
Forks: 2,322
Open Issues: 243
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

awesome-systematic-trading - FinRL - source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance. | ![GitHub stars](https://badgen.net/github/stars/AI4Finance-Foundation/FinRL) | ![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg) | (Machine Learning / Cryptocurrencies)
awesome-list - FinRL - The first open-source framework to show the great potential of financial reinforcement learning. (Reinforcement Learning / Others)
awesome-production-machine-learning - FinRL - Foundation/FinRL.svg?style=social) - FinRL is the first open-source framework to demonstrate the great potential of financial reinforcement learning. (Industry Strength RL)
awesome-systematic-trading - FinRL - commit/AI4Finance-Foundation/FinRL/master) ![GitHub Repo stars](https://img.shields.io/github/stars/AI4Finance-Foundation/FinRL?style=social) | Python | - FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance. (🔥 AI Powered Systematic Trading Systems)

README

        






# FinRL: Financial Reinforcement Learning [![twitter][1.1]][1] [![facebook][1.2]][2] [![google+][1.3]][3] [![linkedin][1.4]][4]

[1.1]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_twitter_22x22.png

[1.2]: http://www.tensorlet.org/wp-content/uploads/2021/01/facebook-button_22x22.png

[1.3]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_google_22.xx_.png

[1.4]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_linkedin_22x22.png

[1]: https://twitter.com/intent/tweet?text=FinRL-Financial-Deep-Reinforcement-Learning%20&url=https://github.com/AI4Finance-Foundation/FinRL&hashtags=DRL&hashtags=AI

[2]: https://www.facebook.com/sharer.php?u=http%3A%2F%2Fgithub.com%2FAI4Finance-Foundation%2FFinRL

[3]: https://plus.google.com/share?url=https://github.com/AI4Finance-Foundation/FinRL

[4]: https://www.linkedin.com/sharing/share-offsite/?url=http%3A%2F%2Fgithub.com%2FAI4Finance-Foundation%2FFinRL







[![Downloads](https://static.pepy.tech/badge/finrl)](https://pepy.tech/project/finrl)

[![Downloads](https://static.pepy.tech/badge/finrl/week)](https://pepy.tech/project/finrl)

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

[![PyPI](https://img.shields.io/pypi/v/finrl.svg)](https://pypi.org/project/finrl/)

[![Documentation Status](https://readthedocs.org/projects/finrl/badge/?version=latest)](https://finrl.readthedocs.io/en/latest/?badge=latest)

![License](https://img.shields.io/github/license/AI4Finance-Foundation/finrl.svg?color=brightgreen)

![](https://img.shields.io/github/issues-raw/AI4Finance-Foundation/finrl?label=Issues)

![](https://img.shields.io/github/issues-closed-raw/AI4Finance-Foundation/finrl?label=Closed+Issues)

![](https://img.shields.io/github/issues-pr-raw/AI4Finance-Foundation/finrl?label=Open+PRs)

![](https://img.shields.io/github/issues-pr-closed-raw/AI4Finance-Foundation/finrl?label=Closed+PRs)

[FinGPT](https://github.com/AI4Finance-Foundation/ChatGPT-for-FinTech): Open-source for open-finance! Revolutionize FinTech.

Check out our latest competition: [ACM ICAIF 2023 FinRL Contest](https://finrl-contest.github.io/)

[![](https://dcbadge.vercel.app/api/server/trsr8SXpW5)](https://discord.gg/trsr8SXpW5)

![Visitors](https://api.visitorbadge.io/api/VisitorHit?user=AI4Finance-Foundation&repo=FinRL&countColor=%23B17A)

**Financial reinforcement learning (FinRL)** ([Document website](https://finrl.readthedocs.io/en/latest/index.html)) is **the first open-source framework** for financial reinforcement learning. FinRL has evolved into an **ecosystem**

| Dev Roadmap  | Stage | Users | Project | Description |

|----|----|----|----|----|

| 0.0 (Preparation) | entrance | practitioners | [FinRL-Meta](https://github.com/AI4Finance-Foundation/FinRL-Meta)| gym-style market environments |

| 1.0 (Proof-of-Concept)| full-stack | developers | [this repo](https://github.com/AI4Finance-Foundation/FinRL) | automatic pipeline |

| 2.0 (Professional) | profession | experts | [ElegantRL](https://github.com/AI4Finance-Foundation/ElegantRL) | algorithms |

| 3.0 (Production) | service | hedge funds | [Podracer](https://github.com/AI4Finance-Foundation/FinRL_Podracer) | cloud-native deployment |

## Outline

  - [Overview](#overview)

  - [File Structure](#file-structure)

  - [Supported Data Sources](#supported-data-sources)

  - [Installation](#installation)

  - [Status Update](#status-update)

  - [Tutorials](#tutorials)

  - [Publications](#publications)

  - [News](#news)

  - [Citing FinRL](#citing-finrl)

  - [Join and Contribute](#join-and-contribute)

    - [Contributors](#contributors)

    - [Sponsorship](#sponsorship)

  - [LICENSE](#license)

## Overview

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.







A quick start: Stock_NeurIPS2018.ipynb. Videos [FinRL](http://www.youtube.com/watch?v=ZSGJjtM-5jA) at [AI4Finance Youtube Channel](https://www.youtube.com/channel/UCrVri6k3KPBa3NhapVV4K5g).

## 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

|Data Source |Type |Range and Frequency |Request Limits|Raw Data|Preprocessed Data|

|  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |

|[Akshare](https://alpaca.markets/docs/introduction/)| CN Securities| 2015-now, 1day| Account-specific| OHLCV| Prices&Indicators|

|[Alpaca](https://alpaca.markets/docs/introduction/)| US Stocks, ETFs| 2015-now, 1min| Account-specific| OHLCV| Prices&Indicators|

|[Baostock](http://baostock.com/baostock/index.php/Python_API%E6%96%87%E6%A1%A3)| CN Securities| 1990-12-19-now, 5min| Account-specific| OHLCV| Prices&Indicators|

|[Binance](https://binance-docs.github.io/apidocs/spot/en/#public-api-definitions)| Cryptocurrency| API-specific, 1s, 1min| API-specific| Tick-level daily aggegrated trades, OHLCV| Prices&Indicators|

|[CCXT](https://docs.ccxt.com/en/latest/manual.html)| Cryptocurrency| API-specific, 1min| API-specific| OHLCV| Prices&Indicators|

|[EODhistoricaldata](https://eodhistoricaldata.com/financial-apis/)| US Securities| Frequency-specific, 1min| API-specific | OHLCV | Prices&Indicators|

|[IEXCloud](https://iexcloud.io/docs/api/)| NMS US securities|1970-now, 1 day|100 per second per IP|OHLCV| Prices&Indicators|

|[JoinQuant](https://www.joinquant.com/)| CN Securities| 2005-now, 1min| 3 requests each time| OHLCV| Prices&Indicators|

|[QuantConnect](https://www.quantconnect.com/docs/home/home)| US Securities| 1998-now, 1s| NA| OHLCV| Prices&Indicators|

|[RiceQuant](https://www.ricequant.com/doc/rqdata/python/)| CN Securities| 2005-now, 1ms| Account-specific| OHLCV| Prices&Indicators|

[Sinopac](https://sinotrade.github.io/zh_TW/tutor/prepare/terms/) | Taiwan securities | 2023-04-13~now, 1min | Account-specific | OHLCV | Prices&Indicators|

|[Tushare](https://tushare.pro/document/1?doc_id=131)| CN Securities, A share| -now, 1 min| Account-specific| OHLCV| Prices&Indicators|

|[WRDS](https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/nyse-trade-and-quote-taq/)| US Securities| 2003-now, 1ms| 5 requests each time| Intraday Trades|Prices&Indicators|

|[YahooFinance](https://pypi.org/project/yfinance/)| 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.

## Installation

+ [Install description for all operating systems (MAC OS, Ubuntu, Windows 10)](./docs/source/start/installation.rst)

+ [FinRL for Quantitative Finance: Install and Setup Tutorial for Beginners](https://ai4finance.medium.com/finrl-for-quantitative-finance-install-and-setup-tutorial-for-beginners-1db80ad39159)

## Status Update

Version History [click to expand]



* 2022-06-25

	0.3.5: Formal release of FinRL, neo_finrl is chenged 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; Remove tensorflow 1.0 at this moment, under development to support tensorflow 2.0

* 2020-11-27

  	0.1: Beta version with tensorflow 1.5



## Tutorials

+ [Towardsdatascience] [Deep Reinforcement Learning for Automated Stock Trading](https://towardsdatascience.com/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02)

A complete list at [blogs](https://github.com/AI4Finance-Foundation/Blogs)

## Publications

|Title |Conference/Journal |Link|Citations|Year|

|  ----  |  ----  |  ----  |  ----  |  ----  |

|Dynamic Datasets and Market Environments for Financial Reinforcement Learning| Machine Learning - Springer Nature| [paper](https://arxiv.org/abs/2304.13174) [code](https://github.com/AI4Finance-Foundation/FinRL-Meta) | 7 | 2024 |

|**FinRL-Meta**: FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning| NeurIPS 2022| [paper](https://arxiv.org/abs/2211.03107) [code](https://github.com/AI4Finance-Foundation/FinRL-Meta) | 37 | 2022 |

|**FinRL**: Deep reinforcement learning framework to automate trading in quantitative finance| ACM International Conference on AI in Finance (ICAIF) | [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3955949) | 49 | 2021 |

|**FinRL**: A deep reinforcement learning library for automated stock trading in quantitative finance| NeurIPS 2020 Deep RL Workshop  | [paper](https://arxiv.org/abs/2011.09607) | 87 | 2020 |

|Deep reinforcement learning for automated stock trading: An ensemble strategy| ACM International Conference on AI in Finance (ICAIF) | [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996) [code](https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb) | 154 | 2020 |

|Practical deep reinforcement learning approach for stock trading | NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services| [paper](https://arxiv.org/abs/1811.07522) [code](https://github.com/AI4Finance-Foundation/DQN-DDPG_Stock_Trading](https://github.com/AI4Finance-Foundation/FinRL/tree/master/examples))| 164 | 2018 |

## 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](http://tech.cnr.cn/techph/20211123/t20211123_525669092.shtml)

+ [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](https://baijiahao.baidu.com/s?id=1717101783873523790&wfr=spider&for=pc)

+ [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](https://idea.edu.cn/news/20211213143128.html)

+ [知乎] [FinRL-Meta基于数据驱动的强化学习金融元宇宙](https://zhuanlan.zhihu.com/p/437804814)

+ [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)](https://www.mdeditor.tw/pl/p5Gg)

+ [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](https://zhuanlan.zhihu.com/p/353557417)

+ [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](https://blog.csdn.net/deeprl/article/details/114828024)

+ [商业新知] [金融科技讲座回顾|AI4Finance: 从AlphaGo到FinRL](https://www.shangyexinzhi.com/article/4170766.html)

+ [Kaggle] [Jane Street Market Prediction](https://www.kaggle.com/c/jane-street-market-prediction/discussion/199313)

+ [矩池云Matpool] [在矩池云上如何运行FinRL股票交易策略框架](http://www.python88.com/topic/111918)

+ [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](https://www.sohu.com/a/486837028_120929319)

+ [Neurohive] [FinRL: глубокое обучение с подкреплением для трейдинга](https://neurohive.io/ru/gotovye-prilozhenija/finrl-glubokoe-obuchenie-s-podkrepleniem-dlya-trejdinga/)

+ [ICHI.PRO] [양적 금융을위한 FinRL: 단일 주식 거래를위한 튜토리얼](https://ichi.pro/ko/yangjeog-geum-yung-eul-wihan-finrl-dan-il-jusig-geolaeleul-wihan-tyutolieol-61395882412716)

+ [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）](https://zhuanlan.zhihu.com/p/563238735)

+ [知乎] [动态数据驱动的金融强化学习](https://zhuanlan.zhihu.com/p/616799055)

+ [知乎] [FinRL的W&B化+超参数搜索和模型优化(基于Stable Baselines 3）](https://zhuanlan.zhihu.com/p/498115373)

+ [知乎] [FinRL-Meta: 未来金融强化学习的元宇宙](https://zhuanlan.zhihu.com/p/544621882)

+

## Citing FinRL

```

@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{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{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{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}

}

```

We published [FinRL papers](http://tensorlet.org/projects/ai-in-finance/) that are listed at [Google Scholar](https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=XsdPXocAAAAJ). Previous papers are given in the [list](https://github.com/AI4Finance-Foundation/FinRL/blob/master/tutorials/FinRL_papers.md).

## Join and Contribute

Welcome to **AI4Finance** community!

Please check [Contributing Guidances](https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/Contributing.md).

### Contributors

Thank you!



  



## LICENSE

MIT License

**Disclaimer: We are sharing codes for academic purpose under the MIT education license. 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.**