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Specialized Domains","Machine Learning for Trading"],"sub_categories":["Crypto Trading","Cryptocurrencies","Others","Books","**Data-Centric Approach for Multimodal Financial Data**","Benchmark Reality Check (real-world tool use)"],"readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg align=\"center\" width=\"30%\" alt=\"image\" src=\"https://github.com/AI4Finance-Foundation/FinGPT/assets/31713746/e0371951-1ce1-488e-aa25-0992dafcc139\"\u003e\n\u003c/div\u003e\n\n# FinRL: Financial Reinforcement Learning [![twitter][1.1]][1] [![facebook][1.2]][2] [![google+][1.3]][3] [![linkedin][1.4]][4]\n\n[1.1]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_twitter_22x22.png\n[1.2]: http://www.tensorlet.org/wp-content/uploads/2021/01/facebook-button_22x22.png\n[1.3]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_google_22.xx_.png\n[1.4]: http://www.tensorlet.org/wp-content/uploads/2021/01/button_linkedin_22x22.png\n\n[1]: https://twitter.com/intent/tweet?text=FinRL-Financial-Deep-Reinforcement-Learning%20\u0026url=https://github.com/AI4Finance-Foundation/FinRL\u0026hashtags=DRL\u0026hashtags=AI\n[2]: https://www.facebook.com/sharer.php?u=http%3A%2F%2Fgithub.com%2FAI4Finance-Foundation%2FFinRL\n[3]: https://plus.google.com/share?url=https://github.com/AI4Finance-Foundation/FinRL\n[4]: https://www.linkedin.com/sharing/share-offsite/?url=http%3A%2F%2Fgithub.com%2FAI4Finance-Foundation%2FFinRL\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg align=\"center\" src=figs/logo_transparent_background.png width=\"55%\"/\u003e\n\u003c/div\u003e\n\n[![Downloads](https://static.pepy.tech/badge/finrl)](https://pepy.tech/project/finrl)\n[![Downloads](https://static.pepy.tech/badge/finrl/week)](https://pepy.tech/project/finrl)\n[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)\n[![PyPI](https://img.shields.io/pypi/v/finrl.svg)](https://pypi.org/project/finrl/)\n[![Documentation Status](https://readthedocs.org/projects/finrl/badge/?version=latest)](https://finrl.readthedocs.io/en/latest/?badge=latest)\n![License](https://img.shields.io/github/license/AI4Finance-Foundation/finrl.svg?color=brightgreen)\n![](https://img.shields.io/github/issues-raw/AI4Finance-Foundation/finrl?label=Issues)\n![](https://img.shields.io/github/issues-closed-raw/AI4Finance-Foundation/finrl?label=Closed+Issues)\n![](https://img.shields.io/github/issues-pr-raw/AI4Finance-Foundation/finrl?label=Open+PRs)\n![](https://img.shields.io/github/issues-pr-closed-raw/AI4Finance-Foundation/finrl?label=Closed+PRs)\n\n[FinGPT](https://github.com/AI4Finance-Foundation/ChatGPT-for-FinTech): Open-source for open-finance! Revolutionize FinTech.\n\n\n[![](https://dcbadge.vercel.app/api/server/trsr8SXpW5)](https://discord.gg/trsr8SXpW5)\n\n![Visitors](https://api.visitorbadge.io/api/VisitorHit?user=AI4Finance-Foundation\u0026repo=FinRL\u0026countColor=%23B17A)\n\n\n\n**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**\n\n| Dev Roadmap  | Stage | Users | Project | Description |\n|----|----|----|----|----|\n| 0.0 (Preparation) | entrance | practitioners | [FinRL-Meta](https://github.com/AI4Finance-Foundation/FinRL-Meta)| gym-style market environments |\n| 1.0 (Proof-of-Concept)| full-stack | developers | [this repo](https://github.com/AI4Finance-Foundation/FinRL) | automatic pipeline |\n| 2.0 (Professional) | profession | experts | [ElegantRL](https://github.com/AI4Finance-Foundation/ElegantRL) | algorithms |\n| 3.0 (Production) | service | hedge funds | [Podracer](https://github.com/AI4Finance-Foundation/FinRL_Podracer) | cloud-native deployment |\n\n\n## Outline\n\n  - [Overview](#overview)\n  - [File Structure](#file-structure)\n  - [Supported Data Sources](#supported-data-sources)\n  - [Installation](#installation)\n  - [Status Update](#status-update)\n  - [Tutorials](#tutorials)\n  - [Publications](#publications)\n  - [News](#news)\n  - [Citing FinRL](#citing-finrl)\n  - [Join and Contribute](#join-and-contribute)\n    - [Contributors](#contributors)\n    - [Sponsorship](#sponsorship)\n  - [LICENSE](#license)\n\n## Overview\n\nFinRL 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.\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg align=\"center\" src=figs/finrl_framework.png\u003e\n\u003c/div\u003e\n\nA 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).\n\n\n## File Structure\n\nThe 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.\n\n```\nFinRL\n├── finrl (main folder)\n│   ├── applications\n│   \t├── Stock_NeurIPS2018\n│   \t├── imitation_learning\n│   \t├── cryptocurrency_trading\n│   \t├── high_frequency_trading\n│   \t├── portfolio_allocation\n│   \t└── stock_trading\n│   ├── agents\n│   \t├── elegantrl\n│   \t├── rllib\n│   \t└── stablebaseline3\n│   ├── meta\n│   \t├── data_processors\n│   \t├── env_cryptocurrency_trading\n│   \t├── env_portfolio_allocation\n│   \t├── env_stock_trading\n│   \t├── preprocessor\n│   \t├── data_processor.py\n│       ├── meta_config_tickers.py\n│   \t└── meta_config.py\n│   ├── config.py\n│   ├── config_tickers.py\n│   ├── main.py\n│   ├── plot.py\n│   ├── train.py\n│   ├── test.py\n│   └── trade.py\n│\n├── examples\n├── unit_tests (unit tests to verify codes on env \u0026 data)\n│   ├── environments\n│   \t└── test_env_cashpenalty.py\n│   └── downloaders\n│   \t├── test_yahoodownload.py\n│   \t└── test_alpaca_downloader.py\n├── setup.py\n├── requirements.txt\n└── README.md\n```\n\n## Supported Data Sources\n\n|Data Source |Type |Range and Frequency |Request Limits|Raw Data|Preprocessed Data|\n|  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |\n|[Akshare](https://alpaca.markets/docs/introduction/)| CN Securities| 2015-now, 1day| Account-specific| OHLCV| Prices\u0026Indicators|\n|[Alpaca](https://alpaca.markets/docs/introduction/)| US Stocks, ETFs| 2015-now, 1min| Account-specific| OHLCV| Prices\u0026Indicators|\n|[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\u0026Indicators|\n|[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\u0026Indicators|\n|[CCXT](https://docs.ccxt.com/en/latest/manual.html)| Cryptocurrency| API-specific, 1min| API-specific| OHLCV| Prices\u0026Indicators|\n|[EODhistoricaldata](https://eodhistoricaldata.com/financial-apis/)| US Securities| Frequency-specific, 1min| API-specific | OHLCV | Prices\u0026Indicators|\n|[IEXCloud](https://iexcloud.io/docs/api/)| NMS US securities|1970-now, 1 day|100 per second per IP|OHLCV| Prices\u0026Indicators|\n|[JoinQuant](https://www.joinquant.com/)| CN Securities| 2005-now, 1min| 3 requests each time| OHLCV| Prices\u0026Indicators|\n|[QuantConnect](https://www.quantconnect.com/docs/home/home)| US Securities| 1998-now, 1s| NA| OHLCV| Prices\u0026Indicators|\n|[RiceQuant](https://www.ricequant.com/doc/rqdata/python/)| CN Securities| 2005-now, 1ms| Account-specific| OHLCV| Prices\u0026Indicators|\n[Sinopac](https://sinotrade.github.io/zh_TW/tutor/prepare/terms/) | Taiwan securities | 2023-04-13~now, 1min | Account-specific | OHLCV | Prices\u0026Indicators|\n|[Tushare](https://tushare.pro/document/1?doc_id=131)| CN Securities, A share| -now, 1 min| Account-specific| OHLCV| Prices\u0026Indicators|\n|[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\u0026Indicators|\n|[YahooFinance](https://pypi.org/project/yfinance/)| US Securities| Frequency-specific, 1min| 2,000/hour| OHLCV | Prices\u0026Indicators|\n\n\n\u003c!-- |Data Source |Type |Max Frequency |Raw Data|Preprocessed Data|\n|  ----  |  ----  |  ----  |  ----  |  ----  |\n|    AkShare |  CN Securities | 1 day  |  OHLCV |  Prices, indicators |\n|    Alpaca |  US Stocks, ETFs |  1 min |  OHLCV |  Prices, indicators |\n|    Alpha Vantage | Stock, ETF, forex, crypto, technical indicators | 1 min |  OHLCV  \u0026 Prices, indicators |\n|    Baostock |  CN Securities |  5 min |  OHLCV |  Prices, indicators |\n|    Binance |  Cryptocurrency |  1 s |  OHLCV |  Prices, indicators |\n|    CCXT |  Cryptocurrency |  1 min  |  OHLCV |  Prices, indicators |\n|    currencyapi |  Exchange rate | 1 day |  Exchange rate | Exchange rate, indicators |\n|    currencylayer |  Exchange rate | 1 day  |  Exchange rate | Exchange rate, indicators |\n|    EOD Historical Data | US stocks, and ETFs |  1 day  |  OHLCV  | Prices, indicators |\n|    Exchangerates |  Exchange rate |  1 day  |  Exchange rate | Exchange rate, indicators |\n|    findatapy |  CN Securities | 1 day  |  OHLCV |  Prices, indicators |\n|    Financial Modeling prep | US stocks, currencies, crypto |  1 min |  OHLCV  | Prices, indicators |\n|    finnhub | US Stocks, currencies, crypto |   1 day |  OHLCV  | Prices, indicators |\n|    Fixer |  Exchange rate |  1 day  |  Exchange rate | Exchange rate, indicators |\n|    IEXCloud |  NMS US securities | 1 day  | OHLCV |  Prices, indicators |\n|    JoinQuant |  CN Securities |  1 min  |  OHLCV |  Prices, indicators |\n|    Marketstack | 50+ countries |  1 day  |  OHLCV | Prices, indicators |\n|    Open Exchange Rates |  Exchange rate |  1 day  |  Exchange rate | Exchange rate, indicators |\n|    pandas\\_datareader |  US Securities |  1 day |  OHLCV | Prices, indicators |\n|    pandas-finance |  US Securities |  1 day  |  OHLCV  \u0026 Prices, indicators |\n|    Polygon |  US Securities |  1 day  |  OHLCV  | Prices, indicators |\n|    Quandl | 250+ sources |  1 day  |  OHLCV  | Prices, indicators |\n|    QuantConnect |  US Securities |  1 s |  OHLCV |  Prices, indicators |\n|    RiceQuant |  CN Securities |  1 ms  |  OHLCV |  Prices, indicators |\n|    Sinopac   | Taiwan securities | 1min | OHLCV |  Prices, indicators |\n|    Tiingo | Stocks, crypto |  1 day  |  OHLCV  | Prices, indicators |\n|    Tushare |  CN Securities | 1 min  |  OHLCV |  Prices, indicators |\n|    WRDS |  US Securities |  1 ms  |  Intraday Trades | Prices, indicators |\n|    XE |  Exchange rate |  1 day  |  Exchange rate | Exchange rate, indicators |\n|    Xignite |  Exchange rate |  1 day  |  Exchange rate | Exchange rate, indicators |\n|    YahooFinance |  US Securities | 1 min  |  OHLCV  |  Prices, indicators |\n|    ystockquote |  US Securities |  1 day  |  OHLCV | Prices, indicators | --\u003e\n\n\n\nOHLCV: open, high, low, and close prices; volume. adjusted_close: adjusted close price\n\nTechnical indicators: 'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'dx_30', 'close_30_sma', 'close_60_sma'. Users also can add new features.\n\n\n## Installation\n+ [Install description for all operating systems (MAC OS, Ubuntu, Windows 10)](./docs/source/start/installation.rst)\n+ [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)\n\n## Status Update\n\u003cdetails\u003e\u003csummary\u003e\u003cb\u003eVersion History\u003c/b\u003e \u003ci\u003e[click to expand]\u003c/i\u003e\u003c/summary\u003e\n\u003cdiv\u003e\n\n* 2022-06-25\n\t0.3.5: Formal release of FinRL, neo_finrl is chenged to FinRL-Meta with related files in directory: *meta*.\n* 2021-08-25\n\t0.3.1: pytorch version with a three-layer architecture, apps (financial tasks), drl_agents (drl algorithms), neo_finrl (gym env)\n* 2020-12-14\n  \tUpgraded to **Pytorch** with stable-baselines3; Remove tensorflow 1.0 at this moment, under development to support tensorflow 2.0\n* 2020-11-27\n  \t0.1: Beta version with tensorflow 1.5\n\u003c/div\u003e\n\u003c/details\u003e\n\n\n## Tutorials\n\n+ [Towardsdatascience] [Deep Reinforcement Learning for Automated Stock Trading](https://towardsdatascience.com/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02)\n\n\n## Publications\n\n|Title |Conference/Journal |Link|Citations|Year|\n|  ----  |  ----  |  ----  |  ----  |  ----  |\n|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 |\n|**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 |\n|**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 |\n|**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 |\n|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 |\n|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 |\n\n\n## News\n+ [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)\n+ [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\u0026wfr=spider\u0026for=pc)\n+ [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)\n+ [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)\n+ [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)\n+ [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)\n+ [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)\n+ [商业新知] [金融科技讲座回顾|AI4Finance: 从AlphaGo到FinRL](https://www.shangyexinzhi.com/article/4170766.html)\n+ [Kaggle] [Jane Street Market Prediction](https://www.kaggle.com/c/jane-street-market-prediction/discussion/199313)\n+ [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)\n+ [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)\n+ [Neurohive] [FinRL: глубокое обучение с подкреплением для трейдинга](https://neurohive.io/ru/gotovye-prilozhenija/finrl-glubokoe-obuchenie-s-podkrepleniem-dlya-trejdinga/)\n+ [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)\n+ [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)\n+ [知乎] [动态数据驱动的金融强化学习](https://zhuanlan.zhihu.com/p/616799055)\n+ [知乎] [FinRL的W\u0026B化+超参数搜索和模型优化(基于Stable Baselines 3）](https://zhuanlan.zhihu.com/p/498115373)\n+ [知乎] [FinRL-Meta: 未来金融强化学习的元宇宙](https://zhuanlan.zhihu.com/p/544621882)\n+\n## Citing FinRL\n\n```\n@article{dynamic_datasets,\n    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},\n    title = {Dynamic Datasets and Market Environments for Financial Reinforcement Learning},\n    journal = {Machine Learning - Springer Nature},\n    year = {2024}\n}\n```\n\n\n```\n@article{liu2022finrl_meta,\n  title={FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning},\n  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},\n  journal={NeurIPS},\n  year={2022}\n}\n```\n\n```\n@article{liu2021finrl,\n    author  = {Liu, Xiao-Yang and Yang, Hongyang and Gao, Jiechao and Wang, Christina Dan},\n    title   = {{FinRL}: Deep reinforcement learning framework to automate trading in quantitative finance},\n    journal = {ACM International Conference on AI in Finance (ICAIF)},\n    year    = {2021}\n}\n\n```\n\n```\n@article{finrl2020,\n    author  = {Liu, Xiao-Yang and Yang, Hongyang and Chen, Qian and Zhang, Runjia and Yang, Liuqing and Xiao, Bowen and Wang, Christina Dan},\n    title   = {{FinRL}: A deep reinforcement learning library for automated stock trading in quantitative finance},\n    journal = {Deep RL Workshop, NeurIPS 2020},\n    year    = {2020}\n}\n```\n\n```\n@article{liu2018practical,\n  title={Practical deep reinforcement learning approach for stock trading},\n  author={Liu, Xiao-Yang and Xiong, Zhuoran and Zhong, Shan and Yang, Hongyang and Walid, Anwar},\n  journal={NeurIPS Workshop on Deep Reinforcement Learning},\n  year={2018}\n}\n```\n\nWe 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\u0026hl=en\u0026hl=en\u0026user=XsdPXocAAAAJ). Previous papers are given in the [list](https://github.com/AI4Finance-Foundation/FinRL/blob/master/tutorials/FinRL_papers.md).\n\n\n## Join and Contribute\n\nWelcome to **AI4Finance** community!\n\nPlease check [Contributing Guidances](https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/Contributing.md).\n\n### Contributors\n\nThank you!\n\n\u003ca href=\"https://github.com/AI4Finance-LLC/FinRL-Library/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=AI4Finance-LLC/FinRL-Library\" /\u003e\n\u003c/a\u003e\n\n\n## LICENSE\n\nMIT License\n\n**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.**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAI4Finance-Foundation%2FFinRL","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAI4Finance-Foundation%2FFinRL","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAI4Finance-Foundation%2FFinRL/lists"}