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FinRL: Deep Reinforcement Learning for Quantitative Finance twitter facebook google+ linkedin

Downloads Downloads Python 3.6 PyPI Documentation Status License

"I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines." — Claude Shannon

"We can only see a short distance ahead, but we can see plenty there that needs to be done." — Alan Turing

Our Mission: to efficiently automate trading. We continuously develop and share codes for finance.

Our Vision: AI community has accumulated an open-source code ocean over the past decade. Applying these intellectual and engineering properties to finance will initiate a paradigm shift from the conventional trading routine to an automated machine learning approach, even RLOps in finance.

FinRL (website) is the first open-source project to explore the great potential of deep reinforcement learning in finance. We help practitioners pipeline a trading strategy using deep reinforcement learning (DRL).

The FinRL ecosystem is a unified framework, including various markets, state-of-the-art algorithms, financial tasks (portfolio allocation, cryptocurrency trading, high-frequency trading), live trading, etc.

Roadmap Level Users Example Desription
0.0 (Preparation) preparation practitioners of financial big data FinRL-Meta a universe of market environments
1.0 (Proof-of-Concept) entry-level beginners this repo demonstration, education
2.0 (Professional) intermediate-level full-stack developers, professionals ElegantRL financially optimized DRL algorithms
3.0 (Production) advance-level investment banks, hedge funds Podracer cloud-native solution

Outline

Overview

FinRL has three layers: applications, agents, and market environments.

For a trading task (on the top), an agent (in the middle) interacts with an environment (at the bottom), making sequential decisions.

Run FinRL_StockTrading_NeurIPS_2018.ipynb step by step for a quick start.

A video about FinRL library at the AI4Finance Youtube Channel.

Star History

Star History Chart

File Structure

Correspondingly, the main folder finrl has three subfolders applications, agents, finrl_meta.

We employ a train-test-trade pipeline by three files: train.py, test.py, and trade.py.

FinRL
├── finrl (main folder)
│   ├── applications
│   	├── cryptocurrency_trading
│   	├── high_frequency_trading
│   	├── portfolio_allocation
│   	└── stock_trading
│   ├── agents
│   	├── elegantrl
│   	├── rllib
│   	└── stablebaseline3
│   ├── finrl_meta
│   	├── data_processors
│   	├── env_cryptocurrency_trading
│   	├── env_portfolio_allocation
│   	├── env_stock_trading
│   	├── preprocessor
│   	├── data_processor.py
│   	└── finrl_meta_config.py
│   ├── config.py
│   ├── config_tickers.py
│   ├── main.py
│   ├── plot.py
│   ├── train.py
│   ├── test.py
│   └── trade.py
│   
├── tutorial (tutorial notebooks and educational files)
├── unit_testing (make sure verified codes working on env & data)
│   ├── test_env
│   	└── test_env_cashpenalty.py
│   └── test_marketdata
│   	└── test_yahoodownload.py
├── setup.py
├── requirements.txt
└── README.md

Supported Data Sources

Data Source Type Max Frequency Raw Data Preprocessed Data
Alpaca US Stocks, ETFs 1 min OHLCV Prices, indicators
Baostock CN Securities 5 min OHLCV Prices, indicators
Binance Cryptocurrency 1 s OHLCV Prices, indicators
CCXT Cryptocurrency 1 min OHLCV Prices, indicators
IEXCloud NMS US securities 1 day OHLCV Prices, indicators
JoinQuant CN Securities 1 min OHLCV Prices, indicators
QuantConnect US Securities 1 s OHLCV Prices, indicators
RiceQuant CN Securities 1 ms OHLCV Prices, indicators
Tushare CN Securities 1 min OHLCV Prices, indicators
WRDS US Securities 1 ms Intraday Trades Prices, indicators
YahooFinance US Securities 1 min OHLCV Prices, indicators
AkShare CN Securities 1 day OHLCV Prices, indicators
findatapy CN Securities 1 day OHLCV Prices, indicators
pandas_datareader US Securities 1 day OHLCV Prices, indicators
pandas-finance US Securities 1 day OHLCV & Prices, indicators
ystockquote US Securities 1 day OHLCV Prices, indicators
Marketstack 50+ countries 1 day OHLCV Prices, indicators
finnhub US Stocks, currencies, crypto 1 day OHLCV Prices, indicators
Financial Modeling prep US stocks, currencies, crypto 1 min OHLCV Prices, indicators
EOD Historical Data US stocks, and ETFs 1 day OHLCV Prices, indicators
Alpha Vantage Stock, ETF, forex, crypto, technical indicators 1 min OHLCV & Prices, indicators
Tiingo Stocks, crypto 1 day OHLCV Prices, indicators
Quandl 250+ sources 1 day OHLCV Prices, indicators
Polygon US Securities 1 day OHLCV Prices, indicators
fixer Exchange rate 1 day Exchange rate Exchange rate, indicators
Exchangerates Exchange rate 1 day Exchange rate Exchange rate, indicators
Fixer Exchange rate 1 day Exchange rate Exchange rate, indicators
currencylayer Exchange rate 1 day Exchange rate Exchange rate, indicators
currencyapi Exchange rate 1 day Exchange rate Exchange rate, indicators
Open Exchange Rates Exchange rate 1 day Exchange rate Exchange rate, indicators
XE Exchange rate 1 day Exchange rate Exchange rate, indicators
Xignite Exchange rate 1 day Exchange rate Exchange rate, 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

Status Update

Version History [click to expand]
  • 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

Contributions

  • FinRL is the first open-source framework to demonstrate the great potential of applying DRL algorithms in quantitative finance. We build an ecosystem around the FinRL framework, which seeds the rapidly growing AI4Finance community.
  • The application layer provides interfaces for users to customize FinRL to their own trading tasks. Automated backtesting tool and performance metrics are provided to help quantitative traders iterate trading strategies at a high turnover rate. Profitable trading strategies are reproducible and hands-on tutorials are provided in a beginner-friendly fashion. Adjusting the trained models to the rapidly changing markets is also possible.
  • The agent layer provides state-of-the-art DRL algorithms that are adapted to finance with fine-tuned hyperparameters. Users can add new DRL algorithms.
  • The environment layer includes not only a collection of historical data APIs, but also live trading APIs. They are reconfigured into standard OpenAI gym-style environments. Moreover, it incorporates market frictions and allows users to customize the trading time granularity.

Tutorials

Publications

Title Conference Link Citations Year
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance NeurIPS 2021 Data-Centric AI Workshop paper: https://arxiv.org/abs/2112.06753 ;
code: https://github.com/AI4Finance-Foundation/FinRL-Meta		 2 2021
Explainable deep reinforcement learning for portfolio management: An empirical approach ICAIF 2021 : ACM International Conference on AI in Finance paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3958005;
code: https://github.com/AI4Finance-Foundation/FinRL		 1 2021
FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance ICAIF 2021 : ACM International Conference on AI in Finance paper: https://arxiv.org/abs/2111.05188;
code: https://github.com/AI4Finance-Foundation/FinRL_Podracer		 2 2021
FinRL: Deep reinforcement learning framework to automate trading in quantitative finance ICAIF 2021 : ACM International Conference on AI in Finance paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3955949;
code: https://github.com/AI4Finance-Foundation/FinRL		 4 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;
code: https://github.com/AI4Finance-Foundation/FinRL		 20 2020
Deep reinforcement learning for automated stock trading: An ensemble strategy ICAIF 2020 : ACM International Conference on AI in Finance paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996;
code: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020		 37 2020
Multi-agent reinforcement learning for liquidation strategy analysis ICML 2019 Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning paper: https://arxiv.org/abs/1906.11046;
code: https://github.com/AI4Finance-Foundation/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019		 19 2019
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		 81 2018

News

Citing FinRL

@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}
}

We published FinTech papers, check Google Scholar, resulting in this project. Closely related papers are given in the list.

Join and Contribute

Welcome to the AI4Finance Foundation community!

Join to discuss FinRL: AI4Finance mailing list, AI4Finance Slack channel:

 Follow us on WeChat:

Please check Contributing Guidances.

Contributors

Thanks!

Sponsorship

Welcome gift money to support AI4Finance, a non-profit academic community. Use the links in the right, or scan the following vemo QR code:

Detailed sponsorship records can be found at Issue #425

LICENSE

MIT License

Disclaimer: 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.