taylor-tian / finrl

FinRL: Financial Reinforcement Learning. Please star. 🔥

FinRL: Financial Reinforcement Learning

                              

FinRL (website) is the first open-source framework to show the great potential of financial reinforcement learning.

FinRL has evolving into an ecosystem, including hundreds of financial markets, state-of-the-art algorithms, financial applications (portfolio allocation, cryptocurrency trading, high-frequency trading), live trading, cloud deployment, etc.

Roadmap	Level	Target Users	Example	Desription
0.0 (Preparation)	preparation	practitioners	FinRL-Meta	a playground
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 solutions

Outline

• Overview
• File Structure
• Supported Data Sources
• Installation
• Status Update
• Contributions
• Tutorials
• Publications
• News
• Citing FinRL
• Welcome Contributions
• Sponsorship
• LICENSE

Overview

FinRL framework 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.

Run Stock_NeurIPS2018.ipynb for a quick start.

A video FinRL at the AI4Finance Youtube Channel.

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
│   	├── 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
│
├── tutorials (educational notebook files)
├── 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
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 aggegrated trades, OHLCV	Prices&Indicators
CCXT	Cryptocurrency	API-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
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.

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]

• 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 financial reinforcement learning. It has evolved into an ecosystem.
• 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

• [Towardsdatascience] Deep Reinforcement Learning for Automated Stock Trading
• [Alpaca][DataDrivenInvestor] A Data Scientist’s Approach for Algorithmic Trading using Deep Reinforcement Learning: An End-to-end Tutorial for Paper Trading
• [Towardsdatascience] FinRL for Quantitative Finance: Tutorial for Multiple Stock Trading
• [Towardsdatascience] FinRL for Quantitative Finance: Tutorial for Portfolio Allocation
• [Towardsdatascience] FinRL for Quantitative Finance: Tutorial for Single Stock Trading
• [Towardsdatascience] ElegantRL-Podracer: A Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
• [Towardsdatascience] ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library
• [Towardsdatascience] ElegantRL: Mastering PPO Algorithms
• [MLearning.ai] Hyperparameter Optimization using Ray tune for FinRL models
• [MLearning.ai] An Empirical Approach to Explain Deep Reinforcement Learning in Portfolio Management Task
• [MLearning.ai] FinRL for Quantitative Finance: plug-and-play DRL algorithms
• [MLearning.ai] ElegantRL Demo: Stock Trading Using DDPG (Part I)
• [MLearning.ai] ElegantRL Demo: Stock Trading Using DDPG (Part II)
• [DataDrivenInvestor] FinRL-Meta: A Universe of Near Real-Market En­vironments for Data­-Driven Financial Reinforcement Learning
• [Analytics Vidhya] Weights and Biases-ify FinRL with Stable Baselines3 models
• [Analytics Vidhya] Hyperparameter tuning using optuna for FinRL
• [Analytics Vidhya] A hitchhikers guide to FinRL: A Deep Reinforcement Learning Framework for Quantitative Finance
• [Analyticsindiamag.com] How To Automate Stock Market Using FinRL (Deep Reinforcement Learning Library)?

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://arxiv.org/abs/2111.03995; 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	7	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	25	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; repo: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020; code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb	46	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; repo: https://github.com/AI4Finance-Foundation/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019; code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/execution_optimizing/execution_optimizing.ipynb	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	87	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
• [央广网] 2021 IDEA大会开启AI**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

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. Please check Google Scholar. Closely related papers are given in the list.

Join and Contribute

Welcome to AI4Finance community!

Discuss FinRL via AI4Finance mailing list and AI4Finance Slack channel:

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Please check Contributing Guidances.

Contributors

Thank you!

Sponsorship

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

Sponsorship records at Issue #425

Network: USDT-TRC20

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.