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FinRL: The indispensable commence-provide mission for monetary reinforcement finding out

FinRL: The indispensable commence-provide mission for monetary reinforcement finding out

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Our Mission: to effectively automate trading. We continuously have and fragment codes for finance.

Our Vision: AI neighborhood has gathered an commence-provide code ocean over the final decade. Making exercise of these intellectual and engineering properties to finance will provoke a paradigm shift from the dilapidated trading routine to an automated machine finding out skill, even RLOps in finance.

FinRL (web inform) is the principle commence-provide mission to search out the immense seemingly of deep reinforcement finding out in finance. We relieve practitioners pipeline a trading approach the exercise of deep reinforcement finding out (DRL).

The FinRL ecosystem is a unified framework, including a lot of markets, inform-of-the-art work algorithms, monetary duties (portfolio administration, cryptocurrency trading, excessive-frequency trading), live trading, etc.

Roadmap Level Customers Instance Desription
0.0 (Preparation) preparation practitioners of enterprise immense knowledge FinRL-Meta a universe of market environments
1.0 (Proof-of-Opinion) entry-level beginners this repo demonstration, education
2.0 (Knowledgeable) intermediate-level fat-stack builders, mavens ElegantRL financially optimized DRL algorithms
3.0 (Production) come-level investment banks, hedge funds Podracer cloud-native solution

Outline

Overview

FinRL has three layers: purposes, drl agents, and market environments.

For a trading project (on the head), an agent (in the heart) interacts with an surroundings (on the bottom), making sequential choices.

Bustle FinRL_StockTrading_NeurIPS_2018.ipynb miniature by miniature for a swiftly commence Up.

A video about FinRL library on the AI4Finance Youtube Channel.

File Structure

Correspondingly, the principle folder finrl has three subfolders apps, drl_agents, finrl_meta.

We use a put collectively-take a look at-exchange pipeline by three files: put collectively.py, take a look at.py, and exchange.py.

FinRL
├── finrl (most indispensable folder)
│   ├── purposes
│   	├── 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
│   ├── most indispensable.py
│   ├── space.py
│   ├── put collectively.py
│   ├── take a look at.py
│   └── exchange.py
│   
├── tutorial (tutorial notebooks and instructional files)
├── unit_testing (be definite that verified codes engaged on env & knowledge)
│   ├── test_env
│   	└── test_env_cashpenalty.py
│   └── test_marketdata
│   	└── test_yahoodownload.py
├── setup.py
├── requirements.txt
└── README.md

Supported Recordsdata Sources

Recordsdata Supply Form Differ and Frequency Seek files from Limits Raw Recordsdata Preprocessed Recordsdata
Alpaca US Stocks, ETFs 2015-now, 1min Fable-particular OHLCV Prices&Indicators
Baostock CN Securities 1990-12-19-now, 5min Fable-particular OHLCV Prices&Indicators
Binance Cryptocurrency API-particular, 1s, 1min API-particular Tick-level each day aggegrated trades, OHLCV Prices&Indicators
CCXT Cryptocurrency API-particular, 1min API-particular OHLCV Prices&Indicators
IEXCloud NMS US securities 1970-now, 1 day 100 per 2nd 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 Fable-particular OHLCV Prices&Indicators
tusharepro CN Securities, A fragment -now, 1 min Fable-particular OHLCV Prices&Indicators
WRDS US Securities 2003-now, 1ms 5 requests each time Intraday Trades Prices&Indicators
YahooFinance US Securities Frequency-particular, 1min 2,000/hour OHLCV Prices&Indicators

OHLCV: commence, excessive, low, and shut costs; volume. adjusted_close: adjusted shut assign

Technical indicators: ‘macd’, ‘boll_ub’, ‘boll_lb’, ‘rsi_30’, ‘dx_30’, ‘close_30_sma’, ‘close_60_sma’. Customers moreover can add contemporary aspects.

Installation

Jam Update

Version Historic past [click to expand]
  • 2021-08-25
    0.3.1: pytorch version with a 3-layer architecture, apps (monetary duties), drl_agents (drl algorithms), neo_finrl (gym env)
  • 2020-12-14
    Upgraded to Pytorch with receive-baselines3; Plan shut tensorflow 1.0 at this 2nd, beneath pattern to augment tensorflow 2.0
  • 2020-11-27
    0.1: Beta version with tensorflow 1.5

Contributions

  • FinRL is the principle commence-provide framework to make clear the immense seemingly of applying DRL algorithms in quantitative finance. We have an ecosystem all by the FinRL framework, which seeds the impulsively rising AI4Finance neighborhood.
  • The utility layer offers interfaces for users to customize FinRL to their include trading duties. Automated backtesting tool and efficiency metrics are supplied to relieve quantitative merchants iterate trading systems at a excessive turnover charge. Winning trading systems are reproducible and hands-on tutorials are supplied in a newbie-obliging kind. Adjusting the trained devices to the impulsively altering markets is moreover that you might per chance per chance per chance accept as true with.
  • The agent layer offers inform-of-the-art work DRL algorithms which can per chance be tailored to finance with swish-tuned hyperparameters. Customers can add contemporary DRL algorithms.
  • The environment layer consists of not entirely a sequence of historic knowledge APIs, but moreover live trading APIs. They are reconfigured into traditional OpenAI gym-style environments. Furthermore, it contains market frictions and lets in users to customize the trading time granularity.

Tutorials

News

Citing FinRL

@article{finrl2020,
    creator ={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 finding out library for automated inventory trading in quantitative finance},
    journal={Deep RL Workshop, NeurIPS 2020},
    Three hundred and sixty five days   ={2020}
}

@article{liu2021finrl,
    creator ={Liu, Xiao-Yang and Yang, Hongyang and Gao, Jiechao and Wang, Christina Dan},
    title  ={{FinRL}: Deep reinforcement finding out framework to automate trading in quantitative finance},
    journal={ACM Global Convention on AI in Finance (ICAIF)},
    Three hundred and sixty five days   ={2021}
}

We printed FinTech papers, overview Google Pupil, ensuing on this mission. Carefully linked papers are given in the list.

Join and Make contributions

Welcome to the AI4Finance Basis neighborhood!

Join to communicate about FinRL: AI4Finance mailing list, AI4Finance Slack channel:


Apply us on WeChat:

Please overview Contributing Guidances.

Contributors

Thanks!



Sponsorship

Welcome gift money to augment AI4Finance, a non-profit tutorial neighborhood. Consume the links in the staunch, or scan the following vemo QR code:

Detailed sponsorship files might per chance per chance moreover be found at Field #425

LICENSE

MIT License

Disclaimer: Nothing herein is monetary advice, and NOT a advice to interchange true money. Please exercise general sense and continuously first seek the advice of a legit earlier than trading or investing.

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About the author: Vanic
“Simplicity, patience, compassion. These three are your greatest treasures. Simple in actions and thoughts, you return to the source of being. Patient with both friends and enemies, you accord with the way things are. Compassionate toward yourself, you reconcile all beings in the world.” ― Lao Tzu, Tao Te Ching
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