TradeMaster is a first-of-its kind, best-in-class open-source platform for quantitative trading (QT) empowered by reinforcement learning (RL), which covers the full pipeline for the design, implementation, evaluation and deployment of RL-based algorithms.
| Update | Status |
|---|---|
| Release TradeMaster 1.0.0 |  Released v1.0.0 on 5 March 2023 |
TradeMaster is composed of 6 key modules: 1) multi-modality market data of different financial assets at multiple granularity; 2) whole data preprocessing pipeline; 3) a series of high-fidelity data-driven market simulators for mainstream QT tasks; 4) efficient implementations of over 13 novel RL-based trading algorithms; 5) systematic evaluation toolkits with 6 axes and 17 measures; 6) different interfaces for interdisciplinary users.
Here are the installation tutorials for different operating systems and docker:
We provide tutorials covering core features of TradeMaster for users to get start with.
| Algorithm | Dataset | Market | Task | Code Link |
|---|---|---|---|---|
| EIIE | DJ30 | US Stock | Portfolio Management | tutorial |
| DeepScalper | FX | Crypto | Intraday Trading | tutorial |
| SARL | DJ30 | US Stock | Portfolio Management | tutorial |
| PPO | SSE 50 | China Stock | Portfolio Management | tutorial |
| ETTO | Bitcoin | Crypto | Order Execution | tutorial |
| Double DQN | Bitcoin | Crypto | High Frequency Trading | tutorial |
- Automatic hyperparameter tuning
- Market dynamic labeling
- Financial data imputation with diffusion models
| Dataset | Data Source | Type | Range and Frequency | Raw Data | Datasheet |
|---|---|---|---|---|---|
| S&P500 | Yahoo | US Stock | 2000/01/01-2022/01/01, 1day | OHLCV | SP500 |
| DJ30 | Yahoo | US Stock | 2012/01/01-2021/12/31, 1day | OHLCV | DJ30 |
| FX | Kaggle | Foreign Exchange | 2000/01/01-2019/12/31, 1day | OHLCV | FX |
| Crypto | Kaggle | Crypto | 2013/04/29-2021/07/06, 1day | OHLCV | Crypto |
| SSE50 | Yahoo | China Stock | 2009/01/02-2021/01/01, 1day | OHLCV | SSE50 |
| Bitcoin | Binance | Crypto | 2021/04/07-2021/04/19, 1min | LOB | Binance |
Dates are in YY/MM/DD format.
OHLCV: open, high, low, and close prices; volume: corresponding trading volume; LOB: Limit order book.
Users can download data of the above datasets from Google Drive or Baidu Cloud (extraction code:x24b)
DeepScalper based on Pytorch (Shuo Sun et al, CIKM 22)
OPD based on Pytorch (Fang et al, AAAI 21)
DeepTrader based on Pytorch (Wang et al, AAAI 21)
SARL based on Pytorch (Yunan Ye et al, AAAI 20)
ETTO based on Pytorch (Lin et al, 20)
Investor-Imitator based on Pytorch (Yi Ding et al, KDD 18)
EIIE based on Pytorch (Jiang et al, 17)
Classic RL based on Pytorch and Ray: PPO A2C Rainbow SAC DDPG DQN PG TD3
TradeMaster provides many visualization toolkits for a systematic evaluation of RL-based quantitative trading methods. Please check this paper and repository for details. Some examples are as follows:
PRIDE-Star is a star plot containing normalized score of 8 key financial measures such total return (TR) and Sharpe ratio (SR) to evaluate profitability,risk-control and diversity:
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| TradeMaster
| ├── configs
| ├── data
| │ ├── algorithmic_trading
| │ ├── high_frequency_trading
| │ ├── order_excution
| │ └── porfolio_management
| ├── deploy
| │ ├── backend_client.py
| │ ├── backend_client_test.py
| │ └── backend_service.py
| │ ├── backend_service_test.py
| ├── docs
| ├── figure
| ├── installation
| │ ├── docker.md
| │ ├── requirements.md
| ├── tools
| │ ├── algorithmic_trading
| │ ├── data_preprocessor
| │ ├── high_frequency_trading
| │ ├── market_dynamics_labeling
| │ ├── missing_value_imputation
| │ ├── order_excution
| │ ├── porfolio_management
| │ ├── __init__.py
| ├── tradmaster
| │ ├── agents
| │ ├── datasets
| │ ├── enviornments
| │ ├── evaluation
| │ ├── imputation
| │ ├── losses
| │ ├── nets
| │ ├── preprocessor
| │ ├── optimizers
| │ ├── pretrained
| │ ├── trainers
| │ ├── transition
| │ ├── utils
| │ └── __init__.py
| ├── unit_testing
| ├── Dockerfile
| ├── LICENSE
| ├── README.md
| ├── pyproject.toml
| └── requirements.txt
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets (2023)
Reinforcement Learning for Quantitative Trading (Survey) (ACM Transactions on Intelligent Systems and Technology 2023)
Deep Reinforcement Learning for Quantitative Trading: Challenges and Opportunities (IEEE Intelligent Systems 2022)
Commission Fee is not Enough: A Hierarchical Reinforced Framework for Portfolio Management (AAAI 21)
[机器之心]南洋理工发布量化交易大师TradeMaster，涵盖15种强化学习算法
- This repository is developed and maintained by AMI group lead by Prof Bo An at Nanyang Technological University.
- We have positions for software engineer, research associate and postdoc. If you are interested in working at the intersection of RL and quantitative trading, feel free to send us an email with your CV.
If you have any further questions of this project, please contact [email protected]