Publication

Deep Reinforcement Learning for Quantitative Trading: Challenges and Opportunities (IEEE Intelligent Systems 2022)

DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities (CIKM 2022)

Commission Fee is not Enough: A Hierarchical Reinforced Framework for Portfolio Management (AAAI 21)

Reinforcement Learning for Quantitative Trading (Survey) (ACM TIST 2023)

PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets

File Structure

Here is the structure of the TradeMaster project.

| 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