Package: trademas
TradeMaster: An RL Platform for TradingTradeMaster is a first-of-its kind, best-in-classopen-source platformforquantitative trading (QT)empowered byreinforcement learning (RL), which covers thefull pipelinefor the design, implementation, evaluation and deployment of RL-based algorithms.:star:What's NEW!:alarm_clock:UpdateStatusSupportautomatic feature generation and selection:hammer: Updatetutorialon 11 May 2023ReleaseColabVersion:speech_balloon:Updatedon 29 March 2023IncldueHK StockandFutureDatasets:compass: Updated#131#132on 27 March 2023SupportAlpha158:hammer: Updated#123#124on 20 March 2023Release TradeMaster 1.0.0:octocat:Released v1.0.0on 5 March 2023OutlineTradeMaster: An RL Platform for TradingOutlineOverviewInstallationTutorialUseful ScriptDatasetModel ZooVisualization ToolkitFile StructurePublicationsNewsTeamCompetitionContact UsOverviewTradeMasteris 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.InstallationHere are the installation tutorials for different operating systems and docker:Installation on Linux/Windows/MacOSInstallation with DockerTutorialWe provide tutorials covering core features of TradeMaster for users to get start with.AlgorithmDatasetMarketTaskCode LinkEIIEDJ30US StockPortfolio ManagementtutorialDeepScalperBTCCryptoIntraday TradingtutorialSARLDJ30US StockPortfolio ManagementtutorialPPOSSE 50China StockPortfolio ManagementtutorialETEOBitcoinCryptoOrder ExecutiontutorialDouble DQNBitcoinCryptoHigh Frequency TradingtutorialWe also provide a colab version of these tutorials that can be run directly. (colab tutorial)Useful ScriptAutomatic hyperparameter tuningAutomatic feature generationMarket dynamic labelingFinancial data imputation with diffusion modelsTrain RL agents with Alpha158 technical indicatorsDatasetDatasetData SourceTypeRange and FrequencyRaw DataDatasheetS&P500YahooUS Stock2000/01/01-2022/01/01, 1dayOHLCVSP500DJ30YahooUS Stock2012/01/01-2021/12/31, 1dayOHLCVDJ30BTCKaggleForeign Exchange2000/01/01-2019/12/31, 1dayOHLCVFXCryptoKaggleCrypto2013/04/29-2021/07/06, 1dayOHLCVCryptoSSE50YahooChina Stock2009/01/02-2021/01/01, 1dayOHLCVSSE50BitcoinBinanceCrypto2021/04/07-2021/04/19, 1minLOBBinanceFutureAKshareFuture2023/03/07-2023/03/28, 5minOHLCVFutureHS30AKShareHK Stock1988/12/30-2023/03/27, 1dayOHLCVHS30Dates 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 fromGoogle DriveorBaidu Cloud(extraction code:x24b)Model ZooTradeMaster provides efficient implementations of the following algorithms: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)ETEO 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:PPOA2CRainbowSACDDPGDQNPGTD3Visualization ToolkitTradeMaster provides many visualization toolkits for a systematic evaluation of RL-based quantitative trading methods. Please check thispaperandrepositoryfor details. Some examples are as follows:PRIDE-Staris 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:File Structure| 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.txtPublicationsPRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets(Transactions on Machine learning Research 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)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)News[知乎]用强化学习在金融市场上赚钱？南洋理工发布全新基于强化学习的量化交易平台TradeMaster[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[运筹OR帷幄]量化交易大师TradeMaster[Medium]Introduction on TradeMasterTeamThis repository is developed and maintained byAMIgroup lead byProf Bo AnatNanyang 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.CompetitionTradeMaster Cup 2022Contact UsIf you have any further questions of this project, please contactTradeMaster.NTU@gmail.com