QF-portfolio-investment-system

Gudience

• DDPG.ipynb: Run this notebook to train and test DDPG-based portfolio investment system
• QFPIS-1.ipynb: Run this notebook to train and test Quantum finance portfolio investment system using one Quantum price level(QPL)
• QFPIS-2.ipynb: Run this notebook to train and test Quantum finance portfolio investment system using two QPL
• backtest.ipynb: Run this notebook to do back test
• ./data: Contain the training and testing forex data, which are obtained form MetaTrader4
• ./environment: Contain the reinforcement learning enviroment:1)QF_env: enviroment for DDPG 2）QF_env_1: environment for QFPIS-1 3) environment for QFPIS-2
• ./model: Contain the trained models
• ./config/config.json: configure the training settings:

  {
  "episode": 100,
  "max step": 1000,
  "buffer size": 100000,
  "batch size": 64,
  "tau": 0.001,
  "gamma": 0.99,
  "actor learning rate": 0.0001,
  "critic learning rate": 0.001,
  "policy learning rate": 0.0001
  }

  Requirement

• Python 3.7
• Jupyter notebook
• Pytorch
• numpy
• pandas
• matplotlib
• detailed requirement will be upadted ...

Contact

• Yitao Qiu m730026088@mail.uic.edu.cn
• Rongkai Liu m730026073@mail.uic.edu.cn