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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 ...

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