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CS277 Deep Reinforcement Learning in Portfolio Management

Introduction

Dataset

  • We use dataset from SSE stock data. It is supported by course.
  • The data we extract stored in utils/datasets.

Structure

|-config
|-data_for_vis
|-environment
|-model
|-results
|-stock_charts
|-utils
|-vis_results
|-weights
|-experiments.ipynb
|-requirements.txt
|-stock_trading.py
  • /config: The hyper parameters of reinforcement learning setting file.
  • /data_for_vis: The data we generate in our test experiments. We visualize it by Excel.
  • /environment: The portfolio management simulation environment, it can inherit from OpenAI Gym.
  • /model: The implements of DDPG. It is our trade agent.
  • /results: The tensorflow logs of training.
  • /stock_charts: The EchartJS project for visualize the weights output by the DDPG agent. If you want to run it can reference the readme.
  • /utils:
    • data.py: data utils.
    • /datasets: The stock_history.h5 used for train and valid, the new_stock_history.h5 used for generalization test.
  • /vis_results: The image in our report.
  • /weights: The weights of tensorflow model trained by DDPG. It include different window size.
  • experiments.ipynb: The test file. It shows how to use the agent to predict and support some visualization example.
  • requirements.txt: All dependencies
  • stock_trading.py: train/test logic. It is the core file of trading.

How to run

python stock_trade.py -p=lstm -w=3 -b=True

p: the predictor.

w: the length of window.

b: whether to use batch normalization

How to test can reference the experiments.ipynb.

Results

  • Our model

train

  • Train performance: train agent in data from 2014-2017.

train_w3

train_w7

  • Test performance: test agent in data from 2018-2019

train

  • Generalization performance: test agent in the new stock datasets which have not exit in train period.

train

  • The action dynamic output by agent in test period.

train

Reference