morningsky/CS277-Project-DRL-in-Portfolio-Management

CS277 Deep Reinforcement Learning in Portfolio Management

Introduction

This repo is part of CS 277 project Deep Reinforcement Learning in Portfolio Management.
It is the implement of Deep Deterministic Policy Gradient part.
The data preprocessing part can be see in https://github.com/WJie12/autotrading_preprocessing .
The DQN part can be see in https://github.com/WJie12/autotrading_DQN .

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.