A parallel-network continuous quantitative trading model with GARCH and PPO

TL;DR

This paper introduces a novel parallel-network trading model combining GARCH and PPO to enhance deep reinforcement learning for stock trading, demonstrating improved profits over traditional methods in Chinese stock market data.

Contribution

The paper presents a new parallel-network architecture integrating GARCH and PPO for continuous trading, addressing data frequency and information limitations in existing RL models.

Findings

Achieves higher profits than baseline RL methods.

Effective handling of multi-frequency data improves trading performance.

Demonstrated success on Chinese stock market data.

Abstract

It is a difficult task for both professional investors and individual traders continuously making profit in stock market. With the development of computer science and deep reinforcement learning, Buy\&Hold (B\&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal with 3 different frequencies data (including GARCH information) and proximal policy optimization (PPO) algorithm interacts actions and rewards with stock trading environment. Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models.