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

Zhishun Wang, Wei Lu, Kaixin Zhang, Tianhao Li, Zixi Zhao
2021 arXiv   pre-print
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
more » ... 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.
arXiv:2105.03625v2 fatcat:rof7n76si5ajjm77zqybeocyrm