  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.

Trading and Market Microstructure;Machine Learning