Before revision:  The former is composed of three parallel network layers , respectively dealing with different frequencies (five minute, one day, one week) data, and day level considers the volatilities of stocks. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares . Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .
After revision:  The former is composed of three 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 different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model .