  With the development of artificial intelligence,more and more financial
practitioners apply deep reinforcement learning to financial trading
strategies.However,It is difficult to extract accurate features due to the
characteristics of considerable noise,highly non-stationary,and non-linearity
of single-scale time series,which makes it hard to obtain high returns.In this
paper,we extract a multi-scale feature matrix on multiple time scales of
financial time series,according to the classic financial theory-Chan Theory,and
put forward to an approach of multi-scale stroke deep deterministic policy
gradient reinforcement learning model(MSSDDPG)to search for the optimal trading
strategy.We carried out experiments on the datasets of the Dow Jones,S&P 500 of
U.S. stocks, and China's CSI 300,SSE Composite,evaluate the performance of our
approach compared with turtle trading strategy, Deep
Q-learning(DQN)reinforcement learning strategy,and deep deterministic policy
gradient (DDPG) reinforcement learning strategy.The result shows that our
approach gets the best performance in China CSI 300,SSE Composite,and get an
outstanding result in Dow Jones,S&P 500 of U.S.
