论文标题
DDPG基于金融时间序列交易策略的多尺度中风
DDPG based on multi-scale strokes for financial time series trading strategy
论文作者
论文摘要
随着人工智能的发展，越来越多的金融从业人员将深入的强化学习应用于金融交易策略。但是，由于相当多的噪音，高度非平稳性和单尺度时间序列的非线性特征，很难提取准确的特征，这是很难获得的较高的回报。提出一种多尺度中风深层确定性政策梯度加强学习模型（MSSDDPG）的方法，以寻找最佳的交易策略。我们在道琼斯股票的数据集，美国股票的标准普尔500指数的数据集上进行了实验深层确定性政策梯度（DDPG）强化学习策略。结果表明，我们的方法在中国获得了CSI 300，SSE Composite的最佳表现，并在美国标准普尔500指数Dow Jones中取得了出色的成果
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.
