Deep hierarchical strategy model for multi-source driven quantitative investment

Abstract

Using deep learning to maximize the benefits of a series of risks in the securities market was a very interesting and widely concerned problem. From the perspective of multi-source driving, we proposed a feature combination method based on prior knowledge and designed a deep hierarchical strategy model to solve this challenge. This model includes a pre-judging module, which implements trend judgment of the time series, and an operation module that performs the trading actions. For the pre-judging module, we designed a regression constraint Wasserstein generative adversarial network (WGAN) to complete the mission instead of the common discriminant methods in the past. As for the operation module, in order to make the machine learning more compatible with human learning characteristics and having the ability to control risks, we designed a network structure based on deep deterministic policy gradient (DDPG), which is one of the reinforcement learning algorithms to decide continuous trading position. We used the Dow Jones Industrial Index and the Shanghai Stock Exchange Index to conduct training and testing. The results show that the proposed model has a good performance in terms of return on investment and robustness.