A Multi-Scale Temporal Feature Aggregation Convolutional Neural Network for Portfolio Management

Financial portfolio management is the process of periodically reallocating a fund into different financial investment products, with the goal of achieving the maximum profits. While conventional financial machine learning methods try to predict the price trends, reinforcement learning based portfolio management methods makes trading decisions according to the price changes directly. However, existing reinforcement learning based methods are limited in extracting the price change information at single-scale level, which makes their performance still not satisfactory. In this paper, inspired by the Inception network that has achieved great success in computer vision and can extract multi-scale features simultaneously, we propose a novel Ensemble of Identical Independent Inception (EI$^3$) convolutional neural network, with the objective of addressing the limitation of existing reinforcement learning based portfolio management methods. With EI$^3$, multiple assets can be processed independently while sharing the same network parameters. Moreover, price movement information for each product can be extracted at multiple scales via wide network and then aggregated to make trading decision. Based on EI$^3$, we further propose a recurrent reinforcement learning framework to provide a deep machine learning solution for the portfolio management problem. Comprehensive experiments on the cryptocurrency datasets demonstrate the superiority of our method over existing competitors, in both upswing and downswing environments.