Affiliations: [a] School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, China | [b] School of Economics, Xihua University, Chengdu, Sichuan, China
Correspondence:
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Corresponding author: Peng Chen, School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, China. E-mail: [email protected].
Abstract: Financial time series prediction and trading decision-making are priorities of computational intelligence for researchers in academia and the finance industry due to their broad application areas and substantial impact. However, these methods remain challenging because they retain various complex statistical properties, and the mechanism behind the processes is unknown to a large extent. A significant number of machine learning-based methods are proposed and demonstrate impressive results, especially deep learning-based models. Nevertheless, due to the high complexity of massive, nonlinear, and nonindependent data and the difficulties and time consumption of complicated training models of deep learning, the performance of online trading decisions is still inadequate for practical application. This paper proposes the Integrated Framework of Forecasting Based Online Trading Strategy (IFF-BOTS) to satisfy better prediction performance and dynamic decisions for real-world online trading systems. Our method adopts a novel isomorphic convolutional neural network (CNN)-based forecaster-classifier-executor architecture to exploit CNN-based price and trend integrated prediction and direct-reinforcement-learning-based trading decision-making. IFF-BOTS can also achieve better real-time performance for online trading. We empirically compare the proposed approach with state-of-the-art prediction and trading methods on real-world S&P and DJI datasets. The results show that the IFF-BOTS outperforms its competitors in predicting metrics, trading profits, and real-time performance.
Keywords: Time series analysis, algorithmic trading, deep learning, reinforcement learning, convolution neural networks
