A Multiscale Convolutional Neural Network for Forex Trading Using Joint Feature Learning

doi:10.3969/j.issn.1006-2475.2018.09.023

Abstract

Abstract: Convolutional neural networks (CNNs) have revolutionized the field of computer vision. In this paper, we explore a particular application of CNNs: using CNNs to predict movements of forex prices from a picture of a time series of past price fluctuations, with the ultimate goal of using them for forex trading in order to make a profit. The main contribution of this research is to set up a novel architecture that uses multiscale CNNs to handle various kinds of features with a joint feature learning mechanism. Experimental results show our approach is more feasible compared with the basic CNNs using only image feature and other traditional machine learning methods.

Key words: multiscale convolutional neural networks, joint feature learning, forex trading

CLC Number:

TP391

CHEN Xi-yuan, ZHU Jia. A Multiscale Convolutional Neural Network for Forex Trading Using Joint Feature Learning[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.09.023.

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