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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2017: 0.96

SCImago Journal Rank (SJR) 2017: 0.193
Source Normalized Impact per Paper (SNIP) 2017: 0.481

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Predict Forex Trend via Convolutional Neural Networks

Yun-Cheng Tsai / Jun-Hao Chen
  • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jun-Jie Wang
  • Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-09-28 | DOI: https://doi.org/10.1515/jisys-2018-0074


Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to train our trading model. Third, we evaluate the model’s performance in terms of the accuracy of classification. The experimental results show that if the strategy is clear enough to make the images obviously distinguishable the CNN model can predict the prices of a financial asset. Hence, our approach can help devise trading strategies and help clients automatically obtain personalized trading strategies.

Keywords: Deep learning; convolutional neural network (CNN); geometric Brownian motion (GBM); Forex (FX); trading strategies


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About the article

Received: 2018-01-31

Published Online: 2018-09-28

Citation Information: Journal of Intelligent Systems, 20180074, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2018-0074.

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