Predict Forex Trend via Convolutional Neural Networks

Yun-Cheng Tsai; Jun-Hao Chen; Jun-Jie Wang

Predict Forex Trend via Convolutional Neural Networks

Computational Engineering, Finance, and Science 2018-01-10 v1 Computational Finance

Authors: Yun-Cheng Tsai , Jun-Hao Chen , Jun-Jie Wang

Abstract

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 three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images. 2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model. 3. We evaluate the model's performance in terms of the accuracy of classification. A trading model is obtained with this approach to help devise trading strategies. The main application is designed to help clients automatically obtain personalized trading strategies.

Keywords

deep learning for image classification deep neural networks convolutional neural network

Cite

@article{arxiv.1801.03018,
  title  = {Predict Forex Trend via Convolutional Neural Networks},
  author = {Yun-Cheng Tsai and Jun-Hao Chen and Jun-Jie Wang},
  journal= {arXiv preprint arXiv:1801.03018},
  year   = {2018}
}

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30 pages, 41 figures

Predict Forex Trend via Convolutional Neural Networks

Abstract

Keywords

Cite

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