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Predict Forex Trend via Convolutional Neural Networks

Author

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  • 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.

Suggested Citation

  • Yun-Cheng Tsai & Jun-Hao Chen & Jun-Jie Wang, 2018. "Predict Forex Trend via Convolutional Neural Networks," Papers 1801.03018, arXiv.org.
    • Handle: RePEc:arx:papers:1801.03018
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    References listed on IDEAS

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    1. Browne, S., 1995. "Optimal Investment Policies for a Firm with a Random Risk Process: Exponential Utility and Minimizing the Probability of Ruin," Papers 95-08, Columbia - Graduate School of Business.
    2. Sid Browne, 1995. "Optimal Investment Policies for a Firm With a Random Risk Process: Exponential Utility and Minimizing the Probability of Ruin," Mathematics of Operations Research, INFORMS, vol. 20(4), pages 937-958, November.
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    Cited by:

    1. Ling Qi & Matloob Khushi & Josiah Poon, 2021. "Event-Driven LSTM For Forex Price Prediction," Papers 2102.01499, arXiv.org.
    2. Hyun Sik Sim & Hae In Kim & Jae Joon Ahn, 2019. "Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?," Complexity, Hindawi, vol. 2019, pages 1-10, February.
    3. Zezheng Zhang & Matloob Khushi, 2020. "GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading," Papers 2008.09471, arXiv.org.

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