Predict Forex Trend via Convolutional Neural Networks

2018/01/09 by Yun‐Cheng Tsai, Junhao Chen, Tsai, Yun-Cheng +3
Computer Science · Decision Sciences · #Computational Engineering #Computational Finance (q-fin.CP) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Economics and business #Finance #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.1801.03018

openalex publication_date 2018/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• On the Origin of Deep Learning

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