基于联合特征学习的多尺度卷积#br#
神经网络在外汇交易市场中的应用

doi:10.3969/j.issn.1006-2475.2018.09.023

计算机与现代化 ›› 2018, Vol. 0 ›› Issue (09): 122-.doi: 10.3969/j.issn.1006-2475.2018.09.023

• 应用与开发 • 上一篇

基于联合特征学习的多尺度卷积#br# 神经网络在外汇交易市场中的应用

(华南师范大学计算机学院，广东广州510631)

收稿日期:2018-03-15 出版日期:2018-09-29 发布日期:2018-09-30
作者简介:陈希远（1993-），男，江西樟树人，华南师范大学计算机学院硕士研究生，研究方向：人工智能与神经网络; 朱佳，男，研究员，博士后，研究方向：机器学习与大数据应用。

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

(School of Computer, South China Normal University, Guangzhou 510631, China)

Received:2018-03-15 Online:2018-09-29 Published:2018-09-30

摘要/Abstract

摘要：
卷积神经网络（CNN）已经引起了计算机视觉领域的变革。本文探讨CNN的一个具体应用：已知价格在过去一段时间内的波动图后，利用CNN对外汇市场的价格进行预测，然后将预测结果用于外汇交易，最终获利。采用联合特征学习机制，创建一种新的可处理多种特征的多尺度CNN应用框架。实验结果表明，相比于只考虑图像特征的传统CNN及其他机器学习算法，本文算法的实用性更强。

关键词: 多尺度卷积神经网络, 联合特征学习, 外汇交易

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

中图分类号:

TP391

陈希远，朱佳. 基于联合特征学习的多尺度卷积#br# 神经网络在外汇交易市场中的应用[J]. 计算机与现代化, 2018, 0(09): 122-.

CHEN Xi-yuan, ZHU Jia. A Multiscale Convolutional Neural Network for Forex Trading Using Joint Feature Learning[J]. Computer and Modernization, 2018, 0(09): 122-.

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基于联合特征学习的多尺度卷积#br# 神经网络在外汇交易市场中的应用

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

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