Open AccessSurvey on the application of deep learning in algorithmic trading
Author(s) -
Yongfeng Wang,
AUTHOR_ID,
Guofeng Yan
Publication year - 2021
Publication title -
data science in finance and economics
Language(s) - English
Resource type - Journals
ISSN - 2769-2140
DOI - 10.3934/dsfe.2021019
Subject(s) - algorithmic trading , trading strategy , computer science , deep learning , high frequency trading , artificial intelligence , dark liquidity , alternative trading system , implementation , pairs trade , snapshot (computer storage) , machine learning , electronic trading , financial economics , algorithm , software engineering , database , economics
Algorithmic trading is one of the most concerned directions in financial applications. Compared with traditional trading strategies, algorithmic trading applications perform forecasting and arbitrage with higher efficiency and more stable performance. Numerous studies on algorithmic trading models using deep learning have been conducted to perform trading forecasting and analysis. In this article, we firstly summarize several deep learning methods that have shown good performance in algorithmic trading applications, and briefly introduce some applications of deep learning in algorithmic trading. We then try to provide the latest snapshot application for algorithmic trading based on deep learning technology, and show the different implementations of the developed algorithmic trading model. Finally, some possible research issues are suggested in the future. The prime objectives of this paper are to provide a comprehensive research progress of deep learning applications in algorithmic trading, and benefit for subsequent research of computer program trading systems.