Survey on the application of deep learning in algorithmic trading

Yongfeng Wang; Guofeng Yan; Yongfeng Wang; Guofeng Yan

doi:10.3934/DSFE.2021019

Data Science in Finance and Economics

2021, Volume 1, Issue 4: 345-361. doi: 10.3934/DSFE.2021019

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Review

Survey on the application of deep learning in algorithmic trading

Yongfeng Wang ,
Guofeng Yan ^,

School of Computer Science and Cyber Engineering, Guangzhou University, China

Received: 08 December 2021 Accepted: 18 December 2021 Published: 27 December 2021
JEL Codes: G15, C63

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.
- deep learning,
- algorithmic trading,
- trading strategy,
- price prediction,
- arbitrage
Citation: Yongfeng Wang, Guofeng Yan. Survey on the application of deep learning in algorithmic trading[J]. Data Science in Finance and Economics, 2021, 1(4): 345-361. doi: 10.3934/DSFE.2021019

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Abstract

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.

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