How Do Reinforcement Learning Algorithms Optimize Trading Strategies in Financial Markets Compared to Traditional Trading Approaches? A Literature Review

Liu Hong Yuan Tom

doi:10.54254/2754-1169/2025.GL25764

Advances in Economics, Management and Political SciencesOpen access

How Do Reinforcement Learning Algorithms Optimize Trading Strategies in Financial Markets Compared to Traditional Trading Approaches? A Literature Review

Research Article

Open Access

How Do Reinforcement Learning Algorithms Optimize Trading Strategies in Financial Markets Compared to Traditional Trading Approaches? A Literature Review

Liu Hong Yuan Tom ^1*

¹ The University of Hong Kong

^*Corresponding author: liutom@connect.hku.hk

Published on 6 August 2025

AEMPS Vol.208

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-315-4

ISBN (Online): 978-1-80590-316-1

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Abstract

Reinforcement learning (RL) has demonstrated significant potential in optimizing sequential decision-making within financial markets' highly dynamic and uncertain environments, offering distinct advantages over traditional trading approaches. This literature review investigates the use of RL in developing and improving trading strategies by integrating the findings of ten recent studies published between 2018 and 2025, selected for their focus on RL applications in different financial domains. These studies employ a range of RL techniques, such as Q-learning, and Proximal Policy Optimization (PPO), across a variety of financial markets, including stocks, Forex, Bitcoin, and derivatives. The review shows that RL-based strategies, which often use innovations such as multi-agent systems, ensemble learning, and sentiment analysis, demonstrate superiority such as better adaptability to non-dynamic stationary market conditions, enhanced risk-adjusted returns, and capability to learn complex relationships directly from market data, thus outperforming conventional methods and market benchmarks. Challenges hindering the practical application of reinforcement learning in trading include sample efficiency, training stability, market complexity, and the necessity for accurate market assumptions. These are areas requiring further examination and enhancement.

Keywords:

Reinforcement Learning, Trading Strategies, Machine Learning, Optimization

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References

[1]. Zhang, Z., Zohren, S., & Roberts, S. (2019). Deep reinforcement learning for trading. arXiv preprint arXiv: 1911.10107.

[2]. Liu, X. Y., Xiong, Z., Zhong, S., Yang, H., & Walid, A. (2018). Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv: 1811.07522.

[3]. Yasin, A. S., & Gill, P. S. (2024). Reinforcement Learning Framework for Quantitative Trading. arXiv preprint arXiv: 2411.07585.

[4]. M. E. Aloud and N. Alkhamees, 2021. “Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change, ” IEEE Access, vol. 9, pp. 114659–114671. doi: 10.1109/ACCESS.2021.3105259.

[5]. F. Liu, Y. Li, B. Li, J. Li, and H. Xie, 2021. “Bitcoin transaction strategy construction based on deep reinforcement learning, ” Appl Soft Comput, vol. 113, Dec, doi: 10.1016/j.asoc.2021.107952.

[6]. Ding, Y., Yuan, G., Zuo, D., & Gao, T. (2025). Hedging with Sparse Reward Reinforcement Learning. arXiv preprint arXiv: 2503.04218.

[7]. F. Li, Z. Wang, and P. Zhou, 2022. “Ensemble Investment Strategies Based on Reinforcement Learning, ” Sci Program, vol. 2022, doi: 10.1155/2022/7648810.

[8]. Ye, A., Xu, J., Veedgav, V., Wang, Y., Yu, Y., Yan, D., ... & Xu, S. (2024). Learning the Market: Sentiment-Based Ensemble Trading Agents. arXiv preprint arXiv: 2402.01441.

[9]. Sarani, D., & Rashidi-Khazaee, P. (2024). A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution. arXiv preprint arXiv: 2405.19982.

[10]. Qiu, D., Wang, J., Wang, J., & Strbac, G. (2021, August). Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market. In IJCAI (pp. 2913-2920).

References

[1]. Zhang, Z., Zohren, S., & Roberts, S. (2019). Deep reinforcement learning for trading. arXiv preprint arXiv: 1911.10107.

[2]. Liu, X. Y., Xiong, Z., Zhong, S., Yang, H., & Walid, A. (2018). Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv: 1811.07522.

[3]. Yasin, A. S., & Gill, P. S. (2024). Reinforcement Learning Framework for Quantitative Trading. arXiv preprint arXiv: 2411.07585.

[6]. Ding, Y., Yuan, G., Zuo, D., & Gao, T. (2025). Hedging with Sparse Reward Reinforcement Learning. arXiv preprint arXiv: 2503.04218.

[7]. F. Li, Z. Wang, and P. Zhou, 2022. “Ensemble Investment Strategies Based on Reinforcement Learning, ” Sci Program, vol. 2022, doi: 10.1155/2022/7648810.

[8]. Ye, A., Xu, J., Veedgav, V., Wang, Y., Yu, Y., Yan, D., ... & Xu, S. (2024). Learning the Market: Sentiment-Based Ensemble Trading Agents. arXiv preprint arXiv: 2402.01441.

[10]. Qiu, D., Wang, J., Wang, J., & Strbac, G. (2021, August). Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market. In IJCAI (pp. 2913-2920).

Cite this article

Tom,L.H.Y. (2025). How Do Reinforcement Learning Algorithms Optimize Trading Strategies in Financial Markets Compared to Traditional Trading Approaches? A Literature Review. Advances in Economics, Management and Political Sciences,208,8-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Financial Framework's Role in Economics and Management of Human-Centered Development

ISBN: 978-1-80590-315-4(Print) / 978-1-80590-316-1(Online)

Editor: Lukáš Vartiak, Habil. Florian Marcel Nuţă

Conference website: https://2025.icftba.org/Galati.html

Conference date: 17 October 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.208

ISSN: 2754-1169(Print) / 2754-1177(Online)

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