Deep Reinforcement Learning-based Algorithmic Optimisation and Risk Management for High Frequency Trading
DOI:
https://doi.org/10.54097/Keywords:
Deep Reinforcement Learning, High Frequency Trading, Algorithm Optimisation, Risk Management, Multi-Intelligent Body SystemAbstract
This paper reviews the current status and challenges of Deep Reinforcement Learning (DRL)-based algorithm optimisation and risk management for high-frequency trading. By analysing the potential application of Deep Reinforcement Learning in high-frequency trading, its unique advantages in algorithm optimisation, trading decision-making and risk management are discussed. Although DRL demonstrates the ability to make self-adaptive and dynamic decisions in complex market environments, it still faces many challenges such as insufficient real-time algorithmic performance, data sparsity, model overfitting, and risk management complexity in practical applications. This paper summarises the main findings of the current research and proposes directions for future research, suggesting that the application of DRL in high-frequency trading can be further enhanced by improving the algorithmic structure, dealing with data sparsity, and optimising risk management strategies.
References
[1] Li, J., Zhang, Y., Yang, X., & Chen, L. (2023). Online portfolio management via deep reinforcement learning with high-frequency data. Information Processing & Management, 60(3), 103247.
[2] Shavandi, A., & Khedmati, M. (2022). A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems with Applications, 208, 118124.
[3] Hirchoua, B., Ouhbi, B., & Frikh, B. (2021). Deep reinforcement learning based trading agents: Risk curiosity driven learning for financial rules-based policy. Expert Systems with Applications, 170, 114553.
[4] Rundo, F. (2019). Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences, 9(20), 4460.
[5] Sahu, S. K., Mokhade, A., & Bokde, N. D. (2023). An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges. Applied Sciences, 13(3), 1956.
[6] Lin, Y. C., Chen, C. T., Sang, C. Y., & Huang, S. H. (2022). Multiagent-based deep reinforcement learning for risk-shifting portfolio management. Applied Soft Computing, 123, 108894.
[7] Wang, R., Wei, H., An, B., Feng, Z., & Yao, J. (2020). Deep stock trading: A hierarchical reinforcement learning framework for portfolio optimization and order execution. arXiv preprint arXiv:2012.12620.
[8] Han, L., Ding, N., Wang, G., Cheng, D., & Liang, Y. (2023, August). Efficient Continuous Space Policy Optimization for High-frequency Trading. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4112-4122).
[9] Salehpour, A., & Samadzamini, K. (2023). Machine learning applications in algorithmic trading: a comprehensive systematic review. International Journal of Education and Management Engineering, 13(6), 41.
[10] Sun, S., Xue, W., Wang, R., He, X., Zhu, J., Li, J., & An, B. (2022, October). DeepScalper: A risk-aware reinforcement learning framework to capture fleeting intraday trading opportunities. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1858-1867).
[11] Lei, K., Zhang, B., Li, Y., Yang, M., & Shen, Y. (2020). Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Systems with Applications, 140, 112872.
[12] Millea, A., & Edalat, A. (2022). Using deep reinforcement learning with hierarchical risk parity for portfolio optimization. International Journal of Financial Studies, 11(1), 10.
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