Leveraging Deep Learning Techniques in High-Frequency Trading: Computational Opportunities and Mathematical Challenges
DOI:
https://doi.org/10.5281/zenodo.12747424ARK:
https://n2t.net/ark:/40704/AJSM.v2n4a05Keywords:
High-frequency Trading, Deep Learning, Predictive Accuracy, Real-time Data Processing, Risk Management, Automated Strategy Development, LSTM Networks, Reinforcement LearningAbstract
High-frequency trading (HFT) has transformed financial markets by leveraging speed and automation to execute large volumes of transactions within microseconds. The integration of deep learning (DL) algorithms into HFT systems presents new opportunities for enhancing prediction accuracy and trading strategies, uncovering complex patterns in large datasets. This paper explores the synergy between DL and HFT, highlighting the potential benefits and inherent challenges. We conduct a comprehensive review of current literature and case studies to provide a detailed understanding of how DL can revolutionize HFT and the obstacles that must be addressed to fully realize its potential.
Downloads
Metrics
References
Aldridge, I. (2013). High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Wiley.
Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2018). Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1), 126-136.
Dixon, M. F., Klabjan, D., & Bang, J. (2020). Machine Learning in Finance: From Theory to Practice. Springer.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Feng, X., Heaton, J. B., & Polson, N. G. (2018). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 34(1), 1-13.
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
Gomber, P., Arndt, B., Lutat, M., & Uhle, T. (2018). High-Frequency Trading. In Handbook on Securities Trading and Derivatives (pp. 61-96). Edward Elgar Publishing.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521 (7553), 436-444.
Liang, J., Zhao, J., Xu, X., Yu, H., & Jin, Y. (2018). Adversarial deep reinforcement learning in portfolio management. arXiv preprint arXiv:1808.09940.
Sahoo, S. R., Chakraborty, S., & Mahapatra, A. (2018). Online incremental machine learning algorithm for time series prediction. Expert Systems with Applications, 102, 29-43.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929-1958.
Zhang, J., Aggarwal, C., & Qi, G. (2017). Stock price prediction through discovering multi-frequency trading patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2141-2149).
Zhao, J., Wang, W., & Sycara, K. (2019). Variational autoencoders for anomaly detection in HFT. IEEE Transactions on Computational Social Systems, 6(3), 543-553.
Liu, T., Cai, Q., Xu, C., Zhou, Z., Ni, F., Qiao, Y., & Yang, T. (2024). Rumor Detection with a novel graph neural network approach. arXiv Preprint arXiv:2403. 16206.
Liu, T., Cai, Q., Xu, C., Zhou, Z., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in news report scenario. arXiv Preprint arXiv:2403. 16209.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024a). Accelerating Semi-Asynchronous Federated Learning. arXiv Preprint arXiv:2402. 10991.
Zhou, J., Liang, Z., Fang, Y., & Zhou, Z. (2024). Exploring Public Response to ChatGPT with Sentiment Analysis and Knowledge Mapping. IEEE Access.
Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13–20.
Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5–12.
Zhou, Z. (2024). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024b). Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach. Computer Life, 12(1), 1–4.
Wang, L., Xiao, W., & Ye, S. (2019). Dynamic Multi-label Learning with Multiple New Labels. Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23--25, 2019, Proceedings, Part III 10, 421–431. Springer.
Wang, L., Fang, W., & Du, Y. (2024). Load Balancing Strategies in Heterogeneous Environments. Journal of Computer Technology and Applied Mathematics, 1(2), 10–18.
Wang, L. (2024). Low-Latency, High-Throughput Load Balancing Algorithms. Journal of Computer Technology and Applied Mathematics, 1(2), 1–9.
Downloads
Published
How to Cite
Issue
Section
ARK
License
Copyright (c) 2024 The author retains copyright and grants the journal the right of first publication.
This work is licensed under a Creative Commons Attribution 4.0 International License.