Optimization of Automated Trading Systems with Deep Learning Strategies

Authors

  • Can Zhang Fudan University
  • Zhanxin Zhou Northern Arizona University
  • Ruibo Wu University of California

DOI:

https://doi.org/10.5281/zenodo.12780180

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n4a02

Keywords:

Automated Trading Systems, Deep Learning, LSTM Networks, Reinforcement Learning, Convolutional Neural Networks, Trading Performance, Financial Markets, Machine Learning, Trading Strategies, Data Preprocessing, Neural Networks, Algorithmic Trading, Model Optimization, Prediction Accuracy, Risk Management

Abstract

Automated trading systems have revolutionized the financial markets by executing trades at speeds and frequencies far beyond human capabilities. The integration of deep learning strategies into these systems promises to enhance their performance by better predicting market movements and making more informed trading decisions. This paper explores various deep learning techniques applied to automated trading systems, examining their effectiveness, implementation challenges, and potential benefits. Specifically, we investigate the use of Convolutional Neural Networks (CNNs) for pattern recognition in price charts, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for time-series prediction, and Deep Reinforcement Learning (DRL) for strategy optimization. We present a comprehensive analysis of these methods, highlighting their strengths and weaknesses in different market conditions. Our experiments demonstrate significant improvements in trading performance, including higher profitability and reduced risk, thus underscoring the transformative potential of deep learning in automated trading.

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Author Biographies

Can Zhang, Fudan University

Fudan University, China.

Zhanxin Zhou, Northern Arizona University

Affiliation: Northern Arizona University.

Ruibo Wu, University of California

Master of quantitative finance, University of California, San Diego.

References

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Cao, L. J., & Tay, F. E. H. (2001). Financial forecasting using support vector machines. Neural Computing & Applications, 10(2), 184-192.

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.

Wang, L. (2024). Network Load Balancing Strategies and Their Implications for Business Continuity. Academic Journal of Sociology and Management, 2(4), 8–13.

Li, W. (2024). The Impact of Apple’s Digital Design on Its Success: An Analysis of Interaction and Interface Design. Academic Journal of Sociology and Management, 2(4), 14–19.

Wu, R., Zhang, T., & Xu, F. (2024). Cross-Market Arbitrage Strategies Based on Deep Learning. Academic Journal of Sociology and Management, 2(4), 20–26.

Wu, R. (2024). Leveraging Deep Learning Techniques in High-Frequency Trading: Computational Opportunities and Mathematical Challenges. Academic Journal of Sociology and Management, 2(4), 27–34.

Wang, L. (2024). The Impact of Network Load Balancing on Organizational Efficiency and Managerial Decision-Making in Digital Enterprises. Academic Journal of Sociology and Management, 2(4), 41–48.

Chen, Q., & Wang, L. (2024). Social Response and Management of Cybersecurity Incidents. Academic Journal of Sociology and Management, 2(4), 49–56.

Song, C. (2024). Optimizing Management Strategies for Enhanced Performance and Energy Efficiency in Modern Computing Systems. Academic Journal of Sociology and Management, 2(4), 57–64.

Chan, E. (2013). Algorithmic Trading: Winning Strategies and Their Rationale. Wiley.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.

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Published

2024-08-01

How to Cite

[1]
C. Zhang, Z. Zhou, and R. Wu, “Optimization of Automated Trading Systems with Deep Learning Strategies”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 4, pp. 8–14, Aug. 2024.

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