This paper is an in-depth study of the data-based capital market trading strategy optimization and risk management methods. With the help of data acquisition, preprocessing, feature extraction and model construction, supplemented by optimization algorithms to enhance the performance of trading strategies. In this paper, the retracement rate, Sharpe ratio and other evaluation criteria of trading strategies are analyzed in detail, and a data-driven risk management framework is constructed, which involves the understanding of market risk, liquidity risk and derivative hedging strategies. The study found that data-based trading strategies significantly improved decision making in capital markets and enhanced risk management capabilities.

Yilin Fu. Data-driven Optimization of Capital Market Trading Strategies and Risk Management. International Journal of Business Management and Economics and Trade (2025), Vol. 6, Issue 1: 221-228. https://doi.org/10.38007/IJBMET.2025.060123.

[1] Liu P, Zhang Y, Bao F, et al. Multi-type data fusion framework based on deep reinforcement learning for algorithmic trading. Applied Intelligence, 2023, 53(2):1683-1706.

[2] ZHOU Rongtian, XIONG Xiong, ZHANG Xiaoxuan. Research on the Short-Selling Rate in China's Capital Market——Based on the Perspective of the Artificial Stock Market. Journal of Systems Science and Mathematical Sciences, 2022, 42(8):2019-2039.

[3] Myers M. Leveraging Your Insurance Carrier's Resources for Risk Management. Engineering news-record, 2023(1):291.

[4] Pomaza-Ponomarenko A, Kryvova S, Hordieiev A, et al. Innovative risk management: identification, assessment and management of risks in the context of innovative project management. GeSec: Revista de Gestao e Secretariado, 2023, 14(10).

[5] Guo Q, Gao L, Chu X, et al. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm. CSEE Journal of Power and Energy Systems, 2022, 8(2):535-547.

[6] Wu Y. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. 2025.

[7] Zhang, X. (2025). Optimization of Financial Fraud Risk Identification System Based on Machine Learning. Journal of Computer, Signal, and System Research, 2(6), 82-89.

[8] Wang, Y. (2025). Exploration and Clinical Practice of the Optimization Path of Sports Rehabilitation Technology. Journal of Medicine and Life Sciences, 1(3), 88-94.

[9] Li W. The Influence of Financial Due Diligence in M&A on Investment Decision Based on Financial Data Analysis. European Journal of AI, Computing & Informatics, 2025, 1(3): 32-38.

[10] Sheng, C. (2025). Innovative Application and Effect Evaluation of Big Data in Cross-Border Tax Compliance Management. Journal of Computer, Signal, and System Research, 2(6), 40-48.

[11] Sheng, C. (2025). Research on the Application of AI in Enterprise Financial Risk Management and Its Optimization Strategy. Economics and Management Innovation, 2(6), 18-24.

[12] Tu, X. (2025). Optimization Strategy for Personalized Recommendation System Based on Data Analysis. Journal of Computer, Signal, and System Research, 2(6), 32-39.

[13] Sun, Q. (2025). Research on Cross-language Intelligent Interaction Integrating NLP and Generative Models. Engineering Advances, 5(4).

[14] Liu, Y. (2025). Use SQL and Python to Advance the Effect Analysis of Financial Data Automation. Financial Economics Insights, 2(1), 110-117.

[15] Ye, J. (2025). Optimization of Neural Motor Control Model Based on EMG Signals. International Journal of Engineering Advances, 2(4), 1-8.

[16] Sun J. Quantile Regression Study on the Impact of Investor Sentiment on Financial Credit from the Perspective of Behavioral Finance. 2025.

[17] Wang Y. Application of Data Completion and Full Lifecycle Cost Optimization Integrating Artificial Intelligence in Supply Chain. 2025.