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Web of Proceedings - Francis Academic Press
Web of Proceedings - Francis Academic Press

Optimization and Practice of Machine Learning Algorithm in Quantitative Trading Strategy

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DOI: 10.25236/iceesr.2024.003


Jiangdan Liu, Qiang Lu, Zhibin Wang, Hongyu Li

Corresponding Author

Hongyu Li


Firstly, this paper introduces the key technologies such as algorithm selection and comparison, parameter optimization method and feature engineering and model fusion, which provides a theoretical basis for the subsequent quantitative trading strategy construction. Then, the paper expounds in detail the steps of data processing and analysis, strategy design and implementation, backtesting and performance evaluation in practical application, and shows the application process of ML (Machine Learning) algorithm in actual transactions. In the case analysis, the article selects a specific case of quantitative trading strategy, and deeply analyzes its background, implementation process and results. By comparing the back test with the actual trading results, the actual effect of ML algorithm in optimizing quantitative trading strategy is verified. In addition, this paper also extracts valuable experience and enlightenment from the case, which provides reference and guidance for the future quantitative trading practice. To sum up, this paper systematically studies the application and effect of ML algorithm in quantitative trading strategy optimization by combining theoretical elaboration, practical application and case analysis, which provides useful reference and enlightenment for research and practice in related fields.


Finance; Quantitative trading strategy; Artificial intelligence; Machine Learning