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Predictive modeling in high-frequency trading using machine learning
- 1 David R. Cheriton School of Computer Science, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
* Author to whom correspondence should be addressed.
Abstract
High-frequency trading (HFT) has transformed financial markets by enabling rapid trade execution and exploiting minute market inefficiencies. This study explores the application of machine learning (ML) techniques to predictive modeling in HFT. Four ensemble boosting methods—Adaptive Boosting, Logic Boosting, Robust Boosting, and Random Under-Sampling (RUS) Boosting—were evaluated using order book data from Euronext Paris. The models were trained and validated on data from a single trading day, with performance assessed using precision, recall, ROC curves, and feature importance analysis. Results indicate that Robust Boosting achieves the highest precision (90%), while Adaptive Boosting and RUS Boosting demonstrate higher recall (94% and 93%, respectively). This research highlights the potential of ML in enhancing HFT strategies, with implications for future trading system developments.
Keywords
High-frequency trading, Predictive modeling, Ensemble boosting, Order book data
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Cite this article
Hou,Y. (2024). Predictive modeling in high-frequency trading using machine learning. Applied and Computational Engineering,90,61-65.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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