Microstructure-Aware Risk Detection in High-Frequency Trading Using Embedding and Sequence Modeling

Abstract

This paper investigates the problem of microstructural risk detection in high-frequency trading environments and proposes a detection framework that integrates feature modeling with temporal dependency. The study first analyzes the structural characteristics of high-frequency trading data. It identifies the complex patterns formed by the interactions of price, trading volume, and order flow at the micro level as the key foundation for risk recognition. An algorithmic mechanism based on embedding mapping and sequence modeling is then designed. Through the joint modeling of feature representation and dynamic dependency, the method captures potential risk signals in trading data. During risk determination, an anomaly scoring function and threshold mechanism are introduced. These enable effective distinction between normal fluctuations and abnormal behaviors, thereby improving the stability and accuracy of detection. To validate the effectiveness of the method, systematic evaluations are conducted through comparative experiments and sensitivity experiments. The comparative experiments show that the proposed method outperforms existing baseline models in AUC, ACC, F1-Score, and Precision, and provides a more comprehensive representation of microstructural risk features. The sensitivity experiments examine hyperparameters, data completeness, and noise levels. They reveal the significant impact of sequence length, embedding dimension, missing rate, and noise intensity on model performance. The results indicate that reasonable parameter settings and data quality assurance play an essential role in maintaining detection performance. Overall, through methodological construction and experimental validation, this paper demonstrates that microstructural risk detection methods based on high-frequency trading data exhibit strong adaptability and effectiveness. The study provides systematic research findings that support financial risk identification in complex trading environments.