Tick Data Quality Control: Detecting and Correcting Inconsistencies in High-Frequency Trading

Abstract

The research undertakes a study of tick data quality control mechanisms that operate in high-frequency trading frameworks to perform crucial inconsistency detection and correction tasks for algorithmic strategy success. A theoretical foundation of quality assessment accompanies the presentation of statistical and machine learning detection methods and it provides correction strategies through filtering and interpolation techniques, and discusses optimized implementations for handling massive data streams. Overall, the paper discusses pipeline design alongside parallel processing optimization and database enhancements and monitoring systems needed to maintain data consistency across distributed market systems. The article establishes a procedure to maintain reliable tick data through hardware acceleration studies and adaptive thresholds and complete audit protocols for trading operations and risk management and regulatory compliance purposes.