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Regular Paper

HXPY: A High-Performance Data Processing Package for Financial Time-Series Data

The Hong Kong University of Science and Technology, Hong Kong, China
International Digital Economy Academy, Shenzhen 518048, China
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China
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A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to explore its potential value. In recent years, we have witnessed the successful adoption of machine learning models on financial data, where the importance of accuracy and timeliness demands highly effective computing frameworks. However, traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues, such as the outlier handling with stock suspension in Pandas and TA-Lib. In this paper, we propose HXPY, a high-performance data processing package with a C++/Python interface for financial time-series data. HXPY supports miscellaneous acceleration techniques such as the streaming algorithm, the vectorization instruction set, and memory optimization, together with various functions such as time window functions, group operations, down-sampling operations, cross-section operations, row-wise or column-wise operations, shape transformations, and alignment functions. The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts. From MiBs to GiBs data, HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.

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Journal of Computer Science and Technology
Pages 3-24
Cite this article:
Guo J, Peng J, Yuan H, et al. HXPY: A High-Performance Data Processing Package for Financial Time-Series Data. Journal of Computer Science and Technology, 2023, 38(1): 3-24.






Web of Science






Received: 30 September 2022
Revised: 29 October 2022
Accepted: 10 January 2023
Published: 28 February 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023