Journal of Computer Science and Technology ›› 2023, Vol. 38 ›› Issue (1): 3-24.doi: 10.1007/s11390-023-2879-5

Special Issue: Data Management and Data Mining; Computer Networks and Distributed Computing

• Special Issue in Honor of Professor Kai Hwang’s 80th Birthday • Previous Articles     Next Articles

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

Jia-dong Guo1,2 (郭家栋), Jing-shu Peng1 (彭靖姝), Hang Yuan2,3 (苑 航), and Lionel Ming-shuan Ni3,1,* (倪明选), Life FellowIEEE        

  1. 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
  • Received:2022-09-30 Revised:2022-10-29 Accepted:2023-01-10 Online:2023-02-28 Published:2023-02-28
  • Contact: Lionel Ming-Shuan Ni E-mail:ni@ust.hk
  • About author:Lionel Ming-shuan Ni is the chair professor in the Data Science and Analytics Thrust at the Hong Kong University of Science and Technology (Guangzhou) and chair professor of computer science and engineering at the Hong Kong University of Science and Technology, Hong Kong, since 2019. He is a life fellow of IEEE. Dr. Ni has chaired over 30 professional conferences and has received eight awards for authoring outstanding papers.

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.

Key words: dataframe; time-series data; SIMD (Single Instruction Multiple Data); CUDA (Compute Unified Device Architecture);

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