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

ABSTRACT: 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, th

SUBMITTER: Guo J 

PROVIDER: S-EPMC10064599 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature