HXPY: A high-performance data processing package for Financial Time-Series Data

MPhil Thesis Defence


Title: "HXPY: A high-performance data processing package for Financial 
Time-Series Data"

By

Mr. Jiadong GUO


Abstract

Tremendous data is created by global financial exchanges day by day, and 
such time-series data needs to be analyzed in real-time for maximum value. 
Besides, with the continuous progress of machine learning technology in 
recent years, more and more machine learning models are being applied to 
financial data. Such scenarios require new computing frameworks, while 
traditional frameworks such as pandas and TA-Lib have shown performance 
and adaptation problems for financial data.

In this paper, we proposed HXPY, a high-performance data processing 
package with a c++/python interface for time-series data. Miscellaneous 
acceleration techniques such as streaming algorithm, SIMD instruction set, 
and memory optimization were used, and various functions for time series 
data such as time window function, group operation, down-sampling 
operation, cross-section operation, row-wise or column-wise operation, 
shape transformation, and alignment were also implemented.

Although HXPY is still at a relatively preliminary stage, the results of 
benchmark and incremental analysis have shown that the performance of HXPY 
is better when compared with its counterparts. From MiBs to GiBs data, our 
performance significantly outperforms other in-memory computing rivals.


Date:  			Friday, 24 June 2022

Time:			3:00pm - 5:00pm

Zoom Meeting:
https://hkust.zoom.us/j/99536928098?pwd=T1ZOc1dvZGtYOEtsSGFINlRtanhTZz09

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Prof. Harry Shum (Supervisor)
 			Prof. Qiong Luo (Chairperson)
 			Prof. James Kwok


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