QuantCloud: A Software with Automated Parallel Python']Python for Quantitative Finance Applications

被引：1
作者：
Zhang, Peng [1 ]
Gao, Yuxiang [2 ]
Shi, Xiang [3 ]
机构：
[1] SUNY Stony Brook, Dept Appl Math, Stony Brook, NY 11794 USA
[2] Midea Emerging Technol Ctr, San Jose, CA 95134 USA
[3] Adv Risk & Portfolio Management ARPM, New York, NY 10023 USA
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2018) | 2018年
关键词：
Quantitative Finance Software; Parallel [!text type='Python']Python[!/text; Big Data; Cloud computing;
D O I：
10.1109/QRS.2018.00052
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Quantitative Finance is a field that replies on data analysis and big data enabling software to discover market signals. In this, a decisive factor is the speed that concerns execution speed and software development speed. So, an efficient software plays a key role in helping trading firms. Inspired by this, we present a novel software: QuantCloud to integrate a parallel Python system with a C++-coded Big Data system. C++ is used to implement this big data system and Python is used to code the user methods. The automated parallel execution of Python codes is built upon a coprocess-based parallel strategy. We test our software using two popular algorithms: moving-window and autoregressive moving-average (ARMA). We conduct an extensive comparative study between Intel Xeon E5 and Xeon Phi processors. The results show that our method achieved a nearly linear speedup for executing Python codes in parallel, prefect for today's multicore processors.
引用
收藏
页码：388 / 396
页数：9
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