QuantCloud:一个带有自动并行Python的定量金融应用软件

2018 IEEE International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2018-07-01 DOI:10.1109/QRS.2018.00052

P. Zhang, Yu-Xiang Gao, Xiang Shi

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引用次数: 2

摘要

定量金融是一个依靠数据分析和大数据使软件发现市场信号的领域。其中，一个决定性的因素是速度，它关系到执行速度和软件开发速度。因此，一个高效的软件在帮助交易公司方面起着关键作用。受此启发，我们提出了一种新颖的软件:QuantCloud，将并行Python系统与c++编码的大数据系统集成在一起。使用c++实现这个大数据系统，使用Python编写用户方法。Python代码的自动并行执行是建立在基于协进程的并行策略之上的。我们使用两种流行的算法:移动窗口和自回归移动平均(ARMA)来测试我们的软件。我们对英特尔至强E5和至强Phi处理器进行了广泛的比较研究。结果表明，我们的方法在并行执行Python代码方面实现了近乎线性的加速，非常适合当今的多核处理器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

2018 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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