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

Abstract

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.