The Design and Implementation of a High-performance Portfolio Optimization Platform

被引：3
作者：
Chen, Yidong [1 ]
Lu, Zhonghua [2 ]
Yang, Xueying [1 ]
机构：
[1] Univ Chinese Acad Sci, Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
[2] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
来源：
2020 IEEE 23RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
high-performance computing; portfolio optimization; portfolio backtesting; periodic data; parameter optimization;
D O I：
10.1109/CSE50738.2020.00008
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallel computing framework and event driven architecture. The platform consists of the data layer, the model layer, and the excursion layer, which is built in a component, pluggable, and loosely coupled way. The platform adopts parallelization acceleration for backtesting and optimizing parameters of portfolio models in a certain historical interval. The platform is able to docking portfolio model with real-time market. Based on the HPPO platform, a parallel program is designed to optimize the parameters of the value at risk(VAR) model. The performance of the platform are summarized by analyzing the experimental results and comparing with the open source framework Zipline and Rqalpha.
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页码：1 / 7
页数：7
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