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

EasyChair Preprint 4447

Abstract

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 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.

Keyphrases: High Performance Computing, parameter optimization, periodic data, portfolio backtesting, portfolio optimization

@booklet{EasyChair:4447,
  author    = {Yidong Chen and Zhonghua Lu and Xueying Yang},
  title     = {The Design and Implementation of a High-performance Portfolio Optimization Platform},
  howpublished = {EasyChair Preprint 4447},
  year      = {EasyChair, 2020}}