论文标题
帕累托驱动的替代（Parden-SUR）的辅助优化多周期投资组合回测模拟
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations
论文作者
论文摘要
投资组合管理是一个多周期的多目标优化问题，但要受广泛约束。但是，实际上，投资组合管理被视为一个单周期问题，部分原因是构建多周期帕累托前沿所需的计算繁重的超参数搜索程序。这项研究介绍了\ gls {parden-sur}建模框架，以有效执行所需的超参数搜索。 \ gls {parden-sur}通过在\ glspl {ea}和传统的接受抽样方案以及传统的接受抽样方案以及传统的接受抽样方案以及传统的接受采样方案以及传统的接受采样方案中，包括一个基于储层抽样的机制来扩展以前的替代框架。我们在两个数据集上针对单个和多期用例的两个数据集上的几个开创性\ gls {mo} \ glspl {ea}评估了此框架。我们的结果表明，\ gls {parden-sur}可以加快探索最佳的超参数$ 2 \ times $，并且在多个数据集和用例中，帕累托边界的统计学显着改善，帕累托边界的统计学显着改善。
Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the \gls{ParDen-Sur} modelling framework to efficiently perform the required hyper-parameter search. \gls{ParDen-Sur} extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in \glspl{EA} alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal \gls{MO} \glspl{EA} on two datasets for both the single- and multi-period use cases. Our results show that \gls{ParDen-Sur} can speed up the exploration for optimal hyper-parameters by almost $2\times$ with a statistically significant improvement of the Pareto frontiers, across multiple \glspl{EA}, for both datasets and use cases.
