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Portfolio management is a multi-period multi-objective optimisation problemsubject to a wide range of constraints. However, in practice, portfoliomanagement is treated as a single-period problem partly due to thecomputationally burdensome hyper-parameter search procedure needed to constructa 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 reservoirsampling-based look-ahead mechanism for offspring generation in \glspl{EA}alongside the traditional acceptance sampling scheme. We evaluate thisframework against, and in conjunction with, several seminal \gls{MO} \glspl{EA}on two datasets for both the single- and multi-period use cases. Our resultsshow that \gls{ParDen-Sur} can speed up the exploration for optimalhyper-parameters by almost $2\times$ with a statistically significantimprovement of the Pareto frontiers, across multiple \glspl{EA}, for bothdatasets and use cases.

Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations