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

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 glsParDen-Sur modelling framework to efficiently perform the required hyper-parameter search. glsParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in glsplEA alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal glsMO glsplEA on two datasets for both the single- and multi-period use cases. Our results show that glsParDen-Sur can speed up the exploration for optimal hyper-parameters by almost 2× with a statistically significant improvement of the Pareto frontiers, across multiple glsplEA, for both datasets and use cases.