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

Portfolio management is a multi-period multi-objective optimization 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 Pareto Driven Surrogate (ParDen-Sur) modelling framework to efficiently perform the required hyper-parameter search. ParDen-Sur extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in Evolutionary Algorithms (EAs) alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal Multi-Objective (MO) EAs on two datasets for both the single- and multi-period use cases. Our results show that ParDen-Sur can speed up the exploration for optimal hyper-parameters by almost 2x with a statistically significant improvement of the Pareto frontiers, across multiple EAs, for both datasets and use cases.