The development of multi-asset quantitative strategies is crucial in the investment industry and requires the analysis of vast amounts of data to gain insight about performance. This project will investigate the use of statistical predictive models and performance measures for risk evaluation in financial investment strategies, aiming to provide increased levels of predictive robustness in modelling under different scenarios. Methodology related to back-testing and statistical machine learning will be used, and model uncertainty will be accounted for under a Bayesian approach.