  We approach the problem of designing an automated trading strategy that can
consistently profit by adapting to changing market conditions. This challenge
can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To
solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses
Bayesian optimization and a ``bandit-over-bandit'' framework to dynamically
adjust strategy parameters in response to market conditions. We use Bristol
Stock Exchange (BSE) to simulate financial markets containing heterogeneous
populations of automated trading agents and compare PRBO with PRSH, a reference
trading strategy that adapts strategy parameters through stochastic
hill-climbing. Results show that PRBO generates significantly more profit than
PRSH, despite having fewer hyperparameters to tune. The code for PRBO and
performing experiments is available online open-source
(https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation).
