  This paper investigates the potential of Bayesian optimization (BO) to
optimize the atr multiplier and atr period -the parameters of the Supertrend
indicator for maximizing trading profits across diverse stock datasets. By
employing BO, the thesis aims to automate the identification of optimal
parameter settings, leading to a more data-driven and potentially more
profitable trading strategy compared to relying on manually chosen parameters.
The effectiveness of the BO-optimized Supertrend strategy will be evaluated
through backtesting on a variety of stock datasets.
