  Financial markets are complex environments that produce enormous amounts of
noisy and non-stationary data. One fundamental problem is online portfolio
selection, the goal of which is to exploit this data to sequentially select
portfolios of assets to achieve positive investment outcomes while managing
risks. Various algorithms have been proposed for solving this problem in fields
such as finance, statistics and machine learning, among others. Most of the
methods have parameters that are estimated from backtests for good performance.
Since these algorithms operate on non-stationary data that reflects the
complexity of financial markets, we posit that adaptively tuning these
parameters in an intelligent manner is a remedy for dealing with this
complexity. In this paper, we model the mapping between the parameter space and
the space of performance metrics using a Gaussian process prior. We then
propose an oracle based on adaptive Bayesian optimization for automatically and
adaptively configuring online portfolio selection methods. We test the efficacy
of our solution on algorithms operating on equity and index data from various
markets.
