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Financial markets are complex environments that produce enormous amounts ofnoisy and non-stationary data. One fundamental problem is online portfolioselection, the goal of which is to exploit this data to sequentially selectportfolios of assets to achieve positive investment outcomes while managingrisks. Various algorithms have been proposed for solving this problem in fieldssuch as finance, statistics and machine learning, among others. Most of themethods have parameters that are estimated from backtests for good performance.Since these algorithms operate on non-stationary data that reflects thecomplexity of financial markets, we posit that adaptively tuning theseparameters in an intelligent manner is a remedy for dealing with thiscomplexity. In this paper, we model the mapping between the parameter space andthe space of performance metrics using a Gaussian process prior. We thenpropose an oracle based on adaptive Bayesian optimization for automatically andadaptively configuring online portfolio selection methods. We test the efficacyof our solution on algorithms operating on equity and index data from variousmarkets.

Adaptive Configuration Oracle for Online Portfolio Selection Methods