Strategy Selection Using Multi-Armed Bandit Algorithms in Financial Markets

Abstract

This paper aims to evaluate the effectiveness of Multi-Armed Bandit (MAB) algorithms in choosing the optimal trading strategy among the sub optimal ones within financial markets. The research aims to addresses the challenge of adapting to dynamic market conditions. By introducing a Composite Trading Strategy that integrates trend-following, mean reversion, and momentum strategies, the study investigates whether increased trading frequency enhances the performance of profitability of various MAB algorithms, including UCB, Thompson Sampling, and epsilon-greedy. The experimental results indicate that while the introduction of complex strategies greatly improves returns in favorable market conditions, MAB algorithms still face limitations in adverse market environments. The findings highlight the potential of MAB algorithms in financial strategy selection and suggest directions for future research to enhance their adaptability in adverse markets.