Bandit Market Makers

TL;DR

This paper presents a modular market making framework that integrates cost-function based automated market makers with bandit algorithms, providing distribution-free profit guarantees and practical simulation insights.

Contribution

It introduces a novel combination of market making and bandit algorithms, offering worst-case profit guarantees and computational efficiency.

Findings

Distribution-free regret guarantees for profits.

Bounded worst-case losses maintained.

Simulation results demonstrate practical effectiveness.

Abstract

We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guarantees on the regret of profits while preserving the bounded worst-case losses and computational tractability over combinatorial spaces of the cost function based approach. We present simulation results to better understand the practical behaviour of market makers from the framework.