Dark-Pool Smart Order Routing: a Combinatorial Multi-armed Bandit Approach

Abstract

We study the problem of developing a Smart Order Routing algorithm that learns how to optimize the dollar volume, i.e., the total value of the traded shares, gained from slicing an order across multiple dark pools. Our work is motivated by two distinct issues: (i) the surge in liquidity fragmentation caused by the rising popularity of electronic trading and by the increasing number of trading venues, and (ii) the growth in popularity of dark pools, an exchange venue characterised by a lack of transparency. This paper critically discusses the known dark pool literature and proposes a novel algorithm, namely the DP-CMAB algorithm, that extends existing solutions by allowing the agent to specify the desired limit price when placing orders. Specifically, we frame the problem of dollar volume optimization in a multi-venue setting as a Combinatorial Multi-Armed Bandit (CMAB) problem, representing a generalization of the well-studied MAB framework. Drawing from the rich MAB and CMAB literature, we present multiple strategies that our algorithm may adopt to select the best allocation options. Furthermore, we analyze how exploiting financial domain knowledge improves the agents' performance. Finally, we evaluate the DP-CMAB performance in an environment built from real market data and show that our algorithm outperforms state-of-the-art solutions.