Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets

TL;DR

This paper introduces methods for classifying and imitating trading agents in continuous double auction markets using opponent modeling and behavioral cloning, with evaluations demonstrating their effectiveness in simulated environments.

Contribution

It presents a novel agent-based modeling approach that combines opponent classification and behavioral cloning for trading agents in limit order book markets.

Findings

Opponent modeling effectively classifies trading agent archetypes.

Behavioral cloning successfully imitates agent behaviors in simulations.

Techniques show promise for application in real-world trading scenarios.

Abstract

Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments. In this work, we develop an agent-based model for trading in a limit order book and show (1) how opponent modelling techniques can be applied to classify trading agent archetypes and (2) how behavioural cloning can be used to imitate these agents in a simulated setting. We experimentally compare a number of techniques for both tasks and evaluate their applicability and use in real-world scenarios.