Learning to simulate realistic limit order book markets from data as a World Agent

TL;DR

This paper introduces a data-driven market simulator that learns a comprehensive 'world' agent from historical limit order book data, eliminating the need for detailed trader calibration and improving realism over traditional models.

Contribution

It presents a novel approach using CGAN and mixture models to learn a market behavior model directly from data, bypassing the need for individual trader strategies.

Findings

Outperforms previous models in realism and responsiveness

Uses CGAN and mixture models for market simulation

Eliminates need for trader calibration

Abstract

Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead to misleading conclusions, potentially causing severe loss when employed by investment banks, hedge funds, and traders to study and evaluate trading strategies. In this paper, we propose a world model simulator that accurately emulates a limit order book market -- it requires no agent calibration but rather learns the simulated market behavior directly from historical data. Traditional approaches fail short to learn and calibrate trader population, as historical labeled data with details on each individual trader strategy is not publicly available. Our approach proposes to learn a unique "world" agent from historical data. It is intended to emulate the overall trader population, without the need of making assumptions about individual market agent strategies. We implement our world agent simulator models as a Conditional Generative Adversarial Network (CGAN), as well as a mixture of parametric distributions, and we compare our models against previous work. Qualitatively and quantitatively, we show that the proposed approaches consistently outperform previous work, providing more realism and responsiveness.