Prospects of Imitating Trading Agents in the Stock Market

Mateusz Wilinski, Juho Kanniainen

Keywords: generative models, limit order book, agent-based modeling, financial computing

TL;DR: We try to train generative models to imitate trading agents.

Abstract: In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propose a modified generative architecture based on the state-space model, and apply it to limit order book data with identified investors. The model is trained on synthetic data, generated from a heterogeneous agent-based model. Finally, we compare model's predicted distribution over different aspects of investors' actions, with the ground truths known from the agent-based model.

Submission Number: 95