ABIDES: Towards High-Fidelity Market Simulation for AI Research
David Byrd, Maria Hybinette, Tucker Hybinette Balch
We introduce ABIDES, an Agent-Based Interactive Discrete Event Simulation
environment. ABIDES is designed from the ground up to support AI agent research
in market applications. While simulations are certainly available within
trading firms for their own internal use, there are no broadly available
high-fidelity market simulation environments. We hope that the availability of
such a platform will facilitate AI research in this important area. ABIDES
currently enables the simulation of tens of thousands of trading agents
interacting with an exchange agent to facilitate transactions. It supports
configurable pairwise network latencies between each individual agent as well
as the exchange. Our simulator's message-based design is modeled after NASDAQ's
published equity trading protocols ITCH and OUCH. We introduce the design of
the simulator and illustrate its use and configuration with sample code,
validating the environment with example trading scenarios. The utility of
ABIDES is illustrated through experiments to develop a market impact model. We
close with discussion of future experimental problems it can be used to
explore, such as the development of ML-based trading algorithms.