ABIDES: Towards High-Fidelity Market Simulation for AI Research

Abstract

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.

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• Explaining Agent-Based Financial Market Simulation
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market
• Reinforcement Learning for Quantitative Trading
• On the Importance of Opponent Modeling in Auction Markets
• Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets
• Similarity metrics for Different Market Scenarios in Abides
• Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets