Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects

TL;DR

This paper presents a versatile, high-performance agent-based simulation framework with a user-friendly Python interface, designed for financial market research and adaptable to various multi-agent system applications.

Contribution

It introduces a novel software toolbox combining Python ease of use with C++ efficiency, supporting customizable simulations of financial markets and other multi-agent systems.

Findings

Demonstrated the toolbox's ability to simulate market delays and dynamics.

Showcased its application in non-financial multi-agent coordination scenarios.

Abstract

We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change. Owing to its general architecture, our toolbox can also be employed as a generic multi-agent system simulator. We provide an example of such a non-financial application by simulating a mechanism for the coordination of no-regret learning agents in a multi-agent network routing scenario previously proposed in the literature.