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

Understand

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.