Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as "over-the-counter" (OTC) trading, and commonly occurring between "market makers". The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications.

翻译：通过基于多智能体的仿真方法探索复杂自适应金融交易环境，为量化金融领域提供了一种创新研究范式。尽管基于多智能体强化学习的方法在数据可观测的金融市场中占据主导地位，但部分系统性重要的金融市场因数据部分缺失或模糊而带来研究挑战。为此，我们设计了一种采用小规模元启发式方法的多智能体仿真框架。该方法旨在模拟澳大利亚政府债券交易中不透明的双边市场特征，刻画银行间交易（即通常发生于"做市商"之间的"场外交易"）的双边属性。由协商交易机制与有限智能体数量构成的双边市场独特性，为基于智能体的建模与量化金融研究提供了重要洞见。该市场结构固有的刚性特征——与全球范围内多边交易平台的普及及金融去中心化趋势形成鲜明对比——凸显了本智能体模型所揭示的独特见解。我们探究了市场刚性对市场结构的影响，并在市场设计中纳入稳定性考量。这拓展了关于复杂金融交易环境的持续学术讨论，深化了对此类市场动态机制及其潜在影响的认知。