  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.
