  The dynamics of financial markets are driven by the interactions between
participants, as well as the trading mechanisms and regulatory frameworks that
govern these interactions. Decision-makers would rather not ignore the impact
of other participants on these dynamics and should employ tools and models that
take this into account. To this end, we demonstrate the efficacy of applying
opponent-modeling in a number of simulated market settings. While our
simulations are simplified representations of actual market dynamics, they
provide an idealized "playground" in which our techniques can be demonstrated
and tested. We present this work with the aim that our techniques could be
refined and, with some effort, scaled up to the full complexity of real-world
market scenarios. We hope that the results presented encourage practitioners to
adopt opponent-modeling methods and apply them online systems, in order to
enable not only reactive but also proactive decisions to be made.
