In this paper we develop a new form of agent-based model for limit order
books based on heterogeneous trading agents, whose motivations are liquidity
driven. These agents are abstractions of real market participants, expressed in
a stochastic model framework. We develop an efficient way to perform
statistical calibration of the model parameters on Level 2 limit order book
data from Chi-X, based on a combination of indirect inference and
multi-objective optimisation. We then demonstrate how such an agent-based
modelling framework can be of use in testing exchange regulations, as well as
informing brokerage decisions and other trading based scenarios
