  The ability to construct a realistic simulator of financial exchanges,
including reproducing the dynamics of the limit order book, can give insight
into many counterfactual scenarios, such as a flash crash, a margin call, or
changes in macroeconomic outlook. In recent years, agent-based models have been
developed that reproduce many features of an exchange, as summarised by a set
of stylised facts and statistics. However, the ability to calibrate simulators
to a specific period of trading remains an open challenge. In this work, we
develop a novel approach to the calibration of market simulators by leveraging
recent advances in deep learning, specifically using neural density estimators
and embedding networks. We demonstrate that our approach is able to correctly
identify high probability parameter sets, both when applied to synthetic and
historical data, and without reliance on manually selected or weighted
ensembles of stylised facts.
