  Modern financial exchanges use an electronic limit order book (LOB) to store
bid and ask orders for a specific financial asset. As the most fine-grained
information depicting the demand and supply of an asset, LOB data is essential
in understanding market dynamics. Therefore, realistic LOB simulations offer a
valuable methodology for explaining empirical properties of markets. Mainstream
simulation models include agent-based models (ABMs) and stochastic models
(SMs). However, ABMs tend not to be grounded on real historical data, while SMs
tend not to enable dynamic agent-interaction. To overcome these limitations, we
propose a novel hybrid LOB simulation paradigm characterised by: (1)
representing the aggregation of market events' logic by a neural stochastic
background trader that is pre-trained on historical LOB data through a neural
point process model; and (2) embedding the background trader in a multi-agent
simulation with other trading agents. We instantiate this hybrid NS-ABM model
using the ABIDES platform. We first run the background trader in isolation and
show that the simulated LOB can recreate a comprehensive list of stylised facts
that demonstrate realistic market behaviour. We then introduce a population of
`trend' and `value' trading agents, which interact with the background trader.
We show that the stylised facts remain and we demonstrate order flow impact and
financial herding behaviours that are in accordance with empirical observations
of real markets.
