Agent-based modelling and simulation of financial markets has proven instrumental in studying a diverse array of phenomena including short selling, bubble formation and bursting, the influence of tick size, usage of dark pools, and many more. Recognising this capability, we present a novel framework designed for simulating agent-based financial markets that constitutes a hybridised framework for discrete event simulation of financial markets.In contrast to existing simulators, our framework adopts a hybrid approach that integrates historically observed limit order book and tick data to inform the simulation, complemented with synthetic agents that drive price progression and shape the market environment. Importantly, our simulator includes a stop-loss order book and mechanisms for agents to generate stop-loss orders. This innovation opens avenues for exploring previously under-explored concepts related to stop-loss hunting strategies. To our knowledge, our framework represents the first general-use simulator to incorporate stop-loss functionality into market simulations, offering opportunities for research and experimentation in this domain.