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.

翻译：现代金融交易所使用电子限价订单簿（LOB）来存储特定金融资产的买入和卖出订单。作为描述资产供需的最细粒度信息，LOB数据对于理解市场动态至关重要。因此，逼真的LOB模拟为解释市场的经验特性提供了有价值的方法。主流模拟模型包括基于主体的模型（ABM）和随机模型（SM）。然而，ABM往往不基于真实历史数据，而SM往往无法实现动态的主体交互。为克服这些局限性，我们提出了一种新颖的混合LOB模拟范式，其特点为：（1）通过神经点过程模型在历史LOB数据上预训练的神经随机背景交易者表示市场事件逻辑的聚合；（2）将背景交易者嵌入到与其他交易主体互动的多主体模拟中。我们使用ABIDES平台实例化了这一混合NS-ABM模型。首先独立运行背景交易者，结果显示模拟的LOB能够重建展示真实市场行为的综合程式化事实列表。随后引入一组“趋势”和“价值”交易主体，它们与背景交易者互动。结果表明程式化事实得以保持，并且我们展示了符合真实市场经验观察的订单流影响和金融从众行为。