Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology
Proposes a hybrid LOB simulation combining neural stochastic models and agent-based models for realistic market dynamics.
What it examines
This paper introduces a hybrid LOB simulation model combining agent-based models (ABMs) and stochastic models (SMs). It aims to overcome limitations of both approaches by using a neural stochastic background trader trained on historical LOB data, embedded in a multi-agent simulation framework.
What it concludes
The study concludes that the NS-ABM model can realistically replicate market characteristics and serve as a dynamic back-test harness for trading strategies. Potential applications include testing trading algorithms and investigating market dynamics. Future research could explore more complex market interactions and abnormal market conditions.
Evidence objects
The study concludes that the NS-ABM model can realistically replicate market characteristics and serve as a dynamic back-test harness for trading strategies. Potential applications include testing trading algorithms and investigating market dynamics. Future research could explore more complex market interactions and abnormal market conditions.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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 simulatio… ▽ More 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. △ Less
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