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Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Market-making strategies in real limit order book markets face substantial model uncertainty and regime-shift risk. Existing adversarial reinforcement learning approaches improve robustness by formulating the Avellaneda--Stoikov market-making problem as a zero-sum game between a market maker and an environmental adversary. However, these approaches typically rely on Poisson order arrivals and neglect trade-induced price impact, limiting their ability to capture important high-frequency market microstructure effects such as clustered order flow, self-excitation, and post-trade price feedback. W

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