Learning Market Making with Closing Auctions
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
In this work, we investigate a market making execution problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the significant liquidity events available in closing auctions, we propose a deep reinforcement learning framework, consisting of a Deep Q-Network and its continuous-control actor-critic extensions (DDPG, TD3 and SAC), that explicitly incorporates this mechanism. We introduce a market making framework designed to explicitly anticipate the closing auction, continuously refining the projected clearing price as the trading session evolves. We develop a generative stochastic market model to simulate the trading session and to emulate the market. Our theoretical model and these deep reinforcement learning methods are applied on the generator in two settings: (1) when the mid price follows a rough Heston model with generative data from this stochastic model; and (2) when the mid price corresponds to historical data of assets from the S&P 500 index and the performance of our algorithm is compared with stylized reference benchmarks from optimal market making.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 60 references that are in the map. First posted on arXiv 24 Jan 2026.
Builds on
17 of 60 references.
References · 17
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5 more
- 2024Logarithmic regret in the ergodic Avellaneda-Stoikov market making modelcited by 2
- 2019Optimal auction duration: A price formation viewpointcited by 1
- 2020AHEAD : Ad-Hoc Electronic Auction Designcited by 1
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- 2025Optimal Rebate Design: Incentives, Competition and Efficiency in Auction Marketscited by 1
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Citing papers per year: 2026: 1.
1 paper.
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- 2025Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
- 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
- 2022Learning to simulate realistic limit order book markets from data as a World Agent
- 2026Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
- 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
- 2026Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator