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Zero-shot adaptation to order book dynamics
Arip Asadula · 2026-05-23 · via cs.LG updates on arXiv.org

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Abstract:We describe an adaptive market-making architecture that preserves the analytical structure of the Avellaneda--Stoikov framework while introducing a successor measure-style adaptation mechanism. In our paper we keep Avellaneda--Stoikov fast Hamilton--Jacobi--Bellman structure and make it adaptive to changing market regimes and trading objectives. The central idea is to separate market dynamics from the trading objective. The market state determines a low-dimensional set of Avellaneda--Stoikov parameters, while recent realized rewards determine a low-dimensional objective vector. The HJB forward map then converts this objective into optimal bid and ask quotes through a scalarization of future reward features.
Subjects: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
Cite as: arXiv:2605.21707 [cs.CE]
  (or arXiv:2605.21707v1 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.2605.21707

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Arip Asadulaev [view email]
[v1] Wed, 20 May 2026 20:11:37 UTC (2,478 KB)