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Concentrated Liquidity Provision: a Reinforcement Learning Perspective — arXiv:2608.19389

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19389
License (as archived): CC0-1.0
Archived: 2026-10-18T11:48:36.127Z
SHA-256 of the archived text: 9fc21a08b4b908fb5a6b498334c911c25f53ea4d99134508c78788e639b70a1b
Stable URL of this page: https://metraipolis.com/documento/dc_bced49ffe06a

How to cite

Concentrated Liquidity Provision: a Reinforcement Learning Perspective — arXiv:2608.19389 — arXiv (en), https://arxiv.org/abs/2608.19389 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bced49ffe06a (Metraipolis archive dc_bced49ffe06a, sha256 9fc21a08b4b908fb5a6b498334c911c25f53ea4d99134508c78788e639b70a1b)

BibTeX

@misc{dcbced49ffe06a,
  title = {Concentrated Liquidity Provision: a Reinforcement Learning Perspective — arXiv:2608.19389},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19389},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bced49ffe06a; sha256 9fc21a08b4b908fb5a6b498334c911c25f53ea4d99134508c78788e639b70a1b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19389. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, such as UniswapV3, are now a well-established design. In these markets, liquidity providers (LPs) face a sequential decision problem: they must decide when to rebalance their positions and which price ranges to allocate capital to as market conditions evolve. We formulate dynamic liquidity provision as a stochastic impulse control problem and use reinforcement learning (RL) to solve it, focusing on providing interpretable solutions. We show that learned policies exhibit rich state-dependent behaviour, allocating liquidity according to mispricing, rebalancing costs, uncertainty, inventory exposure, and heterogeneous risk preferences. These behaviours help compress the left tail of the Profit and Loss (PnL) distribution and avoid catastrophic outcomes under high uncertainty. Finally, we benchmark the RL agents against baseline and sophisticated agents from the AMM microstructure literature and analyse their performance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.LG).
Categorie arXiv: Trading and Market Microstructure (q-fin.TR) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF).
Pagina del lavoro: https://arxiv.org/abs/2608.19389 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:47:43.359Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).