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English(EN) Optimal Regret for Online Market Making with Limit Order Book

新算法实现在线做市最优遗憾

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的在线做市学习算法，该算法实现了 $\widetilde{\mathcal{O}}(\sqrt{T})$ 的高概率遗憾界限。这优于先前针对由限价订单簿驱动的反馈模型的 $\widetilde{\mathcal{O}}(T^{2/3})$ 保证。新算法利用了买卖价差空间的离散化结合 Hedge 算法，并且还解决了市场价格和交易者估值都具有对抗性的环境，证明在这种完全对抗性环境中不可能实现亚线性遗憾。 AI

影响 这项研究推进了金融应用在线学习的理论理解，可能影响算法交易策略。

排序理由 这是一篇详细介绍在线做市新算法和理论结果的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新算法实现在线做市最优遗憾

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这是一篇详细介绍在线做市新算法和理论结果的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Maria Elena Vischi, Francesco Emanuele Stradi, Alberto Marchesi ·

    Optimal Regret for Online Market Making with Limit Order Book

    arXiv:2610.09691v1 Announce Type: cross Abstract: We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the private valuation of an incoming trader. In this setting, Maran et al. 2026 introdu…

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