让做市策略自动适应市场变化，无需重新训练。
Zero-shot adaptation to order book dynamics
- 分离市场状态与交易目标，分别建模
- 通过奖励信号动态调整报价策略
- 适合需要快速响应市场变化的场景
我们提出一种自适应做市架构，在保留Avellaneda--Stoikov框架的分析结构基础上，引入一种基于度量的自适应机制。本文保持Avellaneda--Stoikov的快速哈密顿-雅可比-贝尔曼（HJB）结构，使其能够适应不断变化的市场状态和交易目标。核心思想是将市场动态与交易目标解耦：市场状态决定一组低维的Avellaneda--Stoikov参数，而近期实现的收益则决定一个低维的目标向量。HJB前向映射将该目标向量通过未来收益特征的标量化，转化为最优买卖报价。
原文摘要 · Abstract (English)
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.
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