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.

翻译：我们描述了一种自适应做市架构，该架构在保留Avellaneda-Stoikov框架解析结构的同时，引入了一种后继度量式自适应机制。本文保持Avellaneda-Stoikov快速Hamilton-Jacobi-Bellman结构，使其能够适应不断变化的市场体制和交易目标。核心思想是将市场动态与交易目标分离：市场状态决定一组低维的Avellaneda-Stoikov参数，而近期实现的奖励决定一个低维目标向量。随后，通过HJB正向映射将这一目标转化为最优买卖报价，具体实现方式是对未来奖励特征进行标量化处理。