强化学习视野下带切换的最优执行问题

Optimal Execution with Regime Switching in the Framework of Reinforcement Learning

摘要: 对带切换具有随机价格影响和其他消费者需求压力影响的最优执行问题，提出了一种双深度 Q 学习 (DDQL) 求解方案。首先，构建最优执行模型；其次，进行算法设计，将最优执行模型转化为强化学习语言并进行训练；最后，本文在三种市场条件下对所提出的模型进行验证，发现使用 DDQL 训练出来的策略均优于 TWAP 策略。

Abstract: A Double Deep Q-Learning (DDQL) framework is proposed to address the optimal execution problem under regime switching, incorporating stochastic price impact and demand pressure from other market participants. The approach begins with the con- struction of an optimal execution model. Subsequently, the model is reformulated within the reinforcement learning framework and trained accordingly. Finally, the proposed method is empirically evaluated under three distinct market conditions. The results demonstrate that the strategies derived through DDQL training consistently outperform the Time-Weighted Average Price (TWAP) benchmark.