Reinforcement Learning for Speculative Trading under Exploratory Framework

ArXi:2604.02035v1 Announce Type: cross We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang. The problem is formulated as a sequential optimal stopping problem over entry and exit times under general utility function and price process. We first consider a relaxed version of the problem in which the stopping times are modeled by the jump times of Cox processes driven by bounded, non-randomized intensity controls.