Reinforcement Learning for Speculative Trading under Exploratory Framework
This work addresses algorithmic trading strategies for financial markets, but it is incremental as it builds on an existing exploratory RL framework.
The paper tackles the speculative trading problem by formulating it as a sequential optimal stopping problem under an exploratory reinforcement learning framework, resulting in a closed-form optimal policy and an RL algorithm demonstrated in a pairs-trading application.
We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. 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. Under the exploratory formulation, the agent's randomized control is characterized via the probability measure over the jump intensities, and their objective function is regularized by Shannon's differential entropy. This yields a system of the exploratory HJB equations and Gibbs distributions in closed-form as the optimal policy. Error estimates and convergence of the RL objective to the value function of the original problem are established. Finally, an RL algorithm is designed, and its implementation is showcased in a pairs-trading application.