  Quantitative trading (QT), which refers to the usage of mathematical models
and data-driven techniques in analyzing the financial market, has been a
popular topic in both academia and financial industry since 1970s. In the last
decade, reinforcement learning (RL) has garnered significant interest in many
domains such as robotics and video games, owing to its outstanding ability on
solving complex sequential decision making problems. RL's impact is pervasive,
recently demonstrating its ability to conquer many challenging QT tasks. It is
a flourishing research direction to explore RL techniques' potential on QT
tasks. This paper aims at providing a comprehensive survey of research efforts
on RL-based methods for QT tasks. More concretely, we devise a taxonomy of
RL-based QT models, along with a comprehensive summary of the state of the art.
Finally, we discuss current challenges and propose future research directions
in this exciting field.
