Abstract
Quantitative trading (QT) has been a popular topic in both academia and the financial industry since the 1970s. In the last decade, deep reinforcement learning (DRL) has garnered significant research interest with stellar performance in solving complex sequential decision-making problems, such as Go and video games. The impact of DRL is pervasive, recently demonstrating its ability to conquer some challenging QT tasks. In this article, we outline several key challenges and opportunities that manifest in DRL-based QT to shed light on future research in this field.
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