机器学习与数据科学博士生系列论坛（第三十六期）—— Deep Reinforcement Learning in Quantitative Trading
报告人：Yizheng Hu (PKU)
地点：腾讯会议 723 1564 5542
Abstract: Quantitative Trading (QT) has been vastly growing in recent years, especially in T+0 markets. In fact, it has been a research topic both in the area of finance and computer science for many decades. Classic QT methods, such as stochastic control, rely heavily on model assumptions about the market. In recent years, with the success of Deep Reinforcement Learning (DRL), many QT methods using DRL have been developed, which achieved state-of-the-art performance with few model assumptions.
In this talk, we will introduce recent DRL works on three QT tasks: vanilla QT or optimal execution, portfolio management, and market making. We will also discuss the challenges of DRL in QT and possible future research directions.