Yifeng Hou is the Quantitative Trading Lead of FinFabrik, a Hong Kong-based fintech company providing capital markets technology and services with a focus on the emerging digital assets class. FinFabrik solutions power businesses in assets issuance, market making and trading. Yifeng leads the efforts in algorithmic execution, market making and quantitative investment vehicles in the realm of digital assets. Before joining FinFabrik, he was a FX Quantitative Trader at HSBC and a Visiting Research Scholar at Institute for Scientific Computing and Applied Mathematics, Indiana University Bloomington. His post-doctoral research and publications focused on theoretical and computational partial differential equations. Yifeng is passionate about applying the state-of-the-art machine learning techniques to algorithmic trading. His mission is to explore, enrich and extend humanity’s understanding of computation. He was awarded a PhD in Mathematics by City University of Hong Kong.
Reinforcement Learning and Quantitative Finance
Reinforcement learning has been successfully applied to many areas such as robotics, Go, and video games. This presentation gives a quick introduction of how reinforcement learning can be applied to quantitative finance. It discusses the advantages and caveats of the application, and compares reinforcement learning with classical methods in quantitative finance.