Deep Reinforcement Learning in Optimal Market-Making

On June 1, 2021, Futures First Canada and FinML began a pilot collaborative project involving three Canadian universities by-way-of a MITACS Accelerate internship (IT25712) which jumpstarted an initiative to use cutting edge techniques in machine learning, financial mathematics, and AI for making predictions in financial markets. This goal is integral to the business operations of Futures First and is currently a popular topic in academic research. The pilot project created a basis for this current project application which will continue the effort, extending positive academic results and furthering integration into the company’s infrastructure. This next project will extend previous research from project IT35694 which focused on applying SOC (stochastic optimal control) to algorithmic and HFT (High-Frequency Trading) problems. The trading problem Futures First specifically focuses on is market-making, which is one of the most heavily studied trading problems in the futures trading environment. The main subject of this new project will be to utilize the novel topic of deep reinforcement learning to generate optimal market-making solutions. This will be a challenging project where we uncover the obstacles for applying the academic theory around deep reinforcement learning in practice.

Faculty Supervisor:

Anatoliy Swishchuk

Student:

Partner:

Futures First Canada Inc

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

University of Calgary

Program: