This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management.
By the end of this course, students will be able to
- Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management.
- Practice on valuable examples such as famous Q-learning using financial problems.
- Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project.
Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.
By the end of this course, students will be able to
- Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management.
- Practice on valuable examples such as famous Q-learning using financial problems.
- Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project.
Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.
