Foundations of Reinforcement Learning with Applications in Finance

斯坦福大学的强化学习金融课程

http://web.stanford.edu/class/cme241/

Overview of the Course
Theory of Markov Decision Processes (MDPs)
Dynamic Programming (DP) Algorithms
Backward Induction (BI) and Approximate DP (ADP) Algorithms
Reinforcement Learning (RL) Algorithms
Plenty of Python implementations of models and algorithms
We apply these algorithms to 5 Financial/Trading problems:
(Dynamic) Asset-Allocation to maximize Utility of Consumption
Pricing and Hedging of Derivatives in an Incomplete Market
Optimal Exercise/Stopping of Path-dependent American Options
Optimal Trade Order Execution (managing Price Impact)
Optimal Market-Making (Bid/Ask managing Inventory Risk)
By treating each of the problems as MDPs (i.e., Stochastic Control)
We will go over classical/analytical solutions to these problems
Then we will introduce real-world considerations, and tackle with RL (or DP)
The course blends Theory/Mathematics, Programming/Algorithms and Real-World Financial Nuances