Introduction
This post demonstrates how to use reinforcement learning to price an American Option. An option is a derivative contract that gives its owner the right but not the obligation to buy or sell an underlying asset. Unlike its European-style counterpart, American-style option may exercise at any time before expiry.
American Option is known to be an optimal control MDP (Markov Decision Process) problem where the underlying process is Geometric Brownian motion ([1]). The Markovian state is a price-time tuple and the control is a binary action that decides on each day whether to exercise the option or not.
This post is published on medium here. The accompanying Jupyter notebook is located here on Github.
DISCLAIMER: This post is for the purpose of research and backtest only. The author doesn't promise any future profits and doesn't take responsibility for any trading losses.