Originally, this project was a Homework Project for CME240 - Statistical and Machine Learning Approaches to Problems in Investment Management, taught by Jeremy Evnine at the Stanford Institute for Computational & Mathematical Engineering (ICME) in Spring 2019.
The goal for this little exercise is to detect regime changes in stock price movements using the concept of Minimum Description Length (MDL). The underlying approach is to model up- and downward movements of closing prices as a binary random variable, propose a binomial mixture model for said variable, and then determine a breakpoint in the observed time series of closing prices that maximizes the quality of the fit.
In a stylized experiment on simulated data from a biased coin, the model picks up a regime change with a latency of 5 periods. Applied to S&P 500 closing prices between 2008 and 2009, the model suggests a regime change around June 12th, 2008 - just about two months before the financial meltdown culminated in Lehman Brother's collapse.
All code for this project is written in python 3.6. Stock data is fetched from Alpha Vantage's public API
# clone the git repository
$ git clone https://github.com/bummy93/regime-change-detection.git; cd regime-change-detection
# install all dependencies
$ pip install -r requirements.txt
# expose your API key as an environment variable
$ echo "export API_KEY=<YOUR_API_KEY>" >> set_vars.sh
# run the local notebook server
$ source set_vars.sh; jupyter notebook