Bayesian Hierarchical Hidden Markov Models applied to financial time series, a research replication project for Google Summer of Code 2017.

Hidden Markov Model

Stochastic Volatility Modelling using Hidden Markov Model

Fit hidden Markov model to stock returns and backtest strategy with hidden volatility regime filter

k-means clustering with the Intersection over Union (IoU) metric as described in the YOLO9000 paper

"Active learning through two-stage cluster" be published in Fuzz-IEEE 2018

Active semi-supervised clustering algorithms for scikit-learn

We study capital as it flows through the American equity markets. Using daily returns and volume data we develop a method to identify regimes in the markets over time, regimes which characterize mu…

Master Thesis: Clustering Mutual Funds based on portfolio holding data using machine learning algorithms. Note that the project is highly work in progress.

Using kmeans clustering, hierarchical clustering, and dynamic time warp to find natural groups in mutual funds and broker dealer offices

Code relating to the paper - Stock Embeddings: Learning Distributed Representations for Financial Assets

This repository relates to the paper "Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities" which was published in the proceedings of the Inte…