Blockchain Data Platform Team

Hudson & Thames Quantitative Research is a private research group focused on implementing the most cutting edge research in financial machine learning.

• Implemented portfolio optimisation algorithms in Python - Hierarchical Risk Parity, Black-Litterman - Models, Bayesian Allocation - as part of our open source Python package - portfoliolab.

• Writing technical blog articles on current research in financial machine learning. Articles have featured on Quantocracy and Interactive Brokers Quant Blog.

• Reading academic journals and papers and collaborating with academics in financial machine learning research.

• Developing production quality code used by banks and hedge funds in their research pipeline.