Name: Hudson and Thames Quantitative Research
Type: Organization
Bio: Our mission is to promote the scientific method within investment management by codifying frameworks, algorithms, and best practices.
Twitter: hudson_thames
Location: London
Blog: https://www.hudsonthames.org/
Hudson and Thames Quantitative Research's Projects
Code base for the practitioner's guide to the ONC algorithm paper published with the Journal of Financial Data Science
Jupyter Notebook examples on how to use the ArbitrageLab - pairs trading - python library.
ArbitrageLab is a python library that enables traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals.
This project is based upon the paper: Frazzini, A. & Pedersen, L. (2014). Betting against beta.
Sphinx theme for Hudson and Thames documentation
Interview question for the jr Data Science / Machine Learning Engineer.
Skillset Challenge for the Apprenticeship Program, June 2021.
Read better test failures.
Skillset Challenge for the Apprenticeship Program
Code base for the meta-labeling papers published with the Journal of Financial Data Science
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
My Obsidian Second Brain setup
Applications to the apprenticeship program, October 2021.
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
Kalman Filter, Smoother, and EM Algorithm for Python