Hudson and Thames Quantitative Research
Our mission is to promote the scientific method within investment management by codifying frameworks, algorithms, and best practices.
London
Pinned Repositories
arbitrage_research
Jupyter Notebook examples on how to use the ArbitrageLab - pairs trading - python library.
arbitragelab
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.
backtest_tutorial
example-notebooks
june_applications_21
Skillset Challenge for the Apprenticeship Program, June 2021.
march_applications_21
Skillset Challenge for the Apprenticeship Program
meta-labeling
Code base for the meta-labeling papers published with the Journal of Financial Data Science
mlfinlab
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
portfoliolab
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
SecondBrain
Hudson and Thames Quantitative Research's Repositories
hudson-and-thames/mlfinlab
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
hudson-and-thames/arbitragelab
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.
hudson-and-thames/portfoliolab
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
hudson-and-thames/backtest_tutorial
hudson-and-thames/arbitrage_research
Jupyter Notebook examples on how to use the ArbitrageLab - pairs trading - python library.
hudson-and-thames/meta-labeling
Code base for the meta-labeling papers published with the Journal of Financial Data Science
hudson-and-thames/SecondBrain
hudson-and-thames/march_applications_21
Skillset Challenge for the Apprenticeship Program
hudson-and-thames/example-notebooks
hudson-and-thames/june_applications_21
Skillset Challenge for the Apprenticeship Program, June 2021.
hudson-and-thames/betting-against-beta
This project is based upon the paper: Frazzini, A. & Pedersen, L. (2014). Betting against beta.
hudson-and-thames/guide_to_modern_portfolio_optimization
hudson-and-thames/pykalman
Kalman Filter, Smoother, and EM Algorithm for Python
hudson-and-thames/a-practitioners-guide-to-the-ONC-algorithm
Code base for the practitioner's guide to the ONC algorithm paper published with the Journal of Financial Data Science
hudson-and-thames/definitive_guide_to_pairs_trading
hudson-and-thames/interview_april
Interview question for the jr Data Science / Machine Learning Engineer.
hudson-and-thames/mlfinlab-quickstart
hudson-and-thames/hudsonthames-sphinx-theme
Sphinx theme for Hudson and Thames documentation
hudson-and-thames/marbles
Read better test failures.
hudson-and-thames/oct_applications_21
Applications to the apprenticeship program, October 2021.
hudson-and-thames/MolecularNotes
My Obsidian Second Brain setup
hudson-and-thames/EdgarSEC
hudson-and-thames/example-data