Hudson and Thames Quantitative Research
@hudson-and-thames • Our mission is to promote the scientific method within investment management by codifying frameworks, algorithms, and best practices.. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
92%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
22 repos
Visible snapshot
0 repositories updated in the last 90 days.
Leading language
Unknown
Portfolio mix
Unknown (9), Jupyter Notebook (7), Python (4)
Average size
261
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2024-08-21(780d ago)GitHub API fallback22 repositories
92%
of the visible star count comes from this organization's top three repositories.
261
stars per repository in this same snapshot.
Unknown
is the most common language here, with 0 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 86% of its visible star count.
Flagship share 86%Breakout repo: mlfinlab
Organization pages work best when you separate portfolio breadth from flagship concentration. In Hudson and Thames Quantitative Research's case, the visible top three repositories account for about 92% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Unknown (9), Jupyter Notebook (7), Python (4). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | 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. | Python | 4.9K |
| 2 | 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. | 189 | |
| 3 | hudson-and-thames/backtest_tutorial | Jupyter Notebook | 161 |
| 4 | hudson-and-thames/arbitrage_research Jupyter Notebook examples on how to use the ArbitrageLab - pairs trading - python library. | Jupyter Notebook | 161 |
| 5 | hudson-and-thames/meta-labeling Code base for the meta-labeling papers published with the Journal of Financial Data Science | Jupyter Notebook | 104 |
| 6 | hudson-and-thames/SecondBrain | JavaScript | 47 |
| 7 | 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 | Jupyter Notebook | 22 |
| 8 | hudson-and-thames/example-notebooks | Jupyter Notebook | 21 |
| 9 | hudson-and-thames/march_applications_21 Skillset Challenge for the Apprenticeship Program | Jupyter Notebook | 21 |
| 10 | hudson-and-thames/guide_to_modern_portfolio_optimization | 14 | |
| 11 | hudson-and-thames/pykalman Kalman Filter, Smoother, and EM Algorithm for Python | Python | 13 |
| 12 | hudson-and-thames/june_applications_21 Skillset Challenge for the Apprenticeship Program, June 2021. | Jupyter Notebook | 11 |
| 13 | hudson-and-thames/definitive_guide_to_pairs_trading | 10 | |
| 14 | hudson-and-thames/betting-against-beta This project is based upon the paper: Frazzini, A. & Pedersen, L. (2014). Betting against beta. | Python | 9 |
| 15 | hudson-and-thames/mlfinlab-quickstart | Python | 8 |
| 16 | hudson-and-thames/example-data | 6 | |
| 17 | hudson-and-thames/interview_april Interview question for the jr Data Science / Machine Learning Engineer. | 4 | |
| 18 | hudson-and-thames/hudsonthames-sphinx-theme Sphinx theme for Hudson and Thames documentation | CSS | 3 |
| 19 | hudson-and-thames/marbles Read better test failures. | 3 | |
| 20 | hudson-and-thames/EdgarSEC | 1 | |
| 21 | hudson-and-thames/oct_applications_21 Applications to the apprenticeship program, October 2021. | 1 | |
| 22 | hudson-and-thames/MolecularNotes My Obsidian Second Brain setup | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.