Hudson & Thames

What Hudson & Thames does

Hudson & Thames is a privately held engineering company that builds production-ready quantitative finance Python libraries, founded in 2019 and headquartered in London with primary operations in Pretoria, South Africa. The company is led by three co-founders — Jacques Joubert, Olexandr Proskurin, and Ashutosh Singh — with Dirk Freese serving as advisor, and operates with a disclosed 1-10 person team. Its core products are three subscription-licensed libraries: MLFinLab (machine learning for finance, drawing from Marcos Lopez de Prado's 'Advances in Financial Machine Learning' and the Journal of Financial Data Science), ArbitrageLab (statistical arbitrage and pairs trading across distance, cointegration, copula, and machine learning approaches), and PortfolioLab (portfolio optimization techniques including Hierarchical Risk Parity, Hierarchical Equal Risk Contribution, and Mean-Variance Optimization). Together these libraries comprise 25+ modules with 100% test coverage, marketed as a 'department of PhD researchers' for client quantitative pipelines.

The business model is product-led growth: libraries are distributed via PyPI for self-serve pip installation and via a subscriber portal at portal.hudsonthames.org, supplemented by a native integration with QuantConnect's backtesting and execution platform. Pricing is subscription-based, quote-driven, and not publicly disclosed, with both SMB/individual subscribers and enterprise users. Revenue is complemented by education-led marketing — e-books (e.g., 'The Definitive Guide to Pairs Trading'), Udemy courses ('Mastering Backtesting for Algorithmic Trading'), research blog articles, YouTube tutorials, and a graduate fellowship program run with the University of Pretoria, Beta1, and the Journal of Financial Data Science. The company does not disclose revenue, has no announced funding rounds, and appears bootstrapped and founder-controlled, with named enterprise references including Abu Dhabi Investment Authority, AXA Investment Managers, and QTS.

Name

Hudson & Thames

Legal name

Hudson & Thames

Website

https://hudsonthames.org

Company type

Private

Founded year

2019

Operating status

Operating

Headcount range

1–10 employees

Short description

Hudson & Thames builds production-ready Python libraries — ArbitrageLab, MLFinLab, and PortfolioLab — implementing quantitative finance algorithms from academic literature for portfolio managers, traders, hedge funds, and individual quants, distributed via subscription.

Ownership category

akta.pro rank

Headquarters

HQ city

London

HQ country

United Kingdom

HQ region

Europe

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Marketing or Sales, Operations, Others

Revenue model

1. Software Library Subscriptions: Access to proprietary Python libraries (MLFinLab, ArbitrageLab, PortfolioLab) with subscription-based licensing model
2. Education and Fellowship: Free fellowship program for students at University of Pretoria in partnership with Beta1, focusing on academic excellence and publication

Pricing tiers

Go-to-market motion1 record

Distribution channels4 records

Marketing channels6 records

Core offering

Hudson & Thames is an engineering company that builds production-ready Python libraries implementing top algorithms from academic quantitative finance literature. Its three core libraries — ArbitrageLab (statistical arbitrage and pairs trading), MLFinLab (machine learning for finance), and PortfolioLab (portfolio optimization) — provide portfolio managers and traders with reproducible, tested, 100% code-covered implementations of techniques from leading journals and textbooks.

Product overview

Hudson & Thames is an engineering company that builds quantitative Python libraries consisting of top algorithms from academic literature. The company offers three core Python libraries: ArbitrageLab for pairs trading and statistical arbitrage strategies (including distance, cointegration, copula, and machine learning approaches), MLFinLab for machine learning in finance (based on Advances in Financial Machine Learning), and PortfolioLab for portfolio optimization (including Hierarchical Risk Parity, Hierarchical Equal Risk Contribution, and Mean-Variance Optimization). These libraries are designed as ready-to-use implementations for portfolio managers and traders. The company also provides educational resources including an online course and a graduate fellowship program.

Differentiator

Problem solved

Functional benefit

Brands

• MLFinLab: Python library enabling portfolio managers and traders to leverage machine learning with reproducible, interpretable, and easy-to-use tools for financial machine learning applications.
• ArbitrageLab
• PortfolioLab

Products and services

• ArbitrageLab

Quantifiable outcome

• 25+ modules across three specialized libraries
• +2 more outcomes

Named customers4 records

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

Integration2 records

AI capability5 records

Feature4 records

Partnerships

Four partnerships are on record, tiered flagship and core.

• Journal of Financial Data ScienceflagshipOthers · 1 January 2025Publication partnership for the Fellowship Program, targeting top-tier peer-reviewed journals for Fellow research publications.
• QuantConnectcoreTechnology or Integration · 1 October 2022Full integration of MLFinLab into QuantConnect's powerful backtesting and execution platform. Provides users with access to historical simulations, data feeds, and live execution capabilities using MLFinLab algorithms.
• University of PretoriacoreStrategic or Co-development PartnerAcademic partnership for the Fellowship Program, providing research mentorship and publication opportunities for graduate students in quantitative finance.
• Beta1coreStrategic or Co-development PartnerPartner in the Fellowship Program, helping identify and develop talented graduate students into globally competitive quant researchers and authors.

Scale indicators4 records

Recent moves6 records

Expansion highlights5 records

Emerging players

• Alpha Architect: Quantitative asset manager and research publisher producing factor-investing and portfolio-construction research, directly adjacent to PortfolioLab's portfolio optimization offering.
• Numerai: Crowd-sourced quantitative hedge fund that builds its own ML-on-financial-data stack; overlaps with MLFinLab's machine learning-for-finance positioning and targets a similar quant talent pool.
• CloudQuant: Cloud-based strategy-research and backtesting platform serving individual and institutional quants, with a partially overlapping developer-tool and alt-data focus.

Broad incumbents

• Two Sigma: Established quantitative technology and investment firm that builds proprietary ML-for-finance infrastructure at scale, representing the in-house alternative to outsourced libraries like MLFinLab.
• WorldQuant: Large quantitative investment firm whose internal alpha-research tooling and consultant network compete for the same quant talent and statistical-arbitrage IP that MLFinLab and ArbitrageLab address.

Others

• Qlib (Microsoft): Open-source AI-oriented quantitative investment platform from Microsoft covering alpha seeking, risk modeling and portfolio optimization; the most direct free alternative to MLFinLab and PortfolioLab.
• Pyfolio (Quantopian): Open-source portfolio risk and performance analytics library; overlaps directly with PortfolioLab's portfolio analytics and risk-attribution feature set.
• Zipline: Open-source algorithmic trading / backtesting library originally built at Quantopian; offers an overlapping strategy-development workflow and competes with MLFinLab on accessibility and price.
• QuantInsti: Education platform for algorithmic and quantitative trading that mirrors Hudson & Thames's fellowship and Udemy course motions; an adjacent rather than direct competitor.

Direct peers

• QuantConnect: Algorithmic trading and backtesting platform whose LEAN engine directly overlaps with Hudson & Thames's MLFinLab functionality; QuantConnect is also H&T's distribution partner via full MLFinLab integration.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat5 records

Key risks6 records

Key highlights6 records

Customer concentration

• GitHubgithub.com/hudson-thames
• YouTubeyoutube.com/channel/UC8hI87gt0dmTAIEupEcsckA

Revenue estimate

Valuation estimate

Number of profiles

Profiles4 records

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Hudson & Thames