Unravel is a quantitative investment research firm providing high-performance, institutional-grade, market-neutral factor portfolios based on sound statistical and behavioral edges.
We ingest vast, diverse set of exogenous datasets, creating academically inspired & proprietary factors — designing bespoke, multi-factor portfolios with excellent risk-adjusted returns (Sharpe Ratio of 2-3+). Exploiting cross-sectional alpha, with zero beta or market exposure.
Our public and proprietary research provides unprecedented transparency to make:
Unravel is built by a team of experienced quantitative analysts, software engineers and data scientists. We're pragmatists, committed to delivering results that are testable, repeatable, and transparent.
Co-Founder
Mark combines probabilistic thinking, technological excellence with proven execution, scaling his last tech company to $12m ARR. Post-exit, he's managed systematic trading strategies through his family office for the last 5 years. He co-founded Unravel to institutionalize & scale his quantitative approach.
Co-Founder
Daniel brings deep statistical expertise from his MSc Architectural Computation (UCL) and a track record of building data-driven ventures—from founding a supply-chain startup to helping 150+ Medical AI companies through his GE Healthcare powered accelerator. He co-founded Unravel combining technical depth with entrepreneurial execution.
Quantitative Researcher
Quantitative researcher with a chemistry background and systematic trading expertise. David earned a Research Chemistry degree from Tokyo Institute of Technology and worked as a Senior Data Scientist at WorldQuant Predictive. He specializes in statistical modeling and systematic strategy development.
Unsigned Research
Charles is a serial software entrepreneur with a proven track record in deep-tech ventures (Additive Flow). Frustrated by poor risk-management practices in crypto, he co-founded UR and devoted to developing sophisticated, systematic hedge fund strategies with proper risk controls and institutional-grade execution.
We're building alongside experienced practitioners who share our vision in transparency, reproducible research and opportunities in multi-factor crypto portfolios.
Their involvement reflects a shared belief in our methodology and the significant potential we're unlocking together. We value their strategic perspective as we continue expanding our factor catalog and institutional partnerships.
Advisor
Industry leader in driving institutional growth, investment strategy evaluation, and treasury management. Bo has worked on treasury and investments with a variety of projects and family offices (e.g., Odos, Merit Circle, Function, and more), and has driven allocation of over $2B in capital within the blockchain space. Previously, Bo was a multi-asset portfolio manager in JPM’s Chief Investment Office, helping manage $600B+ in client assets.
Advisor
Kris transitioned from water engineering to trading, where he has worked for over ten years. He co-founded Robot Wealth in 2015, a systematic trading education platform serving 400+ members across six continents. Currently operates his own family office, deploying systematic strategies across multiple asset classes including equities, futures and cryptocurrency markets.
In 2020, Mark exited his previous company, and started his family office with the help of Daniel and a small team of quants. Together they deployed a wide range of quantitative (cross-sectional, long/short) strategies exploiting predictive relationships from exogenous data. Originally focusing on systematic macro, the two realized that there are more opportunities in digital assets and shifted all attention to market-neutral, cross-sectional crypto portfolios.
Unravel is proud on it's reproducible infrastructure and transparency: displaying a handful of cross-sectional factors on the website and auditable, runnable code that helps replicate Unravel's results and show transparent factor analysis.
Unravel is a manifestation of our passion for research and our belief that a well-defined discovery process can uncover many uncorrelated sources of alpha.
114A Friedrichstraße
Berlin
10117
Germany
22 Berners St
London
W1T 3LP
United Kingdom
Our Research & Development path so far.
We publicly launched the research platform uncovering the exogenous predictive factors that matter most for any asset.
Launched a private programmable interface enabling clients to build systematic strategies using our core technology.
Standardized our technology stack to support both in-house research and partner funds.
Built tools to automatically identify and prioritize significant linear patterns in financial data for more effective analysis.
Created systems that combine algorithmic insights with human expertise, ensuring models remain transparent and understandable.
Began exploring advanced forecasting techniques that would enable us to surface exogenous predictive factors reliably.