About the Lab
An educational research publication, built on institutional rigor.
Quant Investing Lab exists because serious individual investors deserve the same methodological standards as institutional allocators.
Mission
Why this publication exists.
The retail investing internet is dominated by two extremes: black-box signal services that ask you to trust them on faith, and generic financial content that teaches nothing you couldn't find on Wikipedia.
Neither respects the reader. A serious individual investor, the kind who reads academic papers on weekends, codes in Python, and wants to understand why a strategy works before deploying capital, has nowhere to go for institutional-grade quantitative research.
Quant Investing Lab fills that gap. We publish the same quality of research a hedge fund would circulate internally, with full methodology disclosed, assumptions stated up front, and academic sources cited, adapted for the individual learner.
Who this is for
Institutional methods, individual access.
Most institutional quantitative strategies are structurally out of reach for individual investors. The capital minimums, the regulatory frameworks, and the execution platforms themselves are built for funds, not for individuals. Even a sophisticated investor willing to learn is excluded by design.
Yet millions of individuals manage their own investment portfolios. They make decisions about allocation, risk, and time horizon with retail-tier tools and retail-tier information, while the methodologies used by institutional allocators sit behind capital thresholds they cannot meet.
Quant Investing Lab narrows that gap through education. We teach the frameworks institutional research desks use: position sizing, leverage discipline, factor exposure, drawdown control, adaptive response to macroeconomic events. The goal is not to replicate institutional strategies. It is to translate institutional thinking into concepts any individual investor can understand and apply to their own decisions.
Founder
John Bergerat
John is a Swiss-French quantitative researcher and trader specializing in factor investing, systematic risk management, and out-of-sample backtest validation. He writes for investors who want to understand the machinery of quantitative finance, not just follow it.
He holds a Master of Science in Finance, Quantitative Asset and Risk Management (2020), and a Bachelor in Economics and Management (minor in Finance, 2017).
His Master's thesis, “On the Relevance of Optimizing Technical Indicators on the US Stock Markets,”applies Bailey et al. (2015) Probability of Backtest Overfitting, Clark and West (2006) out-of-sample predictive testing, and Harvey et al. (2015) multiple-testing adjustments to systematic strategies on the S&P 500 and Nasdaq 100 from 1988 to 2020.
Beyond research and writing, John builds custom quantitative models for institutional clients. He has developed dozens of algorithmic investment and risk-management models, with custom mandates for 5 institutional funds and clients across the United States, United Kingdom, and Canada. His work also extends to financial intelligence platforms serving both institutional and retail audiences, including multi-million-dollar trading applications.
As an independent researcher, John has developed factor strategies on US equities, Nasdaq 100 futures, and cryptocurrency markets, with a consistent focus on survivorship-bias control, walk-forward validation, and rigorous cost assumptions. He previously worked as a Quantitative Analyst at Lightmove SA, contributing to a Swiss buy-side portfolio and developing arbitrage detection for the Swiss real estate market.
John is the founder of Quantalytics (French-speaking research publication) and Astralys LLC (US research entity behind Quant Investing Lab).
Education
- MSc Finance, Quant Asset & Risk Mgmt (2020)
- BSc Economics & Management (2017)
Professional focus
- Factor investing & smart beta
- Algorithmic model design
- Risk management frameworks
- Financial intelligence platforms
Technical stack
Python · MATLAB · R · SQL · Stata · LaTeX · Bloomberg Terminal · Datastream
Methodological references worked with
Bailey, López de Prado, Harvey, Clark & West, Fama-French, Arnott/Hsu (fundamental indexation), Goldberg & Mahmoud (Conditional Expected Drawdown), GARCH family, Extreme Value Theory (Hills estimator).
Operating entity
Astralys LLC.
Quant Investing Lab is a publication of Astralys LLC, a US research entity founded by John Bergerat. Astralys is the research and publication arm supporting the Lab's editorial independence.
Editorial standards
What you can expect from every publication.
Full methodology disclosed
Every assumption is stated before the conclusion. No hidden parameters, no proprietary adjustments, no marketing numbers.
Cited to the original
Research papers cite the literature they build on. Notes cite the statute, the agency page, or the dataset behind every figure they quote. No anonymous authority.
Honest cost assumptions
Backtests include transaction costs, slippage, and survivorship corrections. Performance numbers are never pre-cost.
No advice, only research
Nothing published here is investment advice. We publish educational research; you make your own decisions.
Authorship
Written by John, with research assistance.
Every article on Quant Investing Lab originates with John Bergerat: the question it asks, the angle it takes, the structure it follows, and the argument it makes. He sources and verifies every fact and figure, and edits and signs off on the final text before it is published.
AI tools assist with two things: literature search — locating relevant papers, finding the original publication of a methodology, cross-checking a statistic against its primary source — and drafting. The judgment stays with John: which source is rigorous, which assumption matters, which claim is defensible, and what survives into the published text.
We are explicit about this because the alternative is worse. Quantitative finance does not forgive imprecise claims, and a byline is only worth something if it describes what actually happened. Responsibility for everything published here is John's, without qualification.
Last updated: April 21, 2026