About Altus Labs
Open Research at the Intersection of Finance and Technology
We publish open quantitative research across three workstreams — portfolio, economic, and technology — documented in enough detail to be reproduced. Held together by a single thesis: financial systems are mid-transition into the information age, and the toolkit hasn't caught up.
The Structural Void
Why Current Approaches Fall Short
Modern Portfolio Theory, Risk Parity, and traditional 60/40 allocations were built for an industrial-era economy of physical assets, predictable cash flows, and well-behaved distributions. That economy is gone.
Modern markets run on networks. Information networks, payment networks, attention networks. They compound non-linearly. They concentrate endogenous risk. They generate fat tails as a feature, not an anomaly.
Classical financial frameworks systematically misprice this. They underweight tail risk, ignore path dependence, assume independent draws, and treat code as an afterthought rather than a research output. The toolkit has lagged the system it's meant to model.
The Ergodicity Problem
Traditional finance optimises for ensemble averages — what happens across many investors. But you experience a single sequence of returns over time. These are fundamentally different optimisation targets.
Network Blindness
Most financial models treat assets as independent draws. Modern markets are tightly networked — risk and information flow through positioning chains, not individual securities. Linear models miss this entirely.
The Reproduction Gap
Quantitative research is rarely written for reproduction. Key parameters, assumptions, and validation steps are left implicit; methodology hides behind paywalls and academic jargon. We publish at the level of detail where any technical reader, with modern tools, can rebuild the work.
Our Approach
Research Philosophy
Three principles run through everything we publish: optimise for geometric compounding rather than ensemble averages, measure endogenous risk before it manifests in price, and ship methodology as code so results are fully reproducible.
Geometric Compounding
We focus on what matters for a single investor over time: the geometric mean growth rate. This means understanding why survival and drawdown avoidance dominate expected return maximisation in the long run.
Endogenous Risk
We study how systemic stress builds endogenously — through position crowding, liquidity withdrawal, and reflexive feedback loops — and how convex portfolio construction can exploit the non-ergodic nature of these dynamics.
Open Methodology
All published research includes the methodology, assumptions, parameters, and limitations in sufficient detail for independent reproduction. We treat detail not as a cost but as the price of credibility.
Posture
What We Publish, And How
Two things go out into the world from Labs: long-form research notes and working papers, and engineering write-ups that document our backtesting, validation, and modelling systems in enough depth to be rebuilt. Where the methodology is novel, the write-up is the publication.
Everything ships with enough detail to be reproduced — assumptions, parameter choices, and the limitations of the work all stated explicitly. We treat reproducibility as a feature of credible research, not as a bonus.
Labs remains a research-only entity. We do not sell products or provide investment services — we publish.
Research Areas
Portfolio Research
Convexity, non-ergodicity, geometric compounding, and survival-first construction
Economic Research
Carry trade mechanics, monetary regimes, currency dynamics, and network economies
Technology Research
Backtesting platforms, options pricing, statistical validation, and engineering write-ups documented at reproduction-grade detail