Independent Quantitative Research
Independent research focused on long volatility insights and their implications for long-term market dynamics through systematic thinking.
Our Philosophy
Modern portfolio theory treats investors as casinos—optimizing across many parallel outcomes. But you live a single financial life, experiencing one sequence of returns. We build research infrastructure for this reality: optimizing for geometric compounding and long-term survival, not arithmetic averages.
We dig into flow characteristics to identify market participants and positioning. Isolating price insensitive buyers and their needs helps us understand the dominant drivers of market price.
Markets are complex adaptive systems that are hard to predict. We explore how long volatility positioning relates to capital preservation and risk management in our research framework.
Our research examines factors that affect long-term compounding patterns, exploring non-ergodic time averages versus ensemble averages. Survival is the prerequisite for compounding.
Research
Understanding the four types of market edges and a framework for smaller firms to design winnable games against larger, established players.
Why traditional valuation models fail in a network economy, and how simple heuristics outperform complex optimization under deep uncertainty.
A framework for wealth creation through asymmetric positioning that prioritizes survival, geometric compounding, and convex portfolio design.
Technology
Systematic infrastructure for measuring endogenous market stress. Built for rigour, reproducibility, and institutional-grade research across all volatility regimes.
Proprietary system built on self-organized criticality (SOC) principles to identify the build-up of endogenous market risk—detecting structural stress around bubble formation, pre-correction tremors, and position crowding.
Every research output is anchored by a verifiable, version-controlled audit trail, ensuring 100% reproducibility across all volatility regimes. No black boxes—full transparency in methodology.
Quantitative momentum signal processing that integrates historic price data and convexity inputs to generate forward-looking return probability distributions across multiple timeframes.
Advanced engine utilizing proprietary volatility surface modeling with realistic friction costs—slippage, transaction latency, and market impact—for institutional-grade statistical validation.
Founder & Background
Altus Labs was founded by Gunjeet Singh Mahal, a technology business developer with a 10-year track record serving at major technology firms across P&L management, software engineering, and business value advisory roles.
While the vast majority of investment material focuses on wealth preservation via diversification and low risk, we rarely find rigorous material discussing how to create wealth through the financial markets in the first place. Altus Labs was created to address this gap with systematic, transparent research.
In an increasingly volatile market with an oversupply of money distorting fundamental valuations, volatility as an asset class presents an interesting area for research. Not coming from the traditional finance world, Gunjeet brings a more accessible lens on capital markets delivered via systematic thinking. The aim is to share these insights and learnings with all interested.
None of this is financial advice or solicitation for capital—it is purely research and educational content.
Contact
Interested in our research or potential collaboration? We welcome enquiries from academics, researchers, and fellow practitioners. Please note that we do not provide investment services or advice.
Location
London, United Kingdom
