Philosophy
What We Believe About Portfolio Construction
Conviction-driven, mathematically grounded portfolio construction. Built for people who want to understand their portfolios — not just own them.
Structure Over Guesswork
Portfolios should be engineered, not assembled from marketing narratives. Every allocation in our platform has a mathematical basis — an optimization objective, a constraint set, and a traceable decision chain. There is no "trust us" in the output. You see the logic.
Evidence Over Narrative
Every strategy claim is backed by walk-forward backtesting against real market data. No hypothetical returns. No cherry-picked timeframes. No survivorship bias. Transaction costs are modeled. Results are deterministic and fully reproducible.
Transparency Over Black Boxes
See the full decision chain: from covariance estimation through constraint binding to final weight assignment. Every optimization generates a 17-file artifact bundle — weights, risk decomposition, attribution, constraint reports, and optimizer diagnostics. Nothing is hidden.
Academic Foundations
Every engine in the platform is grounded in peer-reviewed financial mathematics. These are the papers and methods that form the theoretical backbone.
Markowitz (1952)
Modern Portfolio TheoryThe foundation of all portfolio optimization. Risk-return trade-off, efficient frontier, the case for diversification over stock-picking.
Ledoit & Wolf (2004)
Oracle Approximating ShrinkageCovariance matrix shrinkage that makes high-dimensional estimation reliable. Powers the covariance input of our Robust Mean-Variance engine — the only return-aware optimizer we ship.
López de Prado (2016)
Hierarchical Risk ParityClustering-based allocation that works when Markowitz fails. Immune to covariance matrix instability. Powers our Smart Diversification strategy.
Rockafellar & Uryasev (2000)
CVaR OptimizationLinear programming reformulation for minimizing expected tail losses. Powers our Downside Guard strategy — the losses that matter most.
Black & Litterman (1992)
Bayesian Return IntegrationCombines market equilibrium returns with investor views in a Bayesian framework. Produces intuitive portfolios from subjective inputs.
Spinu (2013)
Cyclical Coordinate DescentEfficient solver for the Equal Risk Contribution problem. Powers our Risk Parity and Risk Budgeting engines.
Not a Broker. Not a Robo-Advisor.
Holy Funds is a portfolio intelligence platform. We don't automate your decisions — we give you the tools, data, and structure to make better ones. Research strategies. Test allocations. Understand risk. Build with confidence.