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A score rates one vault at a time. Turning scored vaults into a portfolio is a second, separate job, done by the Atlas Engine — a mean-variance optimizer in the tradition of Harry Markowitz. Markowitz, not magic: deterministic quantitative math, with its policy parameters published in full.

The pipeline — five deterministic steps

1

Eligible universe

Start from every scored vault, keep only those that clear the chosen tier’s hard gates — minimum Atlas Score, minimum TVL, minimum audit count and track record, an allowed asset class, and a permitted withdrawal speed. A vault that misses any single gate is never considered.
2

Return and risk inputs

For each survivor the Engine reads two numbers from public data: current APY as expected return, and the historical variance of that APY — extended to a covariance across vaults, with statistical shrinkage to steady thin histories — as risk. No private inputs, no forecasts.
3

Mean-variance optimization

Solve for the mix of vaults with the best expected return for the risk taken. A single risk-aversion dial, λ, set by the user’s risk score, decides how hard the mix leans toward yield versus away from volatility.
4

Policy constraints

The raw optimum is held inside the tier’s guardrails — caps per vault, per protocol, and per chain, a floor under each position, and a tighter cap on vaults with little history. Concentration is trimmed proportionally until every cap holds.
5

Final allocation

A set of positions with explicit percentages that sum to the full amount. Same inputs in, same portfolio out — every time.
When a candidate set has too little history for the covariance step to be trustworthy, the Engine falls back to a simpler, equally deterministic diversification rule rather than optimize on noise — and the result is labelled when that happens.