Limnal Growth · A 4-sleeve portfolio — stocks, bonds, commodities, and managed futures — that follows the macro regime and automatically de-risks in downturns. See methodology.

Pick up to five lines to compare. Mix and match the three Limnal Growth tiers (Limnal Growth, the 4-sleeve flagship; Growth Smooth, which adds a managed-futures diversifier basket; Growth Aggressive, which adds that basket plus a leverage sleeve for higher returns) against the three passive benchmarks. The first line you pick is the headline and gets the bold ink so the eye reads it as the protagonist.

Growth Aggressive adds a modest SSO sleeve and a diversifier basket on the Growth base, funded entirely from the US large-cap sleeves (SPY and QQQ) so international is left untouched. This is the SSO weight it held each day. The weight is capped at 25% and is driven entirely by Growth's base US large-cap allocation, so it needs no market timing of its own.

The opt-in R&D Yield sleeve picks the 15 cheapest S&P-500 names by R&D-per-dollar-of-market-cap that are also above their 200-day moving average. Here is every name it has held across the last three years — a sticky value core (Gilead, GM, Ford, the pharma/biotech spine) with a rotating tail, drifting from beaten-down tech hardware in 2023 toward pharma in 2025–26 as R&D got cheap in different places. (No AI mega-caps — the screen buys what the market has written off.)

Both opt-in stock-picking backtest lines are scored point-in-time from SEC EDGAR filed-dated fundamentals — no hindsight. Net of that discipline, R&D Yield adds real alpha (27.8% / 1.12 Sharpe vs SPY 15.5% / 0.65), while Quality Compounders ≈ the index(15.3% / 0.64): its earlier outperformance was a today's-fundamentals look-ahead, now corrected to filed-dated quality scoring over the full 2016 window.

Tail-risk view of the Limnal portfolio over the full backtest window. Historical reads percentiles directly off realized daily returns — fat tails baked in. Monte Carlo fits a Gaussian to the same series and draws 10,000 samples; the gap between the two is the size of the fat tail Gaussian VaR understates.

Each rebalance, rolled up into six top-level buckets. Stack order from the ground up is risk-off → risk-on (cash, alts, commodities, credit, fixed income, equity), so the chart shape itself encodes the model's risk posture as a function of time. Trace the cumulative-growth chart above against this one to see what the model was holding when it earned (or lost) ground.

Two envelope-level scores feed the allocation: the macro regime score (growth · inflation · liquidity composite) and the DTS score (Daily Treasury Statement liquidity proxy). Both run on a 1.0 → 3.0 scale where 1.0 is risk-off and 3.0 is risk-on. Trace this against §2 above: when the macro line dips toward 1, you should see the equity band in the allocation chart give ground to cash and fixed income; when it pushes toward 3, the equity band swells.

A standard fund-factsheet view: each cell is one portfolio's return for one calendar month, with cells color-graded by sign and magnitude. Limnal's row should read as fewer extreme months; the model's risk-management goal is to dampen the tails on both sides without giving up the median return.

A single backtest window can be lucky. The honest test is does the edge hold across many overlapping start dates? For each window length below, every possible starting date in the year-long base is tested independently. Limnal's edge is most decisive at the 180-day horizon: the noise washes out and the signal stabilizes.

The full description of how the regime is scored, how the score becomes an allocation, what data feeds the models, and the honesty caveats around the published numbers lives on the methodology page. Short version: six independent quantitative strategies score the regime daily on a 1.0 → 3.0 scale, the consensus drives a sleeve-by-sleeve allocation across a fixed 17-ETF universe, the backtest uses point-in-time inputs (no look-ahead) over the roughly 10-year window (January 2016 to today) for which the full model suite has data, and the published numbers are frictionless — real-world transaction costs, taxes, and slippage will subtract from realized returns.