A policy that looks great on typical days can still ruin a quarter on a rare one. STRIX concentrates its simulation on exactly those days.
Instead of wasting simulations on ordinary days, it concentrates them on the rare bad ones. Importance-sampled tail estimation.
It reduces the noise in the estimate, so the same run gives a consistent answer. Stratified variance reduction.
Every plan comes back with its full outcome range, not a single point. P10/P50/P90 annual-revenue bounds with downside risk (CVaR, worst case).
STRIX stress-tests each plan across thousands of sampled price-and-load futures, so you see its full range of outcomes and its downside. Monte-Carlo scenario analysis on your own history.
During onboarding we validate the method on your own data — a 12-month walk-forward (non-overlapping folds, five benchmarks, Diebold–Mariano significance) — so you can see how it performs before you rely on it.
These are the real methods behind STRIX, each in one plain line. We name the method as a credibility signal — we don't publish the recipe.
Spends its budget on the rare, expensive outcomes crude Monte Carlo misses.
Makes the estimate steadier for the same amount of compute.