Lasso · RF · sentiment · GTAP · regime

Models & Algorithms

Short-form model cards: when to use, when not to use, typical R², fit cadence, retrain triggers.

Lasso (L1)

Feature-importance + sparse weights. Used for the multi-asset rolling-window pipeline (see /backtests). Typical R²~0.18-0.25 on 1-quarter forward.

Random Forest

Non-linear, robust to outliers. Used for crisis-risk classifiers + regime-aware bagging. Permutation feature-importance for interpretability.

Logistic curve

For bounded metrics (fentanyl share, vaccination rate, recidivism rate). Saturation-aware fitting.

Regime-aware modeling

High-vol vs normal split with per-regime coefficients. Covers the Fed-policy + crisis-event heteroskedasticity.

Sentiment fusion

Weighted blend of keyword tonality + (planned) NewsAPI.ai / Permutable / EventRegistry. Used in /sentiment.

GTAP-style trade

Trade-elasticity + Phillips-curve pass-through. The "trade pass-through" pattern in the US-Impact engine.

Hormuz oil model

P_t = P_0 + k·R_t + M_t. Region-risk × event-multiplier. Calibrated against 1990-2024 Brent shocks.

Doomsday composite

10-stream weighted sum (9 base + US-Impact + Domestic-Chaos + trust-inverted). Documented at /doomsday.

Retraining cadence

← Backtests · Pipeline · US-Impact engine