diff --git a/scripts/analysis/regime_lab.py b/scripts/analysis/regime_lab.py
index 40a2257..e7de80d 100644
--- a/scripts/analysis/regime_lab.py
+++ b/scripts/analysis/regime_lab.py
@@ -74,15 +74,20 @@ def _rolling_pct(x: np.ndarray, win: int) -> np.ndarray:
         lambda w: (w.iloc[-1] >= w).mean(), raw=False).values
 
 
-def regime_features(df: pd.DataFrame, pct_win: int = 252, rv_win: int = 24, fund_win: int = 168) -> dict:
-    """Tutte causali. dvol_pct/funding_z usano solo finestra passata. vrp = dvol - rv annualizz."""
+_BARS_PER_YEAR = {"1h": 24 * 365, "4h": 6 * 365, "1d": 365}
+
+
+def regime_features(df: pd.DataFrame, tf: str = "1h", pct_win: int = 252, rv_win: int = 24,
+                    fund_win: int = 168) -> dict:
+    """Tutte causali. dvol_pct/funding_z usano solo finestra passata. vrp = dvol - rv annualizz.
+    tf serve ad annualizzare correttamente la realized vol (sqrt barre/anno per timeframe)."""
     c = df["close"].values.astype(float)
     dvol = df["dvol"].values.astype(float)
     fund = df["funding"].values.astype(float)
     ret = np.zeros_like(c); ret[1:] = np.diff(np.log(c))
-    # realized vol annualizzata (in punti %, scala come DVOL): std rolling * sqrt(barre/anno)
-    bars_per_year = {1: 24 * 365}.get(1, 24 * 365)  # default 1h; per tf diversi e' un proxy
-    rv = pd.Series(ret).rolling(rv_win).std().values * np.sqrt(24 * 365) * 100
+    # realized vol annualizzata (punti %, scala come DVOL): std rolling * sqrt(barre/anno del tf)
+    bpy = _BARS_PER_YEAR.get(tf, 24 * 365)
+    rv = pd.Series(ret).rolling(rv_win).std().values * np.sqrt(bpy) * 100
     dvol_pct = _rolling_pct(dvol, pct_win)
     fmean = pd.Series(fund).rolling(fund_win).mean().values
     fstd = pd.Series(fund).rolling(fund_win).std().values
@@ -142,7 +147,7 @@ def _cache_path(asset: str, tf: str) -> Path:
 def build_cache(asset: str, tf: str, frac_step: int = 6) -> pd.DataFrame:
     """Precompute OHLCV + regime + frattali -> parquet condiviso (per i 100 agenti)."""
     df = load(asset, tf)
-    R = regime_features(df)
+    R = regime_features(df, tf=tf)
     F = frac_features(df, step=frac_step)
     for k in _FEATCOLS_R:
         df[k] = R[k]
@@ -166,9 +171,12 @@ def report(name: str, entries: list[dict], df: pd.DataFrame, asset: str = "", tf
     """Netto-fee full + OOS (ultimo 30%) + sweep fee, via engine onesto di explore_lab.
     Ritorna dict compatto: trades, full/oos (ret%, sharpe, dd, acc), robust (OK su tutte le fee)."""
     if not entries:
-        return {"name": name, "trades": 0, "verdict": False, "note": "no entries"}
-    ev = evaluate(name, entries, df)                        # full + oos + fee sweep
-    return ev
+        # struttura compatibile con robust() (tutti zero) -> robust()=False pulito, niente crash
+        z = {"ret": 0.0, "sharpe": 0.0, "dd": 0.0, "trades": 0, "win": 0.0, "exposure": 0.0, "yearly": {}}
+        print(f"  {name:<24s} NO ENTRIES")
+        return {"full": dict(z), "oos": dict(z), "sweep": {0.0: 0.0, 0.0005: 0.0, 0.001: 0.0, 0.002: 0.0},
+                "sweep_oos": {0.0: 0.0, 0.0005: 0.0, 0.001: 0.0, 0.002: 0.0}, "pos_yrs": 0, "n_yrs": 0}
+    return evaluate(name, entries, df)                      # full + oos + fee sweep
 
 
 if __name__ == "__main__":