harness(causality): guardia look-ahead + calendar-artifact self-policing nel lab intraday

- altlib.causality_ok(target_fn, tf): online-consistency guard (ricalcola il target su un
  prefisso, la coda deve combaciare col full). eval_weights shifta la posizione ma non vede
  una feature non-causale (finestra centrata/shift(-k)/stat full-sample) -> questa sì.
- intra_score integra DUE gate prima/dopo lo scoring: causality (leak -> LEAK, squalificato)
  e day_boundary_robust (ARTIFACT-RISK -> fuori dagli slot). Effetto sul leaderboard intraday:
  open_drive + weekly_seasonality + overnight -> CAL-ARTIFACT (da soli, niente skeptic);
  prevday_range_breakout resta (ROBUST). earns_slot 10 -> 8.
- +2 test (causal-ok / leak), suite intera verde.

Il lab intraday ora auto-becca leak e artefatti-calendario che ieri richiedevano 3 scettici.
Chiude la 3a lezione harness dell'onda intraday.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/research/alt/altlib.py

@@ -640,6 +640,36 @@ def eval_weights_smallcap(df: pd.DataFrame, target, capital: float = 600.0,
                 executed_turnover_per_year=round(float(turn.sum() / (len(turn) / bpy_d)), 1))
 
 
+def causality_ok(target_fn, tf: str = "1h", assets=CERTIFIED,
+                 tail: int = 80, tol: float = 1e-3) -> dict:
+    """Online-consistency / LOOK-AHEAD guard for a continuous target_fn(df) [or (df, asset)].
+    eval_weights SHIFTS the position so you cannot leak by multiplying a weight by the SAME
+    bar's return — but it does NOT verify the FEATURE construction is causal: a centered
+    window, a .shift(-k), or a full-sample statistic would pass eval_weights yet peek at the
+    future. Here we recompute the target on a TRUNCATED prefix and require its tail to MATCH
+    target(full)[:cut] (the bars a deployable signal would have emitted in real time). Any
+    future-peeking diverges. Run this in every altlib-based lab (blind/ortho already do)."""
+    worst = 0.0; bad = False; checked = 0
+    for a in assets:
+        df = get(a, tf)
+        full = np.nan_to_num(np.asarray(_call_target(target_fn, df, a), float))
+        n = len(df)
+        for cut in (int(n * 0.80), int(n * 0.92)):
+            if cut <= tail + 5 or cut >= n:
+                continue
+            sub = df.iloc[:cut].reset_index(drop=True)
+            s = np.nan_to_num(np.asarray(_call_target(target_fn, sub, a), float))
+            if len(s) != cut:
+                bad = True
+                continue
+            d = np.abs(s[cut - tail:cut] - full[cut - tail:cut])
+            worst = max(worst, float(np.max(d)) if len(d) else 0.0)
+            checked += 1
+    return dict(ok=bool((not bad) and worst <= tol),
+                max_tail_diff=round(worst, 8), checked=checked,
+                reason=("length-mismatch on prefix" if bad else None))
+
+
 # ===========================================================================
 # DRIVERS — run a hypothesis across both assets, several TFs, with a fee sweep.
 # ===========================================================================