refactor(core): IV-RV adattivo distinct-days policy + backfill Deribit

Sblocca il warmup hard del gate IV-RV adattivo (~21 giorni residui)
permettendo di mischiare cadenze diverse (tick live 15min + backfill
giornaliero) senza assumere il fattore costante 96 tick/giorno.

API change (no backwards-compat shims):
* compute_adaptive_threshold(history, *, n_days, percentile,
  absolute_floor): rimossi `min_days`/`target_days`. La selezione
  finestra (target_days/min_days/intera storia) si sposta al caller.
  Warmup hard quando `n_days == 0`.
* repository: rimosso `iv_rv_history`; aggiunti
  `count_iv_rv_distinct_days` (COUNT DISTINCT substr(ts,1,10)) e
  `iv_rv_values_for_window`.
* EntryContext aggiunge `iv_rv_n_days: int = 0`. entry_cycle calcola
  n_days, sceglie window_days e popola il context. Audit
  `iv_rv_n_days` reale (non più len/96).
* GUI Calibrazione: counter giorni distinti tramite set di date.
* Spec aggiornata con errata 2026-05-10 e nuova warmup table.

Backfill (scripts/backfill_iv_rv.py, stdlib-only):
* Fetch DVOL daily + ETH/BTC-PERPETUAL closes da Deribit public REST.
* Calcolo RV30d annualizzato (stdev log-return × √365 × 100).
* INSERT OR REPLACE in market_snapshots con timestamp 12:00 UTC e
  fetch_errors_json='{"backfill":true}' per distinzione audit.
* Compute layer testato (9 test): RV su prezzi costanti/monotoni/
  alternati, build_records con cutoff e missing data.

Verifica live post-deploy (10 mag 2026 08:50 UTC):
* ETH: n_days=46, P25=2.21 vol pt, IV-RV=10.05 → gate PASS
* BTC: n_days=46, P25=5.69 vol pt, IV-RV=8.60  → gate PASS

509 test passati (500 esistenti + 9 backfill), ruff pulito.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/integration/test_entry_cycle_iv_rv_adaptive.py

@@ -1,4 +1,8 @@
-"""End-to-end test del gate IV-RV adattivo + Vol-of-Vol guard via Repository."""
+"""End-to-end test del gate IV-RV adattivo + Vol-of-Vol guard via Repository.
+
+Verifica che la nuova API distinct-days componga correttamente repository
+helpers + ``compute_adaptive_threshold``.
+"""
 
 from __future__ import annotations
 
@@ -7,6 +11,7 @@ from decimal import Decimal
 
 import pytest
 
+from cerbero_bite.core.adaptive_threshold import compute_adaptive_threshold
 from cerbero_bite.state.db import connect, run_migrations
 from cerbero_bite.state.models import MarketSnapshotRecord
 from cerbero_bite.state.repository import Repository
@@ -67,26 +72,49 @@ def db_30d(tmp_path):
     return conn, repo
 
 
-def test_iv_rv_history_p25_picks_up_recent_regime(db_30d) -> None:
-    """Sanity: con bimodale 1.0/5.0 e finestra 30g, P25 di tutta la
-    storia è 1.0 (il 25° centile è ancora nella metà bassa)."""
+def test_distinct_days_count_matches_calendar_days(db_30d) -> None:
+    """30 giorni di calendario seedati → COUNT DISTINCT = 30."""
     conn, repo = db_30d
-    history = repo.iv_rv_history(
+    n = repo.count_iv_rv_distinct_days(
         conn,
         asset="ETH",
         max_days=60,
         as_of=datetime(2026, 5, 1, 0, 0, tzinfo=UTC),
     )
-    assert len(history) == 2880
-    from cerbero_bite.core.adaptive_threshold import compute_adaptive_threshold
+    assert n == 30
 
+
+def test_window_values_returned_for_full_history(db_30d) -> None:
+    conn, repo = db_30d
+    values = repo.iv_rv_values_for_window(
+        conn,
+        asset="ETH",
+        window_days=60,
+        as_of=datetime(2026, 5, 1, 0, 0, tzinfo=UTC),
+    )
+    assert len(values) == 2880
+    # Bimodale: 1440 valori 1.0 e 1440 valori 5.0
+    assert sum(1 for v in values if v == Decimal("1.0")) == 1440
+    assert sum(1 for v in values if v == Decimal("5.0")) == 1440
+
+
+def test_p25_of_bimodal_history_picks_low_regime(db_30d) -> None:
+    """Comporre repository + adaptive_threshold come fa entry_cycle."""
+    conn, repo = db_30d
+    as_of = datetime(2026, 5, 1, 0, 0, tzinfo=UTC)
+    n_days = repo.count_iv_rv_distinct_days(
+        conn, asset="ETH", max_days=60, as_of=as_of
+    )
+    values = repo.iv_rv_values_for_window(
+        conn, asset="ETH", window_days=60, as_of=as_of
+    )
     threshold = compute_adaptive_threshold(
-        history=history,
+        history=values,
+        n_days=n_days,
         percentile=Decimal("0.25"),
         absolute_floor=Decimal("0"),
-        min_days=30,
-        target_days=60,
     )
+    # P25 di 2880 valori bimodali: 1440 ×1.0, 1440 ×5.0 → soglia = 1.0
     assert threshold == Decimal("1.0")