feat(live): reconcile resting + orphan single-leg + circuit-breaker venue-lock + FEED_BOOK_GAP

Codice della tornata v1.1.27/28 (gia' in produzione, mai committato):
- reconcile_account: estensione ordini RESTING (FILLED_UNBOOKED/MISSING/STALE,
  caso MR02_BTC: TP fillato di notte scoperto ore dopo) + expected_resting in books
- strategy_worker: orphan_legs su REAL_CLOSE_PARTIAL anche single-leg, persistito
- execution: circuit-breaker su venue-lock admin (stop ordini dopo errori ripetuti)
- runner/hourly_report: alert FEED_BOOK_GAP + timestamp closed trades
- cerbero_client: get_open_orders (merge all + trigger_all)
Test: 12 nuovi, suite completa 126 passed.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

scripts/portfolios/hourly_report.py

@@ -96,8 +96,14 @@ def lossguard_section() -> str:
     return "\n".join(L)
 
 
+# Epoca v1.1.26 (deploy 2026-06-11 ~21:40 UTC): gate TP_PHANTOM attivo. I close
+# precedenti includono il churn da TP fantasma dell'11-06 17:32-17:58 (~24 giri,
+# win-rate inquinato) -> le accuratezze "pulite" si leggono da qui in poi.
+EPOCH_V1126 = "2026-06-11T21:40:00"
+
+
 def collect():
-    closed = []          # (sleeve, reason, net_return, pnl, win)
+    closed = []          # (sleeve, reason, net_return, pnl, win, ts)
     open_pos = []         # dict per posizione aperta
     realized = 0.0
     for sp in sorted(glob.glob(str(PAPER / "*" / "status.json"))):
@@ -119,7 +125,7 @@ def collect():
                 # resta il sim diagnostico: sui TP fantasma da spike testnet diceva
                 # 26/0 mentre il reale era 11/15). Fallback nr>0 per eventi storici.
                 closed.append((_short(wid), ev.get("reason", "?"), nr, pnl,
-                               bool(ev.get("win", nr > 0))))
+                               bool(ev.get("win", nr > 0)), ev.get("ts", "")))
         if "positions" in st or "weights" in st:
             continue        # multi-asset (TR01/ROT02/TSM01): sezione dedicata
         if st.get("in_position"):
@@ -187,7 +193,7 @@ def build_report() -> str:
 
     # breakdown per motivo
     by_reason = defaultdict(lambda: [0, 0, 0.0])   # reason -> [win, loss, pnl]
-    for _, reason, _, pnl, win in closed:
+    for _, reason, _, pnl, win, _ in closed:
         r = by_reason[reason]
         r[0 if win else 1] += 1
         r[2] += pnl
@@ -206,8 +212,12 @@ def build_report() -> str:
     if eq is not None:
         L.append(f"Equity €{eq:.2f} | Cap €{cap:.2f} | maxDD {dd:.3f}%")
 
-    # 1) CHIUSI
+    # 1) CHIUSI — totale storico + epoca corrente (post gate TP_PHANTOM): i
+    # numeri pre-v1.1.26 includono il churn fantasma e non misurano la strategia
+    cur = [c for c in closed if c[5] >= EPOCH_V1126]
+    cpos = sum(1 for c in cur if c[4])
     L.append(f"\n✅ <b>CHIUSI</b>: {pos} positivi / {neg} negativi (netto fee)")
+    L.append(f"   epoca v1.1.26+ (TP_PHANTOM attivo): {cpos}/{len(cur) - cpos}")
     rows = [f"{'motivo':<12}{'✅':>3}{'❌':>4}{'PnL€':>9}"]
     for reason, (w, l, pnl) in sorted(by_reason.items(), key=lambda x: x[1][2]):
         rows.append(f"{reason:<12}{w:>3}{l:>4}{pnl:>+9.2f}")