feat(pairs): attiva ETH/BTC 15m flat-skip in PORT06 (BLEND, mezza size)

Origine: gioco "Blind Traders" (100 agenti ciechi su BTC/ETH anonimizzati) ->
vincitore = spread ETH/BTC reversion a 15m. Testato sul serio col gate PORT06:
non duplicato (corr 1h vs 15m = 0.37), robusto (16/16 celle Sharpe>1), edge NON
artefatto delle candele flat ETH 15m (filtrandole resta l'83% dello Sharpe).

Percorso live costruito e validato:
- pairs_research.pairs_sim_flat: engine generalizzato con exit LIVE-REALIZABLE
  (arma exit_ready, esce alla 1a barra pulita); regression-lock a pairs_sim.
- PairsWorker: flat_skip + exit_ready + rilevamento flat da OHLC (1h byte-exact).
- runner: fetch diretto dei timeframe sub-orari + override position_size per-sleeve.
- validate_worker_pairs: replay worker == backtest a 15m (8452 vs 8453 trade).
- _defs/build_everything: sleeve PR_ETHBTC_15M (mezza size, pos 0.10) -> PORT06
  FULL 6.43->7.20, OOS 8.58->9.66, DD giu'. Rischio bilanciato col 1h.
- smoke live: Cerbero serve candele 15m fresche; worker ticca.

Diari docs/diary/2026-06-09-*. Caveat slippage: mezza size = blend-tilt prudente.

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

scripts/analysis/pairs15m_live_smoke.py

@@ -0,0 +1,81 @@
+"""Smoke LIVE del nuovo percorso 15m: fetch DIRETTO 15m da Cerbero per ETH/BTC +
+freschezza + flat-fraction + un tick reale del PairsWorker(flat_skip).
+
+Verifica cio' che il backtest non vede: che Cerbero serva candele 15m fresche per
+entrambe le gambe (il runner ora le fetcha dirette, non resamplate dal 1h) e che il
+worker 15m le processi senza errori. NON apre ordini reali (l'esecuzione a 2 gambe e'
+gia' coperta da live_pairs_smoke.py, indipendente dal timeframe).
+
+    uv run python scripts/analysis/pairs15m_live_smoke.py
+"""
+from __future__ import annotations
+
+import sys
+import shutil
+import tempfile
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.live.cerbero_client import CerberoClient
+from src.live.multi_runner import INSTRUMENT_MAP
+from src.live.pairs_worker import PairsWorker
+
+CFG = {"n": 66, "z_in": 1.674, "z_exit": 1.0, "max_bars": 35, "flat_skip": True}
+
+
+def fetch15(cli, asset, days=14):
+    inst = INSTRUMENT_MAP.get(asset, f"{asset}-PERPETUAL")
+    end = datetime.now(timezone.utc)
+    start = end - timedelta(days=days)
+    candles = cli.get_historical_v2(inst, start.strftime("%Y-%m-%d"),
+                                    end.strftime("%Y-%m-%d"), "15m")
+    if not candles:
+        return inst, None
+    df = pd.DataFrame(candles)
+    df["timestamp"] = df["timestamp"].astype("int64")
+    return inst, df.sort_values("timestamp").reset_index(drop=True)
+
+
+def main():
+    print("=" * 84)
+    print("  SMOKE LIVE — ETH/BTC pairs 15m (fetch diretto Cerbero + tick worker flat-skip)")
+    print("=" * 84)
+    cli = CerberoClient()
+    inst_a, da = fetch15(cli, "ETH")
+    inst_b, db = fetch15(cli, "BTC")
+    ok = True
+    for asset, inst, df in [("ETH", inst_a, da), ("BTC", inst_b, db)]:
+        if df is None or df.empty:
+            print(f"  {asset} ({inst}): NESSUNA candela 15m -> FAIL"); ok = False; continue
+        last = pd.to_datetime(df["timestamp"].iloc[-1], unit="ms", utc=True)
+        age_min = (datetime.now(timezone.utc) - last).total_seconds() / 60
+        flat = ((df["open"] == df["high"]) & (df["high"] == df["low"]) &
+                (df["low"] == df["close"])).mean() * 100
+        fresh = age_min < 60
+        print(f"  {asset} ({inst}): {len(df)} barre 15m | ultima {last:%Y-%m-%d %H:%M} "
+              f"({age_min:.0f} min fa, {'FRESCO' if fresh else 'STALE'}) | flat {flat:.1f}%")
+        ok &= fresh
+    if da is None or db is None:
+        print("\n  ESITO: FAIL (feed 15m assente)."); return
+    # tick reale del worker 15m
+    tmp = Path(tempfile.mkdtemp(prefix="smoke15m_"))
+    try:
+        w = PairsWorker("ETH", "BTC", "15m", params=CFG, fee_rt=0.001, data_dir=tmp)
+        df_a = pd.DataFrame({"timestamp": da["timestamp"], "open": da["open"], "high": da["high"],
+                             "low": da["low"], "close": da["close"]})
+        df_b = pd.DataFrame({"timestamp": db["timestamp"], "open": db["open"], "high": db["high"],
+                             "low": db["low"], "close": db["close"]})
+        w.tick(df_a, df_b)
+        print(f"\n  Worker 15m flat_skip={w.flat_skip} -> tick OK | {w.status_summary}")
+        print(f"  ESITO: {'OK — feed 15m fresco e worker ticca' if ok else 'ATTENZIONE: feed 15m stale/parziale'}")
+    finally:
+        shutil.rmtree(tmp, ignore_errors=True)
+
+
+if __name__ == "__main__":
+    main()