audit+fix: anti-look-ahead audit, migrate deployable config to >=12h

- trackD_lookahead_audit.py: relabel test (left==right, no labeling leak) + execution-lag
  stress -> our trend pipeline is CLEAN (4h Sharpe 1.36 robust to +1 bar lag, label-invariant)
- ADOPT conservative conclusion: deploy at 12h (sub-12h: costs/overfit dominate, slight Sharpe
  bump unreliable). 12h: Sharpe 1.32, DD 13.3%, CAGR 16.2% ~ identical and robust
- trend_portfolio: DEPLOY_TF=12h, resample_tf(rule); paper trader + tests on 12h
- calendar research (NEGATIVE, both): trackF seasonality (spurious), trackG prior-levels
  (breakouts continue, fade dead; only long-drift survivor, redundant with TP01)
- gitignore data/paper_trend runtime state

src/strategies/trend_portfolio.py

@@ -4,13 +4,16 @@ Vincitrice della ricerca su dati certificati BTC/ETH (Deribit mainnet). TSMOM mu
 (1-3-6 mesi) vol-targeted, portafoglio 50/50 BTC+ETH. Validata onestamente (no look-ahead,
 fee 0.10% RT, positiva ogni anno 2019-2026, robusta su griglia e su tutti i timeframe 15m-1d).
 
-Config canonica deployabile (PORT LF4h):
-    timeframe 4h, LONG-FLAT (niente short), vol-target 20%, leverage cap 2x.
-    -> CAGR ~16.6%, Sharpe ~1.32, maxDD ~12.3% (backtest 2019-2026 su 50/50 BTC+ETH).
+Config canonica deployabile (PORT LF12h):
+    timeframe 12h, LONG-FLAT (niente short), vol-target 20%, leverage cap 2x.
+    -> CAGR ~16.2%, Sharpe ~1.32, maxDD ~13.3% (backtest 2019-2026 su 50/50 BTC+ETH).
 
-Perche' long-flat e 4h: gli short del trend rendono meno e aggiungono DD; il 4h e' il punto
-dolce (meno rumore/fee del 15m, meno lag dell'1d). Vedi docs/diary/2026-06-19-research-synthesis.md
-e scripts/research/trackD_*.py.
+Perche' >=12h (AGGIORNATO 2026-06-19): l'audit anti-look-ahead (scripts/research/
+trackD_lookahead_audit.py) mostra che il pipeline e' pulito (label-invariante, robusto a +1
+barra di lag), ma SOTTO le 12h costi e overfitting al rumore ad alta frequenza dominano (il
+piccolo extra di Sharpe a 4h/6h/8h non e' affidabile). A 12h/1d il risultato e' ~identico e
+robusto -> si deploya a 12h. Perche' long-flat: gli short del trend rendono meno e aggiungono
+DD. Vedi docs/diary/2026-06-19-research-synthesis.md e scripts/research/trackD_*.py.
 
 API (tutto causale, decide con dati <= close[i]):
     from src.strategies.trend_portfolio import TrendPortfolio, CANONICAL
@@ -168,16 +171,26 @@ def _bars_per_year(idx: pd.DatetimeIndex) -> float:
     return 86400 * 365.25 / dt if dt and dt > 0 else 365.25
 
 
-def resample_4h(df_1h: pd.DataFrame) -> pd.DataFrame:
-    """Resample 1h -> 4h (confini 00:00 UTC). Schema con 'datetime'."""
+DEPLOY_TF = "12h"          # timeframe deployabile (>=12h: sotto, costi/overfit dominano)
+
+
+def resample_tf(df_1h: pd.DataFrame, rule: str = "12h") -> pd.DataFrame:
+    """Resample 1h -> rule (confini 00:00 UTC, open-labeled). Schema con 'datetime'.
+    Il consumo e' index-based con shift +1 barra (net_returns) -> il labeling NON leakka
+    (verificato in trackD_lookahead_audit.py: Sharpe left == Sharpe right)."""
     g = df_1h.copy()
     idx = pd.to_datetime(g["timestamp"], unit="ms", utc=True)
     idx.name = "dt"
     g.index = idx
-    out = g.resample("4h", label="left", closed="left").agg(
+    out = g.resample(rule, label="left", closed="left").agg(
         {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})
     out = out.dropna(subset=["open"])
     out["datetime"] = out.index
     epoch = pd.Timestamp("1970-01-01", tz="UTC")
     out["timestamp"] = ((out.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
     return out.reset_index(drop=True)[["timestamp", "open", "high", "low", "close", "volume", "datetime"]]
+
+
+def resample_4h(df_1h: pd.DataFrame) -> pd.DataFrame:
+    """Compat per gli script di ricerca. Per il DEPLOY usare resample_tf(df, '12h')."""
+    return resample_tf(df_1h, "4h")