XAU/XAG: 3-TF analysis (M15+H1+H4 resampled) via per-symbol override

- New _PER_SYMBOL_TFS map in signal_aggregator routes gold/silver to
  M15+H1+H4 confirmation; forex pairs untouched.
- H4 for XAU/XAG is resampled from yfinance H1 bars (yfinance has no
  native 4h candle). New _resample_h1_to_h4 helper.
- Batch prefetch unions per-symbol TFs so future per-pair overrides
  work without extra plumbing. XAU/XAG still excluded from Twelve Data
  batch — H4 resample triggers a single yfinance H1 fetch.
- Telegram SCAN_TIMEFRAMES restored to [M15, H1] so /scan emits both
  M15-primary and H1-primary cards. Shared cache means no extra credits.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

signal_aggregator.py

@@ -31,6 +31,16 @@ from news_engine import NewsEngine, FundamentalContext
 logger = logging.getLogger(__name__)
 
 
+# Per-symbol full TF list (primary + confirmations). Overrides the global
+# config.confirmation_timeframes for these symbols only — forex pairs are
+# unaffected. XAU/XAG use M15+H1+H4 (no D1 since yfinance daily for futures
+# is unreliable; H4 is resampled from H1 in data_provider).
+_PER_SYMBOL_TFS: Dict[str, List[Timeframe]] = {
+    "XAUUSD=X": [Timeframe.M15, Timeframe.H1, Timeframe.H4],
+    "XAGUSD=X": [Timeframe.M15, Timeframe.H1, Timeframe.H4],
+}
+
+
 @dataclass
 class TradeSetup:
     """A complete trade setup recommendation."""
@@ -118,11 +128,12 @@ class SignalAggregator:
         reports = []
         context = self.news_engine.get_fundamental_context()
 
-        # Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21)
-        all_timeframes = (
-            [self.config.primary_timeframe] + self.config.confirmation_timeframes
-        )
-        self.data_provider.prefetch_batch(self.config.watch_pairs, all_timeframes)
+        # Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21).
+        # Union of primary + per-symbol confirmation TFs across the watchlist.
+        tf_set = {self.config.primary_timeframe, *self.config.confirmation_timeframes}
+        for sym in self.config.watch_pairs:
+            tf_set.update(self._confirmation_tfs(sym))
+        self.data_provider.prefetch_batch(self.config.watch_pairs, list(tf_set))
 
         for symbol in self.config.watch_pairs:
             try:
@@ -179,7 +190,7 @@ class SignalAggregator:
             return report
 
         # ── 2. Confirmation Timeframe Analyses ─────────────────────
-        for tf in self.config.confirmation_timeframes:
+        for tf in self._confirmation_tfs(symbol):
             try:
                 cf_df = self.data_provider.fetch_ohlcv(symbol, tf)
                 cf_analysis = self.tech_engine.analyze(cf_df, symbol, tf.value)
@@ -220,6 +231,14 @@ class SignalAggregator:
 
         return report
 
+    def _confirmation_tfs(self, symbol: str) -> List[Timeframe]:
+        """Return confirmation TFs for a symbol, honouring per-symbol overrides."""
+        override = _PER_SYMBOL_TFS.get(symbol)
+        if override is None:
+            return self.config.confirmation_timeframes
+        primary = self.config.primary_timeframe
+        return [tf for tf in override if tf != primary]
+
     def _check_mtf_agreement(self, report: SignalReport) -> bool:
         """Check if multiple timeframes agree on direction."""
         signals = list(report.mtf_signals.values())