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>

data_provider.py

@@ -169,13 +169,24 @@ class ForexDataProvider:
                 return cached
 
         # Symbols not on Twelve Data (gold, silver) always use yfinance.
-        # D1 is intentionally skipped for these — yfinance daily for futures
+        # D1 is skipped (yfinance daily for futures is unreliable).
-        # is unreliable and the engine drops missing confirmation TFs cleanly.
+        # H4 is resampled from H1 since yfinance has no native 4h candle.
         if symbol in _YF_FALLBACK_MAP:
             if timeframe == Timeframe.D1:
                 raise DataUnavailableError(
-                    f"Daily chart skipped for {symbol} — gold/silver use M15/H1 only."
+                    f"Daily chart skipped for {symbol} — gold/silver use M15/H1/H4 only."
                 )
+            if timeframe == Timeframe.H4:
+                h1_df = self._fetch_yfinance_fallback(symbol, Timeframe.H1)
+                if h1_df is None:
+                    raise DataUnavailableError(
+                        f"Yahoo Finance unavailable for {symbol} (H4 resample source H1)."
+                    )
+                df = self._resample_h1_to_h4(h1_df)
+                df = self._validate_and_clean(df, symbol, timeframe)
+                self._cache[cache_key] = df
+                self._cache_timestamps[cache_key] = now
+                return df
             yf_df = self._fetch_yfinance_fallback(symbol, timeframe)
             if yf_df is None:
                 raise DataUnavailableError(
@@ -277,6 +288,17 @@ class ForexDataProvider:
         }
         return fallback_spreads.get(symbol, 0.00020)
 
+    @staticmethod
+    def _resample_h1_to_h4(h1_df: pd.DataFrame) -> pd.DataFrame:
+        """Resample 1-hour OHLCV to 4-hour bars (yfinance has no native 4h)."""
+        return h1_df.resample("4h", label="right", closed="right").agg({
+            "Open":   "first",
+            "High":   "max",
+            "Low":    "min",
+            "Close":  "last",
+            "Volume": "sum",
+        }).dropna()
+
     def get_pair_info(self, symbol: str) -> Dict:
         """Return display name and pip value for a pair."""
         display = SYMBOL_DISPLAY.get(symbol, symbol)

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)
+        # Batch-prefetch all pairs × all needed timeframes (3 API calls instead of 21).
-        all_timeframes = (
+        # Union of primary + per-symbol confirmation TFs across the watchlist.
-            [self.config.primary_timeframe] + self.config.confirmation_timeframes
+        tf_set = {self.config.primary_timeframe, *self.config.confirmation_timeframes}
-        )
+        for sym in self.config.watch_pairs:
-        self.data_provider.prefetch_batch(self.config.watch_pairs, all_timeframes)
+            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())

telegram_bot.py

@@ -40,7 +40,7 @@ logger = logging.getLogger(__name__)
 
 # ── Config ──────────────────────────────────────────────────────────
 
-SCAN_TIMEFRAMES = ["H1"]  # engine pulls M15 + H1 + D1 via confirmations
+SCAN_TIMEFRAMES = ["M15", "H1"]  # two cards (M15 and H1) per /scan; cache shared
 
 # Pairs analyzed by the /scan command. Limited to the most liquid majors
 # plus gold to keep credits low and signal quality high.