Backtest tuning, data freshness guard, Telegram improvements

- Drop USD/JPY from scan pairs (zero signals across all timeframes)
- Disable divergence indicator (zero impact in 14-config lab test)
- Parallelize evaluate.py with ProcessPoolExecutor (16 cores)
- Add --pair CLI filter to evaluate.py for single-pair backtests
- Add data freshness guard: stale cache auto-invalidated, primary TF
  stale data suppresses signals to prevent trading on old data
- Add /cache command: shows fetch age and last bar per pair/TF
- Add /clearcache command: wipes in-memory cache for fresh API fetch
- Remove BB from WebSocket breach alerts (keep structural levels only)
- Show live prices only for WS-monitored pairs (EUR/USD, XAU/USD)
- Replace arrow emojis with text tags in indicator lines

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

data_provider.py

@@ -95,7 +95,11 @@ class ForexDataProvider:
         ):
             cached = self._cache.get(cache_key)
             if cached is not None and not cached.empty:
-                return cached
+                if self._is_data_fresh(cached, symbol, timeframe):
+                    return cached
+                logger.warning(f"{symbol} {timeframe.value}: cached data is stale, refetching")
+                del self._cache[cache_key]
+                del self._cache_timestamps[cache_key]
 
         outputsize = max(
             300,
@@ -104,10 +108,48 @@ class ForexDataProvider:
         df = self._fetch_twelve_data(symbol, timeframe, outputsize)
         df = self._validate_and_clean(df, symbol, timeframe)
 
+        if not self._is_data_fresh(df, symbol, timeframe):
+            logger.warning(
+                f"STALE DATA from API: {symbol} {timeframe.value} — "
+                f"last bar {df.index[-1]} is too old. Signal quality may be degraded."
+            )
+
         self._cache[cache_key] = df
         self._cache_timestamps[cache_key] = now
         return df
 
+    def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
+        """Check if the last candle is within acceptable age for this timeframe."""
+        if df is None or df.empty:
+            return False
+
+        now = pd.Timestamp.now(tz="UTC")
+
+        if now.weekday() >= 5:
+            return True
+
+        last_bar = df.index[-1]
+        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
+        max_age_seconds = candle_seconds * 3
+        age_seconds = (now - last_bar).total_seconds()
+
+        if age_seconds > max_age_seconds:
+            logger.warning(
+                f"{symbol} {timeframe.value}: last bar {last_bar.strftime('%Y-%m-%d %H:%M')} "
+                f"is {age_seconds / 60:.0f}m old (limit: {max_age_seconds / 60:.0f}m)"
+            )
+            return False
+
+        return True
+
+    def clear_cache(self) -> int:
+        """Wipe all cached data. Returns number of entries cleared."""
+        count = len(self._cache)
+        self._cache.clear()
+        self._cache_timestamps.clear()
+        logger.info(f"Cache cleared: {count} entries wiped")
+        return count
+
     def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
         try:
             df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
@@ -255,7 +297,6 @@ class ForexDataProvider:
 
         min_required = max(
             self.config.technical.ema_trend,
-            self.config.technical.ichimoku_senkou_b,
             200,
         ) + 10