Fix scoring math; add 10-min auto-scan A-grade alerts with news block

- engine: composite-score denominator now confidence-weighted, so the
  output actually spans -100..+100 (the prior formula put confidence
  in the numerator only, capping the realistic score around ±25-30
  and making STRONG_BUY ≥40 essentially unreachable).
- engine: ATR percentile uses iloc[-50:-1] to exclude the current bar
  from its own comparison (off-by-one in volatility regime).
- signal_aggregator: always blend primary score with MTF average. The
  prior code only blended on agreement, leaving the primary unblended
  on disagreement — backwards. Disagreement is exactly when the score
  should be pulled toward consensus.
- telegram_bot: JobQueue auto-scan every 10 min that pushes A/A+ to
  ALERT_CHAT_ID with a prominent News & Events block (sentiment +
  headline summary + event-risk + alignment hint). Per-bar dedup so
  persistent setups alert once per new candle, not every 10 min.
  Suppresses alerts when event_risk = "danger".
- telegram_bot: /myid returns chat ID for ALERT_CHAT_ID setup. Auto-scan
  silently disabled when ALERT_CHAT_ID is unset.

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

engine.py

@@ -499,7 +499,8 @@ class TechnicalEngine:
 
         # Compare to historical ATR
         if len(atr) >= 50:
-            atr_percentile = (atr.iloc[-1] > atr.iloc[-50:]).mean() * 100
+            # Exclude current bar so ATR isn't compared against itself
+            atr_percentile = (atr.iloc[-1] > atr.iloc[-50:-1]).mean() * 100
         else:
             atr_percentile = 50.0
 
@@ -844,7 +845,8 @@ class TechnicalEngine:
         - Confidence scaling
         """
         total_score = 0.0
-        total_weight = 0.0
+        score_weight = 0.0       # denominator for score: weight × confidence
+        weight_sum = 0.0         # denominator for confidence average: weight only
         total_confidence = 0.0
 
         regime_weights = {
@@ -863,16 +865,18 @@ class TechnicalEngine:
             regime_mult = active_regime_weights.get(name, 1.0)
             weight *= regime_mult
 
+            conf_factor = ind.confidence / 100.0
             signal_score = ind.signal.value  # -2 to +2
-            weighted_score = signal_score * weight * (ind.confidence / 100.0)
 
-            total_score += weighted_score
-            total_weight += weight
+            total_score += signal_score * weight * conf_factor
+            score_weight += weight * conf_factor
+            weight_sum += weight
             total_confidence += ind.confidence * weight
 
-        if total_weight > 0:
-            normalized_score = (total_score / total_weight) * 50  # scale to -100..+100
-            avg_confidence = total_confidence / total_weight
+        if score_weight > 0:
+            # Confidence-weighted average of signal_score (∈ [-2,+2]); ×50 → [-100,+100]
+            normalized_score = (total_score / score_weight) * 50
+            avg_confidence = total_confidence / weight_sum if weight_sum > 0 else 0
         else:
             normalized_score = 0
             avg_confidence = 0