Serve live recommendation context on trade setup APIs and alerts

Stored TradeSetup rows are point-in-time snapshots from the RR scan, so
the ticker page could show stale confidence/reasoning/composite (e.g.
sentiment=neutral in the setup card while the sentiment panel showed
bullish). Overlay current score/sentiment context onto the API payload
for GET /trades and GET /trades/{symbol}, gate and format Telegram
qualified-setup alerts on the same live values, and apply the
min_confidence/recommended_action filters after the overlay so they
judge what the caller actually sees. Stored setups stay frozen for
outcome analysis and backtests.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

app/services/recommendation_service.py

@@ -524,6 +524,56 @@ def _build_reasoning(
     )
 
 
+def build_recommendation_snapshot(
+    dimension_scores: dict[str, float],
+    sentiment_classification: str | None,
+    config: dict[str, float],
+    available_directions: set[str] | None = None,
+) -> dict[str, Any]:
+    """Build the ticker-level recommendation from the supplied live context."""
+    conflicts = signal_conflict_detector.detect_conflicts(
+        dimension_scores=dimension_scores,
+        sentiment_classification=sentiment_classification,
+        config=config,
+    )
+
+    long_confidence = direction_analyzer.calculate_confidence(
+        direction="long",
+        dimension_scores=dimension_scores,
+        sentiment_classification=sentiment_classification,
+        conflicts=conflicts,
+    )
+    short_confidence = direction_analyzer.calculate_confidence(
+        direction="short",
+        dimension_scores=dimension_scores,
+        sentiment_classification=sentiment_classification,
+        conflicts=conflicts,
+    )
+
+    action = _choose_recommended_action(
+        long_confidence, short_confidence, config, available_directions
+    )
+    reasoning = _build_reasoning(
+        action=action,
+        long_confidence=long_confidence,
+        short_confidence=short_confidence,
+        conflicts=conflicts,
+        dimension_scores=dimension_scores,
+        sentiment_classification=sentiment_classification,
+        config=config,
+        available_directions=available_directions,
+    )
+
+    return {
+        "action": action,
+        "reasoning": reasoning,
+        "risk_level": _risk_level_from_conflicts(conflicts),
+        "long_confidence": long_confidence,
+        "short_confidence": short_confidence,
+        "conflicts": conflicts,
+    }
+
+
 PRIMARY_TARGET_MIN_RR = 1.5
 
 
@@ -559,24 +609,15 @@ async def enhance_trade_setup(
 ) -> TradeSetup:
     config = await get_recommendation_config(db)
 
-    conflicts = signal_conflict_detector.detect_conflicts(
+    snapshot = build_recommendation_snapshot(
         dimension_scores=dimension_scores,
         sentiment_classification=sentiment_classification,
         config=config,
+        available_directions=available_directions,
     )
-
-    long_confidence = direction_analyzer.calculate_confidence(
-        direction="long",
-        dimension_scores=dimension_scores,
-        sentiment_classification=sentiment_classification,
-        conflicts=conflicts,
-    )
-    short_confidence = direction_analyzer.calculate_confidence(
-        direction="short",
-        dimension_scores=dimension_scores,
-        sentiment_classification=sentiment_classification,
-        conflicts=conflicts,
-    )
+    conflicts = list(snapshot["conflicts"])
+    long_confidence = float(snapshot["long_confidence"])
+    short_confidence = float(snapshot["short_confidence"])
 
     direction = setup.direction.lower()
     confidence = long_confidence if direction == "long" else short_confidence
@@ -622,19 +663,8 @@ async def enhance_trade_setup(
 
     # Action and reasoning are ticker-level: they consider both directions and
     # which directions are actually tradeable, and are identical on every setup.
-    action = _choose_recommended_action(
-        long_confidence, short_confidence, config, available_directions
-    )
-    reasoning = _build_reasoning(
-        action=action,
-        long_confidence=long_confidence,
-        short_confidence=short_confidence,
-        conflicts=conflicts,
-        dimension_scores=dimension_scores,
-        sentiment_classification=sentiment_classification,
-        config=config,
-        available_directions=available_directions,
-    )
+    action = str(snapshot["action"])
+    reasoning = str(snapshot["reasoning"])
 
     setup.confidence_score = round(confidence, 2)
     setup.targets_json = json.dumps(targets)