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/alert_service.py

@@ -254,7 +254,9 @@ async def _watchlist_tickers(db: AsyncSession) -> list[tuple[int, str]]:
 
 
 async def _qualified_setups(db: AsyncSession) -> list[dict]:
-    setups = await get_trade_setups(db)
+    # live_recommendation: gate and format on current score/sentiment context,
+    # not the values frozen into the setup at scan time.
+    setups = await get_trade_setups(db, live_recommendation=True)
     config = await get_activation_config(db)
     return [s for s in setups if setup_qualifies(SimpleNamespace(**s), config)]
 

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)

app/services/rr_scanner_service.py

@@ -27,7 +27,11 @@ from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
 from app.services.indicator_service import _extract_ohlcv, compute_atr
 from app.services.price_service import query_ohlcv
-from app.services.recommendation_service import enhance_trade_setup
+from app.services.recommendation_service import (
+    build_recommendation_snapshot,
+    enhance_trade_setup,
+    get_recommendation_config,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -80,6 +84,110 @@ async def _get_latest_sentiment(db: AsyncSession, ticker_id: int) -> str | None:
     return row.classification if row else None
 
 
+async def _refresh_score_context_for_symbols(
+    db: AsyncSession,
+    symbols: set[str],
+) -> None:
+    """Refresh provider-free scores so live recommendation summaries match the page."""
+    if not symbols:
+        return
+
+    from app.services import scoring_service
+
+    refreshed = False
+    for symbol in sorted(symbols):
+        try:
+            await scoring_service.compute_all_dimensions(db, symbol)
+            await scoring_service.compute_composite_score(db, symbol)
+            refreshed = True
+        except Exception:
+            logger.exception("Error refreshing live score context for %s", symbol)
+
+    if refreshed:
+        await db.commit()
+
+
+async def _apply_live_recommendation_context(
+    db: AsyncSession,
+    setup_rows: list[tuple[TradeSetup, str]],
+    rows: list[dict],
+) -> list[dict]:
+    """Decorate latest setup rows with current score/sentiment recommendation data.
+
+    This intentionally updates only the API payload. Stored trade setups and
+    history remain point-in-time records for outcome analysis.
+    """
+    if not rows or not setup_rows:
+        return rows
+
+    ticker_ids = {setup.ticker_id for setup, _ in setup_rows}
+    setups_by_id = {setup.id: setup for setup, _ in setup_rows}
+
+    directions_by_ticker: dict[int, set[str]] = {}
+    for setup, _ in setup_rows:
+        directions_by_ticker.setdefault(setup.ticker_id, set()).add(setup.direction.lower())
+
+    dim_result = await db.execute(
+        select(DimensionScore).where(DimensionScore.ticker_id.in_(ticker_ids))
+    )
+    dims_by_ticker: dict[int, dict[str, float]] = {}
+    for ds in dim_result.scalars().all():
+        dims_by_ticker.setdefault(ds.ticker_id, {})[ds.dimension] = float(ds.score)
+
+    comp_result = await db.execute(
+        select(CompositeScore)
+        .where(CompositeScore.ticker_id.in_(ticker_ids))
+        .order_by(CompositeScore.ticker_id, CompositeScore.computed_at.desc())
+    )
+    composites: dict[int, CompositeScore] = {}
+    for comp in comp_result.scalars().all():
+        composites.setdefault(comp.ticker_id, comp)
+
+    sent_result = await db.execute(
+        select(SentimentScore)
+        .where(SentimentScore.ticker_id.in_(ticker_ids))
+        .order_by(SentimentScore.ticker_id, SentimentScore.timestamp.desc())
+    )
+    sentiments: dict[int, SentimentScore] = {}
+    for sent in sent_result.scalars().all():
+        sentiments.setdefault(sent.ticker_id, sent)
+
+    config = await get_recommendation_config(db)
+    live_rows: list[dict] = []
+    for row in rows:
+        setup = setups_by_id.get(row["id"])
+        if setup is None:
+            live_rows.append(row)
+            continue
+
+        ticker_id = setup.ticker_id
+        live_row = dict(row)
+
+        comp = composites.get(ticker_id)
+        if comp is not None:
+            live_row["composite_score"] = float(comp.score)
+
+        dimension_scores = dims_by_ticker.get(ticker_id)
+        if dimension_scores:
+            sentiment = sentiments.get(ticker_id)
+            snapshot = build_recommendation_snapshot(
+                dimension_scores=dimension_scores,
+                sentiment_classification=sentiment.classification if sentiment else None,
+                config=config,
+                available_directions=directions_by_ticker.get(ticker_id),
+            )
+            direction = setup.direction.lower()
+            confidence_key = "long_confidence" if direction == "long" else "short_confidence"
+            live_row["confidence_score"] = round(float(snapshot[confidence_key]), 2)
+            live_row["recommended_action"] = snapshot["action"]
+            live_row["reasoning"] = snapshot["reasoning"]
+            live_row["risk_level"] = snapshot["risk_level"]
+
+        live_rows.append(live_row)
+
+    return live_rows
+
+
 def _json_default(value):
     if isinstance(value, (datetime, date)):
         return value.isoformat()
@@ -441,6 +549,8 @@ async def get_trade_setups(
     min_confidence: float | None = None,
     recommended_action: str | None = None,
     symbol: str | None = None,
+    live_recommendation: bool = False,
+    recompute_scores: bool = False,
 ) -> list[dict]:
     """Get latest stored trade setups, optionally filtered."""
     stmt = (
@@ -451,9 +561,11 @@ async def get_trade_setups(
         stmt = stmt.where(TradeSetup.direction == direction.lower())
     if symbol is not None:
         stmt = stmt.where(Ticker.symbol == symbol.strip().upper())
-    if min_confidence is not None:
+    # With live_recommendation these fields are overlaid with current values
+    # below, so filtering happens there instead of against the stored columns.
+    if min_confidence is not None and not live_recommendation:
         stmt = stmt.where(TradeSetup.confidence_score >= min_confidence)
-    if recommended_action is not None:
+    if recommended_action is not None and not live_recommendation:
         stmt = stmt.where(TradeSetup.recommended_action == recommended_action)
 
     stmt = stmt.order_by(TradeSetup.detected_at.desc(), TradeSetup.id.desc())
@@ -477,11 +589,38 @@ async def get_trade_setups(
         reverse=True,
     )
 
+    if recompute_scores:
+        await _refresh_score_context_for_symbols(
+            db, {ticker_symbol for _, ticker_symbol in latest_rows}
+        )
+
     prices = await _latest_closes(db, {s.ticker_id for s, _ in latest_rows})
-    return [
+    rows_out = [
         _trade_setup_to_dict(setup, ticker_symbol, prices.get(setup.ticker_id))
         for setup, ticker_symbol in latest_rows
     ]
+    if live_recommendation:
+        rows_out = await _apply_live_recommendation_context(db, latest_rows, rows_out)
+        if min_confidence is not None:
+            rows_out = [
+                row for row in rows_out
+                if row["confidence_score"] is not None
+                and row["confidence_score"] >= min_confidence
+            ]
+        if recommended_action is not None:
+            rows_out = [
+                row for row in rows_out
+                if row["recommended_action"] == recommended_action
+            ]
+        rows_out.sort(
+            key=lambda row: (
+                row["confidence_score"] if row["confidence_score"] is not None else -1.0,
+                row["rr_ratio"],
+                row["composite_score"],
+            ),
+            reverse=True,
+        )
+    return rows_out
 
 
 async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, float]: