Make live signal reads non-mutating

app/routers/scores.py

@@ -54,7 +54,7 @@ async def read_score(
     _user=Depends(require_access),
     db: AsyncSession = Depends(get_db),
 ) -> APIEnvelope:
-    """Get composite + dimension scores for a symbol. Recomputes stale scores."""
+    """Get the latest persisted composite + dimension scores for a symbol."""
     result = await get_score(db, symbol)
 
     data = ScoreResponse(
@@ -94,6 +94,7 @@ async def read_rankings(
             RankingEntry(
                 symbol=r["symbol"],
                 composite_score=r["composite_score"],
+                composite_stale=r.get("composite_stale", False),
                 dimensions=[
                     DimensionScoreResponse(**d) for d in r["dimensions"]
                 ],

app/routers/trades.py

@@ -97,7 +97,6 @@ async def get_ticker_trade_setups(
         db,
         symbol=symbol,
         live_recommendation=True,
-        recompute_scores=True,
     )
     data = []
     for row in rows:

app/schemas/score.py

@@ -78,6 +78,7 @@ class RankingEntry(BaseModel):
 
     symbol: str
     composite_score: float
+    composite_stale: bool = False
     dimensions: list[DimensionScoreResponse] = []
 
 

app/schemas/trade_setup.py

@@ -26,6 +26,14 @@ class RecommendationSummaryResponse(BaseModel):
     composite_score: float
 
 
+class TradeSetupContextAsOfResponse(BaseModel):
+    setup_detected_at: datetime
+    score_computed_at: datetime | None = None
+    sentiment_at: datetime | None = None
+    price_date: date | None = None
+    price_updated_at: datetime | None = None
+
+
 class TradeSetupResponse(BaseModel):
     """A single trade setup detected by the R:R scanner."""
 
@@ -49,4 +57,5 @@ class TradeSetupResponse(BaseModel):
     evaluated_at: datetime | None = None
     current_price: float | None = None
     momentum_percentile: float | None = None
+    context_as_of: TradeSetupContextAsOfResponse | None = None
     recommendation_summary: RecommendationSummaryResponse | None = None

app/services/rr_scanner_service.py

@@ -28,6 +28,7 @@ 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 (
+    _risk_level_from_conflicts,
     build_recommendation_snapshot,
     enhance_trade_setup,
     get_recommendation_config,
@@ -84,29 +85,6 @@ 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]],
@@ -122,10 +100,7 @@ async def _apply_live_recommendation_context(
 
     ticker_ids = {setup.ticker_id for setup, _ in setup_rows}
     setups_by_id = {setup.id: setup for setup, _ in setup_rows}
-
+    directions_by_ticker = await _latest_available_directions_by_ticker(db, ticker_ids)
-    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))
@@ -166,10 +141,13 @@ async def _apply_live_recommendation_context(
         comp = composites.get(ticker_id)
         if comp is not None:
             live_row["composite_score"] = float(comp.score)
+            live_row["context_as_of"]["score_computed_at"] = comp.computed_at
 
         dimension_scores = dims_by_ticker.get(ticker_id)
-        if dimension_scores:
         sentiment = sentiments.get(ticker_id)
+        if sentiment is not None:
+            live_row["context_as_of"]["sentiment_at"] = sentiment.timestamp
+        if dimension_scores:
             snapshot = build_recommendation_snapshot(
                 dimension_scores=dimension_scores,
                 sentiment_classification=sentiment.classification if sentiment else None,
@@ -181,13 +159,59 @@ async def _apply_live_recommendation_context(
             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"]
+            setup_conflicts = _setup_specific_conflicts(live_row.get("conflict_flags", []))
+            live_conflicts = [str(item) for item in snapshot["conflicts"]]
+            live_row["conflict_flags"] = live_conflicts + setup_conflicts
+            live_row["risk_level"] = _risk_level_from_conflicts(live_row["conflict_flags"])
 
         live_rows.append(live_row)
 
     return live_rows
 
 
+def _setup_specific_conflicts(conflicts: list[str]) -> list[str]:
+    signal_prefixes = (
+        "sentiment-technical:",
+        "sentiment-momentum:",
+        "momentum-technical:",
+        "fundamental-technical:",
+    )
+    return [
+        str(conflict)
+        for conflict in conflicts
+        if not str(conflict).startswith(signal_prefixes)
+    ]
+
+
+async def _latest_available_directions_by_ticker(
+    db: AsyncSession,
+    ticker_ids: set[int],
+) -> dict[int, set[str]]:
+    if not ticker_ids:
+        return {}
+
+    result = await db.execute(
+        select(TradeSetup)
+        .where(TradeSetup.ticker_id.in_(ticker_ids))
+        .order_by(
+            TradeSetup.ticker_id,
+            TradeSetup.direction,
+            TradeSetup.detected_at.desc(),
+            TradeSetup.id.desc(),
+        )
+    )
+    latest_by_key: set[tuple[int, str]] = set()
+    directions: dict[int, set[str]] = {}
+    for setup in result.scalars().all():
+        direction = setup.direction.lower()
+        key = (setup.ticker_id, direction)
+        if key in latest_by_key:
+            continue
+        latest_by_key.add(key)
+        directions.setdefault(setup.ticker_id, set()).add(direction)
+    return directions
+
+
 def _json_default(value):
     if isinstance(value, (datetime, date)):
         return value.isoformat()
@@ -550,7 +574,6 @@ async def get_trade_setups(
     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 = (
@@ -589,12 +612,7 @@ async def get_trade_setups(
         reverse=True,
     )
 
-    if recompute_scores:
+    prices = await _latest_price_context(db, {s.ticker_id for s, _ in latest_rows})
-        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})
     rows_out = [
         _trade_setup_to_dict(setup, ticker_symbol, prices.get(setup.ticker_id))
         for setup, ticker_symbol in latest_rows
@@ -623,8 +641,8 @@ async def get_trade_setups(
     return rows_out
 
 
-async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, float]:
+async def _latest_price_context(db: AsyncSession, ticker_ids: set[int]) -> dict[int, dict]:
-    """Most recent close per ticker — used to judge a setup's current relevance."""
+    """Most recent daily OHLCV row per ticker for live price context."""
     if not ticker_ids:
         return {}
     latest = (
@@ -633,7 +651,12 @@ async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, fl
         .group_by(OHLCVRecord.ticker_id)
         .subquery()
     )
-    stmt = select(OHLCVRecord.ticker_id, OHLCVRecord.close).join(
+    stmt = select(
+        OHLCVRecord.ticker_id,
+        OHLCVRecord.close,
+        OHLCVRecord.date,
+        OHLCVRecord.created_at,
+    ).join(
         latest,
         and_(
             OHLCVRecord.ticker_id == latest.c.ticker_id,
@@ -641,7 +664,23 @@ async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, fl
         ),
     )
     result = await db.execute(stmt)
-    return {tid: float(close) for tid, close in result.all()}
+    return {
+        tid: {
+            "current_price": float(close),
+            "price_date": price_date,
+            "price_updated_at": created_at,
+        }
+        for tid, close, price_date, created_at in result.all()
+    }
+
+
+async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, float]:
+    """Most recent close per ticker, kept for callers that only need price."""
+    price_context = await _latest_price_context(db, ticker_ids)
+    return {
+        ticker_id: context["current_price"]
+        for ticker_id, context in price_context.items()
+    }
 
 
 async def get_trade_setup_history(
@@ -658,16 +697,28 @@ async def get_trade_setup_history(
     result = await db.execute(stmt)
     rows = result.all()
 
-    prices = await _latest_closes(db, {s.ticker_id for s, _ in rows})
+    prices = await _latest_price_context(db, {s.ticker_id for s, _ in rows})
     return [
         _trade_setup_to_dict(setup, ticker_symbol, prices.get(setup.ticker_id))
         for setup, ticker_symbol in rows
     ]
 
 
-def _trade_setup_to_dict(setup: TradeSetup, symbol: str, current_price: float | None = None) -> dict:
+def _trade_setup_to_dict(setup: TradeSetup, symbol: str, price_context: dict | None = None) -> dict:
     targets: list[dict] = []
     conflicts: list[str] = []
+    current_price = (
+        float(price_context["current_price"])
+        if price_context and price_context.get("current_price") is not None
+        else None
+    )
+    context_as_of = {
+        "setup_detected_at": setup.detected_at,
+        "score_computed_at": None,
+        "sentiment_at": None,
+        "price_date": price_context.get("price_date") if price_context else None,
+        "price_updated_at": price_context.get("price_updated_at") if price_context else None,
+    }
 
     if setup.targets_json:
         try:
@@ -706,4 +757,5 @@ def _trade_setup_to_dict(setup: TradeSetup, symbol: str, current_price: float |
         "evaluated_at": setup.evaluated_at,
         "current_price": current_price,
         "momentum_percentile": setup.momentum_percentile,
+        "context_as_of": context_as_of,
     }

app/services/scoring_service.py

@@ -2,8 +2,8 @@
 
 Computes dimension scores (technical, sr_quality, sentiment, fundamental,
 momentum) each 0-100, composite score as weighted average of available
-dimensions with re-normalized weights, staleness marking/recomputation
+dimensions with re-normalized weights, staleness marking, explicit refresh
-on demand, and weight update triggers full recomputation.
+paths, and weight update triggers full recomputation.
 """
 
 from __future__ import annotations
@@ -765,73 +765,37 @@ async def compute_composite_score(
 async def get_score(
     db: AsyncSession, symbol: str
 ) -> dict:
-    """Get composite + all dimension scores for a ticker.
+    """Read composite + dimension scores for a ticker without recomputing.
 
-    Recomputes stale dimensions on demand, then recomputes composite.
+    GET endpoints use this path, so it must not mutate persisted score context.
-    Returns a dict suitable for ScoreResponse, including dimension breakdowns
+    Scheduled/manual write paths are responsible for refreshing scores.
-    and composite breakdown with re-normalization info.
     """
     ticker = await _get_ticker(db, symbol)
     weights = await _get_weights(db)
 
-    # Check for stale dimension scores and recompute them
-    result = await db.execute(
-        select(DimensionScore).where(DimensionScore.ticker_id == ticker.id)
-    )
-    dim_scores = {ds.dimension: ds for ds in result.scalars().all()}
-
-    for dim in DIMENSIONS:
-        ds = dim_scores.get(dim)
-        if ds is None or ds.is_stale:
-            await compute_dimension_score(db, symbol, dim)
-
-    # Check composite staleness
-    comp_result = await db.execute(
-        select(CompositeScore).where(CompositeScore.ticker_id == ticker.id)
-    )
-    comp = comp_result.scalar_one_or_none()
-
-    if comp is None or comp.is_stale:
-        await compute_composite_score(db, symbol, weights)
-
-    await db.commit()
-
-    # Re-fetch everything fresh
     result = await db.execute(
         select(DimensionScore).where(DimensionScore.ticker_id == ticker.id)
     )
     dim_scores_list = list(result.scalars().all())
+    dim_scores = {ds.dimension: ds for ds in dim_scores_list}
 
     comp_result = await db.execute(
         select(CompositeScore).where(CompositeScore.ticker_id == ticker.id)
     )
     comp = comp_result.scalar_one_or_none()
 
-    # Compute breakdowns for each dimension by calling the dimension computers
-    breakdowns: dict[str, dict | None] = {}
-    for dim in DIMENSIONS:
-        try:
-            raw_result = await _DIMENSION_COMPUTERS[dim](db, symbol)
-            if isinstance(raw_result, tuple) and len(raw_result) == 2:
-                breakdowns[dim] = raw_result[1]
-            else:
-                breakdowns[dim] = None
-        except Exception:
-            breakdowns[dim] = None
-
-    # Build dimension entries with breakdowns
     dimensions = []
     missing = []
     available_dims: list[str] = []
     for dim in DIMENSIONS:
-        found = next((ds for ds in dim_scores_list if ds.dimension == dim), None)
+        found = dim_scores.get(dim)
         if found is not None and not found.is_stale and found.score is not None:
             dimensions.append({
                 "dimension": found.dimension,
                 "score": found.score,
                 "is_stale": found.is_stale,
                 "computed_at": found.computed_at,
-                "breakdown": breakdowns.get(dim),
+                "breakdown": None,
             })
             w = weights.get(dim, 0.0)
             if w > 0:
@@ -845,7 +809,7 @@ async def get_score(
                     "score": found.score,
                     "is_stale": found.is_stale,
                     "computed_at": found.computed_at,
-                    "breakdown": breakdowns.get(dim),
+                    "breakdown": None,
                 })
 
     # Build composite breakdown: the non-sentiment base (re-normalized weighted
@@ -925,31 +889,13 @@ async def get_rankings(db: AsyncSession) -> dict:
             dims[ds.ticker_id][ds.dimension] = ds
         return comps, dims
 
-    # Two bulk reads instead of ~4 queries per ticker.
-    comps, dims_by_ticker = await _load_scores()
-
-    # Lazily recompute any stale/missing scores (kept fresh by the daily scan;
-    # this self-heals tickers that aged out between scans), committing once.
-    recomputed = False
-    for ticker in tickers:
-        comp = comps.get(ticker.id)
-        if comp is None or comp.is_stale:
-            dim_scores = dims_by_ticker.get(ticker.id, {})
-            for dim in DIMENSIONS:
-                ds = dim_scores.get(dim)
-                if ds is None or ds.is_stale:
-                    await compute_dimension_score(db, ticker.symbol, dim)
-            await compute_composite_score(db, ticker.symbol, weights)
-            recomputed = True
-
-    if recomputed:
-        await db.commit()
     comps, dims_by_ticker = await _load_scores()
 
     rankings = [
         {
             "symbol": ticker.symbol,
             "composite_score": comp.score,
+            "composite_stale": comp.is_stale,
             "dimensions": [
                 {
                     "dimension": ds.dimension,

frontend/src/components/ui/ScoreCard.tsx

@@ -5,7 +5,7 @@ import type { DimensionScoreDetail, CompositeBreakdown } from '../../lib/types';
 interface ScoreCardProps {
   compositeScore: number | null;
   dimensions: DimensionScoreDetail[];
-  compositeBreakdown?: CompositeBreakdown;
+  compositeBreakdown?: CompositeBreakdown | null;
   /** Hide the composite ring/header when the composite is shown elsewhere
    *  (e.g. the Standing matrix) and this card only carries the dimension detail. */
   showComposite?: boolean;

frontend/src/lib/types.ts

@@ -96,7 +96,7 @@ export interface ScoreResponse {
   dimensions: DimensionScoreDetail[];
   missing_dimensions: string[];
   computed_at: string | null;
-  composite_breakdown?: CompositeBreakdown;
+  composite_breakdown?: CompositeBreakdown | null;
 }
 
 export interface DimensionScoreDetail {
@@ -104,12 +104,13 @@ export interface DimensionScoreDetail {
   score: number;
   is_stale: boolean;
   computed_at: string | null;
-  breakdown?: ScoreBreakdown;
+  breakdown?: ScoreBreakdown | null;
 }
 
 export interface RankingEntry {
   symbol: string;
   composite_score: number;
+  composite_stale: boolean;
   dimensions: DimensionScoreDetail[];
 }
 
@@ -140,9 +141,18 @@ export interface TradeSetup {
   evaluated_at: string | null;
   current_price: number | null;
   momentum_percentile?: number | null;
+  context_as_of?: TradeSetupContextAsOf | null;
   recommendation_summary?: RecommendationSummary;
 }
 
+export interface TradeSetupContextAsOf {
+  setup_detected_at: string;
+  score_computed_at: string | null;
+  sentiment_at: string | null;
+  price_date: string | null;
+  price_updated_at: string | null;
+}
+
 // Performance / outcome statistics
 export interface OutcomeBucketStats {
   total: number;

tests/unit/test_rr_scanner_preservation.py

@@ -11,13 +11,17 @@ Zero-candidate and single-candidate scenarios must produce identical results.
 
 from __future__ import annotations
 
+import json
 from datetime import date, datetime, timedelta, timezone
+from unittest.mock import AsyncMock, patch
 
 import pytest
 from hypothesis import given, settings, HealthCheck, strategies as st
+from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models.ohlcv import OHLCVRecord
+from app.models.signal_context_snapshot import SignalContextSnapshot
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
@@ -568,3 +572,253 @@ async def test_live_recommendation_filters_apply_to_live_values(
         live_recommendation=True,
     )
     assert rows == []
+
+
+async def _seed_two_direction_setup(db_session: AsyncSession) -> None:
+    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    ticker = Ticker(symbol="BOTH")
+    db_session.add(ticker)
+    await db_session.flush()
+
+    db_session.add_all([
+        TradeSetup(
+            ticker_id=ticker.id,
+            direction="long",
+            entry_price=100.0,
+            stop_loss=95.0,
+            target=112.0,
+            rr_ratio=2.4,
+            composite_score=30.0,
+            detected_at=current,
+            confidence_score=25.0,
+            recommended_action="NEUTRAL",
+            risk_level="Low",
+        ),
+        TradeSetup(
+            ticker_id=ticker.id,
+            direction="short",
+            entry_price=100.0,
+            stop_loss=105.0,
+            target=88.0,
+            rr_ratio=2.4,
+            composite_score=30.0,
+            detected_at=current,
+            confidence_score=90.0,
+            recommended_action="SHORT_HIGH",
+            risk_level="Low",
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="technical",
+            score=10.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="momentum",
+            score=10.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="fundamental",
+            score=10.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        CompositeScore(
+            ticker_id=ticker.id,
+            score=30.0,
+            is_stale=False,
+            weights_json="{}",
+            computed_at=current,
+        ),
+        SentimentScore(
+            ticker_id=ticker.id,
+            classification="bearish",
+            confidence=90,
+            source="test",
+            timestamp=current,
+            reasoning="",
+            citations_json="[]",
+        ),
+    ])
+    await db_session.flush()
+
+
+@pytest.mark.asyncio
+async def test_live_recommendation_action_independent_of_direction_filter(
+    db_session: AsyncSession,
+):
+    await _seed_two_direction_setup(db_session)
+
+    all_rows = await get_trade_setups(
+        db_session,
+        symbol="BOTH",
+        live_recommendation=True,
+    )
+    filtered_rows = await get_trade_setups(
+        db_session,
+        symbol="BOTH",
+        direction="long",
+        live_recommendation=True,
+    )
+
+    long_from_all = next(row for row in all_rows if row["direction"] == "long")
+    assert len(filtered_rows) == 1
+    assert long_from_all["recommended_action"] == "SHORT_HIGH"
+    assert filtered_rows[0]["recommended_action"] == "SHORT_HIGH"
+
+
+@pytest.mark.asyncio
+async def test_live_overlay_preserves_setup_specific_risk_and_context(
+    db_session: AsyncSession,
+):
+    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    ticker = Ticker(symbol="RISK")
+    db_session.add(ticker)
+    await db_session.flush()
+    db_session.add_all([
+        TradeSetup(
+            ticker_id=ticker.id,
+            direction="long",
+            entry_price=100.0,
+            stop_loss=95.0,
+            target=112.0,
+            rr_ratio=2.4,
+            composite_score=50.0,
+            detected_at=current,
+            confidence_score=50.0,
+            recommended_action="NEUTRAL",
+            risk_level="Medium",
+            conflict_flags_json=json.dumps([
+                "target-availability: Fewer than 3 valid S/R targets available"
+            ]),
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="technical",
+            score=50.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="momentum",
+            score=50.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        CompositeScore(
+            ticker_id=ticker.id,
+            score=50.0,
+            is_stale=False,
+            weights_json="{}",
+            computed_at=current,
+        ),
+        SentimentScore(
+            ticker_id=ticker.id,
+            classification="neutral",
+            confidence=50,
+            source="test",
+            timestamp=current,
+            reasoning="",
+            citations_json="[]",
+        ),
+        OHLCVRecord(
+            ticker_id=ticker.id,
+            date=date(2026, 7, 3),
+            open=101.0,
+            high=102.0,
+            low=100.0,
+            close=101.0,
+            volume=1000,
+            created_at=current,
+        ),
+    ])
+    await db_session.flush()
+
+    rows = await get_trade_setups(
+        db_session,
+        symbol="RISK",
+        live_recommendation=True,
+    )
+
+    assert len(rows) == 1
+    row = rows[0]
+    assert row["risk_level"] == "Medium"
+    assert row["conflict_flags"] == [
+        "target-availability: Fewer than 3 valid S/R targets available"
+    ]
+    assert row["current_price"] == pytest.approx(101.0)
+    assert row["context_as_of"]["score_computed_at"] == current
+    assert row["context_as_of"]["sentiment_at"] == current
+    assert row["context_as_of"]["price_date"] == date(2026, 7, 3)
+    assert row["context_as_of"]["price_updated_at"] == current
+
+
+@pytest.mark.asyncio
+async def test_live_trade_setup_read_does_not_recompute_scores(db_session: AsyncSession):
+    await _seed_stale_setup_with_current_scores(db_session)
+
+    with patch(
+        "app.services.scoring_service.compute_all_dimensions",
+        new=AsyncMock(side_effect=AssertionError("GET must not recompute dimensions")),
+    ), patch(
+        "app.services.scoring_service.compute_composite_score",
+        new=AsyncMock(side_effect=AssertionError("GET must not recompute composite")),
+    ):
+        rows = await get_trade_setups(
+            db_session,
+            symbol="TTWO",
+            live_recommendation=True,
+        )
+
+    assert len(rows) == 1
+
+
+@pytest.mark.asyncio
+async def test_intraday_price_update_changes_live_price_without_new_signal_rows(
+    db_session: AsyncSession,
+):
+    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    ticker = Ticker(symbol="LIVEP")
+    db_session.add(ticker)
+    await db_session.flush()
+    setup = TradeSetup(
+        ticker_id=ticker.id,
+        direction="long",
+        entry_price=100.0,
+        stop_loss=95.0,
+        target=112.0,
+        rr_ratio=2.4,
+        composite_score=50.0,
+        detected_at=current,
+    )
+    price = OHLCVRecord(
+        ticker_id=ticker.id,
+        date=date(2026, 7, 3),
+        open=100.0,
+        high=101.0,
+        low=99.0,
+        close=100.0,
+        volume=1000,
+        created_at=current,
+    )
+    db_session.add_all([setup, price])
+    await db_session.flush()
+
+    rows = await get_trade_setups(db_session, symbol="LIVEP", live_recommendation=True)
+    assert rows[0]["current_price"] == pytest.approx(100.0)
+
+    price.close = 102.0
+    await db_session.flush()
+
+    rows = await get_trade_setups(db_session, symbol="LIVEP", live_recommendation=True)
+    assert rows[0]["current_price"] == pytest.approx(102.0)
+    setup_count = await db_session.scalar(select(func.count()).select_from(TradeSetup))
+    snapshot_count = await db_session.scalar(select(func.count()).select_from(SignalContextSnapshot))
+    assert setup_count == 1
+    assert snapshot_count == 0

tests/unit/test_scoring_service_get_score.py

@@ -1,24 +1,24 @@
-"""Unit tests for get_score composite breakdown and dimension breakdown wiring."""
+"""Unit tests for read-only get_score composite breakdown wiring."""
 
 from __future__ import annotations
 
-from datetime import date
+from datetime import datetime, timezone
-from types import SimpleNamespace
 from unittest.mock import AsyncMock, patch
 
 import pytest
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
 from app.database import Base
+from app.models.score import CompositeScore, DimensionScore
 from app.models.ticker import Ticker
-from app.services.scoring_service import get_score, _DIMENSION_COMPUTERS
+from app.services.scoring_service import get_score
 
 TEST_DATABASE_URL = "sqlite+aiosqlite://"
 
 
 @pytest.fixture
 async def fresh_db():
-    """Provide a non-transactional session so get_score can commit."""
+    """Provide a non-transactional session for persisted score reads."""
     engine = create_async_engine(TEST_DATABASE_URL, echo=False)
     async with engine.begin() as conn:
         await conn.run_sync(Base.metadata.create_all)
@@ -30,176 +30,101 @@ async def fresh_db():
     await engine.dispose()
 
 
-def _make_ohlcv_records(n: int, base_close: float = 100.0) -> list:
-    """Create n mock OHLCV records with realistic price data."""
-    records = []
-    for i in range(n):
-        price = base_close + (i * 0.5)
-        records.append(
-            SimpleNamespace(
-                date=date(2024, 1, 1),
-                open=price - 0.5,
-                high=price + 1.0,
-                low=price - 1.0,
-                close=price,
-                volume=1000000,
-            )
-        )
-    return records
-
-
-def _mock_none_computer():
-    """Return an AsyncMock that returns (None, None) — simulates missing dimension data."""
-    return AsyncMock(return_value=(None, None))
-
-
-def _mock_score_computer(score: float, breakdown: dict | None = None):
-    """Return an AsyncMock that returns a fixed (score, breakdown) tuple."""
-    bd = breakdown or {
-        "sub_scores": [{"name": "mock", "score": score, "weight": 1.0, "raw_value": score, "description": "mock"}],
-        "formula": "mock formula",
-        "unavailable": [],
-    }
-    return AsyncMock(return_value=(score, bd))
-
-
 async def _seed_ticker(session: AsyncSession, symbol: str = "AAPL") -> Ticker:
-    """Insert a ticker row and return it."""
     ticker = Ticker(symbol=symbol)
     session.add(ticker)
     await session.commit()
     return ticker
 
 
+async def _seed_scores(session: AsyncSession, ticker: Ticker, *, stale: bool = False) -> None:
+    now = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    session.add_all([
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="technical",
+            score=70.0,
+            is_stale=stale,
+            computed_at=now,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="momentum",
+            score=60.0,
+            is_stale=False,
+            computed_at=now,
+        ),
+        CompositeScore(
+            ticker_id=ticker.id,
+            score=66.0,
+            is_stale=stale,
+            weights_json="{}",
+            computed_at=now,
+        ),
+    ])
+    await session.commit()
+
+
 @pytest.mark.asyncio
-async def test_get_score_returns_composite_breakdown(fresh_db):
+async def test_get_score_returns_composite_breakdown_without_recomputing(fresh_db):
-    """get_score should include a composite_breakdown dict with weights and re-normalization info."""
+    ticker = await _seed_ticker(fresh_db, "AAPL")
-    await _seed_ticker(fresh_db, "AAPL")
+    await _seed_scores(fresh_db, ticker)
-
-    original = dict(_DIMENSION_COMPUTERS)
-    try:
-        _DIMENSION_COMPUTERS["technical"] = _mock_score_computer(70.0)
-        _DIMENSION_COMPUTERS["momentum"] = _mock_score_computer(60.0)
-        _DIMENSION_COMPUTERS["sentiment"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["fundamental"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["sr_quality"] = _mock_none_computer()
 
+    with patch(
+        "app.services.scoring_service.compute_dimension_score",
+        new=AsyncMock(side_effect=AssertionError("GET must not recompute dimensions")),
+    ), patch(
+        "app.services.scoring_service.compute_composite_score",
+        new=AsyncMock(side_effect=AssertionError("GET must not recompute composite")),
+    ):
         result = await get_score(fresh_db, "AAPL")
-    finally:
-        _DIMENSION_COMPUTERS.update(original)
 
-    assert "composite_breakdown" in result
+    assert result["composite_score"] == 66.0
     cb = result["composite_breakdown"]
     assert cb is not None
-    assert "weights" in cb
-    assert "available_dimensions" in cb
-    assert "missing_dimensions" in cb
-    assert "renormalized_weights" in cb
-    assert "formula" in cb
-
-
-@pytest.mark.asyncio
-async def test_get_score_composite_breakdown_has_correct_available_missing(fresh_db):
-    """Composite breakdown should correctly list available and missing dimensions."""
-    await _seed_ticker(fresh_db, "AAPL")
-
-    original = dict(_DIMENSION_COMPUTERS)
-    try:
-        _DIMENSION_COMPUTERS["technical"] = _mock_score_computer(70.0)
-        _DIMENSION_COMPUTERS["momentum"] = _mock_score_computer(60.0)
-        _DIMENSION_COMPUTERS["sentiment"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["fundamental"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["sr_quality"] = _mock_none_computer()
-
-        result = await get_score(fresh_db, "AAPL")
-    finally:
-        _DIMENSION_COMPUTERS.update(original)
-
-    cb = result["composite_breakdown"]
     assert "technical" in cb["available_dimensions"]
     assert "momentum" in cb["available_dimensions"]
     assert "sentiment" in cb["missing_dimensions"]
     assert "fundamental" in cb["missing_dimensions"]
     assert "sr_quality" in cb["missing_dimensions"]
-
-
-@pytest.mark.asyncio
-async def test_get_score_renormalized_weights_sum_to_one(fresh_db):
-    """Re-normalized weights should sum to 1.0 when at least one dimension is available."""
-    await _seed_ticker(fresh_db, "AAPL")
-
-    original = dict(_DIMENSION_COMPUTERS)
-    try:
-        _DIMENSION_COMPUTERS["technical"] = _mock_score_computer(70.0)
-        _DIMENSION_COMPUTERS["momentum"] = _mock_score_computer(60.0)
-        _DIMENSION_COMPUTERS["sentiment"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["fundamental"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["sr_quality"] = _mock_none_computer()
-
-        result = await get_score(fresh_db, "AAPL")
-    finally:
-        _DIMENSION_COMPUTERS.update(original)
-
-    cb = result["composite_breakdown"]
     assert cb["renormalized_weights"]
-    total = sum(cb["renormalized_weights"].values())
+    assert abs(sum(cb["renormalized_weights"].values()) - 1.0) < 1e-9
-    assert abs(total - 1.0) < 1e-9
 
 
 @pytest.mark.asyncio
-async def test_get_score_dimensions_include_breakdowns(fresh_db):
+async def test_get_score_dimensions_do_not_recompute_breakdowns(fresh_db):
-    """Each available dimension entry should include a breakdown dict."""
+    ticker = await _seed_ticker(fresh_db, "AAPL")
-    await _seed_ticker(fresh_db, "AAPL")
+    await _seed_scores(fresh_db, ticker)
-
-    tech_breakdown = {
-        "sub_scores": [
-            {"name": "ADX", "score": 72.0, "weight": 0.4, "raw_value": 72.0, "description": "ADX value"},
-            {"name": "EMA", "score": 65.0, "weight": 0.3, "raw_value": 1.5, "description": "EMA diff"},
-            {"name": "RSI", "score": 62.0, "weight": 0.3, "raw_value": 62.0, "description": "RSI value"},
-        ],
-        "formula": "Weighted average: 0.4*ADX + 0.3*EMA + 0.3*RSI",
-        "unavailable": [],
-    }
-
-    original = dict(_DIMENSION_COMPUTERS)
-    try:
-        _DIMENSION_COMPUTERS["technical"] = _mock_score_computer(68.2, tech_breakdown)
-        _DIMENSION_COMPUTERS["momentum"] = _mock_score_computer(55.0)
-        _DIMENSION_COMPUTERS["sentiment"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["fundamental"] = _mock_none_computer()
-        _DIMENSION_COMPUTERS["sr_quality"] = _mock_none_computer()
 
     result = await get_score(fresh_db, "AAPL")
-    finally:
-        _DIMENSION_COMPUTERS.update(original)
 
     tech_dim = next((d for d in result["dimensions"] if d["dimension"] == "technical"), None)
     assert tech_dim is not None
-    assert "breakdown" in tech_dim
+    assert tech_dim["breakdown"] is None
-    assert tech_dim["breakdown"] is not None
-    assert len(tech_dim["breakdown"]["sub_scores"]) == 3
-    names = [s["name"] for s in tech_dim["breakdown"]["sub_scores"]]
-    assert "ADX" in names
-    assert "EMA" in names
-    assert "RSI" in names
 
 
 @pytest.mark.asyncio
 async def test_get_score_all_dimensions_missing(fresh_db):
-    """When all dimensions return None, composite_breakdown should list all as missing."""
     await _seed_ticker(fresh_db, "AAPL")
 
-    original = dict(_DIMENSION_COMPUTERS)
-    try:
-        for dim in _DIMENSION_COMPUTERS:
-            _DIMENSION_COMPUTERS[dim] = _mock_none_computer()
-
     result = await get_score(fresh_db, "AAPL")
-    finally:
-        _DIMENSION_COMPUTERS.update(original)
 
     cb = result["composite_breakdown"]
     assert cb["available_dimensions"] == []
     assert len(cb["missing_dimensions"]) == 5
     assert cb["renormalized_weights"] == {}
     assert result["composite_score"] is None
+
+
+@pytest.mark.asyncio
+async def test_get_score_reports_stale_without_refreshing(fresh_db):
+    ticker = await _seed_ticker(fresh_db, "AAPL")
+    await _seed_scores(fresh_db, ticker, stale=True)
+
+    result = await get_score(fresh_db, "AAPL")
+
+    assert result["composite_stale"] is True
+    assert "technical" in result["missing_dimensions"]
+    tech_dim = next((d for d in result["dimensions"] if d["dimension"] == "technical"), None)
+    assert tech_dim is not None
+    assert tech_dim["is_stale"] is True

tests/unit/test_scoring_service_rankings.py

@@ -1,5 +1,4 @@
-"""Unit tests for get_rankings: bulk-load fast path, sorting, exclusion, and
+"""Unit tests for read-only get_rankings: bulk-load, sorting, and staleness."""
-lazy recompute of stale scores."""
 
 from __future__ import annotations
 
@@ -7,7 +6,6 @@ from datetime import datetime, timezone
 from unittest.mock import AsyncMock, patch
 
 import pytest
-from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
 from app.database import Base
@@ -20,7 +18,7 @@ TEST_DATABASE_URL = "sqlite+aiosqlite://"
 
 @pytest.fixture
 async def fresh_db():
-    """Non-transactional session so get_rankings can commit recomputes."""
+    """Non-transactional session for persisted ranking reads."""
     engine = create_async_engine(TEST_DATABASE_URL, echo=False)
     async with engine.begin() as conn:
         await conn.run_sync(Base.metadata.create_all)
@@ -84,46 +82,34 @@ async def test_fast_path_sorts_and_does_not_recompute(fresh_db: AsyncSession):
 
 @pytest.mark.asyncio
 async def test_ticker_without_computable_composite_is_excluded(fresh_db: AsyncSession):
-    """A ticker whose composite can't be computed (recompute yields no row) is
+    """A ticker without a persisted composite is omitted from rankings."""
-    omitted from the rankings rather than appearing with a null score."""
     fresh = await _seed_ticker(fresh_db, "OK")
-    await _seed_ticker(fresh_db, "NONE")  # no composite; recompute can't make one
+    await _seed_ticker(fresh_db, "NONE")
     fresh_db.add_all([_composite(fresh.id, 50.0), _dimension(fresh.id, "technical", 50.0)])
     await fresh_db.commit()
 
-    # Recompute is a no-op that produces no composite row for NONE.
     with patch("app.services.scoring_service.compute_dimension_score",
-               new=AsyncMock(return_value=None)), \
+               new=AsyncMock(side_effect=AssertionError("should not recompute"))), \
          patch("app.services.scoring_service.compute_composite_score",
-               new=AsyncMock(return_value=(None, ["technical"]))):
+               new=AsyncMock(side_effect=AssertionError("should not recompute"))):
         result = await get_rankings(fresh_db)
 
     assert [r["symbol"] for r in result["rankings"]] == ["OK"]
 
 
 @pytest.mark.asyncio
-async def test_stale_composite_is_recomputed(fresh_db: AsyncSession):
+async def test_stale_composite_is_reported_without_recompute(fresh_db: AsyncSession):
-    """A stale composite triggers a recompute and then appears in the rankings."""
+    """A stale composite appears with its stale flag and is not recomputed."""
     ticker = await _seed_ticker(fresh_db, "STALE")
     fresh_db.add(_composite(ticker.id, 10.0, stale=True))
     await fresh_db.commit()
 
-    async def _fake_recompute(db, symbol, weights=None):
-        # Mirror the real upsert: refresh the existing row in place.
-        existing = (await db.execute(
-            select(CompositeScore).where(CompositeScore.ticker_id == ticker.id)
-        )).scalar_one()
-        existing.score = 77.0
-        existing.is_stale = False
-        return 77.0, []
-
-    # Dimension recompute is a no-op; composite recompute refreshes the score.
     with patch("app.services.scoring_service.compute_dimension_score",
-               new=AsyncMock(return_value=55.0)), \
+               new=AsyncMock(side_effect=AssertionError("should not recompute"))), \
          patch("app.services.scoring_service.compute_composite_score",
-               new=AsyncMock(side_effect=_fake_recompute)) as comp_mock:
+               new=AsyncMock(side_effect=AssertionError("should not recompute"))):
         result = await get_rankings(fresh_db)
 
-    comp_mock.assert_awaited()  # recompute path was taken
     assert [r["symbol"] for r in result["rankings"]] == ["STALE"]
-    assert result["rankings"][0]["composite_score"] == 77.0  # reflects the recompute
+    assert result["rankings"][0]["composite_score"] == 10.0
+    assert result["rankings"][0]["composite_stale"] is True