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>

tests/unit/test_rr_scanner_preservation.py

@@ -21,7 +21,8 @@ from app.models.ohlcv import OHLCVRecord
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
-from app.models.score import CompositeScore
+from app.models.score import CompositeScore, DimensionScore
+from app.models.sentiment import SentimentScore
 from app.services.rr_scanner_service import scan_ticker, get_trade_setups
 
 
@@ -431,3 +432,139 @@ async def test_get_trade_setups_sorting_rr_desc_composite_desc(db_session: Async
         f"Expected symbol order ['SORTD', 'SORTC', 'SORTB', 'SORTA'], "
         f"got {symbols}"
     )
+
+
+async def _seed_stale_setup_with_current_scores(db_session: AsyncSession) -> TradeSetup:
+    """Stored setup frozen at scan time (conf 82, neutral) vs. current context
+    (bullish sentiment, composite 96) that yields live confidence 97."""
+    old_scan = datetime(2026, 7, 1, tzinfo=timezone.utc)
+    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    old_reasoning = (
+        "LONG (high confidence): 82% with aligned signals "
+        "(technical=88, momentum=60, sentiment=neutral)."
+    )
+
+    ticker = Ticker(symbol="TTWO")
+    db_session.add(ticker)
+    await db_session.flush()
+
+    stale_setup = TradeSetup(
+        ticker_id=ticker.id,
+        direction="long",
+        entry_price=235.0,
+        stop_loss=220.0,
+        target=265.0,
+        rr_ratio=2.0,
+        composite_score=71.8,
+        detected_at=old_scan,
+        confidence_score=82.0,
+        recommended_action="LONG_HIGH",
+        reasoning=old_reasoning,
+        risk_level="High",
+    )
+    db_session.add(stale_setup)
+
+    db_session.add_all([
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="technical",
+            score=88.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="momentum",
+            score=60.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="fundamental",
+            score=95.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        DimensionScore(
+            ticker_id=ticker.id,
+            dimension="sentiment",
+            score=85.0,
+            is_stale=False,
+            computed_at=current,
+        ),
+        CompositeScore(
+            ticker_id=ticker.id,
+            score=96.0,
+            is_stale=False,
+            weights_json="{}",
+            computed_at=current,
+        ),
+        SentimentScore(
+            ticker_id=ticker.id,
+            classification="bullish",
+            confidence=85,
+            source="test",
+            timestamp=current,
+            reasoning="",
+            citations_json="[]",
+        ),
+    ])
+    await db_session.flush()
+    return stale_setup
+
+
+@pytest.mark.asyncio
+async def test_live_recommendation_payload_uses_current_score_and_sentiment(
+    db_session: AsyncSession,
+):
+    """Latest setup payload should not show stale scan text when score context moved."""
+    stale_setup = await _seed_stale_setup_with_current_scores(db_session)
+    old_reasoning = stale_setup.reasoning
+
+    rows = await get_trade_setups(
+        db_session,
+        symbol="TTWO",
+        live_recommendation=True,
+    )
+
+    assert len(rows) == 1
+    row = rows[0]
+    assert row["composite_score"] == pytest.approx(96.0)
+    assert row["confidence_score"] == pytest.approx(97.0)
+    assert row["recommended_action"] == "LONG_HIGH"
+    assert "sentiment=bullish" in row["reasoning"]
+    assert "sentiment=neutral" not in row["reasoning"]
+
+    persisted = await db_session.get(TradeSetup, stale_setup.id)
+    assert persisted is not None
+    assert persisted.composite_score == pytest.approx(71.8)
+    assert persisted.reasoning == old_reasoning
+
+
+@pytest.mark.asyncio
+async def test_live_recommendation_filters_apply_to_live_values(
+    db_session: AsyncSession,
+):
+    """min_confidence must judge the overlaid live confidence, not the stored one."""
+    await _seed_stale_setup_with_current_scores(db_session)
+
+    # Stored confidence is 82 — a stored-column filter would drop this row.
+    # Live confidence is 97, so it must pass.
+    rows = await get_trade_setups(
+        db_session,
+        symbol="TTWO",
+        min_confidence=90.0,
+        live_recommendation=True,
+    )
+    assert len(rows) == 1
+    assert rows[0]["confidence_score"] == pytest.approx(97.0)
+
+    # And a floor above the live value must drop it.
+    rows = await get_trade_setups(
+        db_session,
+        symbol="TTWO",
+        min_confidence=98.0,
+        live_recommendation=True,
+    )
+    assert rows == []