Make live signal reads non-mutating

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