diff --git a/tests/unit/test_rr_scanner_bug_exploration.py b/tests/unit/test_rr_scanner_bug_exploration.py
deleted file mode 100644
index 933b1b3..0000000
--- a/tests/unit/test_rr_scanner_bug_exploration.py
+++ /dev/null
@@ -1,285 +0,0 @@
-"""Regression: scanner must not headline the most distant (max raw R:R) level.
-
-Historical bug: provisional candidate pick used max R:R / quality only. Production
-headline is probability-based primary after enhance_trade_setup — near levels
-with real reach-probability beat far lotteries.
-
-**Validates: Requirements 1.1, 1.3, 1.4, 2.1, 2.3, 2.4**
-"""
-
-from __future__ import annotations
-
-from datetime import date, timedelta
-
-import pytest
-from hypothesis import given, settings, HealthCheck, strategies as st
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.models.ohlcv import OHLCVRecord
-from app.models.sr_level import SRLevel
-from app.models.ticker import Ticker
-from app.services.rr_scanner_service import scan_ticker
-
-
-# ---------------------------------------------------------------------------
-# Session fixture that allows scan_ticker to commit
-# ---------------------------------------------------------------------------
-# The default db_session fixture wraps in session.begin() which conflicts
-# with scan_ticker's internal commit().  We use a plain session instead.
-
-@pytest.fixture
-async def scan_session() -> AsyncSession:
-    """Provide a DB session compatible with scan_ticker (which commits)."""
-    from tests.conftest import _test_session_factory
-
-    async with _test_session_factory() as session:
-        yield session
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_ohlcv_bars(
-    ticker_id: int,
-    num_bars: int = 20,
-    base_close: float = 100.0,
-) -> list[OHLCVRecord]:
-    """Generate realistic OHLCV bars with small daily variation.
-
-    Produces bars where close ≈ base_close, with enough range for ATR
-    computation (needs >= 15 bars).  The ATR will be roughly 2.0.
-    """
-    bars: list[OHLCVRecord] = []
-    start = date(2024, 1, 1)
-    for i in range(num_bars):
-        close = base_close + (i % 3 - 1) * 0.5  # oscillate ±0.5
-        bars.append(OHLCVRecord(
-            ticker_id=ticker_id,
-            date=start + timedelta(days=i),
-            open=close - 0.3,
-            high=close + 1.0,
-            low=close - 1.0,
-            close=close,
-            volume=100_000,
-        ))
-    return bars
-
-
-# ---------------------------------------------------------------------------
-# Deterministic test: strong-near vs weak-far (long setup)
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-async def test_long_prefers_strong_near_over_weak_far(scan_session: AsyncSession):
-    """With a strong nearby resistance and a weak distant resistance, the
-    probability primary should be the nearby level — NOT the far lottery.
-    """
-    ticker = Ticker(symbol="EXPLR")
-    scan_session.add(ticker)
-    await scan_session.flush()
-
-    # 20 bars closing around 100
-    bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-    scan_session.add_all(bars)
-
-    # With ATR=2.0 and multiplier=1.5, risk=3.0.
-    # R:R threshold=1.5 → min reward=4.5 → min target=104.5
-    # Strong nearby resistance: price=105, strength=90 (R:R≈1.67, quality≈0.66)
-    near_level = SRLevel(
-        ticker_id=ticker.id,
-        price_level=105.0,
-        type="resistance",
-        strength=90,
-        detection_method="volume_profile",
-    )
-    # Weak distant resistance: price=130, strength=5 (R:R=10, quality≈0.58)
-    far_level = SRLevel(
-        ticker_id=ticker.id,
-        price_level=130.0,
-        type="resistance",
-        strength=5,
-        detection_method="volume_profile",
-    )
-    scan_session.add_all([near_level, far_level])
-    await scan_session.flush()
-
-    setups = await scan_ticker(
-        scan_session,
-        "EXPLR",
-        rr_threshold=1.5,
-        gate_levels_override=[near_level, far_level],
-    )
-
-    long_setups = [s for s in setups if s.direction == "long"]
-    assert len(long_setups) == 1, "Expected exactly one long setup"
-
-    selected_target = long_setups[0].target
-    # The scanner must NOT pick the most distant level (130)
-    assert selected_target != pytest.approx(130.0, abs=0.01), (
-        "Bug: scanner picked the weak distant level (130) instead of the "
-        "strong nearby level (105)"
-    )
-    # Probability primary should pick the strong nearby level
-    assert selected_target == pytest.approx(105.0, abs=0.01)
-    primaries = [t for t in long_setups[0].targets if t.get("is_primary")]
-    assert len(primaries) == 1
-    assert primaries[0]["price"] == pytest.approx(105.0, abs=0.01)
-
-
-# ---------------------------------------------------------------------------
-# Deterministic test: strong-near vs weak-far (short setup)
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-async def test_short_prefers_strong_near_over_weak_far(scan_session: AsyncSession):
-    """Short-side mirror: strong nearby support should be preferred over
-    weak distant support.
-    """
-    ticker = Ticker(symbol="EXPLS")
-    scan_session.add(ticker)
-    await scan_session.flush()
-
-    bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-    scan_session.add_all(bars)
-
-    # With ATR=2.0 and multiplier=1.5, risk=3.0.
-    # R:R threshold=1.5 → min reward=4.5 → min target below 95.5
-    # Strong nearby support: price=95, strength=85 (R:R≈1.67, quality≈0.64)
-    near_level = SRLevel(
-        ticker_id=ticker.id,
-        price_level=95.0,
-        type="support",
-        strength=85,
-        detection_method="pivot_point",
-    )
-    # Weak distant support: price=70, strength=5 (R:R=10, quality≈0.58)
-    far_level = SRLevel(
-        ticker_id=ticker.id,
-        price_level=70.0,
-        type="support",
-        strength=5,
-        detection_method="pivot_point",
-    )
-    scan_session.add_all([near_level, far_level])
-    await scan_session.flush()
-
-    setups = await scan_ticker(
-        scan_session,
-        "EXPLS",
-        rr_threshold=1.5,
-        gate_levels_override=[near_level, far_level],
-    )
-
-    short_setups = [s for s in setups if s.direction == "short"]
-    assert len(short_setups) == 1, "Expected exactly one short setup"
-
-    selected_target = short_setups[0].target
-    assert selected_target != pytest.approx(70.0, abs=0.01), (
-        "Bug: scanner picked the weak distant level (70) instead of the "
-        "strong nearby level (95)"
-    )
-    assert selected_target == pytest.approx(95.0, abs=0.01)
-
-
-# ---------------------------------------------------------------------------
-# Hypothesis property test: selection is NOT always the most distant level
-# ---------------------------------------------------------------------------
-
-@st.composite
-def strong_near_weak_far_pair(draw: st.DrawFn) -> dict:
-    """Generate a (strong-near, weak-far) resistance pair above entry=100.
-
-    Guarantees:
-    - near_price < far_price  (both above entry)
-    - near_strength >> far_strength
-    - Both meet the R:R threshold of 1.5 given typical ATR ≈ 2 → risk ≈ 3
-    """
-    # Near level: 5–15 above entry (R:R ≈ 1.7–5.0 with risk≈3)
-    near_dist = draw(st.floats(min_value=5.0, max_value=15.0))
-    near_strength = draw(st.integers(min_value=70, max_value=100))
-
-    # Far level: 25–60 above entry (R:R ≈ 8.3–20 with risk≈3)
-    far_dist = draw(st.floats(min_value=25.0, max_value=60.0))
-    far_strength = draw(st.integers(min_value=1, max_value=15))
-
-    return {
-        "near_price": 100.0 + near_dist,
-        "near_strength": near_strength,
-        "far_price": 100.0 + far_dist,
-        "far_strength": far_strength,
-    }
-
-
-@pytest.mark.asyncio
-@given(pair=strong_near_weak_far_pair())
-@settings(
-    max_examples=15,
-    deadline=None,
-    suppress_health_check=[HealthCheck.function_scoped_fixture],
-)
-async def test_property_scanner_does_not_always_pick_most_distant(
-    pair: dict,
-    scan_session: AsyncSession,
-):
-    """**Validates: Requirements 1.1, 1.3, 1.4, 2.1, 2.3, 2.4**
-
-    Property: when a strong nearby resistance exists alongside a weak distant
-    resistance, the scanner does NOT always select the most distant level.
-
-    On unfixed code this would fail for every example because max-R:R always
-    picks the farthest level.
-    """
-    from tests.conftest import _test_engine, _test_session_factory
-
-    # Each hypothesis example needs a fresh DB state
-    async with _test_engine.begin() as conn:
-        from app.database import Base
-        await conn.run_sync(Base.metadata.drop_all)
-        await conn.run_sync(Base.metadata.create_all)
-
-    async with _test_session_factory() as session:
-        ticker = Ticker(symbol="PROP")
-        session.add(ticker)
-        await session.flush()
-
-        bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-        session.add_all(bars)
-
-        near_level = SRLevel(
-            ticker_id=ticker.id,
-            price_level=pair["near_price"],
-            type="resistance",
-            strength=pair["near_strength"],
-            detection_method="volume_profile",
-        )
-        far_level = SRLevel(
-            ticker_id=ticker.id,
-            price_level=pair["far_price"],
-            type="resistance",
-            strength=pair["far_strength"],
-            detection_method="volume_profile",
-        )
-        session.add_all([near_level, far_level])
-        await session.commit()
-
-        setups = await scan_ticker(
-            session,
-            "PROP",
-            rr_threshold=1.5,
-            gate_levels_override=[near_level, far_level],
-        )
-
-        long_setups = [s for s in setups if s.direction == "long"]
-        assert len(long_setups) == 1, "Expected exactly one long setup"
-
-        selected_target = long_setups[0].target
-        most_distant = round(pair["far_price"], 4)
-
-        # The fixed scanner should prefer the strong nearby level, not the
-        # most distant weak one.
-        assert selected_target != pytest.approx(most_distant, abs=0.01), (
-            f"Bug: scanner picked the most distant level ({most_distant}) "
-            f"with strength={pair['far_strength']} over the nearby level "
-            f"({round(pair['near_price'], 4)}) with strength={pair['near_strength']}"
-        )
diff --git a/tests/unit/test_rr_scanner_fix_check.py b/tests/unit/test_rr_scanner_fix_check.py
deleted file mode 100644
index 5ab1e49..0000000
--- a/tests/unit/test_rr_scanner_fix_check.py
+++ /dev/null
@@ -1,375 +0,0 @@
-"""Fix-checking tests for R:R scanner probability-based primary selection.
-
-Verify that after enhance_trade_setup the headline target is the most likely
-worthwhile primary (R:R + probability floors), for both long and short setups.
-The pre-enhance quality loop only seeds a provisional target.
-
-**Validates: Requirements 2.1, 2.2, 2.3, 2.4**
-"""
-
-from __future__ import annotations
-
-from datetime import date, timedelta
-
-import pytest
-from hypothesis import given, settings, HealthCheck, strategies as st
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.models.ohlcv import OHLCVRecord
-from app.models.sr_level import SRLevel
-from app.models.ticker import Ticker
-from app.services.rr_scanner_service import scan_ticker
-
-
-def _assert_primary_is_most_likely_worthwhile(setup) -> None:
-    """Headline = starred primary = max(probability, rr) among floor-clearing targets."""
-    targets = setup.targets
-    assert targets, "expected generated targets"
-    primaries = [t for t in targets if t.get("is_primary")]
-    assert len(primaries) == 1, "exactly one primary target expected"
-    primary = primaries[0]
-    assert setup.target == pytest.approx(primary["price"], abs=0.01)
-
-    # Mirrors recommendation_service._select_primary_target floors.
-    worthwhile = [
-        t for t in targets
-        if float(t["rr_ratio"]) >= 1.5 and float(t["probability"]) >= 20.0
-    ]
-    pool = worthwhile or targets
-    best = max(pool, key=lambda t: (t["probability"], t["rr_ratio"]))
-    assert primary["price"] == pytest.approx(best["price"], abs=0.01)
-
-
-# ---------------------------------------------------------------------------
-# Session fixture (plain session, not wrapped in begin())
-# ---------------------------------------------------------------------------
-
-@pytest.fixture
-async def scan_session() -> AsyncSession:
-    """Provide a DB session compatible with scan_ticker (which commits)."""
-    from tests.conftest import _test_session_factory
-
-    async with _test_session_factory() as session:
-        yield session
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_ohlcv_bars(
-    ticker_id: int,
-    num_bars: int = 20,
-    base_close: float = 100.0,
-) -> list[OHLCVRecord]:
-    """Generate OHLCV bars closing around base_close with ATR ≈ 2.0."""
-    bars: list[OHLCVRecord] = []
-    start = date(2024, 1, 1)
-    for i in range(num_bars):
-        close = base_close + (i % 3 - 1) * 0.5  # oscillate ±0.5
-        bars.append(OHLCVRecord(
-            ticker_id=ticker_id,
-            date=start + timedelta(days=i),
-            open=close - 0.3,
-            high=close + 1.0,
-            low=close - 1.0,
-            close=close,
-            volume=100_000,
-        ))
-    return bars
-
-
-# ---------------------------------------------------------------------------
-# Hypothesis strategy: multiple resistance levels above entry for longs
-# ---------------------------------------------------------------------------
-
-@st.composite
-def long_candidate_levels(draw: st.DrawFn) -> list[dict]:
-    """Generate 2-5 resistance levels above entry_price=100.
-
-    All levels meet the R:R threshold of 1.5 given ATR≈2, risk≈3,
-    so min reward=4.5, min target=104.5.
-    """
-    num_levels = draw(st.integers(min_value=2, max_value=5))
-    levels = []
-    for _ in range(num_levels):
-        # Distance from entry: 5 to 50 (all above 4.5 threshold)
-        distance = draw(st.floats(min_value=5.0, max_value=50.0))
-        strength = draw(st.integers(min_value=0, max_value=100))
-        levels.append({
-            "price": 100.0 + distance,
-            "strength": strength,
-        })
-    return levels
-
-
-@st.composite
-def short_candidate_levels(draw: st.DrawFn) -> list[dict]:
-    """Generate 2-5 support levels below entry_price=100.
-
-    All levels meet the R:R threshold of 1.5 given ATR≈2, risk≈3,
-    so min reward=4.5, max target=95.5.
-    """
-    num_levels = draw(st.integers(min_value=2, max_value=5))
-    levels = []
-    for _ in range(num_levels):
-        # Distance below entry: 5 to 50 (all above 4.5 threshold)
-        distance = draw(st.floats(min_value=5.0, max_value=50.0))
-        strength = draw(st.integers(min_value=0, max_value=100))
-        levels.append({
-            "price": 100.0 - distance,
-            "strength": strength,
-        })
-    return levels
-
-
-# ---------------------------------------------------------------------------
-# Property test: long setup selects probability-based primary
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-@given(levels=long_candidate_levels())
-@settings(
-    max_examples=20,
-    deadline=None,
-    suppress_health_check=[HealthCheck.function_scoped_fixture],
-)
-async def test_property_long_selects_probability_primary(
-    levels: list[dict],
-    scan_session: AsyncSession,
-):
-    """**Validates: Requirements 2.1, 2.3, 2.4**
-
-    Property: when multiple resistance levels meet the R:R threshold,
-    the headline after enhance is the probability-based primary.
-    """
-    from tests.conftest import _test_engine, _test_session_factory
-    from app.database import Base
-
-    # Fresh DB state per hypothesis example
-    async with _test_engine.begin() as conn:
-        await conn.run_sync(Base.metadata.drop_all)
-        await conn.run_sync(Base.metadata.create_all)
-
-    async with _test_session_factory() as session:
-        ticker = Ticker(symbol="FIXL")
-        session.add(ticker)
-        await session.flush()
-
-        bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-        session.add_all(bars)
-
-        sr_levels = []
-        for lv in levels:
-            sr_levels.append(SRLevel(
-                ticker_id=ticker.id,
-                price_level=lv["price"],
-                type="resistance",
-                strength=lv["strength"],
-                detection_method="volume_profile",
-            ))
-        session.add_all(sr_levels)
-        await session.commit()
-
-        setups = await scan_ticker(
-            session,
-            "FIXL",
-            rr_threshold=1.5,
-            gate_levels_override=sr_levels,
-        )
-
-        long_setups = [s for s in setups if s.direction == "long"]
-        assert len(long_setups) == 1, "Expected exactly one long setup"
-
-        _assert_primary_is_most_likely_worthwhile(long_setups[0])
-
-
-# ---------------------------------------------------------------------------
-# Property test: short setup selects probability-based primary
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-@given(levels=short_candidate_levels())
-@settings(
-    max_examples=20,
-    deadline=None,
-    suppress_health_check=[HealthCheck.function_scoped_fixture],
-)
-async def test_property_short_selects_probability_primary(
-    levels: list[dict],
-    scan_session: AsyncSession,
-):
-    """**Validates: Requirements 2.2, 2.3, 2.4**
-
-    Property: when multiple support levels meet the R:R threshold,
-    the headline after enhance is the probability-based primary.
-    """
-    from tests.conftest import _test_engine, _test_session_factory
-    from app.database import Base
-
-    # Fresh DB state per hypothesis example
-    async with _test_engine.begin() as conn:
-        await conn.run_sync(Base.metadata.drop_all)
-        await conn.run_sync(Base.metadata.create_all)
-
-    async with _test_session_factory() as session:
-        ticker = Ticker(symbol="FIXS")
-        session.add(ticker)
-        await session.flush()
-
-        bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-        session.add_all(bars)
-
-        sr_levels = []
-        for lv in levels:
-            sr_levels.append(SRLevel(
-                ticker_id=ticker.id,
-                price_level=lv["price"],
-                type="support",
-                strength=lv["strength"],
-                detection_method="pivot_point",
-            ))
-        session.add_all(sr_levels)
-        await session.commit()
-
-        setups = await scan_ticker(
-            session,
-            "FIXS",
-            rr_threshold=1.5,
-            gate_levels_override=sr_levels,
-        )
-
-        short_setups = [s for s in setups if s.direction == "short"]
-        assert len(short_setups) == 1, "Expected exactly one short setup"
-
-        _assert_primary_is_most_likely_worthwhile(short_setups[0])
-
-
-# ---------------------------------------------------------------------------
-# Deterministic test: 3 levels with known quality scores (long)
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-async def test_deterministic_long_three_levels(scan_session: AsyncSession):
-    """**Validates: Requirements 2.1, 2.3, 2.4**
-
-    Concrete example with 3 resistance levels of known quality scores.
-    Entry=100, ATR≈2, risk≈3.
-
-    Level A: price=105, strength=90 → rr=5/3≈1.67, dist=5
-      quality = 0.35*(1.67/10) + 0.35*(90/100) + 0.30*(1-5/100)
-             = 0.35*0.167 + 0.35*0.9 + 0.30*0.95
-             = 0.0585 + 0.315 + 0.285 = 0.6585
-
-    Level B: price=112, strength=50 → rr=12/3=4.0, dist=12
-      quality = 0.35*(4/10) + 0.35*(50/100) + 0.30*(1-12/100)
-             = 0.35*0.4 + 0.35*0.5 + 0.30*0.88
-             = 0.14 + 0.175 + 0.264 = 0.579
-
-    Level C: price=130, strength=10 → rr=30/3=10.0, dist=30
-      quality = 0.35*(10/10) + 0.35*(10/100) + 0.30*(1-30/100)
-             = 0.35*1.0 + 0.35*0.1 + 0.30*0.7
-             = 0.35 + 0.035 + 0.21 = 0.595
-
-    Expected winner: Level A (quality=0.6585)
-    """
-    ticker = Ticker(symbol="DET3L")
-    scan_session.add(ticker)
-    await scan_session.flush()
-
-    bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-    scan_session.add_all(bars)
-
-    level_a = SRLevel(
-        ticker_id=ticker.id, price_level=105.0, type="resistance",
-        strength=90, detection_method="volume_profile",
-    )
-    level_b = SRLevel(
-        ticker_id=ticker.id, price_level=112.0, type="resistance",
-        strength=50, detection_method="volume_profile",
-    )
-    level_c = SRLevel(
-        ticker_id=ticker.id, price_level=130.0, type="resistance",
-        strength=10, detection_method="volume_profile",
-    )
-    scan_session.add_all([level_a, level_b, level_c])
-    await scan_session.flush()
-
-    setups = await scan_ticker(
-        scan_session,
-        "DET3L",
-        rr_threshold=1.5,
-        gate_levels_override=[level_a, level_b, level_c],
-    )
-
-    long_setups = [s for s in setups if s.direction == "long"]
-    assert len(long_setups) == 1, "Expected exactly one long setup"
-
-    _assert_primary_is_most_likely_worthwhile(long_setups[0])
-    # Near/strong level A wins on reach-probability over far lottery C.
-    assert long_setups[0].target == pytest.approx(105.0, abs=0.01), (
-        f"Expected primary=105.0 (near, high reach-prob), got {long_setups[0].target}"
-    )
-
-
-# ---------------------------------------------------------------------------
-# Deterministic test: 3 levels with known quality scores (short)
-# ---------------------------------------------------------------------------
-
-@pytest.mark.asyncio
-async def test_deterministic_short_three_levels(scan_session: AsyncSession):
-    """**Validates: Requirements 2.2, 2.3, 2.4**
-
-    Concrete example with 3 support levels of known quality scores.
-    Entry=100, ATR≈2, risk≈3.
-
-    Level A: price=95, strength=85 → rr=5/3≈1.67, dist=5
-      quality = 0.35*(1.67/10) + 0.35*(85/100) + 0.30*(1-5/100)
-             = 0.0585 + 0.2975 + 0.285 = 0.641
-
-    Level B: price=88, strength=45 → rr=12/3=4.0, dist=12
-      quality = 0.35*(4/10) + 0.35*(45/100) + 0.30*(1-12/100)
-             = 0.14 + 0.1575 + 0.264 = 0.5615
-
-    Level C: price=70, strength=8 → rr=30/3=10.0, dist=30
-      quality = 0.35*(10/10) + 0.35*(8/100) + 0.30*(1-30/100)
-             = 0.35 + 0.028 + 0.21 = 0.588
-
-    Expected winner: Level A (quality=0.641)
-    """
-    ticker = Ticker(symbol="DET3S")
-    scan_session.add(ticker)
-    await scan_session.flush()
-
-    bars = _make_ohlcv_bars(ticker.id, num_bars=20, base_close=100.0)
-    scan_session.add_all(bars)
-
-    level_a = SRLevel(
-        ticker_id=ticker.id, price_level=95.0, type="support",
-        strength=85, detection_method="pivot_point",
-    )
-    level_b = SRLevel(
-        ticker_id=ticker.id, price_level=88.0, type="support",
-        strength=45, detection_method="pivot_point",
-    )
-    level_c = SRLevel(
-        ticker_id=ticker.id, price_level=70.0, type="support",
-        strength=8, detection_method="pivot_point",
-    )
-    scan_session.add_all([level_a, level_b, level_c])
-    await scan_session.flush()
-
-    setups = await scan_ticker(
-        scan_session,
-        "DET3S",
-        rr_threshold=1.5,
-        gate_levels_override=[level_a, level_b, level_c],
-    )
-
-    short_setups = [s for s in setups if s.direction == "short"]
-    assert len(short_setups) == 1, "Expected exactly one short setup"
-
-    _assert_primary_is_most_likely_worthwhile(short_setups[0])
-    assert short_setups[0].target == pytest.approx(95.0, abs=0.01), (
-        f"Expected primary=95.0 (near, high reach-prob), got {short_setups[0].target}"
-    )