"""Fix-checking tests for R:R scanner quality-score selection.

Verify that the fixed scan_ticker selects the candidate with the highest
quality score among all candidates meeting the R:R threshold, for both
long and short setups.

**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, _compute_quality_score


# ---------------------------------------------------------------------------
# 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 highest quality score candidate
# ---------------------------------------------------------------------------

@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_highest_quality(
    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 fixed scan_ticker selects the one with the highest quality score.
    """
    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)

        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

        # Compute entry_price and risk from the bars (same logic as scan_ticker)
        # entry_price = last close ≈ 100.0, ATR ≈ 2.0, risk = ATR * 1.5 = 3.0
        entry_price = bars[-1].close
        # Use approximate risk; the exact value comes from ATR computation
        # We reconstruct it from the setup's entry and stop
        risk = long_setups[0].entry_price - long_setups[0].stop_loss

        # Compute quality scores for all candidates that meet threshold
        best_quality = -1.0
        best_target = None
        for lv in levels:
            distance = lv["price"] - entry_price
            if distance > 0:
                rr = distance / risk
                if rr >= 1.5:
                    quality = _compute_quality_score(rr, lv["strength"], distance, entry_price)
                    if quality > best_quality:
                        best_quality = quality
                        best_target = round(lv["price"], 4)

        assert best_target is not None, "At least one candidate should meet threshold"
        assert selected_target == pytest.approx(best_target, abs=0.01), (
            f"Selected target {selected_target} != expected best-quality target "
            f"{best_target} (quality={best_quality:.4f})"
        )


# ---------------------------------------------------------------------------
# Property test: short setup selects highest quality score candidate
# ---------------------------------------------------------------------------

@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_highest_quality(
    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 fixed scan_ticker selects the one with the highest quality score.
    """
    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)

        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

        entry_price = bars[-1].close
        risk = short_setups[0].stop_loss - short_setups[0].entry_price

        # Compute quality scores for all candidates that meet threshold
        best_quality = -1.0
        best_target = None
        for lv in levels:
            distance = entry_price - lv["price"]
            if distance > 0:
                rr = distance / risk
                if rr >= 1.5:
                    quality = _compute_quality_score(rr, lv["strength"], distance, entry_price)
                    if quality > best_quality:
                        best_quality = quality
                        best_target = round(lv["price"], 4)

        assert best_target is not None, "At least one candidate should meet threshold"
        assert selected_target == pytest.approx(best_target, abs=0.01), (
            f"Selected target {selected_target} != expected best-quality target "
            f"{best_target} (quality={best_quality:.4f})"
        )


# ---------------------------------------------------------------------------
# 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)

    long_setups = [s for s in setups if s.direction == "long"]
    assert len(long_setups) == 1, "Expected exactly one long setup"

    # Level A (105, strength=90) should win with highest quality
    assert long_setups[0].target == pytest.approx(105.0, abs=0.01), (
        f"Expected target=105.0 (highest quality), 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)

    short_setups = [s for s in setups if s.direction == "short"]
    assert len(short_setups) == 1, "Expected exactly one short setup"

    # Level A (95, strength=85) should win with highest quality
    assert short_setups[0].target == pytest.approx(95.0, abs=0.01), (
        f"Expected target=95.0 (highest quality), got {short_setups[0].target}"
    )