"""Test all 4 core scorers."""
import pytest
from datetime import date


class TestPriceStructureScorer:
    def test_computes_score(self, db_path):
        from scoring.price_structure import PriceStructureScorer
        scorer = PriceStructureScorer()
        result = scorer.compute(date(2026, 3, 15))
        assert result.name == "Price Structure"
        assert 0 <= result.score <= 100
        assert result.trend_strength >= 0
        assert result.volatility_compression >= 0
        assert result.momentum >= 0
        assert result.label

    def test_bullish_in_trend(self, db_path):
        from scoring.price_structure import PriceStructureScorer
        scorer = PriceStructureScorer()
        result = scorer.compute(date(2025, 11, 15))  # TREND period
        assert result.score > 50  # Should be bullish in uptrend

    def test_bearish_in_panic(self, db_path):
        from scoring.price_structure import PriceStructureScorer
        scorer = PriceStructureScorer()
        result = scorer.compute(date(2026, 5, 15))  # PANIC period
        # In panic period, EMA alignment should be bearish
        assert result.trend_strength < 60

    def test_no_data_handling(self, db_path):
        from scoring.price_structure import PriceStructureScorer
        scorer = PriceStructureScorer()
        result = scorer.compute(date(2020, 1, 1))
        assert result.score == 50.0
        assert result.label == "No Data"


class TestBreadthScorer:
    def test_computes_score(self, db_path):
        from scoring.breadth_scorer import BreadthScorer
        scorer = BreadthScorer()
        result = scorer.compute(date(2026, 3, 15))
        assert result.name == "Breadth"
        assert 0 <= result.score <= 100
        assert result.breadth_bucket
        assert result.breadth_top20 >= 0
        assert result.breadth_top50 >= 0

    def test_tier_values(self, db_path):
        from scoring.breadth_scorer import BreadthScorer
        scorer = BreadthScorer()
        result = scorer.compute(date(2025, 11, 15))  # TREND period
        # Top20 should generally be higher than Top50 (large caps lead)
        assert result.breadth_top20 >= 0
        assert result.breadth_top50 >= 0

    def test_bucket_assignment(self, db_path):
        from scoring.breadth_scorer import BreadthScorer, BreadthBucket
        scorer = BreadthScorer()
        result = scorer.compute(date(2025, 11, 15))  # TREND: adv=42/50
        assert result.breadth_bucket in (
            BreadthBucket.EXTREME, BreadthBucket.STRONG, BreadthBucket.NORMAL
        )

    def test_no_data(self, db_path):
        from scoring.breadth_scorer import BreadthScorer
        scorer = BreadthScorer()
        result = scorer.compute(date(2020, 1, 1))
        assert result.score == 50.0


class TestOIMatrixScorer:
    def test_computes_state(self, db_path):
        from scoring.oi_matrix import OIMatrixScorer, OIState
        scorer = OIMatrixScorer()
        result = scorer.compute(date(2025, 11, 15))  # TREND period, oi_chg=+3.5
        assert result.oi_state in OIState
        assert 0 <= result.score <= 100

    def test_new_longs_in_trend(self, db_path):
        from scoring.oi_matrix import OIMatrixScorer, OIState
        scorer = OIMatrixScorer()
        # Test multiple dates in TREND period — at least one should be NEW_LONGS or NEUTRAL
        found_bullish = False
        for d in ["2025-11-15", "2025-11-20", "2025-12-01", "2025-12-15"]:
            result = scorer.compute(date.fromisoformat(d))
            if result.oi_state in (OIState.NEW_LONGS, OIState.SHORT_COVERING, OIState.NEUTRAL):
                found_bullish = True
                break
        assert found_bullish, "No bullish OI state found in TREND period"

    def test_no_data(self, db_path):
        from scoring.oi_matrix import OIMatrixScorer
        scorer = OIMatrixScorer()
        result = scorer.compute(date(2020, 1, 1))
        assert result.score == 50.0
        assert result.label == "No Data"


class TestVolatilityRegimeScorer:
    def test_computes_regime(self, db_path):
        from scoring.volatility_regime import VolatilityRegimeScorer, VolRegime
        scorer = VolatilityRegimeScorer()
        result = scorer.compute(date(2026, 3, 15))
        assert result.vol_regime in VolRegime
        assert 0 <= result.score <= 100

    def test_higher_vol_in_panic(self, db_path):
        from scoring.volatility_regime import VolatilityRegimeScorer, VolRegime
        scorer = VolatilityRegimeScorer()
        trend_result = scorer.compute(date(2025, 11, 15))
        panic_result = scorer.compute(date(2026, 5, 15))
        # PANIC period has higher ATR → higher vol regime or score
        assert panic_result.atr_pct >= trend_result.atr_pct * 0.5  # at least comparable

    def test_no_data(self, db_path):
        from scoring.volatility_regime import VolatilityRegimeScorer
        scorer = VolatilityRegimeScorer()
        result = scorer.compute(date(2020, 1, 1))
        assert result.score == 50.0