"""Test regime detector and validation."""
import pytest
from datetime import date
import pandas as pd
import numpy as np


class TestRegimeDetector:
    def test_detects_trend(self):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
        assert r.regime == MarketRegime.TREND
        assert r.confidence > 0.5

    def test_detects_range(self):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        r = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
        assert r.regime in (MarketRegime.RANGE, MarketRegime.TREND)

    def test_detects_panic(self):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 24))
        assert r.regime == MarketRegime.PANIC

    def test_2day_confirmation(self):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        # Day 1: RANGE
        r1 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
        assert r1.regime == MarketRegime.RANGE  # first run, no confirmation needed
        # Day 2: still RANGE
        r2 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 25))
        assert r2.regime == MarketRegime.RANGE
        assert r2.confirmation_days == 2

    def test_transition_needs_confirmation(self):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        # Establish TREND
        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
        # Day 3: weak scores → raw best = RANGE, but TREND should persist
        r3 = d.detect(35.0, 40.0, "NORMAL_VOL", date(2026, 6, 26))
        # First day of pending transition — should still be TREND
        assert r3.regime == MarketRegime.TREND
        assert d.pending_regime is not None

    def test_version_is_stored(self):
        from regime_detector import RegimeDetector
        d = RegimeDetector(regime_version="v1_price_breadth_vol")
        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
        assert r.regime_version == "v1_price_breadth_vol"

    def test_load_state(self, db_path):
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        d.load_state(db_path)
        # DB has TREND for first 100 days, so most recent should load
        assert d.current_regime is not None

    def test_confidence_for_confirmed_regime(self):
        """Confidence should be for the confirmed regime, not raw best."""
        from regime_detector import RegimeDetector
        from models import MarketRegime
        d = RegimeDetector()
        # Establish TREND
        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
        # Now feed weak scores → raw best would be PANIC or RANGE
        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 26))
        # Should still report TREND (need 2 confirmations to switch)
        assert r.regime == MarketRegime.TREND


class TestTransitionValidator:
    def test_stable_regime_passes(self):
        from validation.transition_validator import TransitionValidator
        # Create stable regime sequence: long periods
        seq = pd.Series(
            ["TREND"] * 50 + ["RANGE"] * 50 + ["PANIC"] * 40,
            index=pd.date_range("2026-01-01", periods=140),
        )
        tv = TransitionValidator()
        report = tv.validate(seq)
        assert report.is_stable
        assert report.avg_duration > 20
        assert report.flip_rate < 0.05

    def test_unstable_regime_fails(self):
        from validation.transition_validator import TransitionValidator
        # Create unstable sequence: flips every 2 days
        seq = pd.Series(
            ["TREND", "TREND", "RANGE", "RANGE", "TREND", "TREND",
             "PANIC", "PANIC", "RANGE", "RANGE"] * 5,
            index=pd.date_range("2026-01-01", periods=50),
        )
        tv = TransitionValidator()
        report = tv.validate(seq)
        assert not report.is_stable
        assert report.flip_rate > 0.15