"""
Test AMD Detector
"""

import pytest
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

from src.models.amd_detector import AMDDetector, AMDPhase


@pytest.fixture
def sample_ohlcv_data():
    """Create sample OHLCV data for testing"""
    dates = pd.date_range(start='2024-01-01', periods=200, freq='5min')
    np.random.seed(42)

    # Generate synthetic price data
    base_price = 2000
    returns = np.random.randn(200) * 0.001
    prices = base_price * np.cumprod(1 + returns)

    df = pd.DataFrame({
        'open': prices,
        'high': prices * (1 + abs(np.random.randn(200) * 0.001)),
        'low': prices * (1 - abs(np.random.randn(200) * 0.001)),
        'close': prices * (1 + np.random.randn(200) * 0.0005),
        'volume': np.random.randint(1000, 10000, 200)
    }, index=dates)

    # Ensure OHLC consistency
    df['high'] = df[['open', 'high', 'close']].max(axis=1)
    df['low'] = df[['open', 'low', 'close']].min(axis=1)

    return df


def test_amd_detector_initialization():
    """Test AMD detector initialization"""
    detector = AMDDetector(lookback_periods=100)
    assert detector.lookback_periods == 100
    assert len(detector.phase_history) == 0
    assert detector.current_phase is None


def test_detect_phase_insufficient_data():
    """Test phase detection with insufficient data"""
    detector = AMDDetector(lookback_periods=100)

    # Create small dataset
    dates = pd.date_range(start='2024-01-01', periods=50, freq='5min')
    df = pd.DataFrame({
        'open': [2000] * 50,
        'high': [2010] * 50,
        'low': [1990] * 50,
        'close': [2005] * 50,
        'volume': [1000] * 50
    }, index=dates)

    phase = detector.detect_phase(df)

    assert phase.phase == 'unknown'
    assert phase.confidence == 0
    assert phase.strength == 0


def test_detect_phase_with_sufficient_data(sample_ohlcv_data):
    """Test phase detection with sufficient data"""
    detector = AMDDetector(lookback_periods=100)
    phase = detector.detect_phase(sample_ohlcv_data)

    # Should return a valid phase
    assert phase.phase in ['accumulation', 'manipulation', 'distribution']
    assert 0 <= phase.confidence <= 1
    assert 0 <= phase.strength <= 1
    assert isinstance(phase.characteristics, dict)
    assert isinstance(phase.signals, list)


def test_trading_bias_accumulation():
    """Test trading bias for accumulation phase"""
    detector = AMDDetector()

    phase = AMDPhase(
        phase='accumulation',
        confidence=0.7,
        start_time=datetime.utcnow(),
        end_time=None,
        characteristics={},
        signals=[],
        strength=0.6
    )

    bias = detector.get_trading_bias(phase)

    assert bias['phase'] == 'accumulation'
    assert bias['direction'] == 'long'
    assert bias['risk_level'] == 'low'
    assert 'buy_dips' in bias['strategies']


def test_trading_bias_manipulation():
    """Test trading bias for manipulation phase"""
    detector = AMDDetector()

    phase = AMDPhase(
        phase='manipulation',
        confidence=0.7,
        start_time=datetime.utcnow(),
        end_time=None,
        characteristics={},
        signals=[],
        strength=0.6
    )

    bias = detector.get_trading_bias(phase)

    assert bias['phase'] == 'manipulation'
    assert bias['direction'] == 'neutral'
    assert bias['risk_level'] == 'high'
    assert bias['position_size'] == 0.3


def test_trading_bias_distribution():
    """Test trading bias for distribution phase"""
    detector = AMDDetector()

    phase = AMDPhase(
        phase='distribution',
        confidence=0.7,
        start_time=datetime.utcnow(),
        end_time=None,
        characteristics={},
        signals=[],
        strength=0.6
    )

    bias = detector.get_trading_bias(phase)

    assert bias['phase'] == 'distribution'
    assert bias['direction'] == 'short'
    assert bias['risk_level'] == 'medium'
    assert 'sell_rallies' in bias['strategies']


def test_amd_phase_to_dict():
    """Test AMDPhase to_dict conversion"""
    phase = AMDPhase(
        phase='accumulation',
        confidence=0.75,
        start_time=datetime(2024, 1, 1, 12, 0),
        end_time=datetime(2024, 1, 1, 13, 0),
        characteristics={'range_compression': 0.65},
        signals=['breakout_imminent'],
        strength=0.7
    )

    phase_dict = phase.to_dict()

    assert phase_dict['phase'] == 'accumulation'
    assert phase_dict['confidence'] == 0.75
    assert phase_dict['strength'] == 0.7
    assert '2024-01-01' in phase_dict['start_time']
    assert isinstance(phase_dict['characteristics'], dict)
    assert isinstance(phase_dict['signals'], list)


if __name__ == "__main__":
    pytest.main([__file__, "-v"])