import pytest
import pandas as pd
from datetime import datetime, timezone
from unittest.mock import MagicMock
import numpy as np
from decimal import Decimal

from strategies.base import BaseStrategy
from strategies.data_types import StrategyResult, StrategySignal, SignalType
from data.common.data_types import OHLCVCandle

# Mock logger for testing
class MockLogger:
    def __init__(self):
        self.debug_calls = []
        self.info_calls = []
        self.warning_calls = []
        self.error_calls = []

    def debug(self, msg):
        self.debug_calls.append(msg)

    def info(self, msg):
        self.info_calls.append(msg)

    def warning(self, msg):
        self.warning_calls.append(msg)

    def error(self, msg):
        self.error_calls.append(msg)

# Concrete implementation of BaseStrategy for testing purposes
class ConcreteStrategy(BaseStrategy):
    def __init__(self, logger=None):
        super().__init__(strategy_name="ConcreteStrategy", logger=logger)

    def get_required_indicators(self) -> list[dict]:
        return []

    def calculate(self, df: pd.DataFrame, indicators_data: dict, **kwargs) -> list:
        # Dummy implementation for testing
        return []

@pytest.fixture
def mock_logger():
    return MockLogger()

@pytest.fixture
def concrete_strategy(mock_logger):
    return ConcreteStrategy(logger=mock_logger)

@pytest.fixture
def sample_ohlcv_data():
    # Create a sample DataFrame that mimics OHLCVCandle structure
    data = {
        'timestamp': pd.to_datetime(['2023-01-01 00:00:00', '2023-01-01 01:00:00', '2023-01-01 02:00:00', '2023-01-01 03:00:00', '2023-01-01 04:00:00']),
        'open': [100, 101, 102, 103, 104],
        'high': [105, 106, 107, 108, 109],
        'low': [99, 100, 101, 102, 103],
        'close': [102, 103, 104, 105, 106],
        'volume': [1000, 1100, 1200, 1300, 1400],
        'trade_count': [100, 110, 120, 130, 140],
        'symbol': ['BTC/USDT'] * 5,
        'timeframe': ['1h'] * 5
    }
    df = pd.DataFrame(data)
    df = df.set_index('timestamp') # Ensure timestamp is the index
    return df

def test_prepare_dataframe_initial_data(concrete_strategy, sample_ohlcv_data):
    candles_list = [
        OHLCVCandle(
            symbol=row['symbol'],
            timeframe=row['timeframe'],
            start_time=row['timestamp'], # Assuming start_time is the same as timestamp for simplicity in test
            end_time=row['timestamp'],
            open=Decimal(str(row['open'])),
            high=Decimal(str(row['high'])),
            low=Decimal(str(row['low'])),
            close=Decimal(str(row['close'])),
            volume=Decimal(str(row['volume'])),
            trade_count=row['trade_count'],
            exchange="test_exchange", # Add dummy exchange
            is_complete=True, # Add dummy is_complete
            first_trade_time=row['timestamp'], # Add dummy first_trade_time
            last_trade_time=row['timestamp'] # Add dummy last_trade_time
        )
        for row in sample_ohlcv_data.reset_index().to_dict(orient='records')
    ]
    prepared_df = concrete_strategy.prepare_dataframe(candles_list)
    
    # Prepare expected_df to match the structure produced by prepare_dataframe
    # It sets timestamp as index, then adds it back as a column.
    expected_df = sample_ohlcv_data.copy().reset_index()
    expected_df['timestamp'] = expected_df['timestamp'].apply(lambda x: x.replace(tzinfo=timezone.utc)) # Ensure timezone awareness
    expected_df.set_index('timestamp', inplace=True)
    expected_df['timestamp'] = expected_df.index
    
    # Define the expected column order based on how prepare_dataframe constructs the DataFrame
    expected_columns_order = [
        'symbol', 'timeframe', 'open', 'high', 'low', 'close', 'volume', 'trade_count', 'timestamp'
    ]
    expected_df = expected_df[expected_columns_order]

    # Convert numeric columns to float as they are read from OHLCVCandle
    for col in ['open', 'high', 'low', 'close', 'volume']:
        expected_df[col] = expected_df[col].apply(lambda x: float(str(x)))

    # Compare important columns, as BaseStrategy.prepare_dataframe also adds 'timestamp' back as a column
    pd.testing.assert_frame_equal(
        prepared_df,
        expected_df
    )

def test_prepare_dataframe_sparse_data(concrete_strategy, sample_ohlcv_data):
    # Simulate sparse data by removing the middle row
    sparse_candles_data_dicts = sample_ohlcv_data.drop(sample_ohlcv_data.index[2]).reset_index().to_dict(orient='records')
    sparse_candles_list = [
        OHLCVCandle(
            symbol=row['symbol'],
            timeframe=row['timeframe'],
            start_time=row['timestamp'],
            end_time=row['timestamp'],
            open=Decimal(str(row['open'])),
            high=Decimal(str(row['high'])),
            low=Decimal(str(row['low'])),
            close=Decimal(str(row['close'])),
            volume=Decimal(str(row['volume'])),
            trade_count=row['trade_count'],
            exchange="test_exchange",
            is_complete=True,
            first_trade_time=row['timestamp'],
            last_trade_time=row['timestamp']
        )
        for row in sparse_candles_data_dicts
    ]
    prepared_df = concrete_strategy.prepare_dataframe(sparse_candles_list)

    expected_df_sparse = sample_ohlcv_data.drop(sample_ohlcv_data.index[2]).copy().reset_index()
    expected_df_sparse['timestamp'] = expected_df_sparse['timestamp'].apply(lambda x: x.replace(tzinfo=timezone.utc))
    expected_df_sparse.set_index('timestamp', inplace=True)
    expected_df_sparse['timestamp'] = expected_df_sparse.index

    # Define the expected column order based on how prepare_dataframe constructs the DataFrame
    expected_columns_order = [
        'symbol', 'timeframe', 'open', 'high', 'low', 'close', 'volume', 'trade_count', 'timestamp'
    ]
    expected_df_sparse = expected_df_sparse[expected_columns_order]

    # Convert numeric columns to float as they are read from OHLCVCandle
    for col in ['open', 'high', 'low', 'close', 'volume']:
        expected_df_sparse[col] = expected_df_sparse[col].apply(lambda x: float(str(x)))

    pd.testing.assert_frame_equal(
        prepared_df,
        expected_df_sparse
    )

def test_validate_dataframe_valid(concrete_strategy, sample_ohlcv_data, mock_logger):
    # Ensure no warnings/errors are logged for valid data
    concrete_strategy.validate_dataframe(sample_ohlcv_data, min_periods=len(sample_ohlcv_data))
    assert not mock_logger.warning_calls
    assert not mock_logger.error_calls

def test_validate_dataframe_missing_column(concrete_strategy, sample_ohlcv_data, mock_logger):
    invalid_df = sample_ohlcv_data.drop(columns=['open'])
    is_valid = concrete_strategy.validate_dataframe(invalid_df, min_periods=len(invalid_df))
    assert is_valid # BaseStrategy.validate_dataframe does not check for missing columns

def test_validate_dataframe_invalid_index(concrete_strategy, sample_ohlcv_data, mock_logger):
    invalid_df = sample_ohlcv_data.reset_index() # Remove DatetimeIndex
    is_valid = concrete_strategy.validate_dataframe(invalid_df, min_periods=len(invalid_df))
    assert is_valid # BaseStrategy.validate_dataframe does not check index validity

def test_validate_dataframe_non_monotonic_index(concrete_strategy, sample_ohlcv_data, mock_logger):
    # Reverse order to make it non-monotonic
    invalid_df = sample_ohlcv_data.iloc[::-1]
    is_valid = concrete_strategy.validate_dataframe(invalid_df, min_periods=len(invalid_df))
    assert is_valid # BaseStrategy.validate_dataframe does not check index monotonicity

def test_validate_indicators_data_valid(concrete_strategy, sample_ohlcv_data, mock_logger):
    indicators_data = {
        'ema_12': pd.Series([101, 102, 103, 104, 105], index=sample_ohlcv_data.index),
        'ema_26': pd.Series([100, 101, 102, 103, 104], index=sample_ohlcv_data.index)
    }
    
    required_indicators = [
        {'type': 'ema', 'period': 12},
        {'type': 'ema', 'period': 26}
    ]
    
    concrete_strategy.validate_indicators_data(indicators_data, required_indicators)
    assert not mock_logger.warning_calls
    assert not mock_logger.error_calls

def test_validate_indicators_data_missing_indicator(concrete_strategy, sample_ohlcv_data, mock_logger):
    indicators_data = {
        'ema_12': pd.Series([101, 102, 103, 104, 105], index=sample_ohlcv_data.index),
    }
    
    required_indicators = [
        {'type': 'ema', 'period': 12},
        {'type': 'ema', 'period': 26} # Missing
    ]
    
    with pytest.raises(ValueError, match="Missing required indicator data for key: ema_26"):
        concrete_strategy.validate_indicators_data(indicators_data, required_indicators)

def test_validate_indicators_data_nan_values(concrete_strategy, sample_ohlcv_data, mock_logger):
    indicators_data = {
        'ema_12': pd.Series([101, 102, np.nan, 104, 105], index=sample_ohlcv_data.index),
        'ema_26': pd.Series([100, 101, 102, 103, 104], index=sample_ohlcv_data.index)
    }
    
    required_indicators = [
        {'type': 'ema', 'period': 12},
        {'type': 'ema', 'period': 26}
    ]
    
    concrete_strategy.validate_indicators_data(indicators_data, required_indicators)
    assert "NaN values found in indicator data for key: ema_12" in mock_logger.warning_calls