"""
Moving Average Crossover Strategy for Freqtrade
Classic golden cross / death cross strategy with enhancements
"""
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter
import logging

logger = logging.getLogger(__name__)


class MACrossoverStrategy(IStrategy):
    """
    Moving Average Crossover Strategy

    Buy when:
    - Fast MA crosses above Slow MA (Golden Cross)
    - Volume is above average
    - ADX indicates trending market

    Sell when:
    - Fast MA crosses below Slow MA (Death Cross)
    """

    INTERFACE_VERSION = 3

    timeframe = '1h'
    can_short = False

    # ROI table
    minimal_roi = {
        "0": 0.15,
        "60": 0.10,
        "120": 0.05,
        "240": 0.02
    }

    stoploss = -0.08

    trailing_stop = True
    trailing_stop_positive = 0.015
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    process_only_new_candles = True
    use_exit_signal = True

    # Hyperoptable parameters
    fast_ma = IntParameter(10, 30, default=20, space="buy", optimize=True)
    slow_ma = IntParameter(40, 100, default=50, space="buy", optimize=True)
    adx_threshold = IntParameter(20, 35, default=25, space="buy", optimize=True)

    startup_candle_count: int = 100

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Add technical indicators"""

        # Moving Averages
        for period in [10, 20, 30, 50, 100, 200]:
            dataframe[f'sma_{period}'] = ta.SMA(dataframe, timeperiod=period)
            dataframe[f'ema_{period}'] = ta.EMA(dataframe, timeperiod=period)

        # ADX for trend strength
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)

        # RSI for confirmation
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        # Volume SMA
        dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)

        # ATR
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Entry signal logic"""

        fast_ma_col = f'sma_{self.fast_ma.value}'
        slow_ma_col = f'sma_{self.slow_ma.value}'

        # Ensure columns exist
        if fast_ma_col not in dataframe.columns:
            dataframe[fast_ma_col] = ta.SMA(dataframe, timeperiod=self.fast_ma.value)
        if slow_ma_col not in dataframe.columns:
            dataframe[slow_ma_col] = ta.SMA(dataframe, timeperiod=self.slow_ma.value)

        dataframe.loc[
            (
                # Golden Cross
                (dataframe[fast_ma_col] > dataframe[slow_ma_col]) &
                (dataframe[fast_ma_col].shift(1) <= dataframe[slow_ma_col].shift(1)) &
                # ADX indicates trending market
                (dataframe['adx'] > self.adx_threshold.value) &
                # Volume confirmation
                (dataframe['volume'] > dataframe['volume_sma']) &
                # RSI not overbought
                (dataframe['rsi'] < 70) &
                (dataframe['volume'] > 0)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Exit signal logic"""

        fast_ma_col = f'sma_{self.fast_ma.value}'
        slow_ma_col = f'sma_{self.slow_ma.value}'

        dataframe.loc[
            (
                # Death Cross
                (dataframe[fast_ma_col] < dataframe[slow_ma_col]) &
                (dataframe[fast_ma_col].shift(1) >= dataframe[slow_ma_col].shift(1)) &
                (dataframe['volume'] > 0)
            ),
            'exit_long'] = 1

        return dataframe