from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import IntParameter, DecimalParameter
from pandas import DataFrame
import talib.abstract as ta


class TrendFollowingStrategy(IStrategy):
    can_short: bool = True
    timeframe = '5m'
    # Basic ROI/SL - keep modest defaults; can be tuned later
    minimal_roi = {
        "0": 0.03
    }
    stoploss = -0.08
    trailing_stop = True

    # Ensure we have enough candles for indicators (BB 20, EMA 200, etc.)
    startup_candle_count = 210

    # ---- Hyperoptable parameters (buy/sell) to enable default spaces ----
    # These unlock hyperopt for this strategy (avoid "no parameter for this space" error)
    buy_rsi = IntParameter(45, 60, default=50, space='buy')
    buy_bb_mid_offset = DecimalParameter(0.0, 0.02, decimals=3, default=0.0, space='buy')

    sell_rsi = IntParameter(40, 55, default=50, space='sell')
    sell_bb_mid_offset = DecimalParameter(0.0, 0.02, decimals=3, default=0.0, space='sell')

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Moving averages
        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=9)
        dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['ema_trend'] = ta.EMA(dataframe, timeperiod=200)

        # Bollinger Bands (20, 2)
        bb_upper, bb_middle, bb_lower = ta.BBANDS(
            dataframe['close'], timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0
        )
        dataframe['bb_upper'] = bb_upper
        dataframe['bb_mid'] = bb_middle
        dataframe['bb_lower'] = bb_lower

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

        # Volume (ensure positive)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(30).mean()

        return dataframe

    def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs) -> float:
        stop = abs(float(self.stoploss)) if getattr(self, "stoploss", None) is not None else 0.08
        base = 0.05 / stop if stop > 0 else (proposed_leverage or 1.0)
        base = max(1.0, min(float(base), float(max_leverage)))
        return base

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Long entry: EMA9 > EMA21, close breaks above BB mid (trend confirmation), RSI > 50, volume ok
        dataframe.loc[
            (
                (dataframe['ema_short'] > dataframe['ema_long']) &
                (dataframe['close'] > dataframe['bb_mid'] * (1 + float(self.buy_bb_mid_offset.value))) &
                (dataframe['rsi'] > int(self.buy_rsi.value)) &
                (dataframe['close'] > dataframe['ema_trend']) &
                (dataframe['volume'] > 0) &
                (dataframe['volume'] > dataframe['volume_mean_slow'])
            ),
            'enter_long'
        ] = 1

        # Short entry: EMA9 < EMA21, close below BB mid, RSI < 50, price below long EMA, volume ok
        dataframe.loc[
            (
                (dataframe['ema_short'] < dataframe['ema_long']) &
                (dataframe['close'] < dataframe['bb_mid'] * (1 - float(self.buy_bb_mid_offset.value))) &
                (dataframe['rsi'] < 100 - int(self.buy_rsi.value)) &
                (dataframe['close'] < dataframe['ema_trend']) &
                (dataframe['volume'] > 0) &
                (dataframe['volume'] > dataframe['volume_mean_slow'])
            ),
            'enter_short'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Long exit: Cross back down (EMA9 < EMA21) or mean reversion (close < BB mid) or RSI loss of momentum
        dataframe.loc[
            (
                (dataframe['ema_short'] < dataframe['ema_long']) |
                (dataframe['close'] < dataframe['bb_mid'] * (1 - float(self.sell_bb_mid_offset.value))) |
                (dataframe['rsi'] < int(self.sell_rsi.value))
            ),
            'exit_long'
        ] = 1

        # Short exit: Cross back up (EMA9 > EMA21) or mean reversion (close > BB mid) or RSI momentum fades
        dataframe.loc[
            (
                (dataframe['ema_short'] > dataframe['ema_long']) |
                (dataframe['close'] > dataframe['bb_mid'] * (1 + float(self.sell_bb_mid_offset.value))) |
                (dataframe['rsi'] > 100 - int(self.sell_rsi.value))
            ),
            'exit_short'
        ] = 1

        return dataframe