"""
Realistic 10% Monthly Strategy
Based on TemaAdxCmo but optimized for consistent monthly profits

Improvements:
- Remove SOL (worst performer)
- Tighter stop loss (-6% instead of -8%)
- 4x leverage (balanced risk/reward)
- Faster ROI targets
- Stricter entry filters (quality over quantity)
- Only BTC/ETH (most liquid pairs)
"""

import numpy as np
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, BooleanParameter
import talib.abstract as ta
from datetime import datetime
from typing import Optional


class Realistic10Monthly(IStrategy):

    INTERFACE_VERSION = 3
    can_short = False  # Only longs (proven to work better)

    # Faster ROI for 10% monthly target
    minimal_roi = {
        "0": 0.06,   # 6% immediate
        "10": 0.04,  # 4% after 10 min
        "20": 0.025, # 2.5% after 20 min
        "40": 0.015, # 1.5% after 40 min
        "80": 0.01   # 1% after 80 min
    }

    # Tighter stop loss
    stoploss = -0.06  # 6% (was -8%)

    # Aggressive trailing
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True

    timeframe = '15m'
    startup_candle_count = 100

    # 4x leverage (balanced)
    leverage_num = IntParameter(3, 5, default=4, space='buy', optimize=False)

    # Stricter parameters for quality trades
    adx_threshold = IntParameter(25, 35, default=30, space='buy', optimize=True)
    cmo_threshold = IntParameter(15, 25, default=20, space='buy', optimize=True)
    rsi_long_threshold = IntParameter(40, 55, default=48, space='buy', optimize=True)
    volume_factor = DecimalParameter(1.3, 2.0, default=1.5, decimals=1, space='buy', optimize=True)

    # Trend filter
    use_ema_filter = BooleanParameter(default=True, space='buy')
    ema_period = IntParameter(40, 60, default=50, space='buy', optimize=True)

    # TEMA parameters
    tema_rolling_window = IntParameter(2, 4, default=3, space='buy', optimize=True)

    def leverage(self, pair: str, current_time, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:
        return self.leverage_num.value

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # TEMA indicators
        dataframe['tema8'] = ta.TEMA(dataframe, timeperiod=8)
        dataframe['tema13'] = ta.TEMA(dataframe, timeperiod=13)
        dataframe['tema21'] = ta.TEMA(dataframe, timeperiod=21)

        # EMA trend filter
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=self.ema_period.value)

        # Momentum
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['cmo'] = ta.CMO(dataframe, timeperiod=14)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

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

        # Volume
        dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()

        # Bollinger Bands for additional confirmation
        bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
        dataframe['bb_lower'] = bollinger['lowerband']
        dataframe['bb_middle'] = bollinger['middleband']
        dataframe['bb_upper'] = bollinger['upperband']

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Stricter entry conditions for quality trades
        """

        long_conditions = (
            # TEMA alignment (strong trend)
            (dataframe['tema8'] > dataframe['tema13']) &
            (dataframe['tema13'] > dataframe['tema21']) &

            # Strong momentum
            (dataframe['adx'] > self.adx_threshold.value) &
            (dataframe['cmo'] > self.cmo_threshold.value) &

            # Not overbought
            (dataframe['rsi'] > self.rsi_long_threshold.value) &
            (dataframe['rsi'] < 70) &

            # Good volume
            (dataframe['volume'] > dataframe['volume_mean'] * self.volume_factor.value) &

            # Trend filter
            (dataframe['close'] > dataframe['ema50']) &

            # MACD bullish
            (dataframe['macd'] > dataframe['macdsignal']) &

            # Price not extended (within reasonable range of EMA)
            (dataframe['close'] < dataframe['ema50'] * 1.05) &

            # Volatility filter (avoid choppy markets)
            (dataframe['atr'] > dataframe['atr'].rolling(window=20).mean() * 0.8)
        )

        dataframe.loc[long_conditions, 'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Exit when trend weakens
        """

        dataframe.loc[
            # ADX weakening
            (dataframe['adx'] < 20) |
            # TEMA reversal
            (dataframe['tema8'] < dataframe['tema13']) |
            # Below EMA
            (dataframe['close'] < dataframe['ema50'] * 0.98),
            'exit_long'
        ] = 1

        return dataframe

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                           proposed_stake: float, min_stake: Optional[float],
                           max_stake: float, leverage: float, entry_tag: Optional[str],
                           side: str, **kwargs) -> float:
        """
        Dynamic position sizing based on ADX strength
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)

        if dataframe.empty:
            return proposed_stake

        current_adx = dataframe.iloc[-1]['adx']

        # Size positions based on trend strength
        if current_adx > 40:  # Very strong trend
            multiplier = 1.1
        elif current_adx > 30:  # Strong trend
            multiplier = 1.0
        else:  # Moderate trend
            multiplier = 0.85

        stake = proposed_stake * multiplier
        return min(stake, max_stake)