E0V1E

from datetime import datetime, timedelta
import logging
from typing import Optional, Union
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce

logger = logging.getLogger(__name__)

def ewo(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif


class E0V1E(IStrategy):
    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 10
    }

    timeframe = '5m'

    process_only_new_candles = True
    startup_candle_count = 20

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99
    }

    # Disabled
    stoploss = -0.99

    # Custom stoploss
    use_custom_stoploss = True

    is_optimize_ewo = True
    buy_rsi_fast = IntParameter(35, 60, default=55, space='buy', optimize=is_optimize_ewo)
    buy_rsi = IntParameter(15, 50, default=42, space='buy', optimize=is_optimize_ewo)
    buy_ewo = DecimalParameter(-6.0, 5, default=2.058, space='buy', optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.998, default=0.909, space='buy', optimize=is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.082, space='buy', optimize=is_optimize_ewo)

    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=33, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=47, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.995, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.67, decimals=2, space='buy', optimize=is_optimize_32)

    is_optimize_deadfish = True
    sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish)

    sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # buy_1 indicators
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        # ewo indicators
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['EWO'] = ewo(dataframe, 50, 200)

        # profit sell indicators
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # loss sell indicators
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']

        dataframe['bb_width'] = (
                (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])

        dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
        dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy_1 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
                (dataframe['rsi'] > self.buy_rsi_32.value) &
                (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
                (dataframe['cti'] < self.buy_cti_32.value)
        )

        # Keep only the profitable buy_1 branch until ewo is reworked.
        conditions.append(buy_1)
        dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_long'] = 1

        return dataframe

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                        current_profit: float, **kwargs) -> float:
        """
        Indicator-based trailing stoploss for longs with 3x leverage.

        Emergency stop at -25% prevents liquidations (~-30% with 3x leverage).
        Indicator-based exits (RSI, Stochastic) lock in profits when overbought.
        Stops scaled 3x from original spot strategy to maintain same price tolerance.

        Original stops (-0.001, -0.005) scaled to (-0.003, -0.015) for 3x leverage
        to maintain same price movement tolerance as the original spot strategy.

        Args:
            pair: Trading pair
            trade: Trade object
            current_time: Current timestamp
            current_rate: Current price
            current_profit: Current profit/loss ratio
            **kwargs: Additional arguments

        Returns:
            float: Stoploss percentage or 1.0 to keep base stoploss
        """
        # EMERGENCY BACKSTOP: Last resort before liquidation (~-30% with 3x)
        # Tightened from -25% to -20% for more safety margin
        if current_profit <= -0.20:
            logger.warning(f"{trade.pair} EMERGENCY stop at {current_profit*100:.2f}% (preventing liquidation)")
            return -0.21  # Exit immediately

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        enter_tag = ''
        if hasattr(trade, 'enter_tag') and trade.enter_tag is not None:
            enter_tag = trade.enter_tag
        enter_tags = enter_tag.split()

        # EWO entries: Tight trailing at 5% profit (original: -0.005, scaled 3x: -0.015)
        if "ewo" in enter_tags:
            if current_profit >= 0.05:
                return -0.015

        # Profitable trades: Exit on overbought indicators (original: -0.001, scaled 3x: -0.003)
        if current_profit > 0.01:
            if current_candle["fastk"] > self.sell_fastx.value:
                return -0.003

            if current_candle["rsi"] > 80:
                return -0.003

        # Losing trades: Exit on extreme overbought (original: -0.001, scaled 3x: -0.003)
        if current_profit < 0.01:
            if current_candle["rsi"] > 90:
                return -0.003

        # Default: Keep base stoploss (-0.99)
        return 1.0

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        # stoploss - deadfish
        if ((current_profit < self.sell_deadfish_profit.value)
                and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value)
                and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
                and (current_candle['volume_mean_12'] < current_candle[
                    'volume_mean_24'] * self.sell_deadfish_volume_factor.value)):
            logger.info(f"{pair} sell_stoploss_deadfish at {current_profit*100}")
            return "sell_stoploss_deadfish"

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        #dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')

        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float:
        """
        Fixed 3x leverage for all long trades.

        Returns:
            float: Leverage multiplier (3.0 = 3x)
        """
        return 3.0