binance_strategy

from datetime import datetime, timedelta
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


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 binance_strategy(IStrategy):
    minimal_roi = {
        "0": 10
    }

    timeframe = '5m'
    process_only_new_candles = True
    # Corrected startup candle count to match the longest indicator period (EWO uses 200)
    startup_candle_count = 200

    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
    }

    stoploss = -0.99
    use_custom_stoploss = True

    # Your hyperopt parameters remain unchanged...
    is_optimize_ewo = True
    buy_rsi_fast = IntParameter(35, 50, default=50, space='buy', optimize=is_optimize_ewo)
    buy_rsi = IntParameter(15, 35, default=30, space='buy', optimize=is_optimize_ewo)
    buy_ewo = DecimalParameter(-6.0, 5, default=-1.238, space='buy', optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.956, space='buy', optimize=is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=0.986, space='buy', optimize=is_optimize_ewo)
    is_optimize_32 = True
    buy_rsi_fast_32 = IntParameter(20, 70, default=63, space='buy', optimize=is_optimize_32)
    buy_rsi_32 = IntParameter(15, 50, default=16, space='buy', optimize=is_optimize_32)
    buy_sma15_32 = DecimalParameter(0.900, 1, default=0.932, decimals=3, space='buy', optimize=is_optimize_32)
    buy_cti_32 = DecimalParameter(-1, 0, default=-0.8, 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:
        # --- CRITICAL FIX: Enforce consistent data types for TA-Lib ---
        # TA-Lib functions are sensitive to input types. We cast to `float` to ensure consistency.
        dataframe['open'] = dataframe['open'].astype('float')
        dataframe['high'] = dataframe['high'].astype('float')
        dataframe['low'] = dataframe['low'].astype('float')
        dataframe['close'] = dataframe['close'].astype('float')
        dataframe['volume'] = dataframe['volume'].astype('float')
        # --- END OF FIX ---

        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)

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

        # Corrected STOCHF call
        stoch_fast = ta.STOCHF(dataframe, fastk_period=5, fastd_period=3, fastd_matype=0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        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 = []
        # --- FIX: Initialize with a non-empty string for consistent data type ---
        dataframe.loc[:, 'enter_tag'] = ' '

        is_ewo = (
                (dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) &
                (dataframe['EWO'] > self.buy_ewo.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) &
                (dataframe['rsi'] < self.buy_rsi.value)
        )

        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)
        )

        conditions.append(is_ewo)
        dataframe.loc[is_ewo, 'enter_tag'] += 'ewo'

        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
        else:
            dataframe['enter_long'] = 0


        return dataframe

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                        current_profit: float, **kwargs) -> float:
        # ... (Your custom stoploss logic remains unchanged) ...
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        if current_time - timedelta(minutes=60) > trade.open_date_utc:
            if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.01):
                return -0.001

        if current_time - timedelta(days=1) > trade.open_date_utc:
            if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.05):
                return -0.001

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

        if "ewo" in enter_tags:
            if current_profit >= 0.05:
                return -0.005

        if current_profit > 0:
            if current_candle["fastk"] > self.sell_fastx.value:
                return -0.001

        return self.stoploss


    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        # ... (Your custom exit logic remains unchanged) ...
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        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)):
            return "sell_stoploss_deadfish"

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Also ensure the exit column is initialized cleanly
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe