# source: https://raw.githubusercontent.com/secondlinger/WinnieBari/73bedc96c87439cd3ca5ae33aca9cb01ed125468/user_data/strategies/CombinedBinHClucAndMADV9.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter


# SSL Channels
def SSLChannels(dataframe, length=7):
    df = dataframe.copy()
    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
    return df['sslDown'], df['sslUp']


class Github_secondlinger_WinnieBari__CombinedBinHClucAndMADV9__20250919_011108(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {
        "0": 0.038,  # I feel lucky!
        "10": 0.018,
        "40": 0.005,
    }

    stoploss = -0.99  # effectively disabled.

    timeframe = '5m'
    inf_1h = '1h'

    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001  # it doesn't meant anything, just to guarantee there is a minimal profit.
    ignore_roi_if_entry_signal = False

    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025

    # Custom stoploss
    use_custom_stoploss = True

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    # Optional order type mapping.
    order_types = {
        'entry': 'market',
        'exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    buy_params = {
        #############
        # Enable/Disable conditions
        "buy_condition_0_enable": False,
        "buy_condition_1_enable": True,
        "buy_condition_2_enable": True,
        "buy_condition_3_enable": True,
        "buy_condition_4_enable": True,
        "buy_condition_5_enable": True,
        "buy_condition_6_enable": True,
        "buy_condition_7_enable": True,
        "buy_condition_8_enable": True,
        "buy_condition_9_enable": True,
        "buy_condition_10_enable": True,
    }

    ############################################################################

    # Buy

    buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)

    buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space='buy', optimize=False, load=True)
    buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=False,
                                                         load=True)

    buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True)
    buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space='buy', decimals=1, optimize=False, load=True)

    buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True)

    buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True)

    buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True)
    buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True)

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        # Manage losing trades and open room for better ones.

        if (current_profit > 0):
            return 0.99
        else:
            trade_time_50 = current_time - timedelta(minutes=50)

            # Trade open more then 60 minutes. For this strategy it's means -> loss
            # Let's try to minimize the loss

            if (trade_time_50 > trade.open_date_utc):

                try:
                    number_of_candle_shift = int((trade_time_50 - trade.open_date_utc).total_seconds() / 300)
                    dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
                    candle = dataframe.iloc[-number_of_candle_shift].squeeze()

                    # Are we still sinking?
                    if current_rate * 1.015 < candle['open']:
                        return 0.01

                except IndexError as error:

                    # Whoops, set stoploss at 5%
                    return 0.01

        return 0.99

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # RSI
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)

        # SSL Channels
        ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
        informative_1h['ssl_down'] = ssl_down_1h
        informative_1h['ssl_up'] = ssl_up_1h

        return informative_1h

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

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()

        # EMA
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)

        # SMA
        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)

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

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

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

        dataframe.loc[
            (
                    self.buy_condition_1_enable.value &

                    (dataframe['close'] > dataframe['ema_200']) &
                    (dataframe['close'] > dataframe['ema_200_1h']) &

                    (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) &

                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe[
                        'bb_lowerband'].shift(2)) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_2_enable.value &

                    (dataframe['close'] > dataframe['ema_200']) &

                    (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) &

                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe[
                        'bb_lowerband'].shift(2)) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_3_enable.value &

                    (dataframe['close'] > dataframe['ema_200_1h']) &

                    (dataframe['close'] < dataframe['bb_lowerband']) &
                    (dataframe['rsi'] < self.buy_rsi_3.value) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_4_enable.value &

                    (dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) &

                    (dataframe['close'] < dataframe['bb_lowerband']) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_5_enable.value &

                    (dataframe['close'] > dataframe['ema_200']) &
                    (dataframe['close'] > dataframe['ema_200_1h']) &

                    (dataframe['ema_26'] > dataframe['ema_12']) &
                    ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
                    ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                    (dataframe['close'] < (dataframe['bb_lowerband'])) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] > 0)  # Make sure Volume is not 0
            )
            |
            (
                    self.buy_condition_6_enable.value &

                    (dataframe['ema_26'] > dataframe['ema_12']) &
                    ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_2.value)) &
                    ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                    (dataframe['close'] < (dataframe['bb_lowerband'])) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_7_enable.value &

                    (dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) &

                    (dataframe['ema_26'] > dataframe['ema_12']) &
                    ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
                    ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_8_enable.value &

                    (dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) &
                    (dataframe['rsi'] < self.buy_rsi_1.value) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_9_enable.value &

                    (dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
                    (dataframe['rsi'] < self.buy_rsi_2.value) &

                    (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
                    (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(
                        30) * self.buy_volume_pump_1.value) &
                    (dataframe['volume'] > 0)
            )
            |
            (
                    self.buy_condition_10_enable.value &

                    (dataframe['close'] < dataframe['sma_5']) &
                    (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) &
                    (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                    (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) &
                    (dataframe['volume'] > 0)
            ),
            'buy'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['close'] > dataframe['bb_middleband'] * 1.01) &  # Don't be gready, sell fast
                    (dataframe['volume'] > 0)  # Make sure Volume is not 0
            )
            ,
            'exit'
        ] = 1
        return dataframe

    def custom_entry_price(self, pair: str, current_time, proposed_rate: float,
                           entry_tag: str, side: str, **kwargs) -> float:
        latebuy = 0.1
        """
        진입 시 원하는 가격으로 지정가 주문
        ATR 기반으로 현재가보다 낮은 가격에 매수 주문 설정
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)

        # 최신 ATR 값 가져오기
        # ATR 값 가져오기 (항상 최신 ATR 사용)
        latest_atr = dataframe.iloc[-1]['atr']

        # ATR * 0.5보다 낮게 설정
        atr_offset = latest_atr * latebuy

        if side == 'long':
            # ATR * 0.9만큼 현재가보다 낮게 매수 주문
            return dataframe.iloc[-1]['close'] - atr_offset
        else:
            # 숏 포지션의 경우 ATR * 0.9만큼 높게
            return dataframe.iloc[-1]['close'] + atr_offset

    def custom_exit_price(self, pair: str, trade: Trade,
                          current_time: datetime, proposed_rate: float,
                          current_profit: float, exit_tag: str | None, **kwargs) -> float:
        try:
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)

            # 최신 ATR 값 가져오기
            latest_atr = dataframe.iloc[-1]['atr']
            current_close = dataframe.iloc[-1]['close']

            # ATR * 0.1 오프셋
            atr_offset = latest_atr * 0.01

            if trade.is_short:
                # 숏: 현재가에서 ATR * 0.1만큼 아래
                return current_close - atr_offset
            else:
                # 롱: 현재가에서 ATR * 0.1만큼 위
                return current_close + atr_offset
        except:
            return proposed_rate

    def leverage(
        self,
        pair: str,
        current_time: datetime,
        current_rate: float,
        proposed_leverage: float,
        max_leverage: float,
        entry_tag: str | None,
        side: str,
        **kwargs,
    ) -> float:
        return 1.0