# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/CombinedBinHClucAndMADV9_8.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

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_remiotore_freqtrade__CombinedBinHClucAndMADV9_8__20260111_210550(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {
        "0": 0.028,          # I feel lucky!
        "10": 0.018,
        "40": 0.005,
    }

    stoploss = -0.99 # effectively disabled.

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

    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_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025

    use_custom_stoploss = True

    process_only_new_candles = False

    startup_candle_count: int = 200

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    buy_params = {


        "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_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)

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

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

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

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

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

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:


        if (current_profit > 0):
            return 0.99
        else:
            trade_time_60 = current_time - timedelta(minutes=50)



            if (trade_time_60 > trade.open_date_utc):
                number_of_candle_shift = int((trade_time_60 - trade.open_date_utc).total_seconds() / 300)

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

                if current_rate * 1.015 < candle['open']:
                    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."

        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)

        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)

        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)

        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()

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

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

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

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

        return dataframe


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

        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        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
            )
            ,
            'sell'
        ] = 1
        return dataframe