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BinClucMadSMA_v3

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remiotore/ccxt-freqtrade/strategies/BinClucMadSMA_v3_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.9 has minimal roi trailing custom stoploss process only new candles startup candle count: 200 hyperopt hyperopt params: 82
Indicators ATR Bollinger_Bands EMA MFI RSI SMA talib
Concepts trailing
Methods EWO SSLChannels SSLChannels_ATR custom_exit custom_stoploss informative_1h_indicators normal_tf_indicators
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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, IntParameter, stoploss_from_open
from functools import reduce
import logging
logger = logging.getLogger(__name__)

class BinClucMadSMA_v3(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.05, '20': 0.04, '40': 0.03, '60': 0.025, '180': 0.02}
    stoploss = -0.9  # effectively disabled.
    timeframe = '5m'
    informative_timeframe = '1h'
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001
    ignore_roi_if_entry_signal = True
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.049
    use_custom_stoploss = False
    process_only_new_candles = True
    startup_candle_count: int = 200
    buy_params = {'buy_minimum_conditions': 1, 'smaoffset_buy_condition_0_enable': True, 'smaoffset_buy_condition_1_enable': True, 'v6_buy_condition_0_enable': True, 'v6_buy_condition_1_enable': True, 'v6_buy_condition_2_enable': True, 'v6_buy_condition_3_enable': True, 'v8_buy_condition_0_enable': True, 'v8_buy_condition_1_enable': True, 'v8_buy_condition_2_enable': True, 'v8_buy_condition_3_enable': True, 'v8_buy_condition_4_enable': True, 'v9_buy_condition_0_enable': False, 'v9_buy_condition_1_enable': False, 'v9_buy_condition_2_enable': False, 'v9_buy_condition_3_enable': False, 'v9_buy_condition_4_enable': False, 'v9_buy_condition_5_enable': False, 'v9_buy_condition_6_enable': False, 'v9_buy_condition_7_enable': False, 'v9_buy_condition_8_enable': False, 'v9_buy_condition_9_enable': False, 'v9_buy_condition_10_enable': False}
    sell_params = {'v9_sell_condition_0_enable': False, 'v8_sell_condition_0_enable': True, 'v8_sell_condition_1_enable': True, 'smaoffset_sell_condition_0_enable': False}
    cust_log_verbose = False
    smaoffset_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    smaoffset_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    smaoffset_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    base_nb_candles_buy = IntParameter(5, 80, default=20, space='buy', optimize=False, load=True)
    base_nb_candles_sell = IntParameter(5, 80, default=24, space='sell', optimize=False, load=True)
    low_offset = DecimalParameter(0.9, 0.99, default=0.975, space='buy', optimize=False, load=True)
    high_offset = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=False, load=True)
    fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False, load=True)
    slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False, load=True)
    ewo_low = DecimalParameter(-20.0, -8.0, default=-19.881, space='buy', optimize=False, load=True)
    ewo_high = DecimalParameter(2.0, 12.0, default=5.499, space='buy', optimize=False, load=True)
    rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=False, load=True)
    v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='sell', decimals=2, optimize=False, load=True)
    buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=False, load=True)
    buy_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=False, load=True)
    buy_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space='buy', decimals=2, optimize=False, load=True)
    buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space='buy', optimize=False, load=True)
    buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='buy', optimize=False, load=True)
    buy_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space='buy', optimize=False, load=True)
    buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space='buy', optimize=False, load=True)
    buy_bb20_volume = IntParameter(18, 36, default=29, space='buy', optimize=False, load=True)
    buy_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space='buy', decimals=2, optimize=False, load=True)
    buy_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi = DecimalParameter(30.0, 40.0, default=38.5, space='buy', decimals=2, optimize=False, load=True)
    buy_mfi = DecimalParameter(36.0, 65.0, default=36.0, space='buy', decimals=2, optimize=False, load=True)
    buy_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True)
    sell_custom_roi_profit_1 = DecimalParameter(0.02, 0.03, default=0.02, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='sell', decimals=2, optimize=True, load=True)
    v9_buy_condition_0_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_1_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_2_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_3_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_4_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_5_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_6_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_7_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_8_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_9_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_buy_condition_10_enable = CategoricalParameter([True, False], default=False, space='buy', optimize=False, load=True)
    v9_sell_condition_0_enable = CategoricalParameter([True, False], default=False, space='sell', 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)
    buy_minimum_conditions = IntParameter(1, 2, default=1, space='buy', optimize=False, 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_50 = trade.open_date_utc + timedelta(minutes=280)
            if current_time > trade_time_50:
                try:
                    number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
                    dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
                    candle = dataframe.iloc[-number_of_candle_shift].squeeze()
                    if candle['sma_200_dec'] & candle['sma_200_dec_1h']:
                        return 0.01
                    if candle['rsi_1h'] < 30:
                        return 0.99
                    if candle['close'] > candle['ema_200']:
                        if current_rate * 1.025 < candle['open']:
                            return 0.01
                    if current_rate * 1.015 < candle['open']:
                        return 0.01
                except IndexError as error:
                    return 0.1
        return 0.99

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        if last_candle is not None:
            if (current_profit > self.sell_custom_roi_profit_4.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_4.value):
                return 'roi_target_4'
            elif (current_profit > self.sell_custom_roi_profit_3.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_3.value):
                return 'roi_target_3'
            elif (current_profit > self.sell_custom_roi_profit_2.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_2.value):
                return 'roi_target_2'
            elif (current_profit > self.sell_custom_roi_profit_1.value) & (last_candle['rsi'] < self.sell_custom_roi_rsi_1.value):
                return 'roi_target_1'
            elif (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & last_candle['sma_200_dec']:
                return 'roi_target_5'
            elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_trail_down_1.value):
                return 'trail_target_1'
            elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_trail_down_2.value):
                return 'trail_target_2'
        return None

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) 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.informative_timeframe)
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)
        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
        informative_1h['ssl-dir'] = np.where(ssl_up_1h > ssl_down_1h, 'up', 'down')
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        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_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'v9_buy_condition_1_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_2_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_3_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_4_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_5_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_6_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_7_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_8_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_9_enable'] = False
        dataframe.loc[:, 'v9_buy_condition_10_enable'] = False
        dataframe.loc[:, 'v6_buy_condition_0_enable'] = False
        dataframe.loc[:, 'v6_buy_condition_1_enable'] = False
        dataframe.loc[:, 'v6_buy_condition_2_enable'] = False
        dataframe.loc[:, 'v6_buy_condition_3_enable'] = False
        dataframe.loc[:, 'v8_buy_condition_0_enable'] = False
        dataframe.loc[:, 'v8_buy_condition_1_enable'] = False
        dataframe.loc[:, 'v8_buy_condition_2_enable'] = False
        dataframe.loc[:, 'v8_buy_condition_3_enable'] = False
        dataframe.loc[:, 'v8_buy_condition_4_enable'] = False
        dataframe.loc[:, 'smaoffset_buy_condition_0_enable'] = False
        dataframe.loc[:, 'smaoffset_buy_condition_1_enable'] = False
        dataframe.loc[:, 'conditions_count'] = 0
        dataframe['ma_buy'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value
        dataframe.loc[(dataframe['close'] < dataframe['ma_buy']) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (self.smaoffset_buy_condition_0_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] < dataframe['ma_buy']) & (dataframe['EWO'] < self.ewo_low.value) & (self.smaoffset_buy_condition_1_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (self.v8_buy_condition_0_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * self.buy_bb20_volume.value) & (self.v8_buy_condition_1_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_diff.value) & (self.v8_buy_condition_2_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(16)) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & ((dataframe['open'].rolling(24).min() - dataframe['close']) / dataframe['close'] > self.buy_min_inc.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h.value) & (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['mfi'] < self.buy_mfi.value) & (self.v8_buy_condition_3_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_1.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (self.v8_buy_condition_4_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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)) & (self.v9_buy_condition_1_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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)) & (self.v9_buy_condition_2_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_3_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_4_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_5_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_6_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_7_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_8_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_9_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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) & (self.v9_buy_condition_10_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) & ((dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 21) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (self.v6_buy_condition_0_enable.value == True), 'enter_long'] = 1  # Don't buy if someone drop the market.
        dataframe.loc[(dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & ((dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 20) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (dataframe['rsi_1h'] < 15) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (self.v6_buy_condition_1_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(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'] * 0.02) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & ((dataframe['volume'] < dataframe['volume'].shift() * 4) | (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4)) & (dataframe['close'] < dataframe['bb_lowerband']) & (self.v6_buy_condition_2_enable.value == True), 'enter_long'] = 1
        dataframe.loc[(dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.03) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['close'] < dataframe['bb_lowerband']) & (self.v6_buy_condition_3_enable.value == True), 'enter_long'] = 1
        dataframe.loc[:, 'conditions_count'] = dataframe['v9_buy_condition_1_enable'].astype(int) + dataframe['v9_buy_condition_2_enable'].astype(int) + dataframe['v9_buy_condition_3_enable'].astype(int) + dataframe['v9_buy_condition_4_enable'].astype(int) + dataframe['v9_buy_condition_5_enable'].astype(int) + dataframe['v9_buy_condition_6_enable'].astype(int) + dataframe['v9_buy_condition_7_enable'].astype(int) + dataframe['v9_buy_condition_8_enable'].astype(int) + dataframe['v9_buy_condition_9_enable'].astype(int) + dataframe['v9_buy_condition_10_enable'].astype(int) + dataframe['v6_buy_condition_0_enable'].astype(int) + dataframe['v6_buy_condition_1_enable'].astype(int) + dataframe['v6_buy_condition_2_enable'].astype(int) + dataframe['v6_buy_condition_3_enable'].astype(int) + dataframe['v8_buy_condition_0_enable'].astype(int) + dataframe['v8_buy_condition_1_enable'].astype(int) + dataframe['v8_buy_condition_2_enable'].astype(int) + dataframe['v8_buy_condition_3_enable'].astype(int) + dataframe['v8_buy_condition_4_enable'].astype(int) + dataframe['smaoffset_buy_condition_0_enable'].astype(int) + dataframe['smaoffset_buy_condition_1_enable'].astype(int)
        conditions.append(dataframe['conditions_count'] >= self.buy_minimum_conditions.value)
        conditions.append(dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        if self.cust_log_verbose == True:
            for index, row in dataframe.iterrows():
                if row['enter_long'] == 1:
                    buy_cond_details = f"count={int(row['conditions_count'])}/v9_1={int(row['v9_buy_condition_1_enable'])}/v9_2={int(row['v9_buy_condition_2_enable'])}/v9_3={int(row['v9_buy_condition_3_enable'])}/v9_4={int(row['v9_buy_condition_4_enable'])}/v9_5={int(row['v9_buy_condition_5_enable'])}/v9_6={int(row['v9_buy_condition_6_enable'])}/v9_7={int(row['v9_buy_condition_7_enable'])}/v9_8={int(row['v9_buy_condition_8_enable'])}/v9_9={int(row['v9_buy_condition_9_enable'])}/v9_10={int(row['v9_buy_condition_10_enable'])}/v6_0={int(row['v6_buy_condition_0_enable'])}/v6_1={int(row['v6_buy_condition_1_enable'])}/v6_2={int(row['v6_buy_condition_2_enable'])}/v6_3={int(row['v6_buy_condition_3_enable'])}/v8_0={int(row['v8_buy_condition_0_enable'])}/v8_1={int(row['v8_buy_condition_1_enable'])}/v8_2={int(row['v8_buy_condition_2_enable'])}/v8_3={int(row['v8_buy_condition_3_enable'])}/v8_4={int(row['v8_buy_condition_4_enable'])}/sma_0={int(row['smaoffset_buy_condition_0_enable'])}/sma_1={int(row['smaoffset_buy_condition_1_enable'])}"
                    logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}")
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe['ma_sell'] = dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value
        if self.smaoffset_sell_condition_0_enable.value:
            conditions.append(qtpylib.crossed_below(dataframe['close'], dataframe['ma_sell']) & (dataframe['volume'] > 0))
        if self.v9_sell_condition_0_enable.value:  # Don't be gready, sell fast  # Make sure Volume is not 0
            conditions.append((dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0))
        if self.v8_sell_condition_0_enable.value:
            conditions.append((dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0))
        if self.v8_sell_condition_1_enable.value:
            conditions.append((dataframe['rsi_1h'] > self.v8_sell_rsi_main.value) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe

def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    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'])

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'])

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['close'] * 100
    return emadif