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BinClucMadSMA

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DerSalvador/freqtrade/user_data_k8s/kraken-stieves/strategies/BinClucMadSMA_converted.py · first seen 2026-07-28 · repo updated 2024-05-19

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 for parameter merging output, only remove if you remove it further down too! ---------
logger = logging.getLogger(__name__)
# -------------------------------------------------------------------------------------------------

class BinClucMadSMA(IStrategy):
    INTERFACE_VERSION = 3
    # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018, }
    minimal_roi = {'0': 0.05, '20': 0.04, '40': 0.03, '60': 0.025, '180': 0.02}
    # minimal_roi = {"0": 0.20, "38": 0.074, "78": 0.025, "194": 0}
    stoploss = -0.9  # effectively disabled.
    timeframe = '5m'
    informative_timeframe = '1h'
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.001
    ignore_roi_if_entry_signal = True
    # Trailing stoploss
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.049
    # Custom stoploss
    use_custom_stoploss = False
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200
    #############
    # Enable/Disable conditions
    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}
    #############
    # Enable/Disable conditions
    # "v8_sell_rsi_main": 80,
    # "sell_custom_roi_profit_1": 0.02,
    # "sell_custom_roi_profit_2": 0.01,
    # "sell_custom_roi_profit_3": 0.27,
    # "sell_custom_roi_profit_4": 0.51,
    # "sell_custom_roi_profit_5": 0.04,
    # "sell_custom_roi_rsi_1": 54.68,
    # "sell_custom_roi_rsi_2": 46.32,
    # "sell_custom_roi_rsi_3": 54.0,
    # "sell_custom_roi_rsi_4": 56.66,
    # "sell_custom_stoploss_1": -0.05,
    # "sell_trail_down_1": 0.043,
    # "sell_trail_down_2": 0.134,
    # "sell_trail_profit_max_1": 0.34,
    # "sell_trail_profit_max_2": 0.15,
    # "sell_trail_profit_min_1": 0.208,
    # "sell_trail_profit_min_2": 0.035
    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}
    # if you want to see which buy conditions were met
    # or if there is an trade exit override due to high RSI set to True
    # logger will output the buy and trade exit conditions
    cust_log_verbose = False
    ############################################################################
    # Buy SMAOffsetProtectOpt
    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)
    # hyperopt parameters for SMAOffsetProtectOpt
    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)
    # Protection
    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)
    # Buy CombinedBinHClucAndMADV6
    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)
    # Buy CombinedBinHClucV8
    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)
    # Buy  CombinedBinHClucAndMADV9
    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)
    # Sell
    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)
    # minimum conditions to match in buy
    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:
        # Manage losing trades and open room for better ones.
        if current_profit > 0:
            return 0.99
        else:
            trade_time_50 = trade.open_date_utc + timedelta(minutes=280)
            # trade_time_240 = trade.open_date_utc + timedelta(minutes=240)
            # Trade open more then 60 minutes. For this strategy it's means -> loss
            # Let's try to minimize the loss
            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
                    # We are at bottom. Wait...
                    if candle['rsi_1h'] < 30:
                        return 0.99
                    # Are we still sinking?
                    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:
                    # Whoops, set stoploss at 10%
                    return 0.1
        return 0.99

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        # return False
        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'
        # Sell any positions at a loss if they are held for more than one day.
        # if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 2:
        #     return 'unclog'
        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.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
        # EMA
        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)
        # SMA
        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)
        # 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
        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:
        # strategy BinHV45
        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()
        # strategy ClucMay72018
        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_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)
        # SMA
        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)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # MFI
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        # ------ ATR stuff
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        # ------ SMAOffsetProtectOpt
        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        # Elliot
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, 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:
        conditions = []
        # reset additional dataframe rows
        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
        # start from here
        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
        # count the amount of conditions met
        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)
        # append the minimum amount of conditions to be met
        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
        # verbose logging enable only for verbose information or troubleshooting
        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
# --- custom indicators ---------------------------------------------------------------------------

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'])
# 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'])

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