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BinClucMadV1

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DerSalvador/freqtrade-helm-chart/chart/deployed_strategies/binance-michael-k8s-namespace/BinClucMadV1.py · first seen 2026-07-16 · repo updated 2026-04-16 · ⬇ 1 download

Basics mode: spot timeframe: 5m interface version: 3
Settings stoploss: -0.99 has minimal roi custom stoploss process only new candles startup candle count: 200 hyperopt hyperopt params: 69
Indicators ATR Bollinger_Bands EMA MFI RSI SMA talib
Methods SSLChannels custom_exit custom_stoploss custom_stoplossv8 informative_1h_indicators normal_tf_indicators
15 related strategies (⧉ identical code, ≈ similar name)

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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
from functools import reduce
###############################################################################
#   this is the final adjustment version for all clucbinmad snip.
#  here i am goin to ad (v5&v6) & v8 &v9 lets see what we can achive
#   remember its production version no dev. should be lightweight
#
################################################################################
# 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 BinClucMadV1(IStrategy):
    INTERFACE_VERSION = 3
    # minimal_roi = {
    #     "0": 0.028,  # I feel lucky!
    #     "10": 0.018,
    #     "40": 0.005,
    # }
    # I feel lucky!
    # We're going up?
    minimal_roi = {'0': 0.038, '10': 0.028, '40': 0.015, '180': 0.018}
    stoploss = -0.99  # effectively disabled.
    timeframe = '5m'
    informative_timeframe = '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
    # Custom stoploss
    use_custom_stoploss = True
    # 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
    entry_params = {'v6_entry_condition_0_enable': True, 'v6_entry_condition_1_enable': True, 'v6_entry_condition_2_enable': True, 'v6_entry_condition_3_enable': True, 'v8_entry_condition_0_enable': True, 'v8_entry_condition_1_enable': True, 'v8_entry_condition_2_enable': True, 'v8_entry_condition_3_enable': True, 'v8_entry_condition_4_enable': True, 'v9_entry_condition_0_enable': False, 'v9_entry_condition_1_enable': False, 'v9_entry_condition_2_enable': False, 'v9_entry_condition_3_enable': False, 'v9_entry_condition_4_enable': False, 'v9_entry_condition_5_enable': False, 'v9_entry_condition_6_enable': False, 'v9_entry_condition_7_enable': False, 'v9_entry_condition_8_enable': False, 'v9_entry_condition_9_enable': False, 'v9_entry_condition_10_enable': False}
    #############
    # Enable/Disable conditions
    exit_params = {'v9_exit_condition_0_enable': False, 'v8_exit_condition_0_enable': True, 'v8_exit_condition_1_enable': False}
    ############################################################################
    # Buy CombinedBinHClucAndMADV6
    v6_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v6_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v6_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v6_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    # Buy CombinedBinHClucV8
    v8_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v8_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v8_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v8_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v8_entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v8_exit_condition_0_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    v8_exit_condition_1_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    v8_exit_rsi_main = DecimalParameter(72.0, 90.0, default=80, space='exit', decimals=2, optimize=False, load=True)
    entry_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space='entry', decimals=2, optimize=False, load=True)
    entry_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space='entry', decimals=2, optimize=False, load=True)
    entry_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space='entry', decimals=2, optimize=False, load=True)
    entry_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space='entry', optimize=False, load=True)
    entry_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space='entry', optimize=False, load=True)
    entry_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space='entry', optimize=False, load=True)
    entry_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space='entry', optimize=False, load=True)
    entry_bb20_volume = IntParameter(18, 36, default=29, space='entry', optimize=False, load=True)
    entry_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space='entry', decimals=2, optimize=False, load=True)
    entry_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space='entry', decimals=2, optimize=False, load=True)
    entry_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space='entry', decimals=2, optimize=False, load=True)
    entry_rsi = DecimalParameter(30.0, 40.0, default=38.5, space='entry', decimals=2, optimize=False, load=True)
    entry_mfi = DecimalParameter(36.0, 65.0, default=36.0, space='entry', decimals=2, optimize=False, load=True)
    entry_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space='entry', decimals=3, optimize=False, load=True)
    exit_custom_roi_profit_1 = DecimalParameter(0.01, 0.03, default=0.01, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='exit', decimals=2, optimize=False, load=True)
    # Buy  CombinedBinHClucAndMADV9
    v9_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_5_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_6_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_7_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_8_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_9_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    v9_entry_condition_10_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    # Sell
    v9_exit_condition_0_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space='entry', optimize=False, load=True)
    entry_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='entry', optimize=False, load=True)
    entry_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_drop_1 = DecimalParameter(1, 10, default=4, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='entry', decimals=1, optimize=False, load=True)
    entry_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='entry', decimals=2, optimize=False, load=True)
    entry_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='entry', decimals=2, optimize=False, load=True)

    def custom_stoplossv8(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=50)
            # 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_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) & (current_time - timedelta(minutes=280) > trade.open_date_utc):
            return 0.01
        elif current_profit < self.exit_custom_stoploss_1.value:
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            candle = dataframe.iloc[-1].squeeze()
            if candle is not None:
                # 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
        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.exit_custom_roi_profit_4.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_4.value):
                return 'roi_target_4'
            elif (current_profit > self.exit_custom_roi_profit_3.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_3.value):
                return 'roi_target_3'
            elif (current_profit > self.exit_custom_roi_profit_2.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_2.value):
                return 'roi_target_2'
            elif (current_profit > self.exit_custom_roi_profit_1.value) & (last_candle['rsi'] < self.exit_custom_roi_rsi_1.value):
                return 'roi_target_1'
            elif (current_profit > 0) & (current_profit < self.exit_custom_roi_profit_5.value) & last_candle['sma_200_dec']:
                return 'roi_target_5'
            elif (current_profit > self.exit_trail_profit_min_1.value) & (current_profit < self.exit_trail_profit_max_1.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.exit_trail_down_1.value):
                return 'trail_target_1'
            elif (current_profit > self.exit_trail_profit_min_2.value) & (current_profit < self.exit_trail_profit_max_2.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.exit_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.'
        # 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)
        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 = []
        # START  V6
        if self.v6_entry_condition_0_enable.value:  # strategy ClucMay72018
            # Guard is on, candle should dig not so hard (0,99)
            # Try to exclude pumping
            # (dataframe['volume'] < (dataframe['volume'].shift() * 4)) &                        # Don't entry if someone drop the market.
            conditions.append((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_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0))
        if self.v6_entry_condition_1_enable.value:  # strategy ClucMay72018
            # Guard is off, candle should dig hard (0,975)
            # Don't entry if someone drop the market.
            # Buy only at dip
            # Try to exclude pumping  # Make sure Volume is not 0
            conditions.append((dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * 4) & (dataframe['rsi_1h'] < 15) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0))
        if self.v6_entry_condition_2_enable.value:  # strategy MACD Low entry
            # Don't entry if someone drop the market.
            # Try to exclude pumping  # Make sure Volume is not 0
            conditions.append((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['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & (dataframe['volume'] > 0))
        if self.v6_entry_condition_3_enable.value:  # strategy MACD Low entry
            # Don't entry if someone drop the market.
            # Make sure Volume is not 0
            conditions.append((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']) & (dataframe['volume'] > 0))
        # END  V6
        if self.v8_entry_condition_0_enable.value:
            conditions.append((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.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.entry_bb40_bbdelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.entry_bb40_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.entry_bb40_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0))
        if self.v8_entry_condition_1_enable.value:
            conditions.append((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.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.entry_bb20_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * self.entry_bb20_volume.value) & (dataframe['volume'] > 0))
        if self.v8_entry_condition_2_enable.value:
            conditions.append((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.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.entry_rsi_diff.value) & (dataframe['volume'] > 0))
        if self.v8_entry_condition_3_enable.value:
            conditions.append((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.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & ((dataframe['open'].rolling(24).min() - dataframe['close']) / dataframe['close'] > self.entry_min_inc.value) & (dataframe['rsi_1h'] > self.entry_rsi_1h.value) & (dataframe['rsi'] < self.entry_rsi.value) & (dataframe['mfi'] < self.entry_mfi.value) & (dataframe['volume'] > 0))
        if self.v8_entry_condition_4_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_1.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_2.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.entry_dip_threshold_3.value) & (dataframe['volume'].rolling(4).mean() * self.entry_volume_1.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] > 0))
        # START  VERSION9
        if self.v9_entry_condition_1_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_bb20_close_bblowerband_safe_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_2_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.entry_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_3_enable.value:
            conditions.append((dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.entry_rsi_3.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_4_enable.value:
            conditions.append((dataframe['rsi_1h'] < self.entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_5_enable.value:  # Make sure Volume is not 0
            conditions.append((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.entry_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.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_6_enable.value:
            conditions.append((dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_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.entry_volume_drop_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_7_enable.value:
            conditions.append((dataframe['rsi_1h'] < self.entry_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_8_enable.value:
            conditions.append((dataframe['rsi_1h'] < self.entry_rsi_1h_3.value) & (dataframe['rsi'] < self.entry_rsi_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_9_enable.value:
            conditions.append((dataframe['rsi_1h'] < self.entry_rsi_1h_4.value) & (dataframe['rsi'] < self.entry_rsi_2.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * self.entry_volume_pump_1.value) & (dataframe['volume'] > 0))
        if self.v9_entry_condition_10_enable.value:
            conditions.append((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))
        # END  V9
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        if self.v9_exit_condition_0_enable.value:  # Don't be gready, exit fast  # Make sure Volume is not 0
            conditions.append((dataframe['close'] > dataframe['bb_middleband'] * 1.01) & (dataframe['volume'] > 0))
        if self.v8_exit_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['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0))
        if self.v8_exit_condition_1_enable.value:
            conditions.append(qtpylib.crossed_above(dataframe['rsi'], self.v8_exit_rsi_main.value) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit'] = 1
        return dataframe