# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/BcmbigzDevelop.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter
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 Github_DerSalvador_freqtrade_helm_chart__BcmbigzDevelop__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018, }
    # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018, }
    minimal_roi = {'0': 0.2, '38': 0.074, '78': 0.025, '194': 0}
    stoploss = -0.99  # effectively disabled.
    timeframe = '5m'
    inf_1h = '1h'
    # Sell signal
    use_exit_signal = True
    exit_profit_only = False  # it doesn't meant anything, just to guarantee there is a minimal profit.
    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 = 0.01
    trailing_stop_positive_offset = 0.05
    # 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
    # Optional order type mapping.
    order_types = {'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False}
    #############
    # Enable/Disable conditions
    entry_params = {'entry_minimum_conditions': 1, 'bzv7_entry_condition_0_enable': True, 'bzv7_entry_condition_1_enable': True, 'bzv7_entry_condition_2_enable': True, 'bzv7_entry_condition_3_enable': True, 'bzv7_entry_condition_4_enable': True, 'bzv7_entry_condition_5_enable': True, 'bzv7_entry_condition_6_enable': True, 'bzv7_entry_condition_7_enable': True, 'bzv7_entry_condition_8_enable': True, 'bzv7_entry_condition_9_enable': True, 'bzv7_entry_condition_10_enable': False, 'bzv7_entry_condition_11_enable': False, 'bzv7_entry_condition_12_enable': True, 'bzv7_entry_condition_13_enable': False, 'v6_entry_condition_0_enable': False, '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': False, 'v8_entry_condition_2_enable': True, 'v8_entry_condition_3_enable': False, 'v8_entry_condition_4_enable': True}
    #############
    # Enable/Disable conditions
    exit_params = {'bzv7_exit_condition_0_enable': False, 'v8_exit_condition_0_enable': True, 'v8_exit_condition_1_enable': True}
    # if you want to see which entry conditions were met
    # or if there is an trade exit override due to high RSI set to True
    # logger will output the entry and trade exit conditions
    cust_log_verbose = False
    ############################################################################
    # minimum conditions to match in entry
    entry_minimum_conditions = IntParameter(1, 2, default=1, space='entry', optimize=False, load=True)
    #  Strategy: BigZ07
    # Buy HyperParam
    bzv7_entry_condition_0_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_1_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_2_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_3_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_4_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_5_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_6_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_7_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_8_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_9_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_10_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_11_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_12_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_entry_condition_13_enable = CategoricalParameter([True, False], default=True, space='entry', optimize=False, load=True)
    bzv7_exit_condition_0_enable = CategoricalParameter([True, False], default=False, space='exit', optimize=False, load=True)
    bzv7_entry_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='entry', optimize=False, load=True)
    bzv7_entry_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='entry', optimize=False, load=True)
    bzv7_entry_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_2 = DecimalParameter(1, 10, default=3, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='entry', decimals=1, optimize=False, load=True)
    bzv7_entry_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='entry', decimals=2, optimize=False, load=True)
    bzv7_entry_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='entry', decimals=2, optimize=False, load=True)
    #  Strategy: 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)
    # Strategy:  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=True, load=True)
    exit_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space='exit', decimals=2, optimize=True, load=True)
    exit_custom_roi_profit_2 = DecimalParameter(0.01, 0.2, default=0.04, space='exit', decimals=2, optimize=True, load=True)
    exit_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space='exit', decimals=2, optimize=True, load=True)
    exit_custom_roi_profit_3 = DecimalParameter(0.15, 0.3, default=0.08, space='exit', decimals=2, optimize=True, 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=True, 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=True, load=True)
    exit_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='exit', decimals=3, optimize=True, load=True)
    exit_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='exit', decimals=2, optimize=True, load=True)
    exit_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='exit', decimals=3, optimize=True, load=True)
    exit_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=True, load=True)
    exit_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='exit', decimals=2, optimize=True, load=True)
    exit_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='exit', decimals=3, optimize=True, load=True)
    exit_custom_stoploss_1 = DecimalParameter(-0.15, -0.03, default=-0.05, space='exit', decimals=2, optimize=True, load=True)

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool:
        return True
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        last_candle_1 = dataframe.iloc[-2].squeeze()
        if exit_reason == 'roi':
            # Looks like we can get a little have more
            if (last_candle['cmf'] < -0.1) & (last_candle['close'] > last_candle['ema_200_1h']):
                return False
        return True

    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()
        last_candle_2 = dataframe.iloc[-2].squeeze()
        if last_candle is not None:
            # if (last_candle['high'] > last_candle['bb_upperband']) & (last_candle['volume'] > (last_candle_2['volume'] * 1.5)):
            # return 'exit_signal_1'
            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 False

    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=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()
                    # We are at bottom. Wait...
                    if candle['rsi_1h'] < 35:
                        return 0.99
                    if candle['open'] > candle['ema_200']:
                        return 0.1
                    # Are we still sinking?
                    if current_rate * 1.025 < candle['open']:
                        return 0.01
                except IndexError as error:
                    # Whoops, set stoploss at 10%
                    return 0.1
        return 0.99

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

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # RSI
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        informative_1h['bb_lowerband'] = bollinger['lower']
        informative_1h['bb_middleband'] = bollinger['mid']
        informative_1h['bb_upperband'] = bollinger['upper']
        # for BinClucMad
        informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)  # for BinClucMad
        informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)  # for BinClucMad
        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:
        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=48).mean()
        # EMA
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        # MACD
        dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Chaikin A/D Oscillator
        dataframe['mfv'] = MFV(dataframe)
        dataframe['cmf'] = dataframe['mfv'].rolling(20).sum() / dataframe['volume'].rolling(20).sum()
        # for BinClucMad
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)  # for BinClucMad
        dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        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()
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # reset additional dataframe rows
        dataframe.loc[:, 'bzv7_entry_condition_0_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_1_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_2_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_3_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_4_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_5_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_6_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_7_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_8_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_9_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_10_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_11_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_12_enable'] = False
        dataframe.loc[:, 'bzv7_entry_condition_13_enable'] = False
        dataframe.loc[:, 'v6_entry_condition_0_enable'] = False
        dataframe.loc[:, 'v6_entry_condition_1_enable'] = False
        dataframe.loc[:, 'v6_entry_condition_2_enable'] = False
        dataframe.loc[:, 'v6_entry_condition_3_enable'] = False
        dataframe.loc[:, 'v8_entry_condition_0_enable'] = False
        dataframe.loc[:, 'v8_entry_condition_1_enable'] = False
        dataframe.loc[:, 'v8_entry_condition_2_enable'] = False
        dataframe.loc[:, 'v8_entry_condition_3_enable'] = False
        dataframe.loc[:, 'v8_entry_condition_4_enable'] = False
        dataframe.loc[:, 'conditions_count'] = 0
        # Strategy: v8 BUY conditions
        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.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()) & (self.v8_entry_condition_0_enable.value == True), 'v8_entry_condition_0_enable'] = 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.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) & (self.v8_entry_condition_1_enable.value == True), 'v8_entry_condition_1_enable'] = 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.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) & (self.v8_entry_condition_2_enable.value == True), 'v8_entry_condition_2_enable'] = 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.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) & (self.v8_entry_condition_3_enable.value == True), 'v8_entry_condition_3_enable'] = 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.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']) & (self.v8_entry_condition_4_enable.value == True), 'v8_entry_condition_4_enable'] = 1
        # Strategy: v6 BUY conditions
        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_entry_condition_0_enable.value == True), 'v6_entry_condition_0_enable'] = 1  # Don't entry 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_entry_condition_1_enable.value == True), 'v6_entry_condition_1_enable'] = 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_entry_condition_2_enable.value == True), 'v6_entry_condition_2_enable'] = 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_entry_condition_3_enable.value == True), 'v6_entry_condition_3_enable'] = 1
        # Strategy: BigZ07 BUY conditions
        dataframe.loc[self.bzv7_entry_condition_0_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < 30) & (dataframe['close'] * 1.024 < dataframe['open'].shift(3)) & (dataframe['rsi_1h'] < 71) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_0_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_entry_bb20_close_bblowerband_safe_1.value) & (dataframe['rsi_1h'] < 69) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_1_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_entry_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_2_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.bzv7_entry_rsi_3.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_3.value) & (dataframe['volume'] > 0), 'bzv7_entry_condition_3_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_4_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume'] > 0), 'bzv7_entry_condition_4_enable'] = 1  # Make sure Volume is not 0
        dataframe.loc[self.bzv7_entry_condition_5_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.bzv7_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.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_5_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_6_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.bzv7_entry_macd_2.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume'] > 0), 'bzv7_entry_condition_6_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_7_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.bzv7_entry_macd_1.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_7_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_8_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_3.value) & (dataframe['rsi'] < self.bzv7_entry_rsi_1.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_8_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_9_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_4.value) & (dataframe['rsi'] < self.bzv7_entry_rsi_2.value) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_9_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_10_enable.value & (dataframe['rsi_1h'] < self.bzv7_entry_rsi_1h_4.value) & (dataframe['close_1h'] < dataframe['bb_lowerband_1h']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift(2) < 0) & (dataframe['rsi'] < 40.5) & (dataframe['hist'] > dataframe['close'] * 0.0012) & (dataframe['open'] < dataframe['close']) & (dataframe['volume'] > 0), 'bzv7_entry_condition_10_enable'] = 1  # Make sure Volume is not 0
        dataframe.loc[self.bzv7_entry_condition_11_enable.value & (dataframe['high'] - dataframe['low'] < dataframe['open'] / 100) & (dataframe['open'] < dataframe['close']) & (dataframe['high'].shift() - dataframe['low'].shift() < dataframe['open'].shift() / 100) & (dataframe['high'].shift(2) - dataframe['low'].shift(2) < dataframe['open'].shift(2) / 100) & (dataframe['high'].shift(3) - dataframe['low'].shift(3) < dataframe['open'].shift(3) / 100) & (dataframe['high'].shift(4) - dataframe['low'].shift(4) < dataframe['open'].shift(4) / 100) & (dataframe['high'].shift(5) - dataframe['low'].shift(5) < dataframe['open'].shift(5) / 100) & (dataframe['high'].shift(6) - dataframe['low'].shift(6) < dataframe['open'].shift(6) / 100) & (dataframe['high'].shift(7) - dataframe['low'].shift(7) < dataframe['open'].shift(7) / 100) & (dataframe['high'].shift(8) - dataframe['low'].shift(8) < dataframe['open'].shift(8) / 100) & (dataframe['high'].shift(9) - dataframe['low'].shift(9) < dataframe['open'].shift(9) / 100) & (dataframe['bb_middleband'] > dataframe['bb_middleband'].shift(9) * 1.005) & (dataframe['rsi'] < 68) & (dataframe['volume'] > 0), 'bzv7_entry_condition_11_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_12_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.993) & (dataframe['low'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['close'].shift() > dataframe['bb_lowerband']) & (dataframe['rsi_1h'] < 72.8) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] < dataframe['volume'].shift() * self.bzv7_entry_volume_drop_1.value) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_12_enable'] = 1
        dataframe.loc[self.bzv7_entry_condition_13_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['cmf'] < -0.435) & (dataframe['rsi'] < 22) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.bzv7_entry_volume_pump_1.value) & (dataframe['volume_mean_slow'] * self.bzv7_entry_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0), 'bzv7_entry_condition_13_enable'] = 1
        # count the amount of conditions met
        dataframe.loc[:, 'conditions_count'] = dataframe['bzv7_entry_condition_0_enable'].astype(int) + dataframe['bzv7_entry_condition_1_enable'].astype(int) + dataframe['bzv7_entry_condition_2_enable'].astype(int) + dataframe['bzv7_entry_condition_3_enable'].astype(int) + dataframe['bzv7_entry_condition_4_enable'].astype(int) + dataframe['bzv7_entry_condition_5_enable'].astype(int) + dataframe['bzv7_entry_condition_6_enable'].astype(int) + dataframe['bzv7_entry_condition_7_enable'].astype(int) + dataframe['bzv7_entry_condition_8_enable'].astype(int) + dataframe['bzv7_entry_condition_9_enable'].astype(int) + dataframe['bzv7_entry_condition_10_enable'].astype(int) + dataframe['bzv7_entry_condition_11_enable'].astype(int) + dataframe['bzv7_entry_condition_12_enable'].astype(int) + dataframe['bzv7_entry_condition_13_enable'].astype(int) + dataframe['v6_entry_condition_0_enable'].astype(int) + dataframe['v6_entry_condition_1_enable'].astype(int) + dataframe['v6_entry_condition_2_enable'].astype(int) + dataframe['v6_entry_condition_3_enable'].astype(int) + dataframe['v8_entry_condition_0_enable'].astype(int) + dataframe['v8_entry_condition_1_enable'].astype(int) + dataframe['v8_entry_condition_2_enable'].astype(int) + dataframe['v8_entry_condition_3_enable'].astype(int) + dataframe['v8_entry_condition_4_enable'].astype(int)
        # append the minimum amount of conditions to be met
        conditions.append(dataframe['conditions_count'] >= self.entry_minimum_conditions.value)
        conditions.append(dataframe['volume'].gt(0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'entry'] = 1
        # verbose logging enable only for verbose information or troubleshooting
        if self.cust_log_verbose == True:
            for index, row in dataframe.iterrows():
                if row['entry'] == 1:
                    entry_cond_details = f"count={int(row['conditions_count'])}/bzv7_1={int(row['bzv7_entry_condition_1_enable'])}/bzv7_2={int(row['bzv7_entry_condition_2_enable'])}/bzv7_3={int(row['bzv7_entry_condition_3_enable'])}/bzv7_4={int(row['bzv7_entry_condition_4_enable'])}/bzv7_5={int(row['bzv7_entry_condition_5_enable'])}/bzv7_6={int(row['bzv7_entry_condition_6_enable'])}/bzv7_7={int(row['bzv7_entry_condition_7_enable'])}/bzv7_8={int(row['bzv7_entry_condition_8_enable'])}/bzv7_9={int(row['bzv7_entry_condition_9_enable'])}/bzv7_10={int(row['bzv7_entry_condition_10_enable'])}/bzv7_11={int(row['bzv7_entry_condition_11_enable'])}/bzv7_12={int(row['bzv7_entry_condition_12_enable'])}/bzv7_13={int(row['bzv7_entry_condition_13_enable'])}/bzv7_0={int(row['bzv7_entry_condition_0_enable'])}/v6_0={int(row['v6_entry_condition_0_enable'])}/v6_1={int(row['v6_entry_condition_1_enable'])}/v6_2={int(row['v6_entry_condition_2_enable'])}/v6_3={int(row['v6_entry_condition_3_enable'])}/v8_0={int(row['v8_entry_condition_0_enable'])}/v8_1={int(row['v8_entry_condition_1_enable'])}/v8_2={int(row['v8_entry_condition_2_enable'])}/v8_3={int(row['v8_entry_condition_3_enable'])}/v8_4={int(row['v8_entry_condition_4_enable'])}"
                    logger.info(f"{metadata['pair']} - candle: {row['date']} - entry condition - details: {entry_cond_details}")
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # dataframe.loc[:, "exit"] = 0
        if self.bzv7_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((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
# 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'])
# Chaikin Money Flow Volume

def MFV(dataframe):
    df = dataframe.copy()
    N = (df['close'] - df['low'] - (df['high'] - df['close'])) / (df['high'] - df['low'])
    M = N * df['volume']
    return M