BB_RPB_TSLmeneguzzo

# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from freqtrade.exchange import timeframe_to_prev_date
from functools import reduce
from technical.indicators import RMI, zema, ichimoku

# --------------------------------
def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
    return Series(index=bars.index, data=res)

# Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
    """Indicator: Volume Weighted Moving Average (VWMA)"""
    # Calculate Result
    pv = dataframe['close'] * dataframe['volume']
    vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
    return vwma

# Modified Elder Ray Index
def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series:
    slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma))
    return slow_ma >= slow_ma.shift(1)  # we just need true & false for ERI trend

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['low'] * 100
    return emadif

def SROC(dataframe, roclen=21, emalen=13, smooth=21):
    df = dataframe.copy()

    roc = ta.ROC(df, timeperiod=roclen)
    ema = ta.EMA(df, timeperiod=emalen)
    sroc = ta.ROC(ema, timeperiod=smooth)

    return sroc

def range_percent_change(dataframe: DataFrame, method, length: int) -> float:
        """
        Rolling Percentage Change Maximum across interval.
        :param dataframe: DataFrame The original OHLC dataframe
        :param method: High to Low / Open to Close
        :param length: int The length to look back
        """
        if method == 'HL':
            return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min()
        elif method == 'OC':
            return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min()
        else:
            raise ValueError(f"Method {method} not defined!")

# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
        of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
        of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """

    highest_high = dataframe["high"].rolling(center=False, window=period).max()
    lowest_low = dataframe["low"].rolling(center=False, window=period).min()

    WR = Series(
        (highest_high - dataframe["close"]) / (highest_high - lowest_low),
        name=f"{period} Williams %R",
        )

    return WR * -100

# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
    """Chaikin Money Flow (CMF)
    It measures the amount of Money Flow Volume over a specific period.
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
    Args:
        dataframe(pandas.Dataframe): dataframe containing ohlcv
        n(int): n period.
        fillna(bool): if fill nan values.
    Returns:
        pandas.Series: New feature generated.
    """
    mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= dataframe['volume']
    cmf = (mfv.rolling(n, min_periods=0).sum()
           / dataframe['volume'].rolling(n, min_periods=0).sum())
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return Series(cmf, name='cmf')

class BB_RPB_TSLmeneguzzo(IStrategy):
    '''
        BB_RPB_TSL
        @author jilv220
        Simple bollinger brand strategy inspired by this blog  ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )
        RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )
        The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )
        I modified it to better suit my taste and added Hyperopt for this strategy.
    '''

    # (1) sell rework

    ##########################################################################

    # Hyperopt result area

    # buy space
    buy_params = {
        "max_slip": 0.983,
        ##
        "buy_bb_width_1h": 0.954,
        "buy_roc_1h": 86,
        ##
        "buy_threshold": 0.003,
        "buy_bb_factor": 0.999,
        #
        "buy_bb_delta": 0.025,
        "buy_bb_width": 0.095,
        ##
        "buy_cci": -116,
        "buy_cci_length": 25,
        "buy_rmi": 49,
        "buy_rmi_length": 17,
        "buy_srsi_fk": 32,
        ##
        "buy_closedelta": 17.922,
        "buy_ema_diff": 0.026,
        ##
        "buy_ema_high": 0.968,
        "buy_ema_low": 0.935,
        "buy_ewo": -5.001,
        "buy_rsi": 23,
        "buy_rsi_fast": 44,
        ##
        "buy_ema_high_2": 1.087,
        "buy_ema_low_2": 0.970,
        "buy_ewo_high_2": 4.179,
        "buy_rsi_ewo_2": 35,
        "buy_rsi_fast_ewo_2": 45,
        ##
        "buy_r_deadfish_bb_factor": 1.014,
        "buy_r_deadfish_bb_width": 0.299,
        "buy_r_deadfish_ema": 1.054,
        "buy_r_deadfish_volume_factor": 1.59,
        "buy_r_deadfish_cti": -0.115,
        "buy_r_deadfish_r14": -44.34,
        ##
        "buy_clucha_bbdelta_close": 0.049,
        "buy_clucha_bbdelta_tail": 1.146,
        "buy_clucha_close_bblower": 0.018,
        "buy_clucha_closedelta_close": 0.017,
        "buy_clucha_rocr_1h": 0.526,
        ##
        "buy_adx": 13,
        "buy_ewo_high": 8.594,
        "buy_fastd": 28,
        "buy_fastk": 39,
        ##
        "buy_gumbo_ema": 1.121,
        "buy_gumbo_ewo_low": -9.442,
        "buy_gumbo_cti": -0.374,
        "buy_gumbo_r14": -51.971,
        ##
        "buy_sqzmom_ema": 0.981,
        "buy_sqzmom_ewo": -3.966,
        "buy_sqzmom_r14": -45.068,
        ##
        "buy_nfix_49_cti": -0.105,
        "buy_nfix_49_r14": -81.827,
    }

    # sell space
    sell_params = {
        ##
        "sell_cmf": -0.046,
        "sell_ema": 0.988,
        "sell_ema_close_delta": 0.022,
        ##
        "sell_deadfish_profit": -0.063,
        "sell_deadfish_bb_factor": 0.954,
        "sell_deadfish_bb_width": 0.043,
        "sell_deadfish_volume_factor": 2.37,
        ##
        "sell_cti_r_cti": 0.844,
        "sell_cti_r_r": -19.99,
    }

    minimal_roi = {
        "0": 0.205,
        "81": 0.038,
        "292": 0.005,
    }

    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # Disabled
    stoploss = -0.99

    # Custom stoploss
    use_custom_stoploss = True
    use_sell_signal = True

    ############################################################################

    ## Buy params

    is_optimize_dip = False
    buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip)
    buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip)
    buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip)
    buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip)
    buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip)

    is_optimize_break = False
    buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize = is_optimize_break)
    buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = is_optimize_break)

    is_optimize_local_uptrend = False
    buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_uptrend)
    buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False)
    buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_uptrend)



    is_optimize_ewo = False
    buy_rsi_fast = IntParameter(35, 50, default=45, optimize = is_optimize_ewo)
    buy_rsi = IntParameter(15, 35, default=35, optimize = is_optimize_ewo)
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo)

    is_optimize_ewo_2 = False
    buy_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize = is_optimize_ewo_2)
    buy_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize = is_optimize_ewo_2)
    buy_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970 , optimize = is_optimize_ewo_2)
    buy_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_ewo_2)
    buy_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize = is_optimize_ewo_2)

    is_optimize_r_deadfish = False
    buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_r_deadfish)
    buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_r_deadfish)
    buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0 , optimize = is_optimize_r_deadfish)
    buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_r_deadfish)

    is_optimize_r_deadfish_protection = False
    buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5 , optimize = is_optimize_r_deadfish_protection)
    buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60 , optimize = is_optimize_r_deadfish_protection)

    is_optimize_clucha = False
    buy_clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=0.02206, optimize = is_optimize_clucha)
    buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize = is_optimize_clucha)
    buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize = is_optimize_clucha)
    buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize = is_optimize_clucha)

    is_optimize_gumbo = False
    buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo)
    buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo)

    is_optimize_gumbo_protection = False
    buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo_protection)
    buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo_protection)

    is_optimize_sqzmom_protection = False
    buy_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_sqzmom_protection)
    buy_sqzmom_ewo = DecimalParameter(-12 , 12, default= 0 , optimize = is_optimize_sqzmom_protection)
    buy_sqzmom_r14 = DecimalParameter(-100, -22, default=-50 , optimize = is_optimize_sqzmom_protection)

    is_optimize_nfix_49_protection = False
    buy_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_nfix_49_protection)
    buy_nfix_49_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_nfix_49_protection)

    is_optimize_btc_safe = False
    buy_btc_safe = IntParameter(-300, 50, default=-200, optimize = is_optimize_btc_safe)
    buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize = is_optimize_btc_safe)
    buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize = is_optimize_btc_safe)

    is_optimize_check = False
    buy_roc_1h = IntParameter(-25, 200, default=10, optimize = is_optimize_check)
    buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize = is_optimize_check)

    ## Slippage params

    is_optimize_slip = False
    max_slip = DecimalParameter(0.33, 1.00, default=0.33, decimals=3, optimize=is_optimize_slip , space='buy', load=True)

    ## Sell params

    sell_btc_safe = IntParameter(-400, -300, default=-365, optimize = False)

    is_optimize_sell_stoploss = False
    sell_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize = is_optimize_sell_stoploss)
    sell_ema_close_delta = DecimalParameter(0.022, 0.027, default= 0.024, optimize = is_optimize_sell_stoploss)
    sell_ema = DecimalParameter(0.97, 0.99, default=0.987 , optimize = is_optimize_sell_stoploss)

    is_optimize_deadfish = False
    sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05 , optimize = is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , optimize = is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_deadfish)

    is_optimize_bleeding = False
    sell_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_bleeding)
    sell_bleeding_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_bleeding)
    sell_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_bleeding)

    is_optimize_cti_r = False
    sell_cti_r_cti = DecimalParameter(0.55, 1, default=0.5 , optimize = is_optimize_cti_r)
    sell_cti_r_r = DecimalParameter(-15, 0, default=-20 , optimize = is_optimize_cti_r)

    ############################################################################

    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_8'] = ta.EMA(informative_1h, timeperiod=8)
        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)

        # CTI
        informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)
        informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40)

        # CRSI (3, 2, 100)
        crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1)
        crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
        informative_1h['crsi'] =  (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3

        # Williams %R
        informative_1h['r_96'] = williams_r(informative_1h, period=96)
        informative_1h['r_480'] = williams_r(informative_1h, period=480)

        # Bollinger bands
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb_lowerband2'] = bollinger2['lower']
        informative_1h['bb_middleband2'] = bollinger2['mid']
        informative_1h['bb_upperband2'] = bollinger2['upper']
        informative_1h['bb_width'] = ((informative_1h['bb_upperband2'] - informative_1h['bb_lowerband2']) / informative_1h['bb_middleband2'])

        # ROC
        informative_1h['roc'] = ta.ROC(dataframe, timeperiod=9)

        # MOMDIV
        mom = momdiv(informative_1h)
        informative_1h['momdiv_buy'] = mom['momdiv_buy']
        informative_1h['momdiv_sell'] = mom['momdiv_sell']
        informative_1h['momdiv_coh'] = mom['momdiv_coh']
        informative_1h['momdiv_col'] = mom['momdiv_col']

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

        # CMF
        informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20)

        # Heikin Ashi
        inf_heikinashi = qtpylib.heikinashi(informative_1h)
        informative_1h['ha_close'] = inf_heikinashi['close']
        informative_1h['rocr'] = ta.ROCR(informative_1h['ha_close'], timeperiod=168)

        # T3 Average
        informative_1h['T3'] = T3(informative_1h)

        # Elliot
        informative_1h['EWO'] = EWO(informative_1h, 50, 200)

        # nfi 37
        informative_1h['hl_pct_change_5'] = range_percent_change(informative_1h, 'HL', 5)
        informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min()
        informative_1h['safe_dump_50'] = ((informative_1h['hl_pct_change_5'] < 0.66) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))

        # Pump protections
        #informative_1h['hl_pct_change_48'] = range_percent_change(informative_1h, 'HL', length=48)
        #informative_1h['hl_pct_change_36'] = range_percent_change(informative_1h, 'HL', length=36)
        #informative_1h['hl_pct_change_24'] = range_percent_change(informative_1h, 'HL', length=24)
        #informative_1h['hl_pct_change_12'] = range_percent_change(informative_1h, 'HL', length=12)
        #informative_1h['hl_pct_change_6'] = range_percent_change(informative_1h, 'HL', length=6)

        return informative_1h

    ############################################################################

    ### Custom functions

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        sl_new = 1

        if (current_profit > 0.2):
            sl_new = 0.05
        elif (current_profit > 0.1):
            sl_new = 0.03
        elif (current_profit > 0.06):
            sl_new = 0.02
        elif (current_profit > 0.03):
            sl_new = 0.015

        return sl_new

    # From NFIX
    def custom_sell(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]
        previous_candle_1 = dataframe.iloc[-2]
        previous_candle_2 = dataframe.iloc[-3]

        max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
        max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate)

        buy_tag = 'empty'
        if hasattr(trade, 'buy_tag') and trade.buy_tag is not None:
            buy_tag = trade.buy_tag
        buy_tags = buy_tag.split()

        # sell trail
        if 0.012 > current_profit >= 0.0:
            if (max_profit > (current_profit + 0.045)) and (last_candle['rsi'] < 46.0):
                return f"sell_profit_t_0_1( {buy_tag})"
            elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 32.0):
                return f"sell_profit_t_0_2( {buy_tag})"
            elif (max_profit > (current_profit + 0.05)) and (last_candle['rsi'] < 48.0):
                return f"sell_profit_t_0_3( {buy_tag})"
        elif 0.02 > current_profit >= 0.012:
            if (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 39.0):
                return f"sell_profit_t_1_1( {buy_tag})"
            elif (max_profit > (current_profit + 0.035)) and (last_candle['rsi'] < 45.0) and (last_candle['cmf'] < -0.0) and (last_candle['cmf_1h'] < -0.0):
                return f"sell_profit_t_1_2( {buy_tag})"
            elif (max_profit > (current_profit + 0.02)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.0) and (last_candle['cti_1h'] > 0.8):
                return f"sell_profit_t_1_4( {buy_tag})"
            elif (max_profit > (current_profit + 0.04)) and (last_candle['rsi'] < 49.0) and (last_candle['cmf_1h'] < -0.0):
                return f"sell_profit_t_1_5( {buy_tag})"
            elif (max_profit > (current_profit + 0.06)) and (last_candle['rsi'] < 43.0) and (last_candle['cmf'] < -0.0):
                return f"sell_profit_t_1_7( {buy_tag})"
            elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.1) and (last_candle['rsi_1h'] < 50.0):
                return f"sell_profit_t_1_9( {buy_tag})"
            elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 46.0) and (last_candle['cmf'] < -0.0) and (last_candle['r_480_1h'] > -20.0):
                return f"sell_profit_t_1_10( {buy_tag})"
            elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 42.0):
                return f"sell_profit_t_1_11( {buy_tag})"
            elif (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.25):
                return f"sell_profit_t_1_12( {buy_tag})"

        # sell cti_r
        if 0.012 > current_profit >= 0.0 :
            if (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value):
                return f"sell_profit_t_cti_r_0_1( {buy_tag})"

        # main sell
        if current_profit > 0.02:
            if (last_candle['momdiv_sell_1h'] == True):
                return f"signal_profit_q_momdiv_1h( {buy_tag})"
            if (last_candle['momdiv_sell'] == True):
                return f"signal_profit_q_momdiv( {buy_tag})"
            if (last_candle['momdiv_coh'] == True):
                return f"signal_profit_q_momdiv_coh( {buy_tag})"

        # sell bear
        if last_candle['close'] < last_candle['ema_200']:
            if 0.02 > current_profit >= 0.01:
                if (last_candle['rsi'] < 34.0) and (last_candle['cmf'] < 0.0):
                    return f"sell_profit_u_bear_1_1( {buy_tag})"
                elif (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.4):
                    return f"sell_profit_u_bear_1_2( {buy_tag})"

        # sell quick
        if (0.06 > current_profit > 0.02) and (last_candle['rsi'] > 80.0):
            return f"signal_profit_q_1( {buy_tag})"

        if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95):
            return f"signal_profit_q_2( {buy_tag})"

        if (0.06 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1):
            return f"signal_profit_q_pmax_bull( {buy_tag})"
        if (0.06 > current_profit > 0.02) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016):
            return f"signal_profit_q_pmax_bear( {buy_tag})"

        # sell scalp

        if (
                (current_profit < -0.05)
                and (last_candle['close'] < last_candle['ema_200'] * 0.988)
                and (last_candle['cmf'] < -0.046)
                and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < 0.022)
                and last_candle['rsi'] > previous_candle_1['rsi']
                and (last_candle['rsi'] > (last_candle['rsi_1h'] + 10.0))
            ):
            return f"sell_stoploss_u_e_1( {buy_tag})"

        # stoploss - deadfish
        if (    (current_profit < self.sell_deadfish_profit.value)
                and (last_candle['close'] < last_candle['ema_200'])
                and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value)
                and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
                and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value)
            ):
            return f"sell_stoploss_deadfish( {buy_tag})"

        # stoploss - bleeding
        #if (    (current_profit < -0.05)
                #and (last_candle['close'] < last_candle['ema_200'])
                #and (last_candle['cti_mean_24'] < self.sell_bleeding_cti.value)
                #and (last_candle['r_14_mean_24'] < self.sell_bleeding_r14.value)
                #and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_bleeding_volume_factor.value)
            #):
            #return f"sell_stoploss_bleeding( {buy_tag})"

        return None

    ## Confirm Entry
    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)

        max_slip = self.max_slip.value

        if(len(dataframe) < 1):
            return False

        dataframe = dataframe.iloc[-1].squeeze()
        if ((rate > dataframe['close'])) :

            slippage = ( (rate / dataframe['close']) - 1 ) * 100

            if slippage < max_slip:
                return True
            else:
                return False

        return True

    ############################################################################

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

        # Bollinger bands
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']

        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']

        ### Other BB checks
        dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
        dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])

        # CCI hyperopt
        for val in self.buy_cci_length.range:
            dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)

        dataframe['cci'] = ta.CCI(dataframe, 26)
        dataframe['cci_long'] = ta.CCI(dataframe, 170)

        # RMI hyperopt
        for val in self.buy_rmi_length.range:
            dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)

        # SRSI hyperopt
        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe['srsi_fk'] = stoch['fastk']
        dataframe['srsi_fd'] = stoch['fastd']

        # BinH
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()

        # SMA
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20)
        dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)
        dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
        dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75)

        # CTI
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)

        # CMF
        dataframe['cmf'] = chaikin_money_flow(dataframe, 20)

        # CRSI (3, 2, 100)
        crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
        crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
        dataframe['crsi'] =  (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3

        # EMA
        dataframe['ema_4'] = ta.EMA(dataframe, timeperiod=4)
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

        # Elliot
        dataframe['EWO'] = EWO(dataframe, 50, 200)

        # Williams %R
        dataframe['r_14'] = williams_r(dataframe, period=14)
        dataframe['r_32'] = williams_r(dataframe, period=32)
        dataframe['r_64'] = williams_r(dataframe, period=64)
        dataframe['r_96'] = williams_r(dataframe, period=96)
        dataframe['r_480'] = williams_r(dataframe, period=480)

        # Volume
        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
        dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
        dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)

        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)

        # Heiken Ashi
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        ## BB 40
        bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2)
        dataframe['bb_lowerband2_40'] = bollinger2_40['lower']
        dataframe['bb_middleband2_40'] = bollinger2_40['mid']
        dataframe['bb_upperband2_40'] = bollinger2_40['upper']

        # ClucHA
        dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs()
        dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
        dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
        dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        # Cofi
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)

        # Profit Maximizer - PMAX
        dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
        dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4
        dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)

        # MOMDIV
        mom = momdiv(dataframe)
        dataframe['momdiv_buy'] = mom['momdiv_buy']
        dataframe['momdiv_sell'] = mom['momdiv_sell']
        dataframe['momdiv_coh'] = mom['momdiv_coh']
        dataframe['momdiv_col'] = mom['momdiv_col']

        # T3 Average
        dataframe['T3'] = T3(dataframe)

        # True range
        dataframe['trange'] = ta.TRANGE(dataframe)

        # KC
        dataframe['range_ma_28'] = ta.SMA(dataframe['trange'], 28)
        dataframe['kc_upperband_28_1'] = dataframe['sma_28'] + dataframe['range_ma_28']
        dataframe['kc_lowerband_28_1'] = dataframe['sma_28'] - dataframe['range_ma_28']

        # KC 20
        dataframe['range_ma_20'] = ta.SMA(dataframe['trange'], 20)
        dataframe['kc_upperband_20_2'] = dataframe['sma_20'] + dataframe['range_ma_20'] * 2
        dataframe['kc_lowerband_20_2'] = dataframe['sma_20'] - dataframe['range_ma_20'] * 2
        dataframe['kc_bb_delta'] =  ( dataframe['kc_lowerband_20_2'] - dataframe['bb_lowerband2'] ) / dataframe['bb_lowerband2'] * 100

        # Linreg
        dataframe['hh_20'] = ta.MAX(dataframe['high'], 20)
        dataframe['ll_20'] = ta.MIN(dataframe['low'], 20)
        dataframe['avg_hh_ll_20'] = (dataframe['hh_20'] + dataframe['ll_20']) / 2
        dataframe['avg_close_20'] = ta.SMA(dataframe['close'], 20)
        dataframe['avg_val_20'] = (dataframe['avg_hh_ll_20'] + dataframe['avg_close_20']) / 2
        dataframe['linreg_val_20'] = ta.LINEARREG(dataframe['close'] - dataframe['avg_val_20'], 20, 0)

        # fisher
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # Modified Elder Ray Index
        dataframe['moderi_96'] = moderi(dataframe, 96)

        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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []
        dataframe.loc[:, 'buy_tag'] = ''

        is_dip = (
                (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) &
                (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) &
                (dataframe['srsi_fk'] < self.buy_srsi_fk.value)
            )

        is_sqzOff = (
                (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) &
                (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1'])
            )

        is_break = (

                (dataframe['bb_delta'] > self.buy_bb_delta.value) &
                (dataframe['bb_width'] > self.buy_bb_width.value) &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) &    # from BinH
                (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
            )

        is_local_uptrend = (                                                                            # from NFI next gen, credit goes to @iterativ
                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) &
                (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
                (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 )
            )

        is_ewo = (                                                                                      # from SMA offset
                (dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) &
                (dataframe['EWO'] > self.buy_ewo.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) &
                (dataframe['rsi'] < self.buy_rsi.value)
            )

        is_ewo_2 = (
                (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) &
                (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) &
                (dataframe['rsi_fast'] < self.buy_rsi_fast_ewo_2.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) &
                (dataframe['EWO'] > self.buy_ewo_high_2.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) &
                (dataframe['rsi'] < self.buy_rsi_ewo_2.value)
            )

        is_r_deadfish = (                                                                               # reverse deadfish
                (dataframe['ema_100'] < dataframe['ema_200'] * self.buy_r_deadfish_ema.value) &
                (dataframe['bb_width'] > self.buy_r_deadfish_bb_width.value) &
                (dataframe['close'] < dataframe['bb_middleband2'] * self.buy_r_deadfish_bb_factor.value) &
                (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.buy_r_deadfish_volume_factor.value) &
                (dataframe['cti'] < self.buy_r_deadfish_cti.value) &
                (dataframe['r_14'] < self.buy_r_deadfish_r14.value)
            )

        is_clucHA = (
                (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value ) &
                (
                        (dataframe['bb_lowerband2_40'].shift() > 0) &
                        (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value) &
                        (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value) &
                        (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value) &
                        (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) &
                        (dataframe['ha_close'] < dataframe['ha_close'].shift())
                )
            )

        is_gumbo = (                                                                        # Modified from gumbo1, creadit goes to original author @raph92
                (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) &
                (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) &
                (dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) &
                (dataframe['cti'] < self.buy_gumbo_cti.value) &
                (dataframe['r_14'] < self.buy_gumbo_r14.value)
            )

        is_sqzmom = (                                                                       # Modified from squeezeMomentum, credit goes to original author @LazyBear of TradingView
                (is_sqzOff) &
                (dataframe['linreg_val_20'].shift(2) > dataframe['linreg_val_20'].shift(1)) &
                (dataframe['linreg_val_20'].shift(1) < dataframe['linreg_val_20']) &
                (dataframe['linreg_val_20'] < 0) &
                (dataframe['close'] < dataframe['ema_13'] * self.buy_sqzmom_ema.value) &
                (dataframe['EWO'] < self.buy_sqzmom_ewo.value) &
                (dataframe['r_14'] < self.buy_sqzmom_r14.value)
            )

        # NFI quick mode, credit goes to @iterativ
        is_nfi_13 = (
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['close'] < dataframe['sma_30'] * 0.99) &
                (dataframe['cti'] < -0.92) &
                (dataframe['EWO'] < -5.585) &
                (dataframe['cti_1h'] < -0.88) &
                (dataframe['crsi_1h'] > 10.0)
            )

        is_nfi_32 = (  # NFIX 26
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < 46) &
                (dataframe['rsi'] > 25.0) &
                (dataframe['close'] < dataframe['sma_15'] * 0.93) &
                (dataframe['cti'] < -0.9)
            )

        is_nfi_33 = (
                (dataframe['close'] < (dataframe['ema_13'] * 0.978)) &
                (dataframe['EWO'] > 8) &
                (dataframe['cti'] < -0.88) &
                (dataframe['rsi'] < 32) &
                (dataframe['r_14'] < -98.0) &
                (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5))
            )


        is_nfix_5 = (
                (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) &
                (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) &
                (dataframe['close'] < dataframe['sma_75'] * 0.932) &
                (dataframe['EWO'] > 3.6) &
                (dataframe['cti'] < -0.9) &
                (dataframe['r_14'] < -97.0)
            )


        is_nfix_49 = (
                (dataframe['ema_26'].shift(3) > dataframe['ema_12'].shift(3)) &
                (dataframe['ema_26'].shift(3) - dataframe['ema_12'].shift(3) > dataframe['open'].shift(3) * 0.032) &
                (dataframe['ema_26'].shift(9) - dataframe['ema_12'].shift(9) > dataframe['open'].shift(3) / 100) &
                (dataframe['close'].shift(3) < dataframe['ema_20'].shift(3) * 0.916) &
                (dataframe['rsi'].shift(3) < 32.5) &
                (dataframe['crsi'].shift(3) > 18.0) &
                (dataframe['cti'] < self.buy_nfix_49_cti.value) &
                (dataframe['r_14'] < self.buy_nfix_49_r14.value)
            )

        is_nfi7_33 = (
                (dataframe['moderi_96']) &
                (dataframe['cti'] < -0.88) &
                (dataframe['close'] < (dataframe['ema_13'] * 0.988)) &
                (dataframe['EWO'] > 6.4) &
                (dataframe['rsi'] < 32.0) &
                (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0))
            )

        is_nfi7_37 = (
                (dataframe['pm'] > dataframe['pmax_thresh']) &
                (dataframe['close'] < dataframe['sma_75'] * 0.98) &
                (dataframe['EWO'] > 9.8) &
                (dataframe['rsi'] < 56.0) &
                (dataframe['cti'] < -0.7) &
                (dataframe['safe_dump_50_1h'])
            )

        is_additional_check = (
                (dataframe['roc_1h'] < self.buy_roc_1h.value) &
                (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value)
            )

        ## Additional Check
        is_BB_checked = is_dip & is_break

        ## Condition Append
        conditions.append(is_BB_checked)                                           # ~2.32 / 91.1% / 46.27%      D
        dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb '

        conditions.append(is_local_uptrend)                                        # ~3.28 / 92.4% / 69.72%
        dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local_uptrend '

        conditions.append(is_ewo)                                                  # ~0.92 / 92.0% / 43.74%      D
        dataframe.loc[is_ewo, 'buy_tag'] += 'ewo '

        conditions.append(is_r_deadfish)                                           # ~0.99 / 86.9% / 21.93%      D
        dataframe.loc[is_r_deadfish, 'buy_tag'] += 'r_deadfish '

       # conditions.append(is_clucHA)                                               # ~7.2 / 92.5% / 97.98%       D
       # dataframe.loc[is_clucHA, 'buy_tag'] += 'clucHA '

        #conditions.append(is_gumbo)                                                # ~2.63 / 90.6% / 41.49%      D
        #dataframe.loc[is_gumbo, 'buy_tag'] += 'gumbo '

        conditions.append(is_sqzmom)                                               # ~3.14 / 92.4% / 64.14%      D
        dataframe.loc[is_sqzmom, 'buy_tag'] += 'sqzmom '

        conditions.append(is_nfi_13)                                               # ~0.4 / 100%                 D
        dataframe.loc[is_nfi_13, 'buy_tag'] += 'nfi_13 '

        conditions.append(is_nfi_32)                                               # ~0.78 / 92.0 % / 37.41%     D
        dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi_32 '

        conditions.append(is_nfi_33)                                               # ~0.11 / 100%                D
        dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 '
        
        conditions.append(is_nfix_49)                                              # ~0.33 / 100% / 0%           D
        dataframe.loc[is_nfix_49, 'buy_tag'] += 'nfix_49 '

        conditions.append(is_nfi7_33)                                              # ~0.71 / 91.3% / 28.94%      D
        dataframe.loc[is_nfi7_33, 'buy_tag'] += 'nfi7_33 '

        conditions.append(is_nfi7_37)                                              # ~0.46 / 92.6% / 17.05%      D
        dataframe.loc[is_nfi7_37, 'buy_tag'] += 'nfi7_37 '

        if conditions:
            dataframe.loc[
                            
                            reduce(lambda x, y: x | y, conditions)

                        , 'buy' ] = 1

        return dataframe

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

        dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0

        return dataframe


# PMAX
def pmax(df, period, multiplier, length, MAtype, src):

    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)

    mavalue = f'MA_{MAtype}_{length}'
    atr = f'ATR_{period}'
    pm = f'pm_{period}_{multiplier}_{length}_{MAtype}'
    pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}'

    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # MAtype==9 --> zema
    if src == 1:
        masrc = df["close"]
    elif src == 2:
        masrc = (df["high"] + df["low"]) / 2
    elif src == 3:
        masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4

    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)

    df[atr] = ta.ATR(df, timeperiod=period)
    df['basic_ub'] = mavalue + ((multiplier/10) * df[atr])
    df['basic_lb'] = mavalue - ((multiplier/10) * df[atr])


    basic_ub = df['basic_ub'].values
    final_ub = np.full(len(df), 0.00)
    basic_lb = df['basic_lb'].values
    final_lb = np.full(len(df), 0.00)

    for i in range(period, len(df)):
        final_ub[i] = basic_ub[i] if (
            basic_ub[i] < final_ub[i - 1]
            or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
        final_lb[i] = basic_lb[i] if (
            basic_lb[i] > final_lb[i - 1]
            or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]

    df['final_ub'] = final_ub
    df['final_lb'] = final_lb

    pm_arr = np.full(len(df), 0.00)
    for i in range(period, len(df)):
        pm_arr[i] = (
            final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1]
                                    and mavalue[i] <= final_ub[i])
        else final_lb[i] if (
            pm_arr[i - 1] == final_ub[i - 1]
            and mavalue[i] > final_ub[i]) else final_lb[i]
        if (pm_arr[i - 1] == final_lb[i - 1]
            and mavalue[i] >= final_lb[i]) else final_ub[i]
        if (pm_arr[i - 1] == final_lb[i - 1]
            and mavalue[i] < final_lb[i]) else 0.00)

    pm = Series(pm_arr)

    # Mark the trend direction up/down
    pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down',  'up'), np.NaN)

    return pm, pmx

# Mom DIV
def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame:
    mom: Series = ta.MOM(dataframe, timeperiod=mom_length)
    upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0)
    buy = qtpylib.crossed_below(mom, lowerband)
    sell = qtpylib.crossed_above(mom, upperband)
    hh = dataframe['high'].rolling(lookback).max()
    ll = dataframe['low'].rolling(lookback).min()
    coh = dataframe['high'] >= hh
    col = dataframe['low'] <= ll
    df = DataFrame({
            "momdiv_mom": mom,
            "momdiv_upperb": upperband,
            "momdiv_lowerb": lowerband,
            "momdiv_buy": buy,
            "momdiv_sell": sell,
            "momdiv_coh": coh,
            "momdiv_col": col,
        }, index=dataframe['close'].index)
    return df

def T3(dataframe, length=5):
    """
    T3 Average by HPotter on Tradingview
    https://www.tradingview.com/script/qzoC9H1I-T3-Average/
    """
    df = dataframe.copy()

    df['xe1'] = ta.EMA(df['close'], timeperiod=length)
    df['xe2'] = ta.EMA(df['xe1'], timeperiod=length)
    df['xe3'] = ta.EMA(df['xe2'], timeperiod=length)
    df['xe4'] = ta.EMA(df['xe3'], timeperiod=length)
    df['xe5'] = ta.EMA(df['xe4'], timeperiod=length)
    df['xe6'] = ta.EMA(df['xe5'], timeperiod=length)
    b = 0.7
    c1 = -b * b * b
    c2 = 3 * b * b + 3 * b * b * b
    c3 = -6 * b * b - 3 * b - 3 * b * b * b
    c4 = 1 + 3 * b + b * b * b + 3 * b * b
    df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3']

    return df['T3Average']