# --- Do not remove these libs ---
import pandas_ta as pta
import copy
import logging
import pathlib
import rapidjson
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
from freqtrade.exchange import timeframe_to_prev_date
from pandas import DataFrame, Series, concat
from functools import reduce
import math
from typing import Dict
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from technical.util import resample_to_interval, resampled_merge
from technical.indicators import RMI, zema, VIDYA, ichimoku
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter
import time
log = logging.getLogger(__name__)
# --------------------------------

def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0
    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_TSL_Tranz(IStrategy):
    INTERFACE_VERSION = 3
    '\n        BB_RPB_TSL\n        @author jilv220\n        Simple bollinger brand strategy inspired by this blog  ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )\n        RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )\n        The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )\n        I modified it to better suit my taste and added Hyperopt for this strategy.\n    '
    # (1) exit rework
    ##########################################################################
    # Hyperopt result area
    # entry space
    ##
    ##
    #
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    entry_params = {'max_slip': 0.983, 'entry_bb_width_1h': 0.954, 'entry_roc_1h': 86, 'entry_threshold': 0.003, 'entry_bb_factor': 0.999, 'entry_bb_delta': 0.025, 'entry_bb_width': 0.095, 'entry_cci': -116, 'entry_cci_length': 25, 'entry_rmi': 49, 'entry_rmi_length': 17, 'entry_srsi_fk': 32, 'entry_closedelta': 17.922, 'entry_ema_diff': 0.026, 'entry_ema_high': 0.968, 'entry_ema_low': 0.935, 'entry_ewo': -5.001, 'entry_rsi': 23, 'entry_rsi_fast': 44, 'entry_ema_high_2': 1.087, 'entry_ema_low_2': 0.97, 'entry_ewo_high_2': 4.179, 'entry_rsi_ewo_2': 35, 'entry_rsi_fast_ewo_2': 45, 'entry_closedelta_local_dip': 12.044, 'entry_ema_diff_local_dip': 0.024, 'entry_ema_high_local_dip': 1.014, 'entry_rsi_local_dip': 21, 'entry_r_deadfish_bb_factor': 1.014, 'entry_r_deadfish_bb_width': 0.299, 'entry_r_deadfish_ema': 1.054, 'entry_r_deadfish_volume_factor': 1.59, 'entry_r_deadfish_cti': -0.115, 'entry_r_deadfish_r14': -44.34, 'entry_clucha_bbdelta_close': 0.049, 'entry_clucha_bbdelta_tail': 1.146, 'entry_clucha_close_bblower': 0.018, 'entry_clucha_closedelta_close': 0.017, 'entry_clucha_rocr_1h': 0.526, 'entry_adx': 13, 'entry_cofi_r14': -85.016, 'entry_cofi_cti': -0.892, 'entry_ema_cofi': 1.147, 'entry_ewo_high': 8.594, 'entry_fastd': 28, 'entry_fastk': 39, 'entry_gumbo_ema': 1.121, 'entry_gumbo_ewo_low': -9.442, 'entry_gumbo_cti': -0.374, 'entry_gumbo_r14': -51.971, 'entry_sqzmom_ema': 0.981, 'entry_sqzmom_ewo': -3.966, 'entry_sqzmom_r14': -45.068, 'entry_nfix_39_ema': 0.912, 'entry_nfix_49_cti': -0.105, 'entry_nfix_49_r14': -81.827}
    # exit space
    ##
    ##
    ##
    exit_params = {'exit_cmf': -0.046, 'exit_ema': 0.988, 'exit_ema_close_delta': 0.022, 'exit_deadfish_profit': -0.063, 'exit_deadfish_bb_factor': 0.954, 'exit_deadfish_bb_width': 0.043, 'exit_deadfish_volume_factor': 2.37, 'exit_cti_r_cti': 0.844, 'exit_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'
    inf_15m = '15m'
    info_timefame_1d = 'none'
    info_timeframe_1h = '1h'
    info_timeframe_15m = '15m'
    res_timeframe = 'none'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # Disabled
    stoploss = -0.99
    # Custom stoploss
    use_custom_stoploss = True
    use_exit_signal = True
    ############################################################################
    ## Buy params
    is_optimize_dip = False
    entry_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip)
    entry_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip)
    entry_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip)
    entry_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip)
    entry_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip)
    is_optimize_break = False
    entry_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize=is_optimize_break)
    entry_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize=is_optimize_break)
    is_optimize_local_uptrend = False
    entry_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_uptrend)
    entry_bb_factor = DecimalParameter(0.99, 0.999, default=0.995, optimize=False)
    entry_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_uptrend)
    is_optimize_local_dip = False
    entry_ema_diff_local_dip = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_dip)
    entry_ema_high_local_dip = DecimalParameter(0.9, 1.2, default=0.942, optimize=is_optimize_local_dip)
    entry_closedelta_local_dip = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_dip)
    entry_rsi_local_dip = IntParameter(15, 45, default=28, optimize=is_optimize_local_dip)
    entry_crsi_local_dip = IntParameter(10, 18, default=10, optimize=False)
    is_optimize_ewo = False
    entry_rsi_fast = IntParameter(35, 50, default=45, optimize=is_optimize_ewo)
    entry_rsi = IntParameter(15, 35, default=35, optimize=is_optimize_ewo)
    entry_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo)
    entry_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo)
    entry_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo)
    is_optimize_ewo_2 = False
    entry_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize=is_optimize_ewo_2)
    entry_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize=is_optimize_ewo_2)
    entry_ema_low_2 = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_ewo_2)
    entry_ema_high_2 = DecimalParameter(0.9, 1.2, default=1.087, optimize=is_optimize_ewo_2)
    entry_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize=is_optimize_ewo_2)
    is_optimize_r_deadfish = False
    entry_r_deadfish_ema = DecimalParameter(0.9, 1.2, default=1.087, optimize=is_optimize_r_deadfish)
    entry_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_r_deadfish)
    entry_r_deadfish_bb_factor = DecimalParameter(0.9, 1.2, default=1.0, optimize=is_optimize_r_deadfish)
    entry_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_r_deadfish)
    is_optimize_r_deadfish_protection = False
    entry_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, optimize=is_optimize_r_deadfish_protection)
    entry_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, optimize=is_optimize_r_deadfish_protection)
    is_optimize_clucha = False
    entry_clucha_bbdelta_close = DecimalParameter(0.01, 0.05, default=0.02206, optimize=is_optimize_clucha)
    entry_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize=is_optimize_clucha)
    entry_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize=is_optimize_clucha)
    entry_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize=is_optimize_clucha)
    is_optimize_cofi = False
    entry_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97, optimize=is_optimize_cofi)
    entry_fastk = IntParameter(0, 40, default=20, optimize=is_optimize_cofi)
    entry_fastd = IntParameter(0, 40, default=20, optimize=is_optimize_cofi)
    entry_adx = IntParameter(0, 30, default=30, optimize=is_optimize_cofi)
    entry_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi)
    is_optimize_cofi_protection = False
    entry_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_cofi_protection)
    entry_cofi_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_cofi_protection)
    is_optimize_gumbo = False
    entry_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_gumbo)
    entry_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize=is_optimize_gumbo)
    is_optimize_gumbo_protection = False
    entry_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_gumbo_protection)
    entry_gumbo_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_gumbo_protection)
    is_optimize_sqzmom_protection = False
    entry_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_sqzmom_protection)
    entry_sqzmom_ewo = DecimalParameter(-12, 12, default=0, optimize=is_optimize_sqzmom_protection)
    entry_sqzmom_r14 = DecimalParameter(-100, -22, default=-50, optimize=is_optimize_sqzmom_protection)
    is_optimize_nfix_39 = True
    entry_nfix_39_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_nfix_39)
    is_optimize_nfix_49_protection = False
    entry_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_nfix_49_protection)
    entry_nfix_49_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_nfix_49_protection)
    is_optimize_btc_safe = False
    entry_btc_safe = IntParameter(-300, 50, default=-200, optimize=is_optimize_btc_safe)
    entry_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize=is_optimize_btc_safe)
    entry_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize=is_optimize_btc_safe)
    is_optimize_check = False
    entry_roc_1h = IntParameter(-25, 200, default=10, optimize=is_optimize_check)
    entry_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize=is_optimize_check)
    #BB MODDED
    is_optimize_ctt15_protection = False
    entry_ema_open_mult_15 = DecimalParameter(0.01, 0.03, default=0.024, optimize=is_optimize_ctt15_protection)
    entry_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.958, optimize=is_optimize_ctt15_protection)
    entry_rsi_15 = DecimalParameter(20.0, 36.0, default=28.0, optimize=is_optimize_ctt15_protection)
    entry_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.974, optimize=is_optimize_ctt15_protection)
    is_optimize_ctt25_protection = False
    entry_25_ma_offset = DecimalParameter(0.9, 0.99, default=0.922, optimize=is_optimize_ctt25_protection)
    entry_25_rsi_4 = DecimalParameter(26.0, 40.0, default=38.0, optimize=is_optimize_ctt25_protection)
    entry_25_cti = DecimalParameter(-0.99, -0.4, default=-0.76, optimize=is_optimize_ctt25_protection)
    ## Slippage params
    is_optimize_slip = False
    max_slip = DecimalParameter(0.33, 1.0, default=0.33, decimals=3, optimize=is_optimize_slip, space='entry', load=True)
    ## Sell params
    exit_btc_safe = IntParameter(-400, -300, default=-365, optimize=False)
    is_optimize_exit_stoploss = False
    exit_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize=is_optimize_exit_stoploss)
    exit_ema_close_delta = DecimalParameter(0.022, 0.027, default=0.024, optimize=is_optimize_exit_stoploss)
    exit_ema = DecimalParameter(0.97, 0.99, default=0.987, optimize=is_optimize_exit_stoploss)
    is_optimize_deadfish = False
    exit_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_deadfish)
    exit_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, optimize=is_optimize_deadfish)
    exit_deadfish_bb_factor = DecimalParameter(0.9, 1.2, default=1.0, optimize=is_optimize_deadfish)
    exit_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_deadfish)
    is_optimize_bleeding = False
    exit_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_bleeding)
    exit_bleeding_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_bleeding)
    exit_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_bleeding)
    is_optimize_cti_r = False
    exit_cti_r_cti = DecimalParameter(0.55, 1, default=0.5, optimize=is_optimize_cti_r)
    exit_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]
        informative_pairs.extend = [(pair, '15m') 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)
        # RSI
        informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14)
        # SMA
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)
        informative_1h['sma_200_dec_24'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(24)
        # 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)
        informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26)
        informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12)
        informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25)
        informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35)
        # CTI
        informative_1h['cti'] = pta.cti(informative_1h['close'], length=20)
        informative_1h['cti_40'] = pta.cti(informative_1h['close'], length=40)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb20_2_low'] = bollinger['lower']
        informative_1h['bb20_2_mid'] = bollinger['mid']
        informative_1h['bb20_2_upp'] = bollinger['upper']
        informative_1h['bb20_width'] = (informative_1h['bb20_2_upp'] - informative_1h['bb20_2_low']) / informative_1h['bb20_2_mid']
        # 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_entry'] = mom['momdiv_entry']
        informative_1h['momdiv_exit'] = mom['momdiv_exit']
        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

    def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        tik = time.perf_counter()
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe_15m)
        # RSI
        informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14)
        # EMAs
        informative_15m['ema_12'] = ta.EMA(informative_15m, timeperiod=12)
        informative_15m['ema_16'] = ta.EMA(informative_15m, timeperiod=16)
        informative_15m['ema_20'] = ta.EMA(informative_15m, timeperiod=20)
        informative_15m['ema_25'] = ta.EMA(informative_15m, timeperiod=25)
        informative_15m['ema_26'] = ta.EMA(informative_15m, timeperiod=26)
        informative_15m['ema_50'] = ta.EMA(informative_15m, timeperiod=50)
        informative_15m['ema_100'] = ta.EMA(informative_15m, timeperiod=100)
        informative_15m['ema_200'] = ta.EMA(informative_15m, timeperiod=200)
        # SMA
        informative_15m['sma_15'] = ta.SMA(informative_15m, timeperiod=15)
        informative_15m['sma_30'] = ta.SMA(informative_15m, timeperiod=30)
        informative_15m['sma_200'] = ta.SMA(informative_15m, timeperiod=200)
        informative_15m['sma_200_dec_20'] = informative_15m['sma_200'] < informative_15m['sma_200'].shift(20)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_15m), window=20, stds=2)
        informative_15m['bb20_2_low'] = bollinger['lower']
        informative_15m['bb20_2_mid'] = bollinger['mid']
        informative_15m['bb20_2_upp'] = bollinger['upper']
        # BB 40 - STD2
        bb_40_std2 = qtpylib.bollinger_bands(informative_15m['close'], window=40, stds=2)
        informative_15m['bb40_2_low'] = bb_40_std2['lower']
        informative_15m['bb40_2_mid'] = bb_40_std2['mid']
        informative_15m['bb40_2_delta'] = (bb_40_std2['mid'] - informative_15m['bb40_2_low']).abs()
        informative_15m['closedelta'] = (informative_15m['close'] - informative_15m['close'].shift()).abs()
        informative_15m['tail'] = (informative_15m['close'] - informative_15m['bb40_2_low']).abs()
        # CMF
        informative_15m['cmf'] = chaikin_money_flow(informative_15m, 20)
        # CTI
        informative_15m['cti'] = pta.cti(informative_15m['close'], length=20)
        # Williams %R
        informative_15m['r_14'] = williams_r(informative_15m, period=14)
        informative_15m['r_64'] = williams_r(informative_15m, period=64)
        informative_15m['r_96'] = williams_r(informative_15m, period=96)
        # EWO
        informative_15m['ewo'] = ewo(informative_15m, 50, 200)
        # CCI
        informative_15m['cci'] = ta.CCI(informative_15m, source='hlc3', timeperiod=20)
        # CRSI (3, 2, 100)
        crsi_closechange = informative_15m['close'] / informative_15m['close'].shift(1)
        crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
        informative_15m['crsi'] = (ta.RSI(informative_15m['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_15m['close'], 100)) / 3
        tok = time.perf_counter()
        log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.")
        return informative_15m

    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_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        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
        entry_tag = 'empty'
        if hasattr(trade, 'entry_tag') and trade.entry_tag is not None:
            entry_tag = trade.entry_tag
        entry_tags = entry_tag.split()
        # exit trail
        if 0.012 > current_profit >= 0.0:
            if max_profit > current_profit + 0.045 and last_candle['rsi'] < 46.0:
                return f'exit_profit_t_0_1( {entry_tag})'
            elif max_profit > current_profit + 0.025 and last_candle['rsi'] < 32.0:
                return f'exit_profit_t_0_2( {entry_tag})'
            elif max_profit > current_profit + 0.05 and last_candle['rsi'] < 48.0:
                return f'exit_profit_t_0_3( {entry_tag})'
        elif 0.02 > current_profit >= 0.012:
            if max_profit > current_profit + 0.01 and last_candle['rsi'] < 39.0:
                return f'exit_profit_t_1_1( {entry_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'exit_profit_t_1_2( {entry_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'exit_profit_t_1_4( {entry_tag})'
            elif max_profit > current_profit + 0.04 and last_candle['rsi'] < 49.0 and (last_candle['cmf_1h'] < -0.0):
                return f'exit_profit_t_1_5( {entry_tag})'
            elif max_profit > current_profit + 0.06 and last_candle['rsi'] < 43.0 and (last_candle['cmf'] < -0.0):
                return f'exit_profit_t_1_7( {entry_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'exit_profit_t_1_9( {entry_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'exit_profit_t_1_10( {entry_tag})'
            elif max_profit > current_profit + 0.025 and last_candle['rsi'] < 42.0:
                return f'exit_profit_t_1_11( {entry_tag})'
            elif max_profit > current_profit + 0.01 and last_candle['rsi'] < 44.0 and (last_candle['cmf'] < -0.25):
                return f'exit_profit_t_1_12( {entry_tag})'
        # exit cti_r
        if 0.012 > current_profit >= 0.0:
            if last_candle['cti'] > self.exit_cti_r_cti.value and last_candle['r_14'] > self.exit_cti_r_r.value:
                return f'exit_profit_t_cti_r_0_1( {entry_tag})'
        # main exit
        if current_profit > 0.02:
            if last_candle['momdiv_exit_1h'] == True:
                return f'signal_profit_q_momdiv_1h( {entry_tag})'
            if last_candle['momdiv_exit'] == True:
                return f'signal_profit_q_momdiv( {entry_tag})'
            if last_candle['momdiv_coh'] == True:
                return f'signal_profit_q_momdiv_coh( {entry_tag})'
        # exit 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'exit_profit_u_bear_1_1( {entry_tag})'
                elif last_candle['rsi'] < 44.0 and last_candle['cmf'] < -0.4:
                    return f'exit_profit_u_bear_1_2( {entry_tag})'
        # exit quick
        if 0.06 > current_profit > 0.02 and last_candle['rsi'] > 80.0:
            return f'signal_profit_q_1( {entry_tag})'
        if 0.06 > current_profit > 0.02 and last_candle['cti'] > 0.95:
            return f'signal_profit_q_2( {entry_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( {entry_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( {entry_tag})'
        # exit scalp
        if current_profit > 0 and entry_tag in ['nfix_39 ']:
            if current_profit > 0 and last_candle['fisher'] > 0.39075 and (last_candle['ha_high'] <= previous_candle_1['ha_high']) and (previous_candle_1['ha_high'] <= previous_candle_2['ha_high']) and (last_candle['ha_close'] <= previous_candle_1['ha_close']) and (last_candle['ema_4'] > last_candle['ha_close']) and (last_candle['ha_close'] * 0.99754 > last_candle['bb_middleband2']):
                return f'exit_scalp( {entry_tag})'
        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'exit_stoploss_u_e_1( {entry_tag})'
        # stoploss - deadfish
        if current_profit < self.exit_deadfish_profit.value and last_candle['close'] < last_candle['ema_200'] and (last_candle['bb_width'] < self.exit_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.exit_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.exit_deadfish_volume_factor.value):
            return f'exit_stoploss_deadfish( {entry_tag})'
        # stoploss - bleeding
        #if (    (current_profit < -0.05)
        #and (last_candle['close'] < last_candle['ema_200'])
        #and (last_candle['cti_mean_24'] < self.exit_bleeding_cti.value)
        #and (last_candle['r_14_mean_24'] < self.exit_bleeding_r14.value)
        #and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.exit_bleeding_volume_factor.value)
        #):
        #return f"exit_stoploss_bleeding( {entry_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:
        # RSI
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
        # Zero-Lag EMA
        dataframe['zema_61'] = zema(dataframe, period=61)
        # 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']
        # BB 40 - STD2
        bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['bb40_2_low'] = bb_40_std2['lower']
        dataframe['bb40_2_mid'] = bb_40_std2['mid']
        dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
        # BB 20 - STD2
        bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb20_2_low'] = bb_20_std2['lower']
        dataframe['bb20_2_mid'] = bb_20_std2['mid']
        dataframe['bb20_2_upp'] = bb_20_std2['upper']
        # BB 20 - STD3
        bb_20_std3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb20_3_low'] = bb_20_std3['lower']
        dataframe['bb20_3_mid'] = bb_20_std3['mid']
        dataframe['bb20_3_upp'] = bb_20_std3['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.entry_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.entry_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['bb9'] = 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_entry'] = mom['momdiv_entry']
        dataframe['momdiv_exit'] = mom['momdiv_exit']
        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)
        informative_15m = self.informative_15m_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'entry_tag'] = ''
        is_additional_check = (dataframe['roc_1h'] < self.entry_roc_1h.value) & (dataframe['bb_width_1h'] < self.entry_bb_width_1h.value)
        is_dip = (dataframe[f'rmi_length_{self.entry_rmi_length.value}'] < self.entry_rmi.value) & (dataframe[f'cci_length_{self.entry_cci_length.value}'] <= self.entry_cci.value) & (dataframe['srsi_fk'] < self.entry_srsi_fk.value)
        is_sqzOff = (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) & (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1'])  # from BinH
        is_break = (dataframe['bb_delta'] > self.entry_bb_delta.value) & (dataframe['bb_width'] > self.entry_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.entry_bb_factor.value)  # from NFI next gen, credit goes to @iterativ
        is_local_uptrend = is_additional_check & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.entry_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000)
        is_local_dip = is_additional_check & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_diff_local_dip.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['ema_20'] * self.entry_ema_high_local_dip.value) & (dataframe['rsi'] < self.entry_rsi_local_dip.value) & (dataframe['crsi'] > self.entry_crsi_local_dip.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta_local_dip.value / 1000)  # from SMA offset
        is_ewo = is_additional_check & (dataframe['rsi_fast'] < self.entry_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low.value) & (dataframe['EWO'] > self.entry_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high.value) & (dataframe['rsi'] < self.entry_rsi.value)
        is_ewo_2 = is_additional_check & (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.entry_rsi_fast_ewo_2.value) & (dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low_2.value) & (dataframe['EWO'] > self.entry_ewo_high_2.value) & (dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high_2.value) & (dataframe['rsi'] < self.entry_rsi_ewo_2.value)  # reverse deadfish
        is_r_deadfish = is_additional_check & (dataframe['ema_100'] < dataframe['ema_200'] * self.entry_r_deadfish_ema.value) & (dataframe['bb_width'] > self.entry_r_deadfish_bb_width.value) & (dataframe['close'] < dataframe['bb_middleband2'] * self.entry_r_deadfish_bb_factor.value) & (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.entry_r_deadfish_volume_factor.value) & (dataframe['cti'] < self.entry_r_deadfish_cti.value) & (dataframe['r_14'] < self.entry_r_deadfish_r14.value)
        is_clucHA = (dataframe['rocr_1h'] > self.entry_clucha_rocr_1h.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift()))  # Modified from cofi, credit goes to original author "slack user CofiBit"
        is_cofi = is_additional_check & (dataframe['open'] < dataframe['ema_8'] * self.entry_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.entry_fastk.value) & (dataframe['fastd'] < self.entry_fastd.value) & (dataframe['adx'] > self.entry_adx.value) & (dataframe['EWO'] > self.entry_ewo_high.value) & (dataframe['cti'] < self.entry_cofi_cti.value) & (dataframe['r_14'] < self.entry_cofi_r14.value)  # Modified from gumbo1, creadit goes to original author @raph92
        is_gumbo = is_additional_check & (dataframe['EWO'] < self.entry_gumbo_ewo_low.value) & (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) & (dataframe['T3'] <= dataframe['ema_8'] * self.entry_gumbo_ema.value) & (dataframe['cti'] < self.entry_gumbo_cti.value) & (dataframe['r_14'] < self.entry_gumbo_r14.value)  # Modified from squeezeMomentum, credit goes to original author @LazyBear of TradingView
        is_sqzmom = is_additional_check & 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.entry_sqzmom_ema.value) & (dataframe['EWO'] < self.entry_sqzmom_ewo.value) & (dataframe['r_14'] < self.entry_sqzmom_r14.value)
        # NFI quick mode, credit goes to @iterativ
        is_nfi_13 = is_additional_check & (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 = is_additional_check & (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 = is_additional_check & (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_nfi_38 = is_additional_check & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] < -4.4) & (dataframe['cti'] < -0.95) & (dataframe['r_14'] < -97) & (dataframe['crsi_1h'] > 0.5)
        is_nfix_5 = is_additional_check & (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_39 = is_additional_check & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & dataframe['bb_lowerband2_40'].shift().gt(0) & dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056) & dataframe['closedelta'].gt(dataframe['close'] * 0.01) & dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5) & dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['close'] > dataframe['ema_13'] * self.entry_nfix_39_ema.value)
        is_nfix_49 = is_additional_check & (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.entry_nfix_49_cti.value) & (dataframe['r_14'] < self.entry_nfix_49_r14.value)
        is_nfi7_33 = is_additional_check & 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 = is_additional_check & (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']
        ## BB MODDED
        is_nfi_ctt35 = is_additional_check & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.984) & (dataframe['EWO'] > 9.6) & (dataframe['rsi_14'] < 32.0) & (dataframe['cti'] < -0.5)
        is_nfi_ctt25 = is_additional_check & (dataframe['rsi_20'] < dataframe['rsi_20'].shift()) & (dataframe['rsi_4'] < self.entry_25_rsi_4.value) & (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) & (dataframe['close'] < dataframe['sma_20'] * self.entry_25_ma_offset.value) & (dataframe['open'] > dataframe['sma_20'] * self.entry_25_ma_offset.value) & ((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h'])) & (dataframe['cti'] < self.entry_25_cti.value)
        is_nfi_ctt15 = is_additional_check & (dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_15.value) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_15.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['rsi_14'] < self.entry_rsi_15.value) & (dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_15.value)
        is_nfi_9 = is_additional_check & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['ema_20'] * 0.968) & (dataframe['close'] < dataframe['bb20_2_low'] * 0.982) & (dataframe['mfi'] < 50.0) & (dataframe['cti'] < -0.85) & (dataframe['r_14'] < -94.0) & (dataframe['rsi_14_1h'] > 20.0) & (dataframe['rsi_14_1h'] < 88.0) & (dataframe['crsi_1h'] > 21.0)
        is_nfi_10 = is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.94) & (dataframe['close'] < dataframe['bb20_2_low'] * 0.984) & (dataframe['r_14'] < -88.0) & (dataframe['cti_1h'] > -0.5) & (dataframe['cti_1h'] < 0.94)
        is_nfi_26 = is_additional_check & (dataframe['close'] < dataframe['zema_61'] * 0.9405) & (dataframe['cti'] < -0.72) & (dataframe['cci'] < -166.0) & (dataframe['r_14'] < -98.0) & (dataframe['cti_1h'] < 0.95) & (dataframe['volume'] < dataframe['volume_mean_4'] * 2.0)
        is_nfix_53 = is_additional_check & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['ema_200_1h'].shift(24) > dataframe['ema_200_1h'].shift(36)) & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & (dataframe['ema_26_15m'] - dataframe['ema_12_15m'] > dataframe['open_15m'] * 0.02) & (dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3) > dataframe['open_15m'] / 100) & (dataframe['close_15m'] < dataframe['bb20_2_low_15m'] * 0.99) & (dataframe['r_14'] < -90.0) & (dataframe['cti_1h'] > -0.7)
        is_nfix_52 = is_additional_check & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & (dataframe['ema_26_15m'] - dataframe['ema_12_15m'] > dataframe['open_15m'] * 0.032) & (dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3) > dataframe['open_15m'] / 100) & (dataframe['close_15m'] < dataframe['bb20_2_low_15m'] * 0.998) & (dataframe['crsi_1h'] > 10.0)
        is_nfix_51 = is_additional_check & (dataframe['close_15m'] < dataframe['ema_16_15m'] * 0.944) & (dataframe['ewo_15m'] < -1.0) & (dataframe['rsi_14_15m'] > 28.0) & (dataframe['cti_15m'] < -0.84) & (dataframe['r_14_15m'] < -94.0) & (dataframe['rsi_14'] > 30.0) & (dataframe['crsi_1h'] > 1.0)
        is_nfix_48 = is_additional_check & (dataframe['close_15m'].shift(3) < dataframe['sma_15_15m'].shift(3) * 0.95) & (dataframe['close_15m'] > dataframe['open_15m'].shift(3)) & (dataframe['ewo_15m'] > 2.8) & (dataframe['cti_15m'] < -0.75) & (dataframe['r_14_15m'].shift(3) < -94.0) & (dataframe['cti'] < -0.5) & (dataframe['cti_1h'] < 0.1) & (dataframe['crsi_1h'] > 18.0)
        is_nfix_47 = is_additional_check & (dataframe['rsi_14_15m'] < dataframe['rsi_14_15m'].shift(3)) & (dataframe['ema_20_1h'] > dataframe['ema_25_1h']) & (dataframe['close_15m'] < dataframe['sma_15_15m'] * 0.95) & ((dataframe['open_15m'] < dataframe['ema_20_1h']) & (dataframe['low_15m'] < dataframe['ema_20_1h']) | (dataframe['open_15m'] > dataframe['ema_20_1h']) & (dataframe['low_15m'] > dataframe['ema_20_1h'])) & (dataframe['cti_15m'] < -0.9) & (dataframe['r_14_15m'] < -90.0) & (dataframe['r_14'] < -97.0) & (dataframe['cti_1h'] < 0.1) & (dataframe['crsi_1h'] > 8.0)
        is_nfix_41 = is_additional_check & (dataframe['ema_12_15m'] > dataframe['ema_200_1h']) & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & (dataframe['ema_26_15m'] - dataframe['ema_12_15m'] > dataframe['open_15m'] * 0.03) & (dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3) > dataframe['open_15m'] / 100) & (dataframe['close_15m'] < dataframe['bb20_2_low_15m'] * 0.99)
        is_nfix_38 = is_additional_check & (dataframe['ema_200'] > dataframe['ema_200'].shift(12) * 1.01) & (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['mfi'] < 34.5) & (dataframe['r_64'] < -65.0) & (dataframe['r_96'] < -50.0) & (dataframe['r_480_1h'] < -1.0)
        is_nfix_36 = is_additional_check & (dataframe['ema_200'] > dataframe['ema_200'].shift(36) * 1.035) & (dataframe['close'] < dataframe['ema_20'] * 0.956) & (dataframe['rsi_14'] < 34.0) & (dataframe['r_64'] < -80.0) & (dataframe['cti'] < -0.5) & (dataframe['r_480_1h'] < -30.0)
        is_nfix_204 = is_additional_check & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] < -4.4) & (dataframe['cti'] < -0.95) & (dataframe['r_14'] < -97.0) & (dataframe['crsi_1h'] > 0.5)
        is_nfix_203 = is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -6.0) & (dataframe['cti_1h'] < -0.88) & (dataframe['crsi_1h'] > 10.0)
        is_nfix_202 = is_additional_check & (dataframe['close'] > dataframe['ema_200_1h'] * 0.84) & (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['close'] < dataframe['bb20_2_low'] * 0.999) & (dataframe['cti'] < -0.5) & (dataframe['rsi_14'] > 25.0) & (dataframe['mfi'] > 18.0) & (dataframe['r_14'] < -94.0) & (dataframe['r_14'].shift(1) < -94.0) & (dataframe['crsi_1h'] > 12.0) & (dataframe['volume'] < dataframe['volume_mean_4'] * 1.6)
        is_nfix_201 = is_additional_check & (dataframe['rsi_20'] < dataframe['rsi_20'].shift()) & (dataframe['rsi_4'] < 30.0) & (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) & (dataframe['close'] < dataframe['sma_15'] * 0.953) & (dataframe['cti'] < -0.78) & (dataframe['cci'] < -200.0)
        is_nfix_34 = is_additional_check & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < dataframe['bb20_2_low'] * 0.972) & (dataframe['cti'] < -0.8) & (dataframe['rsi_14'] < 18.0)
        is_nfix_28 = is_additional_check & (dataframe['close'] < dataframe['sma_75'] * 0.96) & (dataframe['EWO'] < -8.0) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -97.0) & (dataframe['crsi_1h'] > 14.0)
        is_nfix_27 = is_additional_check & (dataframe['close'] < dataframe['sma_75'] * 0.934) & (dataframe['EWO'] > 6.4) & (dataframe['rsi_14'] < 32.0) & (dataframe['cti'] < -0.8) & (dataframe['r_14'] < -96.0)
        is_nfix_19 = is_additional_check & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & dataframe['bb40_2_low'].shift().gt(0) & dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.045) & dataframe['closedelta'].gt(dataframe['close'] * 0.02) & dataframe['tail'].lt(dataframe['bb40_2_delta'] * 0.28) & dataframe['close'].lt(dataframe['bb40_2_low'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['cti'] < -0.9) & (dataframe['cti_1h'] > -0.75) & (dataframe['cti_1h'] < 0.25)
        is_nfix_11 = is_additional_check & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.027) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['ema_20'] * 0.932) & (dataframe['rsi_14'] < 25.0)
        is_nfix_9 = is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -5.0) & (dataframe['cti_1h'] < -0.88) & (dataframe['crsi_1h'] > 20.0)
        ## 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, 'entry_tag'] += 'bb '
        conditions.append(is_local_uptrend)  # ~3.28 / 92.4% / 69.72%
        dataframe.loc[is_local_uptrend, 'entry_tag'] += 'local_uptrend '
        conditions.append(is_local_dip)  # ~0.76 / 91.1% / 15.54%
        dataframe.loc[is_local_dip, 'entry_tag'] += 'local_dip '
        conditions.append(is_ewo)  # ~0.92 / 92.0% / 43.74%      D
        dataframe.loc[is_ewo, 'entry_tag'] += 'ewo '
        conditions.append(is_ewo_2)  # ~2.86 / 91.5% / 33.31%     D
        dataframe.loc[is_ewo_2, 'entry_tag'] += 'ewo2 '
        conditions.append(is_r_deadfish)  # ~0.99 / 86.9% / 21.93%      D
        dataframe.loc[is_r_deadfish, 'entry_tag'] += 'r_deadfish '
        conditions.append(is_clucHA)  # ~7.2 / 92.5% / 97.98%       D
        dataframe.loc[is_clucHA, 'entry_tag'] += 'clucHA '
        conditions.append(is_cofi)  # ~0.4 / 94.4% / 9.59%        D
        dataframe.loc[is_cofi, 'entry_tag'] += 'cofi '
        conditions.append(is_gumbo)  # ~2.63 / 90.6% / 41.49%      D
        dataframe.loc[is_gumbo, 'entry_tag'] += 'gumbo '
        conditions.append(is_sqzmom)  # ~3.14 / 92.4% / 64.14%      D
        dataframe.loc[is_sqzmom, 'entry_tag'] += 'sqzmom '
        conditions.append(is_nfi_13)  # ~0.4 / 100%                 D
        dataframe.loc[is_nfi_13, 'entry_tag'] += 'nfi_13 '
        conditions.append(is_nfi_32)  # ~0.78 / 92.0 % / 37.41%     D
        dataframe.loc[is_nfi_32, 'entry_tag'] += 'nfi_32 '
        conditions.append(is_nfi_33)  # ~0.11 / 100%                D
        dataframe.loc[is_nfi_33, 'entry_tag'] += 'nfi_33 '
        conditions.append(is_nfi_38)  # ~1.13 / 88.5% / 31.34%      D
        dataframe.loc[is_nfi_38, 'entry_tag'] += 'nfi_38 '
        conditions.append(is_nfix_5)  # ~0.25 / 97.7% / 6.53%       D
        dataframe.loc[is_nfix_5, 'entry_tag'] += 'nfix_5 '
        conditions.append(is_nfix_39)  # ~5.33 / 91.8% / 58.57%      D
        dataframe.loc[is_nfix_39, 'entry_tag'] += 'nfix_39 '
        conditions.append(is_nfix_49)  # ~0.33 / 100% / 0%           D
        dataframe.loc[is_nfix_49, 'entry_tag'] += 'nfix_49 '
        conditions.append(is_nfi7_33)  # ~0.71 / 91.3% / 28.94%      D
        dataframe.loc[is_nfi7_33, 'entry_tag'] += 'nfi7_33 '
        conditions.append(is_nfi7_37)  # ~0.46 / 92.6% / 17.05%      D
        dataframe.loc[is_nfi7_37, 'entry_tag'] += 'nfi7_37 '
        conditions.append(is_nfi_ctt35)  # ~2.32 / 91.1% / 46.27%      D
        dataframe.loc[is_nfi_ctt35, 'entry_tag'] += 'nfi_ctt35 '
        conditions.append(is_nfi_ctt25)  # ~3.28 / 92.4% / 69.72%
        dataframe.loc[is_nfi_ctt25, 'entry_tag'] += 'nfi_ctt25 '
        conditions.append(is_nfi_ctt15)  # ~0.76 / 91.1% / 15.54%
        dataframe.loc[is_nfi_ctt15, 'entry_tag'] += 'nfi_ctt15 '
        conditions.append(is_nfi_10)  # ~0.92 / 92.0% / 43.74%      D
        dataframe.loc[is_nfi_10, 'entry_tag'] += 'nfi_10 '
        conditions.append(is_nfix_53)  # ~7.2 / 92.5% / 97.98%       D
        dataframe.loc[is_nfix_53, 'entry_tag'] += 'nfix_53 '
        conditions.append(is_nfix_52)  # ~0.4 / 94.4% / 9.59%        D
        dataframe.loc[is_nfix_52, 'entry_tag'] += 'nfix_52 '
        conditions.append(is_nfix_51)  # ~2.63 / 90.6% / 41.49%      D
        dataframe.loc[is_nfix_51, 'entry_tag'] += 'nfix_51 '
        conditions.append(is_nfix_48)  # ~3.14 / 92.4% / 64.14%      D
        dataframe.loc[is_nfix_48, 'entry_tag'] += 'nfix_48 '
        conditions.append(is_nfix_47)  # ~0.4 / 100%                 D
        dataframe.loc[is_nfix_47, 'entry_tag'] += 'nfix_47 '
        conditions.append(is_nfix_41)  # ~0.78 / 92.0 % / 37.41%     D
        dataframe.loc[is_nfix_41, 'entry_tag'] += 'nfix_41 '
        conditions.append(is_nfix_38)  # ~0.11 / 100%                D
        dataframe.loc[is_nfix_38, 'entry_tag'] += 'nfix_38 '
        conditions.append(is_nfix_36)  # ~1.13 / 88.5% / 31.34%      D
        dataframe.loc[is_nfix_36, 'entry_tag'] += 'nfix_36 '
        conditions.append(is_nfix_204)  # ~0.25 / 97.7% / 6.53%       D
        dataframe.loc[is_nfix_204, 'entry_tag'] += 'nfix_204 '
        conditions.append(is_nfix_203)  # ~5.33 / 91.8% / 58.57%      D
        dataframe.loc[is_nfix_203, 'entry_tag'] += 'nfix_203 '
        conditions.append(is_nfix_202)  # ~0.33 / 100% / 0%           D
        dataframe.loc[is_nfix_202, 'entry_tag'] += 'nfix_202 '
        conditions.append(is_nfix_201)  # ~0.71 / 91.3% / 28.94%      D
        dataframe.loc[is_nfix_201, 'entry_tag'] += 'nfix_201 '
        conditions.append(is_nfix_34)  # ~0.46 / 92.6% / 17.05%      D
        dataframe.loc[is_nfix_34, 'entry_tag'] += 'nfix_34 '
        conditions.append(is_nfix_28)  # ~0.25 / 97.7% / 6.53%       D
        dataframe.loc[is_nfix_28, 'entry_tag'] += 'nfix_28 '
        conditions.append(is_nfix_27)  # ~5.33 / 91.8% / 58.57%      D
        dataframe.loc[is_nfix_27, 'entry_tag'] += 'nfix_27 '
        conditions.append(is_nfix_19)  # ~0.33 / 100% / 0%           D
        dataframe.loc[is_nfix_19, 'entry_tag'] += 'nfix_19 '
        conditions.append(is_nfix_11)  # ~0.71 / 91.3% / 28.94%      D
        dataframe.loc[is_nfix_11, 'entry_tag'] += 'nfix_11 '
        conditions.append(is_nfix_9)  # ~0.46 / 92.6% / 17.05%      D
        dataframe.loc[is_nfix_9, 'entry_tag'] += 'nfix_9 '
        if conditions:
            dataframe.loc[is_additional_check & reduce(lambda x, y: x | y, conditions), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['volume'] > 0, 'exit'] = 0
        return dataframe

class BB_RPB_TSL_Tranz_TrailingBuy(BB_RPB_TSL_Tranz):
    # Original idea by @MukavaValkku, code by @tirail and @stash86
    #
    # This class is designed to inherit from yours and starts trailing entry with your entry signals
    # Trailing entry starts at any entry signal and will move to next candles if the trailing still active
    # Trailing entry stops  with BUY if : price decreases and rises again more than trailing_entry_offset
    # Trailing entry stops with NO BUY : current price is > initial price * (1 +  trailing_entry_max) OR custom_exit tag
    # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT
    #
    process_only_new_candles = True
    custom_info_trail_entry = dict()
    # Trailing entry parameters
    trailing_entry_order_enabled = True
    trailing_expire_seconds = 1800
    # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, entry the coin
    trailing_entry_uptrend_enabled = False
    trailing_expire_seconds_uptrend = 90
    min_uptrend_trailing_profit = 0.02
    debug_mode = True
    trailing_entry_max_stop = 0.02  # stop trailing entry if current_price > starting_price * (1+trailing_entry_max_stop)
    trailing_entry_max_entry = 0.0  # entry if price between uplimit (=min of serie (current_price * (1 + trailing_entry_offset())) and (start_price * 1+trailing_entry_max_entry))
    init_trailing_dict = {'trailing_entry_order_started': False, 'trailing_entry_order_uplimit': 0, 'start_trailing_price': 0, 'entry_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False}

    def trailing_entry(self, pair, reinit=False):
        # returns trailing entry info for pair (init if necessary)
        if not pair in self.custom_info_trail_entry:
            self.custom_info_trail_entry[pair] = dict()
        if reinit or not 'trailing_entry' in self.custom_info_trail_entry[pair]:
            self.custom_info_trail_entry[pair]['trailing_entry'] = self.init_trailing_dict.copy()
        return self.custom_info_trail_entry[pair]['trailing_entry']

    def trailing_entry_info(self, pair: str, current_price: float):
        # current_time live, dry run
        current_time = datetime.now(timezone.utc)
        if not self.debug_mode:
            return
        trailing_entry = self.trailing_entry(pair)
        duration = 0
        try:
            duration = current_time - trailing_entry['start_trailing_time']
        except TypeError:
            duration = 0
        finally:
            logger.info(f"pair: {pair} : start: {trailing_entry['start_trailing_price']:.4f}, duration: {duration}, current: {current_price:.4f}, uplimit: {trailing_entry['trailing_entry_order_uplimit']:.4f}, profit: {self.current_trailing_profit_ratio(pair, current_price) * 100:.2f}%, offset: {trailing_entry['offset']}")

    def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float:
        trailing_entry = self.trailing_entry(pair)
        if trailing_entry['trailing_entry_order_started']:
            return (trailing_entry['start_trailing_price'] - current_price) / trailing_entry['start_trailing_price']
        else:
            return 0

    def trailing_entry_offset(self, dataframe, pair: str, current_price: float):
        # return rebound limit before a entry in % of initial price, function of current price
        # return None to stop trailing entry (will start again at next entry signal)
        # return 'forceentry' to force immediate entry
        # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no entry, uplimit updated to 99.5), 3price 98 (no entry uplimit updated to 98.5), 4th price 99 -> BUY
        current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price)
        default_offset = 0.005
        trailing_entry = self.trailing_entry(pair)
        if not trailing_entry['trailing_entry_order_started']:
            return default_offset
        # example with duration and indicators
        # dry run, live only
        last_candle = dataframe.iloc[-1]
        current_time = datetime.now(timezone.utc)
        trailing_duration = current_time - trailing_entry['start_trailing_time']
        if trailing_duration.total_seconds() > self.trailing_expire_seconds:
            if current_trailing_profit_ratio > 0 and last_candle['entry'] == 1:
                # more than 1h, price under first signal, entry signal still active -> entry
                return 'forceentry'
            else:
                # wait for next signal
                return None
        elif self.trailing_entry_uptrend_enabled and trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend and (current_trailing_profit_ratio < -1 * self.min_uptrend_trailing_profit):
            # less than 90s and price is rising, entry
            return 'forceentry'
        if current_trailing_profit_ratio < 0:
            # current price is higher than initial price
            return default_offset
        trailing_entry_offset = {0.06: 0.02, 0.03: 0.01, 0: default_offset}
        for key in trailing_entry_offset:
            if current_trailing_profit_ratio > key:
                return trailing_entry_offset[key]
        return default_offset
    # end of trailing entry parameters
    # -----------------------------------------------------

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_indicators(dataframe, metadata)
        self.trailing_entry(metadata['pair'])
        return dataframe

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
        val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs)
        if val:
            if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
                val = False
                dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
                if len(dataframe) >= 1:
                    last_candle = dataframe.iloc[-1].squeeze()
                    current_price = rate
                    trailing_entry = self.trailing_entry(pair)
                    trailing_entry_offset = self.trailing_entry_offset(dataframe, pair, current_price)
                    if trailing_entry['allow_trailing']:
                        if not trailing_entry['trailing_entry_order_started'] and last_candle['entry'] == 1:
                            # start trailing entry
                            # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_started'] = True
                            # self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = last_candle['close']
                            # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_price'] = last_candle['close']
                            # self.custom_info_trail_entry[pair]['trailing_entry']['entry_tag'] = f"initial_entry_tag (strat trail price {last_candle['close']})"
                            # self.custom_info_trail_entry[pair]['trailing_entry']['start_trailing_time'] = datetime.now(timezone.utc)
                            # self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = 0
                            trailing_entry['trailing_entry_order_started'] = True
                            trailing_entry['trailing_entry_order_uplimit'] = last_candle['close']
                            trailing_entry['start_trailing_price'] = last_candle['close']
                            trailing_entry['entry_tag'] = last_candle['entry_tag']
                            trailing_entry['start_trailing_time'] = datetime.now(timezone.utc)
                            trailing_entry['offset'] = 0
                            self.trailing_entry_info(pair, current_price)
                            logger.info(f"start trailing entry for {pair} at {last_candle['close']}")
                        elif trailing_entry['trailing_entry_order_started']:
                            if trailing_entry_offset == 'forceentry':
                                # entry in custom conditions
                                val = True
                                ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full')
                            elif trailing_entry_offset is None:
                                # stop trailing entry custom conditions
                                self.trailing_entry(pair, reinit=True)
                                logger.info(f'STOP trailing entry for {pair} because "trailing entry offset" returned None')
                            elif current_price < trailing_entry['trailing_entry_order_uplimit']:
                                # update uplimit
                                old_uplimit = trailing_entry['trailing_entry_order_uplimit']
                                self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'] = min(current_price * (1 + trailing_entry_offset), self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit'])
                                self.custom_info_trail_entry[pair]['trailing_entry']['offset'] = trailing_entry_offset
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f"update trailing entry for {pair} at {old_uplimit} -> {self.custom_info_trail_entry[pair]['trailing_entry']['trailing_entry_order_uplimit']}")
                            elif current_price < trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry):
                                # entry ! current price > uplimit && lower thant starting price
                                val = True
                                ratio = '%.2f' % (self.current_trailing_profit_ratio(pair, current_price) * 100)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f"current price ({current_price}) > uplimit ({trailing_entry['trailing_entry_order_uplimit']}) and lower than starting price price ({trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_entry)}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full")
                            elif current_price > trailing_entry['start_trailing_price'] * (1 + self.trailing_entry_max_stop):
                                # stop trailing entry because price is too high
                                self.trailing_entry(pair, reinit=True)
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'STOP trailing entry for {pair} because of the price is higher than starting price * {1 + self.trailing_entry_max_stop}')
                            else:
                                # uplimit > current_price > max_price, continue trailing and wait for the price to go down
                                self.trailing_entry_info(pair, current_price)
                                logger.info(f'price too high for {pair} !')
                    else:
                        logger.info(f'Wait for next entry signal for {pair}')
                if val == True:
                    self.trailing_entry_info(pair, rate)
                    self.trailing_entry(pair, reinit=True)
                    logger.info(f'STOP trailing entry for {pair} because I entry it')
        return val

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = super().populate_entry_trend(dataframe, metadata)
        if self.trailing_entry_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
            last_candle = dataframe.iloc[-1].squeeze()
            trailing_entry = self.trailing_entry(metadata['pair'])
            if last_candle['entry'] == 1:
                if not trailing_entry['trailing_entry_order_started']:
                    open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True)]).all()
                    if not open_trades:
                        logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!")
                        # self.custom_info_trail_entry[metadata['pair']]['trailing_entry']['allow_trailing'] = True
                        trailing_entry['allow_trailing'] = True
                        initial_entry_tag = last_candle['entry_tag'] if 'entry_tag' in last_candle else 'entry signal'
                        dataframe.loc[:, 'entry_tag'] = f"{initial_entry_tag} (start trail price {last_candle['close']})"
            elif trailing_entry['trailing_entry_order_started'] == True:
                logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger entry signal!!")
                dataframe.loc[:, 'entry'] = 1
                dataframe.loc[:, 'entry_tag'] = trailing_entry['entry_tag']
        # dataframe['entry'] = 1
        return dataframe
# Elliot Wave Oscillator

def ewo(dataframe, sma1_length=5, sma2_length=35):
    sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
    sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / dataframe['close'] * 100
    return smadif
# Exponential moving average of a volume weighted simple moving average

def ema_vwma_osc(dataframe, len_slow_ma):
    slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma))
    return (slow_ema - slow_ema.shift(1)) / slow_ema.shift(1) * 100

def pivot_points(dataframe: DataFrame, mode='fibonacci') -> Series:
    hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3
    hl_range = (dataframe['high'] - dataframe['low']).shift(1)
    if mode == 'simple':
        res1 = hlc3_pivot * 2 - dataframe['low'].shift(1)
        sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1)
        res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift()
        sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift()
        res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift()
        sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift()
    elif mode == 'fibonacci':
        res1 = hlc3_pivot + 0.382 * hl_range
        sup1 = hlc3_pivot - 0.382 * hl_range
        res2 = hlc3_pivot + 0.618 * hl_range
        sup2 = hlc3_pivot - 0.618 * hl_range
        res3 = hlc3_pivot + 1 * hl_range
        sup3 = hlc3_pivot - 1 * hl_range
    return (hlc3_pivot, res1, res2, res3, sup1, sup2, sup3)

def heikin_ashi(dataframe, smooth_inputs=False, smooth_outputs=False, length=10):
    df = dataframe[['open', 'close', 'high', 'low']].copy().fillna(0)
    if smooth_inputs:
        df['open_s'] = ta.EMA(df['open'], timeframe=length)
        df['high_s'] = ta.EMA(df['high'], timeframe=length)
        df['low_s'] = ta.EMA(df['low'], timeframe=length)
        df['close_s'] = ta.EMA(df['close'], timeframe=length)
        open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2
        high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1)
        low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1)
        close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s']) / 4
    else:
        open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2
        high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1)
        low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1)
        close_ha = (df['open'] + df['high'] + df['low'] + df['close']) / 4
    open_ha = open_ha.fillna(0)
    high_ha = high_ha.fillna(0)
    low_ha = low_ha.fillna(0)
    close_ha = close_ha.fillna(0)
    if smooth_outputs:
        open_sha = ta.EMA(open_ha, timeframe=length)
        high_sha = ta.EMA(high_ha, timeframe=length)
        low_sha = ta.EMA(low_ha, timeframe=length)
        close_sha = ta.EMA(close_ha, timeframe=length)
        return (open_sha, close_sha, low_sha)
    else:
        return (open_ha, close_ha, low_ha)
# 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), 0.0)
    basic_lb = df['basic_lb'].values
    final_lb = np.full(len(df), 0.0)
    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), 0.0)
    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 0.0
    pm = Series(pm_arr)
    # Mark the trend direction up/down
    pmx = np.where(pm_arr > 0.0, 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)
    entry = qtpylib.crossed_below(mom, lowerband)
    exit = 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_entry': entry, 'momdiv_exit': exit, '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']