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true_lambo_2

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freqle/uploads/user-90abf0da8f3178f3/true_lambo_2_converted.py · first seen 2026-07-28

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.99 has minimal roi custom stoploss process only new candles startup candle count: 400 hyperopt hyperopt params: 72
Indicators ADX Bollinger_Bands CCI EMA HMA Heikin_Ashi MFI RSI SMA Stochastic Williams_R pandas_ta talib technical
Methods EWO T3 VWAPB custom_exit custom_stoploss ha_typical_price top_percent_change williams_r
9 related strategies (⧉ identical code, ≈ similar name)

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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
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 functools import reduce
from technical.indicators import RMI, zema

def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0
    return Series(index=bars.index, data=res)

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 VWAPB(dataframe, window_size=20, num_of_std=1):
    df = dataframe.copy()
    df['vwap'] = qtpylib.rolling_vwap(df, window=window_size)
    rolling_std = df['vwap'].rolling(window=window_size).std()
    df['vwap_low'] = df['vwap'] - rolling_std * num_of_std
    df['vwap_high'] = df['vwap'] + rolling_std * num_of_std
    return (df['vwap_low'], df['vwap'], df['vwap_high'])

def top_percent_change(dataframe: DataFrame, length: int) -> float:
    """
        Percentage change of the current close from the range maximum Open price
        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
    if length == 0:
        return (dataframe['open'] - dataframe['close']) / dataframe['close']
    else:
        return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']

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

class true_lambo_2(IStrategy):
    INTERFACE_VERSION = 3
    '\n    @ jilv220\n\n    Based on BB_RPB_3c.\n\n    The discovery is that the pump protection is only needed for EWO level 2 (4 ~ 8)\n    And the dump protection is only needed for EWO level 5 (< -8)\n\n    Higher parameter delta is needed for EWO level 4 (-4 ~ -8)\n\n    Vwap and Cluc can handle rest of the market pretty well\n\n    '
    buy_params = {'buy_pump_2_factor': 1.125, 'buy_crash_4_tpct_0': 0.141, 'buy_crash_4_tpct_3': 0.031, 'buy_crash_4_tpct_9': 0.091, 'buy_adx': 20, 'buy_fastd': 20, 'buy_fastk': 22, 'buy_ema_cofi': 0.98, 'buy_ewo_high': 4.179, 'buy_gumbo_ema': 1.121, 'buy_gumbo_ewo_low': -9.442, 'buy_gumbo_cti': -0.374, 'buy_gumbo_r14': -51.971, 'buy_gumbo_tpct_0': 0.067, 'buy_gumbo_tpct_3': 0.0, 'buy_gumbo_tpct_9': 0.016, 'buy_clucha_bbdelta_close': 0.03734, 'buy_clucha_bbdelta_close_2': 0.0391, 'buy_clucha_bbdelta_close_3': 0.03943, 'buy_clucha_bbdelta_close_4': 0.04202, 'buy_clucha_bbdelta_tail': 0.78624, 'buy_clucha_bbdelta_tail_2': 1.05718, 'buy_clucha_bbdelta_tail_3': 0.77701, 'buy_clucha_bbdelta_tail_4': 0.418, 'buy_clucha_closedelta_close': 0.01382, 'buy_clucha_closedelta_close_2': 0.01092, 'buy_clucha_closedelta_close_3': 0.00935, 'buy_clucha_closedelta_close_4': 0.01234, 'buy_clucha_rocr_1h': 0.70818, 'buy_clucha_rocr_1h_2': 0.15848, 'buy_clucha_rocr_1h_3': 0.88989, 'buy_clucha_rocr_1h_4': 0.99234, 'buy_vwap_closedelta': 19.889, 'buy_vwap_closedelta_2': 20.099, 'buy_vwap_closedelta_3': 27.526, 'buy_vwap_closedelta_4': 22.26, 'buy_vwap_closedelta_5': 23.227, 'buy_vwap_cti': -0.258, 'buy_vwap_cti_2': -0.748, 'buy_vwap_cti_3': -0.227, 'buy_vwap_cti_4': -0.763, 'buy_vwap_cti_5': -0.021, 'buy_vwap_width': 0.266, 'buy_vwap_width_2': 3.212, 'buy_vwap_width_3': 0.47, 'buy_vwap_width_4': 9.76, 'buy_vwap_width_5': 5.374, 'buy_lambo2_ema': 0.986, 'buy_lambo2_rsi14': 32, 'buy_lambo2_rsi4': 30, 'buy_V_bb_width': 0.03, 'buy_V_bb_width_2': 0.08, 'buy_V_bb_width_3': 0.097, 'buy_V_bb_width_4': 0.054, 'buy_V_bb_width_5': 0.063, 'buy_V_cti': -0.872, 'buy_V_cti_2': -0.842, 'buy_V_cti_3': -0.888, 'buy_V_cti_4': -0.57, 'buy_V_cti_5': -0.086, 'buy_V_mfi': 35.342, 'buy_V_mfi_2': 24.775, 'buy_V_mfi_3': 17.053, 'buy_V_mfi_4': 13.907, 'buy_V_mfi_5': 38.158, 'buy_V_r14': -67.209, 'buy_V_r14_2': -6.723, 'buy_V_r14_3': -68.059, 'buy_V_r14_4': -88.943, 'buy_V_r14_5': -41.493}
    sell_params = {'base_nb_candles_sell': 23, 'high_offset': 0.87, 'high_offset_2': 1.166, 'high_offset': 0.963, 'high_offset_2': 1.391, 'pHSL': -0.083, 'pPF_1': 0.011, 'pPF_2': 0.048, 'pSL_1': 0.02, 'pSL_2': 0.041}
    minimal_roi = {'0': 0.092, '29': 0.042, '85': 0.028, '128': 0.005}
    timeframe = '5m'
    inf_1h = '1h'
    stoploss = -0.99
    process_only_new_candles = True
    use_custom_stoploss = True
    use_exit_signal = True
    startup_candle_count: int = 400
    is_optimize_cofi = False
    buy_ema_cofi = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_cofi)
    buy_fastk = IntParameter(20, 45, default=20, optimize=is_optimize_cofi)
    buy_fastd = IntParameter(20, 45, default=20, optimize=is_optimize_cofi)
    buy_adx = IntParameter(20, 45, default=30, optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(-12, 12, default=3.553, optimize=is_optimize_cofi)
    is_optimize_clucha = False
    buy_clucha_bbdelta_close = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha)
    buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha)
    buy_clucha_closedelta_close = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha)
    buy_clucha_rocr_1h = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha)
    is_optimize_clucha_2 = False
    buy_clucha_bbdelta_close_2 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha_2)
    buy_clucha_bbdelta_tail_2 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_2)
    buy_clucha_closedelta_close_2 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_2)
    buy_clucha_rocr_1h_2 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_2)
    is_optimize_clucha_3 = False
    buy_clucha_bbdelta_close_3 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha_3)
    buy_clucha_bbdelta_tail_3 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_3)
    buy_clucha_closedelta_close_3 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_3)
    buy_clucha_rocr_1h_3 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_3)
    is_optimize_clucha_4 = True
    buy_clucha_bbdelta_close_4 = DecimalParameter(0.0005, 0.06, default=0.034, decimals=5, optimize=is_optimize_clucha_4)
    buy_clucha_bbdelta_tail_4 = DecimalParameter(0.35, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_4)
    buy_clucha_closedelta_close_4 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_4)
    buy_clucha_rocr_1h_4 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_4)
    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)
    buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_gumbo)
    buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_gumbo)
    is_optimize_gumbo_protection = False
    buy_gumbo_tpct_0 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection)
    buy_gumbo_tpct_3 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection)
    buy_gumbo_tpct_9 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection)
    is_optimize_vwap = False
    buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap)
    buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap)
    buy_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap)
    is_optimize_vwap_2 = False
    buy_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_2)
    buy_vwap_closedelta_2 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_2)
    buy_vwap_cti_2 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_2)
    is_optimize_vwap_3 = False
    buy_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_3)
    buy_vwap_closedelta_3 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_3)
    buy_vwap_cti_3 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_3)
    is_optimize_vwap_4 = True
    buy_vwap_width_4 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_4)
    buy_vwap_closedelta_4 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_4)
    buy_vwap_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_4)
    is_optimize_lambo_2 = False
    buy_lambo2_ema = DecimalParameter(0.85, 1.15, default=0.942, optimize=is_optimize_lambo_2)
    buy_lambo2_rsi4 = IntParameter(15, 45, default=45, optimize=is_optimize_lambo_2)
    buy_lambo2_rsi14 = IntParameter(15, 45, default=45, optimize=is_optimize_lambo_2)
    is_optimize_V = False
    buy_V_bb_width = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V)
    buy_V_cti = DecimalParameter(-0.95, -0.5, default=-0.6, optimize=is_optimize_V)
    buy_V_r14 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V)
    buy_V_mfi = DecimalParameter(10, 40, default=30, optimize=is_optimize_V)
    is_optimize_V_2 = False
    buy_V_bb_width_2 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_2)
    buy_V_cti_2 = DecimalParameter(-0.95, -0.5, default=-0.6, optimize=is_optimize_V_2)
    buy_V_r14_2 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_2)
    buy_V_mfi_2 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_2)
    is_optimize_V_4 = False
    buy_V_bb_width_4 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_4)
    buy_V_cti_4 = DecimalParameter(-0.95, -0.0, default=-0.6, optimize=is_optimize_V_4)
    buy_V_r14_4 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_4)
    buy_V_mfi_4 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_4)
    is_optimize_V_5 = False
    buy_V_bb_width_5 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_5)
    buy_V_cti_5 = DecimalParameter(-0.95, -0.0, default=-0.6, optimize=is_optimize_V_5)
    buy_V_r14_5 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_5)
    buy_V_mfi_5 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_5)
    buy_V_diff_5 = DecimalParameter(0.1, 0.2, default=0.1, optimize=is_optimize_V_5)
    is_optimize_pump_2 = False
    buy_pump_2_factor = DecimalParameter(1.0, 1.2, default=1.1, optimize=is_optimize_pump_2)
    is_optimize_crash_4 = False
    buy_crash_4_tpct_0 = DecimalParameter(0.02, 0.04, default=0.02, decimals=3, optimize=is_optimize_crash_4)
    buy_crash_4_tpct_3 = DecimalParameter(0.05, 0.1, default=0.05, decimals=2, optimize=is_optimize_crash_4)
    buy_crash_4_tpct_9 = DecimalParameter(0.13, 0.32, default=0.13, decimals=2, optimize=is_optimize_crash_4)
    base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    high_offset = DecimalParameter(0.85, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(0.85, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
    pHSL = DecimalParameter(-0.1, -0.04, default=-0.08, decimals=3, space='sell', load=True, optimize=True)
    pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='sell', load=True, optimize=True)
    pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='sell', load=True, optimize=True)
    pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='sell', load=True, optimize=True)
    pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='sell', load=True, optimize=True)

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

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        HSL = self.pHSL.value
        PF_1 = self.pPF_1.value
        SL_1 = self.pSL_1.value
        PF_2 = self.pPF_2.value
        SL_2 = self.pSL_2.value
        if current_profit > PF_2:
            sl_profit = SL_2 + (current_profit - PF_2)
        elif current_profit > PF_1:
            sl_profit = SL_1 + (current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)
        else:
            sl_profit = HSL
        if sl_profit >= current_profit:
            return -0.99
        return stoploss_from_open(sl_profit, current_profit)

    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]
        max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate
        max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate
        enter_tag = 'empty'
        if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
            enter_tag = trade.buy_tag
        buy_tags = buy_tag.split()
        if last_candle['ema_vwap_diff_50'] > 0.02:
            if current_profit >= 0.005:
                if last_candle['close'] > last_candle['vwap_middleband'] * 0.99 and last_candle['rsi'] > 41:
                    return f'sell_profit_vwap( {enter_tag})'
        return None

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        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']
        dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['rsi_6'] = ta.RSI(dataframe, timeperiod=6)
        dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
        dataframe['EWO'] = EWO(dataframe, 50, 200)
        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)
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']
        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']
        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)
        dataframe['r_14'] = williams_r(dataframe, period=14)
        dataframe['T3'] = T3(dataframe)
        vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1)
        dataframe['vwap_upperband'] = vwap_high
        dataframe['vwap_middleband'] = vwap
        dataframe['vwap_lowerband'] = vwap_low
        dataframe['vwap_width'] = (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] * 100
        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['ema_vwap_diff_50'] = (dataframe['ema_50'] - dataframe['vwap_lowerband']) / dataframe['ema_50']
        dataframe['tpct_0'] = top_percent_change(dataframe, 0)
        dataframe['tpct_3'] = top_percent_change(dataframe, 3)
        dataframe['tpct_9'] = top_percent_change(dataframe, 9)
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        inf_tf = '1h'
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        inf_heikinashi = qtpylib.heikinashi(informative)
        informative['ha_close'] = inf_heikinashi['close']
        informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
        informative['bb_lowerband2'] = bollinger2['lower']
        informative['bb_middleband2'] = bollinger2['mid']
        informative['bb_upperband2'] = bollinger2['upper']
        informative['bb_width'] = (informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2']
        informative['T3'] = T3(informative)
        informative['rsi'] = ta.RSI(informative, timeperiod=14)
        informative['ema_200'] = ta.EMA(informative, timeperiod=200)
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        is_pump_2 = dataframe['close'].rolling(48).max() >= dataframe['close'] * self.buy_pump_2_factor.value
        is_crash_4 = (dataframe['tpct_0'] < self.buy_crash_4_tpct_0.value) & (dataframe['tpct_3'] < self.buy_crash_4_tpct_3.value) & (dataframe['tpct_9'] < self.buy_crash_4_tpct_9.value)
        is_cofi = (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value)  # Modified from gumbo1, creadit goes to original author @raph92
        is_gumbo = (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) & (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) & (dataframe['tpct_0'] < self.buy_gumbo_tpct_0.value) & (dataframe['tpct_3'] < self.buy_gumbo_tpct_3.value) & (dataframe['tpct_9'] < self.buy_gumbo_tpct_9.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())) & (dataframe['EWO'] > 8)
        is_clucHA_2 = (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h_2.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_2.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_2.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail_2.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2
        is_clucHA_3 = (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h_3.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_3.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_3.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail_3.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5)
        is_clucHA_4 = (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h_4.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_4.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_4.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail_4.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] < -4) & (dataframe['EWO'] > -8)
        is_vwap = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta.value / 1000) & (dataframe['cti'] < self.buy_vwap_cti.value) & (dataframe['EWO'] > 8)
        is_vwap_2 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_2.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_2.value / 1000) & (dataframe['cti'] < self.buy_vwap_cti_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2
        is_vwap_3 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_3.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_3.value / 1000) & (dataframe['cti'] < self.buy_vwap_cti_3.value) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5)
        is_vwap_4 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_4.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_4.value / 1000) & (dataframe['cti'] < self.buy_vwap_cti_4.value) & (dataframe['EWO'] < -4) & (dataframe['EWO'] > -8)
        is_lambo_2 = (dataframe['close'] < dataframe['ema_14'] * self.buy_lambo2_ema.value) & (dataframe['rsi_fast'] < self.buy_lambo2_rsi4.value) & (dataframe['rsi'] < self.buy_lambo2_rsi14.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2
        is_V = (dataframe['bb_width'] > self.buy_V_bb_width.value) & (dataframe['cti'] < self.buy_V_cti.value) & (dataframe['r_14'] < self.buy_V_r14.value) & (dataframe['mfi'] < self.buy_V_mfi.value) & (dataframe['EWO'] > 8)
        is_V_2 = (dataframe['bb_width'] > self.buy_V_bb_width_2.value) & (dataframe['cti'] < self.buy_V_cti_2.value) & (dataframe['r_14'] < self.buy_V_r14_2.value) & (dataframe['mfi'] < self.buy_V_mfi_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2
        is_V_5 = (dataframe['bb_width'] > self.buy_V_bb_width_5.value) & (dataframe['cti'] < self.buy_V_cti_5.value) & (dataframe['r_14'] < self.buy_V_r14_5.value) & (dataframe['mfi'] < self.buy_V_mfi_5.value) & (dataframe['ema_vwap_diff_50'] > 0.215) & (dataframe['EWO'] < -10)
        is_additional_check = (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0)
        conditions.append(is_cofi)  # ~3.21 90.8%
        dataframe.loc[is_cofi, 'enter_tag'] += 'cofi '
        conditions.append(is_vwap)  # ~67.3%
        dataframe.loc[is_vwap, 'enter_tag'] += 'vwap '
        conditions.append(is_V)  # ~67.9%
        dataframe.loc[is_V, 'enter_tag'] += 'V '
        conditions.append(is_lambo_2)  # ~67.7%
        dataframe.loc[is_lambo_2, 'enter_tag'] += 'lambo_2 '
        conditions.append(is_clucHA_2)  # ~68.2%
        dataframe.loc[is_clucHA_2, 'enter_tag'] += 'cluc_2 '
        conditions.append(is_vwap_2)  # ~67.3%
        dataframe.loc[is_vwap_2, 'enter_tag'] += 'vwap_2 '
        conditions.append(is_V_2)  # ~67.9%
        dataframe.loc[is_V_2, 'enter_tag'] += 'V_2 '
        conditions.append(is_clucHA_3)  # ~68.2%
        dataframe.loc[is_clucHA_3, 'enter_tag'] += 'cluc_3 '
        conditions.append(is_vwap_3)  # ~67.3%
        dataframe.loc[is_vwap_3, 'enter_tag'] += 'vwap_3 '
        conditions.append(is_clucHA_4)  # ~68.2%
        dataframe.loc[is_clucHA_4, 'enter_tag'] += 'cluc_4 '
        conditions.append(is_vwap_4)  # ~67.3%
        dataframe.loc[is_vwap_4, 'enter_tag'] += 'vwap_4 '
        conditions.append(is_gumbo)  # ~2.63 / 90.6% / 41.49%      F   (263 %)
        dataframe.loc[is_gumbo, 'enter_tag'] += 'gumbo '
        conditions.append(is_V_5)  # ~67.9%
        dataframe.loc[is_V_5, 'enter_tag'] += 'V_5 '
        if conditions:
            dataframe.loc[is_additional_check & reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['sma_9'] > dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1) * 0.005) & (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe

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']