# source: https://raw.githubusercontent.com/ssssi/freqtrade_strs/992dfac1b2ffac6590e43b31bf195e801f5e8d24/binance/BBMod.py
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
import pandas as pd

from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from datetime import datetime
from freqtrade.strategy import DecimalParameter, IntParameter, informative, CategoricalParameter
from functools import reduce
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)


# custom indicators
# ##################################################################################################
def ha_typical_price(bars):
    res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
    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 vwap_b(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']


# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:

    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


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']


def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
    df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
    df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)

    df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]


def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df['ATR'] = ta.ATR(df, timeperiod=14)
    df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
    df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
    df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
    df['hlv'] = df['hlv'].ffill()
    df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
    df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])

    return df['sslDown'], df['sslUp']


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 linear_decay(start: float, end: float, start_time: int, end_time: int, trade_time: int) -> float:
    """
    Simple linear decay function. Decays from start to end after end_time minutes (starts after start_time minutes)
    """
    time = max(0, trade_time - start_time)
    rate = (start - end) / (end_time - start_time)

    return max(end, start - (rate * time))


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


class github_ssssi_freqtrade_strs__BBMod__20220708_090734(IStrategy):

    minimal_roi = {
        "0": 100
    }

    # Optimal timeframe for the strategy
    timeframe = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    startup_candle_count = 120

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,

        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99
    }

    # Disabled
    stoploss = -0.99

    # Custom stoploss
    use_custom_stoploss = True

    custom_trade_info = {}

    # Buy params
    leverage_optimize = True
    leverage_num = IntParameter(low=1, high=3, default=1, space='buy', optimize=leverage_optimize)

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

    is_optimize_break = True
    buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, space='buy', optimize=is_optimize_break)
    buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, space='buy', optimize=is_optimize_break)
    break_closedelta = DecimalParameter(12.0, 18.0, default=15.0, space='buy', optimize=is_optimize_break)
    break_buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, space='buy', optimize=is_optimize_break)

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

    is_optimize_local_uptrend2 = True
    buy_bb_factor2 = DecimalParameter(0.990, 0.999, default=0.995, space='buy', optimize=is_optimize_local_uptrend2)

    is_optimize_local_dip = True
    buy_ema_diff_local_dip = DecimalParameter(0.022, 0.027, default=0.025, space='buy', optimize=is_optimize_local_dip)
    buy_ema_high_local_dip = DecimalParameter(0.90, 1.2, default=0.942, space='buy', optimize=is_optimize_local_dip)
    buy_closedelta_local_dip = DecimalParameter(12.0, 18.0, default=15.0, space='buy', optimize=is_optimize_local_dip)
    buy_rsi_local_dip = IntParameter(15, 45, default=28, space='buy', optimize=is_optimize_local_dip)
    buy_crsi_local_dip = IntParameter(10, 18, default=10, space='buy', optimize=is_optimize_local_dip)

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

    is_optimize_nfix_39 = True
    buy_nfix_39_ema = DecimalParameter(0.9, 1.2, default=0.97, space='buy', optimize=is_optimize_nfix_39)

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

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

    is_optimize_r_deadfish = True
    buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, space='buy', optimize=is_optimize_r_deadfish)
    buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, space='buy', optimize=is_optimize_r_deadfish)

    is_optimize_cofi = True
    buy_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97, space='buy', optimize=is_optimize_cofi)
    buy_fastk = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi)
    buy_fastd = IntParameter(0, 40, default=20, space='buy', optimize=is_optimize_cofi)
    buy_adx = IntParameter(0, 30, default=30, space='buy', optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, space='buy', optimize=is_optimize_cofi)
    buy_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5, space='buy', optimize=is_optimize_cofi)
    buy_cofi_r14 = DecimalParameter(-100, -44, default=-60, space='buy', optimize=is_optimize_cofi)

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

    is_optimize_gumbo = True
    buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, space='buy', optimize=is_optimize_gumbo)
    buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, space='buy', optimize=is_optimize_gumbo)
    buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, space='buy', optimize=is_optimize_gumbo)
    buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60, space='buy', optimize=is_optimize_gumbo)

    # Custom Stoploss
    cs_op = True
    cstop_loss_threshold = DecimalParameter(-0.15, -0.01, default=-0.03, space='sell', load=True, optimize=cs_op)
    cstop_bail_how = CategoricalParameter(['roc', 'time', 'any', 'none'], default='none', space='sell', load=True,
                                          optimize=cs_op)
    cstop_bail_roc = DecimalParameter(-5.0, -1.0, default=-3.0, space='sell', load=True, optimize=cs_op)
    cstop_bail_time = IntParameter(60, 1440, default=720, space='sell', load=True, optimize=cs_op)
    cstop_bail_time_trend = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=cs_op)
    cstop_max_stoploss = DecimalParameter(-0.30, -0.01, default=-0.10, space='sell', load=True, optimize=cs_op)

    # custom exit
    ce_op = True
    csell_pullback_amount = DecimalParameter(0.005, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True,
                                          optimize=ce_op)
    csell_roi_start = DecimalParameter(0.01, 0.15, default=0.01, space='sell', load=True, optimize=ce_op)
    csell_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=ce_op)
    csell_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=ce_op)
    csell_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell',
                                            load=True, optimize=ce_op)
    csell_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=ce_op)
    csell_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)
    csell_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True,
                                                      optimize=ce_op)

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

    @informative('1h')
    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_close'] = heikinashi['close']
        dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)

        # # 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']

        dataframe['T3'] = T3(dataframe)

        return dataframe

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

        if not metadata['pair'] in self.custom_trade_info:
            self.custom_trade_info[metadata['pair']] = {}
            if 'had-trend' not in self.custom_trade_info[metadata["pair"]]:
                self.custom_trade_info[metadata['pair']]['had-trend'] = False

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

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

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

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

        # SMA
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28)

        # CTI
        dataframe['cti'] = pta.cti(dataframe["close"], length=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_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_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)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)

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

        # 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)

        # vmap indicators
        vwap_low, vwap, vwap_high = vwap_b(dataframe, 20, 1)
        dataframe['vwap_low'] = vwap_low
        dataframe['tcp_percent_4'] = top_percent_change(dataframe, 4)

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

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

        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe['srsi_fk'] = stoch['fastk']

        # 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']

        # 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)

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

        dataframe['r_14'] = williams_r(dataframe, period=14)

        # 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)

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

        # custom exit
        dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
        dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
        dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0)

        # Indicators used only for ROI and Custom Stoploss
        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down')

        dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21)

        # Trends, Peaks and Crosses
        dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0)
        dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0)

        return dataframe

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

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

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

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

        is_sqzmom = (
                (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) &
                (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1']) &
                (dataframe['linreg_val_20'].shift(2) > dataframe['linreg_val_20'].shift(1)) &
                (dataframe['linreg_val_20'].shift(1) < dataframe['linreg_val_20']) &
                (dataframe['linreg_val_20'] < 0) &
                (dataframe['close'] < dataframe['ema_13'] * self.buy_sqzmom_ema.value) &
                (dataframe['EWO'] < self.buy_sqzmom_ewo.value) &
                (dataframe['r_14'] < self.buy_sqzmom_r14.value)
        )

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

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

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

        is_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) &
                (dataframe['cti'] < self.buy_cofi_cti.value) &
                (dataframe['r_14'] < self.buy_cofi_r14.value)
        )

        is_gumbo = (
                (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) &
                (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) &
                (dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) &
                (dataframe['cti'] < self.buy_gumbo_cti.value) &
                (dataframe['r_14'] < self.buy_gumbo_r14.value)
        )

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

        is_local_uptrend2 = (  # use origin bb_rpb_tsl value
                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * 0.026) &
                (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
                (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor2.value) &
                (dataframe['closedelta'] > dataframe['close'] * 17.922 / 1000)
        )

        is_local_dip = (
                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff_local_dip.value) &
                (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
                (dataframe['close'] < dataframe['ema_20'] * self.buy_ema_high_local_dip.value) &
                (dataframe['rsi'] < self.buy_rsi_local_dip.value) &
                (dataframe['crsi'] > self.buy_crsi_local_dip.value) &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta_local_dip.value / 1000)
        )

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

        is_nfi_32 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
                (dataframe['rsi_fast'] < 46) &
                (dataframe['rsi'] > 19) &
                (dataframe['close'] < dataframe['sma_15'] * 0.942) &
                (dataframe['cti'] < -0.86)
        )

        is_nfix_39 = (
                (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.buy_nfix_39_ema.value)
        )

        is_vwap = (
                (dataframe['close'] < dataframe['vwap_low']) &
                (dataframe['tcp_percent_4'] > 0.04) &
                (dataframe['cti'] < -0.8) &
                (dataframe['rsi'] < 35) &
                (dataframe['rsi_84'] < 60) &
                (dataframe['rsi_112'] < 60) &
                (dataframe['volume'] > 0)
        )

        is_bb_checked = is_dip & is_break

        conditions.append(is_bb_checked)
        dataframe.loc[is_bb_checked, 'enter_tag'] += 'bb '

        conditions.append(is_sqzmom)
        dataframe.loc[is_sqzmom, 'enter_tag'] += 'sqzmom '

        conditions.append(is_ewo_2)
        dataframe.loc[is_ewo_2, 'enter_tag'] += 'ewo2 '

        conditions.append(is_r_deadfish)
        dataframe.loc[is_r_deadfish, 'enter_tag'] += 'r_deadfish '

        conditions.append(is_clucHA)
        dataframe.loc[is_clucHA, 'enter_tag'] += 'clucHA '

        conditions.append(is_cofi)
        dataframe.loc[is_cofi, 'enter_tag'] += 'cofi '

        conditions.append(is_gumbo)
        dataframe.loc[is_gumbo, 'enter_tag'] += 'gumbo '

        conditions.append(is_local_uptrend)
        dataframe.loc[is_local_uptrend, 'enter_tag'] += 'local_uptrend '

        conditions.append(is_local_dip)
        dataframe.loc[is_local_dip, 'enter_tag'] += 'local_dip '

        conditions.append(is_ewo)
        dataframe.loc[is_ewo, 'enter_tag'] += 'ewo '

        conditions.append(is_nfi_32)
        dataframe.loc[is_nfi_32, 'enter_tag'] += 'nfi_32 '

        conditions.append(is_nfix_39)
        dataframe.loc[is_nfix_39, 'enter_tag'] += 'nfix_39 '

        conditions.append(is_vwap)
        dataframe.loc[is_vwap, 'enter_tag'] += 'vwap '

        conditions.append(is_local_uptrend2)
        dataframe.loc[is_local_uptrend2, 'enter_tag'] += 'local_uptrend2 '

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'enter_long'] = 1

        return dataframe

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

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:

        return self.leverage_num.value

    """
    Custom Stoploss
    """

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

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        in_trend = self.custom_trade_info[trade.pair]['had-trend']

        if current_profit < self.cstop_max_stoploss.value:
            return 0.01

        # Determine how we sell when we are in a loss
        if current_profit < self.cstop_loss_threshold.value:
            if self.cstop_bail_how.value == 'roc' or self.cstop_bail_how.value == 'any':
                # Dynamic bailout based on rate of change
                if last_candle['sroc'] <= self.cstop_bail_roc.value:
                    return 0.01
            if self.cstop_bail_how.value == 'time' or self.cstop_bail_how.value == 'any':
                # Dynamic bailout based on time, unless time_trend is true and there is a potential reversal
                if trade_dur > self.cstop_bail_time.value:
                    if self.cstop_bail_time_trend.value and in_trend:
                        return 1
                    else:
                        return 0.01
        return 1

    """
    Custom Sell
    """

    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=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        max_profit = max(0, trade.calc_profit_ratio(trade.max_rate))
        pullback_value = max(0, (max_profit - self.csell_pullback_amount.value))
        in_trend = False

        # Determine our current ROI point based on the defined type
        if self.csell_roi_type.value == 'static':
            min_roi = self.csell_roi_start.value
        elif self.csell_roi_type.value == 'decay':
            min_roi = linear_decay(self.csell_roi_start.value, self.csell_roi_end.value, 0, self.csell_roi_time.value,
                                   trade_dur)
        elif self.csell_roi_type.value == 'step':
            if trade_dur < self.csell_roi_time.value:
                min_roi = self.csell_roi_start.value
            else:
                min_roi = self.csell_roi_end.value

        # Determine if there is a trend
        if self.csell_trend_type.value == 'rmi' or self.csell_trend_type.value == 'any':
            if last_candle['rmi-up-trend'] == 1:
                in_trend = True
        if self.csell_trend_type.value == 'ssl' or self.csell_trend_type.value == 'any':
            if last_candle['ssl-dir'] == 'up':
                in_trend = True
        if self.csell_trend_type.value == 'candle' or self.csell_trend_type.value == 'any':
            if last_candle['candle-up-trend'] == 1:
                in_trend = True

        # Don't sell if we are in a trend unless the pullback threshold is met
        if in_trend and current_profit > 0:
            # Record that we were in a trend for this trade/pair for a more useful sell message later
            self.custom_trade_info[trade.pair]['had-trend'] = True
            # If pullback is enabled and profit has pulled back allow a sell, maybe
            if self.csell_pullback.value and (current_profit <= pullback_value):
                if self.csell_pullback_respect_roi.value and current_profit > min_roi:
                    return 'intrend_pullback_roi'
                elif not self.csell_pullback_respect_roi.value:
                    if current_profit > min_roi:
                        return 'intrend_pullback_roi'
                    else:
                        return 'intrend_pullback_noroi'
            # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold
            return None
        # If we are not in a trend, just use the roi value
        elif not in_trend:
            if self.custom_trade_info[trade.pair]['had-trend']:
                if current_profit > min_roi:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_roi'
                elif not self.csell_endtrend_respect_roi.value:
                    self.custom_trade_info[trade.pair]['had-trend'] = False
                    return 'trend_noroi'
            elif current_profit > min_roi:
                return 'notrend_roi'
        else:
            return None