BB_RTR

# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import logging
import math
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

logger = logging.getLogger(__name__)


# --------------------------------
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


# 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


# 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


# VWAP bands
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']


class BB_RTR(IStrategy):
    '''
        BB_RPB_TSL_RNG with conditions from true_lambo and dca
    '''

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

    # Hyperopt result area

    # buy space
    buy_params = {
        ##
        "buy_pump_1_factor": 1.096,
        "buy_pump_2_factor": 1.125,
        ##
        "buy_threshold": 0.003,
        "buy_bb_factor": 0.999,
        "buy_bb_delta": 0.025,
        "buy_bb_width": 0.095,
        ##
        "buy_cci": -116,
        "buy_cci_length": 25,
        "buy_rmi": 49,
        "buy_rmi_length": 17,
        "buy_srsi_fk": 32,
        ##
        "buy_closedelta": 12.148,
        "buy_ema_diff": 0.022,
        ##
        "buy_adx": 20,
        "buy_fastd": 20,
        "buy_fastk": 22,
        "buy_ema_cofi": 0.98,
        "buy_ewo_high": 4.179,
        ##
        "buy_ema_high": 0.968,
        "buy_ema_low": 0.935,
        "buy_ewo": -5.001,
        "buy_rsi": 23,
        "buy_rsi_fast": 44,
        ##
        "buy_ema_high_2": 1.087,
        "buy_ema_low_2": 0.970,
        ##
        "buy_no_trend_cti_4": -0.597,
        "buy_no_trend_factor_4": 0.024,
        "buy_no_trend_r14_4": -44.062,
        ##
        "buy_V_bb_width_5": 0.063,
        "buy_V_cti_5": -0.086,
        "buy_V_mfi_5": 38.158,
        "buy_V_r14_5": -41.493,
        ##
        "buy_vwap_closedelta": 26.941,
        "buy_vwap_closedelta_2": 20.099,
        "buy_vwap_closedelta_3": 27.654,
        ##
        "buy_vwap_cti": -0.087,
        "buy_vwap_cti_2": -0.748,
        "buy_vwap_cti_3": -0.2,
        ##
        "buy_vwap_width": 1.308,
        "buy_vwap_width_2": 3.212,
        "buy_vwap_width_3": 0.49,
        ##
    }

    # sell space
    sell_params = {
        "pHSL": -1,  # Disable
        "pPF_1": 0.019,
        "pPF_2": 0.065,
        "pSL_1": 0.019,
        "pSL_2": 0.062,
        ##
        "high_offset_2": 0.997,
        ##
        "sell_cti_r_cti": 0.844,
        "sell_cti_r_r": -19.99,
        ##
        "sell_u_e_2_cmf": -0.0,
        "sell_u_e_2_ema_close_delta": 0.016,
        "sell_u_e_2_rsi": 10,
        ##
        "sell_deadfish_profit": -0.063,
        "sell_deadfish_bb_factor": 0.954,
        "sell_deadfish_bb_width": 0.043,
        "sell_deadfish_volume_factor": 2.37,
    }

    # ROI
    minimal_roi = {
        "0": 0.10,
    }

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

    # Disabled
    stoploss = -1

    # Options
    use_custom_stoploss = True
    use_sell_signal = True
    process_only_new_candles = True
    startup_candle_count: int = 400

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

    ## Buy params

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

    is_optimize_break = True
    buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize=is_optimize_break)
    buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize=is_optimize_break)

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

    is_optimize_ewo = True
    buy_rsi_fast = IntParameter(35, 50, default=45, optimize=True)
    buy_rsi = IntParameter(15, 30, default=35, optimize=True)
    buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo)
    buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo)
    buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo)

    is_optimize_ewo_2 = True
    buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96, optimize=is_optimize_ewo_2)
    buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09, optimize=is_optimize_ewo_2)

    is_optimize_cofi = True
    buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi)
    buy_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi)

    is_optimize_vwap = True
    buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.80, 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 = True
    buy_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.80, 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 = True
    buy_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.80, 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_no_trend_4 = True
    buy_no_trend_factor_4 = DecimalParameter(0.01, 0.05, default=0.030, optimize=is_optimize_no_trend_4)
    buy_no_trend_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_no_trend_4)
    buy_no_trend_r14_4 = DecimalParameter(-100, -44, default=-80, optimize=is_optimize_no_trend_4)

    is_optimize_V_5 = True
    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)

    is_optimize_gumbo = True
    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 = True
    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)

    # Buy params toggle
    buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)

    is_optimize_pump_1 = True
    buy_pump_1_factor = DecimalParameter(1.0, 1.25, default=1.1, optimize=is_optimize_pump_1)

    is_optimize_pump_2 = True
    buy_pump_2_factor = DecimalParameter(1.0, 1.20, default=1.1, optimize=is_optimize_pump_2)

    ## Sell params
    is_optimize_sell_offset = True
    high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell',
                                     optimize=is_optimize_sell_offset)

    is_optimize_sell_u_e_2 = True
    sell_u_e_2_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize=is_optimize_sell_u_e_2)
    sell_u_e_2_ema_close_delta = DecimalParameter(0.001, 0.027, default=0.024, optimize=is_optimize_sell_u_e_2)
    sell_u_e_2_rsi = IntParameter(10, 30, default=24, optimize=is_optimize_sell_u_e_2)

    is_optimize_deadfish = True
    sell_deadfish_bb_width = DecimalParameter(0.015, 0.05, default=0.05, optimize=is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.10, -0.05, default=-0.05, optimize=is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, optimize=is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1.5, 3, default=1.5, optimize=is_optimize_deadfish)

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

    ## Trailing params

    # hard stoploss profit
    is_optimize_trailing = True
    pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True,
                            optimize=is_optimize_trailing)
    # profit threshold 1, trigger point, SL_1 is used
    pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True,
                             optimize=is_optimize_trailing)
    pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True,
                             optimize=is_optimize_trailing)

    # profit threshold 2, SL_2 is used
    pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True,
                             optimize=is_optimize_trailing)
    pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True,
                             optimize=is_optimize_trailing)

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

    def informative_pairs(self):

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

        return informative_pairs

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

    ## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle )
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        # hard stoploss profit
        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

        # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
        # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
        # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.

        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

        # Only for hyperopt invalid return
        if (sl_profit >= current_profit):
            return -0.99

        return stoploss_from_open(sl_profit, current_profit)

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

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

        last_candle = dataframe.iloc[-1]
        previous_candle_1 = dataframe.iloc[-2]
        previous_candle_2 = dataframe.iloc[-3]

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

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

        # sell cti_r
        if 0.012 > current_profit >= 0.0:
            if (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value):
                return f"sell_profit_cti_r_0_1( {buy_tag})"

        # main sell
        if current_profit > 0.02:
            if (last_candle['momdiv_sell_1h'] == True):
                return f"signal_profit_q_momdiv_1h( {buy_tag})"
            if (last_candle['momdiv_sell'] == True):
                return f"signal_profit_q_momdiv( {buy_tag})"
            if (last_candle['momdiv_coh'] == True):
                return f"signal_profit_q_momdiv_coh( {buy_tag})"
            if (last_candle['cti_40_1h'] > 0.844) and (last_candle['r_84_1h'] > -20):
                return f"signal_profit_cti_r( {buy_tag})"

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

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

        # sell recover
        if (max_loss > 0.06) and (0.05 > current_profit > 0.01) and (last_candle['rsi'] < 46):
            return f"signal_profit_r_1( {buy_tag})"

        # sell offset
        if (
                (current_profit > 0.005)
                and (last_candle['close'] > last_candle['sma_9'])
                and (last_candle['close'] > last_candle['ema_24'] * self.high_offset_2.value)
                and (last_candle['rsi'] > 50)
                and (last_candle['rsi_fast'] > last_candle['rsi_slow'])
        ):
            return f"sell_offset( {buy_tag})"

        ## Disable

        # stoploss
        if (
                (current_profit < -0.025)
                and (last_candle['close'] < last_candle['ema_200'])
                and (last_candle['cmf'] < self.sell_u_e_2_cmf.value)
                and (((last_candle['ema_200'] - last_candle['close']) / last_candle[
            'close']) < self.sell_u_e_2_ema_close_delta.value)
                and last_candle['rsi'] > previous_candle_1['rsi']
                and (last_candle['rsi'] > (last_candle['rsi_1h'] + self.sell_u_e_2_rsi.value))
        ):
            return f"sell_stoploss_u_e_2( {buy_tag})"

        # stoploss - deadfish
        if ((current_profit < self.sell_deadfish_profit.value)
                and (last_candle['close'] < last_candle['ema_200'])
                and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value)
                and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
                and (last_candle['volume_mean_12'] < last_candle[
                    'volume_mean_24'] * self.sell_deadfish_volume_factor.value)

        ):
            return f"sell_stoploss_deadfish( {buy_tag})"

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

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

        assert self.dp, "DataProvider is required for multiple timeframes."

        # Bollinger bands (hyperopt hard to implement)
        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 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'])
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype(
            'int')

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

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

        # RMI hyperopt
        for val in self.buy_rmi_length.range:
            dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)
        # dataframe['rmi'] = RMI(dataframe, length=8, mom=4)

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

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

        # SMA
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)

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

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

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

        # 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_24'] = ta.EMA(dataframe, timeperiod=24)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        # SMA
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)

        # VWAP
        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

        # 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)

        # 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)

        # 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)

        # Williams %R
        dataframe['r_14'] = williams_r(dataframe, period=14)

        # 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)

        # Diff
        dataframe['ema_vwap_diff_50'] = ((dataframe['ema_50'] - dataframe['vwap_lowerband']) / dataframe['ema_50'])

        # Dip Protection
        dataframe['tpct_change_1'] = top_percent_change(dataframe, 1)
        dataframe['tpct_change_2'] = top_percent_change(dataframe, 2)
        dataframe['tpct_change_4'] = top_percent_change(dataframe, 4)

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

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

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

        # 1h tf
        inf_tf = '1h'
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)

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

        # Bollinger bands
        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'])

        # RSI
        informative['rsi'] = ta.RSI(informative, timeperiod=14)
        informative['rsi_28'] = ta.RSI(informative, timeperiod=28)
        informative['rsi_42'] = ta.RSI(informative, timeperiod=42)

        # EMA
        informative['ema_20'] = ta.EMA(informative, timeperiod=20)
        informative['ema_26'] = ta.EMA(informative, timeperiod=26)
        informative['ema_200'] = ta.EMA(informative, timeperiod=200)

        # Williams %R
        informative['r_84'] = williams_r(informative, period=84)

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

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

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        return dataframe

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

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

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

        # Utils

        is_pump_1 = ((dataframe['close'].rolling(48).max() >= (dataframe['close'] * self.buy_pump_1_factor.value)))

        is_pump_2 = ((dataframe['close'].rolling(48).max() >= (dataframe['close'] * self.buy_pump_2_factor.value)))

        is_pump_3 = (
                (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.05)) &
                ((dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.125)))
        )

        is_pump_4 = (
                (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.075)) &
                ((dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.17)))
        )

        rsi_check = (
                (dataframe['rsi_84'] < 60) &
                (dataframe['rsi_112'] < 60)
        )

        min_EWO_check = ((dataframe['EWO'] > -5.585))

        max_EWO_check = ((dataframe['EWO'] < 10.6))

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

        if self.buy_is_dip_enabled.value:
            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)
            )

        if self.buy_is_break_enabled.value:
            is_break = (

                    ((dataframe['bb_delta'] > self.buy_bb_delta.value)  # "buy_bb_delta": 0.025 0.036
                     &  # "buy_bb_width": 0.095 0.133
                     (dataframe['bb_width'] > self.buy_bb_width.value)
                     )
                    &
                    (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000) &  # from BinH
                    (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) &
                    (dataframe['tpct_change_1'] < 0.08) &
                    (dataframe['tpct_change_2'] < 0.08)
            )

        is_local_uptrend = (  # from NFI next gen

                (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) &
                (dataframe['EWO'] < 4) &
                (dataframe['EWO'] > -2.5)
        )

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

        is_ewo_2 = (
                (dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
                (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) &
                (dataframe['EWO'] > self.buy_ewo_high.value) &
                (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) &
                (dataframe['rsi'] < self.buy_rsi.value) &
                (rsi_check)
        )

        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) &
                (rsi_check) &
                (is_pump_1)
        )

        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) &
                (rsi_check) &
                (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)
                &
                (dataframe['rsi_28_1h'] < 46) &
                (rsi_check) &
                (is_pump_3)
        )

        is_VWAP = (
                (dataframe['close'] < dataframe['vwap_lowerband']) &
                (dataframe['tpct_change_1'] > 0.04) &
                (dataframe['cti'] < -0.8) &
                (dataframe['rsi'] < 35) &
                (rsi_check)
        )

        is_no_trend_4 = (
                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_no_trend_factor_4.value) &
                (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
                (dataframe['cti'] < self.buy_no_trend_cti_4.value) &
                (dataframe['r_14'] < self.buy_no_trend_r14_4.value) &
                (dataframe['EWO'] < -4) &
                (min_EWO_check) &
                (rsi_check)
        )

        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) &
                # Really Bear, don't engage until dump over
                (dataframe['ema_vwap_diff_50'] > 0.215) &
                (dataframe['EWO'] < -10) &
                (rsi_check)
        )

        is_insta = (
                (dataframe['bb_width_1h'] > 0.13) &
                (dataframe['r_14'] < -50) &
                (dataframe['r_84_1h'] < -69) &
                (dataframe['cti'] < -0.84) &
                (dataframe['cti_40_1h'] < -0.73)
                &
                ((dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.1)))
        )

        # NFI quick mode

        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_nfi_33 = (
                (dataframe['close'] < (dataframe['ema_13'] * 0.978)) &
                (dataframe['EWO'] > 8) &
                (dataframe['cti'] < -0.88) &
                (dataframe['rsi'] < 32) &
                (dataframe['r_14'] < -98.0) &
                (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5))
        )

        is_nfix_39 = (
                (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)) &
                (dataframe['ema_200'] > (dataframe['ema_200'].shift(48) * 1.07)) &
                (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_50'] * 0.912)
        )

        is_nfix_201 = (
                (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift()) &
                (dataframe['rsi_fast'] < 30.0) &
                (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) &
                (dataframe['close'] < dataframe['sma_15'] * 0.953) &
                (dataframe['cti'] < -0.82) &
                (dataframe['cci'] < -210.0)
                &
                (is_pump_1) &
                (rsi_check)
        )

        is_nfi7_33 = (
                (dataframe['moderi_96']) &
                (dataframe['cti'] < -0.88) &
                (dataframe['close'] < (dataframe['ema_13'] * 0.988)) &
                (dataframe['EWO'] > 6.4) &
                (dataframe['rsi'] < 32.0) &
                (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0))
                &
                (is_pump_3) &
                (rsi_check)
        )

        is_nfi_sma_3 = (
                (dataframe['bb_lowerband2_40'].shift() > 0) &
                (dataframe['bb_delta_cluc'] > dataframe['close'] * 0.059) &
                (dataframe['ha_closedelta'] > dataframe['close'] * 0.023) &
                (dataframe['tail'] < dataframe['bb_delta_cluc'] * 0.418) &
                (dataframe['close'] < dataframe['bb_lowerband2_40'].shift()) &
                (dataframe['close'] < dataframe['close'].shift())
        )

        is_BB_checked = is_dip & is_break

        ## condition append
        conditions.append(is_BB_checked)  # P
        dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb '

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

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

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

        conditions.append(is_no_trend_4)
        dataframe.loc[is_no_trend_4, 'buy_tag'] += 'no_trend_4 '

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

        conditions.append(is_vwap_2)
        dataframe.loc[is_vwap_2, 'buy_tag'] += 'vwap_2 '

        conditions.append(is_vwap_3)
        dataframe.loc[is_vwap_3, 'buy_tag'] += 'vwap_3 '

        conditions.append(is_VWAP)
        dataframe.loc[is_VWAP, 'buy_tag'] += 'VWAP '

        conditions.append(is_insta)
        dataframe.loc[is_insta, 'buy_tag'] += 'insta '

        # NFI
        conditions.append(is_nfi_32)
        dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi_32 '

        conditions.append(is_nfi_33)
        dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 '

        conditions.append(is_nfix_39)
        dataframe.loc[is_nfix_39, 'buy_tag'] += 'x_39 '

        conditions.append(is_nfix_201)
        dataframe.loc[is_nfix_201, 'buy_tag'] += 'x_201 '

        conditions.append(is_nfi7_33)
        dataframe.loc[is_nfi7_33, 'buy_tag'] += '7_33 '

        conditions.append(is_nfi_sma_3)
        dataframe.loc[is_nfi_sma_3, 'buy_tag'] += 'sma_3 '

        # Very Bear
        conditions.append(is_V_5)
        dataframe.loc[is_V_5, 'buy_tag'] += 'V_5 '

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

        return dataframe

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

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

        return dataframe


# 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')


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


class BB_RTR_dca(BB_RTR):
    # DCA options
    position_adjustment_enable = True

    initial_safety_order_trigger = -0.08
    max_safety_orders = 2
    safety_order_step_scale = 0.5  # SS
    safety_order_volume_scale = 1.6  # OS

    # Auto compound calculation
    max_dca_multiplier = (1 + max_safety_orders)
    if (max_safety_orders > 0):
        if (safety_order_volume_scale > 1):
            max_dca_multiplier = (2 + (safety_order_volume_scale * (
                    math.pow(safety_order_volume_scale, (max_safety_orders - 1)) - 1) / (
                                               safety_order_volume_scale - 1)))
        elif (safety_order_volume_scale < 1):
            max_dca_multiplier = (2 + (safety_order_volume_scale * (
                    1 - math.pow(safety_order_volume_scale, (max_safety_orders - 1))) / (
                                               1 - safety_order_volume_scale)))

    # Let unlimited stakes leave funds open for DCA orders
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            **kwargs) -> float:

        if self.config['stake_amount'] == 'unlimited':
            return proposed_stake / self.max_dca_multiplier

        return proposed_stake

    # DCA
    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):

        if current_profit > self.initial_safety_order_trigger:
            return None

        count_of_buys = trade.nr_of_successful_buys

        if 1 <= count_of_buys <= self.max_safety_orders:
            safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys)
            if (self.safety_order_step_scale > 1):
                safety_order_trigger = abs(self.initial_safety_order_trigger) + (
                        abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (
                        math.pow(self.safety_order_step_scale, (count_of_buys - 1)) - 1) / (
                                self.safety_order_step_scale - 1))
            elif (self.safety_order_step_scale < 1):
                safety_order_trigger = abs(self.initial_safety_order_trigger) + (
                        abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (
                        1 - math.pow(self.safety_order_step_scale, (count_of_buys - 1))) / (
                                1 - self.safety_order_step_scale))

            if current_profit <= (-1 * abs(safety_order_trigger)):
                try:
                    stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None)
                    # This calculates base order size
                    stake_amount = stake_amount / self.max_dca_multiplier
                    # This then calculates current safety order size
                    stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1))
                    amount = stake_amount / current_rate
                    logger.info(
                        f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}")
                    return stake_amount
                except Exception as exception:
                    logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
                    return None

        return None