BeastBotXBLR7

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from datetime import datetime, timedelta
from pandas import DataFrame, Series, concat
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI,vwmacd
import logging
import pandas_ta as pta
from numpy import where
import time
import datetime

# all conditions true
logger = logging.getLogger(__name__)

# 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

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['close'] * 100
    return emadif

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

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

    return sroc
def 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']

class BeastBotXBLR7(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = '5m'
    inf_1h = '1h'
    info_timeframe_1d = "1d"
    has_BTC_info_tf = True

    # Buy hyperspace params:
    buy_params = {
        "buy_bb_delta": 0.025,
        "buy_bb_factor": 0.996,
        "buy_bb_width": 0.115,
        "buy_c10_1": -96.1,
        "buy_c10_2": -0.95,
        "buy_c6_1": 0.2,
        "buy_c6_2": 0.05,
        "buy_c6_3": 0.007,
        "buy_c6_4": 0.017,
        "buy_c6_5": 0.313,
        "buy_c7_1": 1.05,
        "buy_c7_2": 0.96,
        "buy_c7_3": -85,
        "buy_c7_4": -84,
        "buy_c7_5": 75.5,
        "buy_cci": -134,
        "buy_cci_length": 38,
        "buy_closedelta": 14.098,
        "buy_rmi": 49,
        "buy_rmi_length": 18,
        "buy_srsi_fk": 45,
        "buy_c2_1": 0.02,  # value loaded from strategy
        "buy_c2_2": 0.991,  # value loaded from strategy
        "buy_c2_3": -0.7,  # value loaded from strategy
        "buy_c9_1": 40.0,  # value loaded from strategy
        "buy_c9_2": -69.0,  # value loaded from strategy
        "buy_c9_3": -67.9,  # value loaded from strategy
        "buy_c9_4": 42.3,  # value loaded from strategy
        "buy_c9_5": 32.0,  # value loaded from strategy
        "buy_c9_6": 85.7,  # value loaded from strategy
        "buy_c9_7": -81.9,  # value loaded from strategy
        "buy_con1_enable": True,  # value loaded from strategy
        "buy_con2_enable": True,  # value loaded from strategy
        "buy_con3_1": 0.021,  # value loaded from strategy
        "buy_con3_2": 0.981,  # value loaded from strategy
        "buy_con3_3": 0.973,  # value loaded from strategy
        "buy_con3_4": -0.88,  # value loaded from strategy
        "buy_con3_enable": True,  # value loaded from strategy
        "buy_con4_enable": True,  # value loaded from strategy
        "buy_con6_enable": True,  # value loaded from strategy
        "buy_condition_10_enable": True,  # value loaded from strategy
        "buy_condition_7_enable": True,  # value loaded from strategy
        "buy_condition_8_enable": True,  # value loaded from strategy
        "buy_condition_9_enable": True,  # value loaded from strategy
        "buy_dip_threshold_5": 0.05,  # value loaded from strategy
        "buy_dip_threshold_6": 0.2,  # value loaded from strategy
        "buy_dip_threshold_7": 0.4,  # value loaded from strategy
        "buy_dip_threshold_8": 0.5,  # value loaded from strategy
        "buy_macd_41": 0.09,  # value loaded from strategy
        "buy_mfi_1": 29.8,  # value loaded from strategy
        "buy_min_inc_1": 0.025,  # value loaded from strategy
        "buy_pump_pull_threshold_1": 1.75,  # value loaded from strategy
        "buy_pump_threshold_1": 0.5,  # value loaded from strategy
        "buy_rsi_1": 39.8,  # value loaded from strategy
        "buy_rsi_1h_42": 31.1,  # value loaded from strategy
        "buy_rsi_1h_max_1": 73.8,  # value loaded from strategy
        "buy_rsi_1h_min_1": 36.2,  # value loaded from strategy
        "buy_volume_drop_41": 1.7,  # value loaded from strategy
        "buy_volume_pump_41": 0.2,  # value loaded from strategy
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_bb_relative_8": 1.1,  # value loaded from strategy
        "sell_condition_1_enable": True,  # value loaded from strategy
        "sell_condition_2_enable": True,  # value loaded from strategy
        "sell_condition_3_enable": True,  # value loaded from strategy
        "sell_condition_4_enable": True,  # value loaded from strategy
        "sell_condition_5_enable": True,  # value loaded from strategy
        "sell_condition_6_enable": True,  # value loaded from strategy
        "sell_condition_7_enable": True,  # value loaded from strategy
        "sell_condition_8_enable": True,  # value loaded from strategy
        "sell_custom_dec_profit_1": 0.05,  # value loaded from strategy
        "sell_custom_dec_profit_2": 0.07,  # value loaded from strategy
        "sell_custom_profit_0": 0.01,  # value loaded from strategy
        "sell_custom_profit_1": 0.03,  # value loaded from strategy
        "sell_custom_profit_2": 0.05,  # value loaded from strategy
        "sell_custom_profit_3": 0.08,  # value loaded from strategy
        "sell_custom_profit_4": 0.25,  # value loaded from strategy
        "sell_custom_profit_under_rel_1": 0.024,  # value loaded from strategy
        "sell_custom_profit_under_rsi_diff_1": 4.4,  # value loaded from strategy
        "sell_custom_rsi_0": 33.0,  # value loaded from strategy
        "sell_custom_rsi_1": 38.0,  # value loaded from strategy
        "sell_custom_rsi_2": 43.0,  # value loaded from strategy
        "sell_custom_rsi_3": 48.0,  # value loaded from strategy
        "sell_custom_rsi_4": 50.0,  # value loaded from strategy
        "sell_custom_stoploss_under_rel_1": 0.004,  # value loaded from strategy
        "sell_custom_stoploss_under_rsi_diff_1": 8.0,  # value loaded from strategy
        "sell_custom_under_profit_1": 0.02,  # value loaded from strategy
        "sell_custom_under_profit_2": 0.04,  # value loaded from strategy
        "sell_custom_under_profit_3": 0.6,  # value loaded from strategy
        "sell_custom_under_rsi_1": 56.0,  # value loaded from strategy
        "sell_custom_under_rsi_2": 60.0,  # value loaded from strategy
        "sell_custom_under_rsi_3": 62.0,  # value loaded from strategy
        "sell_dual_rsi_rsi_1h_4": 79.6,  # value loaded from strategy
        "sell_dual_rsi_rsi_4": 73.4,  # value loaded from strategy
        "sell_ema_relative_5": 0.024,  # value loaded from strategy
        "sell_profit_trendstop": 0.02,  # value loaded from strategy
        "sell_rsi_1h_7": 81.7,  # value loaded from strategy
        "sell_rsi_bb_1": 79.5,  # value loaded from strategy
        "sell_rsi_bb_2": 81,  # value loaded from strategy
        "sell_rsi_diff_5": 4.4,  # value loaded from strategy
        "sell_rsi_main_3": 82,  # value loaded from strategy
        "sell_rsi_under_6": 79.0,  # value loaded from strategy
        "sell_time_stoploss": 114,  # value loaded from strategy
        "sell_time_trendstop": 113,  # value loaded from strategy
        "sell_trail_down_1": 0.18,  # value loaded from strategy
        "sell_trail_down_2": 0.14,  # value loaded from strategy
        "sell_trail_down_3": 0.01,  # value loaded from strategy
        "sell_trail_profit_max_1": 0.46,  # value loaded from strategy
        "sell_trail_profit_max_2": 0.12,  # value loaded from strategy
        "sell_trail_profit_max_3": 0.1,  # value loaded from strategy
        "sell_trail_profit_min_1": 0.15,  # value loaded from strategy
        "sell_trail_profit_min_2": 0.01,  # value loaded from strategy
        "sell_trail_profit_min_3": 0.05,  # value loaded from strategy
    }
    minimal_roi = {
        "0": 100
    }

    # new sell
    stoploss = -0.99
    use_custom_stoploss = False

    # Recommended
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Required
    startup_candle_count: int = 300
    process_only_new_candles = False

    # Strategy Specific Variable Storage
    custom_trade_info = {}
    custom_fiat = "USD" # Only relevant if stake is BTC or ETH


    plot_config = {
          "main_plot": {
            "ema_50_1h": {"color": "rgba(255,250,200,2.4)"},
            "bb_lowerband": {"color": "#792bbb","type": "line"},
            "bb_upperband": {"color": "#bc281d","type": "line"}
          },
          "subplots": {
            "RSI/BTC": {
              "mfi": {"color": "#e12a7c","type": "line"},
              "cci": {"color": "#794491","type": "line"},
              "ssl-dir_1h": {"color": "#2773a7","type": "line"},
              "ssl-dir": {"color": "#5379a2","type": "line"}
            }
          }
        }


    custom_trendBTC_info = {}

    if not 'trend' in custom_trendBTC_info:
        custom_trendBTC_info['trend'] = {}
    if not 'not_downtrend' in custom_trendBTC_info['trend']:
        custom_trendBTC_info['trend']['not_downtrend'] = 0
    if not 'st' in custom_trendBTC_info['trend']:
        custom_trendBTC_info['trend']['st'] = 0
    if not 'stx' in custom_trendBTC_info['trend']:
        custom_trendBTC_info['trend']['stx'] = 0


    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration": 120
            },
            {
                "method": "StoplossGuard",
                "lookback_period": 90,
                "trade_limit": 2,
                "stop_duration": 120,
                "only_per_pair": False
            },
            {
                "method": "StoplossGuard",
                "lookback_period": 90,
                "trade_limit": 1,
                "stop_duration": 120,
                "only_per_pair": True
            },
        ]


    ###########################################################################
    # Buy
    Optimize_condition = False
    buy_con1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_con2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_con3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_con4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_con6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)

    optc1 = True
    buy_rmi_length = IntParameter(8, 20, default=8, optimize = optc1, load=True)
    buy_rmi = IntParameter(30, 50, default=35, optimize= optc1, load=True)
    buy_cci_length = IntParameter(25, 45, default=25, optimize = optc1, load=True)
    buy_cci = IntParameter(-135, -90, default=-133, optimize= optc1, load=True)
    buy_srsi_fk = IntParameter(30, 50, default=25, optimize= optc1, load=True)
    buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize = optc1, load=True)
    buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = optc1, load=True)
    buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = optc1, load=True)
    buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = optc1, load=True)

    optc2 = False
    buy_c2_1 = DecimalParameter(0.010, 0.025, default=0.018, space='buy', decimals=3, optimize=optc2, load=True)
    buy_c2_2 = DecimalParameter(0.980, 0.995, default=0.982, space='buy', decimals=3, optimize=optc2, load=True)
    buy_c2_3 = DecimalParameter(-0.8, -0.3, default=-0.5, space='buy', decimals=1, optimize=optc2, load=True)

    optc3 = False
    buy_con3_1 = DecimalParameter(0.010, 0.025, default=0.017, space='buy', decimals=3, optimize=optc3, load=True)
    buy_con3_2 = DecimalParameter(0.980, 0.995, default=0.984, space='buy', decimals=3, optimize=optc3, load=True)
    buy_con3_3 = DecimalParameter(0.955, 0.975, default=0.965, space='buy', decimals=3, optimize=optc3, load=True)
    buy_con3_4 = DecimalParameter(-0.95, -0.70, default=-0.85, space='buy', decimals=2, optimize=optc3, load=True)

    optc4 = False
    buy_rsi_1h_42 = DecimalParameter(10.0, 50.0, default=15.0, space='buy', decimals=1, optimize=optc4, load=True)
    buy_macd_41 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=optc4, load=True)
    buy_volume_pump_41 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=optc4, load=True)
    buy_volume_drop_41 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=optc4, load=True)

    optc6 = True
    buy_c6_2 = DecimalParameter(0.980, 0.999, default=0.985, space='buy', decimals=3, optimize=optc6, load=True)
    buy_c6_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=optc6, load=True)
    buy_c6_2 = DecimalParameter(0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=optc6, load=True)
    buy_c6_3 = DecimalParameter(0.005, 0.04, default=0.031, space='buy', decimals=3, optimize=optc6, load=True)
    buy_c6_4 = DecimalParameter(0.01, 0.03, default=0.021, space='buy', decimals=3, optimize=optc6, load=True)
    buy_c6_5 = DecimalParameter(0.2, 0.4, default=0.264, space='buy', decimals=3, optimize=optc6, load=True)

    optc7 = True
    buy_c7_1 = DecimalParameter(0.95, 1.10, default=1.01, space='buy', decimals=2, optimize=optc7, load=True)
    buy_c7_2 = DecimalParameter(0.95, 1.10, default=0.99, space='buy', decimals=2, optimize=optc7, load=True)
    buy_c7_3 = IntParameter(-100, -80, default=-94, space='buy', optimize= optc7, load=True)
    buy_c7_4 = IntParameter(-90, -60, default=-75, space='buy', optimize= optc7, load=True)
    buy_c7_5 = DecimalParameter(75.1, 90.1, default=80.0, space='buy',decimals=1, optimize= optc7, load=True)

    optc8 = False
    buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=optc8, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=optc8, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=optc8, load=True)
    buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=optc8, load=True)
    buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=optc8, load=True)

    optc9 = False
    buy_c9_1 = DecimalParameter(25.0, 44.0, default=36.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_2 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_3 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_4 = DecimalParameter(35.0, 54.0, default=46.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_5 = DecimalParameter(20.0, 44.0, default=30.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_6 = DecimalParameter(65.0, 94.0, default=84.0, space='buy', decimals=1, optimize=optc9, load=True)
    buy_c9_7 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc9, load=True)

    optc10 = True
    buy_c10_1 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc10, load=True)
    buy_c10_2 = DecimalParameter(-1, -0.5, default=-0.78, space='buy', decimals=2, optimize=optc10, load=True)

    buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)
   # 24 hours
    buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=False, load=True)


    # Sell··································································
    sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)

    sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True)
    sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True)
    sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True)

    sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True)
    sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)

    sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=False, load=True)
    sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True)
    sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True)

    optimize_sell = False
    sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
    sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
    sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
    sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=optimize_sell, load=True)
    sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=optimize_sell, load=True)

    optimize_sell_u = False
    sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
    sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)
    sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
    sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)
    sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.6, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
    sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)

    sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True)

    sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=False, load=True)
    sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)

    sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=False, load=True)
    sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=False, load=True)

    sell_time_stoploss = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True)
    sell_time_trendstop = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True)
    sell_profit_trendstop = DecimalParameter(0.009, 0.02, default=0.015, space='sell', optimize=True, load=True)
    #############################################################

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])



    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].squeeze()

        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()

        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
        max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)

        if (last_candle is not None):
            if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value):
                return f'sf_4( {buy_tag})'
            elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
                return f'sf_3( {buy_tag})'
            elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
                return f'sf_2( {buy_tag})'
            elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
                return f'sf_1( {buy_tag})'
            elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value):
                return f'sf_0( {buy_tag})'

            elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']):
                return f'sf_u_1( {buy_tag})'
            elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']):
                return f'sf_u_2( {buy_tag})'
            elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']):
                return f'sf_u_3( {buy_tag})'

            elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']):
                return f'sf_d_1( {buy_tag})'
            elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']):
                return f'sf_d_2( {buy_tag})'

            elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)):
                return f'sf_t_1( {buy_tag})'
            elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)):
                return f'sf_t_2( {buy_tag})'

            elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)):
                return f'sf_u_t_1( {buy_tag})'

            elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value):
                return f'sf_u_e_1( {buy_tag})'

            elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value):
                return f'stoploss ( {buy_tag})'

            elif (current_profit < -0.05) & (trade_dur > self.sell_time_stoploss.value) & (last_candle['ssl-dir'] == 'down'):
                return f'stoploss5 ( {buy_tag})'

            elif ((buy_tag in [' trend ']) & (trade_dur > self.sell_time_trendstop.value) & ((last_candle['ssl-dir'] == 'down') & (current_profit < self.sell_profit_trendstop.value))):
                return f'trend_stop'

            elif (current_profit < -0.08):
                return f'stoploss8 ( {buy_tag})'

        return None

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
                           rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
        return True


    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.inf_1h) for pair in pairs]
       # informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs])

        if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        else:
            btc_info_pair = "BTC/USDT"

        informative_pairs.append((btc_info_pair, self.timeframe))
        informative_pairs.append((btc_info_pair, self.inf_1h))
        informative_pairs.append((btc_info_pair, self.info_timeframe_1d))

        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        # EMA
        #informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)

        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        #informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi'] > 50))
        informative_1h['r_480'] = williams_r(dataframe, period=480)
        informative_1h['safe_pump_24'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) /
            informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_1.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) /
            self.buy_pump_pull_threshold_1.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min())))

        informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)

        ssldown, sslup = SSLChannels_ATR(informative_1h, 14)
        informative_1h['ssl-dir'] = np.where(sslup > ssldown,'up','down')


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

        return informative_1h

    def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # -----------------------------------------------------------------------------------------
        if not 'trend' in self.custom_trendBTC_info:
            self.custom_trendBTC_info['trend'] = {}
        if not 'not_downtrend' in self.custom_trendBTC_info['trend']:
            self.custom_trendBTC_info['trend']['not_downtrend'] = 0


        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi'] > 50))
        self.custom_trendBTC_info["trend"]['not_downtrend'] = {}
        self.custom_trendBTC_info["trend"]['not_downtrend'] = dataframe['not_downtrend']

      # -----------------------------------------------------------------------------------------
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)

        return dataframe

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

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        # nuevo #

        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']

        dataframe['bb_width'] = ((dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'])
        dataframe['bb_delta'] = ((dataframe['bb_lowerband'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband'])
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # 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)
        # CTI
        dataframe['cti'] = pta.cti(dataframe["close"], length=20)

        # 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']

         # 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)

        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()

        #cols_to_norm = ['vwmacd','signal','hist'] normalize
        #dataframe[cols_to_norm] = dataframe[cols_to_norm].apply(lambda x: (x-x.mean())/ x.std(), axis=0)

        dataframe['r_14'] = williams_r(dataframe, period=14)
        dataframe['r_32'] = williams_r(dataframe, period=32)
        dataframe['r_64'] = williams_r(dataframe, period=64)
#        dataframe['r_480'] = williams_r(dataframe, period=480)

               # EMA 200
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)

        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)

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

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

        dataframe['safe_dips_strict'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_5.value) &
                                  (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_6.value) &
                                  (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_7.value) &
                                  (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_8.value))

        return dataframe



    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        tik = time.perf_counter()

        """
        if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        else:
            btc_info_pair = "BTC/USDT"

        if metadata['pair'] in btc_info_pair:
            btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h)
            btc_info_tfx = self.info_tf_btc_indicators(btc_info_tf, metadata)
            dataframe = merge_informative_pair(dataframe, btc_info_tfx, self.timeframe, self.inf_1h, ffill=True)
            drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        """
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        ssldown, sslup = SSLChannels_ATR(dataframe, 64)
        dataframe['ssl-up'] = sslup
        dataframe['ssl-down'] = ssldown
        dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')
        dataframe['rmi'] =  RMI(dataframe, length=24, mom=5)

        tok = time.perf_counter()
        logger.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.")
        return dataframe

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

        conditions = []

        con1 = (
                self.buy_con1_enable.value &
                (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) &
                ((dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value)) &
                (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) &
                (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
             )

        con2= (
                self.buy_con2_enable.value &
                (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) &
                (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) &
                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_c2_1.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c2_2.value)) &
                (dataframe['cti_1h'] > self.buy_c2_3.value)
            )


        con3 = (
                self.buy_con3_enable.value &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_con3_1.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_con3_2.value)) &
                (dataframe['close'] < dataframe['ema_20'] * self.buy_con3_3.value) &
                (dataframe['cti'] < self.buy_con3_4.value)
            )

        con4 = (
                self.buy_con4_enable.value &

                (dataframe['rsi_1h'] < self.buy_rsi_1h_42.value) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_41.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &

                (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_41.value)) &
                (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_41.value) &
                (dataframe['volume_mean_slow'] * self.buy_volume_pump_41.value < dataframe['volume_mean_slow'].shift(48)) &
                (dataframe['volume'] > 0)
            )

        con6  = (
                self.buy_con6_enable.value &
                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_1.value) &
                (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_2.value) &
                dataframe['bb_lowerband'].shift().gt(0) &
                dataframe['bb_delta'].gt(dataframe['close'] * self.buy_c6_3.value) &
                dataframe['closedelta'].gt(dataframe['close'] * self.buy_c6_4.value) &
                dataframe['tail'].lt(dataframe['bb_delta'] * self.buy_c6_5.value) &
                dataframe['close'].lt(dataframe['bb_lowerband'].shift()) &
                dataframe['close'].le(dataframe['close'].shift()) &
                (dataframe['volume'] > 0)
            )

        con7 = (
                self.buy_condition_7_enable.value &
                (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * self.buy_c7_1.value)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c7_2.value)) &
                (dataframe['r_14'] < self.buy_c7_3.value) &
                (dataframe['r_64'] < self.buy_c7_4.value) &
                (dataframe['rsi_1h'] < self.buy_c7_5.value)
            )

        con8 = (
                self.buy_condition_8_enable.value &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (dataframe['sma_200'] > dataframe['sma_200'].shift(50)) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_24_1h']) &

                (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) &
                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) &
                (dataframe['rsi'] < self.buy_rsi_1.value) &
                (dataframe['mfi'] < self.buy_mfi_1.value) &

                (dataframe['volume'] > 0)
            )

        con9 = (
                self.buy_condition_9_enable.value &
                (((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > 0.032) &
                (dataframe['rsi'] < self.buy_c9_1.value) &
                (dataframe['r_14'] < self.buy_c9_2.value) &
                (dataframe['r_32'] < self.buy_c9_3.value) &
                (dataframe['mfi'] < self.buy_c9_4.value) &
                (dataframe['rsi_1h'] > self.buy_c9_5.value) &
                (dataframe['rsi_1h'] < self.buy_c9_6.value) &
                (dataframe['r_480_1h'] > self.buy_c9_7.value)
            )

        co10 = (
                self.buy_condition_10_enable.value &
                (dataframe['close'].shift(4) < (dataframe['close'].shift(3))) &
                (dataframe['close'].shift(3) < (dataframe['close'].shift(2))) &
                (dataframe['close'].shift(2) < (dataframe['close'].shift())) &
                (dataframe['close'].shift(1) < (dataframe['close'])) &
                (dataframe['ema_26'] > dataframe['ema_12']) &
                (dataframe['close'] > (dataframe['open'])) &
                (dataframe['cci'].shift() < dataframe['cci']) &
                (dataframe['ssl-dir_1h'] == 'up') &
                (dataframe['cci'] < self.buy_c10_1.value) &
                (dataframe['cti'] < self.buy_c10_2.value) &
                (dataframe['volume'] > 0)
            )


        conditions.append(con1)
        conditions.append(con2)
        conditions.append(con3)
        conditions.append(con4)
        conditions.append(con6)
        conditions.append(con7)
        conditions.append(con8)
        conditions.append(con9)
        conditions.append(co10)

        dataframe.loc[con1, 'buy_tag'] = " con1 "
        dataframe.loc[con2, 'buy_tag'] = " Andalusian  "
        dataframe.loc[con3, 'buy_tag'] = " con3 "
        dataframe.loc[con4, 'buy_tag'] = " con4 "
        dataframe.loc[con6, 'buy_tag'] = " con6 "
        dataframe.loc[con7, 'buy_tag'] = " con7 "
        dataframe.loc[con8, 'buy_tag'] = " con8 "
        dataframe.loc[con9, 'buy_tag'] = " con9 "
        dataframe.loc[co10, 'buy_tag'] = " trend "

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

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []

        conditions.append(
            (
                self.sell_condition_1_enable.value &

                (dataframe['rsi'] > self.sell_rsi_bb_1.value) &
                (dataframe['close'] > dataframe['bb_upperband']) &
                (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
                (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
                (dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) &
                (dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) &
                (dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.sell_condition_2_enable.value &

                (dataframe['rsi'] > self.sell_rsi_bb_2.value) &
                (dataframe['close'] > dataframe['bb_upperband']) &
                (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
                (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.sell_condition_3_enable.value &

                (dataframe['rsi'] > self.sell_rsi_main_3.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.sell_condition_4_enable.value &

                (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) &
                (dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.sell_condition_6_enable.value &

                (dataframe['close'] < dataframe['ema_200']) &
                (dataframe['close'] > dataframe['ema_50']) &
                (dataframe['rsi'] > self.sell_rsi_under_6.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.sell_condition_7_enable.value &

                (dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) &
                qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) &
                (dataframe['volume'] > 0)
            )
        )


        """
    for i in self.ma_types:
            conditions.append(
                (
                    (dataframe['close'] > dataframe[f'{i}_offset_sell']) &
                    (dataframe['volume'] > 0)
                )
        )
    """

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

        return dataframe