BeastBotXBLR6

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

##################################################### BeastBot XBLR6 ####################################################################
# don't need hyperopt for binance, thanks free comunity, it have parts other strategies, without freqtrade comunity this strategy is not possible              #
# if you earn money and consider rewarding the author ETH: 0xda884c4dbe47421ba63f033db3cc2ec49d552365    BTC: 1hxiKHLaPDKTWuXhVgTZvBdPZtjC9msxi    #
####################################################################################################################################################
# hyperopt for each conditions and finally all conditions true
#  backtesting: freqtrade backtesting -c config_test.json -s BeastBotXBLR6 --timerange 20210920-20220127 --breakdown day -v --enable-protections
#  119 days
# for each conditions
#           Trades |    Win Draw Loss |   Avg profit |      Profit |    Avg duration |    Max Drawdown
#   Con1        12 |     11    0    1 |        2.63% |    (15.82%) | 0 days 00:15:00 |        (8.27%) |
#   Con2        16 |     15    0    1 |        2.47% |    (19.77%) | 0 days 01:19:00 |        (5.15%) |
#   Con3        12 |     12    0    0 |        2.85% |    (17.11%) | 0 days 00:31:00 |             -- |
#   Con4        18 |     16    0    2 |        2.29% |    (20.61%) | 0 days 03:11:00 |        (5.23%) |
#   Con6        11 |     10    0    1 |        2.73% |    (15.05%) | 0 days 03:15:00 |        (0.75%) |
#   Con7         1 |      1    0    0 |        9.37% |     (4.69%) | 0 days 00:30:00 |             -- |
#   Con8        13 |     12    0    1 |        2.31% |    (15.06%) | 0 days 05:19:00 |        (5.09%) |
#   Con9         7 |      7    0    0 |        2.62% |     (9.17%) | 0 days 00:19:00 |             -- |
#   con10       26 |     19    0    7 |        1.03% |    (13.41%) | 0 days 02:39:00 |        (3.76%)
# 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 BeastBotXBLR6(IStrategy):
    INTERFACE_VERSION = 2

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

    # Buy hyperspace params:
    buy_params = {
        "buy_c10_1": -101.4,
        "buy_c10_2": -0.86,
        "buy_c9_1": 40.0,
        "buy_c9_2": -70.4,
        "buy_c9_3": -67.0,
        "buy_c9_4": 41.8,
        "buy_c9_5": 33.6,
        "buy_c9_6": 82.7,
        "buy_c9_7": -94.3,

        "buy_con3_1": 0.016,
        "buy_con3_2": 0.984,
        "buy_con3_3": 0.966,
        "buy_con3_4": -0.88,

        "buy_macd_41": 0.08,
        "buy_rsi_1h_42": 19.9,
        "buy_volume_drop_41": 3.7,
        "buy_volume_pump_41": 0.1,
        "buy_bb_delta": 0.023,  
        "buy_bb_factor": 0.99,  
        "buy_bb_width": 0.097,  

        "buy_c2_1": 0.02,  
        "buy_c2_2": 0.991,  
        "buy_c2_3": -0.7,

        "buy_c6_1": 0.16,  
        "buy_c6_2": 0.03,  
        "buy_c6_3": 0.02,  
        "buy_c6_4": 0.022,  
        "buy_c6_5": 0.387,

        "buy_c7_1": 1.01,  
        "buy_c7_2": 0.99,  
        "buy_c7_3": -99,  
        "buy_c7_4": -64,  
        "buy_c7_5": 81.4,  
        "buy_cci": -112,

        "buy_cci_length": 25,  
        "buy_closedelta": 12.232,  
        "buy_con1_enable": True,  
        "buy_con2_enable": True,  
        "buy_con3_enable": True,  
        "buy_con4_enable": True,  
        "buy_con6_enable": True,  
        "buy_condition_10_enable": True,  
        "buy_condition_7_enable": True,  
        "buy_condition_8_enable": True,  
        "buy_condition_9_enable": True,  

        "buy_dip_threshold_5": 0.05,  
        "buy_dip_threshold_6": 0.2,  
        "buy_dip_threshold_7": 0.4,  
        "buy_dip_threshold_8": 0.5,  
        "buy_mfi_1": 29.8,  
        "buy_min_inc_1": 0.025,  
        "buy_pump_pull_threshold_1": 1.75,  
        "buy_pump_threshold_1": 0.5,  
        "buy_rmi": 36,  
        "buy_rmi_length": 13,  
        "buy_rsi_1": 39.8,  
        "buy_rsi_1h_max_1": 73.8,  
        "buy_rsi_1h_min_1": 36.2,  
        "buy_srsi_fk": 49,  
    }

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

    minimal_roi = {
        "0": 100
    }

    # new sell
    stoploss = -0.99
    use_custom_stoploss = False

    # Recommended
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_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_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].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_buy_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_sell_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