NostalgiaForInfinityV3

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
from pandas import DataFrame
from functools import reduce
from freqtrade.persistence import Trade


###########################################################################################################
##                NostalgiaForInfinityV3 by iterativ                                                     ##
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##                                                                                                       ##
###########################################################################################################
##               GENERAL RECOMMENDATIONS                                                                 ##
##                                                                                                       ##
##   For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake.        ##
##   A pairlist with 20 to 80 pairs. Volume pairlist works well.                                         ##
##   Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs.                            ##
##   Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc).                    ##
##   Ensure that you don't override any variables in you config.json. Especially                         ##
##   the timeframe (must be 5m).                                                                         ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS                                                                               ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                                     ##
##                                                                                                       ##
###########################################################################################################


class NostalgiaForInfinityV3(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {
        "0": 10,
    }

    stoploss = -0.99 # effectively disabled.

    timeframe = '5m'
    inf_1h = '1h'

    custom_info = {}

    # Sell signal
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
    ignore_roi_if_buy_signal = True

    # Trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.05
    trailing_stop_positive_offset = 0.15

    # Custom stoploss
    use_custom_stoploss = False

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

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 400

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

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

    buy_params = {
        #############
        # Enable/Disable conditions
        "buy_condition_1_enable": True,
        "buy_condition_2_enable": True,
        "buy_condition_3_enable": True,
        "buy_condition_4_enable": True,
        "buy_condition_5_enable": True,
        "buy_condition_6_enable": True,
        "buy_condition_7_enable": True,
        "buy_condition_8_enable": True,
        "buy_condition_9_enable": True,
        "buy_condition_10_enable": True,
    }

    sell_params = {
        #############
        # Enable/Disable conditions
        "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,
    }

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

    # Buy

    buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)

    buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.03, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_1 = DecimalParameter(0.001, 0.2, default=0.12, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_2 = DecimalParameter(0.05, 0.4, default=0.3, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_3 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_1 = DecimalParameter(1.0, 30.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_min_inc_1 = DecimalParameter(0.005, 0.05, default=0.029, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(40.0, 70.0, default=45.25, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=85.06, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi_1 = DecimalParameter(30.0, 40.0, default=36.64, space='buy', decimals=2, optimize=False, load=True)
    buy_mfi_1 = DecimalParameter(36.0, 65.0, default=45.25, space='buy', decimals=2, optimize=False, load=True)

    buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.96, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_relative_2 = DecimalParameter(0.005, 0.08, default=0.006, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_2 = DecimalParameter(40.0, 70.0, default=63.91, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=89.94, space='buy', decimals=2, optimize=False, load=True)
    buy_rsi_1h_diff_2 = DecimalParameter(35.0, 55.0, default=38.69, space='buy', decimals=2, optimize=False, load=True)
    buy_mfi_2 = DecimalParameter(36.0, 65.0, default=53.89, space='buy', decimals=2, optimize=False, load=True)

    buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True)
    buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True)
    buy_bb40_tail_bbdelta = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True)

    buy_bb20_close_bblowerband = DecimalParameter(0.7, 1.1, default=0.98, space='buy', optimize=False, load=True)
    buy_bb20_volume = IntParameter(18, 35, default=24, space='buy', optimize=False, load=True)

    buy_volume_5 = DecimalParameter(1.0, 10.0, default=4.12, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_open_mult_5 = DecimalParameter(0.01, 0.04, default=0.019, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.48, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_open_mult_6 = DecimalParameter(0.025, 0.05, default=0.033, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_7 = DecimalParameter(1.0, 10.0, default=7.04, space='buy', decimals=2, optimize=False, load=True)
    buy_ema_open_mult_7 = DecimalParameter(0.015, 0.03, default=0.02, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_7 = DecimalParameter(24.0, 50.0, default=41.09, space='buy', decimals=2, optimize=False, load=True)

    buy_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=False, load=True)

    buy_volume_9 = DecimalParameter(1.0, 30.0, default=17.0, space='buy', decimals=1, optimize=False, load=True)
    buy_bb_offset_9 = DecimalParameter(0.97, 1.05, default=0.98, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_10 = DecimalParameter(1.0, 26.0, default=9.6, space='buy', decimals=1, optimize=False, load=True)
    buy_bb_offset_10 = DecimalParameter(0.97, 1.05, default=0.994, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_10 = DecimalParameter(15.0, 40.0, default=30.2, space='buy', decimals=1, optimize=False, load=True)

    # Sell

    sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
    sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, 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=4.382, space='sell', optimize=False, load=True)

    sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=87.708, 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)

    sell_custom_profit_1 = DecimalParameter(0.01, 0.20, default=0.01, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.65, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_2 = DecimalParameter(0.01, 0.20, default=0.05, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.37, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_3 = DecimalParameter(0.15, 0.30, default=0.25, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=51.87, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_4 = DecimalParameter(0.3, 0.7, default=0.45, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.35, space='sell', decimals=2, optimize=False, load=True)

    sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.025, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_profit_3 = DecimalParameter(0.05, 0.3, default=0.07, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.166, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.38, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.154, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.035, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.045, space='sell', decimals=3, optimize=False, load=True)

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

    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()

        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 'target_profit_4'
            elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
                return 'target_profit_3'
            elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
                return 'target_profit_2'
            elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
                return 'target_profit_1'

            elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['close'] < last_candle['ema_200']):
                return 'target_profit_u_1'
            elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['sma_200_dec']):
                return 'target_profit_u_2'

            elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)):
                return 'target_profit_t_1'
            elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)):
                return 'target_profit_t_2'

        return None

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, '1h') for pair in pairs]
        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)
        # SMA
        informative_1h['sma_50'] = ta.SMA(informative_1h, timeperiod=50)
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
        # RSI
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb_lowerband'] = bollinger['lower']
        informative_1h['bb_middleband'] = bollinger['mid']
        informative_1h['bb_upperband'] = bollinger['upper']

        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # BB 40
        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()

        # BB 20
        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']
        dataframe['volume_mean_30'] = dataframe['volume'].rolling(window=30).mean()

        # EMA
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        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)

        # SMA
        dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
        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, timeperiod=14)

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

        # Alligator
        dataframe['lips'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['smma_lips'] = dataframe['lips'].rolling(3).mean()
        dataframe['teeth'] = ta.SMA(dataframe, timeperiod=8)
        dataframe['smma_teeth'] = dataframe['teeth'].rolling(5).mean()
        dataframe['jaw'] = ta.SMA(dataframe, timeperiod=13)
        dataframe['smma_jaw'] = dataframe['jaw'].rolling(8).mean()

        # Volume
        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)

        # If don't exceed the dip limits
        dataframe['safe_dips'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_0.value) &
                                  (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) &
                                  (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) &
                                  (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value))

        return dataframe

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

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

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

        conditions.append(
            (
                self.buy_condition_1_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_1.value > dataframe['volume']) &

                (((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)
            )
        )

        conditions.append(
            (
                self.buy_condition_2_enable.value &

                (dataframe['close'] < dataframe['sma_5']) &
                (dataframe['close'] > dataframe['ema_200']) &
                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50'] > dataframe['ema_100']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) &

                (((dataframe['close'] - dataframe['ema_200']) / dataframe['ema_200']) < self.buy_ema_relative_2.value) &
                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) &
                (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) &
                (dataframe['mfi'] < self.buy_mfi_2.value) &
                (dataframe['close'] < (dataframe['bb_lowerband'])) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_3_enable.value &

                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_100'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &

                (dataframe['safe_dips']) &

                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close.value) &
                dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close.value) &
                dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta.value) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift()) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_4_enable.value &

                (dataframe['close'] > dataframe['ema_100']) &
                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50'] > dataframe['ema_100']) &
                (dataframe['ema_15_1h'] > dataframe['ema_50_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) &
                (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) &
                (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) &

                (dataframe['close'] < dataframe['ema_50']) &
                (dataframe['close'] < self.buy_bb20_close_bblowerband.value * dataframe['bb_lowerband']) &
                (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume.value))
            )
        )

        conditions.append(
            (
                self.buy_condition_5_enable.value &

                #(dataframe['close'] > dataframe['ema_200']) &
                (dataframe['close'] > dataframe['ema_100_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'])) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_6_enable.value &

                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_6.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'])) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_7_enable.value &

                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_7.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['rsi'] < self.buy_rsi_7.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_8_enable.value &

                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['close'] > dataframe['open']) &
                (dataframe['close'] > dataframe['smma_lips']) &

                (dataframe['smma_lips'] > dataframe['smma_teeth']) &
                (dataframe['smma_teeth'] > dataframe['smma_jaw']) &
                (dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) &
                (dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) &

                (dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) &
                (dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) &
                (dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) &

                (dataframe['rsi'] < self.buy_rsi_8.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_9_enable.value &

                (dataframe['close'] > dataframe['ema_200']) &
                (dataframe['close'] > dataframe['ema_200_1h']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) &

                (dataframe['close'] < dataframe['ema_50']) &
                (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_10_enable.value &

                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &

                (dataframe['volume_mean_4'] * self.buy_volume_10.value > dataframe['volume']) &

                (dataframe['close'] < dataframe['ema_50']) &
                (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) &
                (dataframe['volume'] > 0)
            )
        )

        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_5_enable.value &

                (dataframe['close'] < dataframe['ema_200']) &
                (((dataframe['ema_200'] - dataframe['close']) / dataframe['close']) < self.sell_ema_relative_5.value) &
                (dataframe['rsi'] > dataframe['rsi_1h'] + self.sell_rsi_diff_5.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)
            )
        )

        conditions.append(
            (
                self.sell_condition_8_enable.value &

                (dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe