ERMD_Strategy_amount_4h_422

# --- Do not remove these libs ---
from datetime import datetime
from functools import reduce
from typing import Optional, Union, List



from freqtrade.persistence import Trade
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, \
    stoploss_from_open
import pandas as pd
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import warnings
import numpy as np
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

DEBUG = False
Debug = False
Debug_Amount = False
Debug_Stoploss = False


class ERMD_Strategy_amount_4h_422(IStrategy):
    INTERFACE_VERSION: int = 3

    can_short = False  # enable short direction
    timeframe = "4h"
    startup_candle_count = 100  # how many candles we need to skip to start receiving robust signals

    class HyperOpt:
        def trailing_space() -> List[Dimension]:
            # All parameters here are mandatory, you can only modify their type or the range.
            return [
                # Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
                Categorical([True, False], name='trailing_stop'),

                SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
                # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
                # so this intermediate parameter is used as the value of the difference between
                # them. The value of the 'trailing_stop_positive_offset' is constructed in the
                # generate_trailing_params() method.
                # This is similar to the hyperspace dimensions used for constructing the ROI tables.
                SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),

                Categorical([True, False], name='trailing_only_offset_is_reached'),
            ]


    stoploss = -0.329  # maximum stop loss distance

    exit_profit_only = False
    trailing_stop = True
    trailing_stop_positive = 0.011
    trailing_stop_positive_offset = 0.066
    trailing_only_offset_is_reached = True

    # take profit
    tp1_dist = 0.2
    tp1_size = 0.3
    tp2_dist = 0.4
    tp2_size = 0.3
    tp3_dist = 0.6
    tp3_size = 0.4
    minimal_roi = {
        "0": 0.268,
        "579": 0.234,
        "1483": 0.11,
        "4606": 0
    }

    buy_total_risk_trade = DecimalParameter(0.007, 0.025, decimals=3, default=0.018, optimize=True)
    buy_cust_stoploss_ = DecimalParameter(0.1, 0.3, decimals=2, default=0.1, optimize=True)
    # Parameters: signal
    buy_ema_enabled = BooleanParameter(default=True, optimize=True)
    buy_ema1_length = IntParameter(3, 100, default=12, optimize=True)
    buy_ema2_length = IntParameter(3, 100, default=65, optimize=True)
    buy_ema3_length = IntParameter(3, 100, default=96, optimize=True)
    buy_rsi_enabled = BooleanParameter(default=False, optimize=True)
    buy_rsi_length = IntParameter(3, 100, default=12, optimize=True)
    buy_rsi_threshold = IntParameter(50, 100, default=61, optimize=True)
    buy_macd_enabled = BooleanParameter(default=True, optimize=True)
    buy_macd_fast = IntParameter(3, 50, default=38, optimize=True)
    buy_macd_slow = IntParameter(20, 200, default=91, optimize=True)
    buy_macd_smooth = IntParameter(1, 30, default=22, optimize=True)
    buy_candlestick_enabled = BooleanParameter(default=False, optimize=True)

    sell_ema_enabled = BooleanParameter(default=False, optimize=True)
    sell_ema1_length = IntParameter(3, 100, default=17, optimize=True)
    sell_ema2_length = IntParameter(3, 100, default=94, optimize=True)
    sell_ema3_length = IntParameter(3, 100, default=40, optimize=True)
    sell_rsi_enabled = BooleanParameter(default=False, optimize=True)
    sell_rsi_length = IntParameter(3, 100, default=36, optimize=True)
    sell_rsi_threshold = IntParameter(0, 50, default=9, optimize=True)
    sell_macd_enabled = BooleanParameter(default=False, optimize=True)
    sell_macd_fast = IntParameter(3, 50, default=49, optimize=True)
    sell_macd_slow = IntParameter(20, 200, default=28, optimize=True)
    sell_macd_smooth = IntParameter(1, 30, default=5, optimize=True)
    sell_candlestick_enabled = BooleanParameter(default=False, optimize=True)

    sell_willy_exit = BooleanParameter(default=True, space='sell', optimize=True)
    sell_high_line = IntParameter(-25, 0, default=-19, space="sell", optimize=True)
    sell_low_line = IntParameter(-100, -75, default=-80, space="sell", optimize=True)
    sell_ema = IntParameter(7, 22, default=8, space='sell', optimize=True)
    sell_willy = IntParameter(15, 30, default=15, space='sell', optimize=True)
    buy_sl_mult = DecimalParameter(0.5, 5, default=3.7, decimals=1, space='buy', optimize=True)

    # signals
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """ adds indicators to dataframe """
        for val in self.buy_sl_mult.range:
            dataframe[f'sl_l{val}'] = self.cust_stoploss(dataframe, length=20, mult=val)['Sl_low']
        for val in self.buy_sl_mult.range:
            dataframe[f'sl_h{val}'] = self.cust_stoploss(dataframe, length=20, mult=val)['Sl_high']
        # BUY
        # EMAs
        for val in self.buy_ema1_length.range:
            dataframe[f"buy_ema1_{val}"] = ta.EMA(dataframe, val)
        for val in self.buy_ema2_length.range:
            dataframe[f"buy_ema2_{val}"] = ta.EMA(dataframe, val)
        for val in self.buy_ema3_length.range:
            dataframe[f"buy_ema3_{val}"] = ta.EMA(dataframe, val)

        # RSI
        for val in self.buy_rsi_length.range:
            dataframe[f"buy_rsi_{val}"] = ta.RSI(dataframe, val)

        # MACD
        for fast_val in self.buy_macd_fast.range:
            for slow_val in self.buy_macd_slow.range:
                for smooth_val in self.buy_macd_smooth.range:
                    if smooth_val < fast_val < slow_val:
                        macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
                        dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                        dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
                    else:
                        macd = ta.MACD(dataframe, 12, 26, 9)
                        dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                        dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']

        # SELL
        # EMAs
        for val in self.sell_ema1_length.range:
            dataframe[f"sell_ema1_{val}"] = ta.EMA(dataframe, val)
        for val in self.sell_ema2_length.range:
            dataframe[f"sell_ema2_{val}"] = ta.EMA(dataframe, val)
        for val in self.sell_ema3_length.range:
            dataframe[f"sell_ema3_{val}"] = ta.EMA(dataframe, val)

        # RSI
        for val in self.sell_rsi_length.range:
            dataframe[f"sell_rsi_{val}"] = ta.RSI(dataframe, val)

        # MACD
        for fast_val in self.sell_macd_fast.range:
            for slow_val in self.sell_macd_slow.range:
                for smooth_val in self.sell_macd_smooth.range:
                    if smooth_val < fast_val < slow_val:
                        macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
                        dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                        dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
                    else:
                        macd = ta.MACD(dataframe, 12, 26, 9)
                        dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
                        dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']

        for val in self.sell_willy.range:
            dataframe[f'sell_willy{val}'] = self.willy_ema(dataframe, low_line=-80,
                                                           up_line=-20, willyLen=val, emaLen=13)[
                'Willy']
        for val in self.sell_ema.range:
            dataframe[f'sell_ema_will{val}'] = \
                self.willy_ema(dataframe, low_line=-80, up_line=-20, willyLen=21, emaLen=val)[
                    'Ema_will']

        # DOJI candlestick pattern
        doji = ta.CDLDOJI(dataframe)
        doji_dir = (dataframe["high"] - dataframe["close"]) / (dataframe["close"] - dataframe["low"])
        dataframe.loc[((doji == 100) & (doji_dir > 1), "doji")] = dataframe["high"]
        dataframe.loc[((doji == 100) & (doji_dir < 1), "doji")] = dataframe["low"]

        # MARUBOZU candlestick pattern
        body = (dataframe["close"] - dataframe["open"]).abs()
        wick_top = dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
        wick_bottom = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
        marubozu = (wick_top / body < 0.25) & (wick_bottom / body < 0.25)
        dataframe.loc[(marubozu & (body > 0), 'marubozu')] = dataframe["high"]
        dataframe.loc[(marubozu & (body < 0), 'marubozu')] = dataframe["low"]

        return dataframe

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

        # BUY signal
        # only trade if there is volume
        conditions = [(dataframe['volume'] > 0)]

        # Close > EMA1 > EMA2 > EMA3
        if self.buy_ema_enabled.value:
            conditions.append((dataframe["close"] > dataframe[f"buy_ema1_{self.buy_ema1_length.value}"]) &
                              (dataframe[f"buy_ema1_{self.buy_ema1_length.value}"] > dataframe[
                                  f"buy_ema2_{self.buy_ema2_length.value}"]) &
                              (dataframe[f"buy_ema2_{self.buy_ema2_length.value}"] > dataframe[
                                  f"buy_ema3_{self.buy_ema3_length.value}"]))

        # RSI > RSI_Threshold_Long
        if self.buy_rsi_enabled:
            conditions.append(dataframe[f"buy_rsi_{self.buy_rsi_length.value}"] > self.buy_rsi_threshold.value)

        # MACD cross MACDs above or MACD > 0
        if self.buy_macd_enabled.value:
            macd = dataframe[
                f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
            macds = dataframe[
                f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
            conditions.append(qtpylib.crossed_above(macd, macds) | (macd > 0))

        # Candlestick patterns: any DOJI and green MARUBOZU
        if self.buy_candlestick_enabled.value:
            doji = dataframe['doji'] == dataframe["high"]
            marubozu = dataframe['marubozu'] == dataframe["high"]
            conditions.append(doji | marubozu)

        # combine conditions and return
        buy_conditions = reduce(lambda x, y: x & y, conditions)
        dataframe.loc[buy_conditions, 'enter_long'] = 1

        # SELL signal
        # only trade if there is volume
        conditions = [(dataframe['volume'] > 0)]

        # Close < EMA1 < EMA2 < EMA3
        if self.sell_ema_enabled.value:
            conditions.append((dataframe["close"] < dataframe[f"sell_ema1_{self.sell_ema1_length.value}"]) &
                              (dataframe[f"sell_ema1_{self.sell_ema1_length.value}"] < dataframe[
                                  f"sell_ema2_{self.sell_ema2_length.value}"]) &
                              (dataframe[f"sell_ema2_{self.sell_ema2_length.value}"] < dataframe[
                                  f"sell_ema3_{self.sell_ema3_length.value}"]))

        # RSI < RSI_Threshold_Long
        if self.sell_rsi_enabled:
            conditions.append(dataframe[f"sell_rsi_{self.sell_rsi_length.value}"] < self.sell_rsi_threshold.value)

        # MACD cross MACDs below or MACD < 0
        if self.sell_macd_enabled.value:
            macd = dataframe[
                f'sell_macd_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
            macds = dataframe[
                f'sell_macds_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
            conditions.append(qtpylib.crossed_below(macd, macds) | (macd < 0))

        # Candlestick patterns, any DOJI and red MARUBOZU
        if self.sell_candlestick_enabled.value:
            doji = dataframe['doji'] == dataframe['low']
            marubozu = dataframe['marubozu'] == dataframe['low']
            conditions.append(doji | marubozu)

        # combine conditions and return
        sell_conditions = reduce(lambda x, y: x & y, conditions)
        dataframe.loc[sell_conditions, 'enter_short'] = 1


        return dataframe

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

        if self.sell_willy_exit.value:
            dataframe.loc[(dataframe[f'sell_ema_will{self.sell_ema.value}'] > self.sell_high_line.value),
            'exit_long'] = 1

        if self.sell_willy_exit.value:
            dataframe.loc[(dataframe[f'sell_ema_will{self.sell_ema.value}'] < self.sell_low_line.value),
            'exit_short'] = 1

        return dataframe

    stop_info = {}

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        """ exit on EMA3 cross, or exit at entry candle low (long) or high (short) """
        # print("cust stoploss", current_profit, self.buy_cust_stoploss_.value * -1)
        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        close = current_rate

        # exit on EMA20 cross
        if trade.is_short:
            ema3 = last_candle[f"sell_ema3_{self.sell_ema3_length.value}"]
            if close > ema3:
                if DEBUG:
                    print(
                        f"{pair} EMA20 exit short at {current_time}, price={current_rate}, level={ema3}, profit={current_profit}")
                return "ema20_cross_exit"
        else:
            ema3 = last_candle[f"buy_ema3_{self.buy_ema3_length.value}"]
            if close < ema3:
                if DEBUG:
                    print(
                        f"{pair} EMA20 exit long at {current_time}, price={current_rate}, level={ema3}, profit={current_profit}")
                return "ema20_cross_exit"

        # exit on entry candle low/high
        if trade.pair in self.stop_info:
            if trade.is_short:
                if close > self.stop_info[trade.pair]["level"]:
                    if DEBUG:
                        print(f"{pair} entry candle high break exit short at {current_time}, price={current_rate}, "
                              f"level={self.stop_info[trade.pair]['level']}, profit={current_profit}")
                    return "candle-based stop_loss"
            else:
                if close < self.stop_info[trade.pair]['level']:
                    if DEBUG:
                        print(f"{pair} entry candle low break exit long at {current_time}, price={current_rate}, "
                              f"level={self.stop_info[trade.pair]['level']}, profit={current_profit}")
                    return "candle-based stop_loss"

        # exit on 3rd take profit, if it sums to 100%
        if self.tp1_size + self.tp2_size + self.tp3_size == 1:
            if trade.is_short:
                ema1 = last_candle[f"sell_ema1_{self.sell_ema1_length.value}"]
                if close < ema1 * (1 - self.tp3_dist):
                    if DEBUG:
                        print(f"{pair} 3rd take profit filled => exit short at {current_time}, price={current_rate}, "
                              f"level={ema1 * (1 - self.tp3_dist)}, profit={current_profit}")
                    return "take profit 3"
            else:
                ema1 = last_candle[f"buy_ema1_{self.buy_ema1_length.value}"]
                if close > ema1 * (1 + self.tp3_dist):
                    if DEBUG:
                        print(f"{pair} 3rd take profit filled => exit long at {current_time}, price={current_rate}, "
                              f"level={ema1 * (1 + self.tp3_dist)}, profit={current_profit}")
                    return "take profit 3"
        if current_profit < self.buy_cust_stoploss_.value * -1:
            if Debug_Stoploss:
                print("cust stoploss", current_profit, self.buy_cust_stoploss_.value * -1)
            return "custon stoploss exit"

        return False

    # use_custom_stoploss = True

    # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
    #                     current_rate: float, current_profit: float, **kwargs) -> float:
    #     dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
    #     candle = dataframe.iloc[-1].squeeze()
    #
    #     stoploss_pricel = candle[f'sl_l{self.buy_sl_mult.value}']
    #     stoploss_prices = candle[f'sl_h{self.buy_sl_mult.value}']
    #     middle_of_cand = (candle['high'] + candle['low'] + candle['close'] + candle['open']) / 4
    #     if Debug_Stoploss:
    #         print('trade ID =>', trade.id, 'Trade is short? =>', trade.is_short)
    #         print('sl hihg =>', candle[f'sl_h{self.buy_sl_mult.value}'], 'sl low =>',
    #               candle[f'sl_l{self.buy_sl_mult.value}'])
    #         print('candle middle =>', middle_of_cand)
    #     if not trade.is_short:
    #         if Debug_Stoploss:
    #             print('   ')
    #             print('Trade is long', 'Stoploss ================================>',
    #                   (middle_of_cand - stoploss_pricel) / candle['close'] * -1)
    #             print('   ')
    #         return (middle_of_cand - stoploss_pricel) / candle['close'] * -1
    #
    #     if trade.is_short:
    #         if Debug_Stoploss:
    #             print('   ')
    #             print('Trade is short', 'Stoploss ==============================>',
    #                   (middle_of_cand - stoploss_prices) / candle['close'])
    #             print('   ')
    #         return (middle_of_cand - stoploss_prices) / candle['close']
    #
    #     return 100

    # Position Size
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
                            leverage: float, entry_tag: Optional[str], side: str,
                            **kwargs) -> float:

        stake_em = self.wallets.get_total_stake_amount() * self.buy_total_risk_trade.value

        if Debug_Amount:
            print(stake_em, (self.buy_cust_stoploss_.value * 100))
            print("Stake amount", stake_em / self.buy_cust_stoploss_.value)
        return stake_em / self.buy_cust_stoploss_.value



        # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        # candle = dataframe.iloc[-1].squeeze()
        # stoploss_pricel = candle[f'sl_l{self.buy_sl_mult.value}']
        # stoploss_prices = candle[f'sl_h{self.buy_sl_mult.value}']
        # middle_of_cand = (candle['high'] + candle['low'] + candle['close'] + candle['open']) / 4
        # if side == "long":
        #     stoploss_ = (middle_of_cand - stoploss_pricel) / candle['close'] * 100
        #     amount_ = self.wallets.get_total_stake_amount() / 100
        #     if Debug_Amount:
        #         print('stoploss from amount = >', stoploss_)
        #         print('full wallet =>>>', self.wallets.get_total_stake_amount())
        #         print('amount stoploss', amount_)
        #         print('Amount final =>>', amount_ / stoploss_ * 100)
        #     return (amount_ / stoploss_) * 100
        #
        # else:
        #     stoploss_ = (middle_of_cand - stoploss_prices) / candle['close'] * -1 * 100
        #     amount_ = self.wallets.get_total_stake_amount() / 100
        #     if Debug_Amount:
        #         print('stoploos from amount =>', stoploss_)
        #         print('full wallet =>>>', self.wallets.get_total_stake_amount())
        #         print('amount stoploss', amount_)
        #         print('Amount final=>>', (amount_ / stoploss_) * 100)
        #     return (amount_ / stoploss_) * 100

        # return proposed_stake

    # Take Profit
    position_adjustment_enable = True
    max_entry_position_adjustment = -1

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: Optional[float], max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs) -> Optional[float]:
        """ custom take profits """

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

        if trade.is_short:
            if trade.nr_of_successful_exits == 0:
                if current_rate < (1 - self.tp1_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
                    -1].squeeze():
                    return -trade.amount_requested * self.tp1_size * current_rate
            if trade.nr_of_successful_exits == 1:
                if current_rate < (1 - self.tp2_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
                    -1].squeeze():
                    return -trade.amount_requested * self.tp2_size * current_rate
            if trade.nr_of_successful_exits == 2:
                if current_rate < (1 - self.tp3_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
                    -1].squeeze():
                    if self.tp1_size + self.tp2_size + self.tp3_size != 1:
                        return -trade.amount_requested * self.tp3_size * current_rate
        else:
            if trade.nr_of_successful_exits == 0:
                if current_rate > (1 + self.tp1_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
                    -1].squeeze():
                    return -trade.amount_requested * self.tp1_size * current_rate
            if trade.nr_of_successful_exits == 1:
                if current_rate > (1 + self.tp2_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
                    -1].squeeze():
                    return -trade.amount_requested * self.tp2_size * current_rate
            if trade.nr_of_successful_exits == 2:
                if current_rate > (1 + self.tp3_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
                    -1].squeeze():
                    if self.tp1_size + self.tp2_size + self.tp3_size != 1:
                        return -trade.amount_requested * self.tp3_size * current_rate

        return None

    # set up plotting
    @property
    def plot_config(self):
        plot_config = {
            'main_plot': {
                f"buy_ema1_{self.buy_ema1_length.value}": {},
                f"buy_ema2_{self.buy_ema2_length.value}": {},
                f"buy_ema3_{self.buy_ema3_length.value}": {},
                f"doji": {"type": "scatter"},
                f"marubozu": {"type": "scatter"}
            },
            'subplots': {
                "MACD": {
                    f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
                    f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
                },
                "RSI": {
                    f'buy_rsi_{self.buy_rsi_length.value}': {}
                }
            }}

        return plot_config

    def willy_ema(self, dataframe: DataFrame, low_line=-80.0, up_line=-20.0, willyLen=21, emaLen=13):

        def max_help(open, high, low, close):
            return max(open, high, low, close)

        def min_help(open, high, low, close):
            return min(open, high, low, close)

        df = dataframe.copy()
        df['highest'] = np.vectorize(max_help)(df['open'], df['high'], df['low'], df['close'])
        df['lowest'] = np.vectorize(min_help)(df['open'], df['high'], df['low'], df['close'])
        df['upper'] = df['highest'].rolling(willyLen).max()
        df['lower'] = df['lowest'].rolling(willyLen).min()
        df['willy'] = 100 * (df['close'] - df['upper']) / (df['upper'] - df['lower'])
        df['ema_will'] = ta.EMA(df['willy'], emaLen)
        # df['ema_willy'] =
        df.drop(['highest', 'lowest', 'upper', 'lower'], inplace=True, axis=1)
        df['low_line'] = low_line
        df['up_line'] = up_line
        return DataFrame(index=df.index, data={
            'Willy': df['willy'],
            'Ema_will': df['ema_will'],
            'Low_line': df['low_line'],
            'Up_line': df['up_line']
        })

    def cust_stoploss(self, dataframe: DataFrame, mult, length):
        df = dataframe.copy()
        df['sma'] = qtpylib.sma(df['close'], length)
        df['tr'] = qtpylib.atr(df, length)

        df['sl_high'] = qtpylib.sma(df['tr'], length)

        def help1(x, mul, high):
            return x * mul + high

        df['sl_high'] = np.vectorize(help1)(df['sl_high'], mult, df['high'])

        df['sl_low'] = qtpylib.sma(df['tr'], length)

        def help1(x, mul, low):
            return low - x * mul

        df['sl_low'] = np.vectorize(help1)(df['sl_low'], mult, df['low'])
        df.drop(['sma', 'tr'], axis=1, inplace=True)
        return DataFrame(index=df.index, data={
            'Sl_high': df['sl_high'],
            'Sl_low': df['sl_low'],
        })

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
                            side: str, **kwargs) -> bool:
        hal_per = self.wallets.get_total_stake_amount() / 200
        usdt_amount = amount * rate
        if usdt_amount > hal_per:
            if Debug_Amount:
                print(f"Amount of trade USDT{usdt_amount} -- (minimal{hal_per})")
            return True
        if Debug_Amount:
            print(f"Trade was rejected ============================>>>>> actual === {usdt_amount} - minimal === {hal_per}")
        return False