CombinedBinHAndClucV5

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
###########################################################################################################
##                CombinedBinHAndClucV5 by iterativ                                                      ##
##                                                                                                       ##
##    Fretrade https://github.com/freqtrade/freqtrade                                                    ##
##    The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies  ##
##    V5 by iterativ.                                                                                    ##
##                                                                                                       ##
###########################################################################################################
##               GENERAL RECOMMENDATIONS                                                                 ##
##                                                                                                       ##
##   For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake.        ##
##   A pairlist with 20 to 40 pairs. Volume pairlist works well.                                         ##
##   Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs.                            ##
##   Ensure that you don't override any variables in you config.json. Especially                         ##
##   the timeframe (must be 5m) & exit_profit_only (must be true).                                       ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS                                                                               ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                                     ##
##                                                                                                       ##
###########################################################################################################

class CombinedBinHAndClucV5(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.018}
    stoploss = -0.99  # effectively disabled.
    timeframe = '5m'
    # Sell signal
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.001  # it doesn't meant anything, just to guarantee there is a minimal profit.
    ignore_roi_if_entry_signal = True
    # Trailing stoploss
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.025
    # Custom stoploss
    use_custom_stoploss = True
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 50
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        # Manage losing trades and open room for better ones.
        if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc):
            return 0.01
        return 0.99

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # strategy BinHV45
        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()
        # strategy ClucMay72018
        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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # strategy BinHV45
        # Make sure Volume is not 0
        # strategy ClucMay72018
        # Make sure Volume is not 0
        dataframe.loc[dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.008) & dataframe['closedelta'].gt(dataframe['close'] * 0.0175) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) | (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_slow'].shift(1) * 20) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Improves the profit slightly.
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe