How to use the freqtrade.vendor.qtpylib.indicators function in freqtrade

def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) &
                (dataframe['cci'] <= -50.0)
            ),
            'buy'] = 1

        return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe

def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['adx'] = ta.ADX(dataframe)
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe)
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
        # Bollinger bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['sar'] = ta.SAR(dataframe)
        return dataframe

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        # Bollinger bands
        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']

        return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort'])
            ),
            'sell'] = 1
        return dataframe

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ##################################################################################
        # buy and sell indicators

        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']

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # dataframe['cci'] = ta.CCI(dataframe)
        # dataframe['mfi'] = ta.MFI(dataframe)
        # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)

        # dataframe['canbuy'] = np.NaN
        # dataframe['canbuy2'] = np.NaN

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
        dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
        dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)

        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']


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

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['adx'] < 25) &
                    (qtpylib.crossed_above(dataframe['long'], dataframe['short']))

            ),
            'sell'] = 1
        return dataframe

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            # Prod
            (
                (qtpylib.crossed_above(dataframe['rsi'], 50)) &
                (dataframe['macd'] < 0) &
                (dataframe['minus_di'] > 0)
            ) |
            (
                (dataframe['sar'] > dataframe['close']) &
                (dataframe['fisher_rsi'] > 0.3)
            ),

            'sell'] = 1
        return dataframe

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """ Adds several different TA indicators to the given DataFrame
        """

        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_SHORT_TERM
        )
        dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_MEDIUM_TERM
        )
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_LONG_TERM
        )

        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['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

        return dataframe