# Import necessary libraries
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta

class # Import necessary libraries
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta

class BollingerBandsStrategy(IStrategy):
    # Define minimal ROI (return on investment) and stoploss
    minimal_roi = {
        "0": 0.10  # 10% profit before we consider selling
    }

    stoploss = -0.10  # 10% stoploss

    # Define the time frame for the strategy (5 minutes)
    timeframe = '5m'

    # Custom settings for Bollinger Bands (period, deviation)
    bollinger_period = 20
    bollinger_deviation = 2

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add Bollinger Bands to the dataframe
        # Upper and Lower Bands + Middle Band (SMA)
        bollinger = ta.BBANDS(dataframe, timeperiod=self.bollinger_period, nbdevup=self.bollinger_deviation, nbdevdn=self.bollinger_deviation, matype=0)
        
        dataframe['bb_upper'] = bollinger['upperband']
        dataframe['bb_lower'] = bollinger['lowerband']
        dataframe['bb_middle'] = bollinger['middleband']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Buy condition: Close price is above the upper Bollinger Band
        dataframe.loc[
            (
                dataframe['close'] > dataframe['bb_upper']
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Sell condition: Close price is below the lower Bollinger Band
        dataframe.loc[
            (
                dataframe['close'] < dataframe['bb_lower']
            ),
            'sell'] = 1
        return dataframe(IStrategy):
    # Define minimal ROI (return on investment) and stoploss
    minimal_roi = {
        "0": 0.10  # 10% profit before we consider selling
    }

    stoploss = -0.10  # 10% stoploss

    # Define the time frame for the strategy (5 minutes)
    timeframe = '5m'

    # Custom settings for Bollinger Bands (period, deviation)
    bollinger_period = 20
    bollinger_deviation = 2

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add Bollinger Bands to the dataframe
        # Upper and Lower Bands + Middle Band (SMA)
        bollinger = ta.BBANDS(dataframe, timeperiod=self.bollinger_period, nbdevup=self.bollinger_deviation, nbdevdn=self.bollinger_deviation, matype=0)
        
        dataframe['bb_upper'] = bollinger['upperband']
        dataframe['bb_lower'] = bollinger['lowerband']
        dataframe['bb_middle'] = bollinger['middleband']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Buy condition: Close price is above the upper Bollinger Band
        dataframe.loc[
            (
                dataframe['close'] > dataframe['bb_upper']
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Sell condition: Close price is below the lower Bollinger Band
        dataframe.loc[
            (
                dataframe['close'] < dataframe['bb_lower']
            ),
            'sell'] = 1
        return dataframe