from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import DecimalParameter
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Low_BBHO(IStrategy):
    """
    author@: Thorsten
    works on new objectify branch!
    idea:
        buy after crossing .98 * lower_bb and sell if trailing stop loss is hit
    """


    minimal_roi = {
        "0": 0.085,
        "7": 0.011,
        "19": 0.007,
        "40": 0
    }

    buy_params = {
        "buy_limit": 0.98,
    }


    stoploss = -0.263

    timeframe = '1m'

    buy_limit = DecimalParameter(0.90, 0.98, default=0.98, space='buy', decimals=3, optimize=True, load=True)
    

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



        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']










        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:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (

                (dataframe['close'] <= self.buy_limit.value * dataframe['bb_lowerband'])

            )
            ,
            '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
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (),
            'sell'] = 1
        return dataframe