import talib.abstract as ta
from pandas import DataFrame

import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.indicator_helpers import fishers_inverse
from freqtrade.strategy.interface import IStrategy


class low_bb(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": 0.9,
        "1": 0.05,
        "10": 0.04,
        "15": 0.5
    }


    stoploss = -0.015



    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    }

    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc',
    }

    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']

        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=20)

        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[
            (

                qtpylib.crossed_below(dataframe['close'], 0.98 * 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'] = 0
        return dataframe