from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import CategoricalParameter, IntParameter
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 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
import numpy  # noqa

class ReinforcedQuickie_86(IStrategy):
    """

    author@: Gert Wohlgemuth

    works on new objectify branch!

    idea:
        only buy on an upward tending market
    """


    minimal_roi = {
        "0": 0.159,
        "34": 0.051,
        "63": 0.028,
        "143": 0.01
    }

    stoploss = -0.99

    trailing_stop = True
    trailing_stop_positive = 0.028
    trailing_stop_positive_offset = 0.109
    trailing_only_offset_is_reached = False

    buy_mfi = IntParameter(low=10, high=100, default=30, space='buy', optimize=True)
    sell_mfi = IntParameter(low=10, high=100, default=80, space='sell', optimize=True)
    buy_rsi = IntParameter(low=10, high=100, default=30, space='buy', optimize=True)
    sell_rsi = IntParameter(low=10, high=100, default=70, space='sell', optimize=True)

    timeframe = '5m'

    resample_factor = 12

    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 12
    EMA_LONG_TERM = 21

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)



        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=1
        )
        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)

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

        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4


        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']

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

        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'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                                    (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
                                    (dataframe['close'] == dataframe['min']) &
                                    (dataframe['close'] <= dataframe['bb_lowerband'])
                            )
                            |



                            (
                                    (dataframe['average'].shift(5) > dataframe['average'].shift(4))
                                    & (dataframe['average'].shift(4) > dataframe['average'].shift(3))
                                    & (dataframe['average'].shift(3) > dataframe['average'].shift(2))
                                    & (dataframe['average'].shift(2) > dataframe['average'].shift(1))
                                    & (dataframe['average'].shift(1) < dataframe['average'].shift(0))
                                    & (dataframe['low'].shift(1) < dataframe['bb_middleband'])
                                    & (dataframe['cci'].shift(1) < -100)
                                    & (dataframe['rsi'].shift(1) < self.buy_rsi.value)
                                    & (dataframe['mfi'].shift(1) < self.buy_mfi.value)

                            )
                    )

                    &
                    (
                            (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
                            (dataframe['resample_sma'] < dataframe['close']) &
                            (dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])
                    )
            )
            ,
            '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[

            (
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
                    (dataframe['close'] >= dataframe['max']) &
                    (dataframe['close'] >= dataframe['bb_upperband']) &
                    (dataframe['mfi'] > self.sell_mfi.value)
            ) |


            (
                    (dataframe['open'] < dataframe['close']) &
                    (dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
                    (dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
                    (dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
                    (dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
                    (dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
                    (dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
                    (dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
                    (dataframe['rsi'] > self.sell_rsi.value)
            )
            ,
            'sell'
        ] = 1
        return dataframe

    def resample(self, dataframe, interval, factor):


        df = dataframe.copy()
        df = df.set_index(DatetimeIndex(df['date']))
        ohlc_dict = {
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last'
        }
        df = df.resample(str(int(interval[:-1]) * factor) + 'min',
                         label="right").agg(ohlc_dict).dropna(how='any')
        df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close')
        df = df.drop(columns=['open', 'high', 'low', 'close'])
        df = df.resample(interval[:-1] + 'min')
        df = df.interpolate(method='time')
        df['date'] = df.index
        df.index = range(len(df))
        dataframe = merge(dataframe, df, on='date', how='left')
        return dataframe