from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from hyperopt import hp
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 hyperopt import hp
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 ReinforcedSmoothScalp_3(IStrategy):
    """
        this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade

        we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
    """


    minimal_roi = {
        "0": 0.02
    }




    stoploss = -0.8


    ticker_interval = '1m'

    resample_factor = 5

    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
        dataframe = ReinforcedSmoothScalp.resample(dataframe, self.ticker_interval, self.resample_factor)

        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, 5.0, 3.0, 0.0, 3.0, 0.0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['mfi'] = ta.MFI(dataframe)

        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) -> DataFrame:
        dataframe.loc[
            (
                (
                        (dataframe['open'] < dataframe['ema_low']) &
                        (dataframe['adx'] > 30) &
                        (dataframe['mfi'] < 30) &
                        (
                                (dataframe['fastk'] < 30) &
                                (dataframe['fastd'] < 30) &
                                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
                        ) &
                        (dataframe['resample_sma'] < dataframe['close'])
                )





            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        dataframe.loc[
            (
                    (
                            (
                                (dataframe['open'] >= dataframe['ema_high'])

                            ) |
                            (
                                    (qtpylib.crossed_above(dataframe['fastk'], 70)) |
                                    (qtpylib.crossed_above(dataframe['fastd'], 70))

                            )
                    ) & (dataframe['cci'] > 100)
            )
            ,
            'sell'] = 1
        return dataframe

    @staticmethod
    def resample(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', how=ohlc_dict).dropna(
            how='any')
        df['resample_sma'] = ta.SMA(df, timeperiod=50, 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