import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import logging
from pandas import DataFrame, DatetimeIndex, merge
from freqtrade.strategy import IStrategy
from typing import Dict, List
from skopt.space import Dimension, Real


logger = logging.getLogger(__name__)



class QuickieHyperStrategy(IStrategy):
    """
    enhanced auto hyperoptable version based on
    https://github.com/back8/github_freqtrade_freqtrade-strategies/blob/master/user_data/strategies/berlinguyinca/ReinforcedQuickie.py

    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    !!! as of today (14.04.2021) you need the freqtrade/develop version to be able     !!!
    !!! to run hyperopt/backtest with this new strategy format                         !!!
    !!!                                                                                !!!
    !!! please check https://github.com/freqtrade/freqtrade/pull/4596 for further      !!!
    !!! information about the new auto-hyperoptable strategies!                        !!!
    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    
    original author@: Gert Wohlgemuth

    idea:
        momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case
    """


    minimal_roi = {
        "100": 0.01,
        "30": 0.03,
        "15": 0.06,
        "10": 0.15,
    }


    stoploss = -0.25

    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=200)

        dataframe['adx'] = ta.ADX(dataframe)

        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['adx'] > 30) &
                    (dataframe['tema'] < dataframe['bb_middleband']) &
                    (dataframe['tema'] > dataframe['tema'].shift(1)) &
                    (dataframe['sma_200'] > dataframe['close'])

            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['adx'] > 70) &
                    (dataframe['tema'] > dataframe['bb_middleband']) &
                    (dataframe['tema'] < dataframe['tema'].shift(1))
            ),
            'sell'] = 1
        return dataframe

    class HyperOpt:



        @staticmethod
        def indicator_space() -> List[Dimension]:
            return []

        @staticmethod
        def sell_indicator_space() -> List[Dimension]:
            return []

        @staticmethod
        def stoploss_space() -> List[Dimension]:
            return [
                Real(-0.25, -0.01, name='stoploss'),
            ]