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


logger = logging.getLogger(__name__)


class BBRSIHyperStrategy(IStrategy):

    """
    enhanced auto hyperoptable version based on the classic Bollinger Bands & RSI

    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    !!! 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!                        !!!
    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

    """

    minimal_roi = {
        "0": 0.01
    }

    stoploss = -0.05

    timeframe = '1h'

    startup_candle_count = 0

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    buy_params = {
        'buy_rsi_enabled': True,
        'buy_rsi_value': 30,
        'buy_bb_trigger': 'bb_lowerband1'
    }

    sell_params = {
        'sell_rsi_value': 80,
        'sell_bb_trigger': 'bb_upperband1'
     }

    buy_rsi_enabled =  CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_rsi_value = IntParameter(5, 30, default=30, space='buy', optimize=True, load=True)
    buy_bb_trigger = CategoricalParameter(
        [
            'bb_lowerband1', 
            'bb_lowerband2', 
            'bb_lowerband3', 
            'bb_lowerband4'
        ], 
        default='bb_lowerband1', space='buy', optimize=True, load=True)
    
    sell_rsi_value = IntParameter(40, 90, default=75, space='sell', optimize=True, load=True)
    sell_bb_trigger = CategoricalParameter(
        [
            'bb_lowerband1', 
            'bb_middleband1', 
            'bb_upperband1'
        ], 
        default='bb_upperband1', space='sell', optimize=True, load=True)

    use_sell_signal = True

    sell_profit_only = True

    ignore_roi_if_buy_signal = False

    def __init__(self, config: dict) -> None:
        super().__init__(config)
        try:
            from mergedeep import merge
        except ImportError as error:

            logger.info("could not import mergedeep, please check if pip is installed: %s", error)
            logger.info("therefor we are not able to merge parameters from config")
        else:
            logger.info('mergedeep found, so attempting to find strategy parameters in config file')
            if self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False):
                cfg_strategy_parameters = self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False)
                logger.info('strategy_parameters from config: %s', repr(cfg_strategy_parameters))
                if cfg_strategy_parameters.get('buy_params', {}):
                    logger.info('merging buy_params from config: %s', cfg_strategy_parameters.get('buy_params'))
                    merge(self.buy_params, cfg_strategy_parameters.get('buy_params'))
                if cfg_strategy_parameters.get('sell_params', {}):
                    logger.info('merging sell_params from config: %s', cfg_strategy_parameters.get('sell_params'))
                    merge(self.sell_params, cfg_strategy_parameters.get('sell_params'))
            else:
                logger.info('no strategy_parameters found in config')
            logger.info('final buy_params: %s', repr(self.buy_params))
            logger.info('final sell_params: %s', repr(self.sell_params))
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['rsi'] = ta.RSI(dataframe)

        bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['bb_lowerband1'] = bollinger1['lower']
        dataframe['bb_middleband1'] = bollinger1['mid']
        dataframe['bb_upperband1'] = bollinger1['upper']
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']
        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']
        bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
        dataframe['bb_lowerband4'] = bollinger4['lower']
        dataframe['bb_middleband4'] = bollinger4['mid']
        dataframe['bb_upperband4'] = bollinger4['upper']
        
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (        
                    ( # rsi enabled
                        (self.buy_rsi_enabled.value == True) &
                        (dataframe['rsi'] > self.buy_rsi_value.value)
                    ) | # rsi disabled
                    (self.buy_rsi_enabled.value == False) 
                ) &
                (dataframe["close"] < dataframe[self.buy_bb_trigger.value])
            ),
            'buy'
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['rsi'] > self.sell_rsi_value.value) &
                (dataframe["close"] > dataframe[self.sell_bb_trigger.value])
            ),
            'sell'
        ] = 1
        return dataframe

    class HyperOpt:

        @staticmethod
        def stoploss_space() -> List[Dimension]:
            return [
                Real(-0.5, -0.02, name='stoploss'),
            ]

        @staticmethod
        def roi_space() -> List[Dimension]:
            """
            Values to search for each ROI steps
            """
            return [
                Integer(10, 120, name='roi_t1'),
                Integer(10, 60, name='roi_t2'),
                Integer(10, 40, name='roi_t3'),
                Real(0.01, 0.04, name='roi_p1'),
                Real(0.01, 0.07, name='roi_p2'),
                Real(0.01, 0.20, name='roi_p3'),
            ]