# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------

import talib.abstract as ta
from datetime import datetime, timedelta
from freqtrade.persistence import Trade


# thanks tirail for original SMAOffset sharing
# added trend detection and stoploss


class SMAOffsetV2(IStrategy):
    minimal_roi = {
        "0": 1,
    }

    stoploss = -0.20
    timeframe = '30m'
    informative_timeframe = '1h'
    use_sell_signal = True
    sell_profit_only = False
    process_only_new_candles = True

    use_custom_stoploss = True
    startup_candle_count = 200

    plot_config = {
        'main_plot': {
            'sma_30_offset': {'color': 'orange'},
            'sma_30_offset_pos': {'color': 'yellow'},
            'ema_fast': {'color': 'blue'},
            'ema_slow': {'color': 'green'},
        },
    }

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        if current_time - timedelta(minutes=40) > trade.open_date_utc and current_profit < -0.1:
            return -0.01

        return -0.99

    def informative_pairs(self):

        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]

        return informative_pairs

    @staticmethod
    def get_informative_indicators(dataframe: DataFrame, metadata: dict):

        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25)

        dataframe['go_long'] = (
                                   (dataframe['ema_fast'] > dataframe['ema_slow'])
                               ).astype('int') * 2

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.dp:
            return dataframe

        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)

        informative = self.get_informative_indicators(informative.copy(), metadata)

        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe,
                                           ffill=True)
        # don't overwrite the base dataframe's HLCV information
        skip_columns = [(s + "_" + self.informative_timeframe) for s in
                        ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.rename(
            columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s,
            inplace=True)

        # ---------------------------------------------------------------------------------

        sma_offset = (1 - 0.04)
        sma_offset_pos = (1 + 0.012)
        base_nb_candles = 20

        dataframe['sma_30_offset'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset
        dataframe['sma_30_offset_pos'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset_pos

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['go_long'] > 0)
                    &
                    (dataframe['close'] < dataframe['sma_30_offset'])
                    &
                    (dataframe['volume'] > 0)
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (
                            (dataframe['go_long'] == 0)
                            |
                            (dataframe['close'] > dataframe['sma_30_offset_pos'])
                    )
                    &
                    (dataframe['volume'] > 0)
            ),
            'sell'] = 1
        return dataframe