# source: https://raw.githubusercontent.com/freqtrade/freqtrade-strategies/98ab175ee301eee699b9de1a27a3cc73a368a889/user_data/strategies/berlinguyinca/CofiBitStrategy.py
# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.strategy import IStrategy
from freqtrade.strategy import IntParameter
from pandas import DataFrame


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


class github_freqtrade_freqtrade_strategies__CofiBitStrategy__20220710_131433(IStrategy):
    """
        taken from slack by user CofiBit
    """

    INTERFACE_VERSION: int = 3
    # Buy hyperspace params:
    buy_params = {
        "buy_fastx": 25,
        "buy_adx": 25,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_fastx": 75,
    }

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "40": 0.05,
        "30": 0.06,
        "20": 0.07,
        "0": 0.10
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.25

    # Optimal timeframe for the strategy
    timeframe = '5m'

    buy_fastx = IntParameter(20, 30, default=25)
    buy_adx = IntParameter(20, 30, default=25)
    sell_fastx = IntParameter(70, 80, default=75)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        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')
        dataframe['adx'] = ta.ADX(dataframe)

        return dataframe

    def populate_entry_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['open'] < dataframe['ema_low']) &
                (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
                (dataframe['fastk'] < self.buy_fastx.value) &
                (dataframe['fastd'] < self.buy_fastx.value) &
                (dataframe['adx'] > self.buy_adx.value)
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_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['open'] >= dataframe['ema_high'])
            ) |
            (
                (qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value)) |
                (qtpylib.crossed_above(dataframe['fastd'], self.sell_fastx.value))
            ),
            'exit_long'] = 1

        return dataframe