stable/tests/strategy/strats/hyperoptable_strategy.py

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

from pandas import DataFrame
from strategy_test_v3 import StrategyTestV3

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter


class HyperoptableStrategy(StrategyTestV3):
    """
    Default Strategy provided by freqtrade bot.
    Please do not modify this strategy, it's  intended for internal use only.
    Please look at the SampleStrategy in the user_data/strategy directory
    or strategy repository https://github.com/freqtrade/freqtrade-strategies
    for samples and inspiration.
    """

    buy_params = {
        'buy_rsi': 35,
        # Intentionally not specified, so "default" is tested
        # 'buy_plusdi': 0.4
    }

    sell_params = {
        'sell_rsi': 74,
        'sell_minusdi': 0.4
    }

    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
                                    load=False)
    protection_enabled = BooleanParameter(default=True)
    protection_cooldown_lookback = IntParameter([0, 50], default=30)

    # Invalid plot config ...
    plot_config = {
        "main_plot": {},
    }

    @property
    def protections(self):
        prot = []
        if self.protection_enabled.value:
            prot.append({
                "method": "CooldownPeriod",
                "stop_duration_candles": self.protection_cooldown_lookback.value
            })
        return prot

    bot_loop_started = False

    def bot_loop_start(self):
        self.bot_loop_started = True

    def bot_start(self, **kwargs) -> None:
        """
        Parameters can also be defined here ...
        """
        self.buy_rsi = IntParameter([0, 50], default=30, space='buy')

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['rsi'] < self.buy_rsi.value) &
                (dataframe['fastd'] < 35) &
                (dataframe['adx'] > 30) &
                (dataframe['plus_di'] > self.buy_plusdi.value)
            ) |
            (
                (dataframe['adx'] > 65) &
                (dataframe['plus_di'] > self.buy_plusdi.value)
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with sell column
        """
        dataframe.loc[
            (
                (
                    (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
                    (qtpylib.crossed_above(dataframe['fastd'], 70))
                ) &
                (dataframe['adx'] > 10) &
                (dataframe['minus_di'] > 0)
            ) |
            (
                (dataframe['adx'] > 70) &
                (dataframe['minus_di'] > self.sell_minusdi.value)
            ),
            'sell'] = 1
        return dataframe