freqtrade_origin/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
    bot_started = False

    def bot_loop_start(self, **kwargs):
        self.bot_loop_started = True

    def bot_start(self, **kwargs) -> None:
        """
        Parameters can also be defined here ...
        """
        self.bot_started = True
        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