115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from pandas import DataFrame
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from strategy_test_v2 import StrategyTestV2
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
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class HyperoptableStrategy(StrategyTestV2):
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"""
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Default Strategy provided by freqtrade bot.
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Please do not modify this strategy, it's intended for internal use only.
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Please look at the SampleStrategy in the user_data/strategy directory
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or strategy repository https://github.com/freqtrade/freqtrade-strategies
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for samples and inspiration.
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"""
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buy_params = {
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'buy_rsi': 35,
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# Intentionally not specified, so "default" is tested
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# 'buy_plusdi': 0.4
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}
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sell_params = {
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'sell_rsi': 74,
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'sell_minusdi': 0.4
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}
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buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
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sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
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load=False)
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protection_enabled = BooleanParameter(default=True)
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protection_cooldown_lookback = IntParameter([0, 50], default=30)
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@property
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def protections(self):
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prot = []
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if self.protection_enabled.value:
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prot.append({
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"method": "CooldownPeriod",
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"stop_duration_candles": self.protection_cooldown_lookback.value
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})
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return prot
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bot_loop_started = False
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def bot_loop_start(self):
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self.bot_loop_started = True
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def bot_start(self, **kwargs) -> None:
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"""
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Parameters can also be defined here ...
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"""
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self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] < self.buy_rsi.value) &
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(dataframe['fastd'] < 35) &
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(dataframe['adx'] > 30) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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) |
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(
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with sell column
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"""
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dataframe.loc[
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(
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(
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(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > self.sell_minusdi.value)
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),
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'sell'] = 1
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return dataframe
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