57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
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from abc import ABC, abstractmethod
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from pandas import DataFrame
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from typing import Dict
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class IStrategy(ABC):
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@property
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def name(self) -> str:
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"""
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Name of the strategy.
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:return: str representation of the class name
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"""
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return self.__class__.__name__
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"""
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Attributes you can use:
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minimal_roi -> Dict: Minimal ROI designed for the strategy
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stoploss -> float: ptimal stoploss designed for the strategy
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"""
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@abstractmethod
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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@abstractmethod
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def populate_buy_trend(self, dataframe: DataFrame) -> 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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:return: DataFrame with buy column
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:return:
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"""
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@abstractmethod
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def populate_sell_trend(self, dataframe: DataFrame) -> 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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:return: DataFrame with buy column
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"""
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@abstractmethod
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def hyperopt_space(self) -> Dict:
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"""
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Define your Hyperopt space for the strategy
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"""
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@abstractmethod
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def buy_strategy_generator(self, params) -> None:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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