81 lines
2.0 KiB
Python
81 lines
2.0 KiB
Python
"""
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IHyperOpt interface
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This module defines the interface to apply for hyperopts
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, Any, Callable, List
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from pandas import DataFrame
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from skopt.space import Dimension
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class IHyperOpt(ABC):
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"""
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Interface for freqtrade hyperopts
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Defines the mandatory structure must follow any custom strategies
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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: optimal stoploss designed for the strategy
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ticker_interval -> int: value of the ticker interval to use for the strategy
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"""
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@staticmethod
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@abstractmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> 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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@staticmethod
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@abstractmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Create a buy strategy generator
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"""
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@staticmethod
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@abstractmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Create a sell strategy generator
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"""
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@staticmethod
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@abstractmethod
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def indicator_space() -> List[Dimension]:
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"""
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Create an indicator space
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"""
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@staticmethod
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@abstractmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Create a sell indicator space
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"""
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@staticmethod
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@abstractmethod
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def generate_roi_table(params: Dict) -> Dict[int, float]:
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"""
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Create an roi table
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"""
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@staticmethod
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@abstractmethod
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def stoploss_space() -> List[Dimension]:
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"""
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Create a stoploss space
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"""
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@staticmethod
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@abstractmethod
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def roi_space() -> List[Dimension]:
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"""
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Create a roi space
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"""
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