96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
# by: Mablue (Masoud Azizi)
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class mabStraHo(IHyperOpt):
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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Real(0, 1, name='buy-div-min'),
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Real(0, 1, name='buy-div-max'),
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]
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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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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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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# div result limited to 0~1 so allways in buy position slowMa is lower than fastMa
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# optimum number will calculate by this two lines
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conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
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> params['buy-div-min'])
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conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
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< params['buy-div-max'])
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Real(0, 1, name='sell-div-min'),
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Real(0, 1, name='sell-div-max'),
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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
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> params['sell-div-min'])
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conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
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< params['sell-div-max'])
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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return dataframe
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return populate_sell_trend
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