98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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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 BinHV45HyperOpt(IHyperOpt):
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"""
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Hyperopt file for optimizing BinHV45Strategy.
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Uses ranges to find best parameter combination for bbdelta, closedelta and tail
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of the buy strategy.
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Sell strategy is ignored, because it's ignored in BinHV45Strategy as well.
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This strategy therefor works without explicit sell signal therefor hyperopting
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for 'roi' is recommend as well
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Also, this is just ONE way to optimize this strategy - others might also include
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disabling certain conditions completely. This file is just a starting point, feel free
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to improve and PR.
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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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conditions.append(dataframe['lower'].shift().gt(0))
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conditions.append(dataframe['bbdelta'].gt(
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dataframe['close'] * params['bbdelta'] / 1000))
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conditions.append(dataframe['closedelta'].gt(
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dataframe['close'] * params['closedelta'] / 1000))
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conditions.append(dataframe['tail'].lt(dataframe['bbdelta'] * params['tail'] / 1000))
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conditions.append(dataframe['close'].lt(dataframe['lower'].shift()))
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conditions.append(dataframe['close'].le(dataframe['close'].shift()))
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# Check that the candle had volume
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conditions.append(dataframe['volume'] > 0)
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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 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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Integer(1, 15, name='bbdelta'),
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Integer(15, 20, name='closedelta'),
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Integer(20, 30, name='tail'),
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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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no sell signal
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"""
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dataframe['sell'] = 0
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return dataframe
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return populate_sell_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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