155 lines
4.9 KiB
Python
155 lines
4.9 KiB
Python
import talib.abstract as ta
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from pandas import DataFrame
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from typing import Dict, Any, Callable, List
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from functools import reduce
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from skopt.space import Categorical, Dimension, Integer, Real
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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shortRangeBegin = 10
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shortRangeEnd = 20
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mediumRangeBegin = 100
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mediumRangeEnd = 120
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class AverageHyperopt(IHyperOpt):
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"""
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Hyperopt file for optimizing AverageStrategy.
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Uses ranges of EMA periods to find the best parameter combination.
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"""
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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for short in range(shortRangeBegin, shortRangeEnd):
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dataframe[f'maShort({short})'] = ta.EMA(dataframe, timeperiod=short)
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for medium in range(mediumRangeBegin, mediumRangeEnd):
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dataframe[f'maMedium({medium})'] = ta.EMA(dataframe, timeperiod=medium)
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return dataframe
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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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# TRIGGERS
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if 'trigger' in params:
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conditions.append(qtpylib.crossed_above(
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dataframe[f"maShort({params['trigger'][0]})"],
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dataframe[f"maMedium({params['trigger'][1]})"])
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)
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# Check that volume is not 0
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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 strategy parameters
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"""
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buyTriggerList = []
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for short in range(shortRangeBegin, shortRangeEnd):
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for medium in range(mediumRangeBegin, mediumRangeEnd):
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# The output will be (short, long)
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buyTriggerList.append(
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(short, medium)
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)
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return [
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Categorical(buyTriggerList, name='trigger')
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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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# print(params)
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conditions = []
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# TRIGGERS
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if 'sell-trigger' in params:
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conditions.append(qtpylib.crossed_above(
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dataframe[f"maMedium({params['sell-trigger'][1]})"],
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dataframe[f"maShort({params['sell-trigger'][0]})"])
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)
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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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@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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sellTriggerList = []
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for short in range(shortRangeBegin, shortRangeEnd):
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for medium in range(mediumRangeBegin, mediumRangeEnd):
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# The output will be (short, long)
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sellTriggerList.append(
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(short, medium)
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)
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return [
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Categorical(sellTriggerList, name='sell-trigger')
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]
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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. Should be a copy of from strategy
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must align to populate_indicators in this file
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Only used when --spaces does not include buy
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"""
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dataframe.loc[
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(
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qtpylib.crossed_above(
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dataframe[f'maShort({shortRangeBegin})'],
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dataframe[f'maMedium({mediumRangeBegin})'])
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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. Should be a copy of from strategy
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must align to populate_indicators in this file
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Only used when --spaces does not include sell
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"""
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dataframe.loc[
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(
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qtpylib.crossed_above(
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dataframe[f'maMedium({mediumRangeBegin})'],
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dataframe[f'maShort({shortRangeBegin})'])
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),
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'sell'] = 1
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return dataframe
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