cosmetics in sample_hyperopt and default_hyperopt
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hroff-1902
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85094a59e6
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e5dcd520ba
@ -14,36 +14,48 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
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class DefaultHyperOpts(IHyperOpt):
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"""
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Default hyperopt provided by the Freqtrade bot.
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You can override it with your own hyperopt
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You can override it with your own Hyperopt
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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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"""
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Add several indicators needed for buy and sell strategies defined below.
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"""
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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# Minus-DI
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_upperband'] = bollinger['upper']
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# SAR
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dataframe['sar'] = ta.SAR(dataframe)
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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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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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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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if 'mfi-enabled' in params and params['mfi-enabled']:
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conditions.append(dataframe['mfi'] < params['mfi-value'])
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@ -79,7 +91,7 @@ class DefaultHyperOpts(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 strategy parameters
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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(10, 25, name='mfi-value'),
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@ -96,14 +108,14 @@ class DefaultHyperOpts(IHyperOpt):
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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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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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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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# GUARDS AND TRENDS
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if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
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conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
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@ -139,7 +151,7 @@ class DefaultHyperOpts(IHyperOpt):
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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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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Integer(75, 100, name='sell-mfi-value'),
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@ -157,9 +169,9 @@ class DefaultHyperOpts(IHyperOpt):
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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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Based on TA indicators. Should be a copy of same method 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 space.
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"""
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dataframe.loc[
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(
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@ -174,9 +186,9 @@ class DefaultHyperOpts(IHyperOpt):
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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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Based on TA indicators. Should be a copy of same method 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 space.
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"""
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dataframe.loc[
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(
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@ -186,4 +198,5 @@ class DefaultHyperOpts(IHyperOpt):
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(dataframe['fastd'] > 54)
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),
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'sell'] = 1
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return dataframe
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@ -1,11 +1,10 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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from functools import reduce
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from math import exp
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from typing import Any, Callable, Dict, List
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from datetime import datetime
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import numpy as np# noqa F401
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import numpy as np
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import talib.abstract as ta
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real
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@ -16,7 +15,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt
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class SampleHyperOpts(IHyperOpt):
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"""
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This is a sample hyperopt to inspire you.
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This is a sample Hyperopt to inspire you.
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Feel free to customize it.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
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@ -37,32 +36,44 @@ class SampleHyperOpts(IHyperOpt):
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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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"""
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Add several indicators needed for buy and sell strategies defined below.
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"""
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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# Minus-DI
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_upperband'] = bollinger['upper']
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# SAR
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dataframe['sar'] = ta.SAR(dataframe)
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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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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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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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if 'mfi-enabled' in params and params['mfi-enabled']:
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conditions.append(dataframe['mfi'] < params['mfi-value'])
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@ -98,7 +109,7 @@ class SampleHyperOpts(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 strategy parameters
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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(10, 25, name='mfi-value'),
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@ -115,14 +126,14 @@ class SampleHyperOpts(IHyperOpt):
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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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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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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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# GUARDS AND TRENDS
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if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
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conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
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@ -158,7 +169,7 @@ class SampleHyperOpts(IHyperOpt):
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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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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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Integer(75, 100, name='sell-mfi-value'),
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@ -176,9 +187,9 @@ class SampleHyperOpts(IHyperOpt):
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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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Based on TA indicators. Should be a copy of same method 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 space.
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"""
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dataframe.loc[
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(
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@ -193,9 +204,9 @@ class SampleHyperOpts(IHyperOpt):
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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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Based on TA indicators. Should be a copy of same method 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 space.
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"""
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dataframe.loc[
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(
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@ -205,4 +216,5 @@ class SampleHyperOpts(IHyperOpt):
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(dataframe['fastd'] > 54)
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),
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'sell'] = 1
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return dataframe
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