stable/user_data/hyperopts/sample_hyperopt.py

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
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import numpy as np # noqa
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
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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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class SampleHyperOpts(IHyperOpt):
"""
This is a sample Hyperopt to inspire you.
Feel free to customize it.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
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"""
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
Define the buy strategy parameters to be used by Hyperopt.
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"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
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"""
conditions = []
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# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
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if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
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if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
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return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
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"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
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@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
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"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
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"""
conditions = []
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# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
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if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
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))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
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))
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if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
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return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
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"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
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'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe