181 lines
7.3 KiB
Python
181 lines
7.3 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# isort: skip_file
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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 SampleHyperOpt(IHyperOpt):
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"""
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This is a sample Hyperopt to inspire you.
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More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
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You should:
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- Rename the class name to some unique name.
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- Add any methods you want to build your hyperopt.
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- Add any lib you need to build your hyperopt.
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An easier way to get a new hyperopt file is by using
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`freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
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You must keep:
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- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
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The methods roi_space, generate_roi_table and stoploss_space are not required
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and are provided by default.
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However, you may override them if you need 'roi' and 'stoploss' spaces that
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differ from the defaults offered by Freqtrade.
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Sample implementation of these methods will be copied to `user_data/hyperopts` when
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creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
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or is available online under the following URL:
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https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
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"""
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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(10, 25, name='mfi-value'),
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Integer(15, 45, name='fastd-value'),
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Integer(20, 50, name='adx-value'),
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Integer(20, 40, name='rsi-value'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='fastd-enabled'),
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Categorical([True, False], name='adx-enabled'),
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Categorical([True, False], name='rsi-enabled'),
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Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
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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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long_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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long_conditions.append(dataframe['mfi'] < params['mfi-value'])
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if 'fastd-enabled' in params and params['fastd-enabled']:
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long_conditions.append(dataframe['fastd'] < params['fastd-value'])
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if 'adx-enabled' in params and params['adx-enabled']:
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long_conditions.append(dataframe['adx'] > params['adx-value'])
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if 'rsi-enabled' in params and params['rsi-enabled']:
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long_conditions.append(dataframe['rsi'] < params['rsi-value'])
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'boll':
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long_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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if params['trigger'] == 'macd_cross_signal':
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long_conditions.append(qtpylib.crossed_above(
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dataframe['macd'],
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dataframe['macdsignal']
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))
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if params['trigger'] == 'sar_reversal':
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long_conditions.append(qtpylib.crossed_above(
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dataframe['close'],
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dataframe['sar']
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))
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# Check that volume is not 0
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long_conditions.append(dataframe['volume'] > 0)
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if long_conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, long_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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Integer(75, 100, name='sell-mfi-value'),
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Integer(50, 100, name='sell-fastd-value'),
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Integer(50, 100, name='sell-adx-value'),
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Integer(60, 100, name='sell-rsi-value'),
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Categorical([True, False], name='sell-mfi-enabled'),
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Categorical([True, False], name='sell-fastd-enabled'),
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Categorical([True, False], name='sell-adx-enabled'),
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Categorical([True, False], name='sell-rsi-enabled'),
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Categorical(['sell-boll',
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'sell-macd_cross_signal',
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'sell-sar_reversal'],
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name='sell-trigger'
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)
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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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exit_long_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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exit_long_conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
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if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
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exit_long_conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
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if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
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exit_long_conditions.append(dataframe['adx'] < params['sell-adx-value'])
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if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
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exit_long_conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
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# TRIGGERS
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'sell-boll':
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exit_long_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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if params['sell-trigger'] == 'sell-macd_cross_signal':
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exit_long_conditions.append(qtpylib.crossed_above(
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dataframe['macdsignal'],
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dataframe['macd']
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))
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if params['sell-trigger'] == 'sell-sar_reversal':
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exit_long_conditions.append(qtpylib.crossed_above(
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dataframe['sar'],
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dataframe['close']
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))
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# Check that volume is not 0
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exit_long_conditions.append(dataframe['volume'] > 0)
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if exit_long_conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, exit_long_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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