389 lines
20 KiB
Python
389 lines
20 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 hyper_adausdt(IHyperOpt):
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"""
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This is a Hyperopt template to get you started.
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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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- Add any lib you need to build your hyperopt.
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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 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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# GUARDS AND TRENDS
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if params.get('tsf_long-enabled'):
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conditions.append((dataframe['close'] - dataframe['tsf_long']) < params['tsf_long-value'])
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if params.get('tsf_mid-enabled'):
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conditions.append((dataframe['close'] - dataframe['tsf_mid']) < params['tsf_mid-value'])
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if params.get('tsf_short-enabled'):
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conditions.append((dataframe['close'] - dataframe['tsf_short']) < params['tsf_short-value'])
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if params.get('angle_short-enabled'):
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conditions.append(dataframe['angle_short'] > params['angle_short-value'])
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if params.get('angle_mid-enabled'):
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conditions.append(dataframe['angle_mid'] > params['angle_mid-value'])
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if params.get('angle_long-enabled'):
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conditions.append(dataframe['angle_long'] > params['angle_long-value'])
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if params.get('correl_h_l-enabled'):
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conditions.append(params['correl_h_l-value'] < dataframe['correl_h_l'])
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if params.get('correl_close_last_close-enabled'):
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conditions.append(params['correl_close_last_close-value'] < dataframe['correl_close_last_close'])
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if params.get('correl_tsf_long_close-enabled'):
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conditions.append(params['correl_tsf_long_close-value'] < dataframe['correl_tsf_long_close'])
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if params.get('correl_tsf_mid_close-enabled'):
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conditions.append(params['correl_tsf_mid_close-value'] < dataframe['correl_tsf_mid_close'])
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if params.get('correl_tsf_short_close-enabled'):
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conditions.append(params['correl_tsf_short_close-value'] < dataframe['correl_tsf_short_close'])
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if params.get('correl_angle_short_close-enabled'):
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conditions.append(params['correl_angle_short_close-value'] < dataframe['correl_angle_short_close'])
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if params.get('correl_angle_mid_close-enabled'):
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conditions.append(params['correl_angle_mid_close-value'] < dataframe['correl_angle_mid_close'])
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if params.get('correl_angle_long_close-enabled'):
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conditions.append(params['correl_angle_long_close-value'] < dataframe['correl_angle_long_close'])
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if params.get('correl_hist_close-enabled'):
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conditions.append(params['correl_hist_close-value'] < dataframe['correl_hist_close'])
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if params.get('correl_mfi_close-enabled'):
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conditions.append(params['correl_mfi_close-value'] < dataframe['correl_mfi_close'])
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if params.get('mfi-enabled'):
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conditions.append(params['mfi-value'] < dataframe['mfi'])
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if params.get('ema-enabled'):
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conditions.append(params['ema-value'] < (dataframe['close'] - dataframe['ema']))
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if params.get('ema_bol-enabled'):
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conditions.append(params['ema_bol-value'] < (dataframe['ema'] - dataframe['middleband']))
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if params.get('macd-enabled'):
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conditions.append(params['macdhist-value'] < dataframe['macdhist'])
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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))
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if params['trigger'] == 'bol_low':
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conditions.append(qtpylib.crossed_above(
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dataframe['close'], dataframe['lowerband']
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))
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if params['trigger'] == 'bol_mid':
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conditions.append(qtpylib.crossed_above(
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dataframe['close'], dataframe['middleband']
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))
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if params['trigger'] == 'mfi':
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conditions.append(qtpylib.crossed_above(
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dataframe['mfi'], 25))
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if params['trigger'] == 'sar':
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conditions.append(qtpylib.crossed_below(
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dataframe['sar'], dataframe['close']))
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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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Real(0.019, 0.7, name='tsf_long-value'),
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Real(0.019, 0.7, name='tsf_mid-value'),
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Real(0.17, 0.7, name='tsf_short-value'),
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Real(-0.26, 0.23, name='angle_short-value'),
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Real(-0.018, 0.023, name='angle_long-value'),
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Real(-0.026, 0.028, name='angle_mid-value'),
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Real(0, 1, name='correl_h_l-value'),
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Real(-1, 1, name='correl_close_last_close-value'),
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Real(0, 1, name='correl_tsf_long_close-value'),
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Real(-1, 1, name='correl_tsf_mid_close-value'),
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Real(-1, 1, name='correl_tsf_short_close-value'),
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Real(-1, 1, name='correl_angle_short_close-value'),
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Real(0, 1, name='correl_angle_mid_close-value'),
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Real(0, 1, name='correl_angle_long_close-value'),
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Real(-1, 1, name='correl_hist_close-value'),
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Real(-1, 1, name='correl_mfi_close-value'),
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Integer(10, 60, name='mfi-value'),
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Real(0.18, 0.7, name='ema-value'),
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Real(-0.018, 0.015, name='ema_bol-value'),
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Real(-0.0042, 0.003, name='macdhist-value'),
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Categorical([True, False], name='angle_short-enabled'),
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Categorical([True, False], name='angle_long-enabled'),
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Categorical([True, False], name='angle_mid-enabled'),
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Categorical([True, False], name='tsf_long-enabled'),
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Categorical([True, False], name='tsf_mid-enabled'),
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Categorical([True, False], name='tsf_short-enabled'),
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Categorical([True, False], name='correl_h_l-enabled'),
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Categorical([True, False], name='correl_close_last_close-enabled'),
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Categorical([True, False], name='correl_tsf_long_close-enabled'),
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Categorical([True, False], name='correl_tsf_short_close-enabled'),
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Categorical([True, False], name='correl_tsf_mid_close-enabled'),
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Categorical([True, False], name='correl_angle_short_close-enabled'),
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Categorical([True, False], name='correl_angle_mid_close-enabled'),
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Categorical([True, False], name='correl_angle_long_close-enabled'),
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Categorical([True, False], name='correl_mfi_close-enabled'),
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Categorical([True, False], name='correl_hist_close-enabled'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='macd-enabled'),
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Categorical([True, False], name='ema-enabled'),
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Categorical([True, False], name='ema_bol-enabled'),
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Categorical(['sar', 'mfi', 'macd_cross_signal', 'bol_low', 'bol_mid'], 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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conditions = []
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# GUARDS AND TRENDS
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if params.get('tsf_long-enabled'):
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conditions.append((dataframe['tsf_long'] - dataframe['close']) > params['tsf_long-value_sell'])
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if params.get('tsf_mid-enabled'):
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conditions.append((dataframe['tsf_mid'] - dataframe['close']) > params['tsf_mid-value_sell'])
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if params.get('tsf_short-enabled'):
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conditions.append((dataframe['tsf_short'] - dataframe['close']) > params['tsf_short-value_sell'])
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if params.get('angle_short-enabled'):
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conditions.append(dataframe['angle_short'] > params['angle_short-value_sell'])
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if params.get('angle_mid-enabled'):
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conditions.append(dataframe['angle_mid'] > params['angle_mid-value_sell'])
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if params.get('angle_long-enabled'):
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conditions.append(dataframe['angle_long'] > params['angle_long-value_sell'])
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if params.get('correl_h_l-enabled'):
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conditions.append(params['correl_h_l-value_sell'] < dataframe['correl_h_l'])
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if params.get('correl_close_last_close-enabled'):
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conditions.append(params['correl_close_last_close-value_sell'] < dataframe['correl_close_last_close'])
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if params.get('correl_tsf_long_close-enabled'):
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conditions.append(params['correl_tsf_long_close-value_sell'] < dataframe['correl_tsf_long_close'])
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if params.get('correl_tsf_mid_close-enabled'):
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conditions.append(params['correl_tsf_mid_close-value_sell'] < dataframe['correl_tsf_mid_close'])
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if params.get('correl_tsf_short_close-enabled'):
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conditions.append(params['correl_tsf_short_close-value_sell'] < dataframe['correl_tsf_short_close'])
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if params.get('correl_angle_short_close-enabled'):
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conditions.append(params['correl_angle_short_close-value_sell'] < dataframe['correl_angle_short_close'])
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if params.get('correl_angle_mid_close-enabled'):
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conditions.append(params['correl_angle_mid_close-value_sell'] < dataframe['correl_angle_mid_close'])
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if params.get('correl_angle_long_close-enabled'):
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conditions.append(params['correl_angle_long_close-value_sell'] < dataframe['correl_angle_long_close'])
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if params.get('correl_hist_close-enabled'):
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conditions.append(params['correl_hist_close-value_sell'] < dataframe['correl_hist_close'])
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if params.get('correl_mfi_close-enabled'):
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conditions.append(params['correl_mfi_close-value_sell'] < dataframe['correl_mfi_close'])
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if params.get('mfi-enabled'):
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conditions.append(params['mfi-value_sell'] > dataframe['mfi'])
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if params.get('ema-enabled'):
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conditions.append(params['ema-value_sell'] < (dataframe['ema'] - dataframe['close']))
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if params.get('sar-enabled'):
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conditions.append(params['sar-value_sell'] < (dataframe['close'] - dataframe['sar']))
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if params.get('ema_bol-enabled'):
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conditions.append(params['ema_bol-value_sell'] < (dataframe['middleband'] - dataframe['ema']))
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if params.get('macd-enabled'):
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conditions.append(params['macdhist-value_sell'] > dataframe['macdhist'])
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(qtpylib.crossed_below(
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dataframe['macd'], dataframe['macdsignal']
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))
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if params['trigger'] == 'bol_high':
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conditions.append(qtpylib.crossed_below(
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dataframe['close'], dataframe['upperband']
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))
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if params['trigger'] == 'bol_mid':
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conditions.append(qtpylib.crossed_below(
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dataframe['close'], dataframe['middleband']
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))
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if params['trigger'] == 'sar':
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conditions.append(qtpylib.crossed_above(
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dataframe['sar'], dataframe['close']))
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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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return [
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Real(0.019, 0.7, name='tsf_long-value_sell'),
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Real(0.019, 0.7, name='tsf_mid-value_sell'),
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Real(0.17, 0.7, name='tsf_short-value_sell'),
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Real(-0.26, 0.23, name='angle_short-value_sell'),
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Real(-0.018, 0.023, name='angle_long-value_sell'),
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Real(-0.026, 0.028, name='angle_mid-value_sell'),
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Real(0, 1, name='correl_h_l-value_sell'),
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Real(-1, 1, name='correl_close_last_close-value_sell'),
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Real(0, 1, name='correl_tsf_long_close-value_sell'),
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Real(-1, 1, name='correl_tsf_mid_close-value_sell'),
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Real(-1, 1, name='correl_tsf_short_close-value_sell'),
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Real(-1, 1, name='correl_angle_short_close-value_sell'),
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Real(0, 1, name='correl_angle_mid_close-value_sell'),
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Real(0, 1, name='correl_angle_long_close-value_sell'),
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Real(-1, 1, name='correl_hist_close-value_sell'),
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Real(-1, 1, name='correl_mfi_close-value_sell'),
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Real(10, 60, name='mfi-value_sell'),
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Real(0.018, 0.7, name='ema-value_sell'),
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Real(0.017, 0.7, name='sar-value_sell'),
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Real(-0.018, 0.015, name='ema_bol-value_sell'),
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Real(-0.0042, 0.003, name='macdhist-value_sell'),
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Categorical([True, False], name='angle_short-enabled'),
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Categorical([True, False], name='angle_long-enabled'),
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Categorical([True, False], name='angle_mid-enabled'),
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Categorical([True, False], name='tsf_long-enabled'),
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Categorical([True, False], name='tsf_mid-enabled'),
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Categorical([True, False], name='tsf_short-enabled'),
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Categorical([True, False], name='correl_h_l-enabled'),
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Categorical([True, False], name='correl_close_last_close-enabled'),
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Categorical([True, False], name='correl_tsf_long_close-enabled'),
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Categorical([True, False], name='correl_tsf_short_close-enabled'),
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Categorical([True, False], name='correl_tsf_mid_close-enabled'),
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Categorical([True, False], name='correl_angle_short_close-enabled'),
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Categorical([True, False], name='correl_angle_mid_close-enabled'),
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Categorical([True, False], name='correl_angle_long_close-enabled'),
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Categorical([True, False], name='correl_mfi_close-enabled'),
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Categorical([True, False], name='correl_hist_close-enabled'),
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Categorical([True, False], name='mfi-enabled'),
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Categorical([True, False], name='macd-enabled'),
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Categorical([True, False], name='ema-enabled'),
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Categorical([True, False], name='sar-enabled'),
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Categorical([True, False], name='ema_bol-enabled'),
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Categorical(['sar', 'macd_cross_signal', 'bol_low', 'bol_mid'], name='trigger')
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]
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@staticmethod
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def generate_roi_table(params: Dict) -> Dict[int, float]:
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"""
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Generate the ROI table that will be used by Hyperopt
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This implementation generates the default legacy Freqtrade ROI tables.
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Change it if you need different number of steps in the generated
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ROI tables or other structure of the ROI tables.
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Please keep it aligned with parameters in the 'roi' optimization
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hyperspace defined by the roi_space method.
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"""
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roi_table = {}
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roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
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roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
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roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
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roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
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return roi_table
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@staticmethod
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def roi_space() -> List[Dimension]:
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"""
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Values to search for each ROI steps
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Override it if you need some different ranges for the parameters in the
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'roi' optimization hyperspace.
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Please keep it aligned with the implementation of the
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generate_roi_table method.
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"""
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return [
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Integer(10, 120, name='roi_t1'),
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Integer(10, 60, name='roi_t2'),
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Integer(10, 40, name='roi_t3'),
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Real(0.01, 0.04, name='roi_p1'),
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Real(0.01, 0.07, name='roi_p2'),
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Real(0.01, 0.20, name='roi_p3'),
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]
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@staticmethod
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def stoploss_space() -> List[Dimension]:
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"""
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Stoploss Value to search
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Override it if you need some different range for the parameter in the
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'stoploss' optimization hyperspace.
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"""
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return [
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Real(-0.25, -0.02, name='stoploss'),
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]
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@staticmethod
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def trailing_space() -> List[Dimension]:
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"""
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Create a trailing stoploss space.
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You may override it in your custom Hyperopt class.
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"""
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return [
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# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
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# is used. Otherwise hyperopt will vary other parameters that won't have effect if
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# trailing_stop is set False.
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# This parameter is included into the hyperspace dimensions rather than assigning
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# it explicitly in the code in order to have it printed in the results along with
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# other 'trailing' hyperspace parameters.
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Categorical([True], name='trailing_stop'),
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Real(0.01, 0.35, name='trailing_stop_positive'),
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# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
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# so this intermediate parameter is used as the value of the difference between
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# them. The value of the 'trailing_stop_positive_offset' is constructed in the
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# generate_trailing_params() method.
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# This is similar to the hyperspace dimensions used for constructing the ROI tables.
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Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
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Categorical([True, False], name='trailing_only_offset_is_reached'),
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]
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