272 lines
11 KiB
Python
272 lines
11 KiB
Python
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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 typing import Any, Callable, Dict, List
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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
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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 DefaultHyperOpt(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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"""
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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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"""
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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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short_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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short_conditions.append(dataframe['mfi'] > params['short-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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short_conditions.append(dataframe['fastd'] > params['short-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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short_conditions.append(dataframe['adx'] < params['short-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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short_conditions.append(dataframe['rsi'] > params['short-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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short_conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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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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short_conditions.append(qtpylib.crossed_below(
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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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short_conditions.append(qtpylib.crossed_below(
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dataframe['close'],
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dataframe['sar']
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))
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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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if short_conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, short_conditions),
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'enter_short'] = 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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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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Integer(75, 90, name='short-mfi-value'),
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Integer(55, 85, name='short-fastd-value'),
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Integer(50, 80, name='short-adx-value'),
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Integer(60, 80, name='short-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 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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exit_short_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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exit_short_conditions.append(dataframe['mfi'] < params['exit-short-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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exit_short_conditions.append(dataframe['fastd'] < params['exit-short-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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exit_short_conditions.append(dataframe['adx'] > params['exit-short-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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exit_short_conditions.append(dataframe['rsi'] < params['exit-short-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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exit_short_conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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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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exit_short_conditions.append(qtpylib.crossed_below(
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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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exit_short_conditions.append(qtpylib.crossed_below(
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dataframe['sar'],
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dataframe['close']
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))
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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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if exit_short_conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, exit_short_conditions),
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'exit-short'] = 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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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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Integer(1, 25, name='exit-short-mfi-value'),
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Integer(1, 50, name='exit-short-fastd-value'),
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Integer(1, 50, name='exit-short-adx-value'),
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Integer(1, 40, name='exit-short-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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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 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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(dataframe['close'] < dataframe['bb_lowerband']) &
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(dataframe['mfi'] < 16) &
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(dataframe['adx'] > 25) &
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(dataframe['rsi'] < 21)
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),
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'buy'] = 1
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dataframe.loc[
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(
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(dataframe['close'] > dataframe['bb_upperband']) &
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(dataframe['mfi'] < 84) &
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(dataframe['adx'] > 75) &
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(dataframe['rsi'] < 79)
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),
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'enter_short'] = 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 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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(qtpylib.crossed_above(
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dataframe['macdsignal'], dataframe['macd']
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)) &
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(dataframe['fastd'] > 54)
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),
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'sell'] = 1
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dataframe.loc[
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(
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(qtpylib.crossed_below(
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dataframe['macdsignal'], dataframe['macd']
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)) &
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(dataframe['fastd'] < 46)
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),
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'exit_short'] = 1
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return dataframe
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