# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement from functools import reduce from typing import Any, Callable, Dict, List import talib.abstract as ta from pandas import DataFrame from skopt.space import Categorical, Dimension, Integer import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.hyperopt_interface import IHyperOpt class DefaultHyperOpt(IHyperOpt): """ Default hyperopt provided by the Freqtrade bot. You can override it with your own Hyperopt """ @staticmethod def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Add several indicators needed for buy and sell strategies defined below. """ # ADX dataframe['adx'] = ta.ADX(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] # Minus-DI dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] # SAR dataframe['sar'] = ta.SAR(dataframe) return dataframe @staticmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the buy strategy parameters to be used by Hyperopt. """ def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Buy strategy Hyperopt will build and use. """ conditions = [] # 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 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'] )) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe return populate_buy_trend @staticmethod def indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching buy strategy parameters. """ 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') ] @staticmethod def short_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the short strategy parameters to be used by Hyperopt. """ def populate_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Buy strategy Hyperopt will build and use. """ conditions = [] # 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 if 'trigger' in params: if params['trigger'] == 'bb_upper': conditions.append(dataframe['close'] > dataframe['bb_upperband']) if params['trigger'] == 'macd_cross_signal': conditions.append(qtpylib.crossed_below( dataframe['macd'], dataframe['macdsignal'] )) if params['trigger'] == 'sar_reversal': conditions.append(qtpylib.crossed_below( dataframe['close'], dataframe['sar'] )) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'short'] = 1 return dataframe return populate_short_trend @staticmethod def short_indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching short strategy parameters. """ return [ Integer(75, 90, name='mfi-value'), Integer(55, 85, name='fastd-value'), Integer(50, 80, name='adx-value'), Integer(60, 80, 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_upper', 'macd_cross_signal', 'sar_reversal'], name='trigger') ] @staticmethod def sell_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the sell strategy parameters to be used by Hyperopt. """ def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Sell strategy Hyperopt will build and use. """ conditions = [] # 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']) 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']) if params['sell-trigger'] == 'sell-macd_cross_signal': conditions.append(qtpylib.crossed_above( dataframe['macdsignal'], dataframe['macd'] )) if params['sell-trigger'] == 'sell-sar_reversal': conditions.append(qtpylib.crossed_above( dataframe['sar'], dataframe['close'] )) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe return populate_sell_trend @staticmethod def exit_short_strategy_generator(params: Dict[str, Any]) -> Callable: """ Define the exit_short strategy parameters to be used by Hyperopt. """ def populate_exit_short_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: """ Exit_short strategy Hyperopt will build and use. """ conditions = [] # GUARDS AND TRENDS if 'exit-short-mfi-enabled' in params and params['exit-short-mfi-enabled']: conditions.append(dataframe['mfi'] < params['exit-short-mfi-value']) if 'exit-short-fastd-enabled' in params and params['exit-short-fastd-enabled']: conditions.append(dataframe['fastd'] < params['exit-short-fastd-value']) if 'exit-short-adx-enabled' in params and params['exit-short-adx-enabled']: conditions.append(dataframe['adx'] > params['exit-short-adx-value']) if 'exit-short-rsi-enabled' in params and params['exit-short-rsi-enabled']: conditions.append(dataframe['rsi'] < params['exit-short-rsi-value']) # TRIGGERS if 'exit-short-trigger' in params: if params['exit-short-trigger'] == 'exit-short-bb_lower': conditions.append(dataframe['close'] < dataframe['bb_lowerband']) if params['exit-short-trigger'] == 'exit-short-macd_cross_signal': conditions.append(qtpylib.crossed_below( dataframe['macdsignal'], dataframe['macd'] )) if params['exit-short-trigger'] == 'exit-short-sar_reversal': conditions.append(qtpylib.crossed_below( dataframe['sar'], dataframe['close'] )) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_short'] = 1 return dataframe return populate_exit_short_trend @staticmethod def sell_indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching sell strategy parameters. """ 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', 'sell-macd_cross_signal', 'sell-sar_reversal'], name='sell-trigger') ] @staticmethod def exit_short_indicator_space() -> List[Dimension]: """ Define your Hyperopt space for searching exit short strategy parameters. """ return [ Integer(1, 25, name='exit_short-mfi-value'), Integer(1, 50, name='exit_short-fastd-value'), Integer(1, 50, name='exit_short-adx-value'), Integer(1, 40, name='exit_short-rsi-value'), Categorical([True, False], name='exit_short-mfi-enabled'), Categorical([True, False], name='exit_short-fastd-enabled'), Categorical([True, False], name='exit_short-adx-enabled'), Categorical([True, False], name='exit_short-rsi-enabled'), Categorical(['exit_short-bb_lower', 'exit_short-macd_cross_signal', 'exit_short-sar_reversal'], name='exit_short-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 def populate_short_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 short space. """ dataframe.loc[ ( (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['mfi'] < 84) & (dataframe['adx'] > 75) & (dataframe['rsi'] < 79) ), 'buy'] = 1 return dataframe def populate_exit_short_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 exit_short space. """ dataframe.loc[ ( (qtpylib.crossed_below( dataframe['macdsignal'], dataframe['macd'] )) & (dataframe['fastd'] < 46) ), 'sell'] = 1 return dataframe