# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement import talib.abstract as ta from pandas import DataFrame from typing import Dict, Any, Callable, List from functools import reduce from skopt.space import Categorical, Dimension, Integer, Real import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.optimize.hyperopt_interface import IHyperOpt class_name = 'DefaultHyperOpts' class DefaultHyperOpts(IHyperOpt): """ Default hyperopt provided by freqtrade bot. You can override it with your own hyperopt """ @staticmethod def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['adx'] = ta.ADX(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] 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'] 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 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 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 """ # print(params) 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 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 generate_roi_table(params: Dict) -> Dict[int, float]: """ Generate the ROI table that will be used by Hyperopt """ roi_table = {} roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2'] roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1'] roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0 return roi_table @staticmethod def stoploss_space() -> List[Dimension]: """ Stoploss Value to search """ return [ Real(-0.5, -0.02, name='stoploss'), ] @staticmethod def roi_space() -> List[Dimension]: """ Values to search for each ROI steps """ return [ Integer(10, 120, name='roi_t1'), Integer(10, 60, name='roi_t2'), Integer(10, 40, name='roi_t3'), Real(0.01, 0.04, name='roi_p1'), Real(0.01, 0.07, name='roi_p2'), Real(0.01, 0.20, name='roi_p3'), ] def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators. Should be a copy of from strategy must align to populate_indicators in this file Only used when --spaces does not include buy """ 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 from strategy must align to populate_indicators in this file Only used when --spaces does not include sell """ dataframe.loc[ ( (qtpylib.crossed_above( dataframe['macdsignal'], dataframe['macd'] )) & (dataframe['fastd'] > 54) ), 'sell'] = 1 return dataframe