# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # --- Do not remove these libs --- from functools import reduce from typing import Any, Callable, Dict, List import numpy # noqa import pandas # noqa from pandas import DataFrame from skopt.space import Categorical, Dimension, Integer, Real # noqa from freqtrade.optimize.hyperopt_interface import IHyperOpt # -------------------------------- # Add your lib to import here import talib.abstract as ta # noqa import freqtrade.vendor.qtpylib.indicators as qtpylib class BBRSIopt(IHyperOpt): @staticmethod def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI dataframe['rsi'] = ta.RSI(dataframe) # Bollinger Bands stds 1-4 bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband1'] = bollinger1['lower'] dataframe['bb_middleband'] = bollinger1['mid'] dataframe['bb_upperband1'] = bollinger1['upper'] bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_upperband2'] = bollinger2['upper'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_upperband3'] = bollinger3['upper'] bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4) dataframe['bb_lowerband4'] = bollinger4['lower'] dataframe['bb_upperband4'] = bollinger4['upper'] return dataframe @staticmethod def buy_strategy_generator(params: Dict[str, Any]) -> Callable: def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS if params.get('rsi-enabled'): conditions.append(dataframe['rsi'] > params['rsi-value']) # TRIGGERS if 'trigger' in params: if params['trigger'] == 'bb_lower1': conditions.append(dataframe['close'] < dataframe['bb_lowerband1']) if params['trigger'] == 'bb_lower2': conditions.append(dataframe['close'] < dataframe['bb_lowerband2']) if params['trigger'] == 'bb_lower3': conditions.append(dataframe['close'] < dataframe['bb_lowerband3']) if params['trigger'] == 'bb_lower4': conditions.append(dataframe['close'] < dataframe['bb_lowerband4']) # Check that the candle had volume conditions.append(dataframe['volume'] > 0) 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]: return [ Integer(5, 50, name='rsi-value'), Categorical([True, False], name='rsi-enabled'), Categorical(['bb_lower1', 'bb_lower2', 'bb_lower3', 'bb_lower4'], name='trigger') ] @staticmethod def sell_strategy_generator(params: Dict[str, Any]) -> Callable: def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS if params.get('sell-rsi-enabled'): conditions.append(dataframe['rsi'] > params['sell-rsi-value']) # TRIGGERS if 'sell-trigger' in params: # All bollinger bands with stds 1-4 if params['sell-trigger'] == 'sell-bb_lower3': conditions.append(dataframe['close'] > dataframe['bb_lowerband3']) if params['sell-trigger'] == 'sell-bb_lower2': conditions.append(dataframe['close'] > dataframe['bb_lowerband2']) if params['sell-trigger'] == 'sell-bb_lower1': conditions.append(dataframe['close'] > dataframe['bb_lowerband1']) if params['sell-trigger'] == 'sell-bb_middle': conditions.append(dataframe['close'] > dataframe['bb_middleband']) if params['sell-trigger'] == 'sell-bb_upper1': conditions.append(dataframe['close'] > dataframe['bb_upperband1']) if params['sell-trigger'] == 'sell-bb_upper2': conditions.append(dataframe['close'] > dataframe['bb_upperband2']) if params['sell-trigger'] == 'sell-bb_upper3': conditions.append(dataframe['close'] > dataframe['bb_upperband3']) if params['sell-trigger'] == 'sell-bb_upper4': conditions.append(dataframe['close'] > dataframe['bb_upperband4']) # Check that the candle had volume conditions.append(dataframe['volume'] > 0) 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]: return [ Integer(5, 100, name='sell-rsi-value'), Categorical([True, False], name='sell-rsi-enabled'), Categorical(['sell-bb_lower3', 'sell-bb_lower2', 'sell-bb_lower1', 'sell-bb_middle', 'sell-bb_upper1', 'sell-bb_upper2', 'sell-bb_upper3', 'sell-bb_upper4', ], name='sell-trigger') ] @staticmethod def generate_roi_table(params: Dict) -> Dict[int, float]: # did not touch """ Generate the ROI table that will be used by Hyperopt This implementation generates the default legacy Freqtrade ROI tables. Change it if you need different number of steps in the generated ROI tables or other structure of the ROI tables. Please keep it aligned with parameters in the 'roi' optimization hyperspace defined by the roi_space method. """ 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 roi_space() -> List[Dimension]: # did not touch """ Values to search for each ROI steps Override it if you need some different ranges for the parameters in the 'roi' optimization hyperspace. Please keep it aligned with the implementation of the generate_roi_table method. """ 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'), ] @staticmethod def stoploss_space() -> List[Dimension]: return [ Real(-0.50, -0.02, name='stoploss'), ] @staticmethod def trailing_space() -> List[Dimension]: # did not touch """ Create a trailing stoploss space. You may override it in your custom Hyperopt class. """ return [ # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace # is used. Otherwise hyperopt will vary other parameters that won't have effect if # trailing_stop is set False. # This parameter is included into the hyperspace dimensions rather than assigning # it explicitly in the code in order to have it printed in the results along with # other 'trailing' hyperspace parameters. Categorical([True], name='trailing_stop'), Real(0.01, 0.35, name='trailing_stop_positive'), # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive', # so this intermediate parameter is used as the value of the difference between # them. The value of the 'trailing_stop_positive_offset' is constructed in the # generate_trailing_params() method. # This is similar to the hyperspace dimensions used for constructing the ROI tables. Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'), Categorical([True, False], name='trailing_only_offset_is_reached'), ] def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # copy paste from bbrsi.py dataframe.loc[ ( (dataframe['rsi'] > 30) & # RSI above 30 (dataframe['close'] < dataframe['bb_lowerband']) # close price under low bb ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # copy paste from bbrsi.py dataframe.loc[ ( (dataframe['close'] > dataframe['bb_middleband']) # close price above the middle bb ), 'sell'] = 1 return dataframe