# source: https://raw.githubusercontent.com/acabreracmx/freqtrade/b03dd84f1da6326bbf7caf4160d7ba6f2793cc2a/Strats%26Configs/strats%20and%20configs%202021-06-24/bbrsi_eth_opt.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement from functools import reduce from typing import Dict, Any, Callable, List import numpy as np import pandas as pd from pandas import DataFrame import talib.abstract as ta from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, IntParameter, RealParameter from skopt.space import Dimension, Integer, Real import freqtrade.vendor.qtpylib.indicators as qtpylib class github_acabreracmx_freqtrade__bbrsi_eth_opt__20210830_101033(IStrategy): """ Hyperopt """ buy_rsi = IntParameter(5, 50, default=40, space="buy") buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_trigger =CategoricalParameter(['bb_lower1', 'bb_lower2', 'bb_lower3'], default="bb_lower3", space="buy") sell_rsi = IntParameter(50, 100, default=53, space="sell") sell_rsi_enabled = CategoricalParameter([True, False], default=False, space="sell") sell_trigger= CategoricalParameter([#'sell-bb_lower2', 'sell-bb_lower1', 'sell-bb_middle1', 'sell-bb_upper1'], default='sell-bb_lower1', space='sell') stoploss = RealParameter(-0.02, -0.5, default=-0.307, space="stoploss") """ # Minimal ROI designed for the strategy minimal_roi = { "0": 0.07916501011003861, "28": 0.035159901969879184, "83": 0.016727420189894475, "131": 0 } """ # Optimal stoploss designed for the strategy #stoploss = -0.4275704128061333 # Optimal ticker interval for the strategy #timeframe = '1h' # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False } # Optional time in force for orders order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc', } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # Momentum Indicator # ----------------------------------- # RSI dataframe['rsi'] = ta.RSI(dataframe) # Overlap Studies # ------------------------------------ # Bollinger bands bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband1'] = bollinger1['lower'] dataframe['bb_middleband1'] = bollinger1['mid'] dataframe['bb_upperband1'] = bollinger1['upper'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Buy strategy Hyperopt will build and use """ conditions = [] # GUARDS AND TRENDS if self.buy_rsi_enabled.value: conditions.append(dataframe['rsi'] > self.buy_rsi.value) # TRIGGERS if self.buy_trigger.value == 'bb_lower1': conditions.append(dataframe['close'] < dataframe['bb_lowerband1']) if self.buy_trigger.value == 'bb_lower2': conditions.append(dataframe['close'] < dataframe['bb_lowerband2']) if self.buy_trigger.value == 'bb_lower3': conditions.append(dataframe['close'] < dataframe['bb_lowerband3']) conditions.append(dataframe['volume'] > 0) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Buy strategy Hyperopt will build and use """ conditions = [] # GUARDS AND TRENDS if self.sell_rsi_enabled.value: conditions.append(dataframe['rsi'] > self.sell_rsi.value) # TRIGGERS if self.sell_trigger.value == 'sell-bb_lower1': conditions.append(dataframe['close'] > dataframe['bb_lowerband1']) if self.sell_trigger.value == 'sell-bb_middle1': conditions.append(dataframe['close'] > dataframe['bb_middleband1']) if self.sell_trigger.value == 'sell-bb_upper1': conditions.append(dataframe['close'] > dataframe['bb_upperband1']) #if self.sell_trigger.value == 'sell-bb_lower2': # conditions.append(dataframe['close'] > dataframe['bb_lowerband2']) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe @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'), ] """ @staticmethod def trailing_space() -> List[Dimension]: #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. CategoricalParameter([True], name='trailing_stop'), Real(0.01, 0.35, decimals=3, 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, decimals=3, name='trailing_stop_positive_offset_p1'), CategoricalParameter([True, False], name='trailing_only_offset_is_reached'), ] """