# --- Do not remove these libs ---------------------------------------------------------------------------------------- import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import logging from pandas import DataFrame from freqtrade.strategy import IStrategy, IntParameter, CategoricalParameter from typing import Dict, List from skopt.space import Dimension, Real # --------------------------------------------------------------------------------------------------------------------- # --- logger for parameter merging output, only remove if you remove it further down too! ----------------------------- logger = logging.getLogger(__name__) # --------------------------------------------------------------------------------------------------------------------- class BBRSIStochHyperStrategy(IStrategy): """ enhanced auto hyperoptable version based on https://github.com/faGH/fa.services.plutus/blob/main/user_data/strategies/fa_m31h_strategy.py !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!! as of today (14.04.2021) you need the freqtrade/develop version to be able !!! !!! to run hyperopt/backtest with this new strategy format !!! !!! !!! !!! please check https://github.com/freqtrade/freqtrade/pull/4596 for further !!! !!! information about the new auto-hyperoptable strategies! !!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! This is FrostAura's mark 3 strategy which aims to make purchase decisions based on the BB, RSI and Stochastic. """ # # user hyperopt for best minimal roi and stoploss! # minimal_roi = { # "0": 0.01 # } # # stoploss = -0.05 # # # best timeframes currently in backtest are 15m and 1h # timeframe = '15m' # # startup_candle_count = 50 # # use_sell_signal = True # sell_profit_only = False # ignore_roi_if_buy_signal = False # # # default buy parameters # buy_params = { # 'buy_stoch_enabled': True, # 'buy_stoch_value': 25, # 'buy_rsi_value': 30, # 'buy_bb_trigger': 'bb_lowerband1' # } # # # default sell parameters # sell_params = { # 'sell_rsi_value': 30, # 'sell_bb_trigger': 'bb_middleband1' # # } # Buy hyperspace params: buy_params = { 'buy_bb_trigger': 'bb_lowerband4', 'buy_rsi_value': 9, 'buy_stoch_enabled': True, 'buy_stoch_value': 10 } # Sell hyperspace params: sell_params = { 'sell_bb_trigger': 'bb_middleband1', 'sell_rsi_value': 43 } # ROI table: minimal_roi = { "0": 0.267, "90": 0.149, "250": 0.043, "356": 0 } # Stoploss: stoploss = -0.49059 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.34 trailing_stop_positive_offset = 0.35 trailing_only_offset_is_reached = False # --- hyperopt parameters > can be selectively turned on/off via optimize=True/False ------------------------------ buy_stoch_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_stoch_value = IntParameter(5, 30, default=25, space='buy', optimize=True, load=True) buy_rsi_value = IntParameter(5, 30, default=30, space='buy', optimize=True, load=True) buy_bb_trigger = CategoricalParameter( [ 'bb_lowerband1', 'bb_lowerband2', 'bb_lowerband3', 'bb_lowerband4' ], default='bb_lowerband1', space='buy', optimize=True, load=True) sell_rsi_value = IntParameter(40, 90, default=75, space='sell', optimize=True, load=True) sell_bb_trigger = CategoricalParameter( [ 'bb_lowerband1', 'bb_middleband1', 'bb_upperband1' ], default='bb_upperband1', space='sell', optimize=True, load=True) use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False def __init__(self, config: dict) -> None: super().__init__(config) try: from mergedeep import merge except ImportError as error: # Output expected ImportErrors. logger.info("could not import mergedeep, please check if pip is installed: %s", error) logger.info("therefor we are not able to merge parameters from config") else: logger.info('mergedeep found, so attempting to find strategy parameters in config file') if self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False): cfg_strategy_parameters = self.config.get('strategy_parameters', {}).get(self.__class__.__name__, False) logger.info('strategy_parameters from config: %s', repr(cfg_strategy_parameters)) if cfg_strategy_parameters.get('buy_params', {}): logger.info('merging buy_params from config: %s', cfg_strategy_parameters.get('buy_params')) merge(self.buy_params, cfg_strategy_parameters.get('buy_params')) if cfg_strategy_parameters.get('sell_params', {}): logger.info('merging sell_params from config: %s', cfg_strategy_parameters.get('sell_params')) merge(self.sell_params, cfg_strategy_parameters.get('sell_params')) else: logger.info('no strategy_parameters found in config') logger.info('final buy_params: %s', repr(self.buy_params)) logger.info('final sell_params: %s', repr(self.sell_params)) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # RSI dataframe['rsi'] = ta.RSI(dataframe) # Stochastic Slow stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Bollinger bands 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'] 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'] 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'] bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4) dataframe['bb_lowerband4'] = bollinger4['lower'] dataframe['bb_middleband4'] = bollinger4['mid'] dataframe['bb_upperband4'] = bollinger4['upper'] return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( ( # stoch enabled (self.buy_stoch_enabled.value == True) & (dataframe['slowd'] > self.buy_stoch_value.value) & (dataframe['slowk'] > self.buy_stoch_value.value) ) | # stoch disabled (self.buy_stoch_enabled.value == False) ) & ( (dataframe['rsi'] > self.buy_rsi_value.value) & (dataframe['slowk'] < dataframe['slowd']) & (dataframe["close"] < dataframe[self.buy_bb_trigger.value]) ) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['slowk'] < dataframe['slowd']) & (dataframe['rsi'] > self.sell_rsi_value.value) & (dataframe["close"] > dataframe[self.sell_bb_trigger.value]) ), 'sell' ] = 1 return dataframe # nested hyperopt class class HyperOpt: # custom stop loss range @staticmethod def stoploss_space() -> List[Dimension]: return [ Real(-0.5, -0.02, name='stoploss'), ]