# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/CombinedBinHAndClucHyperStrategy.py 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, RealParameter from typing import Dict, List from skopt.space import Dimension logger = logging.getLogger(__name__) class Github_remiotore_freqtrade__CombinedBinHAndClucHyperStrategy__20260111_210550(IStrategy): """ enhanced auto hyperoptable version based on https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/berlinguyinca/CombinedBinHAndCluc.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! !!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Based on a backtesting: - the best perfomance is reached with "max_open_trades" = 2 (in average for any market), so it is better to increase "stake_amount" value rather then "max_open_trades" to get more profit - if the market is constantly green(like in JAN 2018) the best performance is reached with "max_open_trades" = 2 and minimal_roi = 0.01 """ minimal_roi = { "0": 0.01 } stoploss = -0.05 timeframe = '5m' startup_candle_count = 50 buy_params = { 'buy_bin_bbdelta_close': 0.008, 'buy_bin_closedelta_close': 0.0175, 'buy_bin_tail_bbdelta': 0.25, 'buy_cluc_close_bblowerband': 0.985, 'buy_cluc_volume': 20 } sell_params = { } # not used but defined for security reasons buy_bin_bbdelta_close = RealParameter(0.0, 0.02, default=0.008, space='buy', optimize=True, load=True) buy_bin_closedelta_close = RealParameter(0.0, 0.03, default=0.0175, space='buy', optimize=True, load=True) buy_bin_tail_bbdelta = RealParameter(0.0, 1.0, default=0.25, space='buy', optimize=True, load=True) buy_cluc_close_bblowerband = RealParameter(0.0, 1.5, default=1.5, space='buy', optimize=True, load=True) buy_cluc_volume = IntParameter(10, 40, default=20, space='buy', 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: 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)) @staticmethod def bollinger_bands(stock_price, window_size, num_of_std): rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: mid, lower = self.bollinger_bands(dataframe['close'], window_size=40, num_of_std=2) dataframe['lower'] = lower dataframe['bbdelta'] = (mid - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # strategy BinHV45 dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bin_closedelta_close.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bin_closedelta_close.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bin_tail_bbdelta.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < self.buy_cluc_close_bblowerband.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * self.buy_cluc_volume.value)) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['close'] > dataframe['bb_middleband']), 'sell' ] = 1 return dataframe class HyperOpt: @staticmethod def sell_indicator_space() -> List[Dimension]: return []