import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import (IStrategy, timeframe_to_prev_date, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter, RealParameter) from pandas import DataFrame from datetime import datetime, timedelta from typing import Dict, List from skopt.space import Dimension ########################################################################################################### ## CombinedBinHAndClucV5 by iterativ ## ## ## ## Fretrade https://github.com/freqtrade/freqtrade ## ## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ## ## V5 by iterativ. ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 20 to 40 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m) & sell_profit_only (must be true). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ########################################################################################################### class CombinedBinHAndClucV5Hyperoptable_2021025_to_20210425_SortinoHyperOptLossDaily(IStrategy): #Best result: # 256/2000: 999 trades. 595/333/71 Wins/Draws/Losses. Avg profit 1.18%. Median profit 0.87%. Total profit 2113.89483855 USDT ( 211.39Σ%). Avg duration 1:36:00 min. Objective: -445.72176 # Buy hyperspace params: buy_params = { "buy_bin_bbdelta_close": 0.04516, "buy_bin_closedelta_close": 0.0176, "buy_bin_tail_bbdelta": 0.24809, "buy_cluc_close_bblowerband": 0.606, "buy_cluc_volume": 19, } # Sell hyperspace params: sell_params = { } # ROI table: minimal_roi = { "0": 0.111, "33": 0.054, "47": 0.035, "76": 0 } # Stoploss: stoploss = -0.173 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.07 trailing_stop_positive_offset = 0.159 trailing_only_offset_is_reached = True #docker-compose run freqtrade hyperopt --config user_data/config_binhclucv5.json --hyperopt-loss SortinoHyperOptLossDaily --spaces all --strategy CombinedBinHAndClucV5Hyperoptable --epochs 2000 --timerange 20210225-20210425 --timeframe 5m --print-all --min-trades 300 stoploss = -0.99 # effectively disabled. timeframe = '5m' # Sell signal use_sell_signal = True sell_profit_only = True sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = True # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 50 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } buy_bin_bbdelta_close = RealParameter(0.004, 0.15, default=0.008, space='buy', optimize=True, load=True) buy_bin_closedelta_close = RealParameter(0.01, 0.03, default=0.0175, space='buy', optimize=True, load=True) buy_bin_tail_bbdelta = RealParameter(0.1, 0.5, default=0.25, space='buy', optimize=True, load=True) buy_cluc_close_bblowerband = RealParameter(0.5, 1.5, default=0.985, space='buy', optimize=True, load=True) buy_cluc_volume = IntParameter(15, 30, default=20, space='buy', optimize=True, load=True) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=300) > trade.open_date_utc): return 0.01 return 0.99 # for hyperopt def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # strategy ClucMay72018 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() > 0 ) & (dataframe['bbdelta'] > (dataframe['close'] * self.buy_bin_closedelta_close.value)) & (dataframe['closedelta'] > (dataframe['close'] * self.buy_bin_closedelta_close.value)) & (dataframe['tail'] < (dataframe['bbdelta'] * self.buy_bin_tail_bbdelta.value)) & (dataframe['close'] < (dataframe['lower'].shift())) & (dataframe['close'] <= (dataframe['close'].shift())) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # 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)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Improves the profit slightly. (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'sell' ] = 1 return dataframe # nested hyperopt class class HyperOpt: # defining as dummy, so that no error is thrown about missing # sell indicator space when hyperopting for all spaces @staticmethod def sell_indicator_space() -> List[Dimension]: return []