import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy.interface import IStrategy from freqtrade.persistence import Trade from pandas import DataFrame from datetime import datetime, timedelta ########################################################################################################### ## CombinedBinHAndClucV6 by iterativ ## ## ## ## Freqtrade https://github.com/freqtrade/freqtrade ## ## The authors of the original CombinedBinHAndCluc https://github.com/freqtrade/freqtrade-strategies ## ## V6 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. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## 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 ## ## ## ########################################################################################################### # SSL Channels def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class HybridMonster(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.023 } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' # informative tf # 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 # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # 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 = 200 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } 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=60) > trade.open_date_utc): return 0.01 return 0.99 def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: if sell_reason == 'force_sell': return True dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Prevent ROI trigger, if there is more potential, in order to maximize profit if (last_candle['rsi'] > 55): #55 return False return True def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h informative_1h['close1h'] = informative_1h['close'] informative_1h['open1h'] = informative_1h['open'] informative_1h['midprice_50'] = ta.MIDPRICE(informative_1h, timeperiod=50) informative_1h['midprice_20'] = ta.MIDPRICE(informative_1h, timeperiod=20) informative_1h['midprice_10'] = ta.MIDPRICE(informative_1h, timeperiod=10) informative_1h['ema_fast'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_slow'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['go_long'] = ((informative_1h['ema_fast'] > informative_1h['ema_slow'])).astype('int') * 2 return informative_1h def normal_tf_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() # EMA dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) bollinger200_3_5m = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=200, stds=3) dataframe['bb_lowerband_3sd_5m'] = bollinger200_3_5m['lower'] dataframe['bb_middleband_3sd_5m'] = bollinger200_3_5m['mid'] dataframe['bb_upperband_3sd_5m'] = bollinger200_3_5m['upper'] # SMA dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) bollinger200_3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=200, stds=3) bollinger20_3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) bollinger20_2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband_3sd'] = bollinger200_3['lower'] dataframe['bb_middleband_3sd'] = bollinger200_3['mid'] dataframe['bb_upperband_3sd'] = bollinger200_3['upper'] dataframe['bb_lowerband_20_3sd'] = bollinger20_3['lower'] dataframe['bb_lowerband_20_2sd'] = bollinger20_2['lower'] dataframe['midprice_50'] = ta.MIDPRICE(dataframe, timeperiod=50) dataframe['midprice_10'] = ta.MIDPRICE(dataframe, timeperiod=10) dataframe['midprice_20'] = ta.MIDPRICE(dataframe, timeperiod=20) #macd macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] macd2 = ta.MACD(dataframe, fastperiod=30, slowperiod=200, signalperiod=50) dataframe['macd2'] = macd2['macd'] dataframe['macdsignal2'] = macd2['macdsignal'] dataframe['macdhist2'] = macd2['macdhist'] sma_offset = (1 - 0.04) sma_offset_pos = (1 + 0.012) base_nb_candles = 20 dataframe['sma_30_offset'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset dataframe['sma_30_offset_pos'] = ta.SMA(dataframe, timeperiod=base_nb_candles) * sma_offset_pos # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['go_long_1h'] > 0) & (dataframe['close'] < dataframe['sma_30_offset']) & (dataframe['macd2'] < dataframe['macdsignal2'])& (dataframe['volume'] > 0) ) | ( (dataframe['sma_5'] < dataframe['midprice_50'])& (dataframe['ema_200'] > dataframe['bb_middleband_3sd'])& (dataframe['bb_middleband'] > dataframe['mid'])& (dataframe['low'] < dataframe['lower'])& (dataframe['midprice_50'] < dataframe['bb_upperband'])& (dataframe['sma_5'] < dataframe['midprice_20_1h'])& (dataframe['close'] < dataframe['bb_lowerband_3sd'])& (dataframe['lower'] < dataframe['midprice_10_1h'])& (dataframe['lower'] < dataframe['midprice_20_1h'])& (dataframe['sma_5'] < dataframe['midprice_50'])& (dataframe['ema_50'] > dataframe['bb_middleband_3sd'])& (dataframe['volume'] > 0) ) | ( #dip buy (dataframe['bb_lowerband_20_3sd'] < dataframe['bb_lowerband_3sd'])& (dataframe['bb_lowerband_20_2sd'] < dataframe['bb_lowerband_3sd'])& (dataframe['close'] < dataframe['bb_lowerband_3sd'])& (dataframe['close'] < dataframe['midprice_20_1h'])& (dataframe['macd'] < dataframe['macdsignal'])& (dataframe['midprice_50_1h'] > dataframe['midprice_20_1h'])& (dataframe['bb_upperband'] > dataframe['midprice_20_1h'])& (dataframe['close'] < dataframe['open'])& (dataframe['bb_upperband'] < dataframe['ema_50_1h'])& (dataframe['sma_5'] < dataframe['bb_middleband_3sd'])& (dataframe['volume'] > 0)#& ) | ( # strategy BinHV45 (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * 0.031) & dataframe['closedelta'].gt(dataframe['close'] * 0.018) & dataframe['tail'].lt(dataframe['bbdelta'] * 0.233) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy ClucMay72018 (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.993 * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 21)) & (dataframe['volume'] > 0) ) | ( (dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) & (dataframe['volume'] > 0) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( (dataframe['go_long_1h'] == 0) & (dataframe['close'] > dataframe['sma_30_offset_pos'])& (dataframe['macd2'] < dataframe['macdsignal2']) ) & (dataframe['volume'] > 0) ) | #( # (dataframe['rsi'] > 60) # #) #| ( # 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