# --- Do not remove these libs --- import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series, DatetimeIndex, merge from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open from functools import reduce from technical.indicators import RMI, zema # -------------------------------- def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 class BB_RPB_TSL_RNG_V2_20211008(IStrategy): ''' BB_RPB_TSL @author jilv220 Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html ) RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py ) The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya ) I modified it to better suit my taste and added Hyperopt for this strategy. ''' ########################################################################## # Hyperopt result area # buy space buy_params = { ## "buy_btc_safe": -289, "buy_btc_safe_1d": -0.05, ## "buy_threshold": 0.003, "buy_bb_factor": 0.999, "buy_bb_delta": 0.025, "buy_bb_width": 0.095, ## "buy_cci": -116, "buy_cci_length": 25, "buy_rmi": 49, "buy_rmi_length": 17, "buy_srsi_fk": 32, ## "buy_closedelta": 12.148, "buy_ema_diff": 0.022, ## "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179, ## "buy_ema_high_2": 1.087, "buy_ema_low_2": 0.970, ## } # sell space sell_params = { "pHSL": -0.178, "pPF_1": 0.019, "pPF_2": 0.065, "pSL_1": 0.019, "pSL_2": 0.062, "base_nb_candles_sell": 23, "high_offset": 1.051, "high_offset_2": 1.02, "sell_btc_safe": -325 } # really hard to use this minimal_roi = { "0": 0.10, } # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' # Disabled stoploss = -0.99 # Custom stoploss use_custom_stoploss = True use_sell_signal = True ############################################################################ ## Buy params is_optimize_dip = False buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip) is_optimize_break = False buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize = is_optimize_break) buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize = is_optimize_break) is_optimize_local_dip = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_dip) is_optimize_ewo = False buy_rsi_fast = IntParameter(35, 50, default=45, optimize = False) buy_rsi = IntParameter(15, 30, default=35, optimize = False) buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo) is_optimize_ewo_2 = False buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96 , optimize = is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09 , optimize = is_optimize_ewo_2) is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) is_optimize_btc_safe = False buy_btc_safe = IntParameter(-300, 50, default=-200, optimize = is_optimize_btc_safe) buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize = is_optimize_btc_safe) buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize = is_optimize_btc_safe) # Buy params toggle buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) ## Sell params sell_btc_safe = IntParameter(-400, -300, default=-365, optimize = True) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) ## Trailing params # hard stoploss profit pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True) ############################################################################ def informative_pairs(self): informative_pairs = [("BTC/BUSD", "5m")] return informative_pairs ############################################################################ ## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle ) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if (current_profit > PF_2): sl_profit = SL_2 + (current_profit - PF_2) elif (current_profit > PF_1): sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # Only for hyperopt invalid return if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) ############################################################################ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Bollinger bands (hyperopt hard to implement) 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'] ### BTC protection # BTC info inf_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/BUSD', timeframe=inf_tf) informative_past = informative.copy().shift(1) # Get recent BTC info # BTC 5m dump protection informative_past_source = (informative_past['open'] + informative_past['close'] + informative_past['high'] + informative_past['low']) / 4 # Get BTC price informative_threshold = informative_past_source * self.buy_threshold.value # BTC dump n% in 5 min informative_past_delta = informative_past['close'].shift(1) - informative_past['close'] # should be positive if dump informative_diff = informative_threshold - informative_past_delta # Need be larger than 0 dataframe['btc_threshold'] = informative_threshold dataframe['btc_diff'] = informative_diff # BTC 1d dump protection informative_past_1d = informative.copy().shift(288) informative_past_source_1d = (informative_past_1d['open'] + informative_past_1d['close'] + informative_past_1d['high'] + informative_past_1d['low']) / 4 dataframe['btc_5m'] = informative_past_source dataframe['btc_1d'] = informative_past_source_1d ### Other checks dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int') # CCI hyperopt for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) dataframe['cci_long'] = ta.CCI(dataframe, 170) # RMI hyperopt for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) #dataframe['rmi'] = RMI(dataframe, length=8, mom=4) # SRSI hyperopt ? stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] # BinH dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # SMA dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # EMA dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # Elliot dataframe['EWO'] = EWO(dataframe, 50, 200) # Cofi stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' if self.buy_is_dip_enabled.value: is_dip = ( (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) ) #conditions.append(is_dip) if self.buy_is_break_enabled.value: is_break = ( ( (dataframe['bb_delta'] > self.buy_bb_delta.value) #"buy_bb_delta": 0.025 0.036 & #"buy_bb_width": 0.095 0.133 (dataframe['bb_width'] > self.buy_bb_width.value) ) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & # from BinH (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) ) #conditions.append(is_break) is_local_uptrend = ( # from NFI next gen (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) ) is_ewo = ( # from SMA offset (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) is_ewo_2 = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & (dataframe['EWO'] > self.buy_ewo_high.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & (dataframe['rsi'] < self.buy_rsi.value) ) is_cofi = ( (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) ) # NFI quick mode is_nfi_32 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) ) is_nfi_33 = ( (dataframe['close'] < (dataframe['ema_13'] * 0.978)) & (dataframe['EWO'] > 8) & (dataframe['cti'] < -0.88) & (dataframe['rsi'] < 32) & (dataframe['r_14'] < -98.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5)) ) # is_btc_safe = ( # (dataframe['btc_diff'] > self.buy_btc_safe.value) # &(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * self.buy_btc_safe_1d.value) # &(dataframe['volume'] > 0) # Make sure Volume is not 0 # ) is_BB_checked = is_dip & is_break ##print(dataframe['btc_5m']) ##print(dataframe['btc_1d']) ##print(dataframe['btc_5m'] - dataframe['btc_1d']) ##print(dataframe['btc_1d'] * -0.025) ##print(dataframe['btc_5m'] - dataframe['btc_1d'] > dataframe['btc_1d'] * -0.025) ## condition append conditions.append(is_BB_checked) # ~1.7 89% dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb ' conditions.append(is_local_uptrend) # ~3.84 90.2% dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local uptrend ' conditions.append(is_ewo) # ~2.26 93.5% dataframe.loc[is_ewo, 'buy_tag'] += 'ewo ' conditions.append(is_ewo_2) # ~3.68 90.3% dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 ' conditions.append(is_cofi) # ~3.21 90.8% dataframe.loc[is_cofi, 'buy_tag'] += 'cofi ' conditions.append(is_nfi_32) # ~2.43 91.3% dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi 32 ' conditions.append(is_nfi_33) # ~0.11 100% dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi 33 ' if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( (dataframe['close'] > dataframe['sma_9'])& (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (dataframe['rsi']>50)& (dataframe['volume'] > 0)& (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) | ( (dataframe['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1)*0.005 )) & (dataframe['close'] < dataframe['hma_50'])& (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0)& (dataframe['rsi_fast']>dataframe['rsi_slow']) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe