# source: https://raw.githubusercontent.com/djienne/YOUTUBE_STRATEGIES_FREQTRADE/b5a1c994a10804e27fed5e53ba588ae9cff6ac9a/Strategies/BB_RPB_TSL/user_data/strategies/BB_RPB_TSL.py # directory_url: https://github.com/djienne/YOUTUBE_STRATEGIES_FREQTRADE/blob/master/Strategies/BB_RPB_TSL/user_data/strategies/ # User: djienne # Repository: YOUTUBE_STRATEGIES_FREQTRADE # --------------------# --- 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 freqtrade.enums import RunMode 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 freqtrade.exchange import timeframe_to_prev_date from functools import reduce from technical.indicators import RMI, zema, ichimoku # -------------------------------- def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma # Modified Elder Ray Index def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend 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 def SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc def range_percent_change(dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min() else: raise ValueError(f"Method {method} not defined!") # 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 # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') class Github_djienne_YOUTUBE_STRATEGIES_FREQTRADE__BB_RPB_TSL__20260927_131411(IStrategy): ''' Github_djienne_YOUTUBE_STRATEGIES_FREQTRADE__BB_RPB_TSL__20260927_131411 @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. ''' # (1) sell rework ########################################################################## # Hyperopt result area # buy space buy_params = { "max_slip": 0.983, ## "buy_bb_width_1h": 0.954, "buy_roc_1h": 86, ## "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": 17.922, "buy_ema_diff": 0.026, ## "buy_ema_high": 0.968, "buy_ema_low": 0.935, "buy_ewo": -5.001, "buy_rsi": 23, "buy_rsi_fast": 44, ## "buy_ema_high_2": 1.087, "buy_ema_low_2": 0.970, "buy_ewo_high_2": 4.179, "buy_rsi_ewo_2": 35, "buy_rsi_fast_ewo_2": 45, ## "buy_closedelta_local_dip": 12.044, "buy_ema_diff_local_dip": 0.024, "buy_ema_high_local_dip": 1.014, "buy_rsi_local_dip": 21, ## "buy_r_deadfish_bb_factor": 1.014, "buy_r_deadfish_bb_width": 0.299, "buy_r_deadfish_ema": 1.054, "buy_r_deadfish_volume_factor": 1.59, "buy_r_deadfish_cti": -0.115, "buy_r_deadfish_r14": -44.34, ## "buy_clucha_bbdelta_close": 0.049, "buy_clucha_bbdelta_tail": 1.146, "buy_clucha_close_bblower": 0.018, "buy_clucha_closedelta_close": 0.017, "buy_clucha_rocr_1h": 0.526, ## "buy_adx": 13, "buy_cofi_r14": -85.016, "buy_cofi_cti": -0.892, "buy_ema_cofi": 1.147, "buy_ewo_high": 8.594, "buy_fastd": 28, "buy_fastk": 39, ## "buy_gumbo_ema": 1.121, "buy_gumbo_ewo_low": -9.442, "buy_gumbo_cti": -0.374, "buy_gumbo_r14": -51.971, ## "buy_sqzmom_ema": 0.981, "buy_sqzmom_ewo": -3.966, "buy_sqzmom_r14": -45.068, ## "buy_nfix_39_ema": 0.912, ## "buy_nfix_49_cti": -0.105, "buy_nfix_49_r14": -81.827, } # sell space sell_params = { ## "sell_cmf": -0.046, "sell_ema": 0.988, "sell_ema_close_delta": 0.022, ## "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37, ## "sell_cti_r_cti": 0.844, "sell_cti_r_r": -19.99, } minimal_roi = { "0": 0.205, "81": 0.038, "292": 0.005, } order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optimal timeframe for the strategy timeframe = '1h' inf_1h = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Disabled stoploss = -0.99 startup_candle_count: int = 605 # Custom stoploss use_custom_stoploss = True use_exit_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(0.065, 0.135, default=0.095, optimize = is_optimize_break) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = is_optimize_break) is_optimize_local_uptrend = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_uptrend) 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_uptrend) is_optimize_local_dip = False buy_ema_diff_local_dip = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip) buy_ema_high_local_dip = DecimalParameter(0.90, 1.2, default=0.942 , optimize = is_optimize_local_dip) buy_closedelta_local_dip = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_dip) buy_rsi_local_dip = IntParameter(15, 45, default=28, optimize = is_optimize_local_dip) buy_crsi_local_dip = IntParameter(10, 18, default=10, optimize = False) is_optimize_ewo = False buy_rsi_fast = IntParameter(35, 50, default=45, optimize = is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=35, optimize = is_optimize_ewo) 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_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize = is_optimize_ewo_2) buy_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize = is_optimize_ewo_2) buy_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970 , optimize = is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_ewo_2) buy_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize = is_optimize_ewo_2) is_optimize_r_deadfish = False buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_r_deadfish) buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_r_deadfish) buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0 , optimize = is_optimize_r_deadfish) buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_r_deadfish) is_optimize_r_deadfish_protection = False buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5 , optimize = is_optimize_r_deadfish_protection) buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60 , optimize = is_optimize_r_deadfish_protection) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=0.02206, optimize = is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize = is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize = is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize = is_optimize_clucha) is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.94, 1.2, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(0, 40, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(0, 40, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(0, 30, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi) is_optimize_cofi_protection = False buy_cofi_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_cofi_protection) buy_cofi_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_cofi_protection) is_optimize_gumbo = False buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo) buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo) is_optimize_gumbo_protection = False buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo_protection) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo_protection) is_optimize_sqzmom_protection = False buy_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_sqzmom_protection) buy_sqzmom_ewo = DecimalParameter(-12 , 12, default= 0 , optimize = is_optimize_sqzmom_protection) buy_sqzmom_r14 = DecimalParameter(-100, -22, default=-50 , optimize = is_optimize_sqzmom_protection) is_optimize_nfix_39 = True buy_nfix_39_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_nfix_39) is_optimize_nfix_49_protection = False buy_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_nfix_49_protection) buy_nfix_49_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_nfix_49_protection) 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) is_optimize_check = False buy_roc_1h = IntParameter(-25, 200, default=10, optimize = is_optimize_check) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize = is_optimize_check) ## Slippage params is_optimize_slip = False max_slip = DecimalParameter(0.33, 1.00, default=0.33, decimals=3, optimize=is_optimize_slip , space='buy', load=True) ## Sell params sell_btc_safe = IntParameter(-400, -300, default=-365, optimize = False) is_optimize_sell_stoploss = False sell_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize = is_optimize_sell_stoploss) sell_ema_close_delta = DecimalParameter(0.022, 0.027, default= 0.024, optimize = is_optimize_sell_stoploss) sell_ema = DecimalParameter(0.97, 0.99, default=0.987 , optimize = is_optimize_sell_stoploss) is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05 , optimize = is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , optimize = is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_deadfish) is_optimize_bleeding = False sell_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_bleeding) sell_bleeding_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_bleeding) sell_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_bleeding) is_optimize_cti_r = False sell_cti_r_cti = DecimalParameter(0.55, 1, default=0.5 , optimize = is_optimize_cti_r) sell_cti_r_r = DecimalParameter(-15, 0, default=-20 , optimize = is_optimize_cti_r) ############################################################################ 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_8'] = ta.EMA(informative_1h, timeperiod=8) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # CTI informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) # CRSI (3, 2, 100) crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative_1h['crsi'] = (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3 # Williams %R informative_1h['r_96'] = williams_r(informative_1h, period=96) informative_1h['r_480'] = williams_r(informative_1h, period=480) # Bollinger bands bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb_lowerband2'] = bollinger2['lower'] informative_1h['bb_middleband2'] = bollinger2['mid'] informative_1h['bb_upperband2'] = bollinger2['upper'] informative_1h['bb_width'] = ((informative_1h['bb_upperband2'] - informative_1h['bb_lowerband2']) / informative_1h['bb_middleband2']) # ROC informative_1h['roc'] = ta.ROC(dataframe, timeperiod=9) # MOMDIV mom = momdiv(informative_1h) informative_1h['momdiv_buy'] = mom['momdiv_buy'] informative_1h['momdiv_sell'] = mom['momdiv_sell'] informative_1h['momdiv_coh'] = mom['momdiv_coh'] informative_1h['momdiv_col'] = mom['momdiv_col'] # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # CMF informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) # Heikin Ashi inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h['ha_close'] = inf_heikinashi['close'] informative_1h['rocr'] = ta.ROCR(informative_1h['ha_close'], timeperiod=168) # T3 Average informative_1h['T3'] = T3(informative_1h) # Elliot informative_1h['EWO'] = EWO(informative_1h, 50, 200) # nfi 37 informative_1h['hl_pct_change_5'] = range_percent_change(informative_1h, 'HL', 5) informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min() informative_1h['safe_dump_50'] = ((informative_1h['hl_pct_change_5'] < 0.66) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])) # Pump protections #informative_1h['hl_pct_change_48'] = range_percent_change(informative_1h, 'HL', length=48) #informative_1h['hl_pct_change_36'] = range_percent_change(informative_1h, 'HL', length=36) #informative_1h['hl_pct_change_24'] = range_percent_change(informative_1h, 'HL', length=24) #informative_1h['hl_pct_change_12'] = range_percent_change(informative_1h, 'HL', length=12) #informative_1h['hl_pct_change_6'] = range_percent_change(informative_1h, 'HL', length=6) return informative_1h ############################################################################ ### Custom functions def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.015 return sl_new # From NFIX def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() # sell trail if 0.012 > current_profit >= 0.0: if (max_profit > (current_profit + 0.045)) and (last_candle['rsi'] < 46.0): return f"sell_profit_t_0_1( {buy_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 32.0): return f"sell_profit_t_0_2( {buy_tag})" elif (max_profit > (current_profit + 0.05)) and (last_candle['rsi'] < 48.0): return f"sell_profit_t_0_3( {buy_tag})" elif 0.02 > current_profit >= 0.012: if (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 39.0): return f"sell_profit_t_1_1( {buy_tag})" elif (max_profit > (current_profit + 0.035)) and (last_candle['rsi'] < 45.0) and (last_candle['cmf'] < -0.0) and (last_candle['cmf_1h'] < -0.0): return f"sell_profit_t_1_2( {buy_tag})" elif (max_profit > (current_profit + 0.02)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.0) and (last_candle['cti_1h'] > 0.8): return f"sell_profit_t_1_4( {buy_tag})" elif (max_profit > (current_profit + 0.04)) and (last_candle['rsi'] < 49.0) and (last_candle['cmf_1h'] < -0.0): return f"sell_profit_t_1_5( {buy_tag})" elif (max_profit > (current_profit + 0.06)) and (last_candle['rsi'] < 43.0) and (last_candle['cmf'] < -0.0): return f"sell_profit_t_1_7( {buy_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 40.0) and (last_candle['cmf'] < -0.1) and (last_candle['rsi_1h'] < 50.0): return f"sell_profit_t_1_9( {buy_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 46.0) and (last_candle['cmf'] < -0.0) and (last_candle['r_480_1h'] > -20.0): return f"sell_profit_t_1_10( {buy_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 42.0): return f"sell_profit_t_1_11( {buy_tag})" elif (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.25): return f"sell_profit_t_1_12( {buy_tag})" # sell cti_r if 0.012 > current_profit >= 0.0 : if (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value): return f"sell_profit_t_cti_r_0_1( {buy_tag})" # main sell if current_profit > 0.02: if (last_candle['momdiv_sell_1h'] == True): return f"signal_profit_q_momdiv_1h( {buy_tag})" if (last_candle['momdiv_sell'] == True): return f"signal_profit_q_momdiv( {buy_tag})" if (last_candle['momdiv_coh'] == True): return f"signal_profit_q_momdiv_coh( {buy_tag})" # sell bear if last_candle['close'] < last_candle['ema_200']: if 0.02 > current_profit >= 0.01: if (last_candle['rsi'] < 34.0) and (last_candle['cmf'] < 0.0): return f"sell_profit_u_bear_1_1( {buy_tag})" elif (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.4): return f"sell_profit_u_bear_1_2( {buy_tag})" # sell quick if (0.06 > current_profit > 0.02) and (last_candle['rsi'] > 80.0): return f"signal_profit_q_1( {buy_tag})" if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95): return f"signal_profit_q_2( {buy_tag})" if (0.06 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1): return f"signal_profit_q_pmax_bull( {buy_tag})" if (0.06 > current_profit > 0.02) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016): return f"signal_profit_q_pmax_bear( {buy_tag})" # sell scalp if (current_profit > 0 and buy_tag in [ 'nfix_39 ']): if ( (current_profit > 0) and (last_candle['fisher'] > 0.39075) and (last_candle['ha_high'] <= previous_candle_1['ha_high']) and (previous_candle_1['ha_high'] <= previous_candle_2['ha_high']) and (last_candle['ha_close'] <= previous_candle_1['ha_close']) and (last_candle['ema_4'] > last_candle['ha_close']) and (last_candle['ha_close'] * 0.99754 > last_candle['bb_middleband2']) ): return f"sell_scalp( {buy_tag})" if ( (current_profit < -0.05) and (last_candle['close'] < last_candle['ema_200'] * 0.988) and (last_candle['cmf'] < -0.046) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < 0.022) and last_candle['rsi'] > previous_candle_1['rsi'] and (last_candle['rsi'] > (last_candle['rsi_1h'] + 10.0)) ): return f"sell_stoploss_u_e_1( {buy_tag})" # stoploss - deadfish if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) ): return f"sell_stoploss_deadfish( {buy_tag})" # stoploss - bleeding #if ( (current_profit < -0.05) #and (last_candle['close'] < last_candle['ema_200']) #and (last_candle['cti_mean_24'] < self.sell_bleeding_cti.value) #and (last_candle['r_14_mean_24'] < self.sell_bleeding_r14.value) #and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_bleeding_volume_factor.value) #): #return f"sell_stoploss_bleeding( {buy_tag})" return None ## Leverage def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. Returns 1x leverage for backtesting, 1x for live/dry-run. """ if self.dp and self.dp.runmode in (RunMode.BACKTEST, RunMode.HYPEROPT): return 1.0 else: return 1.0 ## Confirm Entry def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) max_slip = self.max_slip.value if(len(dataframe) < 1): return False dataframe = dataframe.iloc[-1].squeeze() if ((rate > dataframe['close'])) : slippage = ( (rate / dataframe['close']) - 1 ) * 100 if slippage < max_slip: return True else: return False return True ############################################################################ def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Bollinger bands 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'] ### Other BB 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']) # 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) # 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_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # CMF dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # CRSI (3, 2, 100) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3 # EMA dataframe['ema_4'] = ta.EMA(dataframe, timeperiod=4) 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_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # 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) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_32'] = williams_r(dataframe, period=32) dataframe['r_64'] = williams_r(dataframe, period=64) dataframe['r_96'] = williams_r(dataframe, period=96) dataframe['r_480'] = williams_r(dataframe, period=480) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) # MFI dataframe['mfi'] = ta.MFI(dataframe) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] ## BB 40 bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2) dataframe['bb_lowerband2_40'] = bollinger2_40['lower'] dataframe['bb_middleband2_40'] = bollinger2_40['mid'] dataframe['bb_upperband2_40'] = bollinger2_40['upper'] # ClucHA dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs() dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # 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) # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) # MOMDIV mom = momdiv(dataframe) dataframe['momdiv_buy'] = mom['momdiv_buy'] dataframe['momdiv_sell'] = mom['momdiv_sell'] dataframe['momdiv_coh'] = mom['momdiv_coh'] dataframe['momdiv_col'] = mom['momdiv_col'] # T3 Average dataframe['T3'] = T3(dataframe) # True range dataframe['trange'] = ta.TRANGE(dataframe) # KC dataframe['range_ma_28'] = ta.SMA(dataframe['trange'], 28) dataframe['kc_upperband_28_1'] = dataframe['sma_28'] + dataframe['range_ma_28'] dataframe['kc_lowerband_28_1'] = dataframe['sma_28'] - dataframe['range_ma_28'] # KC 20 dataframe['range_ma_20'] = ta.SMA(dataframe['trange'], 20) dataframe['kc_upperband_20_2'] = dataframe['sma_20'] + dataframe['range_ma_20'] * 2 dataframe['kc_lowerband_20_2'] = dataframe['sma_20'] - dataframe['range_ma_20'] * 2 dataframe['kc_bb_delta'] = ( dataframe['kc_lowerband_20_2'] - dataframe['bb_lowerband2'] ) / dataframe['bb_lowerband2'] * 100 # Linreg dataframe['hh_20'] = ta.MAX(dataframe['high'], 20) dataframe['ll_20'] = ta.MIN(dataframe['low'], 20) dataframe['avg_hh_ll_20'] = (dataframe['hh_20'] + dataframe['ll_20']) / 2 dataframe['avg_close_20'] = ta.SMA(dataframe['close'], 20) dataframe['avg_val_20'] = (dataframe['avg_hh_ll_20'] + dataframe['avg_close_20']) / 2 dataframe['linreg_val_20'] = ta.LINEARREG(dataframe['close'] - dataframe['avg_val_20'], 20, 0) # fisher rsi = 0.1 * (dataframe['rsi'] - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Modified Elder Ray Index dataframe['moderi_96'] = moderi(dataframe, 96) 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) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' 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) ) is_sqzOff = ( (dataframe['bb_lowerband2'] < dataframe['kc_lowerband_28_1']) & (dataframe['bb_upperband2'] > dataframe['kc_upperband_28_1']) ) is_break = ( (dataframe['bb_delta'] > self.buy_bb_delta.value) & (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) ) is_local_uptrend = ( # from NFI next gen, credit goes to @iterativ (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_local_dip = ( (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff_local_dip.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ema_high_local_dip.value) & (dataframe['rsi'] < self.buy_rsi_local_dip.value) & (dataframe['crsi'] > self.buy_crsi_local_dip.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta_local_dip.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['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_ewo_2.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) & (dataframe['EWO'] > self.buy_ewo_high_2.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) & (dataframe['rsi'] < self.buy_rsi_ewo_2.value) ) is_r_deadfish = ( # reverse deadfish (dataframe['ema_100'] < dataframe['ema_200'] * self.buy_r_deadfish_ema.value) & (dataframe['bb_width'] > self.buy_r_deadfish_bb_width.value) & (dataframe['close'] < dataframe['bb_middleband2'] * self.buy_r_deadfish_bb_factor.value) & (dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.buy_r_deadfish_volume_factor.value) & (dataframe['cti'] < self.buy_r_deadfish_cti.value) & (dataframe['r_14'] < self.buy_r_deadfish_r14.value) ) is_clucHA = ( (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value ) & ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift()) ) ) is_cofi = ( # Modified from cofi, credit goes to original author "slack user CofiBit" (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) & (dataframe['cti'] < self.buy_cofi_cti.value) & (dataframe['r_14'] < self.buy_cofi_r14.value) ) is_gumbo = ( # Modified from gumbo1, creadit goes to original author @raph92 (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) & (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) & (dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) & (dataframe['cti'] < self.buy_gumbo_cti.value) & (dataframe['r_14'] < self.buy_gumbo_r14.value) ) is_sqzmom = ( # Modified from squeezeMomentum, credit goes to original author @LazyBear of TradingView (is_sqzOff) & (dataframe['linreg_val_20'].shift(2) > dataframe['linreg_val_20'].shift(1)) & (dataframe['linreg_val_20'].shift(1) < dataframe['linreg_val_20']) & (dataframe['linreg_val_20'] < 0) & (dataframe['close'] < dataframe['ema_13'] * self.buy_sqzmom_ema.value) & (dataframe['EWO'] < self.buy_sqzmom_ewo.value) & (dataframe['r_14'] < self.buy_sqzmom_r14.value) ) # NFI quick mode, credit goes to @iterativ is_nfi_13 = ( (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -5.585) & (dataframe['cti_1h'] < -0.88) & (dataframe['crsi_1h'] > 10.0) ) is_nfi_32 = ( # NFIX 26 (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 25.0) & (dataframe['close'] < dataframe['sma_15'] * 0.93) & (dataframe['cti'] < -0.9) ) 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_nfi_38 = ( (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] < -4.4) & (dataframe['cti'] < -0.95) & (dataframe['r_14'] < -97) & (dataframe['crsi_1h'] > 0.5) ) is_nfix_5 = ( (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['close'] < dataframe['sma_75'] * 0.932) & (dataframe['EWO'] > 3.6) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -97.0) ) is_nfix_39 = ( (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['bb_lowerband2_40'].shift().gt(0)) & (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.01)) & (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)) & (dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['close'] > dataframe['ema_13'] * self.buy_nfix_39_ema.value) ) is_nfix_49 = ( (dataframe['ema_26'].shift(3) > dataframe['ema_12'].shift(3)) & (dataframe['ema_26'].shift(3) - dataframe['ema_12'].shift(3) > dataframe['open'].shift(3) * 0.032) & (dataframe['ema_26'].shift(9) - dataframe['ema_12'].shift(9) > dataframe['open'].shift(3) / 100) & (dataframe['close'].shift(3) < dataframe['ema_20'].shift(3) * 0.916) & (dataframe['rsi'].shift(3) < 32.5) & (dataframe['crsi'].shift(3) > 18.0) & (dataframe['cti'] < self.buy_nfix_49_cti.value) & (dataframe['r_14'] < self.buy_nfix_49_r14.value) ) is_nfi7_33 = ( (dataframe['moderi_96']) & (dataframe['cti'] < -0.88) & (dataframe['close'] < (dataframe['ema_13'] * 0.988)) & (dataframe['EWO'] > 6.4) & (dataframe['rsi'] < 32.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0)) ) is_nfi7_37 = ( (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] > 9.8) & (dataframe['rsi'] < 56.0) & (dataframe['cti'] < -0.7) & (dataframe['safe_dump_50_1h']) ) is_additional_check = ( (dataframe['roc_1h'] < self.buy_roc_1h.value) & (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) ) ## Additional Check is_BB_checked = is_dip & is_break ## Condition Append conditions.append(is_BB_checked) # ~2.32 / 91.1% / 46.27% D dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb ' conditions.append(is_local_uptrend) # ~3.28 / 92.4% / 69.72% dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local_uptrend ' conditions.append(is_local_dip) # ~0.76 / 91.1% / 15.54% dataframe.loc[is_local_dip, 'buy_tag'] += 'local_dip ' conditions.append(is_ewo) # ~0.92 / 92.0% / 43.74% D dataframe.loc[is_ewo, 'buy_tag'] += 'ewo ' conditions.append(is_ewo_2) # ~2.86 / 91.5% / 33.31% D dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 ' conditions.append(is_r_deadfish) # ~0.99 / 86.9% / 21.93% D dataframe.loc[is_r_deadfish, 'buy_tag'] += 'r_deadfish ' conditions.append(is_clucHA) # ~7.2 / 92.5% / 97.98% D dataframe.loc[is_clucHA, 'buy_tag'] += 'clucHA ' conditions.append(is_cofi) # ~0.4 / 94.4% / 9.59% D dataframe.loc[is_cofi, 'buy_tag'] += 'cofi ' conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% D dataframe.loc[is_gumbo, 'buy_tag'] += 'gumbo ' conditions.append(is_sqzmom) # ~3.14 / 92.4% / 64.14% D dataframe.loc[is_sqzmom, 'buy_tag'] += 'sqzmom ' conditions.append(is_nfi_13) # ~0.4 / 100% D dataframe.loc[is_nfi_13, 'buy_tag'] += 'nfi_13 ' conditions.append(is_nfi_32) # ~0.78 / 92.0 % / 37.41% D dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi_32 ' conditions.append(is_nfi_33) # ~0.11 / 100% D dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 ' conditions.append(is_nfi_38) # ~1.13 / 88.5% / 31.34% D dataframe.loc[is_nfi_38, 'buy_tag'] += 'nfi_38 ' conditions.append(is_nfix_5) # ~0.25 / 97.7% / 6.53% D dataframe.loc[is_nfix_5, 'buy_tag'] += 'nfix_5 ' conditions.append(is_nfix_39) # ~5.33 / 91.8% / 58.57% D dataframe.loc[is_nfix_39, 'buy_tag'] += 'nfix_39 ' conditions.append(is_nfix_49) # ~0.33 / 100% / 0% D dataframe.loc[is_nfix_49, 'buy_tag'] += 'nfix_49 ' conditions.append(is_nfi7_33) # ~0.71 / 91.3% / 28.94% D dataframe.loc[is_nfi7_33, 'buy_tag'] += 'nfi7_33 ' conditions.append(is_nfi7_37) # ~0.46 / 92.6% / 17.05% D dataframe.loc[is_nfi7_37, 'buy_tag'] += 'nfi7_37 ' if conditions: dataframe.loc[ is_additional_check & reduce(lambda x, y: x | y, conditions) , 'enter_long' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe['volume'] > 0), 'exit_long' ] = 0 return dataframe # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), None) return pm, pmx # Mom DIV def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe['high'].rolling(lookback).max() ll = dataframe['low'].rolling(lookback).min() coh = dataframe['high'] >= hh col = dataframe['low'] <= ll df = DataFrame({ "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe['close'].index) return df def T3(dataframe, length=5): """ T3 Average by HPotter on Tradingview https://www.tradingview.com/script/qzoC9H1I-T3-Average/ """ df = dataframe.copy() df['xe1'] = ta.EMA(df['close'], timeperiod=length) df['xe2'] = ta.EMA(df['xe1'], timeperiod=length) df['xe3'] = ta.EMA(df['xe2'], timeperiod=length) df['xe4'] = ta.EMA(df['xe3'], timeperiod=length) df['xe5'] = ta.EMA(df['xe4'], timeperiod=length) df['xe6'] = ta.EMA(df['xe5'], timeperiod=length) b = 0.7 c1 = -b * b * b c2 = 3 * b * b + 3 * b * b * b c3 = -6 * b * b - 3 * b - 3 * b * b * b c4 = 1 + 3 * b + b * b * b + 3 * b * b df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3'] return df['T3Average']