# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/true_lambo.py # --- 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 ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0 return Series(index=bars.index, data=res) 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 # VWAP bands def VWAPB(dataframe, window_size=20, num_of_std=1): df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df, window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - rolling_std * num_of_std df['vwap_high'] = df['vwap'] + rolling_std * num_of_std return (df['vwap_low'], df['vwap'], df['vwap_high']) def top_percent_change(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] # 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 Github_DerSalvador_freqtrade_helm_chart__true_lambo__20260115_122204(IStrategy): INTERFACE_VERSION = 3 '\n @ jilv220\n\n Based on BB_RPB_3c.\n\n The discovery is that the pump protection is only needed for EWO level 2 (4 ~ 8)\n And the dump protection is only needed for EWO level 5 (< -8)\n\n Higher parameter delta is needed for EWO level 4 (-4 ~ -8)\n\n Vwap and Cluc can handle rest of the market pretty well\n\n ' ########################################################################## # Hyperopt result area # entry space ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## entry_params = {'entry_pump_2_factor': 1.125, 'entry_crash_4_tpct_0': 0.141, 'entry_crash_4_tpct_3': 0.031, 'entry_crash_4_tpct_9': 0.091, 'entry_adx': 20, 'entry_fastd': 20, 'entry_fastk': 22, 'entry_ema_cofi': 0.98, 'entry_ewo_high': 4.179, 'entry_gumbo_ema': 1.121, 'entry_gumbo_ewo_low': -9.442, 'entry_gumbo_cti': -0.374, 'entry_gumbo_r14': -51.971, 'entry_gumbo_tpct_0': 0.067, 'entry_gumbo_tpct_3': 0.0, 'entry_gumbo_tpct_9': 0.016, 'entry_clucha_bbdelta_close': 0.03734, 'entry_clucha_bbdelta_close_2': 0.0391, 'entry_clucha_bbdelta_close_3': 0.03943, 'entry_clucha_bbdelta_close_4': 0.04202, 'entry_clucha_bbdelta_tail': 0.78624, 'entry_clucha_bbdelta_tail_2': 1.05718, 'entry_clucha_bbdelta_tail_3': 0.77701, 'entry_clucha_bbdelta_tail_4': 0.418, 'entry_clucha_closedelta_close': 0.01382, 'entry_clucha_closedelta_close_2': 0.01092, 'entry_clucha_closedelta_close_3': 0.00935, 'entry_clucha_closedelta_close_4': 0.01234, 'entry_clucha_rocr_1h': 0.70818, 'entry_clucha_rocr_1h_2': 0.15848, 'entry_clucha_rocr_1h_3': 0.88989, 'entry_clucha_rocr_1h_4': 0.99234, 'entry_vwap_closedelta': 19.889, 'entry_vwap_closedelta_2': 20.099, 'entry_vwap_closedelta_3': 27.526, 'entry_vwap_closedelta_4': 22.26, 'entry_vwap_closedelta_5': 23.227, 'entry_vwap_cti': -0.258, 'entry_vwap_cti_2': -0.748, 'entry_vwap_cti_3': -0.227, 'entry_vwap_cti_4': -0.763, 'entry_vwap_cti_5': -0.021, 'entry_vwap_width': 0.266, 'entry_vwap_width_2': 3.212, 'entry_vwap_width_3': 0.47, 'entry_vwap_width_4': 9.76, 'entry_vwap_width_5': 5.374, 'entry_lambo2_ema': 0.986, 'entry_lambo2_rsi14': 32, 'entry_lambo2_rsi4': 30, 'entry_V_bb_width': 0.03, 'entry_V_bb_width_2': 0.08, 'entry_V_bb_width_3': 0.097, 'entry_V_bb_width_4': 0.054, 'entry_V_bb_width_5': 0.063, 'entry_V_cti': -0.872, 'entry_V_cti_2': -0.842, 'entry_V_cti_3': -0.888, 'entry_V_cti_4': -0.57, 'entry_V_cti_5': -0.086, 'entry_V_mfi': 35.342, 'entry_V_mfi_2': 24.775, 'entry_V_mfi_3': 17.053, 'entry_V_mfi_4': 13.907, 'entry_V_mfi_5': 38.158, 'entry_V_r14': -67.209, 'entry_V_r14_2': -6.723, 'entry_V_r14_3': -68.059, 'entry_V_r14_4': -88.943, 'entry_V_r14_5': -41.493} # exit space ## ## exit_params = {'base_nb_candles_exit': 23, 'high_offset': 0.87, 'high_offset_2': 1.166, 'high_offset': 0.963, 'high_offset_2': 1.391, 'pHSL': -0.083, 'pPF_1': 0.011, 'pPF_2': 0.048, 'pSL_1': 0.02, 'pSL_2': 0.041} # ROI minimal_roi = {'0': 0.092, '29': 0.042, '85': 0.028, '128': 0.005} # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' # Disabled stoploss = -0.99 # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Custom stoploss use_custom_stoploss = True use_exit_signal = True startup_candle_count: int = 400 ############################################################################ ## Buy params is_optimize_cofi = False entry_ema_cofi = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_cofi) entry_fastk = IntParameter(20, 45, default=20, optimize=is_optimize_cofi) entry_fastd = IntParameter(20, 45, default=20, optimize=is_optimize_cofi) entry_adx = IntParameter(20, 45, default=30, optimize=is_optimize_cofi) entry_ewo_high = DecimalParameter(-12, 12, default=3.553, optimize=is_optimize_cofi) is_optimize_clucha = False entry_clucha_bbdelta_close = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha) entry_clucha_bbdelta_tail = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha) entry_clucha_closedelta_close = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha) entry_clucha_rocr_1h = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha) is_optimize_clucha_2 = False entry_clucha_bbdelta_close_2 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha_2) entry_clucha_bbdelta_tail_2 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_2) entry_clucha_closedelta_close_2 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_2) entry_clucha_rocr_1h_2 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_2) is_optimize_clucha_3 = False entry_clucha_bbdelta_close_3 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize=is_optimize_clucha_3) entry_clucha_bbdelta_tail_3 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_3) entry_clucha_closedelta_close_3 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_3) entry_clucha_rocr_1h_3 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_3) is_optimize_clucha_4 = True entry_clucha_bbdelta_close_4 = DecimalParameter(0.0005, 0.06, default=0.034, decimals=5, optimize=is_optimize_clucha_4) entry_clucha_bbdelta_tail_4 = DecimalParameter(0.35, 1.1, default=0.95, decimals=5, optimize=is_optimize_clucha_4) entry_clucha_closedelta_close_4 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize=is_optimize_clucha_4) entry_clucha_rocr_1h_4 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize=is_optimize_clucha_4) is_optimize_gumbo = False entry_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_gumbo) entry_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize=is_optimize_gumbo) entry_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_gumbo) entry_gumbo_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_gumbo) is_optimize_gumbo_protection = False entry_gumbo_tpct_0 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection) entry_gumbo_tpct_3 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection) entry_gumbo_tpct_9 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize=is_optimize_gumbo_protection) is_optimize_vwap = False entry_vwap_width = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap) entry_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap) entry_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap) is_optimize_vwap_2 = False entry_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_2) entry_vwap_closedelta_2 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_2) entry_vwap_cti_2 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_2) is_optimize_vwap_3 = False entry_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_3) entry_vwap_closedelta_3 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_3) entry_vwap_cti_3 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_3) is_optimize_vwap_4 = True entry_vwap_width_4 = DecimalParameter(0.05, 10.0, default=0.8, optimize=is_optimize_vwap_4) entry_vwap_closedelta_4 = DecimalParameter(10.0, 30.0, default=15.0, optimize=is_optimize_vwap_4) entry_vwap_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6, optimize=is_optimize_vwap_4) is_optimize_lambo_2 = False entry_lambo2_ema = DecimalParameter(0.85, 1.15, default=0.942, optimize=is_optimize_lambo_2) entry_lambo2_rsi4 = IntParameter(15, 45, default=45, optimize=is_optimize_lambo_2) entry_lambo2_rsi14 = IntParameter(15, 45, default=45, optimize=is_optimize_lambo_2) is_optimize_V = False entry_V_bb_width = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V) entry_V_cti = DecimalParameter(-0.95, -0.5, default=-0.6, optimize=is_optimize_V) entry_V_r14 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V) entry_V_mfi = DecimalParameter(10, 40, default=30, optimize=is_optimize_V) is_optimize_V_2 = False entry_V_bb_width_2 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_2) entry_V_cti_2 = DecimalParameter(-0.95, -0.5, default=-0.6, optimize=is_optimize_V_2) entry_V_r14_2 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_2) entry_V_mfi_2 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_2) is_optimize_V_4 = False entry_V_bb_width_4 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_4) entry_V_cti_4 = DecimalParameter(-0.95, -0.0, default=-0.6, optimize=is_optimize_V_4) entry_V_r14_4 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_4) entry_V_mfi_4 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_4) is_optimize_V_5 = False entry_V_bb_width_5 = DecimalParameter(0.01, 0.1, default=0.01, optimize=is_optimize_V_5) entry_V_cti_5 = DecimalParameter(-0.95, -0.0, default=-0.6, optimize=is_optimize_V_5) entry_V_r14_5 = DecimalParameter(-100, 0, default=-60, optimize=is_optimize_V_5) entry_V_mfi_5 = DecimalParameter(10, 40, default=30, optimize=is_optimize_V_5) entry_V_diff_5 = DecimalParameter(0.1, 0.2, default=0.1, optimize=is_optimize_V_5) is_optimize_pump_2 = False entry_pump_2_factor = DecimalParameter(1.0, 1.2, default=1.1, optimize=is_optimize_pump_2) is_optimize_crash_4 = False entry_crash_4_tpct_0 = DecimalParameter(0.02, 0.04, default=0.02, decimals=3, optimize=is_optimize_crash_4) entry_crash_4_tpct_3 = DecimalParameter(0.05, 0.1, default=0.05, decimals=2, optimize=is_optimize_crash_4) entry_crash_4_tpct_9 = DecimalParameter(0.13, 0.32, default=0.13, decimals=2, optimize=is_optimize_crash_4) ## Sell params base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=False) high_offset = DecimalParameter(0.85, 1.1, default=exit_params['high_offset'], space='exit', optimize=True) high_offset_2 = DecimalParameter(0.85, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True) ## Trailing params # hard stoploss profit pHSL = DecimalParameter(-0.1, -0.04, default=-0.08, decimals=3, space='exit', load=True, optimize=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.016, decimals=3, space='exit', load=True, optimize=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.011, decimals=3, space='exit', load=True, optimize=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.08, decimals=3, space='exit', load=True, optimize=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.04, decimals=3, space='exit', load=True, optimize=True) ############################################################################ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] 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 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 entry_tag = 'empty' if hasattr(trade, 'entry_tag') and trade.entry_tag is not None: entry_tag = trade.entry_tag entry_tags = entry_tag.split() # exit vwap (bear, conservative exit) if last_candle['ema_vwap_diff_50'] > 0.02: if current_profit >= 0.005: if last_candle['close'] > last_candle['vwap_middleband'] * 0.99 and last_candle['rsi'] > 41: return f'exit_profit_vwap( {entry_tag})' return None ############################################################################ 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'] dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'] # BinH dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # SMA dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) # CTI dataframe['cti'] = pta.cti(dataframe['close'], length=20) # EMA dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_6'] = ta.RSI(dataframe, timeperiod=6) dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # 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) # 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) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) # T3 Average dataframe['T3'] = T3(dataframe) # VWAP vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_upperband'] = vwap_high dataframe['vwap_middleband'] = vwap dataframe['vwap_lowerband'] = vwap_low dataframe['vwap_width'] = (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] * 100 # Avg dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 dataframe['cci'] = ta.CCI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) # Diff dataframe['ema_vwap_diff_50'] = (dataframe['ema_50'] - dataframe['vwap_lowerband']) / dataframe['ema_50'] # Crash protection dataframe['tpct_0'] = top_percent_change(dataframe, 0) dataframe['tpct_3'] = top_percent_change(dataframe, 3) dataframe['tpct_9'] = top_percent_change(dataframe, 9) #dataframe['tpct_24'] = top_percent_change(dataframe , 24) #dataframe['tpct_42'] = top_percent_change(dataframe , 42) # Calculate all ma_exit values for val in self.base_nb_candles_exit.range: dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val) ############################################################################ # 1h tf inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) # Heikin Ashi inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) # Bollinger bands bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bb_lowerband2'] = bollinger2['lower'] informative['bb_middleband2'] = bollinger2['mid'] informative['bb_upperband2'] = bollinger2['upper'] informative['bb_width'] = (informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2'] # T3 Average informative['T3'] = T3(informative) # RSI informative['rsi'] = ta.RSI(informative, timeperiod=14) # EMA informative['ema_200'] = ta.EMA(informative, timeperiod=200) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'entry_tag'] = '' is_pump_2 = dataframe['close'].rolling(48).max() >= dataframe['close'] * self.entry_pump_2_factor.value is_crash_4 = (dataframe['tpct_0'] < self.entry_crash_4_tpct_0.value) & (dataframe['tpct_3'] < self.entry_crash_4_tpct_3.value) & (dataframe['tpct_9'] < self.entry_crash_4_tpct_9.value) is_cofi = (dataframe['open'] < dataframe['ema_8'] * self.entry_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.entry_fastk.value) & (dataframe['fastd'] < self.entry_fastd.value) & (dataframe['adx'] > self.entry_adx.value) & (dataframe['EWO'] > self.entry_ewo_high.value) # Modified from gumbo1, creadit goes to original author @raph92 # Crash Protection is_gumbo = (dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) & (dataframe['T3'] <= dataframe['ema_8'] * self.entry_gumbo_ema.value) & (dataframe['cti'] < self.entry_gumbo_cti.value) & (dataframe['r_14'] < self.entry_gumbo_r14.value) & (dataframe['EWO'] < self.entry_gumbo_ewo_low.value) & (dataframe['tpct_0'] < self.entry_gumbo_tpct_0.value) & (dataframe['tpct_3'] < self.entry_gumbo_tpct_3.value) & (dataframe['tpct_9'] < self.entry_gumbo_tpct_9.value) is_clucHA = (dataframe['rocr_1h'] > self.entry_clucha_rocr_1h.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] > 8) is_clucHA_2 = (dataframe['rocr_1h'] > self.entry_clucha_rocr_1h_2.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close_2.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close_2.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail_2.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2 is_clucHA_3 = (dataframe['rocr_1h'] > self.entry_clucha_rocr_1h_3.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close_3.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close_3.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail_3.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5) #& #(is_crash_4) is_clucHA_4 = (dataframe['rocr_1h'] > self.entry_clucha_rocr_1h_4.value) & ((dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close_4.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close_4.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail_4.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['ha_close'] < dataframe['ha_close'].shift())) & (dataframe['EWO'] < -4) & (dataframe['EWO'] > -8) is_vwap = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.entry_vwap_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_vwap_closedelta.value / 1000) & (dataframe['cti'] < self.entry_vwap_cti.value) & (dataframe['EWO'] > 8) is_vwap_2 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.entry_vwap_width_2.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_vwap_closedelta_2.value / 1000) & (dataframe['cti'] < self.entry_vwap_cti_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2 is_vwap_3 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.entry_vwap_width_3.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_vwap_closedelta_3.value / 1000) & (dataframe['cti'] < self.entry_vwap_cti_3.value) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5) #& #(is_crash_4) is_vwap_4 = (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.entry_vwap_width_4.value) & (dataframe['closedelta'] > dataframe['close'] * self.entry_vwap_closedelta_4.value / 1000) & (dataframe['cti'] < self.entry_vwap_cti_4.value) & (dataframe['EWO'] < -4) & (dataframe['EWO'] > -8) is_lambo_2 = (dataframe['close'] < dataframe['ema_14'] * self.entry_lambo2_ema.value) & (dataframe['rsi_fast'] < self.entry_lambo2_rsi4.value) & (dataframe['rsi'] < self.entry_lambo2_rsi14.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2 is_V = (dataframe['bb_width'] > self.entry_V_bb_width.value) & (dataframe['cti'] < self.entry_V_cti.value) & (dataframe['r_14'] < self.entry_V_r14.value) & (dataframe['mfi'] < self.entry_V_mfi.value) & (dataframe['EWO'] > 8) is_V_2 = (dataframe['bb_width'] > self.entry_V_bb_width_2.value) & (dataframe['cti'] < self.entry_V_cti_2.value) & (dataframe['r_14'] < self.entry_V_r14_2.value) & (dataframe['mfi'] < self.entry_V_mfi_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & is_pump_2 # Really Bear, don't engage until dump over is_V_5 = (dataframe['bb_width'] > self.entry_V_bb_width_5.value) & (dataframe['cti'] < self.entry_V_cti_5.value) & (dataframe['r_14'] < self.entry_V_r14_5.value) & (dataframe['mfi'] < self.entry_V_mfi_5.value) & (dataframe['ema_vwap_diff_50'] > 0.215) & (dataframe['EWO'] < -10) is_additional_check = (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) # condition append ## Non EWO conditions.append(is_cofi) # ~3.21 90.8% dataframe.loc[is_cofi, 'entry_tag'] += 'cofi ' # EWO > 8 conditions.append(is_vwap) # ~67.3% dataframe.loc[is_vwap, 'entry_tag'] += 'vwap ' conditions.append(is_V) # ~67.9% dataframe.loc[is_V, 'entry_tag'] += 'V ' # EWO 4 ~ 8 conditions.append(is_lambo_2) # ~67.7% dataframe.loc[is_lambo_2, 'entry_tag'] += 'lambo_2 ' conditions.append(is_clucHA_2) # ~68.2% dataframe.loc[is_clucHA_2, 'entry_tag'] += 'cluc_2 ' conditions.append(is_vwap_2) # ~67.3% dataframe.loc[is_vwap_2, 'entry_tag'] += 'vwap_2 ' conditions.append(is_V_2) # ~67.9% dataframe.loc[is_V_2, 'entry_tag'] += 'V_2 ' # EWO -2.5 ~ 4 conditions.append(is_clucHA_3) # ~68.2% dataframe.loc[is_clucHA_3, 'entry_tag'] += 'cluc_3 ' conditions.append(is_vwap_3) # ~67.3% dataframe.loc[is_vwap_3, 'entry_tag'] += 'vwap_3 ' # EWO -8 ~ -4 conditions.append(is_clucHA_4) # ~68.2% dataframe.loc[is_clucHA_4, 'entry_tag'] += 'cluc_4 ' conditions.append(is_vwap_4) # ~67.3% dataframe.loc[is_vwap_4, 'entry_tag'] += 'vwap_4 ' ## EWO < -8 conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% F (263 %) dataframe.loc[is_gumbo, 'entry_tag'] += 'gumbo ' conditions.append(is_V_5) # ~67.9% dataframe.loc[is_V_5, 'entry_tag'] += 'V_5 ' if conditions: dataframe.loc[is_additional_check & reduce(lambda x, y: x | y, conditions), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.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_exit_{self.base_nb_candles_exit.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), 'exit'] = 1 return dataframe 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']