# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/true_lambo_Hyper.py 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. 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 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'] 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_remiotore_freqtrade__true_lambo_Hyper__20260111_210550(IStrategy): ''' @ jilv220 Based on BB_RPB_3c. The discovery is that the pump protection is only needed for EWO level 2 (4 ~ 8) And the dump protection is only needed for EWO level 5 (< -8) Higher parameter delta is needed for EWO level 4 (-4 ~ -8) Vwap and Cluc can handle rest of the market pretty well ''' buy_params = { "buy_pump_2_factor": 1.125, "buy_crash_4_tpct_0": 0.141, "buy_crash_4_tpct_3": 0.031, "buy_crash_4_tpct_9": 0.091, "buy_adx": 20, "buy_fastd": 20, "buy_fastk": 22, "buy_ema_cofi": 0.98, "buy_ewo_high": 4.179, "buy_gumbo_ema": 1.121, "buy_gumbo_ewo_low": -9.442, "buy_gumbo_cti": -0.374, "buy_gumbo_r14": -51.971, "buy_gumbo_tpct_0": 0.067, "buy_gumbo_tpct_3": 0.0, "buy_gumbo_tpct_9": 0.016, "buy_clucha_bbdelta_close": 0.03734, "buy_clucha_bbdelta_close_2": 0.0391, "buy_clucha_bbdelta_close_3": 0.03943, "buy_clucha_bbdelta_close_4": 0.04202, "buy_clucha_bbdelta_tail": 0.78624, "buy_clucha_bbdelta_tail_2": 1.05718, "buy_clucha_bbdelta_tail_3": 0.77701, "buy_clucha_bbdelta_tail_4": 0.418, "buy_clucha_closedelta_close": 0.01382, "buy_clucha_closedelta_close_2": 0.01092, "buy_clucha_closedelta_close_3": 0.00935, "buy_clucha_closedelta_close_4": 0.01234, "buy_clucha_rocr_1h": 0.70818, "buy_clucha_rocr_1h_2": 0.15848, "buy_clucha_rocr_1h_3": 0.88989, "buy_clucha_rocr_1h_4": 0.99234, "buy_vwap_closedelta": 19.889, "buy_vwap_closedelta_2": 20.099, "buy_vwap_closedelta_3": 27.526, "buy_vwap_closedelta_4": 22.26, "buy_vwap_closedelta_5": 23.227, "buy_vwap_cti": -0.258, "buy_vwap_cti_2": -0.748, "buy_vwap_cti_3": -0.227, "buy_vwap_cti_4": -0.763, "buy_vwap_cti_5": -0.021, "buy_vwap_width": 0.266, "buy_vwap_width_2": 3.212, "buy_vwap_width_3": 0.47, "buy_vwap_width_4": 9.76, "buy_vwap_width_5": 5.374, "buy_lambo2_ema": 0.986, "buy_lambo2_rsi14": 32, "buy_lambo2_rsi4": 30, "buy_V_bb_width": 0.03, "buy_V_bb_width_2": 0.08, "buy_V_bb_width_3": 0.097, "buy_V_bb_width_4": 0.054, "buy_V_bb_width_5": 0.063, "buy_V_cti": -0.872, "buy_V_cti_2": -0.842, "buy_V_cti_3": -0.888, "buy_V_cti_4": -0.57, "buy_V_cti_5": -0.086, "buy_V_mfi": 35.342, "buy_V_mfi_2": 24.775, "buy_V_mfi_3": 17.053, "buy_V_mfi_4": 13.907, "buy_V_mfi_5": 38.158, "buy_V_r14": -67.209, "buy_V_r14_2": -6.723, "buy_V_r14_3": -68.059, "buy_V_r14_4": -88.943, "buy_V_r14_5": -41.493, } sell_params = { "base_nb_candles_sell": 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": { "0": 0.113, "43": 0.067, "178": 0.015, "217": 0 }, timeframe = '5m' inf_1h = '1h' stoploss = -0.028 process_only_new_candles = True use_custom_stoploss = True use_sell_signal = True startup_candle_count: int = 400 "trailing": { "trailing_stop": true, "trailing_stop_positive": 0.016, "trailing_stop_positive_offset": 0.087, "trailing_only_offset_is_reached": false }, "max_open_trades": { "max_open_trades": 10 } is_optimize_cofi = False buy_ema_cofi = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 45, default=20, optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 45, default=20, optimize = is_optimize_cofi) buy_adx = IntParameter(20, 45, default=30, optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(-12, 12, default=3.553, optimize = is_optimize_cofi) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize = is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize = is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize = is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize = is_optimize_clucha) is_optimize_clucha_2 = False buy_clucha_bbdelta_close_2 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize = is_optimize_clucha_2) buy_clucha_bbdelta_tail_2 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize = is_optimize_clucha_2) buy_clucha_closedelta_close_2 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize = is_optimize_clucha_2) buy_clucha_rocr_1h_2 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize = is_optimize_clucha_2) is_optimize_clucha_3 = False buy_clucha_bbdelta_close_3 = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize = is_optimize_clucha_3) buy_clucha_bbdelta_tail_3 = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize = is_optimize_clucha_3) buy_clucha_closedelta_close_3 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize = is_optimize_clucha_3) buy_clucha_rocr_1h_3 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize = is_optimize_clucha_3) is_optimize_clucha_4 = True buy_clucha_bbdelta_close_4 = DecimalParameter(0.0005, 0.06, default=0.034, decimals=5, optimize = is_optimize_clucha_4) buy_clucha_bbdelta_tail_4 = DecimalParameter(0.35, 1.1, default=0.95, decimals=5, optimize = is_optimize_clucha_4) buy_clucha_closedelta_close_4 = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize = is_optimize_clucha_4) buy_clucha_rocr_1h_4 = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize = is_optimize_clucha_4) 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) buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo) is_optimize_gumbo_protection = False buy_gumbo_tpct_0 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) buy_gumbo_tpct_3 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) buy_gumbo_tpct_9 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection) is_optimize_vwap = False buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap) buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap) buy_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap) is_optimize_vwap_2 = False buy_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_2) buy_vwap_closedelta_2 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_2) buy_vwap_cti_2 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_2) is_optimize_vwap_3 = False buy_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_3) buy_vwap_closedelta_3 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_3) buy_vwap_cti_3 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_3) is_optimize_vwap_4 = True buy_vwap_width_4 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_4) buy_vwap_closedelta_4 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_4) buy_vwap_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_4) is_optimize_lambo_2 = False buy_lambo2_ema = DecimalParameter(0.85, 1.15, default=0.942 , optimize = is_optimize_lambo_2) buy_lambo2_rsi4 = IntParameter(15, 45, default=45, optimize = is_optimize_lambo_2) buy_lambo2_rsi14 = IntParameter(15, 45, default=45, optimize = is_optimize_lambo_2) is_optimize_V = False buy_V_bb_width = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V) buy_V_cti = DecimalParameter(-0.95, -0.5, default=-0.6 , optimize = is_optimize_V) buy_V_r14 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V) buy_V_mfi = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V) is_optimize_V_2 = False buy_V_bb_width_2 = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V_2) buy_V_cti_2 = DecimalParameter(-0.95, -0.5, default=-0.6 , optimize = is_optimize_V_2) buy_V_r14_2 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V_2) buy_V_mfi_2 = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V_2) is_optimize_V_4 = False buy_V_bb_width_4 = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V_4) buy_V_cti_4 = DecimalParameter(-0.95, -0.0, default=-0.6 , optimize = is_optimize_V_4) buy_V_r14_4 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V_4) buy_V_mfi_4 = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V_4) is_optimize_V_5 = False buy_V_bb_width_5 = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V_5) buy_V_cti_5 = DecimalParameter(-0.95, -0.0, default=-0.6 , optimize = is_optimize_V_5) buy_V_r14_5 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V_5) buy_V_mfi_5 = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V_5) buy_V_diff_5 = DecimalParameter(0.10, 0.20, default=0.10 , optimize = is_optimize_V_5) is_optimize_pump_2 = False buy_pump_2_factor = DecimalParameter(1.0, 1.20, default= 1.1 , optimize = is_optimize_pump_2) is_optimize_crash_4 = False buy_crash_4_tpct_0 = DecimalParameter(0.02, 0.04, default=0.02, decimals=3, optimize = is_optimize_crash_4) buy_crash_4_tpct_3 = DecimalParameter(0.05, 0.10, default=0.05, decimals=2, optimize = is_optimize_crash_4) buy_crash_4_tpct_9 = DecimalParameter(0.13, 0.32, default=0.13, decimals=2, optimize = is_optimize_crash_4) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) high_offset = DecimalParameter(0.85, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(0.85, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True) pHSL = DecimalParameter(-0.10, -0.040, default=-0.08, decimals=3, space='sell', load=True, optimize=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True, optimize=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True, optimize=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True, optimize=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True, optimize=True) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 if (sl_profit >= current_profit): return -0.99 return stoploss_from_open(sl_profit, current_profit) def custom_sell(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] 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() 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"sell_profit_vwap( {buy_tag})" return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." 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']) dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) dataframe['cti'] = pta.cti(dataframe["close"], length=20) 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) 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) dataframe['EWO'] = EWO(dataframe, 50, 200) 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) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] 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'] 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) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['T3'] = T3(dataframe) 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 dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 dataframe['cci'] = ta.CCI(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['ema_vwap_diff_50'] = ( ( dataframe['ema_50'] - dataframe['vwap_lowerband'] ) / dataframe['ema_50'] ) dataframe['tpct_0'] = top_percent_change(dataframe , 0) dataframe['tpct_3'] = top_percent_change(dataframe , 3) dataframe['tpct_9'] = top_percent_change(dataframe , 9) for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) 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']) informative['T3'] = T3(informative) informative['rsi'] = ta.RSI(informative, timeperiod=14) informative['ema_200'] = ta.EMA(informative, timeperiod=200) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' is_pump_2 = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * self.buy_pump_2_factor.value )) ) is_crash_4 = ( (dataframe['tpct_0'] < self.buy_crash_4_tpct_0.value) & (dataframe['tpct_3'] < self.buy_crash_4_tpct_3.value) & (dataframe['tpct_9'] < self.buy_crash_4_tpct_9.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) ) is_gumbo = ( # Modified from gumbo1, creadit goes to original author @raph92 (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) & (dataframe['EWO'] < self.buy_gumbo_ewo_low.value) & (dataframe['tpct_0'] < self.buy_gumbo_tpct_0.value) & (dataframe['tpct_3'] < self.buy_gumbo_tpct_3.value) & (dataframe['tpct_9'] < self.buy_gumbo_tpct_9.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()) ) & (dataframe['EWO'] > 8) ) is_clucHA_2 = ( (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h_2.value ) & ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_2.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_2.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_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.buy_clucha_rocr_1h_3.value ) & ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_3.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_3.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_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_clucHA_4 = ( (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h_4.value ) & ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close_4.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close_4.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_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.buy_vwap_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti.value) & (dataframe['EWO'] > 8) ) is_vwap_2 = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_2.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_2.value / 1000 ) & (dataframe['cti'] < self.buy_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.buy_vwap_width_3.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_3.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti_3.value) & (dataframe['EWO'] < 4) & (dataframe['EWO'] > -2.5) ) is_vwap_4 = ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['vwap_width'] > self.buy_vwap_width_4.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_4.value / 1000 ) & (dataframe['cti'] < self.buy_vwap_cti_4.value) & (dataframe['EWO'] < -4) & (dataframe['EWO'] > -8) ) is_lambo_2 = ( (dataframe['close'] < dataframe['ema_14'] * self.buy_lambo2_ema.value) & (dataframe['rsi_fast'] < self.buy_lambo2_rsi4.value) & (dataframe['rsi'] < self.buy_lambo2_rsi14.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & (is_pump_2) ) is_V = ( (dataframe['bb_width'] > self.buy_V_bb_width.value) & (dataframe['cti'] < self.buy_V_cti.value) & (dataframe['r_14'] < self.buy_V_r14.value) & (dataframe['mfi'] < self.buy_V_mfi.value) & (dataframe['EWO'] > 8) ) is_V_2 = ( (dataframe['bb_width'] > self.buy_V_bb_width_2.value) & (dataframe['cti'] < self.buy_V_cti_2.value) & (dataframe['r_14'] < self.buy_V_r14_2.value) & (dataframe['mfi'] < self.buy_V_mfi_2.value) & (dataframe['EWO'] > 4) & (dataframe['EWO'] < 8) & (is_pump_2) ) is_V_5 = ( (dataframe['bb_width'] > self.buy_V_bb_width_5.value) & (dataframe['cti'] < self.buy_V_cti_5.value) & (dataframe['r_14'] < self.buy_V_r14_5.value) & (dataframe['mfi'] < self.buy_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) ) conditions.append(is_cofi) # ~3.21 90.8% dataframe.loc[is_cofi, 'buy_tag'] += 'cofi ' conditions.append(is_vwap) # ~67.3% dataframe.loc[is_vwap, 'buy_tag'] += 'vwap ' conditions.append(is_V) # ~67.9% dataframe.loc[is_V, 'buy_tag'] += 'V ' conditions.append(is_lambo_2) # ~67.7% dataframe.loc[is_lambo_2, 'buy_tag'] += 'lambo_2 ' conditions.append(is_clucHA_2) # ~68.2% dataframe.loc[is_clucHA_2, 'buy_tag'] += 'cluc_2 ' conditions.append(is_vwap_2) # ~67.3% dataframe.loc[is_vwap_2, 'buy_tag'] += 'vwap_2 ' conditions.append(is_V_2) # ~67.9% dataframe.loc[is_V_2, 'buy_tag'] += 'V_2 ' conditions.append(is_clucHA_3) # ~68.2% dataframe.loc[is_clucHA_3, 'buy_tag'] += 'cluc_3 ' conditions.append(is_vwap_3) # ~67.3% dataframe.loc[is_vwap_3, 'buy_tag'] += 'vwap_3 ' conditions.append(is_clucHA_4) # ~68.2% dataframe.loc[is_clucHA_4, 'buy_tag'] += 'cluc_4 ' conditions.append(is_vwap_4) # ~67.3% dataframe.loc[is_vwap_4, 'buy_tag'] += 'vwap_4 ' conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% F (263 %) dataframe.loc[is_gumbo, 'buy_tag'] += 'gumbo ' conditions.append(is_V_5) # ~67.9% dataframe.loc[is_V_5, 'buy_tag'] += 'V_5 ' if conditions: dataframe.loc[ is_additional_check & 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 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']