# --- Do not remove these libs --- import pandas_ta as pta import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes from freqtrade.exchange import timeframe_to_prev_date from pandas import DataFrame, Series, concat from functools import reduce import math from typing import Dict from freqtrade.persistence import Trade from datetime import datetime, timedelta from technical.util import resample_to_interval, resampled_merge from technical.indicators import RMI, zema, VIDYA, ichimoku from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) import time log = logging.getLogger(__name__) # -------------------------------- 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 BB_RPB_TSL_discord_tranz_20220104(IStrategy): ''' BB_RPB_TSL @author jilv220 Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html ) RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py ) The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya ) I modified it to better suit my taste and added Hyperopt for this strategy. ''' # (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, } # Optimal timeframe for the strategy timeframe = '5m' inf_1h = '1h' inf_15m = '15m' info_timefame_1d = 'none' info_timeframe_1h = '1h' info_timeframe_15m = '15m' res_timeframe = 'none' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Disabled stoploss = -0.99 # Custom stoploss use_custom_stoploss = True use_sell_signal = True ############################################################################ ## Buy params is_optimize_dip = False buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip) is_optimize_break = False buy_bb_width = DecimalParameter(0.065, 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) #BB MODDED is_optimize_ctt15_protection = False buy_ema_open_mult_15 = DecimalParameter(0.01, 0.03, default=0.024, optimize = is_optimize_ctt15_protection) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.958, optimize = is_optimize_ctt15_protection) buy_rsi_15 = DecimalParameter(20.0, 36.0, default=28.0, optimize = is_optimize_ctt15_protection) buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.974, optimize = is_optimize_ctt15_protection) is_optimize_ctt25_protection = False buy_25_ma_offset = DecimalParameter(0.90, 0.99, default=0.922, optimize = is_optimize_ctt25_protection) buy_25_rsi_4 = DecimalParameter(26.0, 40.0, default=38.0, optimize = is_optimize_ctt25_protection) buy_25_cti = DecimalParameter(-0.99, -0.4, default=-0.76, optimize = is_optimize_ctt25_protection) ## 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] informative_pairs.extend = [(pair, '15m') 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) # RSI informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) # SMA informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) informative_1h['sma_200_dec_24'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(24) # 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) informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35) # CTI informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb20_2_low'] = bollinger['lower'] informative_1h['bb20_2_mid'] = bollinger['mid'] informative_1h['bb20_2_upp'] = bollinger['upper'] informative_1h['bb20_width'] = ((informative_1h['bb20_2_upp'] - informative_1h['bb20_2_low']) / informative_1h['bb20_2_mid']) # 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 def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe_15m) # RSI informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14) # EMAs informative_15m['ema_12'] = ta.EMA(informative_15m, timeperiod=12) informative_15m['ema_16'] = ta.EMA(informative_15m, timeperiod=16) informative_15m['ema_20'] = ta.EMA(informative_15m, timeperiod=20) informative_15m['ema_25'] = ta.EMA(informative_15m, timeperiod=25) informative_15m['ema_26'] = ta.EMA(informative_15m, timeperiod=26) informative_15m['ema_50'] = ta.EMA(informative_15m, timeperiod=50) informative_15m['ema_100'] = ta.EMA(informative_15m, timeperiod=100) informative_15m['ema_200'] = ta.EMA(informative_15m, timeperiod=200) # SMA informative_15m['sma_15'] = ta.SMA(informative_15m, timeperiod=15) informative_15m['sma_30'] = ta.SMA(informative_15m, timeperiod=30) informative_15m['sma_200'] = ta.SMA(informative_15m, timeperiod=200) informative_15m['sma_200_dec_20'] = informative_15m['sma_200'] < informative_15m['sma_200'].shift(20) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_15m), window=20, stds=2) informative_15m['bb20_2_low'] = bollinger['lower'] informative_15m['bb20_2_mid'] = bollinger['mid'] informative_15m['bb20_2_upp'] = bollinger['upper'] # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(informative_15m['close'], window=40, stds=2) informative_15m['bb40_2_low'] = bb_40_std2['lower'] informative_15m['bb40_2_mid'] = bb_40_std2['mid'] informative_15m['bb40_2_delta'] = (bb_40_std2['mid'] - informative_15m['bb40_2_low']).abs() informative_15m['closedelta'] = (informative_15m['close'] - informative_15m['close'].shift()).abs() informative_15m['tail'] = (informative_15m['close'] - informative_15m['bb40_2_low']).abs() # CMF informative_15m['cmf'] = chaikin_money_flow(informative_15m, 20) # CTI informative_15m['cti'] = pta.cti(informative_15m["close"], length=20) # Williams %R informative_15m['r_14'] = williams_r(informative_15m, period=14) informative_15m['r_64'] = williams_r(informative_15m, period=64) informative_15m['r_96'] = williams_r(informative_15m, period=96) # EWO informative_15m['ewo'] = ewo(informative_15m, 50, 200) # CCI informative_15m['cci'] = ta.CCI(informative_15m, source='hlc3', timeperiod=20) # CRSI (3, 2, 100) crsi_closechange = informative_15m['close'] / informative_15m['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative_15m['crsi'] = (ta.RSI(informative_15m['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_15m['close'], 100)) / 3 tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return informative_15m 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_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] 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 ## 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: # RSI dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) # Zero-Lag EMA dataframe['zema_61'] = zema(dataframe, period=61) # 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'] # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['bb40_2_low'] = bb_40_std2['lower'] dataframe['bb40_2_mid'] = bb_40_std2['mid'] dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs() # BB 20 - STD2 bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] # BB 20 - STD3 bb_20_std3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb20_3_low'] = bb_20_std3['lower'] dataframe['bb20_3_mid'] = bb_20_std3['mid'] dataframe['bb20_3_upp'] = bb_20_std3['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['bb9'] = 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) informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' is_additional_check = ( (dataframe['roc_1h'] < self.buy_roc_1h.value) & (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) ) is_dip = ( (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) ) 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 = ( is_additional_check & # 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 = ( is_additional_check & (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 = ( is_additional_check & # 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 = ( is_additional_check & (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 = ( is_additional_check & # 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 = ( is_additional_check & # 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 = ( is_additional_check & # 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 = ( is_additional_check & # 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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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 = ( is_additional_check & (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']) ) ## BB MODDED is_nfi_ctt35 = ( is_additional_check & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.984) & (dataframe['EWO'] > 9.6) & (dataframe['rsi_14'] < 32.0) & (dataframe['cti'] < -0.5) ) is_nfi_ctt25 = ( is_additional_check & (dataframe['rsi_20'] < dataframe['rsi_20'].shift()) & (dataframe['rsi_4'] < self.buy_25_rsi_4.value) & (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) & (dataframe['close'] < (dataframe['sma_20'] * self.buy_25_ma_offset.value)) & (dataframe['open'] > (dataframe['sma_20'] * self.buy_25_ma_offset.value)) & ( (dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h']) ) & (dataframe['cti'] < self.buy_25_cti.value) ) is_nfi_ctt15 = ( is_additional_check & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_15.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi_14'] < self.buy_rsi_15.value) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15.value) ) is_nfi_9 = ( is_additional_check & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['ema_20'] * 0.968) & (dataframe['close'] < dataframe['bb20_2_low'] * 0.982) & (dataframe['mfi'] < 50.0) & (dataframe['cti'] < -0.85) & (dataframe['r_14'] < -94.0) & (dataframe['rsi_14_1h'] > 20.0) & (dataframe['rsi_14_1h'] < 88.0) & (dataframe['crsi_1h'] > 21.0) ) is_nfi_10 = ( is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.94) & (dataframe['close'] < dataframe['bb20_2_low'] * 0.984) & (dataframe['r_14'] < -88.0) & (dataframe['cti_1h'] > -0.5) & (dataframe['cti_1h'] < 0.94) ) is_nfi_26 = ( is_additional_check & (dataframe['close'] < (dataframe['zema_61'] * 0.9405)) & (dataframe['cti'] < -0.72) & (dataframe['cci'] < -166.0) & (dataframe['r_14'] < -98.0) & (dataframe['cti_1h'] < 0.95) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 2.0)) ) is_nfix_53 = ( is_additional_check & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['ema_200_1h'].shift(24) > dataframe['ema_200_1h'].shift(36)) & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & ((dataframe['ema_26_15m'] - dataframe['ema_12_15m']) > (dataframe['open_15m'] * 0.02)) & ((dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3)) > (dataframe['open_15m'] / 100)) & (dataframe['close_15m'] < (dataframe['bb20_2_low_15m'] * 0.99)) & (dataframe['r_14'] < -90.0) & (dataframe['cti_1h'] > -0.7) ) is_nfix_52 = ( is_additional_check & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & ((dataframe['ema_26_15m'] - dataframe['ema_12_15m']) > (dataframe['open_15m'] * 0.032)) & ((dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3)) > (dataframe['open_15m'] / 100)) & (dataframe['close_15m'] < (dataframe['bb20_2_low_15m'] * 0.998)) & (dataframe['crsi_1h'] > 10.0) ) is_nfix_51 = ( is_additional_check & (dataframe['close_15m'] < (dataframe['ema_16_15m'] * 0.944)) & (dataframe['ewo_15m'] < -1.0) & (dataframe['rsi_14_15m'] > 28.0) & (dataframe['cti_15m'] < -0.84) & (dataframe['r_14_15m'] < -94.0) & (dataframe['rsi_14'] > 30.0) & (dataframe['crsi_1h'] > 1.0) ) is_nfix_48 = ( is_additional_check & (dataframe['close_15m'].shift(3) < (dataframe['sma_15_15m'].shift(3) * 0.95)) & (dataframe['close_15m'] > (dataframe['open_15m'].shift(3))) & (dataframe['ewo_15m'] > 2.8) & (dataframe['cti_15m'] < -0.75) & (dataframe['r_14_15m'].shift(3) < -94.0) & (dataframe['cti'] < -0.5) & (dataframe['cti_1h'] < 0.1) & (dataframe['crsi_1h'] > 18.0) ) is_nfix_47 = ( is_additional_check & (dataframe['rsi_14_15m'] < dataframe['rsi_14_15m'].shift(3)) & (dataframe['ema_20_1h'] > dataframe['ema_25_1h']) & (dataframe['close_15m'] < (dataframe['sma_15_15m'] * 0.95)) & ( ((dataframe['open_15m'] < dataframe['ema_20_1h']) & (dataframe['low_15m'] < dataframe['ema_20_1h'])) | ((dataframe['open_15m'] > dataframe['ema_20_1h']) & (dataframe['low_15m'] > dataframe['ema_20_1h'])) ) & (dataframe['cti_15m'] < -0.9) & (dataframe['r_14_15m'] < -90.0) & (dataframe['r_14'] < -97.0) & (dataframe['cti_1h'] < 0.1) & (dataframe['crsi_1h'] > 8.0) ) is_nfix_41 = ( is_additional_check & (dataframe['ema_12_15m'] > dataframe['ema_200_1h']) & (dataframe['ema_26_15m'] > dataframe['ema_12_15m']) & ((dataframe['ema_26_15m'] - dataframe['ema_12_15m']) > (dataframe['open_15m'] * 0.03)) & ((dataframe['ema_26_15m'].shift(3) - dataframe['ema_12_15m'].shift(3)) > (dataframe['open_15m'] / 100)) & (dataframe['close_15m'] < (dataframe['bb20_2_low_15m'] * 0.99)) ) is_nfix_38 = ( is_additional_check & (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['mfi'] < 34.5) & (dataframe['r_64'] < -65.0) & (dataframe['r_96'] < -50.0) & (dataframe['r_480_1h'] < -1.0) ) is_nfix_36 = ( is_additional_check & (dataframe['ema_200'] > (dataframe['ema_200'].shift(36) * 1.035)) & (dataframe['close'] < dataframe['ema_20'] * 0.956) & (dataframe['rsi_14'] < 34.0) & (dataframe['r_64'] < -80.0) & (dataframe['cti'] < -0.5) & (dataframe['r_480_1h'] < -30.0) ) is_nfix_204 = ( is_additional_check & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * 0.98) & (dataframe['EWO'] < -4.4) & (dataframe['cti'] < -0.95) & (dataframe['r_14'] < -97.0) & (dataframe['crsi_1h'] > 0.5) ) is_nfix_203 = ( is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -6.0) & (dataframe['cti_1h'] < -0.88) & (dataframe['crsi_1h'] > 10.0) ) is_nfix_202 = ( is_additional_check & (dataframe['close'] > (dataframe['ema_200_1h'] * 0.84)) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb20_2_low'] * 0.999)) & (dataframe['cti'] < -0.5) & (dataframe['rsi_14'] > 25.0) & (dataframe['mfi'] > 18.0) & (dataframe['r_14'] < -94.0) & (dataframe['r_14'].shift(1) < -94.0) & (dataframe['crsi_1h'] > 12.0) & (dataframe['volume'] < (dataframe['volume_mean_4'] * 1.6)) ) is_nfix_201 = ( is_additional_check & (dataframe['rsi_20'] < dataframe['rsi_20'].shift()) & (dataframe['rsi_4'] < 30.0) & (dataframe['ema_20_1h'] > dataframe['ema_26_1h']) & (dataframe['close'] < dataframe['sma_15'] * 0.953) & (dataframe['cti'] < -0.78) & (dataframe['cci'] < -200.0) ) is_nfix_34 = ( is_additional_check & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < (dataframe['bb20_2_low'] * 0.972)) & (dataframe['cti'] < -0.8) & (dataframe['rsi_14'] < 18.0) ) is_nfix_28 = ( is_additional_check & (dataframe['close'] < dataframe['sma_75'] * 0.96) & (dataframe['EWO'] < -8.0) & (dataframe['cti'] < -0.9) & (dataframe['r_14'] < -97.0) & (dataframe['crsi_1h'] > 14.0) ) is_nfix_27 = ( is_additional_check & (dataframe['close'] < dataframe['sma_75'] * 0.934) & (dataframe['EWO'] > 6.4) & (dataframe['rsi_14'] < 32.0) & (dataframe['cti'] < -0.8) & (dataframe['r_14'] < -96.0) ) is_nfix_19 = ( is_additional_check & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['bb40_2_low'].shift().gt(0)) & (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.045)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.02)) & (dataframe['tail'].lt(dataframe['bb40_2_delta'] * 0.28)) & (dataframe['close'].lt(dataframe['bb40_2_low'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['cti'] < -0.9) & (dataframe['cti_1h'] > -0.75) & (dataframe['cti_1h'] < 0.25) ) is_nfix_11 = ( is_additional_check & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.027)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < dataframe['ema_20'] * 0.932) & (dataframe['rsi_14'] < 25.0) ) is_nfix_9 = ( is_additional_check & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['close'] < dataframe['sma_30'] * 0.99) & (dataframe['cti'] < -0.92) & (dataframe['EWO'] < -5.0) & (dataframe['cti_1h'] < -0.88) & (dataframe['crsi_1h'] > 20.0) ) ## 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 ' conditions.append(is_nfi_ctt35) # ~2.32 / 91.1% / 46.27% D dataframe.loc[is_nfi_ctt35, 'buy_tag'] += 'nfi_ctt35 ' conditions.append(is_nfi_ctt25) # ~3.28 / 92.4% / 69.72% dataframe.loc[is_nfi_ctt25, 'buy_tag'] += 'nfi_ctt25 ' conditions.append(is_nfi_ctt15) # ~0.76 / 91.1% / 15.54% dataframe.loc[is_nfi_ctt15, 'buy_tag'] += 'nfi_ctt15 ' conditions.append(is_nfi_10) # ~0.92 / 92.0% / 43.74% D dataframe.loc[is_nfi_10, 'buy_tag'] += 'nfi_10 ' conditions.append(is_nfix_53) # ~7.2 / 92.5% / 97.98% D dataframe.loc[is_nfix_53, 'buy_tag'] += 'nfix_53 ' conditions.append(is_nfix_52) # ~0.4 / 94.4% / 9.59% D dataframe.loc[is_nfix_52, 'buy_tag'] += 'nfix_52 ' conditions.append(is_nfix_51) # ~2.63 / 90.6% / 41.49% D dataframe.loc[is_nfix_51, 'buy_tag'] += 'nfix_51 ' conditions.append(is_nfix_48) # ~3.14 / 92.4% / 64.14% D dataframe.loc[is_nfix_48, 'buy_tag'] += 'nfix_48 ' conditions.append(is_nfix_47) # ~0.4 / 100% D dataframe.loc[is_nfix_47, 'buy_tag'] += 'nfix_47 ' conditions.append(is_nfix_41) # ~0.78 / 92.0 % / 37.41% D dataframe.loc[is_nfix_41, 'buy_tag'] += 'nfix_41 ' conditions.append(is_nfix_38) # ~0.11 / 100% D dataframe.loc[is_nfix_38, 'buy_tag'] += 'nfix_38 ' conditions.append(is_nfix_36) # ~1.13 / 88.5% / 31.34% D dataframe.loc[is_nfix_36, 'buy_tag'] += 'nfix_36 ' conditions.append(is_nfix_204) # ~0.25 / 97.7% / 6.53% D dataframe.loc[is_nfix_204, 'buy_tag'] += 'nfix_204 ' conditions.append(is_nfix_203) # ~5.33 / 91.8% / 58.57% D dataframe.loc[is_nfix_203, 'buy_tag'] += 'nfix_203 ' conditions.append(is_nfix_202) # ~0.33 / 100% / 0% D dataframe.loc[is_nfix_202, 'buy_tag'] += 'nfix_202 ' conditions.append(is_nfix_201) # ~0.71 / 91.3% / 28.94% D dataframe.loc[is_nfix_201, 'buy_tag'] += 'nfix_201 ' conditions.append(is_nfix_34) # ~0.46 / 92.6% / 17.05% D dataframe.loc[is_nfix_34, 'buy_tag'] += 'nfix_34 ' conditions.append(is_nfix_28) # ~0.25 / 97.7% / 6.53% D dataframe.loc[is_nfix_28, 'buy_tag'] += 'nfix_28 ' conditions.append(is_nfix_27) # ~5.33 / 91.8% / 58.57% D dataframe.loc[is_nfix_27, 'buy_tag'] += 'nfix_27 ' conditions.append(is_nfix_19) # ~0.33 / 100% / 0% D dataframe.loc[is_nfix_19, 'buy_tag'] += 'nfix_19 ' conditions.append(is_nfix_11) # ~0.71 / 91.3% / 28.94% D dataframe.loc[is_nfix_11, 'buy_tag'] += 'nfix_11 ' conditions.append(is_nfix_9) # ~0.46 / 92.6% / 17.05% D dataframe.loc[is_nfix_9, 'buy_tag'] += 'nfix_9 ' 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: dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0 return dataframe class BB_RPB_TSL_trailingbuy_discord_tranz_20220104(BB_RPB_TSL_discord_tranz_20220104): # Original idea by @MukavaValkku, code by @tirail and @stash86 # # This class is designed to inherit from yours and starts trailing buy with your buy signals # Trailing buy starts at any buy signal and will move to next candles if the trailing still active # Trailing buy stops with BUY if : price decreases and rises again more than trailing_buy_offset # Trailing buy stops with NO BUY : current price is > initial price * (1 + trailing_buy_max) OR custom_sell tag # IT IS NOT COMPATIBLE WITH BACKTEST/HYPEROPT # process_only_new_candles = True custom_info_trail_buy = dict() # Trailing buy parameters trailing_buy_order_enabled = True trailing_expire_seconds = 1800 # If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, buy the coin trailing_buy_uptrend_enabled = False trailing_expire_seconds_uptrend = 90 min_uptrend_trailing_profit = 0.02 debug_mode = True trailing_buy_max_stop = 0.02 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop) trailing_buy_max_buy = 0.000 # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy)) init_trailing_dict = { 'trailing_buy_order_started': False, 'trailing_buy_order_uplimit': 0, 'start_trailing_price': 0, 'buy_tag': None, 'start_trailing_time': None, 'offset': 0, 'allow_trailing': False, } def trailing_buy(self, pair, reinit=False): # returns trailing buy info for pair (init if necessary) if not pair in self.custom_info_trail_buy: self.custom_info_trail_buy[pair] = dict() if (reinit or not 'trailing_buy' in self.custom_info_trail_buy[pair]): self.custom_info_trail_buy[pair]['trailing_buy'] = self.init_trailing_dict.copy() return self.custom_info_trail_buy[pair]['trailing_buy'] def trailing_buy_info(self, pair: str, current_price: float): # current_time live, dry run current_time = datetime.now(timezone.utc) if not self.debug_mode: return trailing_buy = self.trailing_buy(pair) duration = 0 try: duration = (current_time - trailing_buy['start_trailing_time']) except TypeError: duration = 0 finally: logger.info( f"pair: {pair} : " f"start: {trailing_buy['start_trailing_price']:.4f}, " f"duration: {duration}, " f"current: {current_price:.4f}, " f"uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, " f"profit: {self.current_trailing_profit_ratio(pair, current_price)*100:.2f}%, " f"offset: {trailing_buy['offset']}") def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float: trailing_buy = self.trailing_buy(pair) if trailing_buy['trailing_buy_order_started']: return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price'] else: return 0 def trailing_buy_offset(self, dataframe, pair: str, current_price: float): # return rebound limit before a buy in % of initial price, function of current price # return None to stop trailing buy (will start again at next buy signal) # return 'forcebuy' to force immediate buy # (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no buy, uplimit updated to 99.5), 3price 98 (no buy uplimit updated to 98.5), 4th price 99 -> BUY current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price) default_offset = 0.005 trailing_buy = self.trailing_buy(pair) if not trailing_buy['trailing_buy_order_started']: return default_offset # example with duration and indicators # dry run, live only last_candle = dataframe.iloc[-1] current_time = datetime.now(timezone.utc) trailing_duration = current_time - trailing_buy['start_trailing_time'] if trailing_duration.total_seconds() > self.trailing_expire_seconds: if ((current_trailing_profit_ratio > 0) and (last_candle['buy'] == 1)): # more than 1h, price under first signal, buy signal still active -> buy return 'forcebuy' else: # wait for next signal return None elif (self.trailing_buy_uptrend_enabled and (trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend) and (current_trailing_profit_ratio < (-1 * self.min_uptrend_trailing_profit))): # less than 90s and price is rising, buy return 'forcebuy' if current_trailing_profit_ratio < 0: # current price is higher than initial price return default_offset trailing_buy_offset = { 0.06: 0.02, 0.03: 0.01, 0: default_offset, } for key in trailing_buy_offset: if current_trailing_profit_ratio > key: return trailing_buy_offset[key] return default_offset # end of trailing buy parameters # ----------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_indicators(dataframe, metadata) self.trailing_buy(metadata['pair']) return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs) if val: if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): val = False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) >= 1): last_candle = dataframe.iloc[-1].squeeze() current_price = rate trailing_buy = self.trailing_buy(pair) trailing_buy_offset = self.trailing_buy_offset(dataframe, pair, current_price) if trailing_buy['allow_trailing']: if (not trailing_buy['trailing_buy_order_started'] and (last_candle['buy'] == 1)): # start trailing buy # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_started'] = True # self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_price'] = last_candle['close'] # self.custom_info_trail_buy[pair]['trailing_buy']['buy_tag'] = f"initial_buy_tag (strat trail price {last_candle['close']})" # self.custom_info_trail_buy[pair]['trailing_buy']['start_trailing_time'] = datetime.now(timezone.utc) # self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = 0 trailing_buy['trailing_buy_order_started'] = True trailing_buy['trailing_buy_order_uplimit'] = last_candle['close'] trailing_buy['start_trailing_price'] = last_candle['close'] trailing_buy['buy_tag'] = last_candle['buy_tag'] trailing_buy['start_trailing_time'] = datetime.now(timezone.utc) trailing_buy['offset'] = 0 self.trailing_buy_info(pair, current_price) logger.info(f'start trailing buy for {pair} at {last_candle["close"]}') elif trailing_buy['trailing_buy_order_started']: if trailing_buy_offset == 'forcebuy': # buy in custom conditions val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_info(pair, current_price) logger.info(f"price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full") elif trailing_buy_offset is None: # stop trailing buy custom conditions self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because "trailing buy offset" returned None') elif current_price < trailing_buy['trailing_buy_order_uplimit']: # update uplimit old_uplimit = trailing_buy["trailing_buy_order_uplimit"] self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = min(current_price * (1 + trailing_buy_offset), self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit']) self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = trailing_buy_offset self.trailing_buy_info(pair, current_price) logger.info(f'update trailing buy for {pair} at {old_uplimit} -> {self.custom_info_trail_buy[pair]["trailing_buy"]["trailing_buy_order_uplimit"]}') elif current_price < (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy)): # buy ! current price > uplimit && lower thant starting price val = True ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100) self.trailing_buy_info(pair, current_price) logger.info(f"current price ({current_price}) > uplimit ({trailing_buy['trailing_buy_order_uplimit']}) and lower than starting price price ({(trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy))}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full") elif current_price > (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop)): # stop trailing buy because price is too high self.trailing_buy(pair, reinit=True) self.trailing_buy_info(pair, current_price) logger.info(f'STOP trailing buy for {pair} because of the price is higher than starting price * {1 + self.trailing_buy_max_stop}') else: # uplimit > current_price > max_price, continue trailing and wait for the price to go down self.trailing_buy_info(pair, current_price) logger.info(f'price too high for {pair} !') else: logger.info(f"Wait for next buy signal for {pair}") if (val == True): self.trailing_buy_info(pair, rate) self.trailing_buy(pair, reinit=True) logger.info(f'STOP trailing buy for {pair} because I buy it') return val def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = super().populate_buy_trend(dataframe, metadata) if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'): last_candle = dataframe.iloc[-1].squeeze() trailing_buy = self.trailing_buy(metadata['pair']) if (last_candle['buy'] == 1): if not trailing_buy['trailing_buy_order_started']: open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True), ]).all() if not open_trades: logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!") # self.custom_info_trail_buy[metadata['pair']]['trailing_buy']['allow_trailing'] = True trailing_buy['allow_trailing'] = True initial_buy_tag = last_candle['buy_tag'] if 'buy_tag' in last_candle else 'buy signal' dataframe.loc[:, 'buy_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})" else: if (trailing_buy['trailing_buy_order_started'] == True): logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!") dataframe.loc[:,'buy'] = 1 dataframe.loc[:, 'buy_tag'] = trailing_buy['buy_tag'] # dataframe['buy'] = 1 return dataframe # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif # Exponential moving average of a volume weighted simple moving average def ema_vwma_osc(dataframe, len_slow_ma): slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma)) return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100 def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series: hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 hl_range = (dataframe['high'] - dataframe['low']).shift(1) if mode == 'simple': res1 = hlc3_pivot * 2 - dataframe['low'].shift(1) sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1) res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift() sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift() res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift() sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift() elif mode == 'fibonacci': res1 = hlc3_pivot + 0.382 * hl_range sup1 = hlc3_pivot - 0.382 * hl_range res2 = hlc3_pivot + 0.618 * hl_range sup2 = hlc3_pivot - 0.618 * hl_range res3 = hlc3_pivot + 1 * hl_range sup3 = hlc3_pivot - 1 * hl_range return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 def heikin_ashi(dataframe, smooth_inputs = False, smooth_outputs = False, length = 10): df = dataframe[['open','close','high','low']].copy().fillna(0) if smooth_inputs: df['open_s'] = ta.EMA(df['open'], timeframe = length) df['high_s'] = ta.EMA(df['high'], timeframe = length) df['low_s'] = ta.EMA(df['low'], timeframe = length) df['close_s'] = ta.EMA(df['close'],timeframe = length) open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2 high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1) low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1) close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s'])/4 else: open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2 high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1) low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1) close_ha = (df['open'] + df['high'] + df['low'] + df['close'])/4 open_ha = open_ha.fillna(0) high_ha = high_ha.fillna(0) low_ha = low_ha.fillna(0) close_ha = close_ha.fillna(0) if smooth_outputs: open_sha = ta.EMA(open_ha, timeframe = length) high_sha = ta.EMA(high_ha, timeframe = length) low_sha = ta.EMA(low_ha, timeframe = length) close_sha = ta.EMA(close_ha, timeframe = length) return open_sha, close_sha, low_sha else: return open_ha, close_ha, low_ha # 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'), np.NaN) 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']