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) def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) return vwma 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!") 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 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(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. ''' 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_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, } timeframe = '5m' inf_1h = '1h' inf_15m = '15m' info_timefame_1d = 'none' info_timeframe_1h = '1h' info_timeframe_15m = '15m' res_timeframe = 'none' process_only_new_candles = True order_types = { 'buy': 'market', 'sell': 'market', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } stoploss = -0.99 use_custom_stoploss = True use_sell_signal = True 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) 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) 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_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." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) 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) 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) informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) 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_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 informative_1h['r_96'] = williams_r(informative_1h, period=96) informative_1h['r_480'] = williams_r(informative_1h, period=480) 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']) informative_1h['roc'] = ta.ROC(dataframe, timeperiod=9) 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'] informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h['ha_close'] = inf_heikinashi['close'] informative_1h['rocr'] = ta.ROCR(informative_1h['ha_close'], timeperiod=168) informative_1h['T3'] = T3(informative_1h) informative_1h['EWO'] = EWO(informative_1h, 50, 200) 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'])) 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." informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe_15m) informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14) 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) 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) 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 = 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() informative_15m['cmf'] = chaikin_money_flow(informative_15m, 20) informative_15m['cti'] = pta.cti(informative_15m["close"], length=20) 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) informative_15m['ewo'] = ewo(informative_15m, 50, 200) informative_15m['cci'] = ta.CCI(informative_15m, source='hlc3', timeperiod=20) 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 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() 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})" 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})" 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})" 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})" 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})" 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})" 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})" return None 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: dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) dataframe['zema_61'] = zema(dataframe, period=61) 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 = 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 = 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 = 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'] dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) 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) for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() 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) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) 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 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) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['EWO'] = EWO(dataframe, 50, 200) 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) 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) dataframe['mfi'] = ta.MFI(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) 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) 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) 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'] dataframe['T3'] = T3(dataframe) dataframe['trange'] = ta.TRANGE(dataframe) 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'] 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 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) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe['moderi_96'] = moderi(dataframe, 96) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) 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) ) 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']) ) 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) ) is_BB_checked = is_dip & is_break 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_Tranz_TrailingBuy(BB_RPB): process_only_new_candles = True custom_info_trail_buy = dict() trailing_buy_order_enabled = True trailing_expire_seconds = 1800 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): 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 = 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): 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 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)): return 'forcebuy' else: 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))): return 'forcebuy' if current_trailing_profit_ratio < 0: 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 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)): 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': 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: 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']: 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)): 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)): 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: 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!!!") 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'] return dataframe 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 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 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}' 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) pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx 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']