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.
Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 20.5%, 81m: 3.8%, 292m: 0.5%
Interface Version
N/A
Startup Candles
N/A
Indicators
25
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
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']