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
# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from freqtrade.exchange import timeframe_to_prev_date
from functools import reduce
from technical.indicators import RMI, zema, ichimoku
# --------------------------------
def ha_typical_price(bars):
res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
return Series(index=bars.index, data=res)
# Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
"""Indicator: Volume Weighted Moving Average (VWMA)"""
# Calculate Result
pv = dataframe['close'] * dataframe['volume']
vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
return vwma
# Modified Elder Ray Index
def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series:
slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma))
return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend
def EWO(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['low'] * 100
return emadif
def SROC(dataframe, roclen=21, emalen=13, smooth=21):
df = dataframe.copy()
roc = ta.ROC(df, timeperiod=roclen)
ema = ta.EMA(df, timeperiod=emalen)
sroc = ta.ROC(ema, timeperiod=smooth)
return sroc
def range_percent_change(dataframe: DataFrame, method, length: int) -> float:
"""
Rolling Percentage Change Maximum across interval.
:param dataframe: DataFrame The original OHLC dataframe
:param method: High to Low / Open to Close
:param length: int The length to look back
"""
if method == 'HL':
return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min()
elif method == 'OC':
return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min()
else:
raise ValueError(f"Method {method} not defined!")
# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
"""Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
"""
highest_high = dataframe["high"].rolling(center=False, window=period).max()
lowest_low = dataframe["low"].rolling(center=False, window=period).min()
WR = Series(
(highest_high - dataframe["close"]) / (highest_high - lowest_low),
name=f"{period} Williams %R",
)
return WR * -100
# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
"""Chaikin Money Flow (CMF)
It measures the amount of Money Flow Volume over a specific period.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
Args:
dataframe(pandas.Dataframe): dataframe containing ohlcv
n(int): n period.
fillna(bool): if fill nan values.
Returns:
pandas.Series: New feature generated.
"""
mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
mfv = mfv.fillna(0.0) # float division by zero
mfv *= dataframe['volume']
cmf = (mfv.rolling(n, min_periods=0).sum()
/ dataframe['volume'].rolling(n, min_periods=0).sum())
if fillna:
cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
return Series(cmf, name='cmf')
class BB_RPB_TSLmeneguzzo_2(IStrategy):
'''
BB_RPB_TSL
@author jilv220
Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )
RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )
The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )
I modified it to better suit my taste and added Hyperopt for this strategy.
'''
# (1) exit rework
##########################################################################
# Hyperopt result area
# entry space
entry_params = {
"max_slip": 0.983,
##
"entry_bb_width_1h": 0.954,
"entry_roc_1h": 86,
##
"entry_threshold": 0.003,
"entry_bb_factor": 0.999,
#
"entry_bb_delta": 0.025,
"entry_bb_width": 0.095,
##
"entry_cci": -116,
"entry_cci_length": 25,
"entry_rmi": 49,
"entry_rmi_length": 17,
"entry_srsi_fk": 32,
##
"entry_closedelta": 17.922,
"entry_ema_diff": 0.026,
##
"entry_ema_high": 0.968,
"entry_ema_low": 0.935,
"entry_ewo": -5.001,
"entry_rsi": 23,
"entry_rsi_fast": 44,
##
"entry_ema_high_2": 1.087,
"entry_ema_low_2": 0.970,
"entry_ewo_high_2": 4.179,
"entry_rsi_ewo_2": 35,
"entry_rsi_fast_ewo_2": 45,
##
"entry_r_deadfish_bb_factor": 1.014,
"entry_r_deadfish_bb_width": 0.299,
"entry_r_deadfish_ema": 1.054,
"entry_r_deadfish_volume_factor": 1.59,
"entry_r_deadfish_cti": -0.115,
"entry_r_deadfish_r14": -44.34,
##
"entry_clucha_bbdelta_close": 0.049,
"entry_clucha_bbdelta_tail": 1.146,
"entry_clucha_close_bblower": 0.018,
"entry_clucha_closedelta_close": 0.017,
"entry_clucha_rocr_1h": 0.526,
##
"entry_adx": 13,
"entry_ewo_high": 8.594,
"entry_fastd": 28,
"entry_fastk": 39,
##
"entry_gumbo_ema": 1.121,
"entry_gumbo_ewo_low": -9.442,
"entry_gumbo_cti": -0.374,
"entry_gumbo_r14": -51.971,
##
"entry_sqzmom_ema": 0.981,
"entry_sqzmom_ewo": -3.966,
"entry_sqzmom_r14": -45.068,
##
"entry_nfix_49_cti": -0.105,
"entry_nfix_49_r14": -81.827,
}
# exit space
exit_params = {
##
"exit_cmf": -0.046,
"exit_ema": 0.988,
"exit_ema_close_delta": 0.022,
##
"exit_deadfish_profit": -0.063,
"exit_deadfish_bb_factor": 0.954,
"exit_deadfish_bb_width": 0.043,
"exit_deadfish_volume_factor": 2.37,
##
"exit_cti_r_cti": 0.844,
"exit_cti_r_r": -19.99,
}
minimal_roi = {
"0": 0.205,
"81": 0.038,
"292": 0.005,
}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_1h = '1h'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Disabled
stoploss = -0.99
# Custom stoploss
use_custom_stoploss = True
use_exit_signal = True
############################################################################
## Buy params
is_optimize_dip = False
entry_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip)
entry_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip)
entry_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip)
entry_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip)
entry_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip)
is_optimize_break = False
entry_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize = is_optimize_break)
entry_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = is_optimize_break)
is_optimize_local_uptrend = False
entry_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_uptrend)
entry_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False)
entry_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_uptrend)
is_optimize_ewo = False
entry_rsi_fast = IntParameter(35, 50, default=45, optimize = is_optimize_ewo)
entry_rsi = IntParameter(15, 35, default=35, optimize = is_optimize_ewo)
entry_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo)
entry_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo)
entry_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo)
is_optimize_ewo_2 = False
entry_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize = is_optimize_ewo_2)
entry_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize = is_optimize_ewo_2)
entry_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970 , optimize = is_optimize_ewo_2)
entry_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_ewo_2)
entry_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize = is_optimize_ewo_2)
is_optimize_r_deadfish = False
entry_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087 , optimize = is_optimize_r_deadfish)
entry_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_r_deadfish)
entry_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0 , optimize = is_optimize_r_deadfish)
entry_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_r_deadfish)
is_optimize_r_deadfish_protection = False
entry_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5 , optimize = is_optimize_r_deadfish_protection)
entry_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60 , optimize = is_optimize_r_deadfish_protection)
is_optimize_clucha = False
entry_clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=0.02206, optimize = is_optimize_clucha)
entry_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize = is_optimize_clucha)
entry_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize = is_optimize_clucha)
entry_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize = is_optimize_clucha)
is_optimize_gumbo = False
entry_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo)
entry_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo)
is_optimize_gumbo_protection = False
entry_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo_protection)
entry_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo_protection)
is_optimize_sqzmom_protection = False
entry_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_sqzmom_protection)
entry_sqzmom_ewo = DecimalParameter(-12 , 12, default= 0 , optimize = is_optimize_sqzmom_protection)
entry_sqzmom_r14 = DecimalParameter(-100, -22, default=-50 , optimize = is_optimize_sqzmom_protection)
is_optimize_nfix_49_protection = False
entry_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_nfix_49_protection)
entry_nfix_49_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_nfix_49_protection)
is_optimize_btc_safe = False
entry_btc_safe = IntParameter(-300, 50, default=-200, optimize = is_optimize_btc_safe)
entry_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize = is_optimize_btc_safe)
entry_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize = is_optimize_btc_safe)
is_optimize_check = False
entry_roc_1h = IntParameter(-25, 200, default=10, optimize = is_optimize_check)
entry_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize = is_optimize_check)
## Slippage params
is_optimize_slip = False
max_slip = DecimalParameter(0.33, 1.00, default=0.33, decimals=3, optimize=is_optimize_slip , space='entry', load=True)
## Sell params
exit_btc_safe = IntParameter(-400, -300, default=-365, optimize = False)
is_optimize_exit_stoploss = False
exit_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize = is_optimize_exit_stoploss)
exit_ema_close_delta = DecimalParameter(0.022, 0.027, default= 0.024, optimize = is_optimize_exit_stoploss)
exit_ema = DecimalParameter(0.97, 0.99, default=0.987 , optimize = is_optimize_exit_stoploss)
is_optimize_deadfish = False
exit_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , optimize = is_optimize_deadfish)
exit_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05 , optimize = is_optimize_deadfish)
exit_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , optimize = is_optimize_deadfish)
exit_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_deadfish)
is_optimize_bleeding = False
exit_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_bleeding)
exit_bleeding_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_bleeding)
exit_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0 , optimize = is_optimize_bleeding)
is_optimize_cti_r = False
exit_cti_r_cti = DecimalParameter(0.55, 1, default=0.5 , optimize = is_optimize_cti_r)
exit_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]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
informative_1h['ema_8'] = ta.EMA(informative_1h, timeperiod=8)
informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# CTI
informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)
informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40)
# CRSI (3, 2, 100)
crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
informative_1h['crsi'] = (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3
# Williams %R
informative_1h['r_96'] = williams_r(informative_1h, period=96)
informative_1h['r_480'] = williams_r(informative_1h, period=480)
# Bollinger bands
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
informative_1h['bb_lowerband2'] = bollinger2['lower']
informative_1h['bb_middleband2'] = bollinger2['mid']
informative_1h['bb_upperband2'] = bollinger2['upper']
informative_1h['bb_width'] = ((informative_1h['bb_upperband2'] - informative_1h['bb_lowerband2']) / informative_1h['bb_middleband2'])
# ROC
informative_1h['roc'] = ta.ROC(dataframe, timeperiod=9)
# MOMDIV
mom = momdiv(informative_1h)
informative_1h['momdiv_entry'] = mom['momdiv_entry']
informative_1h['momdiv_exit'] = mom['momdiv_exit']
informative_1h['momdiv_coh'] = mom['momdiv_coh']
informative_1h['momdiv_col'] = mom['momdiv_col']
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# CMF
informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20)
# Heikin Ashi
inf_heikinashi = qtpylib.heikinashi(informative_1h)
informative_1h['ha_close'] = inf_heikinashi['close']
informative_1h['rocr'] = ta.ROCR(informative_1h['ha_close'], timeperiod=168)
# T3 Average
informative_1h['T3'] = T3(informative_1h)
# Elliot
informative_1h['EWO'] = EWO(informative_1h, 50, 200)
# nfi 37
informative_1h['hl_pct_change_5'] = range_percent_change(informative_1h, 'HL', 5)
informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min()
informative_1h['safe_dump_50'] = ((informative_1h['hl_pct_change_5'] < 0.66) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
# Pump protections
#informative_1h['hl_pct_change_48'] = range_percent_change(informative_1h, 'HL', length=48)
#informative_1h['hl_pct_change_36'] = range_percent_change(informative_1h, 'HL', length=36)
#informative_1h['hl_pct_change_24'] = range_percent_change(informative_1h, 'HL', length=24)
#informative_1h['hl_pct_change_12'] = range_percent_change(informative_1h, 'HL', length=12)
#informative_1h['hl_pct_change_6'] = range_percent_change(informative_1h, 'HL', length=6)
return informative_1h
############################################################################
### Custom functions
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
sl_new = 1
if (current_profit > 0.2):
sl_new = 0.05
elif (current_profit > 0.1):
sl_new = 0.03
elif (current_profit > 0.06):
sl_new = 0.02
elif (current_profit > 0.03):
sl_new = 0.015
return sl_new
# From NFIX
def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
previous_candle_1 = dataframe.iloc[-2]
previous_candle_2 = dataframe.iloc[-3]
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate)
entry_tag = 'empty'
if hasattr(trade, 'entry_tag') and trade.entry_tag is not None:
entry_tag = trade.entry_tag
entry_tags = entry_tag.split()
# exit trail
if 0.012 > current_profit >= 0.0:
if (max_profit > (current_profit + 0.045)) and (last_candle['rsi'] < 46.0):
return f"exit_profit_t_0_1( {entry_tag})"
elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 32.0):
return f"exit_profit_t_0_2( {entry_tag})"
elif (max_profit > (current_profit + 0.05)) and (last_candle['rsi'] < 48.0):
return f"exit_profit_t_0_3( {entry_tag})"
elif 0.02 > current_profit >= 0.012:
if (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 39.0):
return f"exit_profit_t_1_1( {entry_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"exit_profit_t_1_2( {entry_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"exit_profit_t_1_4( {entry_tag})"
elif (max_profit > (current_profit + 0.04)) and (last_candle['rsi'] < 49.0) and (last_candle['cmf_1h'] < -0.0):
return f"exit_profit_t_1_5( {entry_tag})"
elif (max_profit > (current_profit + 0.06)) and (last_candle['rsi'] < 43.0) and (last_candle['cmf'] < -0.0):
return f"exit_profit_t_1_7( {entry_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"exit_profit_t_1_9( {entry_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"exit_profit_t_1_10( {entry_tag})"
elif (max_profit > (current_profit + 0.025)) and (last_candle['rsi'] < 42.0):
return f"exit_profit_t_1_11( {entry_tag})"
elif (max_profit > (current_profit + 0.01)) and (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.25):
return f"exit_profit_t_1_12( {entry_tag})"
# exit cti_r
if 0.012 > current_profit >= 0.0 :
if (last_candle['cti'] > self.exit_cti_r_cti.value) and (last_candle['r_14'] > self.exit_cti_r_r.value):
return f"exit_profit_t_cti_r_0_1( {entry_tag})"
# main exit
if current_profit > 0.02:
if (last_candle['momdiv_exit_1h'] == True):
return f"signal_profit_q_momdiv_1h( {entry_tag})"
if (last_candle['momdiv_exit'] == True):
return f"signal_profit_q_momdiv( {entry_tag})"
if (last_candle['momdiv_coh'] == True):
return f"signal_profit_q_momdiv_coh( {entry_tag})"
# exit bear
if last_candle['close'] < last_candle['ema_200']:
if 0.02 > current_profit >= 0.01:
if (last_candle['rsi'] < 34.0) and (last_candle['cmf'] < 0.0):
return f"exit_profit_u_bear_1_1( {entry_tag})"
elif (last_candle['rsi'] < 44.0) and (last_candle['cmf'] < -0.4):
return f"exit_profit_u_bear_1_2( {entry_tag})"
# exit quick
if (0.06 > current_profit > 0.02) and (last_candle['rsi'] > 80.0):
return f"signal_profit_q_1( {entry_tag})"
if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95):
return f"signal_profit_q_2( {entry_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( {entry_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( {entry_tag})"
# exit scalp
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"exit_stoploss_u_e_1( {entry_tag})"
# stoploss - deadfish
if ( (current_profit < self.exit_deadfish_profit.value)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['bb_width'] < self.exit_deadfish_bb_width.value)
and (last_candle['close'] > last_candle['bb_middleband2'] * self.exit_deadfish_bb_factor.value)
and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.exit_deadfish_volume_factor.value)
):
return f"exit_stoploss_deadfish( {entry_tag})"
# stoploss - bleeding
#if ( (current_profit < -0.05)
#and (last_candle['close'] < last_candle['ema_200'])
#and (last_candle['cti_mean_24'] < self.exit_bleeding_cti.value)
#and (last_candle['r_14_mean_24'] < self.exit_bleeding_r14.value)
#and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.exit_bleeding_volume_factor.value)
#):
#return f"exit_stoploss_bleeding( {entry_tag})"
return None
## Confirm Entry
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
max_slip = self.max_slip.value
if(len(dataframe) < 1):
return False
dataframe = dataframe.iloc[-1].squeeze()
if ((rate > dataframe['close'])) :
slippage = ( (rate / dataframe['close']) - 1 ) * 100
if slippage < max_slip:
return True
else:
return False
return True
############################################################################
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Bollinger bands
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
### Other BB checks
dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])
# CCI hyperopt
for val in self.entry_cci_length.range:
dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)
dataframe['cci'] = ta.CCI(dataframe, 26)
dataframe['cci_long'] = ta.CCI(dataframe, 170)
# RMI hyperopt
for val in self.entry_rmi_length.range:
dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)
# SRSI hyperopt
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe['srsi_fk'] = stoch['fastk']
dataframe['srsi_fd'] = stoch['fastd']
# BinH
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
# SMA
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20)
dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)
dataframe['sma_28'] = ta.SMA(dataframe, timeperiod=28)
dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# CMF
dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
# CRSI (3, 2, 100)
crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3
# EMA
dataframe['ema_4'] = ta.EMA(dataframe, timeperiod=4)
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
# Elliot
dataframe['EWO'] = EWO(dataframe, 50, 200)
# Williams %R
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['r_32'] = williams_r(dataframe, period=32)
dataframe['r_64'] = williams_r(dataframe, period=64)
dataframe['r_96'] = williams_r(dataframe, period=96)
dataframe['r_480'] = williams_r(dataframe, period=480)
# Volume
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Heiken Ashi
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
## BB 40
bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2)
dataframe['bb_lowerband2_40'] = bollinger2_40['lower']
dataframe['bb_middleband2_40'] = bollinger2_40['mid']
dataframe['bb_upperband2_40'] = bollinger2_40['upper']
# ClucHA
dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs()
dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
# Cofi
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
# Profit Maximizer - PMAX
dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4
dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)
# MOMDIV
mom = momdiv(dataframe)
dataframe['momdiv_entry'] = mom['momdiv_entry']
dataframe['momdiv_exit'] = mom['momdiv_exit']
dataframe['momdiv_coh'] = mom['momdiv_coh']
dataframe['momdiv_col'] = mom['momdiv_col']
# T3 Average
dataframe['T3'] = T3(dataframe)
# True range
dataframe['trange'] = ta.TRANGE(dataframe)
# KC
dataframe['range_ma_28'] = ta.SMA(dataframe['trange'], 28)
dataframe['kc_upperband_28_1'] = dataframe['sma_28'] + dataframe['range_ma_28']
dataframe['kc_lowerband_28_1'] = dataframe['sma_28'] - dataframe['range_ma_28']
# KC 20
dataframe['range_ma_20'] = ta.SMA(dataframe['trange'], 20)
dataframe['kc_upperband_20_2'] = dataframe['sma_20'] + dataframe['range_ma_20'] * 2
dataframe['kc_lowerband_20_2'] = dataframe['sma_20'] - dataframe['range_ma_20'] * 2
dataframe['kc_bb_delta'] = ( dataframe['kc_lowerband_20_2'] - dataframe['bb_lowerband2'] ) / dataframe['bb_lowerband2'] * 100
# Linreg
dataframe['hh_20'] = ta.MAX(dataframe['high'], 20)
dataframe['ll_20'] = ta.MIN(dataframe['low'], 20)
dataframe['avg_hh_ll_20'] = (dataframe['hh_20'] + dataframe['ll_20']) / 2
dataframe['avg_close_20'] = ta.SMA(dataframe['close'], 20)
dataframe['avg_val_20'] = (dataframe['avg_hh_ll_20'] + dataframe['avg_close_20']) / 2
dataframe['linreg_val_20'] = ta.LINEARREG(dataframe['close'] - dataframe['avg_val_20'], 20, 0)
# fisher
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Modified Elder Ray Index
dataframe['moderi_96'] = moderi(dataframe, 96)
return dataframe
############################################################################
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'entry_tag'] = ''
is_dip = (
(dataframe[f'rmi_length_{self.entry_rmi_length.value}'] < self.entry_rmi.value) &
(dataframe[f'cci_length_{self.entry_cci_length.value}'] <= self.entry_cci.value) &
(dataframe['srsi_fk'] < self.entry_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.entry_bb_delta.value) &
(dataframe['bb_width'] > self.entry_bb_width.value) &
(dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000 ) & # from BinH
(dataframe['close'] < dataframe['bb_lowerband3'] * self.entry_bb_factor.value)
)
is_local_uptrend = ( # from NFI next gen, credit goes to @iterativ
(dataframe['ema_26'] > dataframe['ema_12']) &
(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_diff.value) &
(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
(dataframe['close'] < dataframe['bb_lowerband2'] * self.entry_bb_factor.value) &
(dataframe['closedelta'] > dataframe['close'] * self.entry_closedelta.value / 1000 )
)
is_ewo = ( # from SMA offset
(dataframe['rsi_fast'] < self.entry_rsi_fast.value) &
(dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low.value) &
(dataframe['EWO'] > self.entry_ewo.value) &
(dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high.value) &
(dataframe['rsi'] < self.entry_rsi.value)
)
is_ewo_2 = (
(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.entry_rsi_fast_ewo_2.value) &
(dataframe['close'] < dataframe['ema_8'] * self.entry_ema_low_2.value) &
(dataframe['EWO'] > self.entry_ewo_high_2.value) &
(dataframe['close'] < dataframe['ema_16'] * self.entry_ema_high_2.value) &
(dataframe['rsi'] < self.entry_rsi_ewo_2.value)
)
is_r_deadfish = ( # reverse deadfish
(dataframe['ema_100'] < dataframe['ema_200'] * self.entry_r_deadfish_ema.value) &
(dataframe['bb_width'] > self.entry_r_deadfish_bb_width.value) &
(dataframe['close'] < dataframe['bb_middleband2'] * self.entry_r_deadfish_bb_factor.value) &
(dataframe['volume_mean_12'] > dataframe['volume_mean_24'] * self.entry_r_deadfish_volume_factor.value) &
(dataframe['cti'] < self.entry_r_deadfish_cti.value) &
(dataframe['r_14'] < self.entry_r_deadfish_r14.value)
)
is_clucHA = (
(dataframe['rocr_1h'] > self.entry_clucha_rocr_1h.value ) &
(
(dataframe['bb_lowerband2_40'].shift() > 0) &
(dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.entry_clucha_bbdelta_close.value) &
(dataframe['ha_closedelta'] > dataframe['ha_close'] * self.entry_clucha_closedelta_close.value) &
(dataframe['tail'] < dataframe['bb_delta_cluc'] * self.entry_clucha_bbdelta_tail.value) &
(dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) &
(dataframe['ha_close'] < dataframe['ha_close'].shift())
)
)
is_gumbo = ( # Modified from gumbo1, creadit goes to original author @raph92
(dataframe['EWO'] < self.entry_gumbo_ewo_low.value) &
(dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) &
(dataframe['T3'] <= dataframe['ema_8'] * self.entry_gumbo_ema.value) &
(dataframe['cti'] < self.entry_gumbo_cti.value) &
(dataframe['r_14'] < self.entry_gumbo_r14.value)
)
is_sqzmom = ( # 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.entry_sqzmom_ema.value) &
(dataframe['EWO'] < self.entry_sqzmom_ewo.value) &
(dataframe['r_14'] < self.entry_sqzmom_r14.value)
)
# NFI quick mode, credit goes to @iterativ
is_nfi_13 = (
(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 = ( # NFIX 26
(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 = (
(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_nfix_5 = (
(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_49 = (
(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.entry_nfix_49_cti.value) &
(dataframe['r_14'] < self.entry_nfix_49_r14.value)
)
is_nfi7_33 = (
(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 = (
(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_additional_check = (
(dataframe['roc_1h'] < self.entry_roc_1h.value) &
(dataframe['bb_width_1h'] < self.entry_bb_width_1h.value)
)
## Additional Check
is_BB_checked = is_dip & is_break
## Condition Append
conditions.append(is_BB_checked) # ~2.32 / 91.1% / 46.27% D
dataframe.loc[is_BB_checked, 'entry_tag'] += 'bb '
conditions.append(is_local_uptrend) # ~3.28 / 92.4% / 69.72%
dataframe.loc[is_local_uptrend, 'entry_tag'] += 'local_uptrend '
conditions.append(is_ewo) # ~0.92 / 92.0% / 43.74% D
dataframe.loc[is_ewo, 'entry_tag'] += 'ewo '
conditions.append(is_r_deadfish) # ~0.99 / 86.9% / 21.93% D
dataframe.loc[is_r_deadfish, 'entry_tag'] += 'r_deadfish '
# conditions.append(is_clucHA) # ~7.2 / 92.5% / 97.98% D
# dataframe.loc[is_clucHA, 'entry_tag'] += 'clucHA '
#conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% D
#dataframe.loc[is_gumbo, 'entry_tag'] += 'gumbo '
conditions.append(is_sqzmom) # ~3.14 / 92.4% / 64.14% D
dataframe.loc[is_sqzmom, 'entry_tag'] += 'sqzmom '
conditions.append(is_nfi_13) # ~0.4 / 100% D
dataframe.loc[is_nfi_13, 'entry_tag'] += 'nfi_13 '
conditions.append(is_nfi_32) # ~0.78 / 92.0 % / 37.41% D
dataframe.loc[is_nfi_32, 'entry_tag'] += 'nfi_32 '
conditions.append(is_nfi_33) # ~0.11 / 100% D
dataframe.loc[is_nfi_33, 'entry_tag'] += 'nfi_33 '
conditions.append(is_nfix_49) # ~0.33 / 100% / 0% D
dataframe.loc[is_nfix_49, 'entry_tag'] += 'nfix_49 '
conditions.append(is_nfi7_33) # ~0.71 / 91.3% / 28.94% D
dataframe.loc[is_nfi7_33, 'entry_tag'] += 'nfi7_33 '
conditions.append(is_nfi7_37) # ~0.46 / 92.6% / 17.05% D
dataframe.loc[is_nfi7_37, 'entry_tag'] += 'nfi7_37 '
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions)
, 'entry' ] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[ (dataframe['volume'] > 0), 'exit' ] = 0
return dataframe
# PMAX
def pmax(df, period, multiplier, length, MAtype, src):
period = int(period)
multiplier = int(multiplier)
length = int(length)
MAtype = int(MAtype)
src = int(src)
mavalue = f'MA_{MAtype}_{length}'
atr = f'ATR_{period}'
pm = f'pm_{period}_{multiplier}_{length}_{MAtype}'
pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}'
# MAtype==1 --> EMA
# MAtype==2 --> DEMA
# MAtype==3 --> T3
# MAtype==4 --> SMA
# MAtype==5 --> VIDYA
# MAtype==6 --> TEMA
# MAtype==7 --> WMA
# MAtype==8 --> VWMA
# MAtype==9 --> zema
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
mavalue = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
mavalue = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
mavalue = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
mavalue = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
mavalue = VIDYA(df, length=length)
elif MAtype == 6:
mavalue = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
mavalue = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
mavalue = vwma(df, length)
elif MAtype == 9:
mavalue = zema(df, period=length)
df[atr] = ta.ATR(df, timeperiod=period)
df['basic_ub'] = mavalue + ((multiplier/10) * df[atr])
df['basic_lb'] = mavalue - ((multiplier/10) * df[atr])
basic_ub = df['basic_ub'].values
final_ub = np.full(len(df), 0.00)
basic_lb = df['basic_lb'].values
final_lb = np.full(len(df), 0.00)
for i in range(period, len(df)):
final_ub[i] = basic_ub[i] if (
basic_ub[i] < final_ub[i - 1]
or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
final_lb[i] = basic_lb[i] if (
basic_lb[i] > final_lb[i - 1]
or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]
df['final_ub'] = final_ub
df['final_lb'] = final_lb
pm_arr = np.full(len(df), 0.00)
for i in range(period, len(df)):
pm_arr[i] = (
final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] <= final_ub[i])
else final_lb[i] if (
pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] > final_ub[i]) else final_lb[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] >= final_lb[i]) else final_ub[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] < final_lb[i]) else 0.00)
pm = Series(pm_arr)
# Mark the trend direction up/down
pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN)
return pm, pmx
# Mom DIV
def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame:
mom: Series = ta.MOM(dataframe, timeperiod=mom_length)
upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0)
entry = qtpylib.crossed_below(mom, lowerband)
exit = 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_entry": entry,
"momdiv_exit": exit,
"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']