BB_RPB_TSL_RNG with conditions from true_lambo and dca
Timeframe
5m
Direction
Long Only
Stoploss
-99.8%
Trailing Stop
No
ROI
0m: 10.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
17
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import freqtrade.vendor.qtpylib.indicators as qtpylib
import logging
import math
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI, zema
logger = 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 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 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 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 VWAPB(dataframe, window_size=20, num_of_std=1):
df = dataframe.copy()
df['vwap'] = qtpylib.rolling_vwap(df,window=window_size)
rolling_std = df['vwap'].rolling(window=window_size).std()
df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std)
df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std)
return df['vwap_low'], df['vwap'], df['vwap_high']
def top_percent_change(dataframe: DataFrame, length: int) -> float:
"""
Percentage change of the current close from the range maximum Open price
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
if length == 0:
return (dataframe['open'] - dataframe['close']) / dataframe['close']
else:
return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']
class BB_DCAv2(IStrategy):
'''
BB_RPB_TSL_RNG with conditions from true_lambo and dca
(1) Improve 7_33 x_201
'''
buy_params = {
"buy_pump_1_factor": 1.096,
"buy_pump_2_factor": 1.125,
"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": 12.148,
"buy_ema_diff": 0.022,
"buy_adx": 20,
"buy_fastd": 20,
"buy_fastk": 22,
"buy_ema_cofi": 0.98,
"buy_ewo_high": 4.179,
"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_no_trend_cti_4": -0.597,
"buy_no_trend_factor_4": 0.024,
"buy_no_trend_r14_4": -44.062,
"buy_V_bb_width_5": 0.063,
"buy_V_cti_5": -0.086,
"buy_V_mfi_5": 38.158,
"buy_V_r14_5": -41.493,
"buy_vwap_closedelta": 26.941,
"buy_vwap_closedelta_2": 20.099,
"buy_vwap_closedelta_3": 27.654,
"buy_vwap_cti": -0.087,
"buy_vwap_cti_2": -0.748,
"buy_vwap_cti_3": -0.2,
"buy_vwap_width": 1.308,
"buy_vwap_width_2": 3.212,
"buy_vwap_width_3": 0.49,
}
sell_params = {
"pHSL": -0.998, # Disable
"pPF_1": 0.019,
"pPF_2": 0.065,
"pSL_1": 0.019,
"pSL_2": 0.062,
"high_offset_2": 0.997,
"sell_cti_r_cti": 0.844,
"sell_cti_r_r": -19.99,
"sell_u_e_2_cmf": -0.0,
"sell_u_e_2_ema_close_delta": 0.016,
"sell_u_e_2_rsi": 10,
"sell_deadfish_profit": -0.063,
"sell_deadfish_bb_factor": 0.954,
"sell_deadfish_bb_width": 0.043,
"sell_deadfish_volume_factor": 2.37,
}
minimal_roi = {
"0": 0.10,
}
timeframe = '5m'
inf_1h = '1h'
stoploss = -0.998
use_custom_stoploss = True
use_sell_signal = True
process_only_new_candles = True
startup_candle_count: int = 400
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(0.05, 0.2, default=0.15, optimize = is_optimize_break)
buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize = is_optimize_break)
is_optimize_local_dip = False
buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip)
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_dip)
is_optimize_ewo = False
buy_rsi_fast = IntParameter(35, 50, default=45, optimize = False)
buy_rsi = IntParameter(15, 30, default=35, optimize = False)
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_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96 , optimize = is_optimize_ewo_2)
buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09 , optimize = is_optimize_ewo_2)
is_optimize_cofi = False
buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi)
buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi)
buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi)
is_optimize_vwap = False
buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap)
buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap)
buy_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap)
is_optimize_vwap_2 = False
buy_vwap_width_2 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_2)
buy_vwap_closedelta_2 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_2)
buy_vwap_cti_2 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_2)
is_optimize_vwap_3 = True
buy_vwap_width_3 = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap_3)
buy_vwap_closedelta_3 = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap_3)
buy_vwap_cti_3 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap_3)
is_optimize_no_trend_4 = False
buy_no_trend_factor_4 = DecimalParameter(0.01, 0.05, default=0.030 , optimize = is_optimize_no_trend_4)
buy_no_trend_cti_4 = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_no_trend_4)
buy_no_trend_r14_4 = DecimalParameter(-100, -44, default=-80 , optimize = is_optimize_no_trend_4)
is_optimize_V_5 = False
buy_V_bb_width_5 = DecimalParameter(0.01, 0.1, default=0.01 , optimize = is_optimize_V_5)
buy_V_cti_5 = DecimalParameter(-0.95, -0.0, default=-0.6 , optimize = is_optimize_V_5)
buy_V_r14_5 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V_5)
buy_V_mfi_5 = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V_5)
is_optimize_gumbo = False
buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo)
buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo)
buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo)
buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo)
is_optimize_gumbo_protection = False
buy_gumbo_tpct_0 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection)
buy_gumbo_tpct_3 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection)
buy_gumbo_tpct_9 = DecimalParameter(0.0, 0.25, default=0.131, decimals=2, optimize = is_optimize_gumbo_protection)
buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
is_optimize_pump_1 = False
buy_pump_1_factor = DecimalParameter(1.0, 1.25, default= 1.1 , optimize = is_optimize_pump_1)
is_optimize_pump_2 = False
buy_pump_2_factor = DecimalParameter(1.0, 1.20, default= 1.1 , optimize = is_optimize_pump_2)
is_optimize_sell_offset = False
high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=is_optimize_sell_offset)
is_optimize_sell_u_e_2 = False
sell_u_e_2_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize = is_optimize_sell_u_e_2)
sell_u_e_2_ema_close_delta = DecimalParameter(0.001, 0.027, default= 0.024, optimize = is_optimize_sell_u_e_2)
sell_u_e_2_rsi = IntParameter(10, 30, default=24, optimize = is_optimize_sell_u_e_2)
is_optimize_deadfish = False
sell_deadfish_bb_width = DecimalParameter(0.015, 0.05, default=0.05 , optimize = is_optimize_deadfish)
sell_deadfish_profit = DecimalParameter(-0.10, -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(1.5, 3, default=1.5 , optimize = is_optimize_deadfish)
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)
is_optimize_trailing = False
pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True, optimize=is_optimize_trailing)
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True, optimize=is_optimize_trailing)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True, optimize=is_optimize_trailing)
pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True, optimize=is_optimize_trailing)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True, optimize=is_optimize_trailing)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
if (current_profit > PF_2):
sl_profit = SL_2 + (current_profit - PF_2)
elif (current_profit > PF_1):
sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
else:
sl_profit = HSL
if (sl_profit >= current_profit):
return -0.99
return stoploss_from_open(sl_profit, current_profit)
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
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 (last_candle['cti'] > self.sell_cti_r_cti.value) and (last_candle['r_14'] > self.sell_cti_r_r.value):
return f"sell_profit_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['cti_40_1h'] > 0.844) and (last_candle['r_84_1h'] > -20):
return f"signal_profit_cti_r( {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 (max_loss > 0.06) and (0.05 > current_profit > 0.01) and (last_candle['rsi'] < 46):
return f"signal_profit_r_1( {buy_tag})"
if (
(current_profit > 0.005)
and (last_candle['close'] > last_candle['sma_9'])
and (last_candle['close'] > last_candle['ema_24'] * self.high_offset_2.value)
and (last_candle['rsi'] > 50)
and (last_candle['rsi_fast'] > last_candle['rsi_slow'])
):
return f"sell_offset( {buy_tag})"
if (
(current_profit > 0.005)
and (last_candle['ema_vwap_diff_50'] < 0.012)
):
return f"sell_vwap_dump( {buy_tag})"
if (
(current_profit < -0.025)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['cmf'] < self.sell_u_e_2_cmf.value)
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_u_e_2_ema_close_delta.value)
and last_candle['rsi'] > previous_candle_1['rsi']
and (last_candle['rsi'] > (last_candle['rsi_1h'] + self.sell_u_e_2_rsi.value))
):
return f"sell_stoploss_u_e_2( {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})"
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
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']
dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
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['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
dataframe['mfi'] = ta.MFI(dataframe)
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_24'] = ta.EMA(dataframe, timeperiod=24)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)
vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1)
dataframe['vwap_upperband'] = vwap_high
dataframe['vwap_middleband'] = vwap
dataframe['vwap_lowerband'] = vwap_low
dataframe['vwap_width'] = ( (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] ) * 100
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
stoch_fast = qtpylib.stoch(dataframe, window=14, fast=True)
dataframe['fastd'] = stoch_fast['fast_d']
dataframe['fastk'] = stoch_fast['fast_k']
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
dataframe['cti_mean'] = dataframe['cti'].rolling(20).mean()
dataframe['cmo'] = ta.CMO(dataframe, timeperiod=24)
dataframe['EWO'] = EWO(dataframe, 50, 200)
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2)
dataframe['bb_lowerband2_40'] = bollinger2_40['lower']
dataframe['bb_middleband2_40'] = bollinger2_40['mid']
dataframe['bb_upperband2_40'] = bollinger2_40['upper']
dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs()
dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['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['ema_vwap_diff_50'] = ( ( dataframe['ema_50'] - dataframe['vwap_lowerband'] ) / dataframe['ema_50'] )
dataframe['tpct_change_1'] = top_percent_change(dataframe, 1)
dataframe['tpct_change_2'] = top_percent_change(dataframe, 2)
dataframe['tpct_change_4'] = top_percent_change(dataframe, 4)
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['moderi_96'] = moderi(dataframe, 96)
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
inf_heikinashi = qtpylib.heikinashi(informative)
informative['ha_close'] = inf_heikinashi['close']
informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
informative['bb_lowerband2'] = bollinger2['lower']
informative['bb_middleband2'] = bollinger2['mid']
informative['bb_upperband2'] = bollinger2['upper']
informative['bb_width'] = ((informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2'])
informative['rsi'] = ta.RSI(informative, timeperiod=14)
informative['rsi_28'] = ta.RSI(informative, timeperiod=28)
informative['rsi_42'] = ta.RSI(informative, timeperiod=42)
informative['ema_20'] = ta.EMA(informative, timeperiod=20)
informative['ema_26'] = ta.EMA(informative, timeperiod=26)
informative['ema_200'] = ta.EMA(informative, timeperiod=200)
informative['r_84'] = williams_r(informative, period=84)
informative['cti_40'] = pta.cti(informative["close"], length=40)
mom = momdiv(informative)
informative['momdiv_buy'] = mom['momdiv_buy']
informative['momdiv_sell'] = mom['momdiv_sell']
informative['momdiv_coh'] = mom['momdiv_coh']
informative['momdiv_col'] = mom['momdiv_col']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'buy_tag'] = ''
is_pump_1 = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * self.buy_pump_1_factor.value )) )
pump_protection_strict = (
(dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) &
( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) )
)
pump_protection_loose = (
(dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.05 )) &
( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.125 )) )
)
is_pump_4 = (
(dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.075 )) &
( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.17 )) )
)
is_crash_1 = (
(dataframe['tpct_change_1'] < 0.08) &
(dataframe['tpct_change_2'] < 0.08)
)
is_crash_2 = (
(dataframe['tpct_change_1'] < 0.06) &
(dataframe['tpct_change_2'] < 0.06)
)
is_crash_3 = (
(dataframe['tpct_change_1'] < 0.055) &
(dataframe['tpct_change_2'] < 0.055)
)
rsi_check = (
(dataframe['rsi_84'] < 60) &
(dataframe['rsi_112'] < 60)
)
min_EWO_check = ( (dataframe['EWO'] > -5.585) )
max_EWO_check = ( (dataframe['EWO'] < 10.6) )
if self.buy_is_dip_enabled.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)
)
if self.buy_is_break_enabled.value:
is_break = (
( (dataframe['bb_delta'] > self.buy_bb_delta.value) #"buy_bb_delta": 0.025 0.036
& #"buy_bb_width": 0.095 0.133
(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_crash_1)
)
is_local_uptrend = ( # from NFI next gen
(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 ) &
(dataframe['EWO'] < 4) &
(dataframe['EWO'] > -2.5)
)
is_ewo = ( # 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 = (
(dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
(dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low_2.value) &
(dataframe['EWO'] > self.buy_ewo_high.value) &
(dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high_2.value) &
(dataframe['rsi'] < self.buy_rsi.value) &
(rsi_check)
)
is_vwap = (
(dataframe['close'] < dataframe['vwap_lowerband']) &
(dataframe['vwap_width'] > self.buy_vwap_width.value) &
(dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta.value / 1000 ) &
(dataframe['cti'] < self.buy_vwap_cti.value) &
(dataframe['EWO'] > 8) &
(rsi_check) &
(pump_protection_strict)
)
is_vwap_2 = (
(dataframe['close'] < dataframe['vwap_lowerband']) &
(dataframe['vwap_width'] > self.buy_vwap_width_2.value) &
(dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_2.value / 1000 ) &
(dataframe['cti'] < self.buy_vwap_cti_2.value) &
(dataframe['EWO'] > 4) &
(dataframe['EWO'] < 8) &
(rsi_check) &
(pump_protection_strict)
)
is_vwap_3 = (
(dataframe['close'] < dataframe['vwap_lowerband']) &
(dataframe['vwap_width'] > self.buy_vwap_width_3.value) &
(dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta_3.value / 1000 ) &
(dataframe['cti'] < self.buy_vwap_cti_3.value) &
(dataframe['EWO'] < 4) &
(dataframe['EWO'] > -2.5) &
(dataframe['rsi_28_1h'] < 46) &
(rsi_check)
&
(pump_protection_loose)
)
is_VWAP = (
(dataframe['close'] < dataframe['vwap_lowerband']) &
(dataframe['tpct_change_1'] > 0.04) &
(dataframe['cti'] < -0.8) &
(dataframe['rsi'] < 35) &
(rsi_check)
)
is_no_trend_4 = (
(dataframe['ema_26'] > dataframe['ema_12']) &
(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_no_trend_factor_4.value) &
(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
(dataframe['cti'] < self.buy_no_trend_cti_4.value) &
(dataframe['r_14'] < self.buy_no_trend_r14_4.value) &
(dataframe['EWO'] < -4) &
(min_EWO_check) &
(rsi_check)
)
is_V_5 = (
(dataframe['bb_width'] > self.buy_V_bb_width_5.value) &
(dataframe['cti'] < self.buy_V_cti_5.value) &
(dataframe['r_14'] < self.buy_V_r14_5.value) &
(dataframe['mfi'] < self.buy_V_mfi_5.value) &
(dataframe['ema_vwap_diff_50'] > 0.215) &
(dataframe['EWO'] < -10) &
(rsi_check)
)
is_insta = (
(dataframe['bb_width_1h'] > 0.13) &
(dataframe['r_14'] < -50) &
(dataframe['r_84_1h'] < -69) &
(dataframe['cti'] < -0.84) &
(dataframe['cti_40_1h'] < -0.73)
&
( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.1 )) )
)
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_39 = (
(dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)) &
(dataframe['ema_200'] > (dataframe['ema_200'].shift(48) * 1.07)) &
(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_50'] * 0.912)
)
is_nfix_201 = (
(dataframe['rsi_slow'] < dataframe['rsi_slow'].shift()) &
(dataframe['rsi_fast'] < 30.0) &
(dataframe['ema_20_1h'] > dataframe['ema_26_1h']) &
(dataframe['close'] < dataframe['sma_15'] * 0.953) &
(dataframe['cti'] < -0.82) &
(dataframe['cci'] < -210.0)
&
(is_pump_1) &
(rsi_check)
)
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))
&
(pump_protection_loose) &
(rsi_check)
)
is_nfi_sma_3 = (
(dataframe['bb_lowerband2_40'].shift() > 0) &
(dataframe['bb_delta_cluc'] > dataframe['close'] * 0.059) &
(dataframe['ha_closedelta'] > dataframe['close'] * 0.023) &
(dataframe['tail'] < dataframe['bb_delta_cluc'] * 0.418) &
(dataframe['close'] < dataframe['bb_lowerband2_40'].shift()) &
(dataframe['close'] < dataframe['close'].shift())
)
is_BB_checked = is_dip & is_break
conditions.append(is_BB_checked) # P
dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb '
conditions.append(is_local_uptrend)
dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local_uptrend '
conditions.append(is_ewo)
dataframe.loc[is_ewo, 'buy_tag'] += 'ewo '
conditions.append(is_ewo_2)
dataframe.loc[is_ewo_2, 'buy_tag'] += 'ewo2 '
conditions.append(is_no_trend_4)
dataframe.loc[is_no_trend_4, 'buy_tag'] += 'no_trend_4 '
conditions.append(is_vwap)
dataframe.loc[is_vwap, 'buy_tag'] += 'vwap '
conditions.append(is_vwap_2)
dataframe.loc[is_vwap_2, 'buy_tag'] += 'vwap_2 '
conditions.append(is_vwap_3)
dataframe.loc[is_vwap_3, 'buy_tag'] += 'vwap_3 '
conditions.append(is_VWAP)
dataframe.loc[is_VWAP, 'buy_tag'] += 'VWAP '
conditions.append(is_insta)
dataframe.loc[is_insta, 'buy_tag'] += 'insta '
conditions.append(is_nfi_33)
dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi_33 '
conditions.append(is_nfix_39)
dataframe.loc[is_nfix_39, 'buy_tag'] += 'x_39 '
conditions.append(is_nfix_201)
dataframe.loc[is_nfix_201, 'buy_tag'] += 'x_201 '
conditions.append(is_nfi7_33)
dataframe.loc[is_nfi7_33, 'buy_tag'] += '7_33 '
conditions.append(is_nfi_sma_3)
dataframe.loc[is_nfi_sma_3, 'buy_tag'] += 'sma_3 '
conditions.append(is_V_5)
dataframe.loc[is_V_5, 'buy_tag'] += 'V_5 '
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy' ] = 1
dataframe.loc[
(dataframe['cti'] > 0.1) |
(dataframe['cti_mean'] > 0.4),
['buy', 'buy_tag']] = (0, "")
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[ (dataframe['volume'] > 0), 'sell' ] = 0
return dataframe
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')
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
class BB_RTR_dca (BB_RTR):
position_adjustment_enable = True
initial_safety_order_trigger = -0.08
max_safety_orders = 2
safety_order_step_scale = 0.5 #SS
safety_order_volume_scale = 1.6 #OS
max_dca_multiplier = (1 + max_safety_orders)
if (max_safety_orders > 0):
if (safety_order_volume_scale > 1):
max_dca_multiplier = (2 + (safety_order_volume_scale * (math.pow(safety_order_volume_scale, (max_safety_orders - 1)) - 1) / (safety_order_volume_scale - 1)))
elif (safety_order_volume_scale < 1):
max_dca_multiplier = (2 + (safety_order_volume_scale * (1 - math.pow(safety_order_volume_scale, (max_safety_orders - 1))) / (1 - safety_order_volume_scale)))
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
**kwargs) -> float:
if self.config['stake_amount'] == 'unlimited':
return proposed_stake / self.max_dca_multiplier
return proposed_stake
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs):
if current_profit > self.initial_safety_order_trigger:
return None
count_of_buys = trade.nr_of_successful_buys
if 1 <= count_of_buys <= self.max_safety_orders:
safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys)
if (self.safety_order_step_scale > 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1))
elif (self.safety_order_step_scale < 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale))
if current_profit <= (-1 * abs(safety_order_trigger)):
try:
stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None)
stake_amount = stake_amount / self.max_dca_multiplier
stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1))
amount = stake_amount / current_rate
logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}")
return stake_amount
except Exception as exception:
logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
return None
return None