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BeastBotXBLR6.py
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BeastBotXBLR6.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from datetime import datetime, timedelta
from pandas import DataFrame, Series, concat
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI,vwmacd
import logging
import pandas_ta as pta
from numpy import where
import time
import datetime
##################################################### BeastBot XBLR6 ####################################################################
# don't need hyperopt for binance, thanks free comunity, it have parts other strategies, without freqtrade comunity this strategy is not possible #
# if you earn money and consider rewarding the author ETH: 0xda884c4dbe47421ba63f033db3cc2ec49d552365 BTC: 1hxiKHLaPDKTWuXhVgTZvBdPZtjC9msxi #
####################################################################################################################################################
# hyperopt for each conditions and finally all conditions true
# backtesting: freqtrade backtesting -c config_test.json -s BeastBotXBLR6 --timerange 20210920-20220127 --breakdown day -v --enable-protections
# 119 days
# for each conditions
# Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Max Drawdown
# Con1 12 | 11 0 1 | 2.63% | (15.82%) | 0 days 00:15:00 | (8.27%) |
# Con2 16 | 15 0 1 | 2.47% | (19.77%) | 0 days 01:19:00 | (5.15%) |
# Con3 12 | 12 0 0 | 2.85% | (17.11%) | 0 days 00:31:00 | -- |
# Con4 18 | 16 0 2 | 2.29% | (20.61%) | 0 days 03:11:00 | (5.23%) |
# Con6 11 | 10 0 1 | 2.73% | (15.05%) | 0 days 03:15:00 | (0.75%) |
# Con7 1 | 1 0 0 | 9.37% | (4.69%) | 0 days 00:30:00 | -- |
# Con8 13 | 12 0 1 | 2.31% | (15.06%) | 0 days 05:19:00 | (5.09%) |
# Con9 7 | 7 0 0 | 2.62% | (9.17%) | 0 days 00:19:00 | -- |
# con10 26 | 19 0 7 | 1.03% | (13.41%) | 0 days 02:39:00 | (3.76%)
# all conditions true
logger = logging.getLogger(__name__)
# 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
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['close'] * 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 SSLChannels_ATR(dataframe, length=7):
"""
SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
Credit to @JimmyNixx for python
"""
df = dataframe.copy()
df['ATR'] = ta.ATR(df, timeperiod=14)
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
return df['sslDown'], df['sslUp']
class BeastBotXBLR6(IStrategy):
INTERFACE_VERSION = 2
timeframe = '5m'
inf_1h = '1h'
info_timeframe_1d = "1d"
has_BTC_info_tf = True
# Buy hyperspace params:
buy_params = {
"buy_c10_1": -101.4,
"buy_c10_2": -0.86,
"buy_c9_1": 40.0,
"buy_c9_2": -70.4,
"buy_c9_3": -67.0,
"buy_c9_4": 41.8,
"buy_c9_5": 33.6,
"buy_c9_6": 82.7,
"buy_c9_7": -94.3,
"buy_con3_1": 0.016,
"buy_con3_2": 0.984,
"buy_con3_3": 0.966,
"buy_con3_4": -0.88,
"buy_macd_41": 0.08,
"buy_rsi_1h_42": 19.9,
"buy_volume_drop_41": 3.7,
"buy_volume_pump_41": 0.1,
"buy_bb_delta": 0.023,
"buy_bb_factor": 0.99,
"buy_bb_width": 0.097,
"buy_c2_1": 0.02,
"buy_c2_2": 0.991,
"buy_c2_3": -0.7,
"buy_c6_1": 0.16,
"buy_c6_2": 0.03,
"buy_c6_3": 0.02,
"buy_c6_4": 0.022,
"buy_c6_5": 0.387,
"buy_c7_1": 1.01,
"buy_c7_2": 0.99,
"buy_c7_3": -99,
"buy_c7_4": -64,
"buy_c7_5": 81.4,
"buy_cci": -112,
"buy_cci_length": 25,
"buy_closedelta": 12.232,
"buy_con1_enable": True,
"buy_con2_enable": True,
"buy_con3_enable": True,
"buy_con4_enable": True,
"buy_con6_enable": True,
"buy_condition_10_enable": True,
"buy_condition_7_enable": True,
"buy_condition_8_enable": True,
"buy_condition_9_enable": True,
"buy_dip_threshold_5": 0.05,
"buy_dip_threshold_6": 0.2,
"buy_dip_threshold_7": 0.4,
"buy_dip_threshold_8": 0.5,
"buy_mfi_1": 29.8,
"buy_min_inc_1": 0.025,
"buy_pump_pull_threshold_1": 1.75,
"buy_pump_threshold_1": 0.5,
"buy_rmi": 36,
"buy_rmi_length": 13,
"buy_rsi_1": 39.8,
"buy_rsi_1h_max_1": 73.8,
"buy_rsi_1h_min_1": 36.2,
"buy_srsi_fk": 49,
}
# Sell hyperspace params:
sell_params = {
"sell_bb_relative_8": 1.1,
"sell_condition_1_enable": True,
"sell_condition_2_enable": True,
"sell_condition_3_enable": True,
"sell_condition_4_enable": True,
"sell_condition_5_enable": True,
"sell_condition_6_enable": True,
"sell_condition_7_enable": True,
"sell_condition_8_enable": True,
"sell_custom_dec_profit_1": 0.05,
"sell_custom_dec_profit_2": 0.07,
"sell_custom_profit_0": 0.01,
"sell_custom_profit_1": 0.03,
"sell_custom_profit_2": 0.05,
"sell_custom_profit_3": 0.08,
"sell_custom_profit_4": 0.25,
"sell_custom_profit_under_rel_1": 0.024,
"sell_custom_profit_under_rsi_diff_1": 4.4,
"sell_custom_rsi_0": 33.0,
"sell_custom_rsi_1": 38.0,
"sell_custom_rsi_2": 43.0,
"sell_custom_rsi_3": 48.0,
"sell_custom_rsi_4": 50.0,
"sell_custom_stoploss_under_rel_1": 0.004,
"sell_custom_stoploss_under_rsi_diff_1": 8.0,
"sell_custom_under_profit_1": 0.02,
"sell_custom_under_profit_2": 0.04,
"sell_custom_under_profit_3": 0.6,
"sell_custom_under_rsi_1": 56.0,
"sell_custom_under_rsi_2": 60.0,
"sell_custom_under_rsi_3": 62.0,
"sell_dual_rsi_rsi_1h_4": 79.6,
"sell_dual_rsi_rsi_4": 73.4,
"sell_ema_relative_5": 0.024,
"sell_profit_trendstop": 0.02,
"sell_rsi_1h_7": 81.7,
"sell_rsi_bb_1": 79.5,
"sell_rsi_bb_2": 81,
"sell_rsi_diff_5": 4.4,
"sell_rsi_main_3": 82,
"sell_rsi_under_6": 79.0,
"sell_time_stoploss": 114,
"sell_time_trendstop": 113,
"sell_trail_down_1": 0.18,
"sell_trail_down_2": 0.14,
"sell_trail_down_3": 0.01,
"sell_trail_profit_max_1": 0.46,
"sell_trail_profit_max_2": 0.12,
"sell_trail_profit_max_3": 0.1,
"sell_trail_profit_min_1": 0.15,
"sell_trail_profit_min_2": 0.01,
"sell_trail_profit_min_3": 0.05,
}
minimal_roi = {
"0": 100
}
# new sell
stoploss = -0.99
use_custom_stoploss = False
# Recommended
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = True
# Required
startup_candle_count: int = 300
process_only_new_candles = False
# Strategy Specific Variable Storage
custom_trade_info = {}
custom_fiat = "USD" # Only relevant if stake is BTC or ETH
plot_config = {
"main_plot": {
"ema_50_1h": {"color": "rgba(255,250,200,2.4)"},
"bb_lowerband": {"color": "#792bbb","type": "line"},
"bb_upperband": {"color": "#bc281d","type": "line"}
},
"subplots": {
"RSI/BTC": {
"mfi": {"color": "#e12a7c","type": "line"},
"cci": {"color": "#794491","type": "line"},
"ssl-dir_1h": {"color": "#2773a7","type": "line"},
"ssl-dir": {"color": "#5379a2","type": "line"}
}
}
}
custom_trendBTC_info = {}
if not 'trend' in custom_trendBTC_info:
custom_trendBTC_info['trend'] = {}
if not 'not_downtrend' in custom_trendBTC_info['trend']:
custom_trendBTC_info['trend']['not_downtrend'] = 0
if not 'st' in custom_trendBTC_info['trend']:
custom_trendBTC_info['trend']['st'] = 0
if not 'stx' in custom_trendBTC_info['trend']:
custom_trendBTC_info['trend']['stx'] = 0
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration": 120
},
{
"method": "StoplossGuard",
"lookback_period": 90,
"trade_limit": 2,
"stop_duration": 120,
"only_per_pair": False
},
{
"method": "StoplossGuard",
"lookback_period": 90,
"trade_limit": 1,
"stop_duration": 120,
"only_per_pair": True
},
]
###########################################################################
# Buy
Optimize_condition = False
buy_con1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_con2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_con3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_con4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_con6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True)
optc1 = True
buy_rmi_length = IntParameter(8, 20, default=8, optimize = optc1, load=True)
buy_rmi = IntParameter(30, 50, default=35, optimize= optc1, load=True)
buy_cci_length = IntParameter(25, 45, default=25, optimize = optc1, load=True)
buy_cci = IntParameter(-135, -90, default=-133, optimize= optc1, load=True)
buy_srsi_fk = IntParameter(30, 50, default=25, optimize= optc1, load=True)
buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize = optc1, load=True)
buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = optc1, load=True)
buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = optc1, load=True)
buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = optc1, load=True)
optc2 = False
buy_c2_1 = DecimalParameter(0.010, 0.025, default=0.018, space='buy', decimals=3, optimize=optc2, load=True)
buy_c2_2 = DecimalParameter(0.980, 0.995, default=0.982, space='buy', decimals=3, optimize=optc2, load=True)
buy_c2_3 = DecimalParameter(-0.8, -0.3, default=-0.5, space='buy', decimals=1, optimize=optc2, load=True)
optc3 = False
buy_con3_1 = DecimalParameter(0.010, 0.025, default=0.017, space='buy', decimals=3, optimize=optc3, load=True)
buy_con3_2 = DecimalParameter(0.980, 0.995, default=0.984, space='buy', decimals=3, optimize=optc3, load=True)
buy_con3_3 = DecimalParameter(0.955, 0.975, default=0.965, space='buy', decimals=3, optimize=optc3, load=True)
buy_con3_4 = DecimalParameter(-0.95, -0.70, default=-0.85, space='buy', decimals=2, optimize=optc3, load=True)
optc4 = False
buy_rsi_1h_42 = DecimalParameter(10.0, 50.0, default=15.0, space='buy', decimals=1, optimize=optc4, load=True)
buy_macd_41 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=optc4, load=True)
buy_volume_pump_41 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=optc4, load=True)
buy_volume_drop_41 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=optc4, load=True)
optc6 = True
buy_c6_2 = DecimalParameter(0.980, 0.999, default=0.985, space='buy', decimals=3, optimize=optc6, load=True)
buy_c6_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=optc6, load=True)
buy_c6_2 = DecimalParameter(0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=optc6, load=True)
buy_c6_3 = DecimalParameter(0.005, 0.04, default=0.031, space='buy', decimals=3, optimize=optc6, load=True)
buy_c6_4 = DecimalParameter(0.01, 0.03, default=0.021, space='buy', decimals=3, optimize=optc6, load=True)
buy_c6_5 = DecimalParameter(0.2, 0.4, default=0.264, space='buy', decimals=3, optimize=optc6, load=True)
optc7 = True
buy_c7_1 = DecimalParameter(0.95, 1.10, default=1.01, space='buy', decimals=2, optimize=optc7, load=True)
buy_c7_2 = DecimalParameter(0.95, 1.10, default=0.99, space='buy', decimals=2, optimize=optc7, load=True)
buy_c7_3 = IntParameter(-100, -80, default=-94, space='buy', optimize= optc7, load=True)
buy_c7_4 = IntParameter(-90, -60, default=-75, space='buy', optimize= optc7, load=True)
buy_c7_5 = DecimalParameter(75.1, 90.1, default=80.0, space='buy',decimals=1, optimize= optc7, load=True)
optc8 = False
buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=optc8, load=True)
buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=optc8, load=True)
buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=optc8, load=True)
buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=optc8, load=True)
buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=optc8, load=True)
optc9 = False
buy_c9_1 = DecimalParameter(25.0, 44.0, default=36.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_2 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_3 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_4 = DecimalParameter(35.0, 54.0, default=46.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_5 = DecimalParameter(20.0, 44.0, default=30.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_6 = DecimalParameter(65.0, 94.0, default=84.0, space='buy', decimals=1, optimize=optc9, load=True)
buy_c9_7 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc9, load=True)
optc10 = True
buy_c10_1 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc10, load=True)
buy_c10_2 = DecimalParameter(-1, -0.5, default=-0.78, space='buy', decimals=2, optimize=optc10, load=True)
buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True)
buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)
# 24 hours
buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=False, load=True)
# Sell··································································
sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True)
sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True)
sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True)
sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True)
sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)
sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=False, load=True)
sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True)
sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True)
optimize_sell = False
sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=optimize_sell, load=True)
sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=optimize_sell, load=True)
optimize_sell_u = False
sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)
sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)
sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.6, space='sell', decimals=3, optimize=optimize_sell_u, load=True)
sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True)
sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=False, load=True)
sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True)
sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=False, load=True)
sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=False, load=True)
sell_time_stoploss = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True)
sell_time_trendstop = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True)
sell_profit_trendstop = DecimalParameter(0.009, 0.02, default=0.015, space='sell', optimize=True, load=True)
#############################################################
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
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].squeeze()
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()
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
if (last_candle is not None):
if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value):
return f'sf_4( {buy_tag})'
elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
return f'sf_3( {buy_tag})'
elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
return f'sf_2( {buy_tag})'
elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
return f'sf_1( {buy_tag})'
elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value):
return f'sf_0( {buy_tag})'
elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']):
return f'sf_u_1( {buy_tag})'
elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']):
return f'sf_u_2( {buy_tag})'
elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']):
return f'sf_u_3( {buy_tag})'
elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']):
return f'sf_d_1( {buy_tag})'
elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']):
return f'sf_d_2( {buy_tag})'
elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)):
return f'sf_t_1( {buy_tag})'
elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)):
return f'sf_t_2( {buy_tag})'
elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)):
return f'sf_u_t_1( {buy_tag})'
elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value):
return f'sf_u_e_1( {buy_tag})'
elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value):
return f'stoploss ( {buy_tag})'
elif (current_profit < -0.05) & (trade_dur > self.sell_time_stoploss.value) & (last_candle['ssl-dir'] == 'down'):
return f'stoploss5 ( {buy_tag})'
elif ((buy_tag in [' trend ']) & (trade_dur > self.sell_time_trendstop.value) & ((last_candle['ssl-dir'] == 'down') & (current_profit < self.sell_profit_trendstop.value))):
return f'trend_stop'
elif (current_profit < -0.08):
return f'stoploss8 ( {buy_tag})'
return None
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
return True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.inf_1h) for pair in pairs]
# informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs])
if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
btc_info_pair = f"BTC/{self.config['stake_currency']}"
else:
btc_info_pair = "BTC/USDT"
informative_pairs.append((btc_info_pair, self.timeframe))
informative_pairs.append((btc_info_pair, self.inf_1h))
informative_pairs.append((btc_info_pair, self.info_timeframe_1d))
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_15'] = ta.EMA(informative_1h, timeperiod=15)
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['rsi'] = ta.RSI(informative_1h, timeperiod=14)
#informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi'] > 50))
informative_1h['r_480'] = williams_r(dataframe, period=480)
informative_1h['safe_pump_24'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) /
informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_1.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) /
self.buy_pump_pull_threshold_1.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min())))
informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)
ssldown, sslup = SSLChannels_ATR(informative_1h, 14)
informative_1h['ssl-dir'] = np.where(sslup > ssldown,'up','down')
# informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)
return informative_1h
def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# -----------------------------------------------------------------------------------------
if not 'trend' in self.custom_trendBTC_info:
self.custom_trendBTC_info['trend'] = {}
if not 'not_downtrend' in self.custom_trendBTC_info['trend']:
self.custom_trendBTC_info['trend']['not_downtrend'] = 0
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi'] > 50))
self.custom_trendBTC_info["trend"]['not_downtrend'] = {}
self.custom_trendBTC_info["trend"]['not_downtrend'] = dataframe['not_downtrend']
# -----------------------------------------------------------------------------------------
ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
return dataframe
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# nuevo #
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_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'])
dataframe['bb_delta'] = ((dataframe['bb_lowerband'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# CCI hyperopt
for val in self.buy_cci_length.range:
dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)
dataframe['cci'] = ta.CCI(dataframe, 26)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# RMI hyperopt
for val in self.buy_rmi_length.range:
dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)
#dataframe['rmi'] = RMI(dataframe, length=8, mom=4)
# SRSI hyperopt ?
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe['srsi_fk'] = stoch['fastk']
dataframe['srsi_fd'] = stoch['fastd']
# 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)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
#cols_to_norm = ['vwmacd','signal','hist'] normalize
#dataframe[cols_to_norm] = dataframe[cols_to_norm].apply(lambda x: (x-x.mean())/ x.std(), axis=0)
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_480'] = williams_r(dataframe, period=480)
# EMA 200
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
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['sma_200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['safe_dips_strict'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_5.value) &
(((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_6.value) &
(((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_7.value) &
(((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_8.value))
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
"""
if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
btc_info_pair = f"BTC/{self.config['stake_currency']}"
else:
btc_info_pair = "BTC/USDT"
if metadata['pair'] in btc_info_pair:
btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h)
btc_info_tfx = self.info_tf_btc_indicators(btc_info_tf, metadata)
dataframe = merge_informative_pair(dataframe, btc_info_tfx, self.timeframe, self.inf_1h, ffill=True)
drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
"""
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
# 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)
ssldown, sslup = SSLChannels_ATR(dataframe, 64)
dataframe['ssl-up'] = sslup
dataframe['ssl-down'] = ssldown
dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')
dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
tok = time.perf_counter()
logger.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.")
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
con1 = (
self.buy_con1_enable.value &
(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) &
((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 ) &
(dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
)
con2= (
self.buy_con2_enable.value &
(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) &
(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_c2_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c2_2.value)) &
(dataframe['cti_1h'] > self.buy_c2_3.value)
)
con3 = (
self.buy_con3_enable.value &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_con3_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
(dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_con3_2.value)) &
(dataframe['close'] < dataframe['ema_20'] * self.buy_con3_3.value) &
(dataframe['cti'] < self.buy_con3_4.value)
)
con4 = (
self.buy_con4_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_42.value) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_41.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_41.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_41.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_41.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0)
)
con6 = (
self.buy_con6_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50'] > dataframe['ema_200']) &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_1.value) &
(((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_2.value) &
dataframe['bb_lowerband'].shift().gt(0) &
dataframe['bb_delta'].gt(dataframe['close'] * self.buy_c6_3.value) &
dataframe['closedelta'].gt(dataframe['close'] * self.buy_c6_4.value) &
dataframe['tail'].lt(dataframe['bb_delta'] * self.buy_c6_5.value) &
dataframe['close'].lt(dataframe['bb_lowerband'].shift()) &
dataframe['close'].le(dataframe['close'].shift()) &
(dataframe['volume'] > 0)
)
con7 = (
self.buy_condition_7_enable.value &
(dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * self.buy_c7_1.value)) &
(dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c7_2.value)) &
(dataframe['r_14'] < self.buy_c7_3.value) &
(dataframe['r_64'] < self.buy_c7_4.value) &
(dataframe['rsi_1h'] < self.buy_c7_5.value)
)
con8 = (
self.buy_condition_8_enable.value &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(dataframe['sma_200'] > dataframe['sma_200'].shift(50)) &
(dataframe['safe_dips_strict']) &
(dataframe['safe_pump_24_1h']) &
(((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) &
(dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) &
(dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) &
(dataframe['rsi'] < self.buy_rsi_1.value) &
(dataframe['mfi'] < self.buy_mfi_1.value) &
(dataframe['volume'] > 0)
)
con9 = (
self.buy_condition_9_enable.value &
(((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > 0.032) &
(dataframe['rsi'] < self.buy_c9_1.value) &
(dataframe['r_14'] < self.buy_c9_2.value) &
(dataframe['r_32'] < self.buy_c9_3.value) &
(dataframe['mfi'] < self.buy_c9_4.value) &
(dataframe['rsi_1h'] > self.buy_c9_5.value) &
(dataframe['rsi_1h'] < self.buy_c9_6.value) &
(dataframe['r_480_1h'] > self.buy_c9_7.value)
)
co10 = (
self.buy_condition_10_enable.value &
(dataframe['close'].shift(4) < (dataframe['close'].shift(3))) &
(dataframe['close'].shift(3) < (dataframe['close'].shift(2))) &
(dataframe['close'].shift(2) < (dataframe['close'].shift())) &
(dataframe['close'].shift(1) < (dataframe['close'])) &
(dataframe['ema_26'] > dataframe['ema_12']) &
(dataframe['close'] > (dataframe['open'])) &
(dataframe['cci'].shift() < dataframe['cci']) &
(dataframe['ssl-dir_1h'] == 'up') &
(dataframe['cci'] < self.buy_c10_1.value) &
(dataframe['cti'] < self.buy_c10_2.value) &
(dataframe['volume'] > 0)
)
conditions.append(con1)
conditions.append(con2)
conditions.append(con3)
conditions.append(con4)
conditions.append(con6)
conditions.append(con7)
conditions.append(con8)
conditions.append(con9)
conditions.append(co10)
dataframe.loc[con1, 'buy_tag'] = " con1 "
dataframe.loc[con2, 'buy_tag'] = " Andalusian "
dataframe.loc[con3, 'buy_tag'] = " con3 "
dataframe.loc[con4, 'buy_tag'] = " con4 "
dataframe.loc[con6, 'buy_tag'] = " con6 "
dataframe.loc[con7, 'buy_tag'] = " con7 "
dataframe.loc[con8, 'buy_tag'] = " con8 "
dataframe.loc[con9, 'buy_tag'] = " con9 "
dataframe.loc[co10, 'buy_tag'] = " trend "
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
self.sell_condition_1_enable.value &
(dataframe['rsi'] > self.sell_rsi_bb_1.value) &
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) &
(dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) &
(dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_2_enable.value &
(dataframe['rsi'] > self.sell_rsi_bb_2.value) &
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_3_enable.value &
(dataframe['rsi'] > self.sell_rsi_main_3.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_4_enable.value &
(dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) &
(dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_6_enable.value &
(dataframe['close'] < dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_50']) &
(dataframe['rsi'] > self.sell_rsi_under_6.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_7_enable.value &
(dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) &
qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) &
(dataframe['volume'] > 0)
)
)
"""
for i in self.ma_types:
conditions.append(
(
(dataframe['close'] > dataframe[f'{i}_offset_sell']) &
(dataframe['volume'] > 0)
)
)
"""
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
] = 1
return dataframe