Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10.0%, 10m: 5.0%, 40m: 2.0%, 180m: 10.0%
Interface Version
2
Startup Candles
N/A
Indicators
8
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
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 pandas import DataFrame, Series
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, timeframe_to_minutes
from functools import reduce
from technical.util import resample_to_interval, resampled_merge
from technical.indicators import zema
###########################################################################################################
## BigZ04 by ilya ##
## ##
## https://github.com/i1ya/freqtrade-strategies ##
## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ##
## ## ##
###########################################################################################################
## The main point of this strat is: ##
## - make drawdown as low as possible ##
## - buy at dip ##
## - sell quick as fast as you can (release money for the next buy) ##
## - soft check if market if rising ##
## - hard check is market if fallen ##
## - 11 buy signals ##
## - stoploss function preventing from big fall ##
## - no sell signal. Whether ROI or stoploss =) ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ##
## ##
## As a pairlist you can use VolumePairlist. ##
## ##
## Ensure that you don't override any variables in your config.json. Especially ##
## the timeframe (must be 5m). ##
## ##
## sell_profit_only: ##
## True - risk more (gives you higher profit and higher Drawdown) ##
## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ##
## ##
###########################################################################################################
## DONATIONS 2 @iterativ (author of the original strategy) ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## ##
###########################################################################################################
class BigZ04NextHO(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.1, # I feel lucky!
"10": 0.05,
"40": 0.02,
"180": 0.1, # We're going up?
}
stoploss = -0.99 # effectively disabled.
timeframe = '5m'
inf_1h = '1h'
res_timeframe = '30m'
info_timeframe = '1h'
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = False
trailing_only_offset_is_reached = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.025
# Custom stoploss
use_custom_stoploss = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
buy_params = {
#############
# Enable/Disable conditions
"buy_condition_0_enable": True,
"buy_condition_1_enable": True,
"buy_condition_2_enable": True,
"buy_condition_3_enable": True,
"buy_condition_4_enable": True,
"buy_condition_5_enable": True,
"buy_condition_6_enable": True,
"buy_condition_7_enable": True,
"buy_condition_8_enable": True,
"buy_condition_9_enable": True,
"buy_condition_10_enable": True,
"buy_condition_11_enable": True,
"buy_condition_12_enable": True,
"buy_condition_13_enable": True,
"buy_bb20_close_bblowerband_safe_1": 0.931,
"buy_bb20_close_bblowerband_safe_2": 0.978,
"buy_volume_drop_1": 6.0,
"buy_volume_drop_2": 9.5,
"buy_volume_drop_3": 1.0,
"buy_volume_pump_1": 0.4,
"buy_rsi_1h_1": 16.0,
"buy_rsi_1h_2": 15.4,
"buy_rsi_1h_3": 19.1,
"buy_rsi_1h_4": 29.1,
"buy_rsi_1h_5": 59.1,
"buy_rsi_1": 27.5,
"buy_rsi_2": 14.8,
"buy_rsi_3": 13.7,
"buy_macd_1": 0.08,
"buy_macd_2": 0.03,
"buy_condition_12_bblower_close": 0.989,
"buy_condition_12_bblower_low": 0.988,
"buy_condition_12_rsi_1h": 78.4,
"buy_condition_11_close_1": 0.143,
"buy_condition_11_close_2": 0.018,
"buy_condition_11_rsi": 50.27,
"buy_condition_0_close": 1.046,
"buy_condition_0_rsi": 31.7,
"buy_condition_0_rsi_1h": 66.2,
"buy_condition_10_hist_close": 0.0009,
"buy_condition_10_rsi": 40.0,
"buy_condition_1_rsi_1h": 73.1,
}
sell_params = {
"custom_stoploss_minutes": 75,
"custom_stoploss_rsi_1h": 36.0,
"custom_stoploss_current_rates_1": 1.032,
"custom_stoploss_current_rates_2": 1.014,
}
############################################################################
# Buy
buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_bb20_optimize=False
buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='buy', optimize=buy_bb20_optimize, load=True)
buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=buy_bb20_optimize, load=True)
buy_volume_pump_optimize=False
buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=buy_volume_pump_optimize, load=True)
buy_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=buy_volume_pump_optimize, load=True)
buy_volume_drop_2 = DecimalParameter(1, 10, default=3, space='buy', decimals=1, optimize=buy_volume_pump_optimize, load=True)
buy_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='buy', decimals=1, optimize=buy_volume_pump_optimize, load=True)
buy_rsi_1h_optimize_1=False
buy_rsi_1h_optimize_2=False
buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=buy_rsi_1h_optimize_1, load=True)
buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=buy_rsi_1h_optimize_1, load=True)
buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=buy_rsi_1h_optimize_1, load=True)
buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=buy_rsi_1h_optimize_2, load=True)
buy_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='buy', decimals=1, optimize=buy_rsi_1h_optimize_2, load=True)
buy_rsi_optimize=False
buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=buy_rsi_optimize, load=True)
buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=buy_rsi_optimize, load=True)
buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=buy_rsi_optimize, load=True)
buy_macd_optimize=False
buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=buy_macd_optimize, load=True)
buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=buy_macd_optimize, load=True)
buy_condition_12_optimize=False
buy_condition_12_bblower_close = DecimalParameter(0.95, 0.995, default=0.993, space='buy', decimals=3, optimize=buy_condition_12_optimize, load=True)
buy_condition_12_bblower_low = DecimalParameter(0.95, 0.99, default=0.985, space='buy', decimals=3, optimize=buy_condition_12_optimize, load=True)
buy_condition_12_rsi_1h = DecimalParameter(60, 80, default=72.8, space='buy', decimals=1, optimize=buy_condition_12_optimize, load=True)
buy_condition_11_optimize=False
buy_condition_11_close_1 = DecimalParameter(0.05, 0.15, default=0.1, space='buy', decimals=3, optimize=buy_condition_11_optimize, load=True)
buy_condition_11_close_2 = DecimalParameter(0.01, 0.03, default=0.018, space='buy', decimals=3, optimize=buy_condition_11_optimize, load=True)
buy_condition_11_rsi = DecimalParameter(49, 53, default=51, space='buy', decimals=2, optimize=buy_condition_11_optimize, load=True)
buy_condition_0_optimize=False
buy_condition_0_rsi = DecimalParameter(26, 34, default=30, space='buy', decimals=1, optimize=buy_condition_0_optimize, load=True)
buy_condition_0_close = DecimalParameter(1, 1.2, default=1.024, space='buy', decimals=3, optimize=buy_condition_0_optimize, load=True)
buy_condition_0_rsi_1h = DecimalParameter(66, 76, default=71, space='buy', decimals=1, optimize=buy_condition_0_optimize, load=True)
buy_condition_1_rsi_1h = DecimalParameter(63, 75, default=69, space='buy', decimals=1, optimize=False, load=True)
buy_condition_10_optimize=False
buy_condition_10_rsi = DecimalParameter(35, 45, default=40.5, space='buy', decimals=1, optimize=buy_condition_10_optimize, load=True)
buy_condition_10_hist_close = DecimalParameter(0.0001, 0.01, default=0.0012, space='buy', decimals=4, optimize=buy_condition_10_optimize, load=True)
custom_stoploss_optimize_1=False
custom_stoploss_optimize_2=False
custom_stoploss_minutes = IntParameter(50, 200, default=50, space='sell', optimize=
custom_stoploss_optimize_2, load=True)
custom_stoploss_rsi_1h = DecimalParameter(20, 50, default=30, space='sell', decimals=1, optimize=custom_stoploss_optimize_2, load=True)
custom_stoploss_current_rates_1 = DecimalParameter(1.001, 1.1, default=1.025, space='sell', decimals=3, optimize=custom_stoploss_optimize_1, load=True)
custom_stoploss_current_rates_2 = DecimalParameter(1.001, 1.1, default=1.015, space='sell', decimals=3, optimize=custom_stoploss_optimize_1, load=True)
# Sell
sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=False, load=True)
# 48h for pump sell checks
sell_pump_threshold_48_1 = DecimalParameter(0.5, 1.2, default=0.9, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_48_2 = DecimalParameter(0.4, 0.9, default=0.7, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_48_3 = DecimalParameter(0.3, 0.7, default=0.5, space='sell', decimals=2, optimize=False, load=True)
# 36h for pump sell checks
sell_pump_threshold_36_1 = DecimalParameter(0.5, 0.9, default=0.72, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_36_2 = DecimalParameter(3.0, 6.0, default=4.0, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_36_3 = DecimalParameter(0.8, 1.6, default=1.0, space='sell', decimals=2, optimize=False, load=True)
# 24h for pump sell checks
sell_pump_threshold_24_1 = DecimalParameter(0.5, 0.9, default=0.68, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_24_2 = DecimalParameter(0.3, 0.6, default=0.62, space='sell', decimals=2, optimize=False, load=True)
sell_pump_threshold_24_3 = DecimalParameter(0.2, 0.5, default=0.88, space='sell', decimals=2, optimize=False, 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)
sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=34.0, space='sell', decimals=3, optimize=False, load=True)
sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=35.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_2 = DecimalParameter(30.0, 50.0, default=37.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_3 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_3 = DecimalParameter(30.0, 50.0, default=42.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_4 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_4 = DecimalParameter(35.0, 50.0, default=43.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_5 = DecimalParameter(0.01, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_5 = DecimalParameter(35.0, 50.0, default=45.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_6 = DecimalParameter(0.01, 0.1, default=0.07, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_6 = DecimalParameter(38.0, 55.0, default=52.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_7 = DecimalParameter(0.01, 0.1, default=0.08, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_7 = DecimalParameter(40.0, 58.0, default=54.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_8 = DecimalParameter(0.06, 0.1, default=0.09, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_8 = DecimalParameter(40.0, 50.0, default=55.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_9 = DecimalParameter(0.05, 0.14, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_9 = DecimalParameter(40.0, 60.0, default=54.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_10 = DecimalParameter(0.1, 0.14, default=0.12, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_10 = DecimalParameter(38.0, 50.0, default=42.0, space='sell', decimals=2, optimize=False, load=True)
sell_custom_profit_11 = DecimalParameter(0.16, 0.45, default=0.20, space='sell', decimals=3, optimize=False, load=True)
sell_custom_rsi_11 = DecimalParameter(28.0, 40.0, default=34.0, space='sell', decimals=2, optimize=False, load=True)
# Profit under EMA200
sell_custom_under_profit_0 = DecimalParameter(0.01, 0.4, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_0 = DecimalParameter(28.0, 40.0, default=38.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=57.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=58.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_4 = DecimalParameter(0.02, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_4 = DecimalParameter(50.0, 68.0, default=59.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_5 = DecimalParameter(0.02, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_5 = DecimalParameter(46.0, 62.0, default=60.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_6 = DecimalParameter(0.03, 0.1, default=0.07, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_6 = DecimalParameter(44.0, 60.0, default=56.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_7 = DecimalParameter(0.04, 0.1, default=0.08, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_7 = DecimalParameter(46.0, 60.0, default=54.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_8 = DecimalParameter(0.06, 0.12, default=0.09, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_8 = DecimalParameter(40.0, 58.0, default=55.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_9 = DecimalParameter(0.08, 0.14, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_9 = DecimalParameter(40.0, 60.0, default=54.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_10 = DecimalParameter(0.1, 0.16, default=0.12, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_10 = DecimalParameter(30.0, 50.0, default=42.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_under_profit_11 = DecimalParameter(0.16, 0.3, default=0.2, space='sell', decimals=3, optimize=False, load=True)
sell_custom_under_rsi_11 = DecimalParameter(24.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
# Profit targets for pumped pairs 48h 1
sell_custom_pump_profit_1_1 = DecimalParameter(0.01, 0.03, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_1_1 = DecimalParameter(26.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_1_2 = DecimalParameter(0.01, 0.6, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_1_2 = DecimalParameter(36.0, 50.0, default=40.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_1_3 = DecimalParameter(0.02, 0.10, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_1_3 = DecimalParameter(38.0, 50.0, default=42.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_1_4 = DecimalParameter(0.06, 0.12, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_1_4 = DecimalParameter(36.0, 48.0, default=42.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_1_5 = DecimalParameter(0.14, 0.24, default=0.2, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_1_5 = DecimalParameter(20.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
# Profit targets for pumped pairs 36h 1
sell_custom_pump_profit_2_1 = DecimalParameter(0.01, 0.03, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_2_1 = DecimalParameter(26.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_2_2 = DecimalParameter(0.01, 0.6, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_2_2 = DecimalParameter(36.0, 50.0, default=40.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_2_3 = DecimalParameter(0.02, 0.10, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_2_3 = DecimalParameter(38.0, 50.0, default=40.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_2_4 = DecimalParameter(0.06, 0.12, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_2_4 = DecimalParameter(36.0, 48.0, default=42.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_2_5 = DecimalParameter(0.14, 0.24, default=0.2, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_2_5 = DecimalParameter(20.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
# Profit targets for pumped pairs 24h 1
sell_custom_pump_profit_3_1 = DecimalParameter(0.01, 0.03, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_3_1 = DecimalParameter(26.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_3_2 = DecimalParameter(0.01, 0.6, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_3_2 = DecimalParameter(34.0, 50.0, default=40.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_3_3 = DecimalParameter(0.02, 0.10, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_3_3 = DecimalParameter(38.0, 50.0, default=40.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_3_4 = DecimalParameter(0.06, 0.12, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_3_4 = DecimalParameter(36.0, 48.0, default=42.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_profit_3_5 = DecimalParameter(0.14, 0.24, default=0.2, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_rsi_3_5 = DecimalParameter(20.0, 40.0, default=34.0, space='sell', decimals=1, optimize=False, load=True)
# SMA descending
sell_custom_dec_profit_min_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_dec_profit_max_1 = DecimalParameter(0.06, 0.16, default=0.12, space='sell', decimals=3, optimize=False, load=True)
# Under EMA100
sell_custom_dec_profit_min_2 = DecimalParameter(0.05, 0.12, default=0.07, space='sell', decimals=3, optimize=False, load=True)
sell_custom_dec_profit_max_2 = DecimalParameter(0.06, 0.2, default=0.16, space='sell', decimals=3, optimize=False, load=True)
# Trail 1
sell_trail_profit_min_1 = DecimalParameter(0.1, 0.2, default=0.16, space='sell', decimals=2, optimize=False, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.4, 0.7, default=0.6, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_1 = DecimalParameter(0.01, 0.08, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_trail_rsi_min_1 = DecimalParameter(16.0, 36.0, default=20.0, space='sell', decimals=1, optimize=False, load=True)
sell_trail_rsi_max_1 = DecimalParameter(30.0, 50.0, default=50.0, space='sell', decimals=1, optimize=False, load=True)
# Trail 2
sell_trail_profit_min_2 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_2 = DecimalParameter(0.02, 0.08, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_trail_rsi_min_2 = DecimalParameter(16.0, 36.0, default=20.0, space='sell', decimals=1, optimize=False, load=True)
sell_trail_rsi_max_2 = DecimalParameter(30.0, 50.0, default=50.0, space='sell', decimals=1, optimize=False, load=True)
# Trail 3
sell_trail_profit_min_3 = DecimalParameter(0.01, 0.12, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_3 = DecimalParameter(0.1, 0.3, default=0.2, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_3 = DecimalParameter(0.01, 0.06, default=0.05, space='sell', decimals=3, optimize=False, load=True)
# Trail 3
sell_trail_profit_min_4 = DecimalParameter(0.01, 0.12, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_4 = DecimalParameter(0.02, 0.1, default=0.06, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_4 = DecimalParameter(0.01, 0.06, default=0.02, space='sell', decimals=3, optimize=False, load=True)
# Under & near EMA200, accept profit
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)
# Under & near EMA200, take the loss
sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.002, space='sell', optimize=False, load=True)
sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=10.0, space='sell', optimize=False, load=True)
# Long duration/recover stoploss 1
sell_custom_stoploss_long_profit_min_1 = DecimalParameter(-0.1, -0.02, default=-0.08, space='sell', optimize=False, load=True)
sell_custom_stoploss_long_profit_max_1 = DecimalParameter(-0.06, -0.01, default=-0.04, space='sell', optimize=False, load=True)
sell_custom_stoploss_long_recover_1 = DecimalParameter(0.05, 0.15, default=0.1, space='sell', optimize=False, load=True)
sell_custom_stoploss_long_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.0, space='sell', optimize=False, load=True)
# Long duration/recover stoploss 2
sell_custom_stoploss_long_recover_2 = DecimalParameter(0.03, 0.15, default=0.06, space='sell', optimize=False, load=True)
sell_custom_stoploss_long_rsi_diff_2 = DecimalParameter(30.0, 50.0, default=40.0, space='sell', optimize=False, load=True)
# Pumped, descending SMA
sell_custom_pump_dec_profit_min_1 = DecimalParameter(0.001, 0.04, default=0.005, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_max_1 = DecimalParameter(0.03, 0.08, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_min_2 = DecimalParameter(0.01, 0.08, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_max_2 = DecimalParameter(0.04, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_min_3 = DecimalParameter(0.02, 0.1, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_max_3 = DecimalParameter(0.06, 0.12, default=0.09, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_min_4 = DecimalParameter(0.01, 0.05, default=0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_dec_profit_max_4 = DecimalParameter(0.02, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True)
# Pumped 48h 1, under EMA200
sell_custom_pump_under_profit_min_1 = DecimalParameter(0.02, 0.06, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_under_profit_max_1 = DecimalParameter(0.04, 0.1, default=0.09, space='sell', decimals=3, optimize=False, load=True)
# Pumped trail 1
sell_custom_pump_trail_profit_min_1 = DecimalParameter(0.01, 0.12, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_trail_profit_max_1 = DecimalParameter(0.06, 0.16, default=0.07, space='sell', decimals=2, optimize=False, load=True)
sell_custom_pump_trail_down_1 = DecimalParameter(0.01, 0.06, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_pump_trail_rsi_min_1 = DecimalParameter(16.0, 36.0, default=20.0, space='sell', decimals=1, optimize=False, load=True)
sell_custom_pump_trail_rsi_max_1 = DecimalParameter(30.0, 50.0, default=70.0, space='sell', decimals=1, optimize=False, load=True)
# Stoploss, pumped, 48h 1
sell_custom_stoploss_pump_max_profit_1 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_min_1 = DecimalParameter(-0.1, -0.01, default=-0.02, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_max_1 = DecimalParameter(-0.1, -0.01, default=-0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_ma_offset_1 = DecimalParameter(0.7, 0.99, default=0.94, space='sell', decimals=2, optimize=False, load=True)
# Stoploss, pumped, 48h 1
sell_custom_stoploss_pump_max_profit_2 = DecimalParameter(0.01, 0.04, default=0.025, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_loss_2 = DecimalParameter(-0.1, -0.01, default=-0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_ma_offset_2 = DecimalParameter(0.7, 0.99, default=0.92, space='sell', decimals=2, optimize=False, load=True)
# Stoploss, pumped, 36h 3
sell_custom_stoploss_pump_max_profit_3 = DecimalParameter(0.01, 0.04, default=0.008, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_loss_3 = DecimalParameter(-0.16, -0.06, default=-0.12, space='sell', decimals=3, optimize=False, load=True)
sell_custom_stoploss_pump_ma_offset_3 = DecimalParameter(0.7, 0.99, default=0.88, space='sell', decimals=2, optimize=False, load=True)
# Recover
sell_custom_recover_profit_1 = DecimalParameter(0.01, 0.06, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_recover_min_loss_1 = DecimalParameter(0.06, 0.16, default=0.12, space='sell', decimals=3, optimize=False, load=True)
sell_custom_recover_profit_min_2 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True)
sell_custom_recover_profit_max_2 = DecimalParameter(0.02, 0.08, default=0.05, space='sell', decimals=3, optimize=False, load=True)
sell_custom_recover_min_loss_2 = DecimalParameter(0.04, 0.16, default=0.06, space='sell', decimals=3, optimize=False, load=True)
sell_custom_recover_rsi_2 = DecimalParameter(32.0, 52.0, default=46.0, space='sell', decimals=1, optimize=False, load=True)
# Profit for long duration trades
sell_custom_long_profit_min_1 = DecimalParameter(0.01, 0.04, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_custom_long_profit_max_1 = DecimalParameter(0.02, 0.08, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_custom_long_duration_min_1 = IntParameter(700, 2000, default=900, space='sell', optimize=False, load=True)
#############################################################
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 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()
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate)
if (last_candle is not None):
if (current_profit > self.sell_custom_profit_11.value) & (last_candle['rsi'] < self.sell_custom_rsi_11.value):
return 'signal_profit_11'
if (self.sell_custom_profit_11.value > current_profit > self.sell_custom_profit_10.value) & (last_candle['rsi'] < self.sell_custom_rsi_10.value):
return 'signal_profit_10'
if (self.sell_custom_profit_10.value > current_profit > self.sell_custom_profit_9.value) & (last_candle['rsi'] < self.sell_custom_rsi_9.value):
return 'signal_profit_9'
if (self.sell_custom_profit_9.value > current_profit > self.sell_custom_profit_8.value) & (last_candle['rsi'] < self.sell_custom_rsi_8.value):
return 'signal_profit_8'
if (self.sell_custom_profit_8.value > current_profit > self.sell_custom_profit_7.value) & (last_candle['rsi'] < self.sell_custom_rsi_7.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_7'
if (self.sell_custom_profit_7.value > current_profit > self.sell_custom_profit_6.value) & (last_candle['rsi'] < self.sell_custom_rsi_6.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_6'
if (self.sell_custom_profit_6.value > current_profit > self.sell_custom_profit_5.value) & (last_candle['rsi'] < self.sell_custom_rsi_5.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_5'
elif (self.sell_custom_profit_5.value > current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_4'
elif (self.sell_custom_profit_4.value > current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_3'
elif (self.sell_custom_profit_3.value > current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_2'
elif (self.sell_custom_profit_2.value > current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_1'
elif (self.sell_custom_profit_1.value > current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value) & (last_candle['cmf'] < 0.0):
return 'signal_profit_0'
# check if close is under EMA200
elif (current_profit > self.sell_custom_under_profit_11.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_11.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_11'
elif (self.sell_custom_under_profit_11.value > current_profit > self.sell_custom_under_profit_10.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_10.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_10'
elif (self.sell_custom_under_profit_10.value > current_profit > self.sell_custom_under_profit_9.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_9.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_9'
elif (self.sell_custom_under_profit_9.value > current_profit > self.sell_custom_under_profit_8.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_8.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_8'
elif (self.sell_custom_under_profit_8.value > current_profit > self.sell_custom_under_profit_7.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_7.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_7'
elif (self.sell_custom_under_profit_7.value > current_profit > self.sell_custom_under_profit_6.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_6.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_6'
elif (self.sell_custom_under_profit_6.value > current_profit > self.sell_custom_under_profit_5.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_5.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_5'
elif (self.sell_custom_under_profit_5.value > current_profit > self.sell_custom_under_profit_4.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_4.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_4'
elif (self.sell_custom_under_profit_4.value > 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 'signal_profit_u_3'
elif (self.sell_custom_under_profit_3.value > 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 'signal_profit_u_2'
elif (self.sell_custom_under_profit_2.value > 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 'signal_profit_u_1'
elif (self.sell_custom_under_profit_1.value > current_profit > self.sell_custom_under_profit_0.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_0.value) & (last_candle['close'] < last_candle['ema_200']) & (last_candle['cmf'] < 0.0):
return 'signal_profit_u_0'
# check if the pair is "pumped"
elif (last_candle['sell_pump_48_1_1h']) & (current_profit > self.sell_custom_pump_profit_1_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_5.value):
return 'signal_profit_p_1_5'
elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_5.value > current_profit > self.sell_custom_pump_profit_1_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_4.value):
return 'signal_profit_p_1_4'
elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_4.value > current_profit > self.sell_custom_pump_profit_1_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_3.value):
return 'signal_profit_p_1_3'
elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_3.value > current_profit > self.sell_custom_pump_profit_1_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_2.value):
return 'signal_profit_p_1_2'
elif (last_candle['sell_pump_48_1_1h']) & (self.sell_custom_pump_profit_1_2.value > current_profit > self.sell_custom_pump_profit_1_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_1_1.value):
return 'signal_profit_p_1_1'
elif (last_candle['sell_pump_36_1_1h']) & (current_profit > self.sell_custom_pump_profit_2_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_5.value):
return 'signal_profit_p_2_5'
elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_5.value > current_profit > self.sell_custom_pump_profit_2_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_4.value):
return 'signal_profit_p_2_4'
elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_4.value > current_profit > self.sell_custom_pump_profit_2_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_3.value):
return 'signal_profit_p_2_3'
elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_3.value > current_profit > self.sell_custom_pump_profit_2_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_2.value):
return 'signal_profit_p_2_2'
elif (last_candle['sell_pump_36_1_1h']) & (self.sell_custom_pump_profit_2_2.value > current_profit > self.sell_custom_pump_profit_2_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_2_1.value):
return 'signal_profit_p_2_1'
elif (last_candle['sell_pump_24_1_1h']) & (current_profit > self.sell_custom_pump_profit_3_5.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_5.value):
return 'signal_profit_p_3_5'
elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_5.value > current_profit > self.sell_custom_pump_profit_3_4.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_4.value):
return 'signal_profit_p_3_4'
elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_4.value > current_profit > self.sell_custom_pump_profit_3_3.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_3.value):
return 'signal_profit_p_3_3'
elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_3.value > current_profit > self.sell_custom_pump_profit_3_2.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_2.value):
return 'signal_profit_p_3_2'
elif (last_candle['sell_pump_24_1_1h']) & (self.sell_custom_pump_profit_3_2.value > current_profit > self.sell_custom_pump_profit_3_1.value) & (last_candle['rsi'] < self.sell_custom_pump_rsi_3_1.value):
return 'signal_profit_p_3_1'
elif (self.sell_custom_dec_profit_max_1.value > current_profit > self.sell_custom_dec_profit_min_1.value) & (last_candle['sma_200_dec_20']):
return 'signal_profit_d_1'
elif (self.sell_custom_dec_profit_max_2.value > current_profit > self.sell_custom_dec_profit_min_2.value) & (last_candle['close'] < last_candle['ema_100']):
return 'signal_profit_d_2'
# Trailing
elif (self.sell_trail_profit_max_1.value > current_profit > self.sell_trail_profit_min_1.value) & (self.sell_trail_rsi_min_1.value < last_candle['rsi'] < self.sell_trail_rsi_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)):
return 'signal_profit_t_1'
elif (self.sell_trail_profit_max_2.value > current_profit > self.sell_trail_profit_min_2.value) & (self.sell_trail_rsi_min_2.value < last_candle['rsi'] < self.sell_trail_rsi_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)):
return 'signal_profit_t_2'
elif (self.sell_trail_profit_max_3.value > current_profit > self.sell_trail_profit_min_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)) & (last_candle['sma_200_dec_20_1h']):
return 'signal_profit_t_3'
elif (self.sell_trail_profit_max_4.value > current_profit > self.sell_trail_profit_min_4.value) & (max_profit > (current_profit + self.sell_trail_down_4.value)) & (last_candle['sma_200_dec_24']) & (last_candle['cmf'] < 0.0):
return 'signal_profit_t_4'
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 'signal_profit_u_t_1'
# elif (last_candle['sell_pump_24_1_1h']) & (0.1 > current_profit > 0.07) & (last_candle['rsi'] < 56.0) & (current_time - timedelta(minutes=20) < trade.open_date_utc):
# return 'signal_profit_p_s_1'
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 'signal_profit_u_e_1'
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) & (last_candle['cmf'] < 0.0) & (last_candle['sma_200_dec_24']) & (current_time - timedelta(minutes=720) > trade.open_date_utc):
return 'signal_stoploss_u_1'
elif (self.sell_custom_stoploss_long_profit_min_1.value < current_profit < self.sell_custom_stoploss_long_profit_max_1.value) & (current_profit > (-max_loss + self.sell_custom_stoploss_long_recover_1.value)) & (last_candle['cmf'] < 0.0) & (last_candle['close'] < last_candle['ema_200']) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_long_rsi_diff_1.value) & (last_candle['sma_200_dec_24']) & (current_time - timedelta(minutes=1200) > trade.open_date_utc):
return 'signal_stoploss_l_r_u_1'
elif (current_profit < -0.0) & (current_profit > (-max_loss + self.sell_custom_stoploss_long_recover_2.value)) & (last_candle['close'] < last_candle['ema_200']) & (last_candle['cmf'] < 0.0) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_long_rsi_diff_2.value) & (last_candle['sma_200_dec_24']) & (current_time - timedelta(minutes=1200) > trade.open_date_utc):
return 'signal_stoploss_l_r_u_2'
elif (self.sell_custom_pump_dec_profit_max_1.value > current_profit > self.sell_custom_pump_dec_profit_min_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['sma_200_dec_20']) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_p_d_1'
elif (self.sell_custom_pump_dec_profit_max_2.value > current_profit > self.sell_custom_pump_dec_profit_min_2.value) & (last_candle['sell_pump_48_2_1h']) & (last_candle['sma_200_dec_20']) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_p_d_2'
elif (self.sell_custom_pump_dec_profit_max_3.value > current_profit > self.sell_custom_pump_dec_profit_min_3.value) & (last_candle['sell_pump_48_3_1h']) & (last_candle['sma_200_dec_20']) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_p_d_3'
elif (self.sell_custom_pump_dec_profit_max_4.value > current_profit > self.sell_custom_pump_dec_profit_min_4.value) & (last_candle['sma_200_dec_20']) & (last_candle['sell_pump_24_2_1h']):
return 'signal_profit_p_d_4'
# Pumped 48h 1, under EMA200
elif (self.sell_custom_pump_under_profit_max_1.value > current_profit > self.sell_custom_pump_under_profit_min_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_p_u_1'
# Pumped 36h 2, trail 1
elif (last_candle['sell_pump_36_2_1h']) & (self.sell_custom_pump_trail_profit_max_1.value > current_profit > self.sell_custom_pump_trail_profit_min_1.value) & (self.sell_custom_pump_trail_rsi_min_1.value < last_candle['rsi'] < self.sell_custom_pump_trail_rsi_max_1.value) & (max_profit > (current_profit + self.sell_custom_pump_trail_down_1.value)):
return 'signal_profit_p_t_1'
# elif (max_profit < self.sell_custom_stoploss_pump_max_profit_1.value) & (self.sell_custom_stoploss_pump_min_1.value < current_profit < self.sell_custom_stoploss_pump_max_1.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['cmf'] < 0.0) & (last_candle['sma_200_dec_20']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_1.value)):
# return 'signal_stoploss_p_1'
elif (max_profit < self.sell_custom_stoploss_pump_max_profit_2.value) & (current_profit < self.sell_custom_stoploss_pump_loss_2.value) & (last_candle['sell_pump_48_1_1h']) & (last_candle['cmf'] < 0.0) & (last_candle['sma_200_dec_20_1h']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_2.value)):
return 'signal_stoploss_p_2'
elif (max_profit < self.sell_custom_stoploss_pump_max_profit_3.value) & (current_profit < self.sell_custom_stoploss_pump_loss_3.value) & (last_candle['sell_pump_36_3_1h']) & (last_candle['close'] < (last_candle['ema_200'] * self.sell_custom_stoploss_pump_ma_offset_3.value)):
return 'signal_stoploss_p_3'
# Recover
elif (max_loss > self.sell_custom_recover_min_loss_1.value) & (current_profit > self.sell_custom_recover_profit_1.value):
return 'signal_profit_r_1'
elif (max_loss > self.sell_custom_recover_min_loss_2.value) & (self.sell_custom_recover_profit_max_2.value > current_profit > self.sell_custom_recover_profit_min_2.value) & (last_candle['rsi'] < self.sell_custom_recover_rsi_2.value):
return 'signal_profit_r_2'
# Take profit for long duration trades
elif (self.sell_custom_long_profit_min_1.value < current_profit < self.sell_custom_long_profit_max_1.value) & (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1.value) > trade.open_date_utc):
return 'signal_profit_l_1'
return None
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Manage losing trades and open room for better ones.
if (current_profit > 0):
return 0.99
else:
trade_time_50 = trade.open_date_utc + timedelta(minutes=int(self.custom_stoploss_minutes.value))
# Trade open more then 60 minutes. For this strategy it's means -> loss
# Let's try to minimize the loss
if (current_time > trade_time_50):
try:
number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
candle = dataframe.iloc[-number_of_candle_shift].squeeze()
# We are at bottom. Wait...
if candle['rsi_1h'] < self.custom_stoploss_rsi_1h.value:
return 0.99
# Are we still sinking?
if candle['close'] > candle['ema_200']:
if current_rate * self.custom_stoploss_current_rates_1.value < candle['open']:
return 0.01
if current_rate * self.custom_stoploss_current_rates_2.value < candle['open']:
return 0.01
except IndexError as error:
# Whoops, set stoploss at 10%
return 0.1
return 0.99
def range_percent_change(self, 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
"""
df = dataframe.copy()
if method == 'HL':
return ((df['high'].rolling(length).max() - df['low'].rolling(length).min()) / df['low'].rolling(length).min())
elif method == 'OC':
return ((df['open'].rolling(length).max() - df['close'].rolling(length).min()) / df['close'].rolling(length).min())
else:
raise ValueError(f"Method {method} not defined!")
def top_percent_change(self, 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
"""
df = dataframe.copy()
if length == 0:
return ((df['open'] - df['close']) / df['close'])
else:
return ((df['open'].rolling(length).max() - df['close']) / df['close'])
def range_maxgap(self, dataframe: DataFrame, length: int) -> float:
"""
Maximum Price Gap across interval.
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
df = dataframe.copy()
return (df['open'].rolling(length).max() - df['close'].rolling(length).min())
def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float:
"""
Maximum Price Gap across interval adjusted.
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
:param adjustment: int The adjustment to be applied
"""
return (self.range_maxgap(dataframe,length) / adjustment)
def range_height(self, dataframe: DataFrame, length: int) -> float:
"""
Current close distance to range bottom.
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
df = dataframe.copy()
return (df['close'] - df['close'].rolling(length).min())
def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool:
"""
Determine if entry after a pump is safe.
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
:param thresh: int Maximum percentage change threshold
:param pull_thresh: int Pullback from interval maximum threshold
"""
df = dataframe.copy()
return (df[f'oc_pct_change_{length}'] < thresh) | (self.range_maxgap_adjusted(df, length, pull_thresh) > self.range_height(df, length))
def safe_dips(self, dataframe: DataFrame, thresh_0, thresh_2, thresh_12, thresh_144) -> bool:
"""
Determine if dip is safe to enter.
:param dataframe: DataFrame The original OHLC dataframe
:param thresh_0: Threshold value for 0 length top pct change
:param thresh_2: Threshold value for 2 length top pct change
:param thresh_12: Threshold value for 12 length top pct change
:param thresh_144: Threshold value for 144 length top pct change
"""
return ((dataframe['tpct_change_0'] < thresh_0) &
(dataframe['tpct_change_2'] < thresh_2) &
(dataframe['tpct_change_12'] < thresh_12) &
(dataframe['tpct_change_144'] < thresh_144))
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)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
informative_1h['bb_lowerband'] = bollinger['lower']
informative_1h['bb_middleband'] = bollinger['mid']
informative_1h['bb_upperband'] = bollinger['upper']
# EMA
informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12)
informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20)
informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26)
informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35)
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)
# SMA
informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# BB
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
informative_1h['bb20_2_low'] = bollinger['lower']
informative_1h['bb20_2_mid'] = bollinger['mid']
informative_1h['bb20_2_upp'] = bollinger['upper']
# Chaikin Money Flow
informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20)
# Pump protections
informative_1h['hl_pct_change_48'] = self.range_percent_change(informative_1h, 'HL', 48)
informative_1h['hl_pct_change_36'] = self.range_percent_change(informative_1h, 'HL', 36)
informative_1h['hl_pct_change_24'] = self.range_percent_change(informative_1h, 'HL', 24)
informative_1h['oc_pct_change_48'] = self.range_percent_change(informative_1h, 'OC', 48)
informative_1h['oc_pct_change_36'] = self.range_percent_change(informative_1h, 'OC', 36)
informative_1h['oc_pct_change_24'] = self.range_percent_change(informative_1h, 'OC', 24)
informative_1h['sell_pump_48_1'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_1.value)
informative_1h['sell_pump_48_2'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_2.value)
informative_1h['sell_pump_48_3'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_3.value)
informative_1h['sell_pump_36_1'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_1.value)
informative_1h['sell_pump_36_2'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_2.value)
informative_1h['sell_pump_36_3'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_3.value)
informative_1h['sell_pump_24_1'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_1.value)
informative_1h['sell_pump_24_2'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_2.value)
informative_1h['sell_pump_24_3'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_3.value)
return informative_1h
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']
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
# MACD
dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
# BB 40 - STD2
bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['bb40_2_low']= bb_40_std2['lower']
dataframe['bb40_2_mid'] = bb_40_std2['mid']
dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
# BB 20 - STD2
bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb20_2_low'] = bb_20_std2['lower']
dataframe['bb20_2_mid'] = bb_20_std2['mid']
dataframe['bb20_2_upp'] = bb_20_std2['upper']
# EMA 200
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15)
dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_35'] = ta.EMA(dataframe, timeperiod=35)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# SMA
dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20)
dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
dataframe['sma_200_dec_24'] = dataframe['sma_200'] < dataframe['sma_200'].shift(24)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# CMF
dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
# EWO
dataframe['ewo'] = EWO(dataframe, 50, 200)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
# Chopiness
dataframe['chop']= qtpylib.chopiness(dataframe, 14)
# Zero-Lag EMA
dataframe['zema'] = zema(dataframe, period=61)
# Dip protection
dataframe['tpct_change_0'] = self.top_percent_change(dataframe,0)
dataframe['tpct_change_2'] = self.top_percent_change(dataframe,2)
dataframe['tpct_change_12'] = self.top_percent_change(dataframe,12)
dataframe['tpct_change_144'] = self.top_percent_change(dataframe,144)
# Volume
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean()
return dataframe
def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
dataframe.rename(columns=lambda s: "btc_" + s if (not s in ignore_columns) else s, inplace=True)
return dataframe
def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
dataframe.rename(columns=lambda s: "btc_" + s if (not s in ignore_columns) else s, inplace=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
'''
--> BTC informative (5m/1h)
___________________________________________________________________________________________
'''
btc_base_tf = self.dp.get_pair_dataframe("BTC/USDT", self.timeframe)
btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata)
dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True)
drop_columns = [(s + "_" + self.timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
btc_info_tf = self.dp.get_pair_dataframe("BTC/USDT", self.info_timeframe)
btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata)
dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.info_timeframe, ffill=True)
drop_columns = [(s + "_" + self.info_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
'''
--> Informative timeframe
___________________________________________________________________________________________
'''
# populate informative indicators
informative_1h = self.informative_1h_indicators(dataframe, metadata)
# Merge informative into dataframe
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe, ffill=True)
drop_columns = [(s + "_" + self.info_timeframe) for s in ['date']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
'''
--> Resampled to another timeframe
___________________________________________________________________________________________
'''
# resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe))
# resampled = self.resampled_tf_indicators(resampled, metadata)
# # Merge resampled info dataframe
# dataframe = resampled_merge(dataframe, resampled, fill_na=True)
# dataframe.rename(columns=lambda s: s+"_{}".format(self.res_timeframe) if "resample_" in s else s, inplace=True)
# dataframe.rename(columns=lambda s: s.replace("resample_{}_".format(self.res_timeframe.replace("m","")), ""), inplace=True)
# drop_columns = [(s + "_" + self.res_timeframe) for s in ['date']]
# dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
'''
--> The indicators for the normal (5m) timeframe
___________________________________________________________________________________________
'''
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
self.buy_condition_12_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_condition_12_bblower_close.value) &
(dataframe['low'] < dataframe['bb_lowerband'] * self.buy_condition_12_bblower_low.value) &
(dataframe['close'].shift() > dataframe['bb_lowerband']) &
(dataframe['rsi_1h'] < self.buy_condition_12_rsi_1h.value) &
(dataframe['open'] > dataframe['close']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_11_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['hist'] > 0) &
(dataframe['hist'].shift() > 0) &
(dataframe['hist'].shift(2) > 0) &
(dataframe['hist'].shift(3) > 0) &
(dataframe['hist'].shift(5) > 0) &
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close']/200) &
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close']/100) &
((dataframe['bb_upperband'] - dataframe['bb_lowerband']) < (dataframe['close']*self.buy_condition_11_close_1.value)) &
((dataframe['open'].shift() - dataframe['close'].shift()) < (dataframe['close'] * self.buy_condition_11_close_2.value)) &
(dataframe['rsi'] > self.buy_condition_11_rsi.value) &
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift() > dataframe['close'].shift()) &
(dataframe['close'] > dataframe['bb_middleband']) &
(dataframe['close'].shift() < dataframe['bb_middleband'].shift()) &
(dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_0_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['rsi'] < self.buy_condition_0_rsi.value) &
(dataframe['close'] * self.buy_condition_0_close.value < dataframe['open'].shift(3)) &
(dataframe['rsi_1h'] < self.buy_condition_0_rsi_1h.value) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_1_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) &
(dataframe['rsi_1h'] < self.buy_condition_1_rsi_1h.value) &
(dataframe['open'] > dataframe['close']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_2_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_3_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['rsi'] < self.buy_rsi_3.value) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_3.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_4_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_5_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_6_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_5.value) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_2.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_7_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_8_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) &
(dataframe['rsi'] < self.buy_rsi_1.value) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_9_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
(dataframe['rsi'] < self.buy_rsi_2.value) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_10_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
(dataframe['close_1h'] < dataframe['bb_lowerband_1h']) &
(dataframe['hist'] > 0) &
(dataframe['hist'].shift(2) < 0) &
(dataframe['rsi'] < self.buy_condition_10_rsi.value) &
(dataframe['hist'] > dataframe['close'] * self.buy_condition_10_hist_close.value) &
(dataframe['open'] < dataframe['close']) &
(dataframe['volume'] > 0)
)
)
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['bb20_2_upp']) &
(dataframe['close'].shift(1) > dataframe['bb20_2_upp'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb20_2_upp'].shift(2)) &
(dataframe['close'].shift(3) > dataframe['bb20_2_upp'].shift(3)) &
(dataframe['close'].shift(4) > dataframe['bb20_2_upp'].shift(4)) &
(dataframe['close'].shift(5) > dataframe['bb20_2_upp'].shift(5)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.sell_condition_2_enable.value &
(dataframe['rsi'] > self.sell_rsi_bb_2.value) &
(dataframe['close'] > dataframe['bb20_2_upp']) &
(dataframe['close'].shift(1) > dataframe['bb20_2_upp'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb20_2_upp'].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)
)
)
conditions.append(
(
self.sell_condition_8_enable.value &
(dataframe['close'] > dataframe['bb20_2_upp_1h'] * self.sell_bb_relative_8.value) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
] = 1
return dataframe
# Elliot Wave Oscillator
def EWO(dataframe, sma1_length=5, sma2_length=35):
df = dataframe.copy()
sma1 = ta.EMA(df, timeperiod=sma1_length)
sma2 = ta.EMA(df, timeperiod=sma2_length)
smadif = (sma1 - sma2) / df['close'] * 100
return smadif
# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False):
"""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 True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
df = dataframe.copy()
mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
mfv = mfv.fillna(0.0) # float division by zero
mfv *= df['volume']
cmf = (mfv.rolling(n, min_periods=0).sum()
/ df['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')