Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
2
Startup Candles
N/A
Indicators
23
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import copy
import logging
import pathlib
import rapidjson
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.misc import json_load, file_dump_json
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes,DecimalParameter,IntParameter,informative
from freqtrade.exchange import timeframe_to_prev_date
from pandas import DataFrame, Series, concat
from functools import reduce
import math
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from technical.util import resample_to_interval, resampled_merge
from technical.indicators import zema, VIDYA, ichimoku
import logging
import technical.indicators as ftt
logger = logging.getLogger(__name__)
log = logging.getLogger(__name__)
try:
import pandas_ta as pta
except ImportError:
log.error("IMPORTANT - please install the pandas_ta python module which is needed for this strategy. If you're running Docker, add RUN pip install pandas_ta to your Dockerfile, otherwise run: pip install pandas_ta")
else:
log.info("pandas_ta successfully imported")
###########################################################################################################
## NostalgiaForInfinityV8 by iterativ ##
## ##
## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 40 to 80 pairs. Volume pairlist works well. ##
## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ##
## Ensure that you don't override any variables in you config.json. Especially ##
## the timeframe (must be 5m). ##
## use_sell_signal must set to true (or not set at all). ##
## sell_profit_only must set to false (or not set at all). ##
## ignore_roi_if_buy_signal must set to true (or not set at all). ##
## ##
###########################################################################################################
## HOLD SUPPORT ##
## In case you want to have SOME of the trades to only be sold when on profit, add a file named ##
## "hold-trades.json" in the same directory as this strategy. ##
## ##
## The contents should be similar to: ##
## ##
## {"trade_ids": [1, 3, 7], "profit_ratio": 0.005} ##
## ##
## Or, for individual profit ratios(Notice the trade ID's as strings: ##
## ##
## {"trade_ids": {"1": 0.001, "3": -0.005, "7": 0.05}} ##
## ##
## NOTE: ##
## * `trade_ids` is a list of integers, the trade ID's, which you can get from the logs or from the ##
## output of the telegram status command. ##
## * Regardless of the defined profit ratio(s), the strategy MUST still produce a SELL signal for the ##
## HOLD support logic to run ##
## * This feature can be completely disabled with the holdSupportEnabled parameter ##
## ##
###########################################################################################################
## DONATIONS ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ##
## ##
###########################################################################################################
class NostalgiaForInfinity772martinsk3(IStrategy):
INTERFACE_VERSION = 2
plot_config = {
'main_plot': {
},
'subplots': {
"buy tag": {
'buy_tag': {'color': 'green'}
},
}
}
# ROI table:
minimal_roi = {
"0": 10,
}
stoploss = -0.99
# Trailing stoploss (not used)
trailing_stop = False
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.03
use_custom_stoploss = False
# Optimal timeframe for the strategy.
timeframe = '5m'
res_timeframe = 'none'
info_timeframe = '1h'
# BTC informative
has_BTC_base_tf = False
has_BTC_info_tf = True
# Backtest Age Filter emulation
has_bt_agefilter = False
bt_min_age_days = 3
# Exchange Downtime protection
has_downtime_protection = False
# Do you want to use the hold feature? (with hold-trades.json)
holdSupportEnabled = False
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 480
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'trailing_stop_loss': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
#############################################################
buy_params = {
#############
# Enable/Disable conditions
"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_condition_14_enable": True,
"buy_condition_15_enable": True,
"buy_condition_16_enable": True,
"buy_condition_17_enable": True,
"buy_condition_18_enable": True,
"buy_condition_19_enable": True,
"buy_condition_20_enable": True,
"buy_condition_21_enable": True,
"buy_condition_22_enable": True,
"buy_condition_23_enable": True,
"buy_condition_24_enable": True,
"buy_condition_25_enable": True,
"buy_condition_26_enable": True,
"buy_condition_27_enable": True,
"buy_condition_28_enable": True,
"buy_condition_29_enable": True,
"buy_condition_30_enable": True,
"buy_condition_31_enable": True,
"buy_condition_32_enable": True,
"buy_condition_33_enable": True,
"buy_condition_34_enable": True,
"buy_condition_35_enable": True,
"buy_condition_36_enable": True,
"buy_condition_37_enable": True,
"buy_condition_38_enable": True,
"buy_condition_39_enable": True,
"buy_condition_40_enable": True,
"buy_condition_41_enable": True,
"buy_condition_42_enable": True,
"buy_condition_43_enable": True,
"buy_condition_44_enable": True,
#############
}
sell_params = {
#############
# Enable/Disable conditions
"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,
"base_nb_candles_sell": 22,
"high_offset": 1.014,
"high_offset_2": 1.01,
## Dead fish
"sell_deadfish_profit": -0.063,
"sell_deadfish_bb_factor": 0.954,
"sell_deadfish_bb_width": 0.043,
"sell_deadfish_volume_factor": 2.37
#############
}
#############################################################
buy_protection_params = {
1: {
"ema_fast" : False,
"ema_fast_len" : "26",
"ema_slow" : True,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "28",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "80",
"safe_pump" : False,
"safe_pump_type" : "70",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
2: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "20",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
3: {
"ema_fast" : True,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "70",
"safe_pump" : True,
"safe_pump_type" : "100",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
4: {
"ema_fast" : True,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : True,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False
},
5: {
"ema_fast" : True,
"ema_fast_len" : "100",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "100",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "100",
"safe_pump" : True,
"safe_pump_type" : "30",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
6: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "50",
"safe_pump" : True,
"safe_pump_type" : "20",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
7: {
"ema_fast" : True,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
8: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "12",
"close_above_ema_fast" : True,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "100",
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
9: {
"ema_fast" : True,
"ema_fast_len" : "100",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "10",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
10: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "24",
"safe_dips" : True,
"safe_dips_type" : "120",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
11: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : True,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
12: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "24",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : True,
"safe_pump_type" : "40",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
13: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "24",
"safe_dips" : True,
"safe_dips_type" : "20",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
14: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "30",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "120",
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
15: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : True,
"safe_pump_type" : "20",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
16: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "50",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "10",
"safe_pump" : True,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
17: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "120",
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
18: {
"ema_fast" : True,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : True,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "44",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "72",
"safe_dips" : True,
"safe_dips_type" : "100",
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
19: {
"ema_fast" : True,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "36",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
20: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "10",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
21: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "90",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
22: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "50",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : True,
"safe_pump_type" : "110",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
23: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "15",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "24",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "110",
"safe_pump" : True,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
24: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "36",
"safe_dips" : True,
"safe_dips_type" : "20",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
25: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "20",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "10",
"safe_pump" : True,
"safe_pump_type" : "20",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
26: {
"ema_fast" : False,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "70",
"safe_pump" : True,
"safe_pump_type" : "20",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True
},
27: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True
},
28: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "50",
"safe_pump" : True,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True
},
29: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
30: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False
},
31: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False
},
32: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "120",
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False
},
33: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "50",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "100",
"safe_pump" : True,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
34: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
35: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
36: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
},
37: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False
},
38: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "100",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "50",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips" : True,
"safe_dips_type" : "130",
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True
},
39: {
"ema_fast" : False,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "15",
"close_above_ema_fast" : True,
"close_above_ema_fast_len" : "100",
"close_above_ema_slow" : True,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : True
},
40: {
"ema_fast" : True,
"ema_fast_len" : "12",
"ema_slow" : True,
"ema_slow_len" : "25",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : True,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : False,
"safe_dips_type" : "130",
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : True
},
41: {
"ema_fast" : False,
"ema_fast_len" : "12",
"ema_slow" : False,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : True,
"safe_dips_type" : "50",
"safe_pump" : False,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True
},
42: {
"ema_fast" : False,
"ema_fast_len" : "12",
"ema_slow" : False,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : True,
"safe_dips_type" : "110",
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True
},
43: {
"ema_fast" : False,
"ema_fast_len" : "12",
"ema_slow" : False,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : True,
"safe_dips_type" : "70",
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True
},
44: {
"ema_fast" : False,
"ema_fast_len" : "12",
"ema_slow" : False,
"ema_slow_len" : "12",
"close_above_ema_fast" : False,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : False,
"close_above_ema_slow_len" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips" : False,
"safe_dips_type" : "100",
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False
}
}
# SMAOffset
#base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
#low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True)
high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True)
# Strict dips - level 10
buy_dip_threshold_10_1 = 0.015
buy_dip_threshold_10_2 = 0.1
buy_dip_threshold_10_3 = 0.24
buy_dip_threshold_10_4 = 0.42
# Strict dips - level 20
buy_dip_threshold_20_1 = 0.016
buy_dip_threshold_20_2 = 0.11
buy_dip_threshold_20_3 = 0.26
buy_dip_threshold_20_4 = 0.44
# Strict dips - level 30
buy_dip_threshold_30_1 = 0.018
buy_dip_threshold_30_2 = 0.12
buy_dip_threshold_30_3 = 0.28
buy_dip_threshold_30_4 = 0.46
# Strict dips - level 40
buy_dip_threshold_40_1 = 0.019
buy_dip_threshold_40_2 = 0.13
buy_dip_threshold_40_3 = 0.3
buy_dip_threshold_40_4 = 0.48
# Normal dips - level 50
buy_dip_threshold_50_1 = 0.02
buy_dip_threshold_50_2 = 0.14
buy_dip_threshold_50_3 = 0.32
buy_dip_threshold_50_4 = 0.5
# Normal dips - level 60
buy_dip_threshold_60_1 = 0.022
buy_dip_threshold_60_2 = 0.18
buy_dip_threshold_60_3 = 0.34
buy_dip_threshold_60_4 = 0.56
# Normal dips - level 70
buy_dip_threshold_70_1 = 0.023
buy_dip_threshold_70_2 = 0.2
buy_dip_threshold_70_3 = 0.36
buy_dip_threshold_70_4 = 0.6
# Normal dips - level 80
buy_dip_threshold_80_1 = 0.024
buy_dip_threshold_80_2 = 0.22
buy_dip_threshold_80_3 = 0.38
buy_dip_threshold_80_4 = 0.66
# Normal dips - level 70
buy_dip_threshold_90_1 = 0.025
buy_dip_threshold_90_2 = 0.23
buy_dip_threshold_90_3 = 0.4
buy_dip_threshold_90_4 = 0.7
# Loose dips - level 100
buy_dip_threshold_100_1 = 0.026
buy_dip_threshold_100_2 = 0.24
buy_dip_threshold_100_3 = 0.42
buy_dip_threshold_100_4 = 0.8
# Loose dips - level 110
buy_dip_threshold_110_1 = 0.027
buy_dip_threshold_110_2 = 0.26
buy_dip_threshold_110_3 = 0.44
buy_dip_threshold_110_4 = 0.84
# Loose dips - level 120
buy_dip_threshold_120_1 = 0.028
buy_dip_threshold_120_2 = 0.28
buy_dip_threshold_120_3 = 0.46
buy_dip_threshold_120_4 = 0.86
# Loose dips - level 130
buy_dip_threshold_130_1 = 0.028
buy_dip_threshold_130_2 = 0.3
buy_dip_threshold_130_3 = 0.48
buy_dip_threshold_130_4 = 0.9
dump= DecimalParameter(-1, 0, default=-0.15, space='buy', optimize=True)
dump_buy= DecimalParameter(0, 0.5, default=0.15, space='buy', optimize=True)
range_filter_length = IntParameter(10, 200, default=200, space='buy', optimize= False)
# 24 hours - level 10
buy_pump_pull_threshold_10_24 = 2.2
buy_pump_threshold_10_24 = 0.42
# 36 hours - level 10
buy_pump_pull_threshold_10_36 = 2.0
buy_pump_threshold_10_36 = 0.58
# 48 hours - level 10
buy_pump_pull_threshold_10_48 = 2.0
buy_pump_threshold_10_48 = 0.8
# 24 hours - level 20
buy_pump_pull_threshold_20_24 = 2.2
buy_pump_threshold_20_24 = 0.46
# 36 hours - level 20
buy_pump_pull_threshold_20_36 = 2.0
buy_pump_threshold_20_36 = 0.6
# 48 hours - level 20
buy_pump_pull_threshold_20_48 = 2.0
buy_pump_threshold_20_48 = 0.81
# 24 hours - level 30
buy_pump_pull_threshold_30_24 = 2.2
buy_pump_threshold_30_24 = 0.5
# 36 hours - level 30
buy_pump_pull_threshold_30_36 = 2.0
buy_pump_threshold_30_36 = 0.62
# 48 hours - level 30
buy_pump_pull_threshold_30_48 = 2.0
buy_pump_threshold_30_48 = 0.82
# 24 hours - level 40
buy_pump_pull_threshold_40_24 = 2.2
buy_pump_threshold_40_24 = 0.54
# 36 hours - level 40
buy_pump_pull_threshold_40_36 = 2.0
buy_pump_threshold_40_36 = 0.63
# 48 hours - level 40
buy_pump_pull_threshold_40_48 = 2.0
buy_pump_threshold_40_48 = 0.84
# 24 hours - level 50
buy_pump_pull_threshold_50_24 = 1.75
buy_pump_threshold_50_24 = 0.6
# 36 hours - level 50
buy_pump_pull_threshold_50_36 = 1.75
buy_pump_threshold_50_36 = 0.64
# 48 hours - level 50
buy_pump_pull_threshold_50_48 = 1.75
buy_pump_threshold_50_48 = 0.85
# 24 hours - level 60
buy_pump_pull_threshold_60_24 = 1.75
buy_pump_threshold_60_24 = 0.62
# 36 hours - level 60
buy_pump_pull_threshold_60_36 = 1.75
buy_pump_threshold_60_36 = 0.66
# 48 hours - level 60
buy_pump_pull_threshold_60_48 = 1.75
buy_pump_threshold_60_48 = 0.9
# 24 hours - level 70
buy_pump_pull_threshold_70_24 = 1.75
buy_pump_threshold_70_24 = 0.63
# 36 hours - level 70
buy_pump_pull_threshold_70_36 = 1.75
buy_pump_threshold_70_36 = 0.67
# 48 hours - level 70
buy_pump_pull_threshold_70_48 = 1.75
buy_pump_threshold_70_48 = 0.95
# 24 hours - level 80
buy_pump_pull_threshold_80_24 = 1.75
buy_pump_threshold_80_24 = 0.64
# 36 hours - level 80
buy_pump_pull_threshold_80_36 = 1.75
buy_pump_threshold_80_36 = 0.68
# 48 hours - level 80
buy_pump_pull_threshold_80_48 = 1.75
buy_pump_threshold_80_48 = 1.0
# 24 hours - level 90
buy_pump_pull_threshold_90_24 = 1.75
buy_pump_threshold_90_24 = 0.65
# 36 hours - level 90
buy_pump_pull_threshold_90_36 = 1.75
buy_pump_threshold_90_36 = 0.69
# 48 hours - level 90
buy_pump_pull_threshold_90_48 = 1.75
buy_pump_threshold_90_48 = 1.1
# 24 hours - level 100
buy_pump_pull_threshold_100_24 = 1.7
buy_pump_threshold_100_24 = 0.66
# 36 hours - level 100
buy_pump_pull_threshold_100_36 = 1.7
buy_pump_threshold_100_36 = 0.7
# 48 hours - level 100
buy_pump_pull_threshold_100_48 = 1.4
buy_pump_threshold_100_48 = 1.6
# 24 hours - level 110
buy_pump_pull_threshold_110_24 = 1.7
buy_pump_threshold_110_24 = 0.7
# 36 hours - level 110
buy_pump_pull_threshold_110_36 = 1.7
buy_pump_threshold_110_36 = 0.74
# 48 hours - level 110
buy_pump_pull_threshold_110_48 = 1.4
buy_pump_threshold_110_48 = 1.8
# 24 hours - level 120
buy_pump_pull_threshold_120_24 = 1.7
buy_pump_threshold_120_24 = 0.78
# 36 hours - level 120
buy_pump_pull_threshold_120_36 = 1.7
buy_pump_threshold_120_36 = 0.78
# 48 hours - level 120
buy_pump_pull_threshold_120_48 = 1.4
buy_pump_threshold_120_48 = 2.0
# 5 hours - level 10
buy_dump_protection_10_5 = 0.4
# 5 hours - level 20
buy_dump_protection_20_5 = 0.44
# 5 hours - level 30
buy_dump_protection_30_5 = 0.50
# 5 hours - level 40
buy_dump_protection_40_5 = 0.58
# 5 hours - level 50
buy_dump_protection_50_5 = 0.66
# 5 hours - level 60
buy_dump_protection_60_5 = 0.74
buy_min_inc_1 = 0.022
buy_rsi_1h_min_1 = 20.0
buy_rsi_1h_max_1 = 84.0
buy_rsi_1 = 36.0
buy_mfi_1 = 50.0
buy_cti_1 = -0.92
buy_rsi_1h_min_2 = 32.0
buy_rsi_1h_max_2 = 84.0
buy_rsi_1h_diff_2 = 38.8
buy_mfi_2 = 49.0
buy_bb_offset_2 = 0.983
buy_volume_2 = 1.6
buy_bb40_bbdelta_close_3 = 0.045
buy_bb40_closedelta_close_3 = 0.023
buy_bb40_tail_bbdelta_3 = 0.418
buy_ema_rel_3 = 0.986
buy_cti_3 = -0.5
buy_bb20_close_bblowerband_4 = 0.979
buy_bb20_volume_4 = 10.0
buy_cti_4 = -0.8
buy_ema_open_mult_5 = 0.018
buy_bb_offset_5 = 0.996
buy_ema_rel_5 = 0.915
buy_cti_5 = -0.84
buy_volume_5 = 1.8
buy_ema_open_mult_6 = 0.021
buy_bb_offset_6 = 0.976
buy_ema_open_mult_7 = 0.030
buy_cti_7 = -0.89
buy_cti_8 = -0.88
buy_rsi_8 = 40.0
buy_bb_offset_8 = 0.99
buy_rsi_1h_8 = 64.0
buy_volume_8 = 1.8
buy_ma_offset_9 = 0.968
buy_bb_offset_9 = 0.942
buy_rsi_1h_min_9 = 20.0
buy_rsi_1h_max_9 = 88.0
buy_mfi_9 = 50.0
buy_ma_offset_10 = 0.98
buy_bb_offset_10 = 0.972
buy_rsi_1h_10 = 50.0
buy_ma_offset_11 = 0.946
buy_min_inc_11 = 0.038
buy_rsi_1h_min_11 = 46.0
buy_rsi_1h_max_11 = 84.0
buy_rsi_11 = 38.0
buy_mfi_11 = 36.0
buy_ma_offset_12 = 0.921
buy_rsi_12 = 28.0
buy_ewo_12 = 1.8
buy_cti_12 = -0.7
buy_ma_offset_13 = 0.99
buy_cti_13 = -0.82
buy_ewo_13 = -9.0
buy_ema_open_mult_14 = 0.014
buy_bb_offset_14 = 0.988
buy_ma_offset_14 = 0.945
buy_cti_14 = -0.86
buy_ema_open_mult_15 = 0.024
buy_ma_offset_15 = 0.958
buy_rsi_15 = 28.0
buy_ema_rel_15 = 0.974
buy_ma_offset_16 = 0.953
buy_rsi_16 = 31.0
buy_ewo_16 = 2.8
buy_cti_16 = -0.84
buy_ma_offset_17 = 0.99
buy_ewo_17 = -9.4
buy_cti_17 = -0.96
buy_volume_17 = 2.0
buy_rsi_18 = 33.0
buy_bb_offset_18 = 0.986
buy_volume_18 = 2.0
buy_cti_18 = -0.86
buy_rsi_1h_min_19 = 30.0
buy_chop_max_19 = 21.3
buy_rsi_20 = 36.0
buy_rsi_1h_20 = 16.0
buy_cti_20 = -0.84
buy_volume_20 = 2.0
buy_rsi_21 = 14.0
buy_rsi_1h_21 = 28.0
buy_cti_21 = -0.902
buy_volume_21 = 2.0
buy_volume_22 = 2.0
buy_bb_offset_22 = 0.984
buy_ma_offset_22 = 0.942
buy_ewo_22 = 5.8
buy_rsi_22 = 36.0
buy_23_bb_offset = 0.984
buy_23_ewo = 7.8
buy_23_rsi = 32.4
buy_23_rsi_1h = 80.0
buy_23_cti = -0.66
buy_23_r = -80.0
buy_23_r_1h = -80.0
buy_24_rsi_max = 50.0
buy_24_rsi_1h_min = 66.9
buy_25_ma_offset = 0.922
buy_25_rsi_4 = 38.0
buy_25_cti = -0.76
buy_26_zema_low_offset = 0.94
buy_26_cti = -0.91
buy_26_r = -35.0
buy_26_r_1h = -60.0
buy_26_volume = 2.0
buy_27_wr_max = 90.0
buy_27_wr_1h_max = 90.0
buy_27_rsi_max = 50
buy_27_cti = -0.93
buy_27_volume = 2.0
buy_28_ma_offset = 0.97
buy_28_ewo = 7.2
buy_28_rsi = 32.5
buy_28_cti = -0.9
buy_29_ma_offset = 0.94
buy_29_ewo = -4.0
buy_29_cti = -0.95
buy_30_ma_offset = 0.97
buy_30_ewo = 7.4
buy_30_rsi = 40.0
buy_30_cti = -0.88
buy_31_ma_offset = 0.962
buy_31_ewo = -10.4
buy_31_wr = -90.0
buy_31_cti = -0.89
buy_32_ma_offset = 0.934
buy_32_dip = 0.005
buy_32_rsi = 46.0
buy_32_cti = -0.8
buy_33_ma_offset = 0.988
buy_33_rsi = 32.0
buy_33_cti = -0.88
buy_33_ewo = 6.4
buy_33_volume = 2.0
buy_34_ma_offset = 0.93
buy_34_dip = 0.005
buy_34_ewo = -6.0
buy_34_cti = -0.88
buy_34_volume = 2.0
buy_35_ma_offset = 0.984
buy_35_ewo = 9.6
buy_35_rsi = 32.0
buy_35_cti = -0.5
buy_36_ma_offset = 0.98
buy_36_ewo = -8.8
buy_36_cti = -0.8
buy_37_ma_offset = 0.98
buy_37_ewo = 9.8
buy_37_rsi = 56.0
buy_37_cti = -0.7
buy_38_ma_offset = 0.98
buy_38_ewo = -5.2
buy_38_cti = -0.96
buy_39_cti = -0.77
buy_39_r = -60.0
buy_39_r_1h = -38.0
buy_40_hrsi = 30.0
buy_40_cci = -240.0
buy_40_rsi = 30.0
buy_40_cti = -0.8
buy_40_r = -90.0
buy_40_r_1h = -90.0
buy_41_cti_1h = -0.84
buy_41_r_1h = -42.0
buy_41_ma_offset = 0.96
buy_41_cti = -0.8
buy_41_r = -75.0
buy_42_cti_1h = 0.5
buy_42_r_1h = -46.0
buy_42_ema_open_mult = 0.018
buy_42_bb_offset = 0.992
buy_43_cti_1h = 0.5
buy_43_r_1h = -80.0
buy_43_bb40_bbdelta_close = 0.046
buy_43_bb40_closedelta_close = 0.02
buy_43_bb40_tail_bbdelta = 0.5
buy_43_cti = -0.6
buy_43_r = -90.0
buy_44_ma_offset = 0.982
buy_44_ewo = -18.143
buy_44_cti = -0.8
buy_44_r_1h = -75.0
# Sell
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
# 48h for pump sell checks
sell_pump_threshold_48_1 = 0.9
sell_pump_threshold_48_2 = 0.7
sell_pump_threshold_48_3 = 0.5
# 36h for pump sell checks
sell_pump_threshold_36_1 = 0.72
sell_pump_threshold_36_2 = 4.0
sell_pump_threshold_36_3 = 1.0
# 24h for pump sell checks
sell_pump_threshold_24_1 = 0.68
sell_pump_threshold_24_2 = 0.62
sell_pump_threshold_24_3 = 0.88
sell_rsi_bb_1 = 79.5
sell_rsi_bb_2 = 81
sell_rsi_main_3 = 82
sell_dual_rsi_rsi_4 = 73.4
sell_dual_rsi_rsi_1h_4 = 79.6
sell_ema_relative_5 = 0.024
sell_rsi_diff_5 = 4.4
sell_rsi_under_6 = 79.0
sell_rsi_1h_7 = 81.7
sell_bb_relative_8 = 1.1
# Profit over EMA200
sell_custom_profit_bull_0 = 0.012
sell_custom_rsi_under_bull_0 = 34.0
sell_custom_profit_bull_1 = 0.02
sell_custom_rsi_under_bull_1 = 35.0
sell_custom_profit_bull_2 = 0.03
sell_custom_rsi_under_bull_2 = 36.0
sell_custom_profit_bull_3 = 0.04
sell_custom_rsi_under_bull_3 = 37.0
sell_custom_profit_bull_4 = 0.05
sell_custom_rsi_under_bull_4 = 42.0
sell_custom_profit_bull_5 = 0.06
sell_custom_rsi_under_bull_5 = 49.0
sell_custom_profit_bull_6 = 0.07
sell_custom_rsi_under_bull_6 = 50.0
sell_custom_profit_bull_7 = 0.08
sell_custom_rsi_under_bull_7 = 54.0
sell_custom_profit_bull_8 = 0.09
sell_custom_rsi_under_bull_8 = 50.0
sell_custom_profit_bull_9 = 0.1
sell_custom_rsi_under_bull_9 = 46.0
sell_custom_profit_bull_10 = 0.12
sell_custom_rsi_under_bull_10 = 42.0
sell_custom_profit_bull_11 = 0.20
sell_custom_rsi_under_bull_11 = 30.0
sell_custom_profit_bear_0 = 0.012
sell_custom_rsi_under_bear_0 = 34.0
sell_custom_profit_bear_1 = 0.02
sell_custom_rsi_under_bear_1 = 35.0
sell_custom_profit_bear_2 = 0.03
sell_custom_rsi_under_bear_2 = 37.0
sell_custom_profit_bear_3 = 0.04
sell_custom_rsi_under_bear_3 = 44.0
sell_custom_profit_bear_4 = 0.05
sell_custom_rsi_under_bear_4 = 48.0
sell_custom_profit_bear_5 = 0.06
sell_custom_rsi_under_bear_5 = 50.0
sell_custom_rsi_over_bear_5 = 78.0
sell_custom_profit_bear_6 = 0.07
sell_custom_rsi_under_bear_6 = 52.0
sell_custom_rsi_over_bear_6 = 78.0
sell_custom_profit_bear_7 = 0.08
sell_custom_rsi_under_bear_7 = 54.0
sell_custom_rsi_over_bear_7 = 80.0
sell_custom_profit_bear_8 = 0.09
sell_custom_rsi_under_bear_8 = 52.0
sell_custom_rsi_over_bear_8 = 82.0
sell_custom_profit_bear_9 = 0.1
sell_custom_rsi_under_bear_9 = 46.0
sell_custom_profit_bear_10 = 0.12
sell_custom_rsi_under_bear_10 = 42.0
sell_custom_profit_bear_11 = 0.20
sell_custom_rsi_under_bear_11 = 30.0
# Profit under EMA200
sell_custom_under_profit_bull_0 = 0.01
sell_custom_under_rsi_under_bull_0 = 38.0
sell_custom_under_profit_bull_1 = 0.02
sell_custom_under_rsi_under_bull_1 = 46.0
sell_custom_under_profit_bull_2 = 0.03
sell_custom_under_rsi_under_bull_2 = 47.0
sell_custom_under_profit_bull_3 = 0.04
sell_custom_under_rsi_under_bull_3 = 48.0
sell_custom_under_profit_bull_4 = 0.05
sell_custom_under_rsi_under_bull_4 = 49.0
sell_custom_under_profit_bull_5 = 0.06
sell_custom_under_rsi_under_bull_5 = 50.0
sell_custom_under_profit_bull_6 = 0.07
sell_custom_under_rsi_under_bull_6 = 52.0
sell_custom_under_profit_bull_7 = 0.08
sell_custom_under_rsi_under_bull_7 = 54.0
sell_custom_under_profit_bull_8 = 0.09
sell_custom_under_rsi_under_bull_8 = 50.0
sell_custom_under_profit_bull_9 = 0.1
sell_custom_under_rsi_under_bull_9 = 46.0
sell_custom_under_profit_bull_10 = 0.12
sell_custom_under_rsi_under_bull_10 = 42.0
sell_custom_under_profit_bull_11 = 0.2
sell_custom_under_rsi_under_bull_11 = 30.0
sell_custom_under_profit_bear_0 = 0.01
sell_custom_under_rsi_under_bear_0 = 38.0
sell_custom_under_profit_bear_1 = 0.02
sell_custom_under_rsi_under_bear_1 = 56.0
sell_custom_under_profit_bear_2 = 0.03
sell_custom_under_rsi_under_bear_2 = 57.0
sell_custom_under_profit_bear_3 = 0.04
sell_custom_under_rsi_under_bear_3 = 58.0
sell_custom_under_profit_bear_4 = 0.05
sell_custom_under_rsi_under_bear_4 = 57.0
sell_custom_under_profit_bear_5 = 0.06
sell_custom_under_rsi_under_bear_5 = 56.0
sell_custom_under_rsi_over_bear_5 = 78.0
sell_custom_under_profit_bear_6 = 0.07
sell_custom_under_rsi_under_bear_6 = 55.0
sell_custom_under_rsi_over_bear_6 = 78.0
sell_custom_under_profit_bear_7 = 0.08
sell_custom_under_rsi_under_bear_7 = 54.0
sell_custom_under_rsi_over_bear_7 = 80.0
sell_custom_under_profit_bear_8 = 0.09
sell_custom_under_rsi_under_bear_8 = 50.0
sell_custom_under_rsi_over_bear_8 = 82.0
sell_custom_under_profit_bear_9 = 0.1
sell_custom_under_rsi_under_bear_9 = 46.0
sell_custom_under_profit_bear_10 = 0.12
sell_custom_under_rsi_under_bear_10 = 42.0
sell_custom_under_profit_bear_11 = 0.2
sell_custom_under_rsi_under_bear_11 = 30.0
# Profit targets for pumped pairs 48h 1
sell_custom_pump_profit_1_1 = 0.01
sell_custom_pump_rsi_1_1 = 34.0
sell_custom_pump_profit_1_2 = 0.02
sell_custom_pump_rsi_1_2 = 40.0
sell_custom_pump_profit_1_3 = 0.04
sell_custom_pump_rsi_1_3 = 42.0
sell_custom_pump_profit_1_4 = 0.1
sell_custom_pump_rsi_1_4 = 34.0
sell_custom_pump_profit_1_5 = 0.2
sell_custom_pump_rsi_1_5 = 30.0
# Profit targets for pumped pairs 36h 1
sell_custom_pump_profit_2_1 = 0.01
sell_custom_pump_rsi_2_1 = 34.0
sell_custom_pump_profit_2_2 = 0.02
sell_custom_pump_rsi_2_2 = 40.0
sell_custom_pump_profit_2_3 = 0.04
sell_custom_pump_rsi_2_3 = 42.0
sell_custom_pump_profit_2_4 = 0.1
sell_custom_pump_rsi_2_4 = 34.0
sell_custom_pump_profit_2_5 = 0.2
sell_custom_pump_rsi_2_5 = 30.0
# Profit targets for pumped pairs 24h 1
sell_custom_pump_profit_3_1 = 0.01
sell_custom_pump_rsi_3_1 = 34.0
sell_custom_pump_profit_3_2 = 0.02
sell_custom_pump_rsi_3_2 = 40.0
sell_custom_pump_profit_3_3 = 0.04
sell_custom_pump_rsi_3_3 = 42.0
sell_custom_pump_profit_3_4 = 0.1
sell_custom_pump_rsi_3_4 = 34.0
sell_custom_pump_profit_3_5 = 0.2
sell_custom_pump_rsi_3_5 = 30.0
# SMA descending
sell_custom_dec_profit_min_1 = 0.05
sell_custom_dec_profit_max_1 = 0.12
# Under EMA100
sell_custom_dec_profit_min_2 = 0.07
sell_custom_dec_profit_max_2 = 0.16
# Trail 1
#sell_trail_profit_min_1 = 0.03
#sell_trail_profit_max_1 = 0.05
#sell_trail_down_1 = 0.05
sell_trail_rsi_min_1 = 10.0
sell_trail_rsi_max_1 = 20.0
sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True)
sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True)
# Trail 2
#sell_trail_profit_min_2 = 0.1
#sell_trail_profit_max_2 = 0.4
#sell_trail_down_2 = 0.03
sell_trail_rsi_min_2 = 20.0
sell_trail_rsi_max_2 = 50.0
# Trail 3
sell_trail_profit_min_3 = 0.06
sell_trail_profit_max_3 = 0.2
sell_trail_down_3 = 0.05
# Trail 4
sell_trail_profit_min_4 = 0.03
sell_trail_profit_max_4 = 0.06
sell_trail_down_4 = 0.02
# Under & near EMA200, accept profit
sell_custom_profit_under_profit_min_1 = 0.001
sell_custom_profit_under_profit_max_1 = 0.008
sell_custom_profit_under_rel_1 = 0.024
sell_custom_profit_under_rsi_diff_1 = 4.4
sell_custom_profit_under_profit_2 = 0.03
sell_custom_profit_under_rel_2 = 0.024
sell_custom_profit_under_rsi_diff_2 = 4.4
# Under & near EMA200, take the loss
sell_custom_stoploss_under_rel_1 = 0.002
sell_custom_stoploss_under_rsi_diff_1 = 10.0
# Long duration/recover stoploss 1
sell_custom_stoploss_long_profit_min_1 = -0.08
sell_custom_stoploss_long_profit_max_1 = -0.04
sell_custom_stoploss_long_recover_1 = 0.14
sell_custom_stoploss_long_rsi_diff_1 = 4.0
# Long duration/recover stoploss 2
sell_custom_stoploss_long_recover_2 = 0.06
sell_custom_stoploss_long_rsi_diff_2 = 40.0
# Pumped, descending SMA
sell_custom_pump_dec_profit_min_1 = 0.005
sell_custom_pump_dec_profit_max_1 = 0.05
sell_custom_pump_dec_profit_min_2 = 0.04
sell_custom_pump_dec_profit_max_2 = 0.06
sell_custom_pump_dec_profit_min_3 = 0.06
sell_custom_pump_dec_profit_max_3 = 0.09
sell_custom_pump_dec_profit_min_4 = 0.02
sell_custom_pump_dec_profit_max_4 = 0.04
# Pumped 48h 1, under EMA200
sell_custom_pump_under_profit_min_1 = 0.04
sell_custom_pump_under_profit_max_1 = 0.09
#Deadfish
is_optimize_deadfish = False
sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , space='sell', optimize = is_optimize_deadfish)
sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08 , space='sell', optimize = is_optimize_deadfish)
sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , space='sell', optimize = is_optimize_deadfish)
sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5 ,space='sell', optimize = is_optimize_deadfish)
# Pumped trail 1
sell_custom_pump_trail_profit_min_1 = 0.05
sell_custom_pump_trail_profit_max_1 = 0.07
sell_custom_pump_trail_down_1 = 0.05
sell_custom_pump_trail_rsi_min_1 = 20.0
sell_custom_pump_trail_rsi_max_1 = 70.0
# Stoploss, pumped, 48h 1
sell_custom_stoploss_pump_max_profit_1 = 0.01
sell_custom_stoploss_pump_min_1 = -0.02
sell_custom_stoploss_pump_max_1 = -0.01
sell_custom_stoploss_pump_ma_offset_1 = 0.94
# Stoploss, pumped, 48h 1
sell_custom_stoploss_pump_max_profit_2 = 0.025
sell_custom_stoploss_pump_loss_2 = -0.05
sell_custom_stoploss_pump_ma_offset_2 = 0.92
# Stoploss, pumped, 36h 3
sell_custom_stoploss_pump_max_profit_3 = 0.008
sell_custom_stoploss_pump_loss_3 = -0.12
sell_custom_stoploss_pump_ma_offset_3 = 0.88
# Recover
sell_custom_recover_profit_1 = 0.06
sell_custom_recover_min_loss_1 = 0.12
sell_custom_recover_profit_min_2 = 0.01
sell_custom_recover_profit_max_2 = 0.05
sell_custom_recover_min_loss_2 = 0.06
sell_custom_recover_rsi_2 = 46.0
# Profit for long duration trades
sell_custom_long_profit_min_1 = 0.03
sell_custom_long_profit_max_1 = 0.04
sell_custom_long_duration_min_1 = 900
#############################################################
hold_trades_cache = None
@informative('15m')
def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# # Dynamic RSI
# dataframe['mom'] = momentum(dataframe, 5)
# dataframe['corr'] = get_rolling_corr(dataframe['mom'], dataframe, 20)
# dataframe['dyn_rsi'] = ta.RSI(dataframe['close'], get_rsi_lenght(dataframe['corr']))
# dataframe['dyn_rsi_3r_mean'] = (dataframe['dyn_rsi']).rolling(3).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
return dataframe
@staticmethod
def get_hold_trades_config_file():
strat_file_path = pathlib.Path(__file__)
hold_trades_config_file_resolve = strat_file_path.resolve().parent / "hold-trades.json"
if hold_trades_config_file_resolve.is_file():
return hold_trades_config_file_resolve
# The resolved path does not exist, is it a symlink?
hold_trades_config_file_absolute = strat_file_path.absolute().parent / "hold-trades.json"
if hold_trades_config_file_absolute.is_file():
return hold_trades_config_file_absolute
if hold_trades_config_file_resolve != hold_trades_config_file_absolute:
looked_in = f"'{hold_trades_config_file_resolve}' and '{hold_trades_config_file_absolute}'"
else:
looked_in = f"'{hold_trades_config_file_resolve}'"
log.warning(
"The 'hold-trades.json' file was not found. Looked in %s. HOLD support disabled.",
looked_in
)
def load_hold_trades_config(self):
if self.hold_trades_cache is None:
hold_trades_config_file = NostalgiaForInfinity772martinsk2.get_hold_trades_config_file()
if hold_trades_config_file:
self.hold_trades_cache = HoldsCache(hold_trades_config_file)
if self.hold_trades_cache:
self.hold_trades_cache.load()
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
if self.holdSupportEnabled and self.config['runmode'].value in ('live', 'dry_run'):
self.load_hold_trades_config()
return super().bot_loop_start(**kwargs)
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
def sell_over_main(self, current_profit: float, last_candle) -> tuple:
if last_candle['close'] > last_candle['ema_200']:
if (last_candle['moderi_96']):
if current_profit >= self.sell_custom_profit_bull_11:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_11:
return True, 'signal_profit_o_bull_11'
elif self.sell_custom_profit_bull_11 > current_profit >= self.sell_custom_profit_bull_10:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_10:
return True, 'signal_profit_o_bull_10'
elif self.sell_custom_profit_bull_10 > current_profit >= self.sell_custom_profit_bull_9:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_9:
return True, 'signal_profit_o_bull_9'
elif self.sell_custom_profit_bull_9 > current_profit >= self.sell_custom_profit_bull_8:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_8:
return True, 'signal_profit_o_bull_8'
elif self.sell_custom_profit_bull_8 > current_profit >= self.sell_custom_profit_bull_7:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_7):
return True, 'signal_profit_o_bull_7'
elif self.sell_custom_profit_bull_7 > current_profit >= self.sell_custom_profit_bull_6:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_6) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_6'
elif self.sell_custom_profit_bull_6 > current_profit >= self.sell_custom_profit_bull_5:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_5'
elif self.sell_custom_profit_bull_5 > current_profit >= self.sell_custom_profit_bull_4:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_4) and (last_candle['cmf'] < 0.0) :
return True, 'signal_profit_o_bull_4'
elif self.sell_custom_profit_bull_4 > current_profit >= self.sell_custom_profit_bull_3:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_3) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_3'
elif self.sell_custom_profit_bull_3 > current_profit >= self.sell_custom_profit_bull_2:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_2) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_2'
elif self.sell_custom_profit_bull_2 > current_profit >= self.sell_custom_profit_bull_1:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_1) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_1'
elif self.sell_custom_profit_bull_1 > current_profit >= self.sell_custom_profit_bull_0:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_0'
else:
if current_profit >= self.sell_custom_profit_bear_11:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_11:
return True, 'signal_profit_o_bear_11'
elif self.sell_custom_profit_bear_11 > current_profit >= self.sell_custom_profit_bear_10:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_10:
return True, 'signal_profit_o_bear_10'
elif self.sell_custom_profit_bear_10 > current_profit >= self.sell_custom_profit_bear_9:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_9:
return True, 'signal_profit_o_bear_9'
elif self.sell_custom_profit_bear_9 > current_profit >= self.sell_custom_profit_bear_8:
if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_8:
return True, 'signal_profit_o_bear_8_1'
elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_8):
return True, 'signal_profit_o_bear_8_2'
elif self.sell_custom_profit_bear_8 > current_profit >= self.sell_custom_profit_bear_7:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_7):
return True, 'signal_profit_o_bear_7_1'
elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_7):
return True, 'signal_profit_o_bear_7_2'
elif self.sell_custom_profit_bear_7 > current_profit >= self.sell_custom_profit_bear_6:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_6):
return True, 'signal_profit_o_bear_6_1'
elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_6):
return True, 'signal_profit_o_bear_6_2'
elif self.sell_custom_profit_bear_6 > current_profit >= self.sell_custom_profit_bear_5:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_5):
return True, 'signal_profit_o_bear_5_1'
elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_5):
return True, 'signal_profit_o_bear_5_2'
elif self.sell_custom_profit_bear_5 > current_profit >= self.sell_custom_profit_bear_4:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_4):
return True, 'signal_profit_o_bear_4'
elif self.sell_custom_profit_bear_4 > current_profit >= self.sell_custom_profit_bear_3:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_3) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_3'
elif self.sell_custom_profit_bear_3 > current_profit >= self.sell_custom_profit_bear_2:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_2) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_2'
elif self.sell_custom_profit_bear_2 > current_profit >= self.sell_custom_profit_bear_1:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_1) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_1'
elif self.sell_custom_profit_bear_1 > current_profit >= self.sell_custom_profit_bear_0:
if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_0'
return False, None
def sell_under_main(self, current_profit: float, last_candle) -> tuple:
if last_candle['close'] < last_candle['ema_200']:
if (last_candle['moderi_96']):
if current_profit >= self.sell_custom_under_profit_bull_11:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_11:
return True, 'signal_profit_u_bull_11'
elif self.sell_custom_under_profit_bull_11 > current_profit >= self.sell_custom_under_profit_bull_10:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_10:
return True, 'signal_profit_u_bull_10'
elif self.sell_custom_under_profit_bull_10 > current_profit >= self.sell_custom_under_profit_bull_9:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_9:
return True, 'signal_profit_u_bull_9'
elif self.sell_custom_under_profit_bull_9 > current_profit >= self.sell_custom_under_profit_bull_8:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_8:
return True, 'signal_profit_u_bull_8'
elif self.sell_custom_under_profit_bull_8 > current_profit >= self.sell_custom_under_profit_bull_7:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_7:
return True, 'signal_profit_u_bull_7'
elif self.sell_custom_under_profit_bull_7 > current_profit >= self.sell_custom_under_profit_bull_6:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_6:
return True, 'signal_profit_u_bull_6'
elif self.sell_custom_under_profit_bull_6 > current_profit >= self.sell_custom_under_profit_bull_5:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_5:
return True, 'signal_profit_u_bull_5'
elif self.sell_custom_under_profit_bull_5 > current_profit >= self.sell_custom_under_profit_bull_4:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_4:
return True, 'signal_profit_u_bull_4'
elif self.sell_custom_under_profit_bull_4 > current_profit >= self.sell_custom_under_profit_bull_3:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_3:
return True, 'signal_profit_u_bull_3'
elif self.sell_custom_under_profit_bull_3 > current_profit >= self.sell_custom_under_profit_bull_2:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_2:
return True, 'signal_profit_u_bull_2'
elif self.sell_custom_under_profit_bull_2 > current_profit >= self.sell_custom_under_profit_bull_1:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_1:
return True, 'signal_profit_u_bull_1'
elif self.sell_custom_under_profit_bull_1 > current_profit >= self.sell_custom_under_profit_bull_0:
if (last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_0'
else:
if current_profit >= self.sell_custom_under_profit_bear_11:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_11:
return True, 'signal_profit_u_bear_11'
elif self.sell_custom_under_profit_bear_11 > current_profit >= self.sell_custom_under_profit_bear_10:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_10:
return True, 'signal_profit_u_bear_10'
elif self.sell_custom_under_profit_bear_10 > current_profit >= self.sell_custom_under_profit_bear_9:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_9:
return True, 'signal_profit_u_bear_9'
elif self.sell_custom_under_profit_bear_9 > current_profit >= self.sell_custom_under_profit_bear_8:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_8:
return True, 'signal_profit_u_bear_8_1'
elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_8):
return True, 'signal_profit_u_bear_8_2'
elif self.sell_custom_under_profit_bear_8 > current_profit >= self.sell_custom_under_profit_bear_7:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_7:
return True, 'signal_profit_u_bear_7_1'
elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_7):
return True, 'signal_profit_u_bear_7_2'
elif self.sell_custom_under_profit_bear_7 > current_profit >= self.sell_custom_under_profit_bear_6:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_6:
return True, 'signal_profit_u_bear_6_1'
elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_6):
return True, 'signal_profit_u_bear_6_2'
elif self.sell_custom_under_profit_bear_6 > current_profit >= self.sell_custom_under_profit_bear_5:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_5:
return True, 'signal_profit_u_bear_5_1'
elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_5):
return True, 'signal_profit_u_bear_5_2'
elif self.sell_custom_under_profit_bear_5 > current_profit >= self.sell_custom_under_profit_bear_4:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_4:
return True, 'signal_profit_u_bear_4'
elif self.sell_custom_under_profit_bear_4 > current_profit >= self.sell_custom_under_profit_bear_3:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_3:
return True, 'signal_profit_u_bear_3'
elif self.sell_custom_under_profit_bear_3 > current_profit >= self.sell_custom_under_profit_bear_2:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_2:
return True, 'signal_profit_u_bear_2'
elif self.sell_custom_under_profit_bear_2 > current_profit >= self.sell_custom_under_profit_bear_1:
if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_1:
return True, 'signal_profit_u_bear_1'
elif self.sell_custom_under_profit_bear_1 > current_profit >= self.sell_custom_under_profit_bear_0:
if (last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_0'
return False, None
def sell_pump_main(self, current_profit: float, last_candle) -> tuple:
if last_candle['sell_pump_48_1_1h']:
if current_profit >= self.sell_custom_pump_profit_1_5:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_5:
return True, 'signal_profit_p_1_5'
elif self.sell_custom_pump_profit_1_5 > current_profit >= self.sell_custom_pump_profit_1_4:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_4:
return True, 'signal_profit_p_1_4'
elif self.sell_custom_pump_profit_1_4 > current_profit >= self.sell_custom_pump_profit_1_3:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_3:
return True, 'signal_profit_p_1_3'
elif self.sell_custom_pump_profit_1_3 > current_profit >= self.sell_custom_pump_profit_1_2:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_2:
return True, 'signal_profit_p_1_2'
elif self.sell_custom_pump_profit_1_2 > current_profit >= self.sell_custom_pump_profit_1_1:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_1:
return True, 'signal_profit_p_1_1'
elif last_candle['sell_pump_36_1_1h']:
if current_profit >= self.sell_custom_pump_profit_2_5:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_5:
return True, 'signal_profit_p_2_5'
elif self.sell_custom_pump_profit_2_5 > current_profit >= self.sell_custom_pump_profit_2_4:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_4:
return True, 'signal_profit_p_2_4'
elif self.sell_custom_pump_profit_2_4 > current_profit >= self.sell_custom_pump_profit_2_3:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_3:
return True, 'signal_profit_p_2_3'
elif self.sell_custom_pump_profit_2_3 > current_profit >= self.sell_custom_pump_profit_2_2:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_2:
return True, 'signal_profit_p_2_2'
elif self.sell_custom_pump_profit_2_2 > current_profit >= self.sell_custom_pump_profit_2_1:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_1:
return True, 'signal_profit_p_2_1'
elif last_candle['sell_pump_24_1_1h']:
if current_profit >= self.sell_custom_pump_profit_3_5:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_5:
return True, 'signal_profit_p_3_5'
elif self.sell_custom_pump_profit_3_5 > current_profit >= self.sell_custom_pump_profit_3_4:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_4:
return True, 'signal_profit_p_3_4'
elif self.sell_custom_pump_profit_3_4 > current_profit >= self.sell_custom_pump_profit_3_3:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_3:
return True, 'signal_profit_p_3_3'
elif self.sell_custom_pump_profit_3_3 > current_profit >= self.sell_custom_pump_profit_3_2:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_2:
return True, 'signal_profit_p_3_2'
elif self.sell_custom_pump_profit_3_2 > current_profit >= self.sell_custom_pump_profit_3_1:
if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_1:
return True, 'signal_profit_p_3_1'
return False, None
def sell_dec_main(self, current_profit: float, last_candle) -> tuple:
if (self.sell_custom_dec_profit_max_1 > current_profit >= self.sell_custom_dec_profit_min_1) and (last_candle['sma_200_dec_20']):
return True, 'signal_profit_d_1'
elif (self.sell_custom_dec_profit_max_2 > current_profit >= self.sell_custom_dec_profit_min_2) and (last_candle['close'] < last_candle['ema_100']):
return True, 'signal_profit_d_2'
return False, None
def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple:
if (self.sell_trail_profit_max_1 > current_profit >= self.sell_trail_profit_min_1) and (self.sell_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_trail_down_1)) and (last_candle['moderi_96'] == False):
return True, 'signal_profit_t_1'
elif (self.sell_trail_profit_max_2 > current_profit >= self.sell_trail_profit_min_2) and (self.sell_trail_rsi_min_2 < last_candle['rsi_14'] < self.sell_trail_rsi_max_2) and (max_profit > (current_profit + self.sell_trail_down_2)) and (last_candle['ema_25'] < last_candle['ema_50']):
return True, 'signal_profit_t_2'
elif (self.sell_trail_profit_max_3 > current_profit >= self.sell_trail_profit_min_3) and (max_profit > (current_profit + self.sell_trail_down_3)) and (last_candle['sma_200_dec_20_1h']):
return True, 'signal_profit_t_3'
elif (self.sell_trail_profit_max_4 > current_profit >= self.sell_trail_profit_min_4) and (max_profit > (current_profit + self.sell_trail_down_4)) and (last_candle['sma_200_dec_24']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_t_4'
return False, None
def sell_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple:
# Pumped pair, short duration
if (last_candle['sell_pump_24_1_1h']) and (0.2 > current_profit >= 0.07) and (current_time - timedelta(minutes=30) < trade.open_date_utc):
return True, 'signal_profit_p_s_1'
elif (self.sell_custom_long_profit_min_1 < current_profit < self.sell_custom_long_profit_max_1) and (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1) > trade.open_date_utc):
return True, 'signal_profit_l_1'
return False, None
def sell_under_min(self, current_profit: float, last_candle) -> tuple:
if ((last_candle['moderi_96']) == False):
# Downtrend
if (self.sell_custom_profit_under_profit_max_1 > current_profit >= self.sell_custom_profit_under_profit_min_1) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_1):
return True, 'signal_profit_u_e_1'
else:
# Uptrend
if (current_profit >= self.sell_custom_profit_under_profit_2) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_2):
return True, 'signal_profit_u_e_2'
return False, None
def sell_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple:
# if (-0.12 <= current_profit < -0.08):
# if (last_candle['close'] < last_candle['atr_high_thresh_1']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_1']):
# return True, 'signal_stoploss_atr_1'
# elif (-0.16 <= current_profit < -0.12):
# if (last_candle['close'] < last_candle['atr_high_thresh_2']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_2']):
# return True, 'signal_stoploss_atr_2'
# elif (-0.2 <= current_profit < -0.16):
# if (last_candle['close'] < last_candle['atr_high_thresh_3']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_3']):
## return True, 'signal_stoploss_atr_3'
# elif (current_profit < -0.2):
# if (last_candle['close'] < last_candle['atr_high_thresh_4']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_4']):
# return True, 'signal_stoploss_atr_4'
return False, None
def sell_pump_dec(self, current_profit: float, last_candle) -> tuple:
if (self.sell_custom_pump_dec_profit_max_1 > current_profit >= self.sell_custom_pump_dec_profit_min_1) and (last_candle['sell_pump_48_1_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']):
return True, 'signal_profit_p_d_1'
elif (self.sell_custom_pump_dec_profit_max_2 > current_profit >= self.sell_custom_pump_dec_profit_min_2) and (last_candle['sell_pump_48_2_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']):
return True, 'signal_profit_p_d_2'
elif (self.sell_custom_pump_dec_profit_max_3 > current_profit >= self.sell_custom_pump_dec_profit_min_3) and (last_candle['sell_pump_48_3_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']):
return True, 'signal_profit_p_d_3'
elif (self.sell_custom_pump_dec_profit_max_4 > current_profit >= self.sell_custom_pump_dec_profit_min_4) and (last_candle['sma_200_dec_20']) and (last_candle['sell_pump_24_2_1h']):
return True, 'signal_profit_p_d_4'
return False, None
def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple:
# Pumped 48h 1, under EMA200
if (self.sell_custom_pump_under_profit_max_1 > current_profit >= self.sell_custom_pump_under_profit_min_1) and (last_candle['sell_pump_48_1_1h']) and (last_candle['close'] < last_candle['ema_200']):
return True, 'signal_profit_p_u_1'
# Pumped 36h 2, trail 1
elif (last_candle['sell_pump_36_2_1h']) and (self.sell_custom_pump_trail_profit_max_1 > current_profit >= self.sell_custom_pump_trail_profit_min_1) and (self.sell_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_custom_pump_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1)):
return True, 'signal_profit_p_t_1'
return False, None
def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple:
if (max_loss > self.sell_custom_recover_min_loss_1) and (current_profit >= self.sell_custom_recover_profit_1):
return True, 'signal_profit_r_1'
elif (max_loss > self.sell_custom_recover_min_loss_2) and (self.sell_custom_recover_profit_max_2 > current_profit >= self.sell_custom_recover_profit_min_2) and (last_candle['rsi_14'] < self.sell_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']):
return True, 'signal_profit_r_2'
return False, None
def sell_r_1(self, current_profit: float, last_candle) -> tuple:
if 0.02 > current_profit >= 0.012:
if last_candle['r_480'] > -0.5:
return True, 'signal_profit_w_1_1'
elif 0.03 > current_profit >= 0.02:
if last_candle['r_480'] > -0.6:
return True, 'signal_profit_w_1_2'
elif 0.04 > current_profit >= 0.03:
if last_candle['r_480'] > -0.7:
return True, 'signal_profit_w_1_3'
elif 0.05 > current_profit >= 0.04:
if last_candle['r_480'] > -0.8:
return True, 'signal_profit_w_1_4'
elif 0.06 > current_profit >= 0.05:
if last_candle['r_480'] > -0.9:
return True, 'signal_profit_w_1_5'
elif 0.07 > current_profit >= 0.06:
if last_candle['r_480'] > -2.0:
return True, 'signal_profit_w_1_6'
elif 0.08 > current_profit >= 0.07:
if last_candle['r_480'] > -2.2:
return True, 'signal_profit_w_1_7'
elif 0.09 > current_profit >= 0.08:
if last_candle['r_480'] > -2.4:
return True, 'signal_profit_w_1_8'
elif 0.1 > current_profit >= 0.09:
if last_candle['r_480'] > -2.6:
return True, 'signal_profit_w_1_9'
elif 0.12 > current_profit >= 0.1:
if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 72.0):
return True, 'signal_profit_w_1_10'
elif 0.2 > current_profit >= 0.12:
if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_w_1_11'
elif current_profit >= 0.2:
if (last_candle['r_480'] > -1.0) and (last_candle['rsi_14'] > 80.0):
return True, 'signal_profit_w_1_12'
return False, None
def sell_r_2(self, current_profit: float, last_candle) -> tuple:
if 0.02 > current_profit >= 0.012:
if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_1'
elif 0.03 > current_profit >= 0.02:
if (last_candle['r_480'] > -4.1) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_2'
elif 0.04 > current_profit >= 0.03:
if (last_candle['r_480'] > -4.2) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_3'
elif 0.05 > current_profit >= 0.04:
if (last_candle['r_480'] > -4.3) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_5'
elif 0.07 > current_profit >= 0.06:
if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_6'
elif 0.08 > current_profit >= 0.07:
if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_7'
elif 0.09 > current_profit >= 0.08:
if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_8'
elif 0.1 > current_profit >= 0.09:
if (last_candle['r_480'] > -4.8) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_9'
elif 0.12 > current_profit >= 0.1:
if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_10'
elif 0.2 > current_profit >= 0.12:
if (last_candle['r_480'] > -3.2) and (last_candle['rsi_14'] > 81.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_11'
elif current_profit >= 0.2:
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 81.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_2_12'
return False, None
def sell_r_3(self, current_profit: float, last_candle) -> tuple:
if 0.02 > current_profit >= 0.012:
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_3_1'
elif 0.03 > current_profit >= 0.02:
if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_3_2'
elif 0.04 > current_profit >= 0.03:
if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_3_3'
elif 0.05 > current_profit >= 0.04:
if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
return True, 'signal_profit_w_3_4'
return False, None
def sell_r_4(self, current_profit: float, last_candle) -> tuple:
if (0.02 > current_profit >= 0.012):
if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 68.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_1'
elif (0.03 > current_profit >= 0.02):
if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 68.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_2'
elif (0.04 > current_profit >= 0.03):
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 68.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_3'
elif (0.05 > current_profit >= 0.04):
if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 68.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_4'
elif (0.06 > current_profit >= 0.05):
if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 68.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_5'
elif (0.07 > current_profit >= 0.06):
if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_6'
elif (0.08 > current_profit >= 0.07):
if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_7'
elif (0.09 > current_profit >= 0.08):
if (last_candle['r_480'] > -5.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_8'
elif (0.1 > current_profit >= 0.09):
if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_9'
elif (0.12 > current_profit >= 0.1):
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_10'
elif (0.2 > current_profit >= 0.12):
if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_11'
elif (current_profit >= 0.2):
if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_w_4_12'
return False, None
def sell_quick_mode(self, current_profit: float, max_profit:float, last_candle, previous_candle_1) -> tuple:
if (0.06 > current_profit > 0.02) and (last_candle['rsi_14'] > 80.0):
return True, 'signal_profit_q_1'
if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95):
return True, 'signal_profit_q_2'
if (last_candle['close'] < last_candle['atr_high_thresh_q']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_q']):
if (0.05 > current_profit > 0.02):
return True, 'signal_profit_q_atr'
elif (current_profit < -0.08):
return True, 'signal_stoploss_q_atr'
if (0.04 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1):
return True, 'signal_profit_q_pmax_bull'
if (0.045 > current_profit > 0.003) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016):
return True, 'signal_profit_q_pmax_bear'
return False, None
def sell_ichi(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
if (0.0 < current_profit < 0.05) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_ichi_u'
elif (-0.03 < current_profit < -0.0) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle['rsi_14'] > 75.0):
return True, 'signal_stoploss_ichi_u'
elif (max_loss > 0.07) and (current_profit > 0.02):
return True, 'signal_profit_ichi_r_0'
elif (max_loss > 0.06) and (current_profit > 0.03):
return True, 'signal_profit_ichi_r_1'
elif (max_loss > 0.05) and (current_profit > 0.04):
return True, 'signal_profit_ichi_r_2'
elif (max_loss > 0.04) and (current_profit > 0.05):
return True, 'signal_profit_ichi_r_3'
elif (max_loss > 0.03) and (current_profit > 0.06):
return True, 'signal_profit_ichi_r_4'
elif (0.05 < current_profit < 0.1) and (current_time - timedelta(minutes=720) > trade.open_date_utc):
return True, 'signal_profit_ichi_slow'
elif (0.07 < current_profit < 0.1) and (max_profit-current_profit > 0.025) and (max_profit > 0.1):
return True, 'signal_profit_ichi_t'
elif (current_profit < -0.1):
return True, 'signal_stoploss_ichi'
return False, None
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
previous_candle_1 = dataframe.iloc[-2]
previous_candle_2 = dataframe.iloc[-3]
previous_candle_3 = dataframe.iloc[-4]
previous_candle_4 = dataframe.iloc[-5]
previous_candle_5 = dataframe.iloc[-6]
buy_tag = 'empty'
if hasattr(trade, 'buy_tag') and trade.buy_tag is not None:
buy_tag = trade.buy_tag
else:
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
buy_signal = dataframe.loc[dataframe['date'] < trade_open_date]
if not buy_signal.empty:
buy_signal_candle = buy_signal.iloc[-1]
buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty'
buy_tags = buy_tag.split()
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate)
filled_buys = trade.select_filled_orders('buy')
count_of_buys = len(filled_buys)
if ( (current_profit < self.sell_deadfish_profit.value)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value)
and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value)
and (last_candle['cmf'] < 0.0)
):
return f"sell_stoploss_deadfish( {buy_tag})"
if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)):
return 'trail_target_1'
elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)):
return 'trail_target_2'
elif (current_profit > 3) & (last_candle['rsi'] > 85):
return 'RSI-85 target'
if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']):
return 'sell signal1'
if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']):
return 'sell signal1 * 1.01'
if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)):
return 'sell signal2'
if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']) :
return 'sell signal2 * 1.01'
if (current_profit > 0.05) & (last_candle['rsi'] < 42.0) & (last_candle['cmf'] < -0.4):
return f"profit exit1( {buy_tag})"
elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.0) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) :
return f"profit exit2( {buy_tag})"
elif (current_profit > 0.05) & (last_candle['rsi'] < 41.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_1h'] < -0.0):
return f"profit exit3( {buy_tag})"
elif (current_profit > 0.05) & (last_candle['rsi'] < 44.0) & (last_candle['cmf'] < -0.1) & (last_candle['cmf_15m'] < -0.1) & (last_candle['cmf_1h'] < -0.1) :
return f"profit exit4( {buy_tag})"
elif (current_profit > 0.05) & (last_candle['rsi'] < 40.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_15m'] < -0.2):
return f"pprofit exit5( {buy_tag})"
elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.4) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) :
return f"profit exit6( {buy_tag})"
elif (current_profit > 0.02) & (last_candle['rsi'] < 30.0) & (last_candle['cmf'] < -0.4):
return f"profit exit bear1( {buy_tag})"
elif (current_profit > 0.03) & (last_candle['rsi'] < 35.0) & (last_candle['cmf'] < -0.4):
return f"profit exit bear2( {buy_tag})"
elif (current_profit > 0.01) & (last_candle['rsi'] < 25.0) & (last_candle['cmf'] < -0.6):
return f"profit exit bear3( {buy_tag})"
return None
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
"""
if method == 'HL':
return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min()
elif method == 'OC':
return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min()
else:
raise ValueError(f"Method {method} not defined!")
def 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
"""
if length == 0:
return (dataframe['open'] - dataframe['close']) / dataframe['close']
else:
return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['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
"""
return dataframe['open'].rolling(length).max() - dataframe['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
"""
return dataframe['close'] - dataframe['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
"""
return (dataframe[f'oc_pct_change_{length}'] < thresh) | (self.range_maxgap_adjusted(dataframe, length, pull_thresh) > self.range_height(dataframe, 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):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, self.info_timeframe) for pair in pairs]
informative_pairs.append(('BTC/USDT', self.timeframe))
informative_pairs.append(('BTC/USDT', self.info_timeframe))
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.info_timeframe)
# 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_25'] = ta.EMA(informative_1h, timeperiod=25)
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_14'] = 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)
# Williams %R
informative_1h['r_480'] = williams_r(informative_1h, period=480)
# CTI
informative_1h['cti'] = pta.cti(informative_1h["close"], length=20)
# Ichimoku
ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
informative_1h['chikou_span'] = ichi['chikou_span']
informative_1h['tenkan_sen'] = ichi['tenkan_sen']
informative_1h['kijun_sen'] = ichi['kijun_sen']
informative_1h['senkou_a'] = ichi['senkou_span_a']
informative_1h['senkou_b'] = ichi['senkou_span_b']
informative_1h['leading_senkou_span_a'] = ichi['leading_senkou_span_a']
informative_1h['leading_senkou_span_b'] = ichi['leading_senkou_span_b']
informative_1h['chikou_span_greater'] = (informative_1h['chikou_span'] > informative_1h['senkou_a']).shift(30).fillna(False)
informative_1h.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1)
# EFI - Elders Force Index
informative_1h['efi'] = pta.efi(informative_1h["close"], informative_1h["volume"], length=13)
# SSL
ssl_down, ssl_up = SSLChannels(informative_1h, 10)
informative_1h['ssl_down'] = ssl_down
informative_1h['ssl_up'] = ssl_up
# 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['hl_pct_change_5'] = self.range_percent_change(informative_1h, 'HL', 5)
informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min()
informative_1h['safe_pump_24_10'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_10_24, self.buy_pump_pull_threshold_10_24)
informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_10_36, self.buy_pump_pull_threshold_10_36)
informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_10_48, self.buy_pump_pull_threshold_10_48)
informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_20_24, self.buy_pump_pull_threshold_20_24)
informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_20_36, self.buy_pump_pull_threshold_20_36)
informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_20_48, self.buy_pump_pull_threshold_20_48)
informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_30_24, self.buy_pump_pull_threshold_30_24)
informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_30_36, self.buy_pump_pull_threshold_30_36)
informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_30_48, self.buy_pump_pull_threshold_30_48)
informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_40_24, self.buy_pump_pull_threshold_40_24)
informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_40_36, self.buy_pump_pull_threshold_40_36)
informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_40_48, self.buy_pump_pull_threshold_40_48)
informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_50_24, self.buy_pump_pull_threshold_50_24)
informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_50_36, self.buy_pump_pull_threshold_50_36)
informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_50_48, self.buy_pump_pull_threshold_50_48)
informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_60_24, self.buy_pump_pull_threshold_60_24)
informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_60_36, self.buy_pump_pull_threshold_60_36)
informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_60_48, self.buy_pump_pull_threshold_60_48)
informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_70_24, self.buy_pump_pull_threshold_70_24)
informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_70_36, self.buy_pump_pull_threshold_70_36)
informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_70_48, self.buy_pump_pull_threshold_70_48)
informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_80_24, self.buy_pump_pull_threshold_80_24)
informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_80_36, self.buy_pump_pull_threshold_80_36)
informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_80_48, self.buy_pump_pull_threshold_80_48)
informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_90_24, self.buy_pump_pull_threshold_90_24)
informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_90_36, self.buy_pump_pull_threshold_90_36)
informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_90_48, self.buy_pump_pull_threshold_90_48)
informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_100_24, self.buy_pump_pull_threshold_100_24)
informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_100_36, self.buy_pump_pull_threshold_100_36)
informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_100_48, self.buy_pump_pull_threshold_100_48)
informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_110_24, self.buy_pump_pull_threshold_110_24)
informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_110_36, self.buy_pump_pull_threshold_110_36)
informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_110_48, self.buy_pump_pull_threshold_110_48)
informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_120_24, self.buy_pump_pull_threshold_120_24)
informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_120_36, self.buy_pump_pull_threshold_120_36)
informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_120_48, self.buy_pump_pull_threshold_120_48)
informative_1h['safe_dump_10'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_10_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['safe_dump_20'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_20_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['safe_dump_30'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_30_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['safe_dump_40'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_40_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['safe_dump_50'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_50_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['safe_dump_60'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_60_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open']))
informative_1h['sell_pump_48_1'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_1)
informative_1h['sell_pump_48_2'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_2)
informative_1h['sell_pump_48_3'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_3)
informative_1h['sell_pump_36_1'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_1)
informative_1h['sell_pump_36_2'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_2)
informative_1h['sell_pump_36_3'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_3)
informative_1h['sell_pump_24_1'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_1)
informative_1h['sell_pump_24_2'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_2)
informative_1h['sell_pump_24_3'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_3)
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 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']
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
dataframe['hma_5'] = qtpylib.hull_moving_average(dataframe['close'], window=30)
dataframe['hma_4'] = qtpylib.hull_moving_average(dataframe['close'], window=15)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe = dataframe.copy()
# EMA 200
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15)
dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25)
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)
#pump stregth
dataframe['zema_30'] = ftt.zema(dataframe, period=30)
dataframe['zema_200'] = ftt.zema(dataframe, period=100)
dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']
# SMA
dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
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_4'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
# Chopiness
dataframe['chop']= qtpylib.chopiness(dataframe, 14)
# Zero-Lag EMA
dataframe['zema_61'] = zema(dataframe, period=61)
# Williams %R
dataframe['r_480'] = williams_r(dataframe, period=480)
# Stochastic RSI
stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0)
dataframe['stochrsi_fastk_96'] = stochrsi['fastk']
dataframe['stochrsi_fastd_96'] = stochrsi['fastd']
# Modified Elder Ray Index
dataframe['moderi_32'] = moderi(dataframe, 32)
dataframe['moderi_64'] = moderi(dataframe, 64)
dataframe['moderi_96'] = moderi(dataframe, 96)
# hull
dataframe['hull_75'] = hull(dataframe, 75)
# zlema
dataframe['zlema_68'] = zlema(dataframe, 68)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# For sell checks
dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26'])
# Heiken Ashi
heikinashi = qtpylib.heikinashi(dataframe)
heikinashi["volume"] = dataframe["volume"]
# Profit Maximizer - PMAX
dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4
dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)
dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)
dataframe['sma_68'] = ta.SMA(dataframe, timeperiod=68)
dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75)
# HLC3
dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
# HRSI
dataframe['hull'] = (2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2))
dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2)
# ZLEMA
dataframe['zlema_2'] = pta.zlma(dataframe['hlc3'], length = 2)
dataframe['zlema_4'] = pta.zlma(dataframe['hlc3'], length = 4)
# CCI
dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20)
# ATR
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
dataframe['atr_high_thresh_1'] = (dataframe['high'] - (dataframe['atr'] * 5.4))
dataframe['atr_high_thresh_2'] = (dataframe['high'] - (dataframe['atr'] * 5.2))
dataframe['atr_high_thresh_3'] = (dataframe['high'] - (dataframe['atr'] * 5.0))
dataframe['atr_high_thresh_4'] = (dataframe['high'] - (dataframe['atr'] * 2.0))
dataframe['atr_high_thresh_q'] = (dataframe['high'] - (dataframe['atr'] * 3.0))
# 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)
dataframe['safe_dips_10'] = self.safe_dips(dataframe, self.buy_dip_threshold_10_1, self.buy_dip_threshold_10_2, self.buy_dip_threshold_10_3, self.buy_dip_threshold_10_4)
dataframe['safe_dips_20'] = self.safe_dips(dataframe, self.buy_dip_threshold_20_1, self.buy_dip_threshold_20_2, self.buy_dip_threshold_20_3, self.buy_dip_threshold_20_4)
dataframe['safe_dips_30'] = self.safe_dips(dataframe, self.buy_dip_threshold_30_1, self.buy_dip_threshold_30_2, self.buy_dip_threshold_30_3, self.buy_dip_threshold_30_4)
dataframe['safe_dips_40'] = self.safe_dips(dataframe, self.buy_dip_threshold_40_1, self.buy_dip_threshold_40_2, self.buy_dip_threshold_40_3, self.buy_dip_threshold_40_4)
dataframe['safe_dips_50'] = self.safe_dips(dataframe, self.buy_dip_threshold_50_1, self.buy_dip_threshold_50_2, self.buy_dip_threshold_50_3, self.buy_dip_threshold_50_4)
dataframe['safe_dips_60'] = self.safe_dips(dataframe, self.buy_dip_threshold_60_1, self.buy_dip_threshold_60_2, self.buy_dip_threshold_60_3, self.buy_dip_threshold_60_4)
dataframe['safe_dips_70'] = self.safe_dips(dataframe, self.buy_dip_threshold_70_1, self.buy_dip_threshold_70_2, self.buy_dip_threshold_70_3, self.buy_dip_threshold_70_4)
dataframe['safe_dips_80'] = self.safe_dips(dataframe, self.buy_dip_threshold_80_1, self.buy_dip_threshold_80_2, self.buy_dip_threshold_80_3, self.buy_dip_threshold_80_4)
dataframe['safe_dips_90'] = self.safe_dips(dataframe, self.buy_dip_threshold_90_1, self.buy_dip_threshold_90_2, self.buy_dip_threshold_90_3, self.buy_dip_threshold_90_4)
dataframe['safe_dips_100'] = self.safe_dips(dataframe, self.buy_dip_threshold_100_1, self.buy_dip_threshold_100_2, self.buy_dip_threshold_100_3, self.buy_dip_threshold_100_4)
dataframe['safe_dips_110'] = self.safe_dips(dataframe, self.buy_dip_threshold_110_1, self.buy_dip_threshold_110_2, self.buy_dip_threshold_110_3, self.buy_dip_threshold_110_4)
dataframe['safe_dips_120'] = self.safe_dips(dataframe, self.buy_dip_threshold_120_1, self.buy_dip_threshold_120_2, self.buy_dip_threshold_120_3, self.buy_dip_threshold_120_4)
dataframe['safe_dips_130'] = self.safe_dips(dataframe, self.buy_dip_threshold_130_1, self.buy_dip_threshold_130_2, self.buy_dip_threshold_130_3, self.buy_dip_threshold_130_4)
# Bollinger bands (hyperopt hard to implement)
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
### Other checks
dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
# Volume
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean()
dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)
dataframe[f'range{self.range_filter_length.value}'] = range_filter(dataframe=dataframe,length=self.range_filter_length.value)
dataframe ['test'] = (
(dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) &
( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) )
)
if not self.config['runmode'].value in ('live', 'dry_run'):
# Backtest age filter
dataframe['bt_agefilter_ok'] = False
dataframe.loc[dataframe.index > (12 * 24 * self.bt_min_age_days),'bt_agefilter_ok'] = True
else:
# Exchange downtime protection
dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)
return dataframe
def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi_14'] = 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_14'] = ta.RSI(dataframe, timeperiod=14)
dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi_14'] > 50))
# 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)
___________________________________________________________________________________________
'''
if self.has_BTC_base_tf:
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)
if self.has_BTC_info_tf:
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
___________________________________________________________________________________________
'''
if self.info_timeframe != 'none':
informative_1h = self.informative_1h_indicators(dataframe, metadata)
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
___________________________________________________________________________________________
'''
if self.res_timeframe != 'none':
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 = []
dataframe.loc[:, 'buy_tag'] = ''
for index in self.buy_protection_params:
item_buy_protection_list = [True]
global_buy_protection_params = self.buy_protection_params[index]
if self.buy_params['buy_condition_' + str(index) + '_enable']:
# Standard protections - Common to every condition
# -----------------------------------------------------------------------------------------
if global_buy_protection_params["ema_fast"]:
item_buy_protection_list.append(dataframe[f"ema_{global_buy_protection_params['ema_fast_len']}"] > dataframe['ema_200'])
if global_buy_protection_params["ema_slow"]:
item_buy_protection_list.append(dataframe[f"ema_{global_buy_protection_params['ema_slow_len']}_1h"] > dataframe['ema_200_1h'])
if global_buy_protection_params["close_above_ema_fast"]:
item_buy_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_fast_len']}"])
if global_buy_protection_params["close_above_ema_slow"]:
item_buy_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_slow_len']}_1h"])
if global_buy_protection_params["sma200_rising"]:
item_buy_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_buy_protection_params['sma200_rising_val'])))
if global_buy_protection_params["sma200_1h_rising"]:
item_buy_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_buy_protection_params['sma200_1h_rising_val'])))
if global_buy_protection_params["safe_dips"]:
item_buy_protection_list.append(dataframe[f"safe_dips_{global_buy_protection_params['safe_dips_type']}"])
if global_buy_protection_params["safe_pump"]:
item_buy_protection_list.append(dataframe[f"safe_pump_{global_buy_protection_params['safe_pump_period']}_{global_buy_protection_params['safe_pump_type']}_1h"])
if global_buy_protection_params['btc_1h_not_downtrend']:
item_buy_protection_list.append(dataframe['btc_not_downtrend_1h'])
if not self.config['runmode'].value in ('live', 'dry_run'):
if self.has_bt_agefilter:
item_buy_protection_list.append(dataframe['bt_agefilter_ok'])
else:
if self.has_downtime_protection:
item_buy_protection_list.append(dataframe['live_data_ok'])
item_buy_protection_list.append(dataframe['pump_strength'] < 0.15)
item_buy_protection_list.append(dataframe['pump_strength'] > self.dump.value)
item_buy_protection_list.append(dataframe['test'])
item_buy_protection_list.append(dataframe[f'range{self.range_filter_length.value}'] < 1 )
dataframe['pump_strength'] < self.dump.value
# Buy conditions
# -----------------------------------------------------------------------------------------
item_buy_logic = []
item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list))
# Condition #1
if index == 1:
# Non-Standard protections
# Logic
item_buy_logic.append(((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_1)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_1)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_1)
item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_1)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_1)
# Condition #2
elif index == 2:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.buy_rsi_1h_diff_2)
item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_2)
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_2))
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_2))
# Condition #3
elif index == 3:
# Non-Standard protections
item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_3))
# Logic
item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0))
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3))
item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3))
item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_bb40_tail_bbdelta_3))
item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
item_buy_logic.append(dataframe['cti'] < self.buy_cti_3)
# Condition #4
elif index == 4:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_50'])
item_buy_logic.append(dataframe['close'] < self.buy_bb20_close_bblowerband_4 * dataframe['bb20_2_low'])
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4))
item_buy_logic.append(dataframe['cti'] < self.buy_cti_4)
# Condition #5
elif index == 5:
# Non-Standard protections
item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_5))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_5))
item_buy_logic.append(dataframe['cti'] < self.buy_cti_5)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_5))
# Condition #6
elif index == 6:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_6))
# Condition #7
elif index == 7:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['cti'] < self.buy_cti_7)
# Condition #8
elif index == 8:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_96'])
item_buy_logic.append(dataframe['cti'] < self.buy_cti_8)
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_8))
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_8)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_8))
# Condition #9
elif index == 9:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_50'] > dataframe['ema_200'])
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_9)
item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_9)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_9)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_9)
item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_9)
# Condition #10
elif index == 10:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10)
item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_10)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_10)
# Condition #11
elif index == 11:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
# Logic
item_buy_logic.append(((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_11)
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_11)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_11)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_11)
item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_11)
# Condition #12
elif index == 12:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12)
item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_12)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_12)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_12)
# Condition #13
elif index == 13:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_13)
item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_13)
# Condition #14
elif index == 14:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_14))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_14))
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_14)
# Condition #15
elif index == 15:
# Non-Standard protections
item_buy_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15)
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_15))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_15)
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15)
# Condition #16
elif index == 16:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16)
item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_16)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_16)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_16)
# Condition #17
elif index == 17:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17)
item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_17)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_17)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_17))
# Condition #18
elif index == 18:
# Non-Standard protections
item_buy_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20))
item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36))
# Logic
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_18)
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_18))
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_18))
item_buy_logic.append(dataframe['cti'] < self.buy_cti_18)
# Condition #19
elif index == 19:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h'])
item_buy_logic.append(dataframe['low'] < dataframe['ema_100_1h'])
item_buy_logic.append(dataframe['close'] > dataframe['ema_100_1h'])
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_19)
item_buy_logic.append(dataframe['chop'] < self.buy_chop_max_19)
item_buy_logic.append(dataframe['moderi_32'] == True)
item_buy_logic.append(dataframe['moderi_64'] == True)
item_buy_logic.append(dataframe['moderi_96'] == True)
# Condition #20
elif index == 20:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_20)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_20)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_20)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_20))
# Condition #21
elif index == 21:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_21)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_21)
item_buy_logic.append(dataframe['cti'] < self.buy_cti_21)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_21))
# Condition #22
elif index == 22:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12))
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36))
# Logic
item_buy_logic.append((dataframe['volume_mean_4'] * self.buy_volume_22) > dataframe['volume'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_22)
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_22))
item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_22)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_22)
# Condition #23
elif index == 23:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_23_bb_offset))
item_buy_logic.append(dataframe['ewo'] > self.buy_23_ewo)
item_buy_logic.append(dataframe['cti'] < self.buy_23_cti)
item_buy_logic.append(dataframe['r_480'] > self.buy_23_r)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_23_r_1h)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_23_rsi)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_23_rsi_1h)
# Condition #24
elif index == 24:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12))
item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h'])
item_buy_logic.append(dataframe['cmf_1h'].shift(12) < 0)
item_buy_logic.append(dataframe['cmf_1h'] > 0)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_24_rsi_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_24_rsi_1h_min)
# Condition #25
elif index == 25:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift())
item_buy_logic.append(dataframe['rsi_4'] < self.buy_25_rsi_4)
item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h'])
item_buy_logic.append(dataframe['close'] < (dataframe['sma_20'] * self.buy_25_ma_offset))
item_buy_logic.append(dataframe['open'] > (dataframe['sma_20'] * self.buy_25_ma_offset))
item_buy_logic.append(
(dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) |
(dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h'])
)
item_buy_logic.append(dataframe['cti'] < self.buy_25_cti)
# Condition #26
elif index == 26:
# Non-Standard protections
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'])
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['zema_61'] * self.buy_26_zema_low_offset))
item_buy_logic.append(dataframe['cti'] < self.buy_26_cti)
item_buy_logic.append(dataframe['r_480'] > self.buy_26_r)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_26_r_1h)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_26_volume))
# Condition #27
elif index == 27:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['r_480'] < -self.buy_27_wr_max)
item_buy_logic.append(dataframe['r_480_1h'] < -self.buy_27_wr_1h_max)
item_buy_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.buy_27_rsi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_27_cti)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_27_volume))
# Condition #28
elif index == 28:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_64'] == True)
item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_28_ma_offset)
item_buy_logic.append(dataframe['ewo'] > self.buy_28_ewo)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_28_rsi)
item_buy_logic.append(dataframe['cti'] < self.buy_28_cti)
# Condition #29
elif index == 29:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_64'] == True)
item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_29_ma_offset)
item_buy_logic.append(dataframe['ewo'] < self.buy_29_ewo)
item_buy_logic.append(dataframe['cti'] < self.buy_29_cti)
# Condition #30
elif index == 30:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_64'] == False)
item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_30_ma_offset)
item_buy_logic.append(dataframe['ewo'] > self.buy_30_ewo)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_30_rsi)
item_buy_logic.append(dataframe['cti'] < self.buy_30_cti)
# Condition #31
elif index == 31:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_64'] == False)
item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_31_ma_offset )
item_buy_logic.append(dataframe['ewo'] < self.buy_31_ewo)
item_buy_logic.append(dataframe['r_480'] < self.buy_31_wr)
item_buy_logic.append(dataframe['cti'] < self.buy_31_cti)
# Condition #32 - Quick mode buy
elif index == 32:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_32'])
item_buy_logic.append(dataframe['moderi_64'])
item_buy_logic.append(dataframe['moderi_96'])
item_buy_logic.append(dataframe['cti'] < self.buy_32_cti)
item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1))
item_buy_logic.append(dataframe['rsi_4'] < self.buy_32_rsi)
item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h'])
item_buy_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_32_dip)
item_buy_logic.append(dataframe['close'] < (dataframe['sma_15'] * self.buy_32_ma_offset))
item_buy_logic.append(
((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h'])) |
((dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h'])))
# Condition #33 - Quick mode buy
elif index == 33:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['moderi_96'])
item_buy_logic.append(dataframe['cti'] < self.buy_33_cti)
item_buy_logic.append(dataframe['close'] < (dataframe['ema_13'] * self.buy_33_ma_offset))
item_buy_logic.append(dataframe['ewo'] > self.buy_33_ewo)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_33_rsi)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_33_volume))
# Condition #34 - Quick mode buy
elif index == 34:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['cti'] < self.buy_34_cti)
item_buy_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_34_dip)
item_buy_logic.append(dataframe['close'] < dataframe['ema_13'] * self.buy_34_ma_offset)
item_buy_logic.append(dataframe['ewo'] < self.buy_34_ewo)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_34_volume))
# Condition #35 - PMAX0 buy
elif index == 35:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_35_ma_offset)
item_buy_logic.append(dataframe['ewo'] > self.buy_35_ewo)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_35_rsi)
item_buy_logic.append(dataframe['cti'] < self.buy_35_cti)
# Condition #36 - PMAX1 buy
elif index == 36:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_36_ma_offset)
item_buy_logic.append(dataframe['ewo'] < self.buy_36_ewo)
item_buy_logic.append(dataframe['cti'] < self.buy_36_cti)
# Condition #37 - PMAX2 buy
elif index == 37:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset)
item_buy_logic.append(dataframe['ewo'] > self.buy_37_ewo)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_37_rsi)
item_buy_logic.append(dataframe['cti'] < self.buy_37_cti)
item_buy_logic.append(dataframe['safe_dump_50_1h'])
# Condition #38 - PMAX3 buy
elif index == 38:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_38_ma_offset)
item_buy_logic.append(dataframe['ewo'] < self.buy_38_ewo)
item_buy_logic.append(dataframe['cti'] < self.buy_38_cti)
# Condition #39 - Ichimoku
elif index == 39:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h'])
item_buy_logic.append(dataframe['close'] > dataframe['cloud_top_1h'])
item_buy_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h'])
item_buy_logic.append(dataframe['chikou_span_greater_1h'])
item_buy_logic.append(dataframe['efi_1h'] > 0)
item_buy_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h'])
item_buy_logic.append(dataframe['close'] < dataframe['ssl_up_1h'])
item_buy_logic.append(dataframe['cti'] < self.buy_39_cti)
item_buy_logic.append(dataframe['r_480'] > self.buy_39_r)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_39_r_1h)
item_buy_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12))
# Start of trend
item_buy_logic.append(
(dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) |
(dataframe['ssl_up_1h'].shift(12) < dataframe['ssl_down_1h'].shift(12))
)
# Condition #40 - ZLEMA X buy
elif index == 40:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(qtpylib.crossed_above(dataframe['zlema_2'], dataframe['zlema_4']))
item_buy_logic.append(dataframe['hrsi'] < self.buy_40_hrsi)
item_buy_logic.append(dataframe['cci'] < self.buy_40_cci)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_40_rsi)
item_buy_logic.append(dataframe['cti'] < self.buy_40_cti)
item_buy_logic.append(dataframe['r_480'] > self.buy_40_r)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_40_r_1h)
# Condition #41
elif index == 41:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
item_buy_logic.append(dataframe['cti_1h'] < self.buy_41_cti_1h)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_41_r_1h)
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_41_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_41_cti)
item_buy_logic.append(dataframe['r_480'] < self.buy_41_r)
# Condition #42
elif index == 42:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
item_buy_logic.append(dataframe['cti_1h'] < self.buy_42_cti_1h)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_42_r_1h)
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_42_ema_open_mult))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_42_bb_offset))
# Condition #43
elif index == 43:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
item_buy_logic.append(dataframe['cti_1h'] < self.buy_43_cti_1h)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_43_r_1h)
item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0))
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_43_bb40_bbdelta_close))
item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_43_bb40_closedelta_close))
item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_43_bb40_tail_bbdelta))
item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
item_buy_logic.append(dataframe['cti'] < self.buy_43_cti)
item_buy_logic.append(dataframe['r_480'] > self.buy_43_r)
# Condition #44
elif index == 44:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset))
item_buy_logic.append(dataframe['ewo'] < self.buy_44_ewo)
item_buy_logic.append(dataframe['cti'] < self.buy_44_cti)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_44_r_1h)
item_buy_logic.append(dataframe['volume'] > 0)
item_buy = reduce(lambda x, y: x & y, item_buy_logic)
dataframe.loc[item_buy, 'buy_tag'] += str(index) + ' '
conditions.append(item_buy)
if conditions:
dataframe.loc[:, 'buy'] = reduce(lambda x, y: x | y, conditions)
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[:, 'sell'] = 0
return dataframe
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:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
# Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
if self.holdSupportEnabled and self.config['runmode'].value in ('live', 'dry_run'):
self.load_hold_trades_config()
if not self.hold_trades_cache:
# Cache hasn't been setup, likely because the corresponding file does not exist, sell
return True
if not self.hold_trades_cache.data:
# We have no pairs we want to hold until profit, sell
return True
if trade.id not in self.hold_trades_cache.data:
# This pair is not on the list to hold until profit, sell
return True
trade_profit_ratio = self.hold_trades_cache.data[trade.id]
current_profit_ratio = trade.calc_profit_ratio(rate)
if sell_reason == "force_sell":
formatted_profit_ratio = "{}%".format(trade_profit_ratio * 100)
formatted_current_profit_ratio = "{}%".format(current_profit_ratio * 100)
log.warning(
"Force selling %s even though the current profit of %s < %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return True
elif current_profit_ratio >= trade_profit_ratio:
# This pair is on the list to hold, and we reached minimum profit, sell
return True
# This pair is on the list to hold, and we haven't reached minimum profit, hold
return False
else:
return True
initial_safety_order_trigger = -0.018
max_safety_orders = 8
safety_order_step_scale = 1.2
safety_order_volume_scale = 1.4
def top_percent_change_dca(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
"""
if length == 0:
return (dataframe['open'] - dataframe['close']) / dataframe['close']
else:
return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs):
if current_profit > self.initial_safety_order_trigger:
return None
# credits to reinuvader for not blindly executing safety orders
# Obtain pair dataframe.
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
# Only buy when it seems it's climbing back up
last_candle = dataframe.iloc[-1].squeeze()
#previous_candle = dataframe.iloc[-2].squeeze()
#previous2_candle = dataframe.iloc[-3].squeeze()
#if last_candle['close'] / previous_candle['close'] < 1.02 :
#if last_candle['close'] < previous_candle['close']:
filled_buys = trade.select_filled_orders('buy')
count_of_buys = len(filled_buys)
if last_candle['pump_strength'] < self.dump.value :
logger.info(f"DCA for {trade.pair} waiting for pump_strength ({last_candle['pump_strength']}) to rise above -0.15")
return None
#elif (count_of_buys > 3):
# if (last_candle['cmf_1h'] < 0.00) and (last_candle['rsi_1h'] < 28) and (last_candle['close'] < last_candle['open']):
# logger.info(f"DCA for {trade.pair} waiting RSI 1h ({last_candle['rsi_1h']}) to rise above 30 and CMF 1h ({last_candle['cmf_1h']}) to rise above 0")
# return None
#if (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30):
# logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30")
#if (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']):
# return None
#count_of_buys = 0
#for order in trade.orders:
# if order.ft_is_open or order.ft_order_side != 'buy':
# continue
# if order.status == "closed":
# count_of_buys += 1
if 1 <= count_of_buys <= self.max_safety_orders:
safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys)
if (self.safety_order_step_scale > 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1))
elif (self.safety_order_step_scale < 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale))
if current_profit <= (-1 * abs(safety_order_trigger)):
try:
# This returns first order stake size
stake_amount = filled_buys[0].cost
# This then calculates current safety order size
stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1))
amount = stake_amount / current_rate
logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}")
return stake_amount
except Exception as exception:
logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
return None
return None
# Elliot Wave Oscillator
def ewo(dataframe, sma1_length=5, sma2_length=35):
sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
smadif = (sma1 - sma2) / dataframe['close'] * 100
return smadif
# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
"""Chaikin Money Flow (CMF)
It measures the amount of Money Flow Volume over a specific period.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
Args:
dataframe(pandas.Dataframe): dataframe containing ohlcv
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
mfv = mfv.fillna(0.0) # float division by zero
mfv *= dataframe['volume']
cmf = (mfv.rolling(n, min_periods=0).sum()
/ dataframe['volume'].rolling(n, min_periods=0).sum())
if fillna:
cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
return Series(cmf, name='cmf')
# 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="{0} Williams %R".format(period),
)
return WR * -100
# Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
"""Indicator: Volume Weighted Moving Average (VWMA)"""
# Calculate Result
pv = dataframe['close'] * dataframe['volume']
vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
return vwma
# Modified Elder Ray Index
def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series:
slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma))
return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend
# zlema
def zlema(dataframe, timeperiod):
lag = int(math.floor((timeperiod - 1) / 2) )
if isinstance(dataframe, Series):
ema_data = dataframe + (dataframe - dataframe.shift(lag))
else:
ema_data = dataframe['close'] + (dataframe['close'] - dataframe['close'] .shift(lag))
return ta.EMA(ema_data, timeperiod = timeperiod)
# zlhull
def zlhull(dataframe, timeperiod):
lag = int(math.floor((timeperiod - 1) / 2) )
if isinstance(dataframe, Series):
wma_data = dataframe + (dataframe - dataframe.shift(lag))
else:
wma_data = dataframe['close'] + (dataframe['close'] - dataframe['close'] .shift(lag))
return ta.WMA(
2 * ta.WMA(wma_data, int(math.floor(timeperiod/2))) - ta.WMA(wma_data, timeperiod), int(round(np.sqrt(timeperiod)))
)
# hull
def hull(dataframe, timeperiod):
if isinstance(dataframe, Series):
return ta.WMA(
2 * ta.WMA(dataframe, int(math.floor(timeperiod/2))) - ta.WMA(dataframe, timeperiod), int(round(np.sqrt(timeperiod)))
)
else:
return ta.WMA(
2 * ta.WMA(dataframe['close'], int(math.floor(timeperiod/2))) - ta.WMA(dataframe['close'], timeperiod), int(round(np.sqrt(timeperiod)))
)
# PMAX
def pmax(df, period, multiplier, length, MAtype, src):
period = int(period)
multiplier = int(multiplier)
length = int(length)
MAtype = int(MAtype)
src = int(src)
mavalue = 'MA_' + str(MAtype) + '_' + str(length)
atr = 'ATR_' + str(period)
pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
# MAtype==1 --> EMA
# MAtype==2 --> DEMA
# MAtype==3 --> T3
# MAtype==4 --> SMA
# MAtype==5 --> VIDYA
# MAtype==6 --> TEMA
# MAtype==7 --> WMA
# MAtype==8 --> VWMA
# MAtype==9 --> zema
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
mavalue = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
mavalue = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
mavalue = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
mavalue = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
mavalue = VIDYA(df, length=length)
elif MAtype == 6:
mavalue = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
mavalue = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
mavalue = vwma(df, length)
elif MAtype == 9:
mavalue = zema(df, period=length)
df[atr] = ta.ATR(df, timeperiod=period)
df['basic_ub'] = mavalue + ((multiplier/10) * df[atr])
df['basic_lb'] = mavalue - ((multiplier/10) * df[atr])
basic_ub = df['basic_ub'].values
final_ub = np.full(len(df), 0.00)
basic_lb = df['basic_lb'].values
final_lb = np.full(len(df), 0.00)
for i in range(period, len(df)):
final_ub[i] = basic_ub[i] if (
basic_ub[i] < final_ub[i - 1]
or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
final_lb[i] = basic_lb[i] if (
basic_lb[i] > final_lb[i - 1]
or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]
df['final_ub'] = final_ub
df['final_lb'] = final_lb
pm_arr = np.full(len(df), 0.00)
for i in range(period, len(df)):
pm_arr[i] = (
final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] <= final_ub[i])
else final_lb[i] if (
pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] > final_ub[i]) else final_lb[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] >= final_lb[i]) else final_ub[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] < final_lb[i]) else 0.00)
pm = Series(pm_arr)
# Mark the trend direction up/down
pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN)
return pm, pmx
def calc_streaks(series: Series):
# logic tables
geq = series >= series.shift(1) # True if rising
eq = series == series.shift(1) # True if equal
logic_table = concat([geq, eq], axis=1)
streaks = [0] # holds the streak duration, starts with 0
for row in logic_table.iloc[1:].itertuples(): # iterate through logic table
if row[2]: # same value as before
streaks.append(0)
continue
last_value = streaks[-1]
if row[1]: # higher value than before
streaks.append(last_value + 1 if last_value >=
0 else 1) # increase or reset to +1
else: # lower value than before
streaks.append(last_value - 1 if last_value <
0 else -1) # decrease or reset to -1
return streaks
# SSL Channels
def SSLChannels(dataframe, length = 7):
df = dataframe.copy()
ATR = ta.ATR(dataframe, timeperiod=14)
smaHigh = dataframe['high'].rolling(length).mean() + ATR
smaLow = dataframe['low'].rolling(length).mean() - ATR
hlv = Series(np.where(dataframe['close'] > smaHigh, 1, np.where(dataframe['close'] < smaLow, -1, np.NAN)))
hlv = hlv.ffill()
sslDown = np.where(hlv < 0, smaHigh, smaLow)
sslUp = np.where(hlv < 0, smaLow, smaHigh)
return sslDown, sslUp
class Cache:
def __init__(self, path):
self.path = path
self.data = {}
self._mtime = None
self._previous_data = {}
try:
self.load()
except FileNotFoundError:
pass
def load(self):
if not self._mtime or self.path.stat().st_mtime_ns != self._mtime:
self._load()
def save(self):
if self.data != self._previous_data:
self._save()
def process_loaded_data(self, data):
return data
def _load(self):
# This method only exists to simplify unit testing
with self.path.open("r") as rfh:
try:
data = json_load(rfh)
except rapidjson.JSONDecodeError as exc:
log.error("Failed to load JSON from %s: %s", self.path, exc)
else:
self.data = self.process_loaded_data(data)
self._previous_data = copy.deepcopy(self.data)
self._mtime = self.path.stat().st_mtime_ns
def _save(self):
# This method only exists to simplify unit testing
file_dump_json(self.path, self.data, is_zip=False, log=True)
self._mtime = self.path.stat().st_mtime
self._previous_data = copy.deepcopy(self.data)
class HoldsCache(Cache):
def save(self):
raise RuntimeError("The holds cache does not allow programatical save")
def process_loaded_data(self, data):
trade_ids = data.get("trade_ids")
if not trade_ids:
return {}
rdata = {}
open_trades = {
trade.id: trade for trade in Trade.get_trades_proxy(is_open=True)
}
if isinstance(trade_ids, dict):
# New syntax
for trade_id, profit_ratio in trade_ids.items():
try:
trade_id = int(trade_id)
except ValueError:
log.error(
"The trade_id(%s) defined under 'trade_ids' in %s is not an integer",
trade_id, self.path
)
continue
if not isinstance(profit_ratio, float):
log.error(
"The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float",
profit_ratio,
trade_id,
self.path
)
if trade_id in open_trades:
formatted_profit_ratio = "{}%".format(profit_ratio * 100)
log.warning(
"The trade %s is configured to HOLD until the profit ratio of %s is met",
open_trades[trade_id],
formatted_profit_ratio
)
rdata[trade_id] = profit_ratio
else:
log.warning(
"The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s",
trade_id,
self.path
)
else:
# Initial Syntax
profit_ratio = data.get("profit_ratio")
if profit_ratio:
if not isinstance(profit_ratio, float):
log.error(
"The 'profit_ratio' config value(%s) in %s is not a float",
profit_ratio,
self.path
)
else:
profit_ratio = 0.005
formatted_profit_ratio = "{}%".format(profit_ratio * 100)
for trade_id in trade_ids:
if not isinstance(trade_id, int):
log.error(
"The trade_id(%s) defined under 'trade_ids' in %s is not an integer",
trade_id, self.path
)
continue
if trade_id in open_trades:
log.warning(
"The trade %s is configured to HOLD until the profit ratio of %s is met",
open_trades[trade_id],
formatted_profit_ratio
)
rdata[trade_id] = profit_ratio
else:
log.warning(
"The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s",
trade_id,
self.path
)
return rdata
def range_filter(dataframe, length = 10, min_rate_of_change=0.01):
df = dataframe[['date','open','high','low','close','volume']].copy()
highest_high = df['high'].rolling(length).max()
lowest_low = df['low'].rolling(length).min()
df['range'] = ((highest_high - lowest_low) / lowest_low)
return df['range']