Timeframe
5m
Direction
Long Only
Stoploss
-50.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
2
Startup Candles
N/A
Indicators
25
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.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.data.dataprovider import DataProvider
from pandas import DataFrame, Series, concat
from functools import reduce
import math
from typing import Dict
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 time
log = logging.getLogger(__name__)
#log.setLevel(logging.DEBUG)
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 ##
## https://github.com/iterativv/NostalgiaForInfinity ##
## ##
## 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 ##
## ##
## -------- SPECIFIC TRADES ---------------------------------------------------------------------------- ##
## In case you want to have SOME of the trades to only be sold when on profit, add a file named ##
## "nfi-hold-trades.json" in the user_data directory ##
## ##
## 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 class attribute ##
## ##
## -------- SPECIFIC PAIRS ----------------------------------------------------------------------------- ##
## In case you want to have some pairs to always be on held until a specific profit, using the same ##
## "hold-trades.json" file add something like: ##
## ##
## {"trade_pairs": {"BTC/USDT": 0.001, "ETH/USDT": -0.005}} ##
## ##
## -------- SPECIFIC TRADES AND PAIRS ------------------------------------------------------------------ ##
## It is also valid to include specific trades and pairs on the holds file, for example: ##
## ##
## {"trade_ids": {"1": 0.001}, "trade_pairs": {"BTC/USDT": 0.001}} ##
###########################################################################################################
## DONATIONS ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ##
## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ##
## ##
## REFERRAL LINKS ##
## ##
## Binance: https://accounts.binance.com/en/register?ref=EAZC47FM (5% discount on trading fees) ##
## Kucoin: https://www.kucoin.com/r/QBSSSPYV (5% discount on trading fees) ##
## Gate.io: https://www.gate.io/signup/8054544 ##
## Huobi: https://www.huobi.com/en-us/topic/double-reward/?invite_code=ubpt2223 ##
###########################################################################################################
class NostalgiaForInfinityNext(IStrategy):
INTERFACE_VERSION = 2
# ROI table:
minimal_roi = {
"0": 10,
}
stoploss = -0.50
# 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 = '1h'
info_timeframe_1d = '1d'
# BTC informative
has_BTC_base_tf = False
has_BTC_info_tf = True
has_BTC_daily_tf = False
# 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 = True
# Coin Metrics
coin_metrics = {}
coin_metrics['top_traded_enabled'] = False
coin_metrics['top_traded_updated'] = False
coin_metrics['top_traded_len'] = 10
coin_metrics['tt_dataframe'] = DataFrame()
coin_metrics['top_grossing_enabled'] = False
coin_metrics['top_grossing_updated'] = False
coin_metrics['top_grossing_len'] = 20
coin_metrics['tg_dataframe'] = DataFrame()
coin_metrics['current_whitelist'] = []
# 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,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
#############################################################
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": False,
"buy_condition_36_enable": False,
"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,
"buy_condition_45_enable": True,
"buy_condition_46_enable": True,
"buy_condition_47_enable": True,
"buy_condition_48_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,
#############
}
profit_target_params = {
#############
# Enable/Disable conditions
"profit_target_1_enable": False,
#############
}
#############################################################
buy_protection_params = {
1: {
"ema_fast" : False,
"ema_fast_len" : "26",
"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" : "28",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "70",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
2: {
"ema_fast" : True,
"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" : True,
"sma200_1h_rising_val" : "48",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "20",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.4
},
3: {
"ema_fast" : False,
"ema_fast_len" : "100",
"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" : "200",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "36",
"safe_dips_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : True,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
4: {
"ema_fast" : True,
"ema_fast_len" : "50",
"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" : "20",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
5: {
"ema_fast" : False,
"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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "80",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
8: {
"ema_fast" : True,
"ema_fast_len" : "12",
"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" : "36",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "20",
"safe_dips_threshold_0" : 0.016,
"safe_dips_threshold_2" : 0.11,
"safe_dips_threshold_12" : 0.26,
"safe_dips_threshold_144" : 0.44,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.05
},
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_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.1
},
10: {
"ema_fast" : True,
"ema_fast_len" : "35",
"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" : "24",
"safe_dips_threshold_0" : 0.016,
"safe_dips_threshold_2" : 0.11,
"safe_dips_threshold_12" : 0.26,
"safe_dips_threshold_144" : 0.44,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.6
},
11: {
"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" : "24",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "36",
"safe_dips_threshold_0" : 0.022,
"safe_dips_threshold_2" : 0.18,
"safe_dips_threshold_12" : 0.34,
"safe_dips_threshold_144" : 0.56,
"safe_pump" : False,
"safe_pump_type" : "120",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.3
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.5
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "80",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.027,
"safe_dips_threshold_2" : 0.26,
"safe_dips_threshold_12" : 0.44,
"safe_dips_threshold_144" : 0.84,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.026,
"safe_dips_threshold_2" : 0.24,
"safe_dips_threshold_12" : 0.42,
"safe_dips_threshold_144" : 0.8,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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" : "36",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.025,
"safe_dips_threshold_2" : 0.23,
"safe_dips_threshold_12" : 0.4,
"safe_dips_threshold_144" : 0.7,
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "110",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.6
},
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_threshold_0" : 0.022,
"safe_dips_threshold_2" : 0.1,
"safe_dips_threshold_12" : 0.3,
"safe_dips_threshold_144" : 0.84,
"safe_pump" : True,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.016,
"safe_dips_threshold_2" : 0.11,
"safe_dips_threshold_12" : 0.26,
"safe_dips_threshold_144" : 0.44,
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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" : False,
"sma200_rising_val" : "20",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "36",
"safe_dips_threshold_0" : 0.024,
"safe_dips_threshold_2" : 0.22,
"safe_dips_threshold_12" : 0.38,
"safe_dips_threshold_144" : 0.66,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 0.98,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.4
},
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" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips_threshold_0" : 0.016,
"safe_dips_threshold_2" : 0.1,
"safe_dips_threshold_12" : 0.11,
"safe_dips_threshold_144" : 0.22,
"safe_pump" : True,
"safe_pump_type" : "100",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.35
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "50",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
28: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "12",
"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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 0.99,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.32
},
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_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.01
},
30: {
"ema_fast" : False,
"ema_fast_len" : "50",
"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" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "110",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.02,
"safe_dips_threshold_2" : 0.14,
"safe_dips_threshold_12" : 0.32,
"safe_dips_threshold_144" : 0.5,
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "sup3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 0.98,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
32: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "12",
"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" : True,
"sma200_1h_rising_val" : "50",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "80",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
33: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "12",
"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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 0.99,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
35: {
"ema_fast" : False,
"ema_fast_len" : "50",
"ema_slow" : False,
"ema_slow_len" : "12",
"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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.1
},
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_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
37: {
"ema_fast" : True,
"ema_fast_len" : "50",
"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" : "100",
"sma200_rising" : False,
"sma200_rising_val" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "50",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.5
},
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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "10",
"safe_pump_period" : "36",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
39: {
"ema_fast" : False,
"ema_fast_len" : "100",
"ema_slow" : True,
"ema_slow_len" : "12",
"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" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "50",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
40: {
"ema_fast" : True,
"ema_fast_len" : "50",
"ema_slow" : True,
"ema_slow_len" : "12",
"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_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : True,
"safe_pump_type" : "100",
"safe_pump_period" : "48",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.2
},
41: {
"ema_fast" : False,
"ema_fast_len" : "50",
"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" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips_threshold_0" : 0.015,
"safe_dips_threshold_2" : 0.1,
"safe_dips_threshold_12" : 0.24,
"safe_dips_threshold_144" : 0.42,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.027,
"safe_dips_threshold_2" : 0.26,
"safe_dips_threshold_12" : 0.44,
"safe_dips_threshold_144" : 0.84,
"safe_pump" : True,
"safe_pump_type" : "10",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : 0.024,
"safe_dips_threshold_2" : 0.22,
"safe_dips_threshold_12" : 0.38,
"safe_dips_threshold_144" : 0.66,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
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_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
45: {
"ema_fast" : True,
"ema_fast_len" : "15",
"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" : "20",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.3,
"safe_dips_threshold_12" : 0.48,
"safe_dips_threshold_144" : 0.9,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
46: {
"ema_fast" : False,
"ema_fast_len" : "50",
"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" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "20",
"safe_dips_threshold_0" : 0.028,
"safe_dips_threshold_2" : 0.06,
"safe_dips_threshold_12" : 0.25,
"safe_dips_threshold_144" : 0.26,
"safe_pump" : False,
"safe_pump_type" : "100",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : True,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 2.0
},
47: {
"ema_fast" : False,
"ema_fast_len" : "12",
"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" : "30",
"sma200_1h_rising" : False,
"sma200_1h_rising_val" : "24",
"safe_dips_threshold_0" : 0.025,
"safe_dips_threshold_2" : 0.05,
"safe_dips_threshold_12" : 0.25,
"safe_dips_threshold_144" : 0.5,
"safe_pump" : True,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
},
48: {
"ema_fast" : True,
"ema_fast_len" : "12",
"ema_slow" : True,
"ema_slow_len" : "12",
"close_above_ema_fast" : True,
"close_above_ema_fast_len" : "200",
"close_above_ema_slow" : True,
"close_above_ema_slow_len" : "200",
"sma200_rising" : True,
"sma200_rising_val" : "30",
"sma200_1h_rising" : True,
"sma200_1h_rising_val" : "24",
"safe_dips_threshold_0" : None,
"safe_dips_threshold_2" : None,
"safe_dips_threshold_12" : None,
"safe_dips_threshold_144" : None,
"safe_pump" : False,
"safe_pump_type" : "120",
"safe_pump_period" : "24",
"btc_1h_not_downtrend" : False,
"close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_over_pivot_offset" : 1.0,
"close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3
"close_under_pivot_offset" : 1.0
}
}
# 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_1_min_inc = 0.022
buy_1_rsi_max = 32.0
buy_2_r_14_max = -75.0
buy_1_mfi_max = 46.0
buy_1_rsi_1h_min = 30.0
buy_1_rsi_1h_max = 84.0
buy_2_rsi_1h_diff = 39.0
buy_2_mfi = 49.0
buy_2_cti_max = -0.9
buy_2_r_480_min = -95.0
buy_2_r_480_max = -46.0
buy_2_cti_1h_max = 0.9
buy_2_volume = 2.0
buy_3_bb40_bbdelta_close = 0.057
buy_3_bb40_closedelta_close = 0.023
buy_3_bb40_tail_bbdelta = 0.418
buy_3_cti_max = -0.5
buy_3_cci_36_osc_min = -0.25
buy_3_crsi_1h_min = 20.0
buy_3_r_480_1h_min = -48.0
buy_3_cti_1h_max = 0.82
buy_4_bb20_close_bblowerband = 0.98
buy_4_bb20_volume = 10.0
buy_4_cti_max = -0.8
buy_5_ema_rel = 0.84
buy_5_ema_open_mult = 0.02
buy_5_bb_offset = 0.999
buy_5_cti_max = -0.5
buy_5_r_14_max = -94.0
buy_5_rsi_14_min = 25.0
buy_5_mfi_min = 18.0
buy_5_crsi_1h_min = 12.0
buy_5_volume = 1.6
buy_6_ema_open_mult = 0.019
buy_6_bb_offset = 0.984
buy_6_r_14_max = -85.0
buy_6_crsi_1h_min = 15.0
buy_6_cti_1h_min = 0.0
buy_7_ema_open_mult = 0.031
buy_7_ma_offset = 0.978
buy_7_cti_max = -0.9
buy_7_rsi_max = 45.0
buy_8_bb_offset = 0.986
buy_8_r_14_max = -98.0
buy_8_cti_1h_max = 0.95
buy_8_r_480_1h_max = -18.0
buy_8_volume = 1.8
buy_9_ma_offset = 0.968
buy_9_bb_offset = 0.982
buy_9_mfi_max = 50.0
buy_9_cti_max = -0.85
buy_9_r_14_max = -94.0
buy_9_rsi_1h_min = 20.0
buy_9_rsi_1h_max = 88.0
buy_9_crsi_1h_min = 21.0
buy_10_ma_offset_high = 0.94
buy_10_bb_offset = 0.984
buy_10_r_14_max = -88.0
buy_10_cti_1h_min = -0.5
buy_10_cti_1h_max = 0.94
buy_11_ma_offset = 0.956
buy_11_min_inc = 0.022
buy_11_rsi_max = 37.0
buy_11_mfi_max = 46.0
buy_11_cci_max = -120.0
buy_11_r_480_max = -32.0
buy_11_rsi_1h_min = 30.0
buy_11_rsi_1h_max = 84.0
buy_11_cti_1h_max = 0.91
buy_11_r_480_1h_max = -25.0
buy_11_crsi_1h_min = 26.0
buy_12_ma_offset = 0.927
buy_12_ewo_min = 2.0
buy_12_rsi_max = 32.0
buy_12_cti_max = -0.9
buy_13_ma_offset = 0.99
buy_13_cti_max = -0.92
buy_13_ewo_max = -6.0
buy_13_cti_1h_max = -0.88
buy_13_crsi_1h_min = 10.0
buy_14_ema_open_mult = 0.014
buy_14_bb_offset = 0.989
buy_14_ma_offset = 0.945
buy_14_cti_max = -0.85
buy_15_ema_open_mult = 0.0238
buy_15_ma_offset = 0.958
buy_15_rsi_min = 28.0
buy_15_cti_1h_min = -0.2
buy_16_ma_offset = 0.942
buy_16_ewo_min = 2.0
buy_16_rsi_max = 36.0
buy_16_cti_max = -0.9
buy_17_ma_offset = 0.999
buy_17_ewo_max = -7.0
buy_17_cti_max = -0.96
buy_17_crsi_1h_min = 12.0
buy_17_volume = 2.0
buy_18_bb_offset = 0.986
buy_18_rsi_max = 33.5
buy_18_cti_max = -0.85
buy_18_cti_1h_max = 0.91
buy_18_volume = 2.0
buy_19_rsi_1h_min = 30.0
buy_19_chop_max = 21.3
buy_20_rsi_14_max = 36.0
buy_20_rsi_14_1h_max = 16.0
buy_20_cti_max = -0.84
buy_20_volume = 2.0
buy_21_rsi_14_max = 14.0
buy_21_rsi_14_1h_max = 28.0
buy_21_cti_max = -0.902
buy_21_volume = 2.0
buy_22_volume = 2.0
buy_22_bb_offset = 0.984
buy_22_ma_offset = 0.98
buy_22_ewo_min = 5.6
buy_22_rsi_14_max = 36.0
buy_22_cti_max = -0.54
buy_22_r_480_max = -40.0
buy_22_cti_1h_min = -0.5
buy_23_bb_offset = 0.984
buy_23_ewo_min = 3.4
buy_23_rsi_14_max = 28.0
buy_23_cti_max = -0.74
buy_23_rsi_14_1h_max = 80.0
buy_23_r_480_1h_min = -95.0
buy_23_cti_1h_max = 0.92
buy_24_rsi_14_max = 50.0
buy_24_rsi_14_1h_min = 66.9
buy_25_ma_offset = 0.953
buy_25_rsi_4_max = 30.0
buy_25_cti_max = -0.78
buy_25_cci_max = -200.0
buy_26_zema_low_offset = 0.9405
buy_26_cti_max = -0.72
buy_26_cci_max = -166.0
buy_26_r_14_max = -98.0
buy_26_cti_1h_max = 0.95
buy_26_volume = 2.0
buy_27_wr_max = -95.0
buy_27_r_14 = -100.0
buy_27_wr_1h_max = -90.0
buy_27_rsi_max = 46.0
buy_27_volume = 2.0
buy_28_ma_offset = 0.928
buy_28_ewo_min = 2.0
buy_28_rsi_14_max = 33.4
buy_28_cti_max = -0.84
buy_28_r_14_max = -97.0
buy_28_cti_1h_max = 0.95
buy_29_ma_offset = 0.984
buy_29_ewo_max = -4.2
buy_29_cti_max = -0.96
buy_30_ma_offset = 0.962
buy_30_ewo_min = 6.4
buy_30_rsi_14_max = 34.0
buy_30_cti_max = -0.87
buy_30_r_14_max = -97.0
buy_31_ma_offset = 0.962
buy_31_ewo_max = -5.2
buy_31_r_14_max = -94.0
buy_31_cti_max = -0.9
buy_32_ma_offset = 0.942
buy_32_rsi_4_max = 46.0
buy_32_cti_max = -0.86
buy_32_rsi_14_min = 19.0
buy_32_crsi_1h_min = 10.0
buy_32_crsi_1h_max = 60.0
buy_33_ma_offset = 0.988
buy_33_ewo_min = 9.0
buy_33_rsi_max = 32.0
buy_33_cti_max = -0.88
buy_33_r_14_max = -98.0
buy_33_cti_1h_max = 0.92
buy_33_volume = 2.0
buy_34_ma_offset = 0.97
buy_34_ewo_max = -4.0
buy_34_cti_max = -0.95
buy_34_r_14_max = -99.9
buy_34_crsi_1h_min = 8.0
buy_34_volume = 2.0
buy_35_ma_offset = 0.984
buy_35_ewo_min = 7.8
buy_35_rsi_max = 32.0
buy_35_cti_max = -0.8
buy_35_r_14_max = -95.0
buy_36_ma_offset = 0.98
buy_36_ewo_max = -5.0
buy_36_cti_max = -0.82
buy_36_r_14_max = -97.0
buy_36_crsi_1h_min = 12.0
buy_37_ma_offset = 0.984
buy_37_ewo_min = 8.3
buy_37_ewo_max = 11.1
buy_37_rsi_14_min = 26.0
buy_37_rsi_14_max = 46.0
buy_37_crsi_1h_min = 12.0
buy_37_crsi_1h_max = 56.0
buy_37_cti_max = -0.85
buy_37_cti_1h_max = 0.92
buy_37_r_14_max = -97.0
buy_37_close_1h_max = 0.1
buy_38_ma_offset = 0.98
buy_38_ewo_max = -4.4
buy_38_cti_max = -0.95
buy_38_r_14_max = -97.0
buy_38_crsi_1h_min = 0.5
buy_39_cti_max = -0.1
buy_39_r_1h_max = -22.0
buy_39_cti_1h_min = -0.1
buy_39_cti_1h_max = 0.4
buy_40_cci_max = -150.0
buy_40_rsi_max = 30.0
buy_40_r_14_max = -99.9
buy_40_cti_max = -0.8
buy_41_ma_offset_high = 0.95
buy_41_cti_max = -0.95
buy_41_cci_max = -178.0
buy_41_ewo_1h_min = 0.5
buy_41_r_480_1h_max = -14.0
buy_41_crsi_1h_min = 14.0
buy_42_ema_open_mult = 0.018
buy_42_bb_offset = 0.992
buy_42_ewo_1h_min = 2.8
buy_42_cti_1h_min = -0.5
buy_42_cti_1h_max = 0.88
buy_42_r_480_1h_max = -12.0
buy_43_bb40_bbdelta_close = 0.045
buy_43_bb40_closedelta_close = 0.02
buy_43_bb40_tail_bbdelta = 0.5
buy_43_cti_max = -0.75
buy_43_r_480_min = -94.0
buy_43_cti_1h_min = -0.75
buy_43_cti_1h_max = 0.45
buy_43_r_480_1h_min = -80.0
buy_44_ma_offset = 0.982
buy_44_ewo_max = -18.0
buy_44_cti_max = -0.73
buy_44_crsi_1h_min = 8.0
buy_45_bb40_bbdelta_close = 0.039
buy_45_bb40_closedelta_close = 0.0231
buy_45_bb40_tail_bbdelta = 0.24
buy_45_ma_offset = 0.948
buy_45_ewo_min = 2.0
buy_45_ewo_1h_min = 2.0
buy_45_cti_1h_max = 0.76
buy_45_r_480_1h_max = -20.0
buy_46_ema_open_mult = 0.0332
buy_46_ewo_1h_min = 0.5
buy_46_cti_1h_min = -0.9
buy_46_cti_1h_max = 0.5
buy_47_ewo_min = 3.2
buy_47_ma_offset = 0.952
buy_47_rsi_14_max = 46.0
buy_47_cti_max = -0.93
buy_47_r_14_max = -97.0
buy_47_ewo_1h_min = 2.0
buy_47_cti_1h_min = -0.9
buy_47_cti_1h_max = 0.3
buy_48_ewo_min = 8.5
buy_48_ewo_1h_min = 14.0
buy_48_r_480_min = -25.0
buy_48_r_480_1h_min = -50.0
buy_48_r_480_1h_max = -10.0
buy_48_cti_1h_min = 0.5
buy_48_crsi_1h_min = 10.0
# 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 = 79.0
sell_rsi_bb_2 = 80.0
sell_rsi_main_3 = 83.0
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 = 44.0
sell_custom_profit_bull_4 = 0.05
sell_custom_rsi_under_bull_4 = 45.0
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 = 57.0
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 = 57.0
sell_custom_rsi_over_bear_7 = 77.0
sell_custom_profit_bear_8 = 0.09
sell_custom_rsi_under_bear_8 = 55.0
sell_custom_rsi_over_bear_8 = 75.5
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 = 57.0
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 = 57.0
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 = 57.0
sell_custom_under_rsi_over_bear_5 = 78.0
sell_custom_under_profit_bear_6 = 0.07
sell_custom_under_rsi_under_bear_6 = 57.0
sell_custom_under_rsi_over_bear_6 = 78.0
sell_custom_under_profit_bear_7 = 0.08
sell_custom_under_rsi_under_bear_7 = 57.0
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
# 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
# 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 48h 1, under EMA200
sell_custom_pump_under_profit_min_1 = 0.04
sell_custom_pump_under_profit_max_1 = 0.09
# 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
# Profit Target Signal
profit_target_1_enable = False
#############################################################
plot_config = {
'main_plot': {
'ema_12_1h': { 'color': 'rgba(200,200,100,0.4)' },
'ema_15_1h': { 'color': 'rgba(200,180,100,0.4)' },
'ema_20_1h': { 'color': 'rgba(200,160,100,0.4)' },
'ema_25_1h': { 'color': 'rgba(200,140,100,0.4)' },
'ema_26_1h': { 'color': 'rgba(200,120,100,0.4)' },
'ema_35_1h': { 'color': 'rgba(200,100,100,0.4)' },
'ema_50_1h': { 'color': 'rgba(200,80,100,0.4)' },
'ema_100_1h': { 'color': 'rgba(200,60,100,0.4)' },
'ema_200_1h': { 'color': 'rgba(200,40,100,0.4)' },
'sma_200_1h': { 'color': 'rgba(150,20,100,0.4)' },
'pm': { 'color': 'rgba(100,20,100,0.5)' }
},
'subplots': {
'buy tag': { 'buy_tag': {'color': 'green'} },
'RSI/BTC': {
'btc_not_downtrend_1h': { 'color': 'yellow' },
'btc_rsi_14_1h': { 'color': 'green' },
'rsi_14_1h': { 'color': '#f41cd1' },
'crsi': {'color': 'blue' }
},
'pump': {
'cti_1h': {'color': 'pink' },
'safe_pump_24_10_1h': {'color': '#481110' },
'safe_pump_24_20_1h': {'color': '#481120' },
'safe_pump_24_30_1h': {'color': '#481130' },
'safe_pump_24_40_1h': {'color': '#481140' },
'safe_pump_24_50_1h': {'color': '#481150' },
'safe_pump_24_60_1h': {'color': '#481160' },
'safe_pump_24_70_1h': {'color': '#481170' },
'safe_pump_24_80_1h': {'color': '#481180' },
'safe_pump_24_90_1h': {'color': '#481190' },
'safe_pump_24_100_1h': {'color': '#4811A0' },
'safe_pump_24_120_1h': {'color': '#4811C0' },
'safe_pump_36_10_1h': {'color': '#721110' },
'safe_pump_36_20_1h': {'color': '#721120' },
'safe_pump_36_30_1h': {'color': '#721130' },
'safe_pump_36_40_1h': {'color': '#721140' },
'safe_pump_36_50_1h': {'color': '#721150' },
'safe_pump_36_60_1h': {'color': '#721160' },
'safe_pump_36_70_1h': {'color': '#721170' },
'safe_pump_36_80_1h': {'color': '#721180' },
'safe_pump_36_90_1h': {'color': '#721190' },
'safe_pump_36_100_1h': {'color': '#7211A0' },
'safe_pump_36_120_1h': {'color': '#7211C0' },
'safe_pump_48_10_1h': {'color': '#961110' },
'safe_pump_48_20_1h': {'color': '#961120' },
'safe_pump_48_30_1h': {'color': '#961130' },
'safe_pump_48_40_1h': {'color': '#961140' },
'safe_pump_48_50_1h': {'color': '#961150' },
'safe_pump_48_60_1h': {'color': '#961160' },
'safe_pump_48_70_1h': {'color': '#961170' },
'safe_pump_48_80_1h': {'color': '#961180' },
'safe_pump_48_90_1h': {'color': '#961190' },
'safe_pump_48_100_1h': {'color': '#9611A0' },
'safe_pump_48_120_1h': {'color': '#9611C0' }
}
}
}
#############################################################
# CACHES
hold_trades_cache = None
target_profit_cache = None
#############################################################
def __init__(self, config: dict) -> None:
super().__init__(config)
#self.dp = DataProvider(config, config['exchange'])
if self.target_profit_cache is None:
self.target_profit_cache = Cache(
self.config["user_data_dir"] / "data-nfi-profit_target_by_pair.json"
)
# If the cached data hasn't changed, it's a no-op
self.target_profit_cache.save()
def get_hold_trades_config_file(self):
proper_holds_file_path = self.config["user_data_dir"].resolve() / "nfi-hold-trades.json"
if proper_holds_file_path.is_file():
return proper_holds_file_path
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():
log.warning(
"Please move %s to %s which is now the expected path for the holds file",
hold_trades_config_file_resolve,
proper_holds_file_path,
)
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():
log.warning(
"Please move %s to %s which is now the expected path for the holds file",
hold_trades_config_file_absolute,
proper_holds_file_path,
)
return hold_trades_config_file_absolute
def load_hold_trades_config(self):
if self.hold_trades_cache is None:
hold_trades_config_file = self.get_hold_trades_config_file()
if hold_trades_config_file:
log.warning("Loading hold support data from %s", hold_trades_config_file)
self.hold_trades_cache = HoldsCache(hold_trades_config_file)
if self.hold_trades_cache:
self.hold_trades_cache.load()
def whitelist_tracker(self):
if sorted(self.coin_metrics['current_whitelist']) != sorted(self.dp.current_whitelist()):
log.info("Whitelist has changed...")
self.coin_metrics['top_traded_updated'] = False
self.coin_metrics['top_grossing_updated'] = False
# Update pairlist
self.coin_metrics['current_whitelist'] = self.dp.current_whitelist()
# Move up BTC for largest data footprint
self.coin_metrics['current_whitelist'].insert(0, self.coin_metrics['current_whitelist'].pop(self.coin_metrics['current_whitelist'].index(f"BTC/{self.config['stake_currency']}")))
def top_traded_list(self):
log.info("Updating top traded pairlist...")
tik = time.perf_counter()
self.coin_metrics['tt_dataframe'] = DataFrame()
# Build traded volume dataframe
for coin_pair in self.coin_metrics['current_whitelist']:
coin = coin_pair.split('/')[0]
# Get the volume for the daily informative timeframe and name the column for the coin
pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d)
pair_dataframe.set_index('date')
if self.config['runmode'].value in ('live', 'dry_run'):
pair_dataframe = pair_dataframe.iloc[-7:,:]
# Set the date index of the self.coin_metrics['tt_dataframe'] once
if not 'date' in self.coin_metrics['tt_dataframe']:
self.coin_metrics['tt_dataframe']['date'] = pair_dataframe['date']
self.coin_metrics['tt_dataframe'].set_index('date')
# Calculate daily traded volume
pair_dataframe[coin] = pair_dataframe['volume'] * qtpylib.typical_price(pair_dataframe)
# Drop the columns we don't need
pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True)
# Merge it in on the date key
self.coin_metrics['tt_dataframe'] = self.coin_metrics['tt_dataframe'].merge(pair_dataframe, on='date', how='left')
# Forward fill empty cells (due to different df shapes)
self.coin_metrics['tt_dataframe'].fillna(0, inplace=True)
# Store and drop date column for value sorting
pair_dates = self.coin_metrics['tt_dataframe']['date']
self.coin_metrics['tt_dataframe'].drop(columns=['date'], inplace=True)
# Build columns and top traded coins
column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_traded_len'] + 1)]
self.coin_metrics['tt_dataframe'][column_names] = self.coin_metrics['tt_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_traded_len']).index.values, axis=1, result_type='expand')
self.coin_metrics['tt_dataframe'].drop(columns=[col for col in self.coin_metrics['tt_dataframe'] if col not in column_names], inplace=True)
# Re-add stored date column
self.coin_metrics['tt_dataframe'].insert(loc = 0, column = 'date', value = pair_dates)
self.coin_metrics['tt_dataframe'].set_index('date')
self.coin_metrics['top_traded_updated'] = True
log.info("Updated top traded pairlist (tail-5):")
log.info(f"\n{self.coin_metrics['tt_dataframe'].tail(5)}")
tok = time.perf_counter()
log.info(f"Updating top traded pairlist took {tok - tik:0.4f} seconds...")
def top_grossing_list(self):
log.info("Updating top grossing pairlist...")
tik = time.perf_counter()
self.coin_metrics['tg_dataframe'] = DataFrame()
# Build grossing volume dataframe
for coin_pair in self.coin_metrics['current_whitelist']:
coin = coin_pair.split('/')[0]
# Get the volume for the daily informative timeframe and name the column for the coin
pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d)
pair_dataframe.set_index('date')
if self.config['runmode'].value in ('live', 'dry_run'):
pair_dataframe = pair_dataframe.iloc[-7:,:]
# Set the date index of the self.coin_metrics['tg_dataframe'] once
if not 'date' in self.coin_metrics['tg_dataframe']:
self.coin_metrics['tg_dataframe']['date'] = pair_dataframe['date']
self.coin_metrics['tg_dataframe'].set_index('date')
# Calculate daily grossing rate
pair_dataframe[coin] = pair_dataframe['close'].pct_change() * 100
# Drop the columns we don't need
pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True)
# Merge it in on the date key
self.coin_metrics['tg_dataframe'] = self.coin_metrics['tg_dataframe'].merge(pair_dataframe, on='date', how='left')
# Forward fill empty cells (due to different df shapes)
self.coin_metrics['tg_dataframe'].fillna(0, inplace=True)
# Store and drop date column for value sorting
pair_dates = self.coin_metrics['tg_dataframe']['date']
self.coin_metrics['tg_dataframe'].drop(columns=['date'], inplace=True)
# Build columns and top grossing coins
column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_grossing_len'] + 1)]
self.coin_metrics['tg_dataframe'][column_names] = self.coin_metrics['tg_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_grossing_len']).index.values, axis=1, result_type='expand')
self.coin_metrics['tg_dataframe'].drop(columns=[col for col in self.coin_metrics['tg_dataframe'] if col not in column_names], inplace=True)
# Re-add stored date column
self.coin_metrics['tg_dataframe'].insert(loc = 0, column = 'date', value = pair_dates)
self.coin_metrics['tg_dataframe'].set_index('date')
self.coin_metrics['top_grossing_updated'] = True
log.info("Updated top grossing pairlist (tail-5):")
log.info(f"\n{self.coin_metrics['tg_dataframe'].tail(5)}")
tok = time.perf_counter()
log.info(f"Updating top grossing pairlist took {tok - tik:0.4f} seconds...")
def is_top_coin(self, coin_pair, row_data, top_length) -> bool:
return coin_pair.split('/')[0] in row_data.loc['Coin #1':f"Coin #{top_length}"].values
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.
"""
# Coin metrics mechanism
if self.coin_metrics['top_traded_enabled'] or self.coin_metrics['top_grossing_enabled']:
self.whitelist_tracker()
if self.coin_metrics['top_traded_enabled'] and not self.coin_metrics['top_traded_updated']:
self.top_traded_list()
if self.coin_metrics['top_grossing_enabled'] and not self.coin_metrics['top_grossing_updated']:
self.top_grossing_list()
if self.config["runmode"].value not in ("live", "dry_run"):
return super().bot_loop_start(**kwargs)
if self.holdSupportEnabled:
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 >= 0.20:
if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_12_1'
elif (last_candle['rsi_14'] < 27.0):
return True, 'signal_profit_o_bull_12_9'
elif 0.20 > current_profit >= 0.12:
if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_11_1'
elif (last_candle['rsi_14'] < 39.0):
return True, 'signal_profit_o_bull_11_9'
elif 0.12 > current_profit >= 0.1:
if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_10_1'
elif (last_candle['rsi_14'] < 48.0):
return True, 'signal_profit_o_bull_10_9'
elif 0.1 > current_profit >= 0.09:
if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_9_1'
elif (last_candle['rsi_14'] < 49.0):
return True, 'signal_profit_o_bull_9_9'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_8_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_8_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_8_4'
elif (last_candle['rsi_14'] < 48.0):
return True, 'signal_profit_o_bull_8_9'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_7_1'
if last_candle['rsi_14'] > 83.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_7_2'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_7_3'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_7_4'
elif (last_candle['rsi_14'] < 45.0):
return True, 'signal_profit_o_bull_7_9'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_6_1'
if last_candle['rsi_14'] > 82.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_6_2'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_6_3'
elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_6_4'
elif (last_candle['cti'] > 0.95):
return True, 'signal_profit_o_bull_6_5'
elif (last_candle['rsi_14'] < 42.0):
return True, 'signal_profit_o_bull_6_9'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_5_1'
if last_candle['rsi_14'] > 80.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_5_2'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_5_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_5_4'
elif (last_candle['cti'] > 0.952):
return True, 'signal_profit_o_bull_5_5'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_5_6'
elif (last_candle['rsi_14'] < 41.0):
return True, 'signal_profit_o_bull_5_9'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 45.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_4_1'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_4_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_4_4'
elif (last_candle['cti'] > 0.954):
return True, 'signal_profit_o_bull_4_5'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_4_6'
elif (last_candle['rsi_14'] < 40.0):
return True, 'signal_profit_o_bull_4_9'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_3_1'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_3_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_3_4'
elif (last_candle['cti'] > 0.956):
return True, 'signal_profit_o_bull_3_5'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_3_6'
elif (last_candle['rsi_14'] < 35.0):
return True, 'signal_profit_o_bull_3_9'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_2_1'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_2_3'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_2_4'
elif (last_candle['cti'] > 0.958):
return True, 'signal_profit_o_bull_2_5'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_2_6'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_o_bull_2_7'
elif last_candle['rsi_14'] < 40.0 and (last_candle['cmf'] < -0.25):
return True, 'signal_profit_o_bull_2_8'
elif (last_candle['rsi_14'] < 34.0):
return True, 'signal_profit_o_bull_2_9'
elif 0.02 > current_profit >= 0.012:
if (last_candle['rsi_14'] < 34.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_1_1'
elif (last_candle['rsi_14'] < 41.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bull_1_3'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bull_1_4'
elif (last_candle['cti'] > 0.96):
return True, 'signal_profit_o_bull_1_5'
elif (last_candle['rsi_14'] < 41.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bull_1_6'
elif (last_candle['rsi_14'] < 41.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_o_bull_1_7'
elif last_candle['rsi_14'] < 39.0 and (last_candle['cmf'] < -0.25):
return True, 'signal_profit_o_bull_1_8'
elif (last_candle['rsi_14'] < 32.0):
return True, 'signal_profit_o_bull_1_9'
else:
if current_profit >= 0.20:
if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_12_1'
elif (last_candle['rsi_14'] < 28.0):
return True, 'signal_profit_o_bear_12_9'
elif 0.20 > current_profit >= 0.12:
if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_11_1'
elif (last_candle['rsi_14'] < 40.0):
return True, 'signal_profit_o_bear_11_9'
elif 0.12 > current_profit >= 0.10:
if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_10_1'
elif (last_candle['rsi_14'] < 49.0):
return True, 'signal_profit_o_bear_10_9'
elif 0.10 > current_profit >= 0.09:
if last_candle['rsi_14'] < 55.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_9_1'
elif (last_candle['rsi_14'] > 75.5):
return True, 'signal_profit_o_bear_9_2'
elif (last_candle['rsi_14'] < 50.0):
return True, 'signal_profit_o_bear_9_9'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_8_1'
elif (last_candle['rsi_14'] > 77.0):
return True, 'signal_profit_o_bear_8_2'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_8_3'
elif (last_candle['rsi_14'] < 59.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_8_4'
elif (last_candle['rsi_14'] < 49.0):
return True, 'signal_profit_o_bear_8_9'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_7_1'
elif (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_o_bear_7_2'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_7_3'
elif (last_candle['rsi_14'] < 57.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_7_4'
elif (last_candle['rsi_14'] < 46.0):
return True, 'signal_profit_o_bear_7_9'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_6_1'
elif (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_o_bear_6_2'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_6_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_6_4'
elif (last_candle['cti'] > 0.94):
return True, 'signal_profit_o_bear_6_5'
elif (last_candle['rsi_14'] < 43.0):
return True, 'signal_profit_o_bear_6_9'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 49.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_5_1'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_5_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_5_4'
elif (last_candle['cti'] > 0.942):
return True, 'signal_profit_o_bear_5_5'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_5_6'
elif (last_candle['rsi_14'] < 42.0):
return True, 'signal_profit_o_bear_5_9'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_4_1'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_4_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_4_4'
elif (last_candle['cti'] > 0.944):
return True, 'signal_profit_o_bear_4_5'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_4_6'
elif (last_candle['rsi_14'] < 41.0):
return True, 'signal_profit_o_bear_4_9'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_3_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_3_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_3_4'
elif (last_candle['cti'] > 0.946):
return True, 'signal_profit_o_bear_3_5'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_3_6'
elif (last_candle['rsi_14'] < 36.0):
return True, 'signal_profit_o_bear_3_9'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_2_1'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_o_bear_2_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_2_4'
elif (last_candle['cti'] > 0.948):
return True, 'signal_profit_o_bear_2_5'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_2_6'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_o_bear_2_7'
elif (last_candle['rsi_14'] < 35.0):
return True, 'signal_profit_o_bear_2_9'
elif 0.02 > current_profit >= 0.012:
if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_1_1'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_o_bear_1_3'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_o_bear_1_4'
elif (last_candle['cti'] > 0.95):
return True, 'signal_profit_o_bear_1_5'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_o_bear_1_6'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_o_bear_1_7'
elif (last_candle['rsi_14'] < 33.0):
return True, 'signal_profit_o_bear_1_9'
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 >= 0.20:
if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_12_1'
elif (last_candle['rsi_14'] < 28.0):
return True, 'signal_profit_u_bull_12_9'
elif 0.20 > current_profit >= 0.12:
if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_11_1'
elif (last_candle['rsi_14'] < 43.0):
return True, 'signal_profit_u_bull_11_9'
elif 0.12 > current_profit >= 0.10:
if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_10_1'
elif (last_candle['rsi_14'] < 49.0):
return True, 'signal_profit_u_bull_10_9'
elif 0.10 > current_profit >= 0.09:
if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_9_1'
elif (last_candle['rsi_14'] < 50.0):
return True, 'signal_profit_u_bull_9_9'
elif 0.09 > current_profit >= 0.08:
if last_candle['rsi_14'] < 57.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_8_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_8_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_8_4'
elif (last_candle['rsi_14'] < 49.0):
return True, 'signal_profit_u_bull_8_9'
elif 0.08 > current_profit >= 0.07:
if last_candle['rsi_14'] < 52.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_7_1'
if last_candle['rsi_14'] > 83.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_7_2'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_7_3'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_7_4'
elif (last_candle['rsi_14'] < 46.0):
return True, 'signal_profit_u_bull_7_9'
elif 0.07 > current_profit >= 0.06:
if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_6_1'
if last_candle['rsi_14'] > 82.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_6_2'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_6_3'
elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_6_4'
elif (last_candle['cti'] > 0.95):
return True, 'signal_profit_u_bull_6_5'
elif (last_candle['rsi_14'] < 43.0):
return True, 'signal_profit_u_bull_6_9'
elif 0.06 > current_profit >= 0.05:
if last_candle['rsi_14'] < 48.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_5_1'
if last_candle['rsi_14'] > 80.0 and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_5_2'
elif (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_5_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_5_4'
elif (last_candle['cti'] > 0.952):
return True, 'signal_profit_u_bull_5_5'
elif (last_candle['rsi_14'] < 51.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_5_6'
elif (last_candle['rsi_14'] < 42.0):
return True, 'signal_profit_u_bull_5_9'
elif 0.05 > current_profit >= 0.04:
if last_candle['rsi_14'] < 47.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_4_1'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_4_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_4_4'
elif (last_candle['cti'] > 0.954):
return True, 'signal_profit_u_bull_4_5'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_4_6'
elif (last_candle['rsi_14'] < 41.0):
return True, 'signal_profit_u_bull_4_9'
elif 0.04 > current_profit >= 0.03:
if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_3_1'
elif (last_candle['rsi_14'] < 49.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_3_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_3_4'
elif (last_candle['cti'] > 0.956):
return True, 'signal_profit_u_bull_3_5'
elif (last_candle['rsi_14'] < 49.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_3_6'
elif (last_candle['rsi_14'] < 36.0):
return True, 'signal_profit_u_bull_3_9'
elif 0.03 > current_profit >= 0.02:
if last_candle['rsi_14'] < 45.0 and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_2_1'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_2_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_2_4'
elif (last_candle['cti'] > 0.958):
return True, 'signal_profit_u_bull_2_5'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_2_6'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_u_bull_2_7'
elif last_candle['rsi_14'] < 41.0 and (last_candle['cmf'] < -0.25):
return True, 'signal_profit_u_bull_2_8'
elif (last_candle['rsi_14'] < 35.0):
return True, 'signal_profit_u_bull_2_9'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_1_1'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4):
return True, 'signal_profit_u_bull_1_3'
elif (last_candle['rsi_14'] < 47.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bull_1_4'
elif (last_candle['cti'] > 0.96):
return True, 'signal_profit_u_bull_1_5'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bull_1_6'
elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_u_bull_1_7'
elif last_candle['rsi_14'] < 40.0 and (last_candle['cmf'] < -0.25):
return True, 'signal_profit_u_bull_1_8'
elif (last_candle['rsi_14'] < 33.0):
return True, 'signal_profit_u_bull_1_9'
else:
if current_profit >= 0.20:
if last_candle['rsi_14'] < 30.0:
return True, 'signal_profit_u_bear_12_1'
elif 0.20 > current_profit >= 0.12:
if last_candle['rsi_14'] < 42.0:
return True, 'signal_profit_u_bear_11_1'
elif 0.12 > current_profit >= 0.10:
if last_candle['rsi_14'] < 46.0:
return True, 'signal_profit_u_bear_10_1'
elif 0.10 > current_profit >= 0.09:
if last_candle['rsi_14'] < 50.0:
return True, 'signal_profit_u_bear_9_1'
elif (last_candle['rsi_14'] > 82.0):
return True, 'signal_profit_u_bear_9_2'
elif 0.09 > current_profit >= 0.08:
if last_candle['rsi_14'] < 57.0:
return True, 'signal_profit_u_bear_8_1'
elif (last_candle['rsi_14'] > 80.0):
return True, 'signal_profit_u_bear_8_2'
elif 0.08 > current_profit >= 0.07:
if last_candle['rsi_14'] < 56.0:
return True, 'signal_profit_u_bear_7_1'
elif (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_u_bear_7_2'
elif 0.07 > current_profit >= 0.06:
if last_candle['rsi_14'] < 54.0:
return True, 'signal_profit_u_bear_6_1'
elif (last_candle['rsi_14'] > 78.0):
return True, 'signal_profit_u_bear_6_2'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_u_bear_6_3'
elif (last_candle['cti'] > 0.94):
return True, 'signal_profit_u_bear_6_5'
elif 0.06 > current_profit >= 0.05:
if last_candle['rsi_14'] < 52.0:
return True, 'signal_profit_u_bear_5_1'
elif (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_u_bear_5_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bear_5_4'
elif (last_candle['cti'] > 0.942):
return True, 'signal_profit_u_bear_5_5'
elif (last_candle['rsi_14'] < 57.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_5_6'
elif 0.05 > current_profit >= 0.04:
if last_candle['rsi_14'] < 50.0:
return True, 'signal_profit_u_bear_4_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.05):
return True, 'signal_profit_u_bear_4_3'
elif (last_candle['rsi_14'] < 57.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bear_4_4'
elif (last_candle['cti'] > 0.944):
return True, 'signal_profit_u_bear_4_5'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_4_6'
elif 0.04 > current_profit >= 0.03:
if last_candle['rsi_14'] < 48.0:
return True, 'signal_profit_u_bear_3_1'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['cmf'] < -0.05):
return True, 'signal_profit_u_bear_3_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bear_3_4'
elif (last_candle['cti'] > 0.946):
return True, 'signal_profit_u_bear_3_5'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_3_6'
elif 0.03 > current_profit >= 0.02:
if last_candle['rsi_14'] < 55.0: #46
return True, 'signal_profit_u_bear_2_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.05):
return True, 'signal_profit_u_bear_2_3'
elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bear_2_4'
elif (last_candle['cti'] > 0.948):
return True, 'signal_profit_u_bear_2_5'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_2_6'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_u_bear_2_7'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_1_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.05):
return True, 'signal_profit_u_bear_1_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0):
return True, 'signal_profit_u_bear_1_4'
elif (last_candle['cti'] > 0.95):
return True, 'signal_profit_u_bear_1_5'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_u_bear_1_6'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85):
return True, 'signal_profit_u_bear_1_7'
elif (last_candle['rsi_14'] < 34.0):
return True, 'signal_profit_u_bear_1_9'
return False, None
def sell_pump_main(self, current_profit: float, last_candle) -> tuple:
if last_candle['sell_pump_48_1_1h']:
if (last_candle['moderi_96']):
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_6_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_5_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_4_3'
elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_3_1'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_3_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_2_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_2_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_48_1_1'
elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bull_48_1_3'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_48_1_4'
else:
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_6_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_5_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_4_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_3_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_3_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_2_1'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_2_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_48_1_1'
elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.12):
return True, 'signal_profit_p_bear_48_1_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_48_1_4'
elif last_candle['sell_pump_36_1_1h']:
if (last_candle['moderi_96']):
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_6_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_5_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_4_3'
elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_3_1'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_3_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_2_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_2_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_36_1_1'
elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bull_36_1_3'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_36_1_4'
else:
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_6_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_5_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_4_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_3_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_3_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_2_1'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_2_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_36_1_1'
elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.2):
return True, 'signal_profit_p_bear_36_1_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_36_1_4'
elif last_candle['sell_pump_24_1_1h']:
if (last_candle['moderi_96']):
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_6_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_5_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_4_3'
elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_3_1'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_3_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_2_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_2_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bull_24_1_1'
elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bull_24_1_3'
elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bull_24_1_4'
else:
if current_profit >= 0.2:
if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_12_1'
elif 0.2 > current_profit >= 0.12:
if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_11_1'
elif 0.12 > current_profit >= 0.1:
if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_10_1'
elif 0.1 > current_profit >= 0.09:
if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_9_1'
elif 0.09 > current_profit >= 0.08:
if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_8_1'
elif 0.08 > current_profit >= 0.07:
if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_7_1'
elif 0.07 > current_profit >= 0.06:
if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_6_1'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_6_3'
elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_6_4'
elif 0.06 > current_profit >= 0.05:
if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_5_1'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_5_3'
elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_5_4'
elif 0.05 > current_profit >= 0.04:
if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_4_1'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_4_3'
elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_4_4'
elif 0.04 > current_profit >= 0.03:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_3_1'
elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_3_3'
elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_3_4'
elif 0.03 > current_profit >= 0.02:
if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_2_1'
elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_2_3'
elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_2_4'
elif 0.02 > current_profit >= 0.01:
if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0):
return True, 'signal_profit_p_bear_24_1_1'
elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.3):
return True, 'signal_profit_p_bear_24_1_3'
elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0):
return True, 'signal_profit_p_bear_24_1_4'
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, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
# Under & near EMA200, local uptrend move
if (
(current_profit < -0.05)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['cmf'] < 0.0)
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < 0.004)
and last_candle['rsi_14'] > previous_candle_1['rsi_14']
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + 10.0))
and (last_candle['sma_200_dec_24'])
and (current_time - timedelta(minutes=2880) > trade.open_date_utc)
):
return True, 'signal_stoploss_u_e_1'
# Under EMA200, local strong uptrend move
if (
(current_profit < -0.08)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['cmf'] < 0.0)
and last_candle['rsi_14'] > previous_candle_1['rsi_14']
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + 24.0))
and (last_candle['sma_200_dec_20'])
and (last_candle['sma_200_dec_24'])
and (current_time - timedelta(minutes=2880) > trade.open_date_utc)
):
return True, 'signal_stoploss_u_e_2'
# Under EMA200, pair negative, low max rate
if (
(current_profit < -0.08)
and (max_profit < 0.04)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['ema_25'] < last_candle['ema_50'])
and (last_candle['sma_200_dec_20'])
and (last_candle['sma_200_dec_24'])
and (last_candle['sma_200_dec_20_1h'])
and (last_candle['ema_vwma_osc_32'] < 0.0)
and (last_candle['ema_vwma_osc_64'] < 0.0)
and (last_candle['ema_vwma_osc_96'] < 0.0)
and (last_candle['cmf'] < -0.0)
and (last_candle['cmf_1h'] < -0.0)
and (last_candle['btc_not_downtrend_1h'] == False)
and (current_time - timedelta(minutes=1440) > trade.open_date_utc)
):
return True, 'signal_stoploss_u_e_doom'
# Under EMA200, pair and BTC negative, low max rate
if (
(-0.05 > current_profit > -0.09)
and (last_candle['btc_not_downtrend_1h'] == False)
and (last_candle['ema_vwma_osc_32'] < 0.0)
and (last_candle['ema_vwma_osc_64'] < 0.0)
and (max_profit < 0.005)
and (max_loss < 0.09)
and (last_candle['sma_200_dec_24'])
and (last_candle['cmf'] < -0.0)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['ema_25'] < last_candle['ema_50'])
and (last_candle['cti'] < -0.8)
and (last_candle['r_480'] < -50.0)
):
return True, 'signal_stoploss_u_e_b_1'
# Under EMA200, pair and BTC negative, CTI, Elder Ray Index negative, normal max rate
elif (
(-0.1 > current_profit > -0.2)
and (last_candle['btc_not_downtrend_1h'] == False)
and (last_candle['ema_vwma_osc_32'] < 0.0)
and (last_candle['ema_vwma_osc_64'] < 0.0)
and (last_candle['ema_vwma_osc_96'] < 0.0)
and (max_profit < 0.05)
and (max_loss < 0.2)
and (last_candle['sma_200_dec_24'])
and (last_candle['sma_200_dec_20_1h'])
and (last_candle['cmf'] < -0.45)
and (last_candle['close'] < last_candle['ema_200'])
and (last_candle['ema_25'] < last_candle['ema_50'])
and (last_candle['cti'] < -0.8)
and (last_candle['r_480'] < -97.0)
):
return True, 'signal_stoploss_u_e_b_2'
return False, None
def sell_pump_dec(self, current_profit: float, last_candle) -> tuple:
if (0.03 > current_profit >= 0.005) 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 (0.06 > current_profit >= 0.04) 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 (0.09 > current_profit >= 0.06) 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 (0.04 > current_profit >= 0.02) 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'] > -0.4:
return True, 'signal_profit_w_1_1'
elif 0.03 > current_profit >= 0.02:
if last_candle['r_480'] > -0.5:
return True, 'signal_profit_w_1_2'
elif 0.04 > current_profit >= 0.03:
if last_candle['r_480'] > -0.6:
return True, 'signal_profit_w_1_3'
elif 0.05 > current_profit >= 0.04:
if last_candle['r_480'] > -0.7:
return True, 'signal_profit_w_1_4'
elif 0.06 > current_profit >= 0.05:
if last_candle['r_480'] > -1.0:
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'] > 78.0) 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'] > 78.0) 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'] > 78.0) 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'] > 78.0) 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'] > 78.0) 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_r_5(self, current_profit: float, last_candle) -> tuple:
if (0.02 > current_profit >= 0.012):
if (last_candle['r_480'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_1'
elif (0.03 > current_profit >= 0.02):
if (last_candle['r_480'] > -1.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_2'
elif (0.04 > current_profit >= 0.03):
if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_3'
elif (0.05 > current_profit >= 0.04):
if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_4'
elif (0.06 > current_profit >= 0.05):
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_5'
elif (0.07 > current_profit >= 0.06):
if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_6'
elif (0.08 > current_profit >= 0.07):
if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_7'
elif (0.09 > current_profit >= 0.08):
if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_8'
elif (0.1 > current_profit >= 0.09):
if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_9'
elif (0.12 > current_profit >= 0.1):
if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_10'
elif (0.2 > current_profit >= 0.12):
if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_11'
elif (current_profit >= 0.2):
if (last_candle['r_480'] > -1.5) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti_1h'] > 0.92):
return True, 'signal_profit_w_5_12'
return False, None
def sell_r_6(self, current_profit: float, last_candle) -> tuple:
if (0.02 > current_profit >= 0.012):
if (last_candle['r_14'] > -0.1) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_1'
elif (0.03 > current_profit >= 0.02):
if (last_candle['r_14'] > -0.2) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_2'
elif (0.04 > current_profit >= 0.03):
if (last_candle['r_14'] > -0.3) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_3'
elif (0.05 > current_profit >= 0.04):
if (last_candle['r_14'] > -0.4) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_4'
elif (0.06 > current_profit >= 0.05):
if (last_candle['r_14'] > -0.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_5'
elif (0.07 > current_profit >= 0.06):
if (last_candle['r_14'] > -0.6) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_6'
elif (0.08 > current_profit >= 0.07):
if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_7'
elif (0.09 > current_profit >= 0.08):
if (last_candle['r_14'] > -1.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_8'
elif (0.1 > current_profit >= 0.09):
if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_9'
elif (0.12 > current_profit >= 0.1):
if (last_candle['r_14'] > -0.75) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_10'
elif (0.2 > current_profit >= 0.12):
if (last_candle['r_14'] > -0.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_11'
elif (current_profit >= 0.2):
if (last_candle['r_14'] > -0.1) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_w_6_12'
return False, None
def mark_profit_target(self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple:
# if self.profit_target_1_enable:
# if (current_profit > 0) and (last_candle['zlema_4_lowKF'] > last_candle['lowKF']) and (previous_candle_1['zlema_4_lowKF'] < previous_candle_1['lowKF']) and (last_candle['cci'] > -100) and (last_candle['hrsi'] > 70):
# return pair, "mark_profit_target_01"
return None, None
def sell_profit_target(self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached) -> tuple:
# if self.profit_target_1_enable and previous_sell_reason == "mark_profit_target_01":
# if (current_profit > 0) and (current_rate < (previous_rate - 0.005)):
# return True, 'sell_profit_target_01'
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 (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 > 0.005) 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'
if (last_candle['momdiv_sell_1h'] == True) and (current_profit > 0.02):
return True, 'signal_profit_q_momdiv_1h'
if (last_candle['momdiv_sell'] == True) and (current_profit > 0.02):
return True, 'signal_profit_q_momdiv'
if (last_candle['momdiv_coh'] == True) and (current_profit > 0.02):
return True, 'signal_profit_q_momdiv_coh'
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 (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'
return False, None
def sell_long_mode(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple:
# Sell signal 1
if (last_candle['rsi_14'] > 78.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']) and (previous_candle_5['close'] > previous_candle_5['bb20_2_upp']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_1_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_1_2_1'
# Sell signal 2
elif (last_candle['rsi_14'] > 79.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_2_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_2_2_1'
# Sell signal 3
elif (last_candle['rsi_14'] > 82.0):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_3_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_3_2_1'
# Sell signal 4
elif (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_1h'] > 80.0):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_4_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_4_2_1'
# Sell signal 6
elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.5):
if (current_profit > 0.01):
return True, 'sell_long_6_1'
# Sell signal 7
elif (last_candle['rsi_14_1h'] > 82.0) and (last_candle['crossed_below_ema_12_26']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_7_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_7_2_1'
# Sell signal 8
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.05):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return True, 'sell_long_8_1_1'
else:
if (current_profit > 0.01):
return True, 'sell_long_8_2_1'
elif (0.02 < current_profit <= 0.06) and (max_profit - current_profit > 0.04) and (last_candle['cmf'] < 0.0) and (last_candle['sma_200_dec_24']):
return True, 'sell_long_t_1'
elif (0.06 < current_profit <= 0.12) and (max_profit - current_profit > 0.06) and (last_candle['cmf'] < 0.0):
return True, 'sell_long_t_2'
elif (0.12 < current_profit <= 0.24) and (max_profit - current_profit > 0.08) and (last_candle['cmf'] < 0.0):
return True, 'sell_long_t_3'
elif (0.24 < current_profit <= 0.5) and (max_profit - current_profit > 0.09) and (last_candle['cmf'] < 0.0):
return True, 'sell_long_t_4'
elif (0.5 < current_profit <= 0.9) and (max_profit - current_profit > 0.1) and (last_candle['cmf'] < 0.0):
return True, 'sell_long_t_5'
elif (0.03 < current_profit <= 0.06) and (current_time - timedelta(minutes=720) > trade.open_date_utc) and (last_candle['r_480'] > -20.0):
return True, 'sell_long_l_1'
return self.sell_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time)
def sell_pivot(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
if (last_candle['close'] > (last_candle['res3_1d'] * 2.2)):
if (0.02 > current_profit >= 0.012):
if (last_candle['r_14'] >= -0.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['r_480'] > -3.0):
return True, 'signal_profit_pv_1_1_1'
elif (0.03 > current_profit >= 0.02):
if (last_candle['r_14'] > -0.4) and (last_candle['rsi_14'] > 76.0) and (last_candle['r_480'] > -5.0):
return True, 'signal_profit_pv_1_2_1'
elif (0.04 > current_profit >= 0.03):
if (last_candle['r_14'] > -0.8) and (last_candle['rsi_14'] > 74.0) and (last_candle['r_480'] > -10.0):
return True, 'signal_profit_pv_1_3_1'
elif (0.05 > current_profit >= 0.04):
if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 70.0) and (last_candle['r_480'] > -15.0):
return True, 'signal_profit_pv_1_4_1'
elif (0.06 > current_profit >= 0.05):
if (last_candle['r_14'] > -1.2) and (last_candle['rsi_14'] > 66.0) and (last_candle['r_480'] > -20.0):
return True, 'signal_profit_pv_1_5_1'
elif (0.07 > current_profit >= 0.06):
if (last_candle['r_14'] > -1.6) and (last_candle['rsi_14'] > 60.0) and (last_candle['r_480'] > -25.0):
return True, 'signal_profit_pv_1_6_1'
elif (0.08 > current_profit >= 0.07):
if (last_candle['r_14'] > -2.0) and (last_candle['rsi_14'] > 56.0) and (last_candle['r_480'] > -30.0):
return True, 'signal_profit_pv_1_7_1'
elif (last_candle['close'] > (last_candle['res3_1d'] * 1.3)):
if (0.02 > current_profit >= 0.012):
if (last_candle['rsi_14'] > 80.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_1_1'
elif (last_candle['rsi_14'] > 79.0) and (last_candle['r_14'] > -1.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_1_2'
elif (0.03 > current_profit >= 0.02):
if (last_candle['rsi_14'] > 78.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_2_1'
elif (last_candle['rsi_14'] > 77.0) and (last_candle['r_14'] > -3.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_2_2'
elif (0.04 > current_profit >= 0.03):
if (last_candle['rsi_14'] > 76.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_3_1'
elif (last_candle['rsi_14'] > 75.0) and (last_candle['r_14'] > -5.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_3_2'
elif (0.05 > current_profit >= 0.04):
if (last_candle['rsi_14'] > 72.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_4_1'
elif (last_candle['rsi_14'] > 71.0) and (last_candle['r_14'] > -7.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_4_2'
elif (0.06 > current_profit >= 0.05):
if (last_candle['rsi_14'] > 68.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_5_1'
elif (last_candle['rsi_14'] > 67.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_5_2'
elif (0.07 > current_profit >= 0.06):
if (last_candle['rsi_14'] > 60.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_6_1'
elif (last_candle['rsi_14'] > 59.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_6_2'
elif (0.08 > current_profit >= 0.07):
if (last_candle['rsi_14'] > 58.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0):
return True, 'signal_profit_pv_2_7_1'
elif (last_candle['rsi_14'] > 57.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9):
return True, 'signal_profit_pv_2_7_2'
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
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)
# Long mode
if all(c in ['45', '46', '47'] for c in buy_tags):
sell, signal_name = self.sell_long_mode(current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, buy_tag)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Skip remaining sell logic for long mode
return None
# Quick sell mode
if all(c in ['empty', '32', '33', '34', '35', '36', '37', '38', '40'] for c in buy_tags):
sell, signal_name = self.sell_quick_mode(current_profit, max_profit, last_candle, previous_candle_1)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Ichi Trade management
if all(c in ['39'] for c in buy_tags):
sell, signal_name = self.sell_ichi(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Over EMA200, main profit targets
sell, signal_name = self.sell_over_main(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Under EMA200, main profit targets
sell, signal_name = self.sell_under_main(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# The pair is pumped
sell, signal_name = self.sell_pump_main(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# The pair is descending
sell, signal_name = self.sell_dec_main(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Trailing
sell, signal_name = self.sell_trail_main(current_profit, last_candle, max_profit)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Duration based
sell, signal_name = self.sell_duration_main(current_profit, last_candle, trade, current_time)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Under EMA200, exit with any profit
sell, signal_name = self.sell_under_min(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Stoplosses
sell, signal_name = self.sell_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Pumped descending pairs
sell, signal_name = self.sell_pump_dec(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Extra sells for pumped pairs
sell, signal_name = self.sell_pump_extra(current_profit, last_candle, max_profit)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Extra sells for trades that recovered
sell, signal_name = self.sell_recover(current_profit, last_candle, max_loss)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 1
sell, signal_name = self.sell_r_1(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 2
sell, signal_name = self.sell_r_2(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 3
sell, signal_name = self.sell_r_3(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 4, plus CTI
sell, signal_name = self.sell_r_4(current_profit, last_candle)
if (sell) and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 5, plus RSI and CTI 1h
sell, signal_name = self.sell_r_5(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Williams %R based sell 6, plus RSI, CTI, CCI
sell, signal_name = self.sell_r_6(current_profit, last_candle)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Pivot points based sells
sell, signal_name = self.sell_pivot(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time)
if sell and (signal_name is not None):
return f"{signal_name} ( {buy_tag})"
# Profit Target Signal
# Check if pair exist on target_profit_cache
if self.target_profit_cache is not None and pair in self.target_profit_cache.data:
previous_rate = self.target_profit_cache.data[pair]['rate']
previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason']
previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached'])
sell, signal_name = self.sell_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached)
if sell and signal_name is not None:
return f"{signal_name} ( {buy_tag})"
pair, mark_signal = self.mark_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
if pair:
self._set_profit_target(pair, mark_signal, current_rate, current_time)
# Sell signal 1
if self.sell_condition_1_enable and (last_candle['rsi_14'] > self.sell_rsi_bb_1) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']) and (previous_candle_5['close'] > previous_candle_5['bb20_2_upp']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_1_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_1_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_1_2_2 ( {buy_tag})"
# Sell signal 2
elif (self.sell_condition_2_enable) and (last_candle['rsi_14'] > self.sell_rsi_bb_2) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_2_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_2_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_2_2_2 ( {buy_tag})"
# Sell signal 3
elif (self.sell_condition_3_enable) and (last_candle['rsi_14'] > self.sell_rsi_main_3):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_3_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_3_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_3_2_2 ( {buy_tag})"
# Sell signal 4
elif self.sell_condition_4_enable and (last_candle['rsi_14'] > self.sell_dual_rsi_rsi_4) and (last_candle['rsi_14_1h'] > self.sell_dual_rsi_rsi_1h_4):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_4_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_4_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_4_2_2 ( {buy_tag})"
# Sell signal 6
elif self.sell_condition_6_enable and (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > self.sell_rsi_under_6):
if (current_profit > 0.01):
return f"sell_signal_6_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_6_2 ( {buy_tag})"
# Sell signal 7
elif self.sell_condition_7_enable and (last_candle['rsi_14_1h'] > self.sell_rsi_1h_7) and (last_candle['crossed_below_ema_12_26']):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_7_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_7_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_7_2_2 ( {buy_tag})"
# Sell signal 8
elif self.sell_condition_8_enable and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * self.sell_bb_relative_8):
if (last_candle['close'] > last_candle['ema_200']):
if (current_profit > 0.01):
return f"sell_signal_8_1_1 ( {buy_tag})"
else:
if (current_profit > 0.01):
return f"sell_signal_8_2_1 ( {buy_tag})"
elif (max_loss > 0.5):
return f"sell_signal_8_2_2 ( {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_1h) for pair in pairs]
informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs])
if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
btc_info_pair = f"BTC/{self.config['stake_currency']}"
else:
btc_info_pair = "BTC/USDT"
informative_pairs.append((btc_info_pair, self.timeframe))
informative_pairs.append((btc_info_pair, self.info_timeframe_1h))
informative_pairs.append((btc_info_pair, self.info_timeframe_1d))
return informative_pairs
def informative_1d_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe_1d)
# Top traded coins
if self.coin_metrics['top_traded_enabled']:
informative_1d = informative_1d.merge(self.coin_metrics['tt_dataframe'], on='date', how='left')
informative_1d['is_top_traded'] = informative_1d.apply(lambda row: self.is_top_coin(metadata['pair'], row, self.coin_metrics['top_traded_len']), axis=1)
column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_traded_len'] + 1)]
informative_1d.drop(columns = column_names, inplace=True)
# Top grossing coins
if self.coin_metrics['top_grossing_enabled']:
informative_1d = informative_1d.merge(self.coin_metrics['tg_dataframe'], on='date', how='left')
informative_1d['is_top_grossing'] = informative_1d.apply(lambda row: self.is_top_coin(metadata['pair'], row, self.coin_metrics['top_grossing_len']), axis=1)
column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_grossing_len'] + 1)]
informative_1d.drop(columns = column_names, inplace=True)
# Pivots
informative_1d['pivot'], informative_1d['res1'], informative_1d['res2'], informative_1d['res3'], informative_1d['sup1'], informative_1d['sup2'], informative_1d['sup3'] = pivot_points(informative_1d, mode='fibonacci')
# Smoothed Heikin-Ashi
informative_1d['open_sha'], informative_1d['close_sha'], informative_1d['low_sha'] = HeikinAshi(informative_1d, smooth_inputs=True, smooth_outputs=False, length=10)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.")
return informative_1d
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
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_1h)
# 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)
# EWO
informative_1h['ewo_sma'] = ewo_sma(informative_1h, 50, 200)
# 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)
# CRSI (3, 2, 100)
crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
informative_1h['crsi'] = (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3
# 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)
# SSL
ssl_down, ssl_up = SSLChannels(informative_1h, 10)
informative_1h['ssl_down'] = ssl_down
informative_1h['ssl_up'] = ssl_up
# MOMDIV
mom = momdiv(informative_1h)
informative_1h['momdiv_buy'] = mom['momdiv_buy']
informative_1h['momdiv_sell'] = mom['momdiv_sell']
informative_1h['momdiv_coh'] = mom['momdiv_coh']
informative_1h['momdiv_col'] = mom['momdiv_col']
# 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['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)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.")
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
# BB 40 - STD2
bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['bb40_2_low'] = bb_40_std2['lower']
dataframe['bb40_2_mid'] = bb_40_std2['mid']
dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
# BB 20 - STD2
bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb20_2_low'] = bb_20_std2['lower']
dataframe['bb20_2_mid'] = bb_20_std2['mid']
dataframe['bb20_2_upp'] = bb_20_std2['upper']
# EMA 200
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_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)
# 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_sma'] = ewo_sma(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_14'] = williams_r(dataframe, period=14)
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)
# EMA of VWMA Oscillator
dataframe['ema_vwma_osc_32'] = ema_vwma_osc(dataframe, 32)
dataframe['ema_vwma_osc_64'] = ema_vwma_osc(dataframe, 64)
dataframe['ema_vwma_osc_96'] = ema_vwma_osc(dataframe, 96)
# hull
dataframe['hull_75'] = hull(dataframe, 75)
# CRSI (3, 2, 100)
crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0))
dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3
# 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
# CCI
dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20)
# CCI Oscillator
cci_36 = ta.CCI(dataframe, timeperiod=36)
cci_36_max = cci_36.rolling(self.startup_candle_count).max()
cci_36_min = cci_36.rolling(self.startup_candle_count).min()
dataframe['cci_36_osc'] = (cci_36 / cci_36_max).where(cci_36 > 0, -cci_36 / cci_36_min)
# MOMDIV
mom = momdiv(dataframe)
dataframe['momdiv_buy'] = mom['momdiv_buy']
dataframe['momdiv_sell'] = mom['momdiv_sell']
dataframe['momdiv_coh'] = mom['momdiv_coh']
dataframe['momdiv_col'] = mom['momdiv_col']
# Dip protection
dataframe['tpct_change_0'] = self.top_percent_change(dataframe,0)
dataframe['tpct_change_2'] = self.top_percent_change(dataframe,2)
dataframe['tpct_change_12'] = self.top_percent_change(dataframe,12)
dataframe['tpct_change_144'] = self.top_percent_change(dataframe,144)
# Volume
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean()
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)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] normal_tf_indicators took: {tok - tik:0.4f} seconds.")
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:
tik = time.perf_counter()
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] base_tf_btc_indicators took: {tok - tik:0.4f} seconds.")
return dataframe
def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
# 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: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] info_tf_btc_indicators took: {tok - tik:0.4f} seconds.")
return dataframe
def daily_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
# Indicators
# -----------------------------------------------------------------------------------------
dataframe['pivot'], dataframe['res1'], dataframe['res2'], dataframe['res3'], dataframe['sup1'], dataframe['sup2'], dataframe['sup3'] = pivot_points(dataframe, mode='fibonacci')
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] daily_tf_btc_indicators took: {tok - tik:0.4f} seconds.")
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
'''
--> BTC informative (5m/1h)
___________________________________________________________________________________________
'''
if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
btc_info_pair = f"BTC/{self.config['stake_currency']}"
else:
btc_info_pair = "BTC/USDT"
if self.has_BTC_daily_tf:
btc_daily_tf = self.dp.get_pair_dataframe(btc_info_pair, '1d')
btc_daily_tf = self.daily_tf_btc_indicators(btc_daily_tf, metadata)
dataframe = merge_informative_pair(dataframe, btc_daily_tf, self.timeframe, '1d', ffill=True)
drop_columns = [f"{s}_1d" 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_info_pair, self.info_timeframe_1h)
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_1h, ffill=True)
drop_columns = [f"{s}_{self.info_timeframe_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
if self.has_BTC_base_tf:
btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, 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 = [f"{s}_{self.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_1d != 'none':
informative_1d = self.informative_1d_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, self.info_timeframe_1d, ffill=True)
drop_columns = [f"{s}_{self.info_timeframe_1d}" for s in ['date','open', 'high', 'low', 'close', 'volume']]
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
if self.info_timeframe_1h != 'none':
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe_1h, ffill=True)
drop_columns = [f"{s}_{self.info_timeframe_1h}" 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: f"{s}_{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 = [f"{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)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.")
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[f"buy_condition_{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_threshold_0"] is not None:
item_buy_protection_list.append(dataframe['tpct_change_0'] < global_buy_protection_params["safe_dips_threshold_0"])
if global_buy_protection_params["safe_dips_threshold_2"] is not None:
item_buy_protection_list.append(dataframe['tpct_change_2'] < global_buy_protection_params["safe_dips_threshold_2"])
if global_buy_protection_params["safe_dips_threshold_12"] is not None:
item_buy_protection_list.append(dataframe['tpct_change_12'] < global_buy_protection_params["safe_dips_threshold_12"])
if global_buy_protection_params["safe_dips_threshold_144"] is not None:
item_buy_protection_list.append(dataframe['tpct_change_144'] < global_buy_protection_params["safe_dips_threshold_144"])
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 global_buy_protection_params['close_over_pivot_type'] != 'none':
item_buy_protection_list.append(dataframe['close'] > dataframe[f"{global_buy_protection_params['close_over_pivot_type']}_1d"] * global_buy_protection_params['close_over_pivot_offset'])
if global_buy_protection_params['close_under_pivot_type'] != 'none':
item_buy_protection_list.append(dataframe['close'] < dataframe[f"{global_buy_protection_params['close_under_pivot_type']}_1d"] * global_buy_protection_params['close_under_pivot_offset'])
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'])
# 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(12).min()) / dataframe['open'].rolling(12).min()) > self.buy_1_min_inc)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_1_rsi_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_2_r_14_max)
item_buy_logic.append(dataframe['mfi'] < self.buy_1_mfi_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_1_rsi_1h_min)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_1_rsi_1h_max)
# Condition #2
elif index == 2:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.buy_2_rsi_1h_diff)
item_buy_logic.append(dataframe['mfi'] < self.buy_2_mfi)
item_buy_logic.append(dataframe['cti'] < self.buy_2_cti_max)
item_buy_logic.append(dataframe['r_480'] > self.buy_2_r_480_min)
item_buy_logic.append(dataframe['r_480'] < self.buy_2_r_480_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_2_cti_1h_max)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_2_volume))
# Condition #3
elif index == 3:
# Non-Standard protections
# 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_3_bb40_bbdelta_close))
item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_3_bb40_closedelta_close))
item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_3_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['cci_36_osc'] > self.buy_3_cci_36_osc_min)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_3_crsi_1h_min)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_3_r_480_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_3_cti_1h_max)
# 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_4_bb20_close_bblowerband * dataframe['bb20_2_low'])
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_4_bb20_volume))
item_buy_logic.append(dataframe['cti'] < self.buy_4_cti_max)
# Condition #5
elif index == 5:
# Non-Standard protections
item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_5_ema_rel))
# 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_5_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_5_bb_offset))
item_buy_logic.append(dataframe['cti'] < self.buy_5_cti_max)
item_buy_logic.append(dataframe['rsi_14'] > self.buy_5_rsi_14_min)
item_buy_logic.append(dataframe['mfi'] > self.buy_5_mfi_min)
item_buy_logic.append(dataframe['r_14'] < self.buy_5_r_14_max)
item_buy_logic.append(dataframe['r_14'].shift(1) < self.buy_5_r_14_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_5_crsi_1h_min)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_5_volume))
# 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_6_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_6_bb_offset))
item_buy_logic.append(dataframe['r_14'] < self.buy_6_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_6_cti_1h_min)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_6_crsi_1h_min)
# 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_7_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['sma_30'] * self.buy_7_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_7_cti_max)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_7_rsi_max)
# Condition #8
elif index == 8:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_20'] > dataframe['ema_50'])
item_buy_logic.append(dataframe['ema_15'] > dataframe['ema_100'])
item_buy_logic.append(dataframe['ema_200'] > dataframe['sma_200'])
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_8_bb_offset))
item_buy_logic.append(dataframe['r_14'] < self.buy_8_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_8_cti_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_8_r_480_1h_max)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_8_volume))
# 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_9_ma_offset)
item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_9_bb_offset)
item_buy_logic.append(dataframe['mfi'] < self.buy_9_mfi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_9_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_9_r_14_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_9_rsi_1h_min)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_9_rsi_1h_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_9_crsi_1h_min)
# 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_10_ma_offset_high)
item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_10_bb_offset)
item_buy_logic.append(dataframe['r_14'] < self.buy_10_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_10_cti_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_10_cti_1h_max)
# Condition #11
elif index == 11:
# Non-Standard protections
# Logic
item_buy_logic.append(((dataframe['close'] - dataframe['open'].rolling(6).min()) / dataframe['open'].rolling(6).min()) > self.buy_11_min_inc)
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_11_ma_offset)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_11_rsi_max)
item_buy_logic.append(dataframe['mfi'] < self.buy_11_mfi_max)
item_buy_logic.append(dataframe['cci'] < self.buy_11_cci_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_11_rsi_1h_min)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_11_rsi_1h_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_11_cti_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_11_r_480_1h_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_11_crsi_1h_min)
# Condition #12
elif index == 12:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_12_ma_offset)
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_12_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_12_rsi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_12_cti_max)
# 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_13_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_13_cti_max)
item_buy_logic.append(dataframe['ewo_sma'] < self.buy_13_ewo_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_13_cti_1h_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_13_crsi_1h_min)
# 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_14_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_14_bb_offset))
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_14_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_14_cti_max)
# Condition #15
elif index == 15:
# 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_15_ema_open_mult))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['rsi_14'] < self.buy_15_rsi_min)
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_15_ma_offset)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_15_cti_1h_min)
# Condition #16
elif index == 16:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_16_ma_offset)
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_16_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_16_rsi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_16_cti_max)
# Condition #17
elif index == 17:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_17_ma_offset)
item_buy_logic.append(dataframe['ewo_sma'] < self.buy_17_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_17_cti_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_17_crsi_1h_min)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_17_volume))
# 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['close'] < (dataframe['bb20_2_low'] * self.buy_18_bb_offset))
item_buy_logic.append(dataframe['rsi_14'] < self.buy_18_rsi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_18_cti_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_18_cti_1h_max)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_18_volume))
# Condition #19
elif index == 19:
# Non-Standard protections
item_buy_logic.append(dataframe['moderi_32'] == True)
item_buy_logic.append(dataframe['moderi_64'] == True)
item_buy_logic.append(dataframe['moderi_96'] == True)
# 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['chop'] < self.buy_19_chop_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_19_rsi_1h_min)
# Condition #20
elif index == 20:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < self.buy_20_rsi_14_max)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_20_rsi_14_1h_max)
item_buy_logic.append(dataframe['cti'] < self.buy_20_cti_max)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_20_volume))
# Condition #21
elif index == 21:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['rsi_14'] < self.buy_21_rsi_14_max)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_21_rsi_14_1h_max)
item_buy_logic.append(dataframe['cti'] < self.buy_21_cti_max)
item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_21_volume))
# 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_22_volume) > dataframe['volume'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_22_ma_offset)
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_22_bb_offset))
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_22_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_22_rsi_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_22_cti_max)
item_buy_logic.append(dataframe['r_480'] < self.buy_22_r_480_max)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_22_cti_1h_min)
# 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_sma'] > self.buy_23_ewo_min)
item_buy_logic.append(dataframe['cti'] < self.buy_23_cti_max)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_23_rsi_14_max)
item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_23_rsi_14_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_23_r_480_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < 0.92)
# 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_14_max)
item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_24_rsi_14_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_max)
item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h'])
item_buy_logic.append(dataframe['close'] < dataframe['sma_15'] * self.buy_25_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_25_cti_max)
item_buy_logic.append(dataframe['cci'] < self.buy_25_cci_max)
# Condition #26
elif index == 26:
# Non-Standard protections
# 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_max)
item_buy_logic.append(dataframe['cci'] < self.buy_26_cci_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_26_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_26_cti_1h_max)
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_14'] == self.buy_27_r_14)
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['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_sma'] > self.buy_28_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_28_rsi_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_28_cti_max)
item_buy_logic.append(dataframe['cti'].shift(1) < self.buy_28_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_28_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_28_cti_1h_max)
# 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_sma'] < self.buy_29_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_29_cti_max)
# 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_sma'] > self.buy_30_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_30_rsi_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_30_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_30_r_14_max)
# 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_sma'] < self.buy_31_ewo_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_31_r_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_31_cti_max)
# Condition #32 - Quick mode buy
elif index == 32:
# Non-Standard protections
item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h'])
# Logic
item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1))
item_buy_logic.append(dataframe['rsi_4'] < self.buy_32_rsi_4_max)
item_buy_logic.append(dataframe['rsi_14'] > self.buy_32_rsi_14_min)
item_buy_logic.append(dataframe['close'] < dataframe['sma_15'] * self.buy_32_ma_offset)
item_buy_logic.append(dataframe['cti'] < self.buy_32_cti_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_32_crsi_1h_min)
item_buy_logic.append(dataframe['crsi_1h'] < self.buy_32_crsi_1h_max)
# Condition #33 - Quick mode buy
elif index == 33:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['ema_13'] * self.buy_33_ma_offset))
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_33_ewo_min)
item_buy_logic.append(dataframe['cti'] < self.buy_33_cti_max)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_33_rsi_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_33_r_14_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_33_cti_1h_max)
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['close'] < dataframe['ema_13'] * self.buy_34_ma_offset)
item_buy_logic.append(dataframe['ewo_sma'] < self.buy_34_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_34_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_34_r_14_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_34_crsi_1h_min)
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_sma'] > self.buy_35_ewo_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_35_rsi_max)
item_buy_logic.append(dataframe['cti'] < self.buy_35_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_35_r_14_max)
# 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_sma'] < self.buy_36_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_36_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_36_r_14_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_36_crsi_1h_min)
# Condition #37 - Quick mode buy
elif index == 37:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset)
item_buy_logic.append(((dataframe['close_1h'].shift(12) - dataframe['close_1h']) / dataframe['close_1h']) < self.buy_37_close_1h_max)
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_37_ewo_min)
item_buy_logic.append(dataframe['ewo_sma'] < self.buy_37_ewo_max)
item_buy_logic.append(dataframe['rsi_14'] > self.buy_37_rsi_14_min)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_37_rsi_14_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_37_crsi_1h_min)
item_buy_logic.append(dataframe['crsi_1h'] < self.buy_37_crsi_1h_max)
item_buy_logic.append(dataframe['cti'] < self.buy_37_cti_max)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_37_cti_1h_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_37_r_14_max)
# 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_sma'] < self.buy_38_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_38_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_38_r_14_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_38_crsi_1h_min)
# 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['ssl_up_1h'] > dataframe['ssl_down_1h'])
item_buy_logic.append(dataframe['close'] < dataframe['ssl_up_1h'])
item_buy_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12))
item_buy_logic.append(dataframe['cti'] < self.buy_39_cti_max)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_39_r_1h_max)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_39_cti_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_39_cti_1h_max)
# Start of trend
item_buy_logic.append(
(dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12))
)
# Condition #40
elif index == 40:
# Non-Standard protections (add below)
# Logic
item_buy_logic.append(dataframe['momdiv_buy_1h'] == True)
item_buy_logic.append(dataframe['cci'] < self.buy_40_cci_max)
item_buy_logic.append(dataframe['rsi_14'] < self.buy_40_rsi_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_40_r_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_40_cti_max)
# 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['close'] < dataframe['sma_75'] * self.buy_41_ma_offset_high)
item_buy_logic.append(dataframe['cti'] < self.buy_41_cti_max)
item_buy_logic.append(dataframe['cci'] < self.buy_41_cci_max)
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_41_ewo_1h_min)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_41_r_480_1h_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_41_crsi_1h_min)
# 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['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))
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_42_ewo_1h_min)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_42_cti_1h_min)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_42_r_480_1h_max)
# 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['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_max)
item_buy_logic.append(dataframe['r_480'] > self.buy_43_r_480_min)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_43_cti_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_43_cti_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_43_r_480_1h_min)
# 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_sma'] < self.buy_44_ewo_max)
item_buy_logic.append(dataframe['cti'] < self.buy_44_cti_max)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_44_crsi_1h_min)
# Condition #45 - Long mode
elif index == 45:
# Non-Standard protections
# 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_45_bb40_bbdelta_close))
item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_45_bb40_closedelta_close))
item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_45_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['close'] < dataframe['sma_30'] * self.buy_45_ma_offset)
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_45_ewo_min)
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_45_ewo_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_45_cti_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_45_r_480_1h_max)
# Condition #46 - Long mode
elif index == 46:
# 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_46_ema_open_mult))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_46_ewo_1h_min)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_46_cti_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_46_cti_1h_max)
# Condition #47 - Long mode
elif index == 47:
# Non-Standard protections
# Logic
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_47_ewo_min)
item_buy_logic.append(dataframe['close'] < (dataframe['sma_30'] * self.buy_47_ma_offset))
item_buy_logic.append(dataframe['rsi_14'] < self.buy_47_rsi_14_max)
item_buy_logic.append(dataframe['cti'] < self.buy_47_cti_max)
item_buy_logic.append(dataframe['r_14'] < self.buy_47_r_14_max)
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_47_ewo_1h_min)
item_buy_logic.append(dataframe['cti_1h'] > self.buy_47_cti_1h_min)
item_buy_logic.append(dataframe['cti_1h'] < self.buy_47_cti_1h_max)
# Condition #48 - Uptrend mode
elif index == 48:
# Non-Standard protections
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['moderi_32'])
item_buy_logic.append(dataframe['moderi_64'])
item_buy_logic.append(dataframe['moderi_96'])
# Logic
item_buy_logic.append(dataframe['ewo_sma'] > self.buy_48_ewo_min)
item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_48_ewo_1h_min)
item_buy_logic.append(dataframe['r_480'] > self.buy_48_r_480_min)
item_buy_logic.append(dataframe['r_480_1h'] > self.buy_48_r_480_1h_min)
item_buy_logic.append(dataframe['r_480_1h'] < self.buy_48_r_480_1h_max)
item_buy_logic.append(dataframe['r_480_1h'] > dataframe['r_480_1h'].shift(12))
item_buy_logic.append(dataframe['cti_1h'] > self.buy_48_cti_1h_min)
item_buy_logic.append(dataframe['crsi_1h'] > self.buy_48_crsi_1h_min)
item_buy_logic.append(dataframe['cti'].shift(1).rolling(12).min() < -0.5)
item_buy_logic.append(dataframe['cti'].shift(1).rolling(12).max() < 0.0)
item_buy_logic.append(dataframe['cti'].shift(1) < 0.0)
item_buy_logic.append(dataframe['cti'] > 0.0)
item_buy_logic.append(dataframe['volume'] > 0)
item_buy = reduce(lambda x, y: x & y, item_buy_logic)
dataframe.loc[item_buy, 'buy_tag'] += f"{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
"""
if self._should_hold_trade(trade, rate, sell_reason):
return False
self._remove_profit_target(pair)
return True
def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_time: "datetime"):
self.target_profit_cache.data[pair] = {
"rate": rate,
"sell_reason": sell_reason,
"time_profit_reached": current_time.isoformat()
}
self.target_profit_cache.save()
def _remove_profit_target(self, pair: str):
if self.target_profit_cache is not None:
self.target_profit_cache.data.pop(pair, None)
self.target_profit_cache.save()
def _should_hold_trade(self, trade: "Trade", rate: float, sell_reason: str) -> bool:
if self.config['runmode'].value not in ('live', 'dry_run'):
return False
if not self.holdSupportEnabled:
return False
# Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
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 False
if not self.hold_trades_cache.data:
# We have no pairs we want to hold until profit, sell
return False
# By default, no hold should be done
hold_trade = False
trade_ids: dict = self.hold_trades_cache.data.get("trade_ids")
if trade_ids and trade.id in trade_ids:
trade_profit_ratio = trade_ids[trade.id]
current_profit_ratio = trade.calc_profit_ratio(rate)
if sell_reason == "force_sell":
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{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 False
elif current_profit_ratio >= trade_profit_ratio:
# This pair is on the list to hold, and we reached minimum profit, sell
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Selling %s because the current profit of %s >= %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
# This pair is on the list to hold, and we haven't reached minimum profit, hold
hold_trade = True
trade_pairs: dict = self.hold_trades_cache.data.get("trade_pairs")
if trade_pairs and trade.pair in trade_pairs:
trade_profit_ratio = trade_pairs[trade.pair]
current_profit_ratio = trade.calc_profit_ratio(rate)
if sell_reason == "force_sell":
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{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 False
elif current_profit_ratio >= trade_profit_ratio:
# This pair is on the list to hold, and we reached minimum profit, sell
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Selling %s because the current profit of %s >= %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
# This pair is on the list to hold, and we haven't reached minimum profit, hold
hold_trade = True
return hold_trade
# 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
def ewo_sma(dataframe, sma1_length=5, sma2_length=35):
sma1 = ta.SMA(dataframe, timeperiod=sma1_length)
sma2 = ta.SMA(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=f"{period} Williams %R",
)
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
# Exponential moving average of a volume weighted simple moving average
def ema_vwma_osc(dataframe, len_slow_ma):
slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma))
return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100
# 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 = f'MA_{MAtype}_{length}'
atr = f'ATR_{period}'
pm = f'pm_{period}_{multiplier}_{length}_{MAtype}'
pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}'
# MAtype==1 --> EMA
# MAtype==2 --> DEMA
# MAtype==3 --> T3
# MAtype==4 --> SMA
# MAtype==5 --> VIDYA
# MAtype==6 --> TEMA
# MAtype==7 --> WMA
# MAtype==8 --> VWMA
# MAtype==9 --> zema
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
mavalue = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
mavalue = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
mavalue = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
mavalue = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
mavalue = VIDYA(df, length=length)
elif MAtype == 6:
mavalue = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
mavalue = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
mavalue = vwma(df, length)
elif MAtype == 9:
mavalue = zema(df, period=length)
df[atr] = ta.ATR(df, timeperiod=period)
df['basic_ub'] = mavalue + ((multiplier/10) * df[atr])
df['basic_lb'] = mavalue - ((multiplier/10) * df[atr])
basic_ub = df['basic_ub'].values
final_ub = np.full(len(df), 0.00)
basic_lb = df['basic_lb'].values
final_lb = np.full(len(df), 0.00)
for i in range(period, len(df)):
final_ub[i] = basic_ub[i] if (
basic_ub[i] < final_ub[i - 1]
or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
final_lb[i] = basic_lb[i] if (
basic_lb[i] > final_lb[i - 1]
or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]
df['final_ub'] = final_ub
df['final_lb'] = final_lb
pm_arr = np.full(len(df), 0.00)
for i in range(period, len(df)):
pm_arr[i] = (
final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] <= final_ub[i])
else final_lb[i] if (
pm_arr[i - 1] == final_ub[i - 1]
and mavalue[i] > final_ub[i]) else final_lb[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] >= final_lb[i]) else final_ub[i]
if (pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] < final_lb[i]) else 0.00)
pm = Series(pm_arr)
# Mark the trend direction up/down
pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN)
return pm, pmx
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):
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
def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series:
hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3
hl_range = (dataframe['high'] - dataframe['low']).shift(1)
if mode == 'simple':
res1 = hlc3_pivot * 2 - dataframe['low'].shift(1)
sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1)
res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift()
sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift()
res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift()
sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift()
elif mode == 'fibonacci':
res1 = hlc3_pivot + 0.382 * hl_range
sup1 = hlc3_pivot - 0.382 * hl_range
res2 = hlc3_pivot + 0.618 * hl_range
sup2 = hlc3_pivot - 0.618 * hl_range
res3 = hlc3_pivot + 1 * hl_range
sup3 = hlc3_pivot - 1 * hl_range
return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3
def HeikinAshi(dataframe, smooth_inputs = False, smooth_outputs = False, length = 10):
df = dataframe[['open','close','high','low']].copy().fillna(0)
if smooth_inputs:
df['open_s'] = ta.EMA(df['open'], timeframe = length)
df['high_s'] = ta.EMA(df['high'], timeframe = length)
df['low_s'] = ta.EMA(df['low'], timeframe = length)
df['close_s'] = ta.EMA(df['close'],timeframe = length)
open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2
high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1)
low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1)
close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s'])/4
else:
open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2
high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1)
low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1)
close_ha = (df['open'] + df['high'] + df['low'] + df['close'])/4
open_ha = open_ha.fillna(0)
high_ha = high_ha.fillna(0)
low_ha = low_ha.fillna(0)
close_ha = close_ha.fillna(0)
if smooth_outputs:
open_sha = ta.EMA(open_ha, timeframe = length)
high_sha = ta.EMA(high_ha, timeframe = length)
low_sha = ta.EMA(low_ha, timeframe = length)
close_sha = ta.EMA(close_ha, timeframe = length)
return open_sha, close_sha, low_sha
else:
return open_ha, close_ha, low_ha
# Mom DIV
def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame:
mom: Series = ta.MOM(dataframe, timeperiod=mom_length)
upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0)
buy = qtpylib.crossed_below(mom, lowerband)
sell = qtpylib.crossed_above(mom, upperband)
hh = dataframe['high'].rolling(lookback).max()
ll = dataframe['low'].rolling(lookback).min()
coh = dataframe['high'] >= hh
col = dataframe['low'] <= ll
df = DataFrame({
"momdiv_mom": mom,
"momdiv_upperb": upperband,
"momdiv_lowerb": lowerband,
"momdiv_buy": buy,
"momdiv_sell": sell,
"momdiv_coh": coh,
"momdiv_col": col,
}, index=dataframe['close'].index)
return df
class Cache:
def __init__(self, path):
self.path = path
self.data = {}
self._mtime = None
self._previous_data = {}
try:
self.load()
except FileNotFoundError:
pass
@staticmethod
def rapidjson_load_kwargs():
return {"number_mode": rapidjson.NM_NATIVE}
@staticmethod
def rapidjson_dump_kwargs():
return {"number_mode": rapidjson.NM_NATIVE}
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 = rapidjson.load(
rfh,
**self.rapidjson_load_kwargs()
)
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
rapidjson.dump(
self.data,
self.path.open("w"),
**self.rapidjson_dump_kwargs()
)
self._mtime = self.path.stat().st_mtime
self._previous_data = copy.deepcopy(self.data)
class HoldsCache(Cache):
@staticmethod
def rapidjson_load_kwargs():
return {
"number_mode": rapidjson.NM_NATIVE,
"object_hook": HoldsCache._object_hook,
}
@staticmethod
def rapidjson_dump_kwargs():
return {
"number_mode": rapidjson.NM_NATIVE,
"mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS,
}
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")
trade_pairs = data.get("trade_pairs")
if not trade_ids and not trade_pairs:
return data
open_trades = {}
for trade in Trade.get_trades_proxy(is_open=True):
open_trades[trade.id] = open_trades[trade.pair] = trade
r_trade_ids = {}
if trade_ids:
if isinstance(trade_ids, dict):
# New syntax
for trade_id, profit_ratio in trade_ids.items():
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 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 = f"{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
)
r_trade_ids[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 = f"{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
)
r_trade_ids[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
)
r_trade_pairs = {}
if trade_pairs:
for trade_pair, profit_ratio in trade_pairs.items():
if not isinstance(trade_pair, str):
log.error(
"The trade_pair(%s) defined under 'trade_pairs' in %s is not a string",
trade_pair, self.path
)
continue
if "/" not in trade_pair:
log.error(
"The trade_pair(%s) defined under 'trade_pairs' in %s does not look like "
"a valid '<TOKEN_NAME>/<STAKE_CURRENCY>' formatted pair.",
trade_pair, self.path
)
continue
if not isinstance(profit_ratio, float):
log.error(
"The 'profit_ratio' config value(%s) for trade_pair %s in %s is not a float",
profit_ratio,
trade_pair,
self.path
)
formatted_profit_ratio = f"{profit_ratio * 100}%"
if trade_pair in open_trades:
log.warning(
"The trade %s is configured to HOLD until the profit ratio of %s is met",
open_trades[trade_pair],
formatted_profit_ratio
)
else:
log.warning(
"The trade pair %s is configured to HOLD until the profit ratio of %s is met",
trade_pair,
formatted_profit_ratio
)
r_trade_pairs[trade_pair] = profit_ratio
r_data = {}
if r_trade_ids:
r_data["trade_ids"] = r_trade_ids
if r_trade_pairs:
r_data["trade_pairs"] = r_trade_pairs
return r_data
@staticmethod
def _object_hook(data):
_data = {}
for key, value in data.items():
try:
key = int(key)
except ValueError:
pass
_data[key] = value
return _data