NostalgiaForInfinityNext_ChangeToTower_V5_3

import logging
import pathlib
import rapidjson
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.misc import json_load
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter
from freqtrade.exchange import timeframe_to_prev_date
from pandas import DataFrame, Series, concat
from functools import reduce
import math
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from technical.util import resample_to_interval, resampled_merge
from technical.indicators import zema, VIDYA, ichimoku
import pandas_ta as pta
log = logging.getLogger(__name__)
###########################################################################################################
##                NostalgiaForInfinityV8 by iterativ                                                     ##
##                                                                                                       ##
##    Strategy for Freqtrade https://github.com/freqtrade/freqtrade                                      ##
##                                                                                                       ##
###########################################################################################################
##               GENERAL RECOMMENDATIONS                                                                 ##
##                                                                                                       ##
##   For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake.        ##
##   A pairlist with 40 to 80 pairs. Volume pairlist works well.                                         ##
##   Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs.                            ##
##   Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc).                    ##
##   Ensure that you don't override any variables in you config.json. Especially                         ##
##   the timeframe (must be 5m).                                                                         ##
##     use_exit_signal must set to true (or not set at all).                                             ##
##     exit_profit_only must set to false (or not set at all).                                           ##
##     ignore_roi_if_entry_signal must set to true (or not set at all).                                    ##
##                                                                                                       ##
###########################################################################################################
##               HOLD SUPPORT                                                                            ##
##   In case you want to have SOME of the trades to only be sold when on profit, add a file named        ##
##   "hold-trades.json" in the same directory as this strategy.                                          ##
##                                                                                                       ##
##   The contents should be similar to:                                                                  ##
##                                                                                                       ##
##   {"trade_ids": [1, 3, 7], "profit_ratio": 0.005}                                                     ##
##                                                                                                       ##
##   Or, for individual profit ratios(Notice the trade ID's as strings:                                  ##
##                                                                                                       ##
##   {"trade_ids": {"1": 0.001, "3": -0.005, "7": 0.05}}                                                 ##
##                                                                                                       ##
##   NOTE:                                                                                               ##
##    * `trade_ids` is a list of integers, the trade ID's, which you can get from the logs or from the   ##
##      output of the telegram status command.                                                           ##
##    * Regardless of the defined profit ratio(s), the strategy MUST still produce a SELL signal for the ##
##      HOLD support logic to run                                                                        ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS                                                                               ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                             ##
##   BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe                               ##
##                                                                                                       ##
###########################################################################################################

class NostalgiaForInfinityNext_ChangeToTower_V5_3(IStrategy):
    INTERFACE_VERSION = 3
    plot_config = {'main_plot': {}, 'subplots': {'entry tag': {'enter_tag': {'color': 'green'}}}}
    # ROI table:
    minimal_roi = {'0': 10}
    stoploss = -0.99
    # Trailing stoploss (not used)
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03
    use_custom_stoploss = False
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    res_timeframe = 'none'
    info_timeframe = '1h'
    # BTC informative
    has_BTC_base_tf = False
    has_BTC_info_tf = True
    # Backtest Age Filter emulation
    has_bt_agefilter = False
    bt_min_age_days = 3
    # Exchange Downtime protection
    has_downtime_protection = False
    # 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_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 480
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False}
    #############################################################
    #############
    # Enable/Disable conditions
    #############
    entry_params = {'entry_condition_1_enable': True, 'entry_condition_2_enable': True, 'entry_condition_3_enable': True, 'entry_condition_4_enable': True, 'entry_condition_5_enable': True, 'entry_condition_6_enable': True, 'entry_condition_7_enable': True, 'entry_condition_8_enable': True, 'entry_condition_9_enable': True, 'entry_condition_10_enable': True, 'entry_condition_11_enable': True, 'entry_condition_12_enable': True, 'entry_condition_13_enable': True, 'entry_condition_14_enable': True, 'entry_condition_15_enable': True, 'entry_condition_16_enable': True, 'entry_condition_17_enable': True, 'entry_condition_18_enable': True, 'entry_condition_19_enable': True, 'entry_condition_20_enable': True, 'entry_condition_21_enable': True, 'entry_condition_22_enable': True, 'entry_condition_23_enable': True, 'entry_condition_24_enable': True, 'entry_condition_25_enable': True, 'entry_condition_26_enable': True, 'entry_condition_27_enable': True, 'entry_condition_28_enable': True, 'entry_condition_29_enable': True, 'entry_condition_30_enable': True, 'entry_condition_31_enable': True, 'entry_condition_32_enable': True, 'entry_condition_33_enable': True, 'entry_condition_34_enable': True, 'entry_condition_35_enable': True, 'entry_condition_36_enable': True, 'entry_condition_37_enable': True, 'entry_condition_38_enable': True, 'entry_condition_39_enable': True, 'entry_condition_40_enable': True}
    #############
    # Enable/Disable conditions
    #############
    exit_params = {'exit_condition_1_enable': True, 'exit_condition_2_enable': True, 'exit_condition_3_enable': True, 'exit_condition_4_enable': True, 'exit_condition_5_enable': True, 'exit_condition_6_enable': True, 'exit_condition_7_enable': True, 'exit_condition_8_enable': True}
    #############################################################
    entry_protection_params = {1: {'ema_fast': False, 'ema_fast_len': '26', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '28', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '80', 'safe_pump': False, 'safe_pump_type': '70', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 2: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '20', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 3: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': True, 'safe_pump_type': '100', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 4: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 5: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '100', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '30', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 6: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 7: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 8: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 9: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 10: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 11: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 12: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '40', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 13: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 14: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 15: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 16: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '50', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 17: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 18: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '44', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '72', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 19: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '36', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 20: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 21: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '90', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 22: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 23: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 24: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '36', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 25: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '20', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 26: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '60', 'safe_pump': True, 'safe_pump_type': '100', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 27: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 28: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 29: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 30: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 31: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '110', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 32: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 33: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 34: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 35: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 36: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 37: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 38: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 39: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '50', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}, 40: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '50', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}}
    # Strict dips - level 10
    entry_dip_threshold_10_1 = DecimalParameter(0.001, 0.05, default=0.015, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_10_2 = DecimalParameter(0.01, 0.2, default=0.1, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_10_3 = DecimalParameter(0.1, 0.3, default=0.24, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_10_4 = DecimalParameter(0.3, 0.5, default=0.42, space='entry', decimals=3, optimize=False, load=True)
    # Strict dips - level 20
    entry_dip_threshold_20_1 = DecimalParameter(0.001, 0.05, default=0.016, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_20_2 = DecimalParameter(0.01, 0.2, default=0.11, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_20_3 = DecimalParameter(0.1, 0.4, default=0.26, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_20_4 = DecimalParameter(0.36, 0.56, default=0.44, space='entry', decimals=3, optimize=False, load=True)
    # Strict dips - level 30
    entry_dip_threshold_30_1 = DecimalParameter(0.001, 0.05, default=0.018, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_30_2 = DecimalParameter(0.01, 0.2, default=0.12, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_30_3 = DecimalParameter(0.1, 0.4, default=0.28, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_30_4 = DecimalParameter(0.36, 0.56, default=0.46, space='entry', decimals=3, optimize=False, load=True)
    # Strict dips - level 40
    entry_dip_threshold_40_1 = DecimalParameter(0.001, 0.05, default=0.019, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_40_2 = DecimalParameter(0.01, 0.2, default=0.13, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_40_3 = DecimalParameter(0.1, 0.4, default=0.3, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_40_4 = DecimalParameter(0.36, 0.56, default=0.48, space='entry', decimals=3, optimize=False, load=True)
    # Normal dips - level 50
    entry_dip_threshold_50_1 = DecimalParameter(0.001, 0.05, default=0.02, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_50_2 = DecimalParameter(0.01, 0.2, default=0.14, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_50_3 = DecimalParameter(0.05, 0.4, default=0.32, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_50_4 = DecimalParameter(0.2, 0.5, default=0.5, space='entry', decimals=3, optimize=False, load=True)
    # Normal dips - level 60
    entry_dip_threshold_60_1 = DecimalParameter(0.001, 0.05, default=0.022, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_60_2 = DecimalParameter(0.1, 0.22, default=0.18, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_60_3 = DecimalParameter(0.2, 0.4, default=0.34, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_60_4 = DecimalParameter(0.4, 0.6, default=0.56, space='entry', decimals=3, optimize=False, load=True)
    # Normal dips - level 70
    entry_dip_threshold_70_1 = DecimalParameter(0.001, 0.05, default=0.023, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_70_2 = DecimalParameter(0.16, 0.28, default=0.2, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_70_3 = DecimalParameter(0.2, 0.4, default=0.36, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_70_4 = DecimalParameter(0.5, 0.7, default=0.6, space='entry', decimals=3, optimize=False, load=True)
    # Normal dips - level 80
    entry_dip_threshold_80_1 = DecimalParameter(0.001, 0.05, default=0.024, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_80_2 = DecimalParameter(0.16, 0.28, default=0.22, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_80_3 = DecimalParameter(0.2, 0.4, default=0.38, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_80_4 = DecimalParameter(0.5, 0.7, default=0.66, space='entry', decimals=3, optimize=False, load=True)
    # Normal dips - level 70
    entry_dip_threshold_90_1 = DecimalParameter(0.001, 0.05, default=0.025, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_90_2 = DecimalParameter(0.16, 0.28, default=0.23, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_90_3 = DecimalParameter(0.3, 0.5, default=0.4, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_90_4 = DecimalParameter(0.6, 0.8, default=0.7, space='entry', decimals=3, optimize=False, load=True)
    # Loose dips - level 100
    entry_dip_threshold_100_1 = DecimalParameter(0.001, 0.05, default=0.026, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_100_2 = DecimalParameter(0.16, 0.3, default=0.24, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_100_3 = DecimalParameter(0.3, 0.5, default=0.42, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_100_4 = DecimalParameter(0.6, 1.0, default=0.8, space='entry', decimals=3, optimize=False, load=True)
    # Loose dips - level 110
    entry_dip_threshold_110_1 = DecimalParameter(0.001, 0.05, default=0.027, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_110_2 = DecimalParameter(0.16, 0.3, default=0.26, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_110_3 = DecimalParameter(0.3, 0.5, default=0.44, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_110_4 = DecimalParameter(0.6, 1.0, default=0.84, space='entry', decimals=3, optimize=False, load=True)
    # Loose dips - level 120
    entry_dip_threshold_120_1 = DecimalParameter(0.001, 0.05, default=0.028, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_120_2 = DecimalParameter(0.16, 0.3, default=0.28, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_120_3 = DecimalParameter(0.3, 0.5, default=0.46, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_120_4 = DecimalParameter(0.6, 1.0, default=0.86, space='entry', decimals=3, optimize=False, load=True)
    # Loose dips - level 130
    entry_dip_threshold_130_1 = DecimalParameter(0.001, 0.05, default=0.028, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_130_2 = DecimalParameter(0.16, 0.34, default=0.3, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_130_3 = DecimalParameter(0.36, 0.56, default=0.48, space='entry', decimals=3, optimize=False, load=True)
    entry_dip_threshold_130_4 = DecimalParameter(0.6, 1.0, default=0.9, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 10
    entry_pump_pull_threshold_10_24 = DecimalParameter(1.5, 3.0, default=2.2, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_10_24 = DecimalParameter(0.4, 1.0, default=0.42, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 10
    entry_pump_pull_threshold_10_36 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_10_36 = DecimalParameter(0.4, 1.0, default=0.58, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 10
    entry_pump_pull_threshold_10_48 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_10_48 = DecimalParameter(0.4, 1.0, default=0.8, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 20
    entry_pump_pull_threshold_20_24 = DecimalParameter(1.5, 3.0, default=2.2, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_20_24 = DecimalParameter(0.4, 1.0, default=0.46, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 20
    entry_pump_pull_threshold_20_36 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_20_36 = DecimalParameter(0.4, 1.0, default=0.6, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 20
    entry_pump_pull_threshold_20_48 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_20_48 = DecimalParameter(0.4, 1.0, default=0.81, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 30
    entry_pump_pull_threshold_30_24 = DecimalParameter(1.5, 3.0, default=2.2, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_30_24 = DecimalParameter(0.4, 1.0, default=0.5, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 30
    entry_pump_pull_threshold_30_36 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_30_36 = DecimalParameter(0.4, 1.0, default=0.62, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 30
    entry_pump_pull_threshold_30_48 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_30_48 = DecimalParameter(0.4, 1.0, default=0.82, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 40
    entry_pump_pull_threshold_40_24 = DecimalParameter(1.5, 3.0, default=2.2, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_40_24 = DecimalParameter(0.4, 1.0, default=0.54, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 40
    entry_pump_pull_threshold_40_36 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_40_36 = DecimalParameter(0.4, 1.0, default=0.63, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 40
    entry_pump_pull_threshold_40_48 = DecimalParameter(1.5, 3.0, default=2.0, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_40_48 = DecimalParameter(0.4, 1.0, default=0.84, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 50
    entry_pump_pull_threshold_50_24 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_50_24 = DecimalParameter(0.4, 1.0, default=0.6, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 50
    entry_pump_pull_threshold_50_36 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_50_36 = DecimalParameter(0.4, 1.0, default=0.64, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 50
    entry_pump_pull_threshold_50_48 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_50_48 = DecimalParameter(0.4, 1.0, default=0.85, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 60
    entry_pump_pull_threshold_60_24 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_60_24 = DecimalParameter(0.4, 1.0, default=0.62, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 60
    entry_pump_pull_threshold_60_36 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_60_36 = DecimalParameter(0.4, 1.0, default=0.66, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 60
    entry_pump_pull_threshold_60_48 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_60_48 = DecimalParameter(0.4, 1.0, default=0.9, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 70
    entry_pump_pull_threshold_70_24 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_70_24 = DecimalParameter(0.4, 1.0, default=0.63, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 70
    entry_pump_pull_threshold_70_36 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_70_36 = DecimalParameter(0.4, 1.0, default=0.67, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 70
    entry_pump_pull_threshold_70_48 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_70_48 = DecimalParameter(0.4, 1.0, default=0.95, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 80
    entry_pump_pull_threshold_80_24 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_80_24 = DecimalParameter(0.4, 1.0, default=0.64, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 80
    entry_pump_pull_threshold_80_36 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_80_36 = DecimalParameter(0.4, 1.0, default=0.68, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 80
    entry_pump_pull_threshold_80_48 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_80_48 = DecimalParameter(0.8, 1.1, default=1.0, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 90
    entry_pump_pull_threshold_90_24 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_90_24 = DecimalParameter(0.4, 1.0, default=0.65, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 90
    entry_pump_pull_threshold_90_36 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_90_36 = DecimalParameter(0.4, 1.0, default=0.69, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 90
    entry_pump_pull_threshold_90_48 = DecimalParameter(1.5, 3.0, default=1.75, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_90_48 = DecimalParameter(0.8, 1.2, default=1.1, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 100
    entry_pump_pull_threshold_100_24 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_100_24 = DecimalParameter(0.4, 1.0, default=0.66, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 100
    entry_pump_pull_threshold_100_36 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_100_36 = DecimalParameter(0.4, 1.0, default=0.7, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 100
    entry_pump_pull_threshold_100_48 = DecimalParameter(1.3, 2.0, default=1.4, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_100_48 = DecimalParameter(0.4, 1.8, default=1.6, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 110
    entry_pump_pull_threshold_110_24 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_110_24 = DecimalParameter(0.4, 1.0, default=0.7, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 110
    entry_pump_pull_threshold_110_36 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_110_36 = DecimalParameter(0.4, 1.0, default=0.74, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 110
    entry_pump_pull_threshold_110_48 = DecimalParameter(1.3, 2.0, default=1.4, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_110_48 = DecimalParameter(1.4, 2.0, default=1.8, space='entry', decimals=3, optimize=False, load=True)
    # 24 hours - level 120
    entry_pump_pull_threshold_120_24 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_120_24 = DecimalParameter(0.4, 1.0, default=0.78, space='entry', decimals=3, optimize=False, load=True)
    # 36 hours - level 120
    entry_pump_pull_threshold_120_36 = DecimalParameter(1.5, 3.0, default=1.7, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_120_36 = DecimalParameter(0.4, 1.0, default=0.78, space='entry', decimals=3, optimize=False, load=True)
    # 48 hours - level 120
    entry_pump_pull_threshold_120_48 = DecimalParameter(1.3, 2.0, default=1.4, space='entry', decimals=2, optimize=False, load=True)
    entry_pump_threshold_120_48 = DecimalParameter(1.4, 2.8, default=2.0, space='entry', decimals=3, optimize=False, load=True)
    # 5 hours - level 10
    entry_dump_protection_10_5 = DecimalParameter(0.3, 0.8, default=0.4, space='entry', decimals=2, optimize=False, load=True)
    # 5 hours - level 20
    entry_dump_protection_20_5 = DecimalParameter(0.3, 0.8, default=0.44, space='entry', decimals=2, optimize=False, load=True)
    # 5 hours - level 30
    entry_dump_protection_30_5 = DecimalParameter(0.3, 0.8, default=0.5, space='entry', decimals=2, optimize=False, load=True)
    # 5 hours - level 40
    entry_dump_protection_40_5 = DecimalParameter(0.3, 0.8, default=0.58, space='entry', decimals=2, optimize=False, load=True)
    # 5 hours - level 50
    entry_dump_protection_50_5 = DecimalParameter(0.3, 0.8, default=0.66, space='entry', decimals=2, optimize=False, load=True)
    # 5 hours - level 60
    entry_dump_protection_60_5 = DecimalParameter(0.3, 0.8, default=0.74, space='entry', decimals=2, optimize=False, load=True)
    entry_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='entry', decimals=1, optimize=False, load=True)
    entry_mfi_1 = DecimalParameter(20.0, 40.0, default=50.0, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_1 = DecimalParameter(-0.99, -0.5, default=-0.92, space='entry', decimals=2, optimize=False, load=True)
    entry_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=38.8, space='entry', decimals=1, optimize=False, load=True)
    entry_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space='entry', decimals=1, optimize=False, load=True)
    entry_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space='entry', decimals=3, optimize=False, load=True)
    entry_volume_2 = DecimalParameter(0.6, 6.0, default=1.6, space='entry', decimals=1, optimize=False, load=True)
    entry_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.045, space='entry', optimize=False, load=True)
    entry_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='entry', optimize=False, load=True)
    entry_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='entry', optimize=False, load=True)
    entry_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space='entry', decimals=3, optimize=False, load=True)
    entry_cti_3 = DecimalParameter(-0.99, -0.5, default=-0.5, space='entry', decimals=2, optimize=False, load=True)
    entry_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space='entry', optimize=False, load=True)
    entry_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space='entry', decimals=2, optimize=False, load=True)
    entry_cti_4 = DecimalParameter(-0.99, -0.5, default=-0.8, space='entry', decimals=2, optimize=False, load=True)
    entry_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.018, space='entry', decimals=3, optimize=False, load=True)
    entry_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.996, space='entry', decimals=3, optimize=False, load=True)
    entry_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.915, space='entry', decimals=3, optimize=False, load=True)
    entry_cti_5 = DecimalParameter(-0.99, -0.5, default=-0.84, space='entry', decimals=2, optimize=False, load=True)
    entry_volume_5 = DecimalParameter(0.6, 6.0, default=1.8, space='entry', decimals=1, optimize=False, load=True)
    entry_ema_open_mult_6 = DecimalParameter(0.02, 0.03, default=0.021, space='entry', decimals=3, optimize=False, load=True)
    entry_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.976, space='entry', decimals=3, optimize=False, load=True)
    entry_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='entry', decimals=3, optimize=False, load=True)
    entry_cti_7 = DecimalParameter(-0.99, -0.5, default=-0.89, space='entry', decimals=2, optimize=False, load=True)
    entry_cti_8 = DecimalParameter(-0.99, -0.5, default=-0.88, space='entry', decimals=2, optimize=False, load=True)
    entry_rsi_8 = DecimalParameter(20.0, 50.0, default=40.0, space='entry', decimals=1, optimize=False, load=True)
    entry_bb_offset_8 = DecimalParameter(0.98, 1.0, default=0.99, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_1h_8 = DecimalParameter(40.0, 66.0, default=64.0, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_8 = DecimalParameter(0.6, 6.0, default=1.8, space='entry', decimals=1, optimize=False, load=True)
    entry_ma_offset_9 = DecimalParameter(0.91, 0.94, default=0.968, space='entry', decimals=3, optimize=False, load=True)
    entry_bb_offset_9 = DecimalParameter(0.96, 0.98, default=0.942, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=20.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space='entry', decimals=1, optimize=False, load=True)
    entry_mfi_9 = DecimalParameter(36.0, 56.0, default=50.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ma_offset_10 = DecimalParameter(0.94, 0.99, default=0.98, space='entry', decimals=3, optimize=False, load=True)
    entry_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.972, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_1h_10 = DecimalParameter(30.0, 60.0, default=50.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.946, space='entry', decimals=3, optimize=False, load=True)
    entry_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.038, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=46.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_11 = DecimalParameter(34.0, 50.0, default=38.0, space='entry', decimals=1, optimize=False, load=True)
    entry_mfi_11 = DecimalParameter(30.0, 46.0, default=36.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.921, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_12 = DecimalParameter(26.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ewo_12 = DecimalParameter(1.0, 6.0, default=1.8, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_12 = DecimalParameter(-0.99, -0.5, default=-0.7, space='entry', decimals=2, optimize=False, load=True)
    entry_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.99, space='entry', decimals=3, optimize=False, load=True)
    entry_cti_13 = DecimalParameter(-0.99, -0.5, default=-0.82, space='entry', decimals=2, optimize=False, load=True)
    entry_ewo_13 = DecimalParameter(-14.0, -7.0, default=-9.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='entry', decimals=3, optimize=False, load=True)
    entry_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.988, space='entry', decimals=3, optimize=False, load=True)
    entry_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.945, space='entry', decimals=3, optimize=False, load=True)
    entry_cti_14 = DecimalParameter(-0.99, -0.5, default=-0.86, space='entry', decimals=2, optimize=False, load=True)
    entry_ema_open_mult_15 = DecimalParameter(0.01, 0.03, default=0.024, space='entry', decimals=3, optimize=False, load=True)
    entry_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.958, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_15 = DecimalParameter(20.0, 36.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.974, space='entry', decimals=3, optimize=False, load=True)
    entry_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.953, space='entry', decimals=3, optimize=False, load=True)
    entry_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space='entry', decimals=1, optimize=False, load=True)
    entry_ewo_16 = DecimalParameter(2.0, 6.0, default=2.8, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_16 = DecimalParameter(-0.99, -0.5, default=-0.84, space='entry', decimals=2, optimize=False, load=True)
    entry_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.99, space='entry', decimals=3, optimize=False, load=True)
    entry_ewo_17 = DecimalParameter(-18.0, -10.0, default=-9.4, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_17 = DecimalParameter(-0.99, -0.5, default=-0.96, space='entry', decimals=2, optimize=False, load=True)
    entry_volume_17 = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_18 = DecimalParameter(20.0, 36.0, default=33.0, space='entry', decimals=1, optimize=False, load=True)
    entry_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.986, space='entry', decimals=3, optimize=False, load=True)
    entry_volume_18 = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_18 = DecimalParameter(-0.99, -0.5, default=-0.86, space='entry', decimals=2, optimize=False, load=True)
    entry_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=30.0, space='entry', decimals=1, optimize=False, load=True)
    entry_chop_max_19 = DecimalParameter(20.0, 60.0, default=21.3, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_20 = DecimalParameter(20.0, 36.0, default=36.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=16.0, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_20 = DecimalParameter(-0.99, -0.5, default=-0.84, space='entry', decimals=2, optimize=False, load=True)
    entry_volume_20 = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_21 = DecimalParameter(10.0, 28.0, default=14.0, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=28.0, space='entry', decimals=1, optimize=False, load=True)
    entry_cti_21 = DecimalParameter(-0.99, -0.4, default=-0.9, space='entry', decimals=2, optimize=False, load=True)
    entry_volume_21 = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_volume_22 = DecimalParameter(0.5, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_bb_offset_22 = DecimalParameter(0.98, 1.0, default=0.984, space='entry', decimals=3, optimize=False, load=True)
    entry_ma_offset_22 = DecimalParameter(0.93, 0.98, default=0.942, space='entry', decimals=3, optimize=False, load=True)
    entry_ewo_22 = DecimalParameter(2.0, 10.0, default=5.8, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_22 = DecimalParameter(26.0, 56.0, default=36.0, space='entry', decimals=1, optimize=False, load=True)
    entry_bb_offset_23 = DecimalParameter(0.97, 1.0, default=0.985, space='entry', decimals=3, optimize=False, load=True)
    entry_ewo_23 = DecimalParameter(2.0, 10.0, default=6.2, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_23 = DecimalParameter(20.0, 40.0, default=32.4, space='entry', decimals=1, optimize=False, load=True)
    entry_rsi_1h_23 = DecimalParameter(60.0, 80.0, default=70.0, space='entry', decimals=1, optimize=False, load=True)
    entry_24_rsi_max = DecimalParameter(26.0, 60.0, default=50.0, space='entry', decimals=1, optimize=False, load=True)
    entry_24_rsi_1h_min = DecimalParameter(40.0, 90.0, default=66.9, space='entry', decimals=1, optimize=False, load=True)
    entry_25_ma_offset = DecimalParameter(0.9, 0.99, default=0.922, space='entry', optimize=False, load=True)
    entry_25_rsi_4 = DecimalParameter(26.0, 40.0, default=38.0, space='entry', decimals=1, optimize=False, load=True)
    entry_25_cti = DecimalParameter(-0.99, -0.4, default=-0.76, space='entry', decimals=2, optimize=False, load=True)
    entry_26_zema_low_offset = DecimalParameter(0.9, 0.99, default=0.932, space='entry', optimize=False, load=True)
    entry_26_cti = DecimalParameter(-0.99, -0.4, default=-0.82, space='entry', decimals=2, optimize=False, load=True)
    entry_26_volume = DecimalParameter(0.6, 6.0, default=1.2, space='entry', decimals=1, optimize=False, load=True)
    entry_27_wr_max = DecimalParameter(90, 99, default=90.0, space='entry', decimals=1, optimize=False, load=True)
    entry_27_wr_1h_max = DecimalParameter(90, 99, default=90.0, space='entry', decimals=1, optimize=False, load=True)
    entry_27_rsi_max = DecimalParameter(40, 70, default=50, space='entry', decimals=0, optimize=False, load=True)
    entry_27_cti = DecimalParameter(-0.99, -0.4, default=-0.93, space='entry', decimals=2, optimize=False, load=True)
    entry_27_volume = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_28_ma_offset = DecimalParameter(0.9, 0.99, default=0.97, space='entry', optimize=False, load=True)
    entry_28_ewo = DecimalParameter(2.0, 14.0, default=7.2, space='entry', decimals=1, optimize=False, load=True)
    entry_28_rsi = DecimalParameter(24.0, 44.0, default=32.5, space='entry', decimals=1, optimize=False, load=True)
    entry_28_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space='entry', decimals=2, optimize=False, load=True)
    entry_29_ma_offset = DecimalParameter(0.9, 0.99, default=0.94, space='entry', optimize=False, load=True)
    entry_29_ewo = DecimalParameter(-14.0, -2.0, default=-4.0, space='entry', decimals=1, optimize=False, load=True)
    entry_29_cti = DecimalParameter(-0.99, -0.4, default=-0.95, space='entry', decimals=2, optimize=False, load=True)
    entry_30_ma_offset = DecimalParameter(0.9, 0.99, default=0.97, space='entry', optimize=False, load=True)
    entry_30_ewo = DecimalParameter(2.0, 14.0, default=7.4, space='entry', decimals=1, optimize=False, load=True)
    entry_30_rsi = DecimalParameter(24.0, 48.0, default=40.0, space='entry', decimals=1, optimize=False, load=True)
    entry_30_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space='entry', decimals=2, optimize=False, load=True)
    entry_31_ma_offset = DecimalParameter(0.9, 0.99, default=0.94, space='entry', optimize=False, load=True)
    entry_31_ewo = DecimalParameter(-22.0, -8.0, default=-19.0, space='entry', decimals=1, optimize=False, load=True)
    entry_31_wr = DecimalParameter(-99.9, -95.0, default=-98.4, space='entry', decimals=1, optimize=False, load=True)
    entry_32_ma_offset = DecimalParameter(0.9, 0.99, default=0.934, space='entry', optimize=False, load=True)
    entry_32_dip = DecimalParameter(0.001, 0.02, default=0.005, space='entry', decimals=3, optimize=False, load=True)
    entry_32_rsi = DecimalParameter(24.0, 50.0, default=46.0, space='entry', decimals=1, optimize=False, load=True)
    entry_32_cti = DecimalParameter(-0.99, -0.4, default=-0.8, space='entry', decimals=2, optimize=False, load=True)
    entry_33_ma_offset = DecimalParameter(0.9, 0.99, default=0.988, space='entry', optimize=False, load=True)
    entry_33_rsi = DecimalParameter(24.0, 50.0, default=32.0, space='entry', decimals=1, optimize=False, load=True)
    entry_33_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space='entry', decimals=2, optimize=False, load=True)
    entry_33_ewo = DecimalParameter(2.0, 14.0, default=6.5, space='entry', decimals=1, optimize=False, load=True)
    entry_33_volume = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    entry_34_ma_offset = DecimalParameter(0.9, 0.99, default=0.93, space='entry', optimize=False, load=True)
    entry_34_dip = DecimalParameter(0.001, 0.02, default=0.005, space='entry', decimals=3, optimize=False, load=True)
    entry_34_ewo = DecimalParameter(-24.0, -5.0, default=-6.0, space='entry', decimals=1, optimize=False, load=True)
    entry_34_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space='entry', decimals=2, optimize=False, load=True)
    entry_34_volume = DecimalParameter(0.6, 6.0, default=2.0, space='entry', decimals=1, optimize=False, load=True)
    # Sell
    exit_condition_1_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_2_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_3_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_4_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_5_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_6_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_7_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    exit_condition_8_enable = CategoricalParameter([True, False], default=True, space='exit', optimize=False, load=True)
    # 48h for pump exit checks
    exit_pump_threshold_48_1 = DecimalParameter(0.5, 1.2, default=0.9, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_48_2 = DecimalParameter(0.4, 0.9, default=0.7, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_48_3 = DecimalParameter(0.3, 0.7, default=0.5, space='exit', decimals=2, optimize=False, load=True)
    # 36h for pump exit checks
    exit_pump_threshold_36_1 = DecimalParameter(0.5, 0.9, default=0.72, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_36_2 = DecimalParameter(3.0, 6.0, default=4.0, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_36_3 = DecimalParameter(0.8, 1.6, default=1.0, space='exit', decimals=2, optimize=False, load=True)
    # 24h for pump exit checks
    exit_pump_threshold_24_1 = DecimalParameter(0.5, 0.9, default=0.68, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_24_2 = DecimalParameter(0.3, 0.6, default=0.62, space='exit', decimals=2, optimize=False, load=True)
    exit_pump_threshold_24_3 = DecimalParameter(0.2, 0.5, default=0.88, space='exit', decimals=2, optimize=False, load=True)
    exit_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='exit', decimals=1, optimize=False, load=True)
    exit_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='exit', decimals=1, optimize=False, load=True)
    exit_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='exit', decimals=1, optimize=False, load=True)
    exit_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='exit', decimals=1, optimize=False, load=True)
    exit_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='exit', decimals=1, optimize=False, load=True)
    exit_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='exit', optimize=False, load=True)
    exit_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='exit', optimize=False, load=True)
    exit_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='exit', decimals=1, optimize=False, load=True)
    exit_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='exit', decimals=1, optimize=False, load=True)
    exit_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='exit', decimals=3, optimize=False, load=True)
    # Profit over EMA200
    exit_custom_profit_bull_0 = DecimalParameter(0.01, 0.1, default=0.012, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_0 = DecimalParameter(30.0, 40.0, default=34.0, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_profit_bull_1 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_1 = DecimalParameter(30.0, 50.0, default=35.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_2 = DecimalParameter(0.01, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_2 = DecimalParameter(30.0, 50.0, default=36.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_3 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_3 = DecimalParameter(30.0, 50.0, default=37.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_4 = DecimalParameter(0.01, 0.1, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_4 = DecimalParameter(35.0, 50.0, default=42.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_5 = DecimalParameter(0.01, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_5 = DecimalParameter(35.0, 50.0, default=49.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_6 = DecimalParameter(0.01, 0.1, default=0.07, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_6 = DecimalParameter(38.0, 55.0, default=50.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_7 = DecimalParameter(0.01, 0.1, default=0.08, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_7 = DecimalParameter(40.0, 58.0, default=54.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_8 = DecimalParameter(0.06, 0.1, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_8 = DecimalParameter(40.0, 50.0, default=50.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_9 = DecimalParameter(0.05, 0.14, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_9 = DecimalParameter(40.0, 60.0, default=46.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_10 = DecimalParameter(0.1, 0.14, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_10 = DecimalParameter(38.0, 50.0, default=42.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bull_11 = DecimalParameter(0.16, 0.45, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bull_11 = DecimalParameter(28.0, 40.0, default=30.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_0 = DecimalParameter(0.01, 0.1, default=0.012, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_0 = DecimalParameter(30.0, 40.0, default=34.0, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_profit_bear_1 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_1 = DecimalParameter(30.0, 50.0, default=35.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_2 = DecimalParameter(0.01, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_2 = DecimalParameter(30.0, 50.0, default=37.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_3 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_3 = DecimalParameter(30.0, 50.0, default=44.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_4 = DecimalParameter(0.01, 0.1, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_4 = DecimalParameter(35.0, 50.0, default=48.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_5 = DecimalParameter(0.01, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_5 = DecimalParameter(35.0, 50.0, default=50.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_rsi_over_bear_5 = DecimalParameter(70.0, 85.0, default=78.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_6 = DecimalParameter(0.01, 0.1, default=0.07, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_6 = DecimalParameter(38.0, 55.0, default=52.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_rsi_over_bear_6 = DecimalParameter(70.0, 85.0, default=78.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_7 = DecimalParameter(0.01, 0.1, default=0.08, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_7 = DecimalParameter(40.0, 58.0, default=54.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_rsi_over_bear_7 = DecimalParameter(70.0, 85.0, default=80.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_8 = DecimalParameter(0.06, 0.1, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_8 = DecimalParameter(40.0, 50.0, default=52.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_rsi_over_bear_8 = DecimalParameter(70.0, 85.0, default=82.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_9 = DecimalParameter(0.05, 0.14, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_9 = DecimalParameter(40.0, 60.0, default=50.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_10 = DecimalParameter(0.1, 0.14, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_10 = DecimalParameter(38.0, 50.0, default=42.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_profit_bear_11 = DecimalParameter(0.16, 0.45, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_rsi_under_bear_11 = DecimalParameter(28.0, 40.0, default=30.0, space='exit', decimals=2, optimize=False, load=True)
    # Profit under EMA200
    exit_custom_under_profit_bull_0 = DecimalParameter(0.01, 0.4, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_0 = DecimalParameter(28.0, 40.0, default=38.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_1 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_1 = DecimalParameter(36.0, 60.0, default=54.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_2 = DecimalParameter(0.01, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_2 = DecimalParameter(46.0, 66.0, default=55.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_3 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_3 = DecimalParameter(50.0, 68.0, default=56.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_4 = DecimalParameter(0.02, 0.1, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_4 = DecimalParameter(50.0, 68.0, default=57.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_5 = DecimalParameter(0.02, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_5 = DecimalParameter(46.0, 62.0, default=58.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_6 = DecimalParameter(0.03, 0.1, default=0.07, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_6 = DecimalParameter(44.0, 60.0, default=48.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_7 = DecimalParameter(0.04, 0.1, default=0.08, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_7 = DecimalParameter(46.0, 60.0, default=44.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_8 = DecimalParameter(0.06, 0.12, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_8 = DecimalParameter(40.0, 58.0, default=42.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_9 = DecimalParameter(0.08, 0.14, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_9 = DecimalParameter(40.0, 60.0, default=38.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_10 = DecimalParameter(0.1, 0.16, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_10 = DecimalParameter(30.0, 50.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bull_11 = DecimalParameter(0.16, 0.3, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bull_11 = DecimalParameter(24.0, 40.0, default=30.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_0 = DecimalParameter(0.01, 0.4, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_0 = DecimalParameter(28.0, 40.0, default=38.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_1 = DecimalParameter(0.01, 0.1, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_1 = DecimalParameter(36.0, 60.0, default=59.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_2 = DecimalParameter(0.01, 0.1, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_2 = DecimalParameter(46.0, 66.0, default=60.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_3 = DecimalParameter(0.01, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_3 = DecimalParameter(50.0, 68.0, default=61.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_4 = DecimalParameter(0.02, 0.1, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_4 = DecimalParameter(50.0, 68.0, default=60.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_5 = DecimalParameter(0.02, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_5 = DecimalParameter(46.0, 62.0, default=58.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_rsi_over_bear_5 = DecimalParameter(70.0, 85.0, default=78.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_under_profit_bear_6 = DecimalParameter(0.03, 0.1, default=0.07, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_6 = DecimalParameter(44.0, 60.0, default=50.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_rsi_over_bear_6 = DecimalParameter(70.0, 85.0, default=78.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_under_profit_bear_7 = DecimalParameter(0.04, 0.1, default=0.08, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_7 = DecimalParameter(46.0, 60.0, default=46.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_rsi_over_bear_7 = DecimalParameter(70.0, 85.0, default=80.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_under_profit_bear_8 = DecimalParameter(0.06, 0.12, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_8 = DecimalParameter(40.0, 58.0, default=42.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_rsi_over_bear_8 = DecimalParameter(70.0, 85.0, default=82.0, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_under_profit_bear_9 = DecimalParameter(0.08, 0.14, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_9 = DecimalParameter(40.0, 60.0, default=36.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_10 = DecimalParameter(0.1, 0.16, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_10 = DecimalParameter(30.0, 50.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_under_profit_bear_11 = DecimalParameter(0.16, 0.3, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_under_rsi_under_bear_11 = DecimalParameter(24.0, 40.0, default=30.0, space='exit', decimals=1, optimize=False, load=True)
    # Profit targets for pumped pairs 48h 1
    exit_custom_pump_profit_1_1 = DecimalParameter(0.01, 0.03, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_1_1 = DecimalParameter(26.0, 40.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_1_2 = DecimalParameter(0.01, 0.6, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_1_2 = DecimalParameter(36.0, 50.0, default=40.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_1_3 = DecimalParameter(0.02, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_1_3 = DecimalParameter(38.0, 50.0, default=42.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_1_4 = DecimalParameter(0.06, 0.12, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_1_4 = DecimalParameter(36.0, 48.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_1_5 = DecimalParameter(0.14, 0.24, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_1_5 = DecimalParameter(20.0, 40.0, default=30.0, space='exit', decimals=1, optimize=False, load=True)
    # Profit targets for pumped pairs 36h 1
    exit_custom_pump_profit_2_1 = DecimalParameter(0.01, 0.03, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_2_1 = DecimalParameter(26.0, 40.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_2_2 = DecimalParameter(0.01, 0.6, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_2_2 = DecimalParameter(36.0, 50.0, default=40.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_2_3 = DecimalParameter(0.02, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_2_3 = DecimalParameter(38.0, 50.0, default=42.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_2_4 = DecimalParameter(0.06, 0.12, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_2_4 = DecimalParameter(36.0, 48.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_2_5 = DecimalParameter(0.14, 0.24, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_2_5 = DecimalParameter(20.0, 40.0, default=30.0, space='exit', decimals=1, optimize=False, load=True)
    # Profit targets for pumped pairs 24h 1
    exit_custom_pump_profit_3_1 = DecimalParameter(0.01, 0.03, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_3_1 = DecimalParameter(26.0, 40.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_3_2 = DecimalParameter(0.01, 0.6, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_3_2 = DecimalParameter(34.0, 50.0, default=40.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_3_3 = DecimalParameter(0.02, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_3_3 = DecimalParameter(38.0, 50.0, default=42.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_3_4 = DecimalParameter(0.06, 0.12, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_3_4 = DecimalParameter(36.0, 48.0, default=34.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_profit_3_5 = DecimalParameter(0.14, 0.24, default=0.2, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_rsi_3_5 = DecimalParameter(20.0, 40.0, default=30.0, space='exit', decimals=1, optimize=False, load=True)
    # SMA descending
    exit_custom_dec_profit_min_1 = DecimalParameter(0.01, 0.1, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_dec_profit_max_1 = DecimalParameter(0.06, 0.16, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    # Under EMA100
    exit_custom_dec_profit_min_2 = DecimalParameter(0.05, 0.12, default=0.07, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_dec_profit_max_2 = DecimalParameter(0.06, 0.2, default=0.16, space='exit', decimals=3, optimize=False, load=True)
    # Trail 1
    exit_trail_profit_min_1 = DecimalParameter(0.1, 0.2, default=0.03, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_profit_max_1 = DecimalParameter(0.4, 0.7, default=0.05, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_1 = DecimalParameter(0.01, 0.08, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_rsi_min_1 = DecimalParameter(16.0, 36.0, default=10.0, space='exit', decimals=1, optimize=False, load=True)
    exit_trail_rsi_max_1 = DecimalParameter(30.0, 50.0, default=20.0, space='exit', decimals=1, optimize=False, load=True)
    # Trail 2
    exit_trail_profit_min_2 = DecimalParameter(0.08, 0.16, default=0.1, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_max_2 = DecimalParameter(0.3, 0.5, default=0.4, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_2 = DecimalParameter(0.02, 0.08, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_rsi_min_2 = DecimalParameter(16.0, 36.0, default=20.0, space='exit', decimals=1, optimize=False, load=True)
    exit_trail_rsi_max_2 = DecimalParameter(30.0, 50.0, default=50.0, space='exit', decimals=1, optimize=False, load=True)
    # Trail 3
    exit_trail_profit_min_3 = DecimalParameter(0.01, 0.12, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_max_3 = DecimalParameter(0.1, 0.3, default=0.2, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_3 = DecimalParameter(0.01, 0.06, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    # Trail 4
    exit_trail_profit_min_4 = DecimalParameter(0.01, 0.12, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_trail_profit_max_4 = DecimalParameter(0.02, 0.1, default=0.06, space='exit', decimals=2, optimize=False, load=True)
    exit_trail_down_4 = DecimalParameter(0.01, 0.06, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    # Under & near EMA200, accept profit
    exit_custom_profit_under_profit_min_1 = DecimalParameter(0.0, 0.01, default=0.0, space='exit', optimize=False, load=True)
    exit_custom_profit_under_profit_max_1 = DecimalParameter(0.0, 0.05, default=0.02, space='exit', optimize=False, load=True)
    exit_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='exit', optimize=False, load=True)
    exit_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='exit', optimize=False, load=True)
    exit_custom_profit_under_profit_2 = DecimalParameter(0.0, 0.05, default=0.03, space='exit', optimize=False, load=True)
    exit_custom_profit_under_rel_2 = DecimalParameter(0.01, 0.04, default=0.024, space='exit', optimize=False, load=True)
    exit_custom_profit_under_rsi_diff_2 = DecimalParameter(0.0, 20.0, default=4.4, space='exit', optimize=False, load=True)
    # Under & near EMA200, take the loss
    exit_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.002, space='exit', optimize=False, load=True)
    exit_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=10.0, space='exit', optimize=False, load=True)
    # Long duration/recover stoploss 1
    exit_custom_stoploss_long_profit_min_1 = DecimalParameter(-0.1, -0.02, default=-0.08, space='exit', optimize=False, load=True)
    exit_custom_stoploss_long_profit_max_1 = DecimalParameter(-0.06, -0.01, default=-0.04, space='exit', optimize=False, load=True)
    exit_custom_stoploss_long_recover_1 = DecimalParameter(0.05, 0.15, default=0.14, space='exit', optimize=False, load=True)
    exit_custom_stoploss_long_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.0, space='exit', optimize=False, load=True)
    # Long duration/recover stoploss 2
    exit_custom_stoploss_long_recover_2 = DecimalParameter(0.03, 0.15, default=0.06, space='exit', optimize=False, load=True)
    exit_custom_stoploss_long_rsi_diff_2 = DecimalParameter(30.0, 50.0, default=40.0, space='exit', optimize=False, load=True)
    # Pumped, descending SMA
    exit_custom_pump_dec_profit_min_1 = DecimalParameter(0.001, 0.04, default=0.005, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_max_1 = DecimalParameter(0.03, 0.08, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_min_2 = DecimalParameter(0.01, 0.08, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_max_2 = DecimalParameter(0.04, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_min_3 = DecimalParameter(0.02, 0.1, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_max_3 = DecimalParameter(0.06, 0.12, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_min_4 = DecimalParameter(0.01, 0.05, default=0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_dec_profit_max_4 = DecimalParameter(0.02, 0.1, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    # Pumped 48h 1, under EMA200
    exit_custom_pump_under_profit_min_1 = DecimalParameter(0.02, 0.06, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_under_profit_max_1 = DecimalParameter(0.04, 0.1, default=0.09, space='exit', decimals=3, optimize=False, load=True)
    # Pumped trail 1
    exit_custom_pump_trail_profit_min_1 = DecimalParameter(0.01, 0.12, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_trail_profit_max_1 = DecimalParameter(0.06, 0.16, default=0.07, space='exit', decimals=2, optimize=False, load=True)
    exit_custom_pump_trail_down_1 = DecimalParameter(0.01, 0.06, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_pump_trail_rsi_min_1 = DecimalParameter(16.0, 36.0, default=20.0, space='exit', decimals=1, optimize=False, load=True)
    exit_custom_pump_trail_rsi_max_1 = DecimalParameter(30.0, 50.0, default=70.0, space='exit', decimals=1, optimize=False, load=True)
    # Stoploss, pumped, 48h 1
    exit_custom_stoploss_pump_max_profit_1 = DecimalParameter(0.01, 0.04, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_min_1 = DecimalParameter(-0.1, -0.01, default=-0.02, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_max_1 = DecimalParameter(-0.1, -0.01, default=-0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_ma_offset_1 = DecimalParameter(0.7, 0.99, default=0.94, space='exit', decimals=2, optimize=False, load=True)
    # Stoploss, pumped, 48h 1
    exit_custom_stoploss_pump_max_profit_2 = DecimalParameter(0.01, 0.04, default=0.025, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_loss_2 = DecimalParameter(-0.1, -0.01, default=-0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_ma_offset_2 = DecimalParameter(0.7, 0.99, default=0.92, space='exit', decimals=2, optimize=False, load=True)
    # Stoploss, pumped, 36h 3
    exit_custom_stoploss_pump_max_profit_3 = DecimalParameter(0.01, 0.04, default=0.008, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_loss_3 = DecimalParameter(-0.16, -0.06, default=-0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_stoploss_pump_ma_offset_3 = DecimalParameter(0.7, 0.99, default=0.88, space='exit', decimals=2, optimize=False, load=True)
    # Recover
    exit_custom_recover_profit_1 = DecimalParameter(0.01, 0.06, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_recover_min_loss_1 = DecimalParameter(0.06, 0.16, default=0.12, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_recover_profit_min_2 = DecimalParameter(0.01, 0.04, default=0.01, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_recover_profit_max_2 = DecimalParameter(0.02, 0.08, default=0.05, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_recover_min_loss_2 = DecimalParameter(0.04, 0.16, default=0.06, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_recover_rsi_2 = DecimalParameter(32.0, 52.0, default=46.0, space='exit', decimals=1, optimize=False, load=True)
    # Profit for long duration trades
    exit_custom_long_profit_min_1 = DecimalParameter(0.01, 0.04, default=0.03, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_long_profit_max_1 = DecimalParameter(0.02, 0.08, default=0.04, space='exit', decimals=3, optimize=False, load=True)
    exit_custom_long_duration_min_1 = IntParameter(700, 2000, default=900, space='exit', optimize=False, load=True)
    #############################################################
    hold_trade_ids = None

    @staticmethod
    def get_hold_trades_config_file():
        strat_file_path = pathlib.Path(__file__)
        hold_trades_config_file_resolve = strat_file_path.resolve().parent / 'hold-trades.json'
        if hold_trades_config_file_resolve.is_file():
            return hold_trades_config_file_resolve
        # The resolved path does not exist, is it a symlink?
        hold_trades_config_file_absolute = strat_file_path.absolute().parent / 'hold-trades.json'
        if hold_trades_config_file_absolute.is_file():
            return hold_trades_config_file_absolute
        if hold_trades_config_file_resolve != hold_trades_config_file_absolute:
            looked_in = f"'{hold_trades_config_file_resolve}' and '{hold_trades_config_file_absolute}'"
        else:
            looked_in = f"'{hold_trades_config_file_resolve}'"
        log.warning("The 'hold-trades.json' file was not found. Looked in %s. HOLD support disabled.", looked_in)

    def load_hold_trades_config(self):
        if self.hold_trade_ids is not None:
            # Already loaded
            return
        # Default Values
        self.hold_trade_ids = {}
        # Update values from config file, if it exists
        hold_trades_config_file = NostalgiaForInfinityNext_ChangeToTower_V5_3.get_hold_trades_config_file()
        if not hold_trades_config_file:
            return
        with hold_trades_config_file.open('r') as f:
            trade_ids = None
            hold_trades_config = None
            try:
                hold_trades_config = json_load(f)
            except rapidjson.JSONDecodeError as exc:
                log.error('Failed to load JSON from %s: %s', hold_trades_config_file, exc)
            else:
                trade_ids = hold_trades_config.get('trade_ids')
            if not trade_ids:
                return
            open_trades = {trade.id: trade for trade in Trade.get_trades_proxy(is_open=True)}
            if isinstance(trade_ids, dict):
                # New syntax
                for trade_id, profit_ratio in trade_ids.items():
                    try:
                        trade_id = int(trade_id)
                    except ValueError:
                        log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, hold_trades_config_file)
                        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, hold_trades_config_file)
                    if trade_id in open_trades:
                        formatted_profit_ratio = '{}%'.format(profit_ratio * 100)
                        log.warning('The trade %s is configured to HOLD until the profit ratio of %s is met', open_trades[trade_id], formatted_profit_ratio)
                        self.hold_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, hold_trades_config_file)
            else:
                # Initial Syntax
                profit_ratio = hold_trades_config.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, hold_trades_config_file)
                else:
                    profit_ratio = 0.005
                formatted_profit_ratio = '{}%'.format(profit_ratio * 100)
                for trade_id in trade_ids:
                    if not isinstance(trade_id, int):
                        log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, hold_trades_config_file)
                        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)
                        self.hold_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, hold_trades_config_file)

    def bot_loop_start(self, **kwargs) -> None:
        """
        Called at the start of the bot iteration (one loop).
        Might be used to perform pair-independent tasks
        (e.g. gather some remote resource for comparison)
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        """
        if self.config['runmode'].value in ('live', 'dry_run'):
            self.load_hold_trades_config()
        return super().bot_loop_start(**kwargs)

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def exit_over_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['close'] > last_candle['ema_200']:
            if last_candle['moderi_96']:
                if current_profit >= self.exit_custom_profit_bull_11.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_11.value:
                        return (True, 'signal_profit_o_bull_11')
                elif self.exit_custom_profit_bull_11.value > current_profit >= self.exit_custom_profit_bull_10.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_10.value:
                        return (True, 'signal_profit_o_bull_10')
                elif self.exit_custom_profit_bull_10.value > current_profit >= self.exit_custom_profit_bull_9.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_9.value:
                        return (True, 'signal_profit_o_bull_9')
                elif self.exit_custom_profit_bull_9.value > current_profit >= self.exit_custom_profit_bull_8.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_8.value:
                        return (True, 'signal_profit_o_bull_8')
                elif self.exit_custom_profit_bull_8.value > current_profit >= self.exit_custom_profit_bull_7.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_7.value:
                        return (True, 'signal_profit_o_bull_7')
                elif self.exit_custom_profit_bull_7.value > current_profit >= self.exit_custom_profit_bull_6.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_6.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_6')
                elif self.exit_custom_profit_bull_6.value > current_profit >= self.exit_custom_profit_bull_5.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_5.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_5')
                elif self.exit_custom_profit_bull_5.value > current_profit >= self.exit_custom_profit_bull_4.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_4.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_4')
                elif self.exit_custom_profit_bull_4.value > current_profit >= self.exit_custom_profit_bull_3.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_3.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_3')
                elif self.exit_custom_profit_bull_3.value > current_profit >= self.exit_custom_profit_bull_2.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_2.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_2')
                elif self.exit_custom_profit_bull_2.value > current_profit >= self.exit_custom_profit_bull_1.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_1.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_1')
                elif self.exit_custom_profit_bull_1.value > current_profit >= self.exit_custom_profit_bull_0.value:
                    if last_candle['rsi_14'] < self.exit_custom_rsi_under_bull_0.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_0')
            elif current_profit >= self.exit_custom_profit_bear_11.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_11.value:
                    return (True, 'signal_profit_o_bear_11')
            elif self.exit_custom_profit_bear_11.value > current_profit >= self.exit_custom_profit_bear_10.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_10.value:
                    return (True, 'signal_profit_o_bear_10')
            elif self.exit_custom_profit_bear_10.value > current_profit >= self.exit_custom_profit_bear_9.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_9.value:
                    return (True, 'signal_profit_o_bear_9')
            elif self.exit_custom_profit_bear_9.value > current_profit >= self.exit_custom_profit_bear_8.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_8.value:
                    return (True, 'signal_profit_o_bear_8_1')
                elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_8.value:
                    return (True, 'signal_profit_o_bear_8_2')
            elif self.exit_custom_profit_bear_8.value > current_profit >= self.exit_custom_profit_bear_7.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_7.value:
                    return (True, 'signal_profit_o_bear_7_1')
                elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_7.value:
                    return (True, 'signal_profit_o_bear_7_2')
            elif self.exit_custom_profit_bear_7.value > current_profit >= self.exit_custom_profit_bear_6.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_6.value:
                    return (True, 'signal_profit_o_bear_6_1')
                elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_6.value:
                    return (True, 'signal_profit_o_bear_6_2')
            elif self.exit_custom_profit_bear_6.value > current_profit >= self.exit_custom_profit_bear_5.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_5.value:
                    return (True, 'signal_profit_o_bear_5_1')
                elif last_candle['rsi_14'] > self.exit_custom_rsi_over_bear_5.value:
                    return (True, 'signal_profit_o_bear_5_2')
            elif self.exit_custom_profit_bear_5.value > current_profit >= self.exit_custom_profit_bear_4.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_4.value:
                    return (True, 'signal_profit_o_bear_4')
            elif self.exit_custom_profit_bear_4.value > current_profit >= self.exit_custom_profit_bear_3.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_3.value and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_3')
            elif self.exit_custom_profit_bear_3.value > current_profit >= self.exit_custom_profit_bear_2.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_2.value and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_2')
            elif self.exit_custom_profit_bear_2.value > current_profit >= self.exit_custom_profit_bear_1.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_1.value and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_1')
            elif self.exit_custom_profit_bear_1.value > current_profit >= self.exit_custom_profit_bear_0.value:
                if last_candle['rsi_14'] < self.exit_custom_rsi_under_bear_0.value and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_0')
        return (False, None)

    def exit_under_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['close'] < last_candle['ema_200']:
            if last_candle['moderi_96']:
                if current_profit >= self.exit_custom_under_profit_bull_11.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_11.value:
                        return (True, 'signal_profit_u_bull_11')
                elif self.exit_custom_under_profit_bull_11.value > current_profit >= self.exit_custom_under_profit_bull_10.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_10.value:
                        return (True, 'signal_profit_u_bull_10')
                elif self.exit_custom_under_profit_bull_10.value > current_profit >= self.exit_custom_under_profit_bull_9.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_9.value:
                        return (True, 'signal_profit_u_bull_9')
                elif self.exit_custom_under_profit_bull_9.value > current_profit >= self.exit_custom_under_profit_bull_8.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_8.value:
                        return (True, 'signal_profit_u_bull_8')
                elif self.exit_custom_under_profit_bull_8.value > current_profit >= self.exit_custom_under_profit_bull_7.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_7.value:
                        return (True, 'signal_profit_u_bull_7')
                elif self.exit_custom_under_profit_bull_7.value > current_profit >= self.exit_custom_under_profit_bull_6.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_6.value:
                        return (True, 'signal_profit_u_bull_6')
                elif self.exit_custom_under_profit_bull_6.value > current_profit >= self.exit_custom_under_profit_bull_5.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_5.value:
                        return (True, 'signal_profit_u_bull_5')
                elif self.exit_custom_under_profit_bull_5.value > current_profit >= self.exit_custom_under_profit_bull_4.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_4.value:
                        return (True, 'signal_profit_u_bull_4')
                elif self.exit_custom_under_profit_bull_4.value > current_profit >= self.exit_custom_under_profit_bull_3.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_3.value:
                        return (True, 'signal_profit_u_bull_3')
                elif self.exit_custom_under_profit_bull_3.value > current_profit >= self.exit_custom_under_profit_bull_2.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_2.value:
                        return (True, 'signal_profit_u_bull_2')
                elif self.exit_custom_under_profit_bull_2.value > current_profit >= self.exit_custom_under_profit_bull_1.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_1.value:
                        return (True, 'signal_profit_u_bull_1')
                elif self.exit_custom_under_profit_bull_1.value > current_profit >= self.exit_custom_under_profit_bull_0.value:
                    if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bull_0.value and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_u_bull_0')
            elif current_profit >= self.exit_custom_under_profit_bear_11.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_11.value:
                    return (True, 'signal_profit_u_bear_11')
            elif self.exit_custom_under_profit_bear_11.value > current_profit >= self.exit_custom_under_profit_bear_10.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_10.value:
                    return (True, 'signal_profit_u_bear_10')
            elif self.exit_custom_under_profit_bear_10.value > current_profit >= self.exit_custom_under_profit_bear_9.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_9.value:
                    return (True, 'signal_profit_u_bear_9')
            elif self.exit_custom_under_profit_bear_9.value > current_profit >= self.exit_custom_under_profit_bear_8.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_8.value:
                    return (True, 'signal_profit_u_bear_8_1')
                elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_8.value:
                    return (True, 'signal_profit_u_bear_8_2')
            elif self.exit_custom_under_profit_bear_8.value > current_profit >= self.exit_custom_under_profit_bear_7.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_7.value:
                    return (True, 'signal_profit_u_bear_7_1')
                elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_7.value:
                    return (True, 'signal_profit_u_bear_7_2')
            elif self.exit_custom_under_profit_bear_7.value > current_profit >= self.exit_custom_under_profit_bear_6.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_6.value:
                    return (True, 'signal_profit_u_bear_6_1')
                elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_6.value:
                    return (True, 'signal_profit_u_bear_6_2')
            elif self.exit_custom_under_profit_bear_6.value > current_profit >= self.exit_custom_under_profit_bear_5.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_5.value:
                    return (True, 'signal_profit_u_bear_5_1')
                elif last_candle['rsi_14'] > self.exit_custom_under_rsi_over_bear_5.value:
                    return (True, 'signal_profit_u_bear_5_2')
            elif self.exit_custom_under_profit_bear_5.value > current_profit >= self.exit_custom_under_profit_bear_4.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_4.value:
                    return (True, 'signal_profit_u_bear_4')
            elif self.exit_custom_under_profit_bear_4.value > current_profit >= self.exit_custom_under_profit_bear_3.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_3.value:
                    return (True, 'signal_profit_u_bear_3')
            elif self.exit_custom_under_profit_bear_3.value > current_profit >= self.exit_custom_under_profit_bear_2.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_2.value:
                    return (True, 'signal_profit_u_bear_2')
            elif self.exit_custom_under_profit_bear_2.value > current_profit >= self.exit_custom_under_profit_bear_1.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_1.value:
                    return (True, 'signal_profit_u_bear_1')
            elif self.exit_custom_under_profit_bear_1.value > current_profit >= self.exit_custom_under_profit_bear_0.value:
                if last_candle['rsi_14'] < self.exit_custom_under_rsi_under_bear_0.value and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_u_bear_0')
        return (False, None)

    def exit_pump_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['exit_pump_48_1_1h']:
            if current_profit >= self.exit_custom_pump_profit_1_5.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_5.value:
                    return (True, 'signal_profit_p_1_5')
            elif self.exit_custom_pump_profit_1_5.value > current_profit >= self.exit_custom_pump_profit_1_4.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_4.value:
                    return (True, 'signal_profit_p_1_4')
            elif self.exit_custom_pump_profit_1_4.value > current_profit >= self.exit_custom_pump_profit_1_3.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_3.value:
                    return (True, 'signal_profit_p_1_3')
            elif self.exit_custom_pump_profit_1_3.value > current_profit >= self.exit_custom_pump_profit_1_2.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_2.value:
                    return (True, 'signal_profit_p_1_2')
            elif self.exit_custom_pump_profit_1_2.value > current_profit >= self.exit_custom_pump_profit_1_1.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_1_1.value:
                    return (True, 'signal_profit_p_1_1')
        elif last_candle['exit_pump_36_1_1h']:
            if current_profit >= self.exit_custom_pump_profit_2_5.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_5.value:
                    return (True, 'signal_profit_p_2_5')
            elif self.exit_custom_pump_profit_2_5.value > current_profit >= self.exit_custom_pump_profit_2_4.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_4.value:
                    return (True, 'signal_profit_p_2_4')
            elif self.exit_custom_pump_profit_2_4.value > current_profit >= self.exit_custom_pump_profit_2_3.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_3.value:
                    return (True, 'signal_profit_p_2_3')
            elif self.exit_custom_pump_profit_2_3.value > current_profit >= self.exit_custom_pump_profit_2_2.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_2.value:
                    return (True, 'signal_profit_p_2_2')
            elif self.exit_custom_pump_profit_2_2.value > current_profit >= self.exit_custom_pump_profit_2_1.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_2_1.value:
                    return (True, 'signal_profit_p_2_1')
        elif last_candle['exit_pump_24_1_1h']:
            if current_profit >= self.exit_custom_pump_profit_3_5.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_5.value:
                    return (True, 'signal_profit_p_3_5')
            elif self.exit_custom_pump_profit_3_5.value > current_profit >= self.exit_custom_pump_profit_3_4.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_4.value:
                    return (True, 'signal_profit_p_3_4')
            elif self.exit_custom_pump_profit_3_4.value > current_profit >= self.exit_custom_pump_profit_3_3.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_3.value:
                    return (True, 'signal_profit_p_3_3')
            elif self.exit_custom_pump_profit_3_3.value > current_profit >= self.exit_custom_pump_profit_3_2.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_2.value:
                    return (True, 'signal_profit_p_3_2')
            elif self.exit_custom_pump_profit_3_2.value > current_profit >= self.exit_custom_pump_profit_3_1.value:
                if last_candle['rsi_14'] < self.exit_custom_pump_rsi_3_1.value:
                    return (True, 'signal_profit_p_3_1')
        return (False, None)

    def exit_dec_main(self, current_profit: float, last_candle) -> tuple:
        if self.exit_custom_dec_profit_max_1.value > current_profit >= self.exit_custom_dec_profit_min_1.value and last_candle['sma_200_dec_20']:
            return (True, 'signal_profit_d_1')
        elif self.exit_custom_dec_profit_max_2.value > current_profit >= self.exit_custom_dec_profit_min_2.value and last_candle['close'] < last_candle['ema_100']:
            return (True, 'signal_profit_d_2')
        return (False, None)

    def exit_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        if self.exit_trail_profit_max_1.value > current_profit >= self.exit_trail_profit_min_1.value and self.exit_trail_rsi_min_1.value < last_candle['rsi_14'] < self.exit_trail_rsi_max_1.value and (max_profit > current_profit + self.exit_trail_down_1.value) and (last_candle['moderi_96'] == False):
            return (True, 'signal_profit_t_1')
        elif self.exit_trail_profit_max_2.value > current_profit >= self.exit_trail_profit_min_2.value and self.exit_trail_rsi_min_2.value < last_candle['rsi_14'] < self.exit_trail_rsi_max_2.value and (max_profit > current_profit + self.exit_trail_down_2.value) and (last_candle['ema_25'] < last_candle['ema_50']):
            return (True, 'signal_profit_t_2')
        elif self.exit_trail_profit_max_3.value > current_profit >= self.exit_trail_profit_min_3.value and max_profit > current_profit + self.exit_trail_down_3.value and last_candle['sma_200_dec_20_1h']:
            return (True, 'signal_profit_t_3')
        elif self.exit_trail_profit_max_4.value > current_profit >= self.exit_trail_profit_min_4.value and max_profit > current_profit + self.exit_trail_down_4.value and last_candle['sma_200_dec_24'] and (last_candle['cmf'] < 0.0):
            return (True, 'signal_profit_t_4')
        return (False, None)

    def exit_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple:
        # Pumped pair, short duration
        if last_candle['exit_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.exit_custom_long_profit_min_1.value < current_profit < self.exit_custom_long_profit_max_1.value and current_time - timedelta(minutes=self.exit_custom_long_duration_min_1.value) > trade.open_date_utc:
            return (True, 'signal_profit_l_1')
        return (False, None)

    def exit_under_min(self, current_profit: float, last_candle) -> tuple:
        if last_candle['moderi_96'] == False:
            # Downtrend
            if self.exit_custom_profit_under_profit_max_1.value > current_profit >= self.exit_custom_profit_under_profit_min_1.value and last_candle['close'] < last_candle['ema_200'] and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.exit_custom_profit_under_rel_1.value) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_1.value):
                return (True, 'signal_profit_u_e_1')
        # Uptrend
        elif current_profit >= self.exit_custom_profit_under_profit_2.value and last_candle['close'] < last_candle['ema_200'] and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.exit_custom_profit_under_rel_2.value) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_2.value):
            return (True, 'signal_profit_u_e_2')
        return (False, None)

    def exit_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple:
        if -0.12 <= current_profit < -0.08:
            if last_candle['close'] < last_candle['atr_high_thresh_1'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_1']:
                return (True, 'signal_stoploss_atr_1')
        elif -0.16 <= current_profit < -0.12:
            if last_candle['close'] < last_candle['atr_high_thresh_2'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_2']:
                return (True, 'signal_stoploss_atr_2')
        elif -0.2 <= current_profit < -0.16:
            if last_candle['close'] < last_candle['atr_high_thresh_3'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_3']:
                return (True, 'signal_stoploss_atr_3')
        elif current_profit < -0.2:
            if last_candle['close'] < last_candle['atr_high_thresh_4'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_4']:
                return (True, 'signal_stoploss_atr_4')
        return (False, None)

    def exit_pump_dec(self, current_profit: float, last_candle) -> tuple:
        if self.exit_custom_pump_dec_profit_max_1.value > current_profit >= self.exit_custom_pump_dec_profit_min_1.value and last_candle['exit_pump_48_1_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_1')
        elif self.exit_custom_pump_dec_profit_max_2.value > current_profit >= self.exit_custom_pump_dec_profit_min_2.value and last_candle['exit_pump_48_2_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_2')
        elif self.exit_custom_pump_dec_profit_max_3.value > current_profit >= self.exit_custom_pump_dec_profit_min_3.value and last_candle['exit_pump_48_3_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_3')
        elif self.exit_custom_pump_dec_profit_max_4.value > current_profit >= self.exit_custom_pump_dec_profit_min_4.value and last_candle['sma_200_dec_20'] and last_candle['exit_pump_24_2_1h']:
            return (True, 'signal_profit_p_d_4')
        return (False, None)

    def exit_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        # Pumped 48h 1, under EMA200
        if self.exit_custom_pump_under_profit_max_1.value > current_profit >= self.exit_custom_pump_under_profit_min_1.value and last_candle['exit_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['exit_pump_36_2_1h'] and self.exit_custom_pump_trail_profit_max_1.value > current_profit >= self.exit_custom_pump_trail_profit_min_1.value and (self.exit_custom_pump_trail_rsi_min_1.value < last_candle['rsi_14'] < self.exit_custom_pump_trail_rsi_max_1.value) and (max_profit > current_profit + self.exit_custom_pump_trail_down_1.value):
            return (True, 'signal_profit_p_t_1')
        return (False, None)

    def exit_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple:
        if max_loss > self.exit_custom_recover_min_loss_1.value and current_profit >= self.exit_custom_recover_profit_1.value:
            return (True, 'signal_profit_r_1')
        elif max_loss > self.exit_custom_recover_min_loss_2.value and self.exit_custom_recover_profit_max_2.value > current_profit >= self.exit_custom_recover_profit_min_2.value and (last_candle['rsi_14'] < self.exit_custom_recover_rsi_2.value) and (last_candle['ema_25'] < last_candle['ema_50']):
            return (True, 'signal_profit_r_2')
        return (False, None)

    def exit_r_1(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle['r_480'] > -0.1:
                return (True, 'signal_profit_w_1_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -0.2:
                return (True, 'signal_profit_w_1_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -0.4:
                return (True, 'signal_profit_w_1_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -0.6:
                return (True, 'signal_profit_w_1_4')
        elif 0.06 > current_profit >= 0.05:
            if last_candle['r_480'] > -0.8:
                return (True, 'signal_profit_w_1_5')
        elif 0.07 > current_profit >= 0.06:
            if last_candle['r_480'] > -1.0:
                return (True, 'signal_profit_w_1_6')
        elif 0.08 > current_profit >= 0.07:
            if last_candle['r_480'] > -1.2:
                return (True, 'signal_profit_w_1_7')
        elif 0.09 > current_profit >= 0.08:
            if last_candle['r_480'] > -1.4:
                return (True, 'signal_profit_w_1_8')
        elif 0.1 > current_profit >= 0.09:
            if last_candle['r_480'] > -1.6:
                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 exit_r_2(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'] > 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'] > -2.1 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'] > -2.2 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'] > -2.3 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'] > -2.4 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'] > -2.5 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'] > -3.0 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'] > -3.0 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'] > -2.8 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'] > -2.4 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'] > -2.2 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'] > -2.0 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 exit_r_3(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'] > 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'] > -1.5 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'] > -2.0 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'] > -3.0 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_3_4')
        return (False, None)

    def exit_r_4(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'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -1.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_4')
        elif 0.06 > current_profit >= 0.05:
            if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_5')
        elif 0.07 > current_profit >= 0.06:
            if last_candle['r_480'] > -3.5 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'] > -4.0 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'] > -4.5 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'] > -3.0 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'] > -2.0 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'] > -2.0 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 exit_quick_mode(self, current_profit: float, max_profit: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
        if 0.06 > current_profit > 0.02 and last_candle['rsi_14'] > 79.0:
            return (True, 'signal_profit_q_1')
        if 0.06 > current_profit > 0.02 and last_candle['cti'] > 0.9:
            return (True, 'signal_profit_q_2')
        if last_candle['close'] < last_candle['atr_high_thresh_q'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_q']:
            if current_profit > 0.0:
                return (True, 'signal_profit_q_atr')
            elif current_profit < -0.05:
                return (True, 'signal_stoploss_q_atr')
        if current_profit > 0.0:
            if 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 last_candle['pm'] > last_candle['pmax_thresh'] and last_candle['close'] > last_candle['sma_21'] * 1.014:
                return (True, 'signal_profit_q_pmax_bear')
        if last_candle['zlema_4'] > last_candle['zlema_1'] and previous_candle_1['zlema_4'] < previous_candle_1['zlema_1'] and (last_candle['cci'] > 200) and (last_candle['hrsi'] > 80) and (last_candle['moderi_32'] == True) and (current_profit > 0):
            return (True, 'signal_profit_zlema_up')
        if last_candle['zlema_4_std'] > last_candle['zlema_1_std'] and previous_candle_1['zlema_4_std'] < previous_candle_1['zlema_1_std'] and (last_candle['cci'] > -100) and (last_candle['hrsi'] > 80) and (last_candle['moderi_32'] == False) and (current_profit > 0):
            return (True, 'signal_profit_zlema_dn')
        #        if ((max_profit - current_profit) > 0.01) and (current_profit > 0.00):
        #            return True, 'quick_trailing'
        return (False, None)

    def exit_ichi(self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
        if -0.03 < current_profit < 0.05 and current_time - timedelta(minutes=1440) > trade.open_date_utc and (last_candle['rsi_14'] > 75):
            return (True, 'signal_ichi_underwater')
        if max_loss > 0.07 and current_profit > 0.02:
            return (True, 'signal_ichi_recover_0')
        if max_loss > 0.06 and current_profit > 0.03:
            return (True, 'signal_ichi_recover_1')
        if max_loss > 0.05 and current_profit > 0.04:
            return (True, 'signal_ichi_recover_2')
        if max_loss > 0.04 and current_profit > 0.05:
            return (True, 'signal_ichi_recover_3')
        if max_loss > 0.03 and current_profit > 0.06:
            return (True, 'signal_ichi_recover_4')
        if 0.05 < current_profit < 0.1 and current_time - timedelta(minutes=720) > trade.open_date_utc:
            return (True, 'signal_ichi_slow_trade')
        if 0.07 < current_profit < 0.1 and max_profit - current_profit > 0.025 and (max_profit > 0.1):
            return (True, 'signal_ichi_trailing')
        if current_profit < -0.1:
            return (True, 'signal_ichi_stoploss')
        if last_candle['zlema_4'] > last_candle['zlema_1'] and previous_candle_1['zlema_4'] < previous_candle_1['zlema_1'] and (last_candle['cci'] > 100) and (last_candle['hrsi'] > 85) and (current_profit > 0):
            return (True, 'signal_ichi_zlema')
        return (False, None)

    def custom_exit(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]
        enter_tag = 'empty'
        if hasattr(trade, 'enter_tag') and trade.entry_tag is not None:
            enter_tag = trade.entry_tag
        else:
            trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            entry_signal = dataframe.loc[dataframe['date'] < trade_open_date]
            if not entry_signal.empty:
                entry_signal_candle = entry_signal.iloc[-1]
                enter_tag = entry_signal_candle['enter_tag'] if entry_signal_candle['enter_tag'] != '' else 'empty'
        entry_tags = entry_tag.split()
        max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate
        max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate
        # Quick exit mode
        if all((c in ['32', '33', '34', '35', '36', '37', '38', '40'] for c in entry_tags)):
            exit_long, signal_name = self.exit_quick_mode(current_profit, max_profit, last_candle, previous_candle_1, trade, current_time)
            if exit_long and signal_name is not None:
                return signal_name + ' ( ' + enter_tag + ')'
        # Ichi Trade management
        if all((c in ['39'] for c in entry_tags)):
            exit_long, signal_name = self.exit_ichi(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time)
            if exit_long and signal_name is not None:
                return signal_name + ' ( ' + enter_tag + ')'
        # Over EMA200, main profit targets
        exit_long, signal_name = self.exit_over_main(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Under EMA200, main profit targets
        exit_long, signal_name = self.exit_under_main(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # The pair is pumped
        exit_long, signal_name = self.exit_pump_main(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # The pair is descending
        exit_long, signal_name = self.exit_dec_main(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Trailing
        exit_long, signal_name = self.exit_trail_main(current_profit, last_candle, max_profit)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Duration based
        exit_long, signal_name = self.exit_duration_main(current_profit, last_candle, trade, current_time)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Under EMA200, exit with any profit
        exit_long, signal_name = self.exit_under_min(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Stoplosses
        exit_long, signal_name = self.exit_stoploss(current_profit, last_candle, previous_candle_1)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Pumped descending pairs
        exit_long, signal_name = self.exit_pump_dec(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Extra exits for pumped pairs
        exit_long, signal_name = self.exit_pump_extra(current_profit, last_candle, max_profit)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Extra exits for trades that recovered
        exit_long, signal_name = self.exit_recover(current_profit, last_candle, max_loss)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Williams %R based exit 1
        exit_long, signal_name = self.exit_r_1(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Williams %R based exit 2
        exit_long, signal_name = self.exit_r_2(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Williams %R based exit 3
        exit_long, signal_name = self.exit_r_3(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Williams %R based exit 4, plus CTI
        exit_long, signal_name = self.exit_r_4(current_profit, last_candle)
        if exit_long and signal_name is not None:
            return signal_name + ' ( ' + enter_tag + ')'
        # Sell signal 1
        if self.exit_condition_1_enable.value and last_candle['rsi_14'] > self.exit_rsi_bb_1.value 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 > 0.0:
                    return 'exit_signal_1_1_1' + ' ( ' + enter_tag + ')'
            elif current_profit > 0.0:
                return 'exit_signal_1_2_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_1_2_2' + ' ( ' + enter_tag + ')'
        # Sell signal 2
        elif self.exit_condition_2_enable.value and last_candle['rsi_14'] > self.exit_rsi_bb_2.value 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 > 0.0:
                    return 'exit_signal_2_1_1' + ' ( ' + enter_tag + ')'
            elif current_profit > 0.0:
                return 'exit_signal_2_2_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_2_2_2' + ' ( ' + enter_tag + ')'
        # Sell signal 4
        elif self.exit_condition_4_enable.value and last_candle['rsi_14'] > self.exit_dual_rsi_rsi_4.value and (last_candle['rsi_14_1h'] > self.exit_dual_rsi_rsi_1h_4.value):
            if last_candle['close'] > last_candle['ema_200']:
                if current_profit > 0.0:
                    return 'exit_signal_4_1_1' + ' ( ' + enter_tag + ')'
            elif current_profit > 0.0:
                return 'exit_signal_4_2_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_4_2_2' + ' ( ' + enter_tag + ')'
        # Sell signal 6
        elif self.exit_condition_6_enable.value and last_candle['close'] < last_candle['ema_200'] and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > self.exit_rsi_under_6.value):
            if current_profit > 0.0:
                return 'exit_signal_6_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_6_2' + ' ( ' + enter_tag + ')'
        # Sell signal 7
        elif self.exit_condition_7_enable.value and last_candle['rsi_14_1h'] > self.exit_rsi_1h_7.value and last_candle['crossed_below_ema_12_26']:
            if last_candle['close'] > last_candle['ema_200']:
                if current_profit > 0.0:
                    return 'exit_signal_7_1_1' + ' ( ' + enter_tag + ')'
            elif current_profit > 0.0:
                return 'exit_signal_7_2_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_7_2_2' + ' ( ' + enter_tag + ')'
        # Sell signal 8
        elif self.exit_condition_8_enable.value and last_candle['close'] > last_candle['bb20_2_upp_1h'] * self.exit_bb_relative_8.value:
            if last_candle['close'] > last_candle['ema_200']:
                if current_profit > 0.0:
                    return 'exit_signal_8_1_1' + ' ( ' + enter_tag + ')'
            elif current_profit > 0.0:
                return 'exit_signal_8_2_1' + ' ( ' + enter_tag + ')'
            elif max_loss > 0.25:
                return 'exit_signal_8_2_2' + ' ( ' + enter_tag + ')'
        return None

    def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float:
        """
        Rolling Percentage Change Maximum across interval.

        :param dataframe: DataFrame The original OHLC dataframe
        :param method: High to Low / Open to Close
        :param length: int The length to look back
        """
        if method == 'HL':
            return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min()
        elif method == 'OC':
            return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min()
        else:
            raise ValueError(f'Method {method} not defined!')

    def top_percent_change(self, dataframe: DataFrame, length: int) -> float:
        """
        Percentage change of the current close from the range maximum Open price

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
        if length == 0:
            return (dataframe['open'] - dataframe['close']) / dataframe['close']
        else:
            return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']

    def range_maxgap(self, dataframe: DataFrame, length: int) -> float:
        """
        Maximum Price Gap across interval.

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
        return dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()

    def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float:
        """
        Maximum Price Gap across interval adjusted.

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        :param adjustment: int The adjustment to be applied
        """
        return self.range_maxgap(dataframe, length) / adjustment

    def range_height(self, dataframe: DataFrame, length: int) -> float:
        """
        Current close distance to range bottom.

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        """
        return dataframe['close'] - dataframe['close'].rolling(length).min()

    def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool:
        """
        Determine if entry after a pump is safe.

        :param dataframe: DataFrame The original OHLC dataframe
        :param length: int The length to look back
        :param thresh: int Maximum percentage change threshold
        :param pull_thresh: int Pullback from interval maximum threshold
        """
        return (dataframe[f'oc_pct_change_{length}'] < thresh) | (self.range_maxgap_adjusted(dataframe, length, pull_thresh) > self.range_height(dataframe, length))

    def safe_dips(self, dataframe: DataFrame, thresh_0, thresh_2, thresh_12, thresh_144) -> bool:
        """
        Determine if dip is safe to enter.

        :param dataframe: DataFrame The original OHLC dataframe
        :param thresh_0: Threshold value for 0 length top pct change
        :param thresh_2: Threshold value for 2 length top pct change
        :param thresh_12: Threshold value for 12 length top pct change
        :param thresh_144: Threshold value for 144 length top pct change
        """
        return (dataframe['tpct_change_0'] < thresh_0) & (dataframe['tpct_change_2'] < thresh_2) & (dataframe['tpct_change_12'] < thresh_12) & (dataframe['tpct_change_144'] < thresh_144)

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, self.info_timeframe) for pair in pairs]
        informative_pairs.append(('BTC/USDT', self.timeframe))
        informative_pairs.append(('BTC/USDT', self.info_timeframe))
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe)
        # EMA
        informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12)
        informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
        informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25)
        informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26)
        informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35)
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # SMA
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)
        # RSI
        informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb20_2_low'] = bollinger['lower']
        informative_1h['bb20_2_mid'] = bollinger['mid']
        informative_1h['bb20_2_upp'] = bollinger['upper']
        # Chaikin Money Flow
        informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20)
        # Williams %R
        informative_1h['r_480'] = williams_r(informative_1h, period=480)
        # 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.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1)
        # EFI - Elders Force Index
        informative_1h['efi'] = pta.efi(informative_1h['close'], informative_1h['volume'], length=13)
        # SSL
        ssl_down, ssl_up = SSLChannels(informative_1h, 10)
        informative_1h['ssl_down'] = ssl_down
        informative_1h['ssl_up'] = ssl_up
        # Pump protections
        informative_1h['hl_pct_change_48'] = self.range_percent_change(informative_1h, 'HL', 48)
        informative_1h['hl_pct_change_36'] = self.range_percent_change(informative_1h, 'HL', 36)
        informative_1h['hl_pct_change_24'] = self.range_percent_change(informative_1h, 'HL', 24)
        informative_1h['oc_pct_change_48'] = self.range_percent_change(informative_1h, 'OC', 48)
        informative_1h['oc_pct_change_36'] = self.range_percent_change(informative_1h, 'OC', 36)
        informative_1h['oc_pct_change_24'] = self.range_percent_change(informative_1h, 'OC', 24)
        informative_1h['hl_pct_change_5'] = self.range_percent_change(informative_1h, 'HL', 5)
        informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min()
        informative_1h['safe_pump_24_10'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_10_24.value, self.entry_pump_pull_threshold_10_24.value)
        informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_10_36.value, self.entry_pump_pull_threshold_10_36.value)
        informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_10_48.value, self.entry_pump_pull_threshold_10_48.value)
        informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_20_24.value, self.entry_pump_pull_threshold_20_24.value)
        informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_20_36.value, self.entry_pump_pull_threshold_20_36.value)
        informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_20_48.value, self.entry_pump_pull_threshold_20_48.value)
        informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_30_24.value, self.entry_pump_pull_threshold_30_24.value)
        informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_30_36.value, self.entry_pump_pull_threshold_30_36.value)
        informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_30_48.value, self.entry_pump_pull_threshold_30_48.value)
        informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_40_24.value, self.entry_pump_pull_threshold_40_24.value)
        informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_40_36.value, self.entry_pump_pull_threshold_40_36.value)
        informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_40_48.value, self.entry_pump_pull_threshold_40_48.value)
        informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_50_24.value, self.entry_pump_pull_threshold_50_24.value)
        informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_50_36.value, self.entry_pump_pull_threshold_50_36.value)
        informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_50_48.value, self.entry_pump_pull_threshold_50_48.value)
        informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_60_24.value, self.entry_pump_pull_threshold_60_24.value)
        informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_60_36.value, self.entry_pump_pull_threshold_60_36.value)
        informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_60_48.value, self.entry_pump_pull_threshold_60_48.value)
        informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_70_24.value, self.entry_pump_pull_threshold_70_24.value)
        informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_70_36.value, self.entry_pump_pull_threshold_70_36.value)
        informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_70_48.value, self.entry_pump_pull_threshold_70_48.value)
        informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_80_24.value, self.entry_pump_pull_threshold_80_24.value)
        informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_80_36.value, self.entry_pump_pull_threshold_80_36.value)
        informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_80_48.value, self.entry_pump_pull_threshold_80_48.value)
        informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_90_24.value, self.entry_pump_pull_threshold_90_24.value)
        informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_90_36.value, self.entry_pump_pull_threshold_90_36.value)
        informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_90_48.value, self.entry_pump_pull_threshold_90_48.value)
        informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_100_24.value, self.entry_pump_pull_threshold_100_24.value)
        informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_100_36.value, self.entry_pump_pull_threshold_100_36.value)
        informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_100_48.value, self.entry_pump_pull_threshold_100_48.value)
        informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_110_24.value, self.entry_pump_pull_threshold_110_24.value)
        informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_110_36.value, self.entry_pump_pull_threshold_110_36.value)
        informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_110_48.value, self.entry_pump_pull_threshold_110_48.value)
        informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_120_24.value, self.entry_pump_pull_threshold_120_24.value)
        informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_120_36.value, self.entry_pump_pull_threshold_120_36.value)
        informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_120_48.value, self.entry_pump_pull_threshold_120_48.value)
        informative_1h['safe_dump_10'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_10_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_20'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_20_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_30'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_30_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_40'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_40_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_50'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_50_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_60'] = (informative_1h['hl_pct_change_5'] < self.entry_dump_protection_60_5.value) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['exit_pump_48_1'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_1.value
        informative_1h['exit_pump_48_2'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_2.value
        informative_1h['exit_pump_48_3'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_3.value
        informative_1h['exit_pump_36_1'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_1.value
        informative_1h['exit_pump_36_2'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_2.value
        informative_1h['exit_pump_36_3'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_3.value
        informative_1h['exit_pump_24_1'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_1.value
        informative_1h['exit_pump_24_2'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_2.value
        informative_1h['exit_pump_24_3'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_3.value
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # BB 40 - STD2
        bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['bb40_2_low'] = bb_40_std2['lower']
        dataframe['bb40_2_mid'] = bb_40_std2['mid']
        dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
        # BB 20 - STD2
        bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb20_2_low'] = bb_20_std2['lower']
        dataframe['bb20_2_mid'] = bb_20_std2['mid']
        dataframe['bb20_2_upp'] = bb_20_std2['upper']
        # 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_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'] = ewo(dataframe, 50, 200)
        # RSI
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
        # Chopiness
        dataframe['chop'] = qtpylib.chopiness(dataframe, 14)
        # Zero-Lag EMA
        dataframe['zema_61'] = zema(dataframe, period=61)
        # Williams %R
        dataframe['r_480'] = williams_r(dataframe, period=480)
        # Stochastic RSI
        stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0)
        dataframe['stochrsi_fastk_96'] = stochrsi['fastk']
        dataframe['stochrsi_fastd_96'] = stochrsi['fastd']
        # Modified Elder Ray Index
        dataframe['moderi_32'] = moderi(dataframe, 32)
        dataframe['moderi_64'] = moderi(dataframe, 64)
        dataframe['moderi_96'] = moderi(dataframe, 96)
        # hull
        dataframe['hull_75'] = hull(dataframe, 75)
        # zlema
        dataframe['zlema_68'] = zlema(dataframe, 68)
        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        # For exit 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']
        #HLC3
        dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
        #HRSI
        dataframe['hull'] = 2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2)
        dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2)
        dataframe['hull_exit'] = 2 * dataframe['low'] - ta.WMA(dataframe['low'], 2)
        dataframe['hrsi_exit'] = ta.RSI(dataframe['hull_exit'], 2)
        #Kalman Filter HLC3
        dataframe['hlc3KF'] = KalmanFilter(dataframe, source='hlc3')
        #Kalman Filter LOW
        dataframe['lowKF'] = KalmanFilter(dataframe, source='low')
        #ZLEMA BUY
        dataframe['zlema_1'] = dataframe['hlc3KF']
        dataframe['zlema_1_std'] = dataframe['hlc3']
        dataframe['ema_data'] = dataframe['hlc3KF'] + (dataframe['hlc3KF'] - dataframe['hlc3KF'].shift(2))
        dataframe['ema_data_2'] = dataframe['hlc3KF'] + (dataframe['hlc3KF'] - dataframe['hlc3KF'].shift(1))
        dataframe['zlema_4'] = ta.EMA(dataframe['ema_data'], timeperiod=4)
        dataframe['zlema_2'] = ta.EMA(dataframe['ema_data_2'], timeperiod=2)
        dataframe['zlema_4_std'] = pta.zlma(dataframe['hlc3'], length=4)
        #ZLEMA SELL
        dataframe['zlema_1_exit'] = dataframe['lowKF']
        dataframe['zlema_1_std_exit'] = dataframe['low']
        dataframe['ema_data_exit'] = dataframe['lowKF'] + (dataframe['lowKF'] - dataframe['lowKF'].shift(2))
        dataframe['zlema_4_exit'] = ta.EMA(dataframe['ema_data_exit'], timeperiod=4)
        dataframe['zlema_4_std_exit'] = pta.zlma(dataframe['low'], length=4)
        #CCI
        dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20)
        # 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)
        # ATR
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_high_thresh_1'] = dataframe['high'] - dataframe['atr'] * 5.4
        dataframe['atr_high_thresh_2'] = dataframe['high'] - dataframe['atr'] * 5.2
        dataframe['atr_high_thresh_3'] = dataframe['high'] - dataframe['atr'] * 5.0
        dataframe['atr_high_thresh_4'] = dataframe['high'] - dataframe['atr'] * 2.0
        dataframe['atr_high_thresh_q'] = dataframe['high'] - dataframe['atr'] * 3.0
        # Dip protection
        dataframe['tpct_change_0'] = self.top_percent_change(dataframe, 0)
        dataframe['tpct_change_2'] = self.top_percent_change(dataframe, 2)
        dataframe['tpct_change_12'] = self.top_percent_change(dataframe, 12)
        dataframe['tpct_change_144'] = self.top_percent_change(dataframe, 144)
        dataframe['safe_dips_10'] = self.safe_dips(dataframe, self.entry_dip_threshold_10_1.value, self.entry_dip_threshold_10_2.value, self.entry_dip_threshold_10_3.value, self.entry_dip_threshold_10_4.value)
        dataframe['safe_dips_20'] = self.safe_dips(dataframe, self.entry_dip_threshold_20_1.value, self.entry_dip_threshold_20_2.value, self.entry_dip_threshold_20_3.value, self.entry_dip_threshold_20_4.value)
        dataframe['safe_dips_30'] = self.safe_dips(dataframe, self.entry_dip_threshold_30_1.value, self.entry_dip_threshold_30_2.value, self.entry_dip_threshold_30_3.value, self.entry_dip_threshold_30_4.value)
        dataframe['safe_dips_40'] = self.safe_dips(dataframe, self.entry_dip_threshold_40_1.value, self.entry_dip_threshold_40_2.value, self.entry_dip_threshold_40_3.value, self.entry_dip_threshold_40_4.value)
        dataframe['safe_dips_50'] = self.safe_dips(dataframe, self.entry_dip_threshold_50_1.value, self.entry_dip_threshold_50_2.value, self.entry_dip_threshold_50_3.value, self.entry_dip_threshold_50_4.value)
        dataframe['safe_dips_60'] = self.safe_dips(dataframe, self.entry_dip_threshold_60_1.value, self.entry_dip_threshold_60_2.value, self.entry_dip_threshold_60_3.value, self.entry_dip_threshold_60_4.value)
        dataframe['safe_dips_70'] = self.safe_dips(dataframe, self.entry_dip_threshold_70_1.value, self.entry_dip_threshold_70_2.value, self.entry_dip_threshold_70_3.value, self.entry_dip_threshold_70_4.value)
        dataframe['safe_dips_80'] = self.safe_dips(dataframe, self.entry_dip_threshold_80_1.value, self.entry_dip_threshold_80_2.value, self.entry_dip_threshold_80_3.value, self.entry_dip_threshold_80_4.value)
        dataframe['safe_dips_90'] = self.safe_dips(dataframe, self.entry_dip_threshold_90_1.value, self.entry_dip_threshold_90_2.value, self.entry_dip_threshold_90_3.value, self.entry_dip_threshold_90_4.value)
        dataframe['safe_dips_100'] = self.safe_dips(dataframe, self.entry_dip_threshold_100_1.value, self.entry_dip_threshold_100_2.value, self.entry_dip_threshold_100_3.value, self.entry_dip_threshold_100_4.value)
        dataframe['safe_dips_110'] = self.safe_dips(dataframe, self.entry_dip_threshold_110_1.value, self.entry_dip_threshold_110_2.value, self.entry_dip_threshold_110_3.value, self.entry_dip_threshold_110_4.value)
        dataframe['safe_dips_120'] = self.safe_dips(dataframe, self.entry_dip_threshold_120_1.value, self.entry_dip_threshold_120_2.value, self.entry_dip_threshold_120_3.value, self.entry_dip_threshold_120_4.value)
        dataframe['safe_dips_130'] = self.safe_dips(dataframe, self.entry_dip_threshold_130_1.value, self.entry_dip_threshold_130_2.value, self.entry_dip_threshold_130_3.value, self.entry_dip_threshold_130_4.value)
        # 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
        return dataframe

    def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Indicators
        # -----------------------------------------------------------------------------------------
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Indicators
        # -----------------------------------------------------------------------------------------
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        # Add prefix
        # -----------------------------------------------------------------------------------------
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: 'btc_' + s if not s in ignore_columns else s, inplace=True)
        return dataframe

    def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Indicators
        # -----------------------------------------------------------------------------------------
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['not_downtrend'] = (dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi_14'] > 50)
        # Add prefix
        # -----------------------------------------------------------------------------------------
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: 'btc_' + s if not s in ignore_columns else s, inplace=True)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        --> BTC informative (5m/1h)
        ___________________________________________________________________________________________
        """
        if self.has_BTC_base_tf:
            btc_base_tf = self.dp.get_pair_dataframe('BTC/USDT', self.timeframe)
            btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata)
            dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True)
            drop_columns = [s + '_' + self.timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        if self.has_BTC_info_tf:
            btc_info_tf = self.dp.get_pair_dataframe('BTC/USDT', self.info_timeframe)
            btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata)
            dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.info_timeframe, ffill=True)
            drop_columns = [s + '_' + self.info_timeframe for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        '\n        --> Informative timeframe\n        ___________________________________________________________________________________________\n        '
        if self.info_timeframe != 'none':
            informative_1h = self.informative_1h_indicators(dataframe, metadata)
            dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe, ffill=True)
            drop_columns = [s + '_' + self.info_timeframe for s in ['date']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        '\n        --> Resampled to another timeframe\n        ___________________________________________________________________________________________\n        '
        if self.res_timeframe != 'none':
            resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe))
            resampled = self.resampled_tf_indicators(resampled, metadata)
            # Merge resampled info dataframe
            dataframe = resampled_merge(dataframe, resampled, fill_na=True)
            dataframe.rename(columns=lambda s: s + '_{}'.format(self.res_timeframe) if 'resample_' in s else s, inplace=True)
            dataframe.rename(columns=lambda s: s.replace('resample_{}_'.format(self.res_timeframe.replace('m', '')), ''), inplace=True)
            drop_columns = [s + '_' + self.res_timeframe for s in ['date']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        '\n        --> The indicators for the normal (5m) timeframe\n        ___________________________________________________________________________________________\n        '
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        for index in self.entry_protection_params:
            item_entry_protection_list = [True]
            global_entry_protection_params = self.entry_protection_params[index]
            if self.entry_params['entry_condition_' + str(index) + '_enable']:
                # Standard protections - Common to every condition
                # -----------------------------------------------------------------------------------------
                if global_entry_protection_params['ema_fast']:
                    item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_fast_len']}"] > dataframe['ema_200'])
                if global_entry_protection_params['ema_slow']:
                    item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_slow_len']}_1h"] > dataframe['ema_200_1h'])
                if global_entry_protection_params['close_above_ema_fast']:
                    item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_fast_len']}"])
                if global_entry_protection_params['close_above_ema_slow']:
                    item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_slow_len']}_1h"])
                if global_entry_protection_params['sma200_rising']:
                    item_entry_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_entry_protection_params['sma200_rising_val'])))
                if global_entry_protection_params['sma200_1h_rising']:
                    item_entry_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_entry_protection_params['sma200_1h_rising_val'])))
                if global_entry_protection_params['safe_dips']:
                    item_entry_protection_list.append(dataframe[f"safe_dips_{global_entry_protection_params['safe_dips_type']}"])
                if global_entry_protection_params['safe_pump']:
                    item_entry_protection_list.append(dataframe[f"safe_pump_{global_entry_protection_params['safe_pump_period']}_{global_entry_protection_params['safe_pump_type']}_1h"])
                if global_entry_protection_params['btc_1h_not_downtrend']:
                    item_entry_protection_list.append(dataframe['btc_not_downtrend_1h'])
                if not self.config['runmode'] in ('live', 'dry_run'):
                    if self.has_bt_agefilter:
                        item_entry_protection_list.append(dataframe['bt_agefilter_ok'])
                elif self.has_downtime_protection:
                    item_entry_protection_list.append(dataframe['live_data_ok'])
                # Buy conditions
                # -----------------------------------------------------------------------------------------
                item_entry_logic = []
                item_entry_logic.append(reduce(lambda x, y: x & y, item_entry_protection_list))
                # Condition #1
                if index == 1:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.entry_min_inc_1.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_1.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_1.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_1.value)
                    item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_1.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_1.value)
                # Condition #2
                elif index == 2:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.entry_rsi_1h_diff_2.value)
                    item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_2.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_2.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_2.value)
                # Condition #3
                elif index == 3:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_3.value)
                    # Logic
                    item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0))
                    item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_bb40_bbdelta_close_3.value))
                    item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_bb40_closedelta_close_3.value))
                    item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_bb40_tail_bbdelta_3.value))
                    item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
                    item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift()))
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_3.value)
                # Condition #4
                elif index == 4:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_50'])
                    item_entry_logic.append(dataframe['close'] < self.entry_bb20_close_bblowerband_4.value * dataframe['bb20_2_low'])
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_30'].shift(1) * self.entry_bb20_volume_4.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_4.value)
                # Condition #5
                elif index == 5:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_5.value)
                    # Logic
                    item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_5.value)
                    item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_5.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_5.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_5.value)
                # Condition #6
                elif index == 6:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_6.value)
                    item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_6.value)
                # Condition #7
                elif index == 7:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_7.value)
                    item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_7.value)
                # Condition #8
                elif index == 8:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_96'])
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_8.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_8.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_8.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_8.value)
                # Condition #9
                elif index == 9:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['ema_50'] > dataframe['ema_200'])
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_9.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_9.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_9.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_9.value)
                    item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_9.value)
                # Condition #10
                elif index == 10:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_10.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_10.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_10.value)
                # Condition #11
                elif index == 11:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.entry_min_inc_11.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_11.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_11.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_max_11.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_11.value)
                    item_entry_logic.append(dataframe['mfi'] < self.entry_mfi_11.value)
                # Condition #12
                elif index == 12:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_12.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_12.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_12.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_12.value)
                # Condition #13
                elif index == 13:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_13.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_13.value)
                    item_entry_logic.append(dataframe['ewo'] < self.entry_ewo_13.value)
                # Condition #14
                elif index == 14:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_14.value)
                    item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_14.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_14.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_14.value)
                # Condition #15
                elif index == 15:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_ema_rel_15.value)
                    # Logic
                    item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_ema_open_mult_15.value)
                    item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_15.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_15.value)
                # Condition #16
                elif index == 16:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_16.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_16.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_16.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_16.value)
                # Condition #17
                elif index == 17:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_ma_offset_17.value)
                    item_entry_logic.append(dataframe['ewo'] < self.entry_ewo_17.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_17.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_17.value)
                # Condition #18
                elif index == 18:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20))
                    item_entry_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36))
                    # Logic
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_18.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_18.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_18.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_18.value)
                # Condition #19
                elif index == 19:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h'])
                    item_entry_logic.append(dataframe['low'] < dataframe['ema_100_1h'])
                    item_entry_logic.append(dataframe['close'] > dataframe['ema_100_1h'])
                    item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_rsi_1h_min_19.value)
                    item_entry_logic.append(dataframe['chop'] < self.entry_chop_max_19.value)
                    item_entry_logic.append(dataframe['moderi_32'] == True)
                    item_entry_logic.append(dataframe['moderi_64'] == True)
                    item_entry_logic.append(dataframe['moderi_96'] == True)
                # Condition #20
                elif index == 20:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_20.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_20.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_20.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_20.value)
                # Condition #21
                elif index == 21:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_21.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_21.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_cti_21.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_volume_21.value)
                # Condition #22
                elif index == 22:
                    # Non-Standard protections
                    item_entry_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12))
                    item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36))
                    # Logic
                    item_entry_logic.append(dataframe['volume_mean_4'] * self.entry_volume_22.value > dataframe['volume'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_ma_offset_22.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_22.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_22.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_22.value)
                # Condition #23
                elif index == 23:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_bb_offset_23.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_ewo_23.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_rsi_23.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_rsi_1h_23.value)
                # Condition #24
                elif index == 24:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12))
                    item_entry_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h'])
                    item_entry_logic.append(dataframe['cmf_1h'].shift(12) < 0)
                    item_entry_logic.append(dataframe['cmf_1h'] > 0)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_24_rsi_max.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_24_rsi_1h_min.value)
                # Condition #25
                elif index == 25:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift())
                    item_entry_logic.append(dataframe['rsi_4'] < self.entry_25_rsi_4.value)
                    item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_20'] * self.entry_25_ma_offset.value)
                    item_entry_logic.append(dataframe['open'] > dataframe['sma_20'] * self.entry_25_ma_offset.value)
                    item_entry_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h']))
                    item_entry_logic.append(dataframe['cti'] < self.entry_25_cti.value)
                # Condition #26
                elif index == 26:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['close'] < dataframe['zema_61'] * self.entry_26_zema_low_offset.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_26_cti.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_26_volume.value)
                # Condition #27
                elif index == 27:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['r_480'] < -self.entry_27_wr_max.value)
                    item_entry_logic.append(dataframe['r_480_1h'] < -self.entry_27_wr_1h_max.value)
                    item_entry_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.entry_27_rsi_max.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_27_cti.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_27_volume.value)
                # Condition #28
                elif index == 28:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_64'] == True)
                    item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_28_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_28_ewo.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_28_rsi.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_28_cti.value)
                # Condition #29
                elif index == 29:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_64'] == True)
                    item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_29_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] < self.entry_29_ewo.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_29_cti.value)
                # Condition #30
                elif index == 30:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_64'] == False)
                    item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_30_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_30_ewo.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_30_rsi.value)
                    item_entry_logic.append(dataframe['cti'] < self.entry_30_cti.value)
                # Condition #31
                elif index == 31:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_64'] == False)
                    item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_31_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] < self.entry_31_ewo.value)
                    item_entry_logic.append(dataframe['r_480'] < self.entry_31_wr.value)
                # Condition #32 - Quick mode entry
                elif index == 32:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_32'])
                    item_entry_logic.append(dataframe['moderi_64'])
                    item_entry_logic.append(dataframe['moderi_96'])
                    item_entry_logic.append(dataframe['cti'] < self.entry_32_cti.value)
                    item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1))
                    item_entry_logic.append(dataframe['rsi_4'] < self.entry_32_rsi.value)
                    item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h'])
                    item_entry_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.entry_32_dip.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_15'] * self.entry_32_ma_offset.value)
                    item_entry_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h']))
                # Condition #33 - Quick mode entry
                elif index == 33:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['moderi_96'])
                    item_entry_logic.append(dataframe['cti'] < self.entry_33_cti.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_33_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] > self.entry_33_ewo.value)
                    item_entry_logic.append(dataframe['rsi_14'] < self.entry_33_rsi.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_33_volume.value)
                # Condition #34 - Quick mode entry
                elif index == 34:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['cti'] < self.entry_34_cti.value)
                    item_entry_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.entry_34_dip.value)
                    item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_34_ma_offset.value)
                    item_entry_logic.append(dataframe['ewo'] < self.entry_34_ewo.value)
                    item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_34_volume.value)
                # Condition #35 - PMAX0 entry
                elif index == 35:
                    # Non-Standard protections
                    # Logic
                    item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * 0.984)
                    item_entry_logic.append(dataframe['ewo'] > 9.6)
                    item_entry_logic.append(dataframe['rsi_14'] < 32.0)
                    item_entry_logic.append(dataframe['cti'] < -0.5)
                # Condition #36 - PMAX1 entry
                elif index == 36:
                    # Non-Standard protections (add below)
                    # Logic
                    item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * 0.98)
                    item_entry_logic.append(dataframe['ewo'] < -8.8)
                    item_entry_logic.append(dataframe['cti'] < -0.8)
                # Condition #37 - PMAX2 entry
                elif index == 37:
                    # Non-Standard protections (add below)
                    # Logic
                    item_entry_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * 0.98)
                    item_entry_logic.append(dataframe['ewo'] > 9.8)
                    item_entry_logic.append(dataframe['rsi_14'] < 56.0)
                    item_entry_logic.append(dataframe['cti'] < -0.7)
                    item_entry_logic.append(dataframe['safe_dump_50_1h'])
                # Condition #38 - PMAX3 entry
                elif index == 38:
                    # Non-Standard protections (add below)
                    # Logic
                    item_entry_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
                    item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * 0.7)
                    item_entry_logic.append(dataframe['ewo'] < -2.0)
                    item_entry_logic.append(dataframe['cti'] < -0.86)
                # Condition #39 - Ichimoku
                elif index == 39:
                    # Non-Standard protections (add below)
                    # Logic
                    item_entry_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h'])
                    item_entry_logic.append(dataframe['close'] > dataframe['cloud_top_1h'])
                    item_entry_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h'])
                    item_entry_logic.append(dataframe['chikou_span_1h'] > dataframe['senkou_a_1h'])
                    item_entry_logic.append(dataframe['efi_1h'] > 0)
                    item_entry_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h'])
                    item_entry_logic.append(dataframe['close'] < dataframe['ssl_up_1h'])
                    item_entry_logic.append(dataframe['cti'] < -0.73)
                    # Start of trend
                    item_entry_logic.append((dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) | (dataframe['efi_1h'] < 0) | (dataframe['ssl_up_1h'].shift(12) < dataframe['ssl_down_1h'].shift(12)))
                # Condition #40 - ZLEMA X entry
                elif index == 40:
                    # Non-Standard protections (add below)
                    # Logic
                    item_entry_logic.append(qtpylib.crossed_above(dataframe['zlema_2'], dataframe['zlema_4']))
                    item_entry_logic.append(dataframe['hrsi'] < 30)
                    item_entry_logic.append(dataframe['cci'] < -200)
                    item_entry_logic.append(dataframe['rsi_14'] < 30)
                item_entry_logic.append(dataframe['volume'] > 0)
                item_entry = reduce(lambda x, y: x & y, item_entry_logic)
                dataframe.loc[item_entry, 'enter_tag'] += str(index) + ' '
                conditions.append(item_entry)
        if conditions:
            dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        return dataframe

    def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool:
        """
        Called right before placing a regular exit 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 exit_reason: Sell reason.
            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
                           'exit_signal', 'force_exit', 'emergency_exit']
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return bool: When True is returned, then the exit-order is placed on the exchange.
            False aborts the process
        """
        # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
        if self.config['runmode'].value in ('live', 'dry_run'):
            self.load_hold_trades_config()
            if not self.hold_trade_ids:
                # We have no pairs we want to hold until profit, exit
                return True
            if trade.id not in self.hold_trade_ids:
                # This pair is not on the list to hold until profit, exit
                return True
            trade_profit_ratio = self.hold_trade_ids[trade.id]
            current_profit_ratio = trade.calc_profit_ratio(rate)
            if exit_reason == 'force_exit':
                formatted_profit_ratio = '{}%'.format(trade_profit_ratio * 100)
                formatted_current_profit_ratio = '{}%'.format(current_profit_ratio * 100)
                log.warning('Force exiting %s even though the current profit of %s < %s', trade, formatted_current_profit_ratio, formatted_profit_ratio)
                return True
            elif current_profit_ratio >= trade_profit_ratio:
                # This pair is on the list to hold, and we reached minimum profit, exit
                return True
            # This pair is on the list to hold, and we haven't reached minimum profit, hold
            return False
        else:
            return True
# Elliot Wave Oscillator

def ewo(dataframe, sma1_length=5, sma2_length=35):
    sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
    sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / dataframe['close'] * 100
    return smadif
# Chaikin Money Flow

def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
    """Chaikin Money Flow (CMF)
    It measures the amount of Money Flow Volume over a specific period.
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
    Args:
        dataframe(pandas.Dataframe): dataframe containing ohlcv
        n(int): n period.
        fillna(bool): if True, fill nan values.
    Returns:
        pandas.Series: New feature generated.
    """
    mfv = (dataframe['close'] - dataframe['low'] - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= dataframe['volume']
    cmf = mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return Series(cmf, name='cmf')
# Williams %R

def williams_r(dataframe: DataFrame, period: int=14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
        of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
        of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """
    highest_high = dataframe['high'].rolling(center=False, window=period).max()
    lowest_low = dataframe['low'].rolling(center=False, window=period).min()
    WR = Series((highest_high - dataframe['close']) / (highest_high - lowest_low), name='{0} Williams %R'.format(period))
    return WR * -100
# Volume Weighted Moving Average

def vwma(dataframe: DataFrame, length: int=10):
    """Indicator: Volume Weighted Moving Average (VWMA)"""
    # Calculate Result
    pv = dataframe['close'] * dataframe['volume']
    vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
    return vwma
# Modified Elder Ray Index

def moderi(dataframe: DataFrame, len_slow_ma: int=32) -> Series:
    slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma))
    return slow_ma >= slow_ma.shift(1)  # we just need true & false for ERI trend
# zlema

def zlema(dataframe, timeperiod):
    lag = int(math.floor((timeperiod - 1) / 2))
    if isinstance(dataframe, Series):
        ema_data = dataframe + (dataframe - dataframe.shift(lag))
    else:
        ema_data = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(lag))
    return ta.EMA(ema_data, timeperiod=timeperiod)
# zlhull

def zlhull(dataframe, timeperiod):
    lag = int(math.floor((timeperiod - 1) / 2))
    if isinstance(dataframe, Series):
        wma_data = dataframe + (dataframe - dataframe.shift(lag))
    else:
        wma_data = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(lag))
    return ta.WMA(2 * ta.WMA(wma_data, int(math.floor(timeperiod / 2))) - ta.WMA(wma_data, timeperiod), int(round(np.sqrt(timeperiod))))
# hull

def hull(dataframe, timeperiod):
    if isinstance(dataframe, Series):
        return ta.WMA(2 * ta.WMA(dataframe, int(math.floor(timeperiod / 2))) - ta.WMA(dataframe, timeperiod), int(round(np.sqrt(timeperiod))))
    else:
        return ta.WMA(2 * ta.WMA(dataframe['close'], int(math.floor(timeperiod / 2))) - ta.WMA(dataframe['close'], timeperiod), int(round(np.sqrt(timeperiod))))
# PMAX

def pmax(df, period, multiplier, length, MAtype, src):
    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)
    mavalue = 'MA_' + str(MAtype) + '_' + str(length)
    atr = 'ATR_' + str(period)
    pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
    pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # MAtype==9 --> zema
    if src == 1:
        masrc = df['close']
    elif src == 2:
        masrc = (df['high'] + df['low']) / 2
    elif src == 3:
        masrc = (df['high'] + df['low'] + df['close'] + df['open']) / 4
    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)
    df[atr] = ta.ATR(df, timeperiod=period)
    df['basic_ub'] = mavalue + multiplier / 10 * df[atr]
    df['basic_lb'] = mavalue - multiplier / 10 * df[atr]
    basic_ub = df['basic_ub'].values
    final_ub = np.full(len(df), 0.0)
    basic_lb = df['basic_lb'].values
    final_lb = np.full(len(df), 0.0)
    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), 0.0)
    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 0.0
    pm = Series(pm_arr)
    # Mark the trend direction up/down
    pmx = np.where(pm_arr > 0.0, np.where(mavalue < pm_arr, 'down', 'up'), np.NaN)
    return (pm, pmx)

def calc_streaks(series: Series):
    # logic tables
    geq = series >= series.shift(1)  # True if rising
    eq = series == series.shift(1)  # True if equal
    logic_table = concat([geq, eq], axis=1)
    streaks = [0]  # holds the streak duration, starts with 0
    for row in logic_table.iloc[1:].itertuples():  # iterate through logic table
        if row[2]:  # same value as before
            streaks.append(0)
            continue
        last_value = streaks[-1]
        if row[1]:  # higher value than before
            streaks.append(last_value + 1 if last_value >= 0 else 1)  # increase or reset to +1
        else:  # lower value than before
            streaks.append(last_value - 1 if last_value < 0 else -1)  # decrease or reset to -1
    return streaks
# SSL Channels

def SSLChannels(dataframe, length=7):
    df = dataframe.copy()
    ATR = ta.ATR(dataframe, timeperiod=14)
    smaHigh = dataframe['high'].rolling(length).mean() + ATR
    smaLow = dataframe['low'].rolling(length).mean() - ATR
    hlv = Series(np.where(dataframe['close'] > smaHigh, 1, np.where(dataframe['close'] < smaLow, -1, np.NAN)))
    hlv = hlv.ffill()
    sslDown = np.where(hlv < 0, smaHigh, smaLow)
    sslUp = np.where(hlv < 0, smaLow, smaHigh)
    return (sslDown, sslUp)
#Kalman Filter

def KalmanFilter(dtloc, source='close'):
    dtKF = dtloc.copy().fillna(0)
    dtKF['TRANGE'] = ta.TRANGE(dtloc).fillna(0)

    def calc_dtKF(dfr, init=0):
        global calc_dtKF_value_1
        global calc_dtKF_value_2
        global calc_dtKF_value_3
        global calc_dtKF_source
        if init == 1:
            calc_dtKF_value_1 = 0.0
            calc_dtKF_value_2 = 0.0
            calc_dtKF_value_3 = 0.0
            calc_dtKF_source = 0.0
            return
        calc_dtKF_value_1 = 0.2 * (dfr[source] - calc_dtKF_source) + 0.8 * calc_dtKF_value_1
        calc_dtKF_value_2 = 0.1 * dfr['TRANGE'] + 0.8 * calc_dtKF_value_2
        if calc_dtKF_value_2 != 0:
            vlambda = abs(calc_dtKF_value_1 / calc_dtKF_value_2)
        else:
            vlambda = 0
        valpha = (-1 * math.pow(vlambda, 2) + math.sqrt(math.pow(vlambda, 4) + 16 * math.pow(vlambda, 2))) / 8
        calc_dtKF_value_3 = valpha * dfr[source] + (1 - valpha) * calc_dtKF_value_3
        calc_dtKF_source = dfr[source]
        return calc_dtKF_value_3
    calc_dtKF(None, init=1)
    dtKF['KF'] = dtKF.apply(calc_dtKF, axis=1)
    return dtKF['KF']