NostalgiaForInfinityNext_ChangeToTower_V6

# -*- coding: utf-8 -*-
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_V6(IStrategy):
    INTERFACE_VERSION = 2

    plot_config = {
        "main_plot": {},
        "subplots": {
            "buy tag": {"buy_tag": {"color": "green"}},
        },
    }

    # ROI table: tightened for faster profit capture & reduced exposure
    minimal_roi = {
        "0": 0.10,
        "20": 0.05,
        "45": 0.03,
        "90": 0.01,
    }

    stoploss = -0.07  # Tightened from -0.10, moderate compromise

    # Trailing stoploss - moderately tighter than original
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.008  # Moderate: between original 0.01 and previous 0.005
    trailing_stop_positive_offset = 0.02  # Moderate: between original 0.03 and previous 0.015

    use_custom_stoploss = True  # Enable tiered custom stoploss

    # 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 = True  # Restored: strategy relies on hold-for-recovery behavior
    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 = {
        "buy": "limit",
        "sell": "limit",
        "trailing_stop_loss": "limit",
        "stoploss": "limit",
        "stoploss_on_exchange": False,
    }

    #############################################################

    buy_params = {
        #############
        # Enable/Disable conditions
        "buy_condition_1_enable": True,
        "buy_condition_2_enable": True,
        "buy_condition_3_enable": True,
        "buy_condition_4_enable": True,
        "buy_condition_5_enable": True,
        "buy_condition_6_enable": True,
        "buy_condition_7_enable": True,
        "buy_condition_8_enable": True,
        "buy_condition_9_enable": False,  # Disabled: minimal protection filters
        "buy_condition_10_enable": True,
        "buy_condition_11_enable": False,  # Disabled: weak protection, RSI-only
        "buy_condition_12_enable": True,
        "buy_condition_13_enable": True,
        "buy_condition_14_enable": True,
        "buy_condition_15_enable": True,
        "buy_condition_16_enable": True,
        "buy_condition_17_enable": True,
        "buy_condition_18_enable": True,
        "buy_condition_19_enable": True,
        "buy_condition_20_enable": False,  # Disabled: unprotected entries
        "buy_condition_21_enable": True,
        "buy_condition_22_enable": True,
        "buy_condition_23_enable": True,
        "buy_condition_24_enable": True,
        "buy_condition_25_enable": True,
        "buy_condition_26_enable": True,
        "buy_condition_27_enable": True,
        "buy_condition_28_enable": True,
        "buy_condition_29_enable": True,
        "buy_condition_30_enable": True,
        "buy_condition_31_enable": True,
        "buy_condition_32_enable": True,
        "buy_condition_33_enable": True,
        "buy_condition_34_enable": True,
        "buy_condition_35_enable": False,  # Disabled: no safe_dips/pump/sma200
        "buy_condition_36_enable": False,  # Disabled: no safe_dips/pump/sma200
        "buy_condition_37_enable": False,  # Disabled: no safe_dips/pump/sma200
        "buy_condition_38_enable": False,  # Disabled: no safe_dips/pump/sma200
        "buy_condition_39_enable": False,  # Disabled: no safe_dips/pump/sma200
        "buy_condition_40_enable": False,  # Disabled: no safe_dips/pump/sma200
        #############
    }

    sell_params = {
        #############
        # Enable/Disable conditions
        "sell_condition_1_enable": True,
        "sell_condition_2_enable": True,
        "sell_condition_3_enable": True,
        "sell_condition_4_enable": True,
        "sell_condition_5_enable": True,
        "sell_condition_6_enable": True,
        "sell_condition_7_enable": True,
        "sell_condition_8_enable": True,
        #############
    }

    #############################################################

    buy_protection_params = {
        1: {
            "ema_fast": False,
            "ema_fast_len": "26",
            "ema_slow": True,
            "ema_slow_len": "100",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": True,
            "sma200_rising_val": "28",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": False,
            "safe_dips_type": "80",
            "safe_pump": False,
            "safe_pump_type": "70",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        2: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "20",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "50",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        3: {
            "ema_fast": True,
            "ema_fast_len": "100",
            "ema_slow": True,
            "ema_slow_len": "100",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "70",
            "safe_pump": True,
            "safe_pump_type": "100",
            "safe_pump_period": "36",
            "btc_1h_not_downtrend": False,
        },
        4: {
            "ema_fast": True,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "20",
            "safe_dips": True,
            "safe_dips_type": "50",
            "safe_pump": False,
            "safe_pump_type": "110",
            "safe_pump_period": "48",
            "btc_1h_not_downtrend": False,
        },
        5: {
            "ema_fast": True,
            "ema_fast_len": "100",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "100",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "100",
            "safe_pump": True,
            "safe_pump_type": "30",
            "safe_pump_period": "36",
            "btc_1h_not_downtrend": False,
        },
        6: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "100",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "50",
            "safe_pump": True,
            "safe_pump_type": "20",
            "safe_pump_period": "36",
            "btc_1h_not_downtrend": False,
        },
        7: {
            "ema_fast": True,
            "ema_fast_len": "100",
            "ema_slow": True,
            "ema_slow_len": "12",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "130",
            "safe_pump": True,
            "safe_pump_type": "120",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        8: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "12",
            "close_above_ema_fast": True,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "100",
            "safe_pump": True,
            "safe_pump_type": "120",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        9: {
            "ema_fast": True,
            "ema_fast_len": "100",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": False,
            "safe_dips_type": "10",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        10: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "24",
            "safe_dips": True,
            "safe_dips_type": "120",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        11: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": False,
            "safe_dips_type": "100",
            "safe_pump": True,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        12: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": True,
            "sma200_1h_rising_val": "24",
            "safe_dips": True,
            "safe_dips_type": "130",
            "safe_pump": True,
            "safe_pump_type": "40",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        13: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": True,
            "sma200_1h_rising_val": "24",
            "safe_dips": True,
            "safe_dips_type": "20",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        14: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": True,
            "sma200_rising_val": "30",
            "sma200_1h_rising": True,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "120",
            "safe_pump": False,
            "safe_pump_type": "100",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        15: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "130",
            "safe_pump": True,
            "safe_pump_type": "20",
            "safe_pump_period": "36",
            "btc_1h_not_downtrend": False,
        },
        16: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "50",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "10",
            "safe_pump": True,
            "safe_pump_type": "10",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        17: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "120",
            "safe_pump": True,
            "safe_pump_type": "120",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        18: {
            "ema_fast": True,
            "ema_fast_len": "100",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": True,
            "close_above_ema_slow_len": "200",
            "sma200_rising": True,
            "sma200_rising_val": "44",
            "sma200_1h_rising": True,
            "sma200_1h_rising_val": "72",
            "safe_dips": True,
            "safe_dips_type": "100",
            "safe_pump": True,
            "safe_pump_type": "120",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        19: {
            "ema_fast": True,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "100",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "36",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "130",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        20: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": False,
            "safe_dips_type": "10",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        21: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": True,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "90",
            "safe_pump": False,
            "safe_pump_type": "50",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        22: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": False,
            "ema_slow_len": "50",
            "close_above_ema_fast": False,
            "close_above_ema_fast_len": "200",
            "close_above_ema_slow": False,
            "close_above_ema_slow_len": "200",
            "sma200_rising": False,
            "sma200_rising_val": "50",
            "sma200_1h_rising": False,
            "sma200_1h_rising_val": "50",
            "safe_dips": True,
            "safe_dips_type": "130",
            "safe_pump": True,
            "safe_pump_type": "110",
            "safe_pump_period": "24",
            "btc_1h_not_downtrend": False,
        },
        23: {
            "ema_fast": False,
            "ema_fast_len": "50",
            "ema_slow": 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
    buy_dip_threshold_10_1 = DecimalParameter(
        0.001, 0.05, default=0.015, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_10_2 = DecimalParameter(
        0.01, 0.2, default=0.1, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_10_3 = DecimalParameter(
        0.1, 0.3, default=0.24, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_10_4 = DecimalParameter(
        0.3, 0.5, default=0.42, space="buy", decimals=3, optimize=False, load=True
    )
    # Strict dips - level 20
    buy_dip_threshold_20_1 = DecimalParameter(
        0.001, 0.05, default=0.016, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_20_2 = DecimalParameter(
        0.01, 0.2, default=0.11, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_20_3 = DecimalParameter(
        0.1, 0.4, default=0.26, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_20_4 = DecimalParameter(
        0.36, 0.56, default=0.44, space="buy", decimals=3, optimize=False, load=True
    )
    # Strict dips - level 30
    buy_dip_threshold_30_1 = DecimalParameter(
        0.001, 0.05, default=0.018, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_30_2 = DecimalParameter(
        0.01, 0.2, default=0.12, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_30_3 = DecimalParameter(
        0.1, 0.4, default=0.28, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_30_4 = DecimalParameter(
        0.36, 0.56, default=0.46, space="buy", decimals=3, optimize=False, load=True
    )
    # Strict dips - level 40
    buy_dip_threshold_40_1 = DecimalParameter(
        0.001, 0.05, default=0.019, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_40_2 = DecimalParameter(
        0.01, 0.2, default=0.13, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_40_3 = DecimalParameter(
        0.1, 0.4, default=0.3, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_40_4 = DecimalParameter(
        0.36, 0.56, default=0.48, space="buy", decimals=3, optimize=False, load=True
    )
    # Normal dips - level 50
    buy_dip_threshold_50_1 = DecimalParameter(
        0.001, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_50_2 = DecimalParameter(
        0.01, 0.2, default=0.14, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_50_3 = DecimalParameter(
        0.05, 0.4, default=0.32, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_50_4 = DecimalParameter(
        0.2, 0.5, default=0.5, space="buy", decimals=3, optimize=False, load=True
    )
    # Normal dips - level 60
    buy_dip_threshold_60_1 = DecimalParameter(
        0.001, 0.05, default=0.022, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_60_2 = DecimalParameter(
        0.1, 0.22, default=0.18, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_60_3 = DecimalParameter(
        0.2, 0.4, default=0.34, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_60_4 = DecimalParameter(
        0.4, 0.6, default=0.56, space="buy", decimals=3, optimize=False, load=True
    )
    # Normal dips - level 70
    buy_dip_threshold_70_1 = DecimalParameter(
        0.001, 0.05, default=0.023, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_70_2 = DecimalParameter(
        0.16, 0.28, default=0.2, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_70_3 = DecimalParameter(
        0.2, 0.4, default=0.36, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_70_4 = DecimalParameter(
        0.5, 0.7, default=0.6, space="buy", decimals=3, optimize=False, load=True
    )
    # Normal dips - level 80
    buy_dip_threshold_80_1 = DecimalParameter(
        0.001, 0.05, default=0.024, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_80_2 = DecimalParameter(
        0.16, 0.28, default=0.22, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_80_3 = DecimalParameter(
        0.2, 0.4, default=0.38, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_80_4 = DecimalParameter(
        0.5, 0.7, default=0.66, space="buy", decimals=3, optimize=False, load=True
    )
    # Normal dips - level 70
    buy_dip_threshold_90_1 = DecimalParameter(
        0.001, 0.05, default=0.025, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_90_2 = DecimalParameter(
        0.16, 0.28, default=0.23, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_90_3 = DecimalParameter(
        0.3, 0.5, default=0.4, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_90_4 = DecimalParameter(
        0.6, 0.8, default=0.7, space="buy", decimals=3, optimize=False, load=True
    )
    # Loose dips - level 100
    buy_dip_threshold_100_1 = DecimalParameter(
        0.001, 0.05, default=0.026, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_100_2 = DecimalParameter(
        0.16, 0.3, default=0.24, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_100_3 = DecimalParameter(
        0.3, 0.5, default=0.42, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_100_4 = DecimalParameter(
        0.6, 1.0, default=0.8, space="buy", decimals=3, optimize=False, load=True
    )
    # Loose dips - level 110
    buy_dip_threshold_110_1 = DecimalParameter(
        0.001, 0.05, default=0.027, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_110_2 = DecimalParameter(
        0.16, 0.3, default=0.26, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_110_3 = DecimalParameter(
        0.3, 0.5, default=0.44, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_110_4 = DecimalParameter(
        0.6, 1.0, default=0.84, space="buy", decimals=3, optimize=False, load=True
    )
    # Loose dips - level 120
    buy_dip_threshold_120_1 = DecimalParameter(
        0.001, 0.05, default=0.028, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_120_2 = DecimalParameter(
        0.16, 0.3, default=0.28, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_120_3 = DecimalParameter(
        0.3, 0.5, default=0.46, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_120_4 = DecimalParameter(
        0.6, 1.0, default=0.86, space="buy", decimals=3, optimize=False, load=True
    )
    # Loose dips - level 130
    buy_dip_threshold_130_1 = DecimalParameter(
        0.001, 0.05, default=0.028, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_130_2 = DecimalParameter(
        0.16, 0.34, default=0.3, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_130_3 = DecimalParameter(
        0.36, 0.56, default=0.48, space="buy", decimals=3, optimize=False, load=True
    )
    buy_dip_threshold_130_4 = DecimalParameter(
        0.6, 1.0, default=0.9, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 10
    buy_pump_pull_threshold_10_24 = DecimalParameter(
        1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_10_24 = DecimalParameter(
        0.4, 1.0, default=0.42, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 10
    buy_pump_pull_threshold_10_36 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_10_36 = DecimalParameter(
        0.4, 1.0, default=0.58, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 10
    buy_pump_pull_threshold_10_48 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_10_48 = DecimalParameter(
        0.4, 1.0, default=0.8, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 20
    buy_pump_pull_threshold_20_24 = DecimalParameter(
        1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_20_24 = DecimalParameter(
        0.4, 1.0, default=0.46, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 20
    buy_pump_pull_threshold_20_36 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_20_36 = DecimalParameter(
        0.4, 1.0, default=0.6, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 20
    buy_pump_pull_threshold_20_48 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_20_48 = DecimalParameter(
        0.4, 1.0, default=0.81, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 30
    buy_pump_pull_threshold_30_24 = DecimalParameter(
        1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_30_24 = DecimalParameter(
        0.4, 1.0, default=0.5, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 30
    buy_pump_pull_threshold_30_36 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_30_36 = DecimalParameter(
        0.4, 1.0, default=0.62, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 30
    buy_pump_pull_threshold_30_48 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_30_48 = DecimalParameter(
        0.4, 1.0, default=0.82, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 40
    buy_pump_pull_threshold_40_24 = DecimalParameter(
        1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_40_24 = DecimalParameter(
        0.4, 1.0, default=0.54, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 40
    buy_pump_pull_threshold_40_36 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_40_36 = DecimalParameter(
        0.4, 1.0, default=0.63, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 40
    buy_pump_pull_threshold_40_48 = DecimalParameter(
        1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_40_48 = DecimalParameter(
        0.4, 1.0, default=0.84, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 50
    buy_pump_pull_threshold_50_24 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_50_24 = DecimalParameter(
        0.4, 1.0, default=0.6, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 50
    buy_pump_pull_threshold_50_36 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_50_36 = DecimalParameter(
        0.4, 1.0, default=0.64, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 50
    buy_pump_pull_threshold_50_48 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_50_48 = DecimalParameter(
        0.4, 1.0, default=0.85, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 60
    buy_pump_pull_threshold_60_24 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_60_24 = DecimalParameter(
        0.4, 1.0, default=0.62, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 60
    buy_pump_pull_threshold_60_36 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_60_36 = DecimalParameter(
        0.4, 1.0, default=0.66, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 60
    buy_pump_pull_threshold_60_48 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_60_48 = DecimalParameter(
        0.4, 1.0, default=0.9, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 70
    buy_pump_pull_threshold_70_24 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_70_24 = DecimalParameter(
        0.4, 1.0, default=0.63, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 70
    buy_pump_pull_threshold_70_36 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_70_36 = DecimalParameter(
        0.4, 1.0, default=0.67, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 70
    buy_pump_pull_threshold_70_48 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_70_48 = DecimalParameter(
        0.4, 1.0, default=0.95, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 80
    buy_pump_pull_threshold_80_24 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_80_24 = DecimalParameter(
        0.4, 1.0, default=0.64, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 80
    buy_pump_pull_threshold_80_36 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_80_36 = DecimalParameter(
        0.4, 1.0, default=0.68, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 80
    buy_pump_pull_threshold_80_48 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_80_48 = DecimalParameter(
        0.8, 1.1, default=1.0, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 90
    buy_pump_pull_threshold_90_24 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_90_24 = DecimalParameter(
        0.4, 1.0, default=0.65, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 90
    buy_pump_pull_threshold_90_36 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_90_36 = DecimalParameter(
        0.4, 1.0, default=0.69, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 90
    buy_pump_pull_threshold_90_48 = DecimalParameter(
        1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_90_48 = DecimalParameter(
        0.8, 1.2, default=1.1, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 100
    buy_pump_pull_threshold_100_24 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_100_24 = DecimalParameter(
        0.4, 1.0, default=0.66, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 100
    buy_pump_pull_threshold_100_36 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_100_36 = DecimalParameter(
        0.4, 1.0, default=0.7, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 100
    buy_pump_pull_threshold_100_48 = DecimalParameter(
        1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_100_48 = DecimalParameter(
        0.4, 1.8, default=1.6, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 110
    buy_pump_pull_threshold_110_24 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_110_24 = DecimalParameter(
        0.4, 1.0, default=0.7, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 110
    buy_pump_pull_threshold_110_36 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_110_36 = DecimalParameter(
        0.4, 1.0, default=0.74, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 110
    buy_pump_pull_threshold_110_48 = DecimalParameter(
        1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_110_48 = DecimalParameter(
        1.4, 2.0, default=1.8, space="buy", decimals=3, optimize=False, load=True
    )

    # 24 hours - level 120
    buy_pump_pull_threshold_120_24 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_120_24 = DecimalParameter(
        0.4, 1.0, default=0.78, space="buy", decimals=3, optimize=False, load=True
    )
    # 36 hours - level 120
    buy_pump_pull_threshold_120_36 = DecimalParameter(
        1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_120_36 = DecimalParameter(
        0.4, 1.0, default=0.78, space="buy", decimals=3, optimize=False, load=True
    )
    # 48 hours - level 120
    buy_pump_pull_threshold_120_48 = DecimalParameter(
        1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True
    )
    buy_pump_threshold_120_48 = DecimalParameter(
        1.4, 2.8, default=2.0, space="buy", decimals=3, optimize=False, load=True
    )

    # 5 hours - level 10
    buy_dump_protection_10_5 = DecimalParameter(
        0.3, 0.8, default=0.4, space="buy", decimals=2, optimize=False, load=True
    )

    # 5 hours - level 20
    buy_dump_protection_20_5 = DecimalParameter(
        0.3, 0.8, default=0.44, space="buy", decimals=2, optimize=False, load=True
    )

    # 5 hours - level 30
    buy_dump_protection_30_5 = DecimalParameter(
        0.3, 0.8, default=0.50, space="buy", decimals=2, optimize=False, load=True
    )

    # 5 hours - level 40
    buy_dump_protection_40_5 = DecimalParameter(
        0.3, 0.8, default=0.58, space="buy", decimals=2, optimize=False, load=True
    )

    # 5 hours - level 50
    buy_dump_protection_50_5 = DecimalParameter(
        0.3, 0.8, default=0.66, space="buy", decimals=2, optimize=False, load=True
    )

    # 5 hours - level 60
    buy_dump_protection_60_5 = DecimalParameter(
        0.3, 0.8, default=0.74, space="buy", decimals=2, optimize=False, load=True
    )

    buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space="buy", decimals=1, optimize=False, load=True)
    buy_mfi_1 = DecimalParameter(20.0, 40.0, default=50.0, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_1 = DecimalParameter(-0.99, -0.5, default=-0.92, space="buy", decimals=2, optimize=False, load=True)

    buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=38.8, space="buy", decimals=1, optimize=False, load=True)
    buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space="buy", decimals=1, optimize=False, load=True)
    buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space="buy", decimals=3, optimize=False, load=True)
    buy_volume_2 = DecimalParameter(0.6, 6.0, default=1.6, space="buy", decimals=1, optimize=False, load=True)

    buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.045, space="buy", optimize=False, load=True)
    buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space="buy", optimize=False, load=True)
    buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space="buy", optimize=False, load=True)
    buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space="buy", decimals=3, optimize=False, load=True)
    buy_cti_3 = DecimalParameter(-0.99, -0.5, default=-0.5, space="buy", decimals=2, optimize=False, load=True)

    buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space="buy", optimize=False, load=True)
    buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space="buy", decimals=2, optimize=False, load=True)
    buy_cti_4 = DecimalParameter(-0.99, -0.5, default=-0.8, space="buy", decimals=2, optimize=False, load=True)

    buy_ema_open_mult_5 = DecimalParameter(
        0.016, 0.03, default=0.018, space="buy", decimals=3, optimize=False, load=True
    )
    buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.996, space="buy", decimals=3, optimize=False, load=True)
    buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.915, space="buy", decimals=3, optimize=False, load=True)
    buy_cti_5 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True)
    buy_volume_5 = DecimalParameter(0.6, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True)

    buy_ema_open_mult_6 = DecimalParameter(
        0.02, 0.03, default=0.021, space="buy", decimals=3, optimize=False, load=True
    )
    buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.976, space="buy", decimals=3, optimize=False, load=True)

    buy_ema_open_mult_7 = DecimalParameter(
        0.02, 0.04, default=0.030, space="buy", decimals=3, optimize=False, load=True
    )
    buy_cti_7 = DecimalParameter(-0.99, -0.5, default=-0.89, space="buy", decimals=2, optimize=False, load=True)

    buy_cti_8 = DecimalParameter(-0.99, -0.5, default=-0.88, space="buy", decimals=2, optimize=False, load=True)
    buy_rsi_8 = DecimalParameter(20.0, 50.0, default=40.0, space="buy", decimals=1, optimize=False, load=True)
    buy_bb_offset_8 = DecimalParameter(0.98, 1.0, default=0.99, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_1h_8 = DecimalParameter(40.0, 66.0, default=64.0, space="buy", decimals=1, optimize=False, load=True)
    buy_volume_8 = DecimalParameter(0.6, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True)

    buy_ma_offset_9 = DecimalParameter(0.91, 0.94, default=0.968, space="buy", decimals=3, optimize=False, load=True)
    buy_bb_offset_9 = DecimalParameter(0.96, 0.98, default=0.942, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space="buy", decimals=1, optimize=False, load=True)
    buy_mfi_9 = DecimalParameter(36.0, 56.0, default=50.0, space="buy", decimals=1, optimize=False, load=True)

    buy_ma_offset_10 = DecimalParameter(0.94, 0.99, default=0.98, space="buy", decimals=3, optimize=False, load=True)
    buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.972, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_1h_10 = DecimalParameter(30.0, 60.0, default=50.0, space="buy", decimals=1, optimize=False, load=True)

    buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.946, space="buy", decimals=3, optimize=False, load=True)
    buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.038, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=46.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_11 = DecimalParameter(34.0, 50.0, default=38.0, space="buy", decimals=1, optimize=False, load=True)
    buy_mfi_11 = DecimalParameter(30.0, 46.0, default=36.0, space="buy", decimals=1, optimize=False, load=True)

    buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.921, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_12 = DecimalParameter(26.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True)
    buy_ewo_12 = DecimalParameter(1.0, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_12 = DecimalParameter(-0.99, -0.5, default=-0.7, space="buy", decimals=2, optimize=False, load=True)

    buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.99, space="buy", decimals=3, optimize=False, load=True)
    buy_cti_13 = DecimalParameter(-0.99, -0.5, default=-0.82, space="buy", decimals=2, optimize=False, load=True)
    buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-9.0, space="buy", decimals=1, optimize=False, load=True)

    buy_ema_open_mult_14 = DecimalParameter(
        0.01, 0.03, default=0.014, space="buy", decimals=3, optimize=False, load=True
    )
    buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.988, space="buy", decimals=3, optimize=False, load=True)
    buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.945, space="buy", decimals=3, optimize=False, load=True)
    buy_cti_14 = DecimalParameter(-0.99, -0.5, default=-0.86, space="buy", decimals=2, optimize=False, load=True)

    buy_ema_open_mult_15 = DecimalParameter(
        0.01, 0.03, default=0.024, space="buy", decimals=3, optimize=False, load=True
    )
    buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.958, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_15 = DecimalParameter(20.0, 36.0, default=28.0, space="buy", decimals=1, optimize=False, load=True)
    buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.974, space="buy", decimals=3, optimize=False, load=True)

    buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.953, space="buy", decimals=3, optimize=False, load=True)
    buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space="buy", decimals=1, optimize=False, load=True)
    buy_ewo_16 = DecimalParameter(2.0, 6.0, default=2.8, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_16 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True)

    buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.99, space="buy", decimals=3, optimize=False, load=True)
    buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-9.4, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_17 = DecimalParameter(-0.99, -0.5, default=-0.96, space="buy", decimals=2, optimize=False, load=True)
    buy_volume_17 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    buy_rsi_18 = DecimalParameter(20.0, 36.0, default=33.0, space="buy", decimals=1, optimize=False, load=True)
    buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.986, space="buy", decimals=3, optimize=False, load=True)
    buy_volume_18 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_18 = DecimalParameter(-0.99, -0.5, default=-0.86, space="buy", decimals=2, optimize=False, load=True)

    buy_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=30.0, space="buy", decimals=1, optimize=False, load=True)
    buy_chop_max_19 = DecimalParameter(20.0, 60.0, default=21.3, space="buy", decimals=1, optimize=False, load=True)

    buy_rsi_20 = DecimalParameter(20.0, 36.0, default=36.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=16.0, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_20 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True)
    buy_volume_20 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    buy_rsi_21 = DecimalParameter(10.0, 28.0, default=14.0, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True)
    buy_cti_21 = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True)
    buy_volume_21 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    buy_volume_22 = DecimalParameter(0.5, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)
    buy_bb_offset_22 = DecimalParameter(0.98, 1.0, default=0.984, space="buy", decimals=3, optimize=False, load=True)
    buy_ma_offset_22 = DecimalParameter(0.93, 0.98, default=0.942, space="buy", decimals=3, optimize=False, load=True)
    buy_ewo_22 = DecimalParameter(2.0, 10.0, default=5.8, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_22 = DecimalParameter(26.0, 56.0, default=36.0, space="buy", decimals=1, optimize=False, load=True)

    buy_bb_offset_23 = DecimalParameter(0.97, 1.0, default=0.985, space="buy", decimals=3, optimize=False, load=True)
    buy_ewo_23 = DecimalParameter(2.0, 10.0, default=6.2, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_23 = DecimalParameter(20.0, 40.0, default=32.4, space="buy", decimals=1, optimize=False, load=True)
    buy_rsi_1h_23 = DecimalParameter(60.0, 80.0, default=70.0, space="buy", decimals=1, optimize=False, load=True)

    buy_24_rsi_max = DecimalParameter(26.0, 60.0, default=50.0, space="buy", decimals=1, optimize=False, load=True)
    buy_24_rsi_1h_min = DecimalParameter(40.0, 90.0, default=66.9, space="buy", decimals=1, optimize=False, load=True)

    buy_25_ma_offset = DecimalParameter(0.90, 0.99, default=0.922, space="buy", optimize=False, load=True)
    buy_25_rsi_4 = DecimalParameter(26.0, 40.0, default=38.0, space="buy", decimals=1, optimize=False, load=True)
    buy_25_cti = DecimalParameter(-0.99, -0.4, default=-0.76, space="buy", decimals=2, optimize=False, load=True)

    buy_26_zema_low_offset = DecimalParameter(0.90, 0.99, default=0.932, space="buy", optimize=False, load=True)
    buy_26_cti = DecimalParameter(-0.99, -0.4, default=-0.82, space="buy", decimals=2, optimize=False, load=True)
    buy_26_volume = DecimalParameter(0.6, 6.0, default=1.2, space="buy", decimals=1, optimize=False, load=True)

    buy_27_wr_max = DecimalParameter(90, 99, default=90.0, space="buy", decimals=1, optimize=False, load=True)
    buy_27_wr_1h_max = DecimalParameter(90, 99, default=90.0, space="buy", decimals=1, optimize=False, load=True)
    buy_27_rsi_max = DecimalParameter(40, 70, default=50, space="buy", decimals=0, optimize=False, load=True)
    buy_27_cti = DecimalParameter(-0.99, -0.4, default=-0.93, space="buy", decimals=2, optimize=False, load=True)
    buy_27_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    buy_28_ma_offset = DecimalParameter(0.90, 0.99, default=0.97, space="buy", optimize=False, load=True)
    buy_28_ewo = DecimalParameter(2.0, 14.0, default=7.2, space="buy", decimals=1, optimize=False, load=True)
    buy_28_rsi = DecimalParameter(24.0, 44.0, default=32.5, space="buy", decimals=1, optimize=False, load=True)
    buy_28_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True)

    buy_29_ma_offset = DecimalParameter(0.90, 0.99, default=0.94, space="buy", optimize=False, load=True)
    buy_29_ewo = DecimalParameter(-14.0, -2.0, default=-4.0, space="buy", decimals=1, optimize=False, load=True)
    buy_29_cti = DecimalParameter(-0.99, -0.4, default=-0.95, space="buy", decimals=2, optimize=False, load=True)

    buy_30_ma_offset = DecimalParameter(0.90, 0.99, default=0.97, space="buy", optimize=False, load=True)
    buy_30_ewo = DecimalParameter(2.0, 14.0, default=7.4, space="buy", decimals=1, optimize=False, load=True)
    buy_30_rsi = DecimalParameter(24.0, 48.0, default=40.0, space="buy", decimals=1, optimize=False, load=True)
    buy_30_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space="buy", decimals=2, optimize=False, load=True)

    buy_31_ma_offset = DecimalParameter(0.90, 0.99, default=0.94, space="buy", optimize=False, load=True)
    buy_31_ewo = DecimalParameter(-22.0, -8.0, default=-19.0, space="buy", decimals=1, optimize=False, load=True)
    buy_31_wr = DecimalParameter(-99.9, -95.0, default=-98.4, space="buy", decimals=1, optimize=False, load=True)

    buy_32_ma_offset = DecimalParameter(0.90, 0.99, default=0.934, space="buy", optimize=False, load=True)
    buy_32_dip = DecimalParameter(0.001, 0.02, default=0.005, space="buy", decimals=3, optimize=False, load=True)
    buy_32_rsi = DecimalParameter(24.0, 50.0, default=46.0, space="buy", decimals=1, optimize=False, load=True)
    buy_32_cti = DecimalParameter(-0.99, -0.4, default=-0.8, space="buy", decimals=2, optimize=False, load=True)

    buy_33_ma_offset = DecimalParameter(0.90, 0.99, default=0.988, space="buy", optimize=False, load=True)
    buy_33_rsi = DecimalParameter(24.0, 50.0, default=32.0, space="buy", decimals=1, optimize=False, load=True)
    buy_33_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True)
    buy_33_ewo = DecimalParameter(2.0, 14.0, default=6.5, space="buy", decimals=1, optimize=False, load=True)
    buy_33_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    buy_34_ma_offset = DecimalParameter(0.90, 0.99, default=0.93, space="buy", optimize=False, load=True)
    buy_34_dip = DecimalParameter(0.001, 0.02, default=0.005, space="buy", decimals=3, optimize=False, load=True)
    buy_34_ewo = DecimalParameter(-24.0, -5.0, default=-6.0, space="buy", decimals=1, optimize=False, load=True)
    buy_34_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space="buy", decimals=2, optimize=False, load=True)
    buy_34_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True)

    # Sell

    sell_condition_1_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_2_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_3_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_4_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_5_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_6_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_7_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )
    sell_condition_8_enable = CategoricalParameter(
        [True, False], default=True, space="sell", optimize=False, load=True
    )

    # 48h for pump sell checks
    sell_pump_threshold_48_1 = DecimalParameter(
        0.5, 1.2, default=0.9, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_48_2 = DecimalParameter(
        0.4, 0.9, default=0.7, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_48_3 = DecimalParameter(
        0.3, 0.7, default=0.5, space="sell", decimals=2, optimize=False, load=True
    )

    # 36h for pump sell checks
    sell_pump_threshold_36_1 = DecimalParameter(
        0.5, 0.9, default=0.72, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_36_2 = DecimalParameter(
        3.0, 6.0, default=4.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_36_3 = DecimalParameter(
        0.8, 1.6, default=1.0, space="sell", decimals=2, optimize=False, load=True
    )

    # 24h for pump sell checks
    sell_pump_threshold_24_1 = DecimalParameter(
        0.5, 0.9, default=0.68, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_24_2 = DecimalParameter(
        0.3, 0.6, default=0.62, space="sell", decimals=2, optimize=False, load=True
    )
    sell_pump_threshold_24_3 = DecimalParameter(
        0.2, 0.5, default=0.88, space="sell", decimals=2, optimize=False, load=True
    )

    sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space="sell", decimals=1, optimize=False, load=True)

    sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space="sell", decimals=1, optimize=False, load=True)

    sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space="sell", decimals=1, optimize=False, load=True)

    sell_dual_rsi_rsi_4 = DecimalParameter(
        72.0, 84.0, default=73.4, space="sell", decimals=1, optimize=False, load=True
    )
    sell_dual_rsi_rsi_1h_4 = DecimalParameter(
        78.0, 92.0, default=79.6, space="sell", decimals=1, optimize=False, load=True
    )

    sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space="sell", optimize=False, load=True)
    sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space="sell", optimize=False, load=True)

    sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space="sell", decimals=1, optimize=False, load=True)

    sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space="sell", decimals=1, optimize=False, load=True)

    sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space="sell", decimals=3, optimize=False, load=True)

    # Profit over EMA200
    sell_custom_profit_bull_0 = DecimalParameter(
        0.01, 0.1, default=0.012, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_0 = DecimalParameter(
        30.0, 40.0, default=34.0, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_profit_bull_1 = DecimalParameter(
        0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_1 = DecimalParameter(
        30.0, 50.0, default=35.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_2 = DecimalParameter(
        0.01, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_2 = DecimalParameter(
        30.0, 50.0, default=36.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_3 = DecimalParameter(
        0.01, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_3 = DecimalParameter(
        30.0, 50.0, default=37.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_4 = DecimalParameter(
        0.01, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_4 = DecimalParameter(
        35.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_5 = DecimalParameter(
        0.01, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_5 = DecimalParameter(
        35.0, 50.0, default=49.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_6 = DecimalParameter(
        0.01, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_6 = DecimalParameter(
        38.0, 55.0, default=50.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_7 = DecimalParameter(
        0.01, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_7 = DecimalParameter(
        40.0, 58.0, default=54.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_8 = DecimalParameter(
        0.06, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_8 = DecimalParameter(
        40.0, 50.0, default=50.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_9 = DecimalParameter(
        0.05, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_9 = DecimalParameter(
        40.0, 60.0, default=46.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_10 = DecimalParameter(
        0.1, 0.14, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_10 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bull_11 = DecimalParameter(
        0.16, 0.45, default=0.20, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bull_11 = DecimalParameter(
        28.0, 40.0, default=30.0, space="sell", decimals=2, optimize=False, load=True
    )

    sell_custom_profit_bear_0 = DecimalParameter(
        0.01, 0.1, default=0.012, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_0 = DecimalParameter(
        30.0, 40.0, default=34.0, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_profit_bear_1 = DecimalParameter(
        0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_1 = DecimalParameter(
        30.0, 50.0, default=35.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_2 = DecimalParameter(
        0.01, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_2 = DecimalParameter(
        30.0, 50.0, default=37.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_3 = DecimalParameter(
        0.01, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_3 = DecimalParameter(
        30.0, 50.0, default=44.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_4 = DecimalParameter(
        0.01, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_4 = DecimalParameter(
        35.0, 50.0, default=48.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_5 = DecimalParameter(
        0.01, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_5 = DecimalParameter(
        35.0, 50.0, default=50.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_rsi_over_bear_5 = DecimalParameter(
        70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_6 = DecimalParameter(
        0.01, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_6 = DecimalParameter(
        38.0, 55.0, default=52.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_rsi_over_bear_6 = DecimalParameter(
        70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_7 = DecimalParameter(
        0.01, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_7 = DecimalParameter(
        40.0, 58.0, default=54.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_rsi_over_bear_7 = DecimalParameter(
        70.0, 85.0, default=80.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_8 = DecimalParameter(
        0.06, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_8 = DecimalParameter(
        40.0, 50.0, default=52.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_rsi_over_bear_8 = DecimalParameter(
        70.0, 85.0, default=82.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_9 = DecimalParameter(
        0.05, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_9 = DecimalParameter(
        40.0, 60.0, default=50.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_10 = DecimalParameter(
        0.1, 0.14, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_10 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_profit_bear_11 = DecimalParameter(
        0.16, 0.45, default=0.20, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_rsi_under_bear_11 = DecimalParameter(
        28.0, 40.0, default=30.0, space="sell", decimals=2, optimize=False, load=True
    )

    # Profit under EMA200
    sell_custom_under_profit_bull_0 = DecimalParameter(
        0.01, 0.4, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_0 = DecimalParameter(
        28.0, 40.0, default=38.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_1 = DecimalParameter(
        0.01, 0.10, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_1 = DecimalParameter(
        36.0, 60.0, default=54.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_2 = DecimalParameter(
        0.01, 0.10, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_2 = DecimalParameter(
        46.0, 66.0, default=55.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_3 = DecimalParameter(
        0.01, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_3 = DecimalParameter(
        50.0, 68.0, default=56.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_4 = DecimalParameter(
        0.02, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_4 = DecimalParameter(
        50.0, 68.0, default=57.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_5 = DecimalParameter(
        0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_5 = DecimalParameter(
        46.0, 62.0, default=58.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_6 = DecimalParameter(
        0.03, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_6 = DecimalParameter(
        44.0, 60.0, default=48.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_7 = DecimalParameter(
        0.04, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_7 = DecimalParameter(
        46.0, 60.0, default=44.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_8 = DecimalParameter(
        0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_8 = DecimalParameter(
        40.0, 58.0, default=42.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_9 = DecimalParameter(
        0.08, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_9 = DecimalParameter(
        40.0, 60.0, default=38.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_10 = DecimalParameter(
        0.1, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_10 = DecimalParameter(
        30.0, 50.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bull_11 = DecimalParameter(
        0.16, 0.3, default=0.2, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bull_11 = DecimalParameter(
        24.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True
    )

    sell_custom_under_profit_bear_0 = DecimalParameter(
        0.01, 0.4, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_0 = DecimalParameter(
        28.0, 40.0, default=38.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_1 = DecimalParameter(
        0.01, 0.10, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_1 = DecimalParameter(
        36.0, 60.0, default=59.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_2 = DecimalParameter(
        0.01, 0.10, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_2 = DecimalParameter(
        46.0, 66.0, default=60.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_3 = DecimalParameter(
        0.01, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_3 = DecimalParameter(
        50.0, 68.0, default=61.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_4 = DecimalParameter(
        0.02, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_4 = DecimalParameter(
        50.0, 68.0, default=60.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_5 = DecimalParameter(
        0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_5 = DecimalParameter(
        46.0, 62.0, default=58.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_rsi_over_bear_5 = DecimalParameter(
        70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_under_profit_bear_6 = DecimalParameter(
        0.03, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_6 = DecimalParameter(
        44.0, 60.0, default=50.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_rsi_over_bear_6 = DecimalParameter(
        70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_under_profit_bear_7 = DecimalParameter(
        0.04, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_7 = DecimalParameter(
        46.0, 60.0, default=46.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_rsi_over_bear_7 = DecimalParameter(
        70.0, 85.0, default=80.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_under_profit_bear_8 = DecimalParameter(
        0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_8 = DecimalParameter(
        40.0, 58.0, default=42.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_rsi_over_bear_8 = DecimalParameter(
        70.0, 85.0, default=82.0, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_under_profit_bear_9 = DecimalParameter(
        0.08, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_9 = DecimalParameter(
        40.0, 60.0, default=36.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_10 = DecimalParameter(
        0.1, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_10 = DecimalParameter(
        30.0, 50.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_under_profit_bear_11 = DecimalParameter(
        0.16, 0.3, default=0.2, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_under_rsi_under_bear_11 = DecimalParameter(
        24.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Profit targets for pumped pairs 48h 1
    sell_custom_pump_profit_1_1 = DecimalParameter(
        0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_1_1 = DecimalParameter(
        26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_1_2 = DecimalParameter(
        0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_1_2 = DecimalParameter(
        36.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_1_3 = DecimalParameter(
        0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_1_3 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_1_4 = DecimalParameter(
        0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_1_4 = DecimalParameter(
        36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_1_5 = DecimalParameter(
        0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_1_5 = DecimalParameter(
        20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Profit targets for pumped pairs 36h 1
    sell_custom_pump_profit_2_1 = DecimalParameter(
        0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_2_1 = DecimalParameter(
        26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_2_2 = DecimalParameter(
        0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_2_2 = DecimalParameter(
        36.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_2_3 = DecimalParameter(
        0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_2_3 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_2_4 = DecimalParameter(
        0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_2_4 = DecimalParameter(
        36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_2_5 = DecimalParameter(
        0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_2_5 = DecimalParameter(
        20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Profit targets for pumped pairs 24h 1
    sell_custom_pump_profit_3_1 = DecimalParameter(
        0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_3_1 = DecimalParameter(
        26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_3_2 = DecimalParameter(
        0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_3_2 = DecimalParameter(
        34.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_3_3 = DecimalParameter(
        0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_3_3 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_3_4 = DecimalParameter(
        0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_3_4 = DecimalParameter(
        36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_profit_3_5 = DecimalParameter(
        0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_rsi_3_5 = DecimalParameter(
        20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True
    )

    # SMA descending
    sell_custom_dec_profit_min_1 = DecimalParameter(
        0.01, 0.10, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_dec_profit_max_1 = DecimalParameter(
        0.06, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )

    # Under EMA100
    sell_custom_dec_profit_min_2 = DecimalParameter(
        0.05, 0.12, default=0.07, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_dec_profit_max_2 = DecimalParameter(
        0.06, 0.2, default=0.16, space="sell", decimals=3, optimize=False, load=True
    )

    # Trail 1
    sell_trail_profit_min_1 = DecimalParameter(
        0.1, 0.2, default=0.03, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_profit_max_1 = DecimalParameter(
        0.4, 0.7, default=0.05, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_1 = DecimalParameter(0.01, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True)
    sell_trail_rsi_min_1 = DecimalParameter(
        16.0, 36.0, default=10.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_trail_rsi_max_1 = DecimalParameter(
        30.0, 50.0, default=20.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Trail 2
    sell_trail_profit_min_2 = DecimalParameter(
        0.08, 0.16, default=0.1, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_2 = DecimalParameter(
        0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_2 = DecimalParameter(0.02, 0.08, default=0.03, space="sell", decimals=3, optimize=False, load=True)
    sell_trail_rsi_min_2 = DecimalParameter(
        16.0, 36.0, default=20.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_trail_rsi_max_2 = DecimalParameter(
        30.0, 50.0, default=50.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Trail 3
    sell_trail_profit_min_3 = DecimalParameter(
        0.01, 0.12, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_3 = DecimalParameter(
        0.1, 0.3, default=0.2, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_3 = DecimalParameter(0.01, 0.06, default=0.05, space="sell", decimals=3, optimize=False, load=True)

    # Trail 4
    sell_trail_profit_min_4 = DecimalParameter(
        0.01, 0.12, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_trail_profit_max_4 = DecimalParameter(
        0.02, 0.1, default=0.06, space="sell", decimals=2, optimize=False, load=True
    )
    sell_trail_down_4 = DecimalParameter(0.01, 0.06, default=0.02, space="sell", decimals=3, optimize=False, load=True)

    # Under & near EMA200, accept profit
    sell_custom_profit_under_profit_min_1 = DecimalParameter(
        0.0, 0.01, default=0.0, space="sell", optimize=False, load=True
    )
    sell_custom_profit_under_profit_max_1 = DecimalParameter(
        0.0, 0.05, default=0.02, space="sell", optimize=False, load=True
    )
    sell_custom_profit_under_rel_1 = DecimalParameter(
        0.01, 0.04, default=0.024, space="sell", optimize=False, load=True
    )
    sell_custom_profit_under_rsi_diff_1 = DecimalParameter(
        0.0, 20.0, default=4.4, space="sell", optimize=False, load=True
    )

    sell_custom_profit_under_profit_2 = DecimalParameter(
        0.0, 0.05, default=0.03, space="sell", optimize=False, load=True
    )
    sell_custom_profit_under_rel_2 = DecimalParameter(
        0.01, 0.04, default=0.024, space="sell", optimize=False, load=True
    )
    sell_custom_profit_under_rsi_diff_2 = DecimalParameter(
        0.0, 20.0, default=4.4, space="sell", optimize=False, load=True
    )

    # Under & near EMA200, take the loss
    sell_custom_stoploss_under_rel_1 = DecimalParameter(
        0.001, 0.02, default=0.002, space="sell", optimize=False, load=True
    )
    sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(
        0.0, 20.0, default=10.0, space="sell", optimize=False, load=True
    )

    # Long duration/recover stoploss 1
    sell_custom_stoploss_long_profit_min_1 = DecimalParameter(
        -0.1, -0.02, default=-0.08, space="sell", optimize=False, load=True
    )
    sell_custom_stoploss_long_profit_max_1 = DecimalParameter(
        -0.06, -0.01, default=-0.04, space="sell", optimize=False, load=True
    )
    sell_custom_stoploss_long_recover_1 = DecimalParameter(
        0.05, 0.15, default=0.14, space="sell", optimize=False, load=True
    )
    sell_custom_stoploss_long_rsi_diff_1 = DecimalParameter(
        0.0, 20.0, default=4.0, space="sell", optimize=False, load=True
    )

    # Long duration/recover stoploss 2
    sell_custom_stoploss_long_recover_2 = DecimalParameter(
        0.03, 0.15, default=0.06, space="sell", optimize=False, load=True
    )
    sell_custom_stoploss_long_rsi_diff_2 = DecimalParameter(
        30.0, 50.0, default=40.0, space="sell", optimize=False, load=True
    )

    # Pumped, descending SMA
    sell_custom_pump_dec_profit_min_1 = DecimalParameter(
        0.001, 0.04, default=0.005, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_max_1 = DecimalParameter(
        0.03, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_min_2 = DecimalParameter(
        0.01, 0.08, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_max_2 = DecimalParameter(
        0.04, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_min_3 = DecimalParameter(
        0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_max_3 = DecimalParameter(
        0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_min_4 = DecimalParameter(
        0.01, 0.05, default=0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_dec_profit_max_4 = DecimalParameter(
        0.02, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )

    # Pumped 48h 1, under EMA200
    sell_custom_pump_under_profit_min_1 = DecimalParameter(
        0.02, 0.06, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_under_profit_max_1 = DecimalParameter(
        0.04, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True
    )

    # Pumped trail 1
    sell_custom_pump_trail_profit_min_1 = DecimalParameter(
        0.01, 0.12, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_trail_profit_max_1 = DecimalParameter(
        0.06, 0.16, default=0.07, space="sell", decimals=2, optimize=False, load=True
    )
    sell_custom_pump_trail_down_1 = DecimalParameter(
        0.01, 0.06, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_pump_trail_rsi_min_1 = DecimalParameter(
        16.0, 36.0, default=20.0, space="sell", decimals=1, optimize=False, load=True
    )
    sell_custom_pump_trail_rsi_max_1 = DecimalParameter(
        30.0, 50.0, default=70.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Stoploss, pumped, 48h 1
    sell_custom_stoploss_pump_max_profit_1 = DecimalParameter(
        0.01, 0.04, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_min_1 = DecimalParameter(
        -0.1, -0.01, default=-0.02, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_max_1 = DecimalParameter(
        -0.1, -0.01, default=-0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_ma_offset_1 = DecimalParameter(
        0.7, 0.99, default=0.94, space="sell", decimals=2, optimize=False, load=True
    )

    # Stoploss, pumped, 48h 1
    sell_custom_stoploss_pump_max_profit_2 = DecimalParameter(
        0.01, 0.04, default=0.025, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_loss_2 = DecimalParameter(
        -0.1, -0.01, default=-0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_ma_offset_2 = DecimalParameter(
        0.7, 0.99, default=0.92, space="sell", decimals=2, optimize=False, load=True
    )

    # Stoploss, pumped, 36h 3
    sell_custom_stoploss_pump_max_profit_3 = DecimalParameter(
        0.01, 0.04, default=0.008, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_loss_3 = DecimalParameter(
        -0.16, -0.06, default=-0.12, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_stoploss_pump_ma_offset_3 = DecimalParameter(
        0.7, 0.99, default=0.88, space="sell", decimals=2, optimize=False, load=True
    )

    # Recover
    sell_custom_recover_profit_1 = DecimalParameter(
        0.01, 0.06, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_recover_min_loss_1 = DecimalParameter(
        0.06, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True
    )

    sell_custom_recover_profit_min_2 = DecimalParameter(
        0.01, 0.04, default=0.01, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_recover_profit_max_2 = DecimalParameter(
        0.02, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_recover_min_loss_2 = DecimalParameter(
        0.04, 0.16, default=0.06, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_recover_rsi_2 = DecimalParameter(
        32.0, 52.0, default=46.0, space="sell", decimals=1, optimize=False, load=True
    )

    # Profit for long duration trades
    sell_custom_long_profit_min_1 = DecimalParameter(
        0.01, 0.04, default=0.03, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_long_profit_max_1 = DecimalParameter(
        0.02, 0.08, default=0.04, space="sell", decimals=3, optimize=False, load=True
    )
    sell_custom_long_duration_min_1 = IntParameter(700, 2000, default=900, space="sell", optimize=False, load=True)

    #############################################################

    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_V6.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 sell_over_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle["close"] > last_candle["ema_200"]:
            if last_candle["moderi_96"]:
                if current_profit >= self.sell_custom_profit_bull_11.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_11.value:
                        return True, "signal_profit_o_bull_11"
                elif self.sell_custom_profit_bull_11.value > current_profit >= self.sell_custom_profit_bull_10.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_10.value:
                        return True, "signal_profit_o_bull_10"
                elif self.sell_custom_profit_bull_10.value > current_profit >= self.sell_custom_profit_bull_9.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_9.value:
                        return True, "signal_profit_o_bull_9"
                elif self.sell_custom_profit_bull_9.value > current_profit >= self.sell_custom_profit_bull_8.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_8.value:
                        return True, "signal_profit_o_bull_8"
                elif self.sell_custom_profit_bull_8.value > current_profit >= self.sell_custom_profit_bull_7.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_7.value:
                        return True, "signal_profit_o_bull_7"
                elif self.sell_custom_profit_bull_7.value > current_profit >= self.sell_custom_profit_bull_6.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_6.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_6"
                elif self.sell_custom_profit_bull_6.value > current_profit >= self.sell_custom_profit_bull_5.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_5.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_5"
                elif self.sell_custom_profit_bull_5.value > current_profit >= self.sell_custom_profit_bull_4.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_4.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_4"
                elif self.sell_custom_profit_bull_4.value > current_profit >= self.sell_custom_profit_bull_3.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_3.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_3"
                elif self.sell_custom_profit_bull_3.value > current_profit >= self.sell_custom_profit_bull_2.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_2.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_2"
                elif self.sell_custom_profit_bull_2.value > current_profit >= self.sell_custom_profit_bull_1.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_1.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_1"
                elif self.sell_custom_profit_bull_1.value > current_profit >= self.sell_custom_profit_bull_0.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_0.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bull_0"
            else:
                if current_profit >= self.sell_custom_profit_bear_11.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_11.value:
                        return True, "signal_profit_o_bear_11"
                elif self.sell_custom_profit_bear_11.value > current_profit >= self.sell_custom_profit_bear_10.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_10.value:
                        return True, "signal_profit_o_bear_10"
                elif self.sell_custom_profit_bear_10.value > current_profit >= self.sell_custom_profit_bear_9.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_9.value:
                        return True, "signal_profit_o_bear_9"
                elif self.sell_custom_profit_bear_9.value > current_profit >= self.sell_custom_profit_bear_8.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_8.value:
                        return True, "signal_profit_o_bear_8_1"
                    elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_8.value:
                        return True, "signal_profit_o_bear_8_2"
                elif self.sell_custom_profit_bear_8.value > current_profit >= self.sell_custom_profit_bear_7.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_7.value:
                        return True, "signal_profit_o_bear_7_1"
                    elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_7.value:
                        return True, "signal_profit_o_bear_7_2"
                elif self.sell_custom_profit_bear_7.value > current_profit >= self.sell_custom_profit_bear_6.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_6.value:
                        return True, "signal_profit_o_bear_6_1"
                    elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_6.value:
                        return True, "signal_profit_o_bear_6_2"
                elif self.sell_custom_profit_bear_6.value > current_profit >= self.sell_custom_profit_bear_5.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_5.value:
                        return True, "signal_profit_o_bear_5_1"
                    elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_5.value:
                        return True, "signal_profit_o_bear_5_2"
                elif self.sell_custom_profit_bear_5.value > current_profit >= self.sell_custom_profit_bear_4.value:
                    if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_4.value:
                        return True, "signal_profit_o_bear_4"
                elif self.sell_custom_profit_bear_4.value > current_profit >= self.sell_custom_profit_bear_3.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_3.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bear_3"
                elif self.sell_custom_profit_bear_3.value > current_profit >= self.sell_custom_profit_bear_2.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_2.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bear_2"
                elif self.sell_custom_profit_bear_2.value > current_profit >= self.sell_custom_profit_bear_1.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_1.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bear_1"
                elif self.sell_custom_profit_bear_1.value > current_profit >= self.sell_custom_profit_bear_0.value:
                    if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_0.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_o_bear_0"

        return False, None

    def sell_under_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle["close"] < last_candle["ema_200"]:
            if last_candle["moderi_96"]:
                if current_profit >= self.sell_custom_under_profit_bull_11.value:
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_11.value:
                        return True, "signal_profit_u_bull_11"
                elif (
                    self.sell_custom_under_profit_bull_11.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_10.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_10.value:
                        return True, "signal_profit_u_bull_10"
                elif (
                    self.sell_custom_under_profit_bull_10.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_9.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_9.value:
                        return True, "signal_profit_u_bull_9"
                elif (
                    self.sell_custom_under_profit_bull_9.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_8.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_8.value:
                        return True, "signal_profit_u_bull_8"
                elif (
                    self.sell_custom_under_profit_bull_8.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_7.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_7.value:
                        return True, "signal_profit_u_bull_7"
                elif (
                    self.sell_custom_under_profit_bull_7.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_6.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_6.value:
                        return True, "signal_profit_u_bull_6"
                elif (
                    self.sell_custom_under_profit_bull_6.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_5.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_5.value:
                        return True, "signal_profit_u_bull_5"
                elif (
                    self.sell_custom_under_profit_bull_5.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_4.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_4.value:
                        return True, "signal_profit_u_bull_4"
                elif (
                    self.sell_custom_under_profit_bull_4.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_3.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_3.value:
                        return True, "signal_profit_u_bull_3"
                elif (
                    self.sell_custom_under_profit_bull_3.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_2.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_2.value:
                        return True, "signal_profit_u_bull_2"
                elif (
                    self.sell_custom_under_profit_bull_2.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_1.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_1.value:
                        return True, "signal_profit_u_bull_1"
                elif (
                    self.sell_custom_under_profit_bull_1.value
                    > current_profit
                    >= self.sell_custom_under_profit_bull_0.value
                ):
                    if (last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_0.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_u_bull_0"
            else:
                if current_profit >= self.sell_custom_under_profit_bear_11.value:
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_11.value:
                        return True, "signal_profit_u_bear_11"
                elif (
                    self.sell_custom_under_profit_bear_11.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_10.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_10.value:
                        return True, "signal_profit_u_bear_10"
                elif (
                    self.sell_custom_under_profit_bear_10.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_9.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_9.value:
                        return True, "signal_profit_u_bear_9"
                elif (
                    self.sell_custom_under_profit_bear_9.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_8.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_8.value:
                        return True, "signal_profit_u_bear_8_1"
                    elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_8.value:
                        return True, "signal_profit_u_bear_8_2"
                elif (
                    self.sell_custom_under_profit_bear_8.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_7.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_7.value:
                        return True, "signal_profit_u_bear_7_1"
                    elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_7.value:
                        return True, "signal_profit_u_bear_7_2"
                elif (
                    self.sell_custom_under_profit_bear_7.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_6.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_6.value:
                        return True, "signal_profit_u_bear_6_1"
                    elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_6.value:
                        return True, "signal_profit_u_bear_6_2"
                elif (
                    self.sell_custom_under_profit_bear_6.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_5.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_5.value:
                        return True, "signal_profit_u_bear_5_1"
                    elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_5.value:
                        return True, "signal_profit_u_bear_5_2"
                elif (
                    self.sell_custom_under_profit_bear_5.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_4.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_4.value:
                        return True, "signal_profit_u_bear_4"
                elif (
                    self.sell_custom_under_profit_bear_4.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_3.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_3.value:
                        return True, "signal_profit_u_bear_3"
                elif (
                    self.sell_custom_under_profit_bear_3.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_2.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_2.value:
                        return True, "signal_profit_u_bear_2"
                elif (
                    self.sell_custom_under_profit_bear_2.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_1.value
                ):
                    if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_1.value:
                        return True, "signal_profit_u_bear_1"
                elif (
                    self.sell_custom_under_profit_bear_1.value
                    > current_profit
                    >= self.sell_custom_under_profit_bear_0.value
                ):
                    if (last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_0.value) and (
                        last_candle["cmf"] < 0.0
                    ):
                        return True, "signal_profit_u_bear_0"

        return False, None

    def sell_pump_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle["sell_pump_48_1_1h"]:
            if current_profit >= self.sell_custom_pump_profit_1_5.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_5.value:
                    return True, "signal_profit_p_1_5"
            elif self.sell_custom_pump_profit_1_5.value > current_profit >= self.sell_custom_pump_profit_1_4.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_4.value:
                    return True, "signal_profit_p_1_4"
            elif self.sell_custom_pump_profit_1_4.value > current_profit >= self.sell_custom_pump_profit_1_3.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_3.value:
                    return True, "signal_profit_p_1_3"
            elif self.sell_custom_pump_profit_1_3.value > current_profit >= self.sell_custom_pump_profit_1_2.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_2.value:
                    return True, "signal_profit_p_1_2"
            elif self.sell_custom_pump_profit_1_2.value > current_profit >= self.sell_custom_pump_profit_1_1.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_1.value:
                    return True, "signal_profit_p_1_1"

        elif last_candle["sell_pump_36_1_1h"]:
            if current_profit >= self.sell_custom_pump_profit_2_5.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_5.value:
                    return True, "signal_profit_p_2_5"
            elif self.sell_custom_pump_profit_2_5.value > current_profit >= self.sell_custom_pump_profit_2_4.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_4.value:
                    return True, "signal_profit_p_2_4"
            elif self.sell_custom_pump_profit_2_4.value > current_profit >= self.sell_custom_pump_profit_2_3.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_3.value:
                    return True, "signal_profit_p_2_3"
            elif self.sell_custom_pump_profit_2_3.value > current_profit >= self.sell_custom_pump_profit_2_2.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_2.value:
                    return True, "signal_profit_p_2_2"
            elif self.sell_custom_pump_profit_2_2.value > current_profit >= self.sell_custom_pump_profit_2_1.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_1.value:
                    return True, "signal_profit_p_2_1"

        elif last_candle["sell_pump_24_1_1h"]:
            if current_profit >= self.sell_custom_pump_profit_3_5.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_5.value:
                    return True, "signal_profit_p_3_5"
            elif self.sell_custom_pump_profit_3_5.value > current_profit >= self.sell_custom_pump_profit_3_4.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_4.value:
                    return True, "signal_profit_p_3_4"
            elif self.sell_custom_pump_profit_3_4.value > current_profit >= self.sell_custom_pump_profit_3_3.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_3.value:
                    return True, "signal_profit_p_3_3"
            elif self.sell_custom_pump_profit_3_3.value > current_profit >= self.sell_custom_pump_profit_3_2.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_2.value:
                    return True, "signal_profit_p_3_2"
            elif self.sell_custom_pump_profit_3_2.value > current_profit >= self.sell_custom_pump_profit_3_1.value:
                if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_1.value:
                    return True, "signal_profit_p_3_1"

        return False, None

    def sell_dec_main(self, current_profit: float, last_candle) -> tuple:
        if (self.sell_custom_dec_profit_max_1.value > current_profit >= self.sell_custom_dec_profit_min_1.value) and (
            last_candle["sma_200_dec_20"]
        ):
            return True, "signal_profit_d_1"
        elif (
            self.sell_custom_dec_profit_max_2.value > current_profit >= self.sell_custom_dec_profit_min_2.value
        ) and (last_candle["close"] < last_candle["ema_100"]):
            return True, "signal_profit_d_2"

        return False, None

    def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        if (
            (self.sell_trail_profit_max_1.value > current_profit >= self.sell_trail_profit_min_1.value)
            and (self.sell_trail_rsi_min_1.value < last_candle["rsi_14"] < self.sell_trail_rsi_max_1.value)
            and (max_profit > (current_profit + self.sell_trail_down_1.value))
            and (last_candle["moderi_96"] == False)
        ):
            return True, "signal_profit_t_1"
        elif (
            (self.sell_trail_profit_max_2.value > current_profit >= self.sell_trail_profit_min_2.value)
            and (self.sell_trail_rsi_min_2.value < last_candle["rsi_14"] < self.sell_trail_rsi_max_2.value)
            and (max_profit > (current_profit + self.sell_trail_down_2.value))
            and (last_candle["ema_25"] < last_candle["ema_50"])
        ):
            return True, "signal_profit_t_2"
        elif (
            (self.sell_trail_profit_max_3.value > current_profit >= self.sell_trail_profit_min_3.value)
            and (max_profit > (current_profit + self.sell_trail_down_3.value))
            and (last_candle["sma_200_dec_20_1h"])
        ):
            return True, "signal_profit_t_3"
        elif (
            (self.sell_trail_profit_max_4.value > current_profit >= self.sell_trail_profit_min_4.value)
            and (max_profit > (current_profit + self.sell_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 sell_duration_main(
        self, current_profit: float, last_candle, trade: "Trade", current_time: "datetime"
    ) -> tuple:
        # Pumped pair, short duration
        if (
            (last_candle["sell_pump_24_1_1h"])
            and (0.2 > current_profit >= 0.07)
            and (current_time - timedelta(minutes=30) < trade.open_date_utc)
        ):
            return True, "signal_profit_p_s_1"

        elif (
            self.sell_custom_long_profit_min_1.value < current_profit < self.sell_custom_long_profit_max_1.value
        ) and (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1.value) > trade.open_date_utc):
            return True, "signal_profit_l_1"

        return False, None

    def sell_under_min(self, current_profit: float, last_candle) -> tuple:
        if (last_candle["moderi_96"]) == False:
            # Downtrend
            if (
                (
                    self.sell_custom_profit_under_profit_max_1.value
                    > current_profit
                    >= self.sell_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.sell_custom_profit_under_rel_1.value
                )
                and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_1.value)
            ):
                return True, "signal_profit_u_e_1"
        else:
            # Uptrend
            if (
                (current_profit >= self.sell_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.sell_custom_profit_under_rel_2.value
                )
                and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_2.value)
            ):
                return True, "signal_profit_u_e_2"

        return False, None

    def sell_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple:
        # ATR-based stoplosses: tightened moderately from original (-8/-12/-16/-20)
        if -0.06 <= current_profit < -0.03:
            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 -0.10 <= current_profit < -0.06:
            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 -0.14 <= current_profit < -0.10:
            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 < -0.14:
            if (last_candle["close"] < last_candle["atr_high_thresh_4"]) and (
                previous_candle_1["close"] > previous_candle_1["atr_high_thresh_4"]
            ):
                return True, "signal_stoploss_atr_4"

        return False, None

    def sell_pump_dec(self, current_profit: float, last_candle) -> tuple:
        if (
            (
                self.sell_custom_pump_dec_profit_max_1.value
                > current_profit
                >= self.sell_custom_pump_dec_profit_min_1.value
            )
            and (last_candle["sell_pump_48_1_1h"])
            and (last_candle["sma_200_dec_20"])
            and (last_candle["close"] < last_candle["ema_200"])
        ):
            return True, "signal_profit_p_d_1"
        elif (
            (
                self.sell_custom_pump_dec_profit_max_2.value
                > current_profit
                >= self.sell_custom_pump_dec_profit_min_2.value
            )
            and (last_candle["sell_pump_48_2_1h"])
            and (last_candle["sma_200_dec_20"])
            and (last_candle["close"] < last_candle["ema_200"])
        ):
            return True, "signal_profit_p_d_2"
        elif (
            (
                self.sell_custom_pump_dec_profit_max_3.value
                > current_profit
                >= self.sell_custom_pump_dec_profit_min_3.value
            )
            and (last_candle["sell_pump_48_3_1h"])
            and (last_candle["sma_200_dec_20"])
            and (last_candle["close"] < last_candle["ema_200"])
        ):
            return True, "signal_profit_p_d_3"
        elif (
            (
                self.sell_custom_pump_dec_profit_max_4.value
                > current_profit
                >= self.sell_custom_pump_dec_profit_min_4.value
            )
            and (last_candle["sma_200_dec_20"])
            and (last_candle["sell_pump_24_2_1h"])
        ):
            return True, "signal_profit_p_d_4"

        return False, None

    def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        # Pumped 48h 1, under EMA200
        if (
            (
                self.sell_custom_pump_under_profit_max_1.value
                > current_profit
                >= self.sell_custom_pump_under_profit_min_1.value
            )
            and (last_candle["sell_pump_48_1_1h"])
            and (last_candle["close"] < last_candle["ema_200"])
        ):
            return True, "signal_profit_p_u_1"

            # Pumped 36h 2, trail 1
        elif (
            (last_candle["sell_pump_36_2_1h"])
            and (
                self.sell_custom_pump_trail_profit_max_1.value
                > current_profit
                >= self.sell_custom_pump_trail_profit_min_1.value
            )
            and (
                self.sell_custom_pump_trail_rsi_min_1.value
                < last_candle["rsi_14"]
                < self.sell_custom_pump_trail_rsi_max_1.value
            )
            and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1.value))
        ):
            return True, "signal_profit_p_t_1"

        return False, None

    def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple:
        if (max_loss > self.sell_custom_recover_min_loss_1.value) and (
            current_profit >= self.sell_custom_recover_profit_1.value
        ):
            return True, "signal_profit_r_1"

        elif (
            (max_loss > self.sell_custom_recover_min_loss_2.value)
            and (
                self.sell_custom_recover_profit_max_2.value
                > current_profit
                >= self.sell_custom_recover_profit_min_2.value
            )
            and (last_candle["rsi_14"] < self.sell_custom_recover_rsi_2.value)
            and (last_candle["ema_25"] < last_candle["ema_50"])
        ):
            return True, "signal_profit_r_2"

        return False, None

    def sell_r_1(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle["r_480"] > -0.1:
                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 sell_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 sell_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 sell_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 sell_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"] > -100)
            and (last_candle["hrsi"] > 70)
            and (current_profit > 0)
        ):
            return True, "signal_profit_zlema"

        #        if ((max_profit - current_profit) > 0.01) and (current_profit > 0.00):
        #            return True, 'quick_trailing'

        return False, None

    def sell_ichi(
        self,
        current_profit: float,
        max_profit: float,
        max_loss: float,
        last_candle,
        previous_candle_1,
        trade: "Trade",
        current_time: "datetime",
    ) -> tuple:
        if (
            (-0.03 < 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_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: "datetime",
        current_rate: float,
        current_profit: float,
        after_fill: bool,
        **kwargs,
    ) -> float:
        """
        Tiered custom stoploss to prevent catastrophic losses.
        Tightens the stop progressively as losses deepen.
        """
        # In profit: let trailing_stop settings handle it (don't conflict)
        if current_profit > 0.04:
            return -0.02  # Lock at least 2% once 4% is reached
        elif current_profit > 0.0:
            return -1  # Use default stoploss / trailing while building profit
        elif current_profit > -0.04:
            # Loss <4%: let default -0.07 handle it, allow recovery
            return -1
        elif current_profit > -0.06:
            # Loss 4-6%: tighten slightly
            return -0.06
        else:
            # Loss > 6%: near hard stop, let it trigger
            return -0.05

    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]

        # Time-based exit: only cut stale trades losing > 3% after 12 hours
        if (current_profit < -0.03) and (current_time - timedelta(minutes=720) > trade.open_date_utc):
            return "exit_stale_loss_12h"

        buy_tag = "empty"
        if hasattr(trade, "buy_tag") and trade.buy_tag is not None:
            buy_tag = trade.buy_tag
        else:
            trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            buy_signal = dataframe.loc[dataframe["date"] < trade_open_date]
            if not buy_signal.empty:
                buy_signal_candle = buy_signal.iloc[-1]
                buy_tag = buy_signal_candle["buy_tag"] if buy_signal_candle["buy_tag"] != "" else "empty"
        buy_tags = buy_tag.split()
        max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate
        max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate

        # Quick sell mode
        if all(c in ["32", "33", "34", "35", "36", "37", "38", "40"] for c in buy_tags):
            sell, signal_name = self.sell_quick_mode(
                current_profit, max_profit, last_candle, previous_candle_1, trade, current_time
            )
            if sell and (signal_name is not None):
                return signal_name + " ( " + buy_tag + ")"

        # Ichi Trade management
        if all(c in ["39"] for c in buy_tags):
            sell, signal_name = self.sell_ichi(
                current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time
            )
            if sell and (signal_name is not None):
                return signal_name + " ( " + buy_tag + ")"

        # Over EMA200, main profit targets
        sell, signal_name = self.sell_over_main(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Under EMA200, main profit targets
        sell, signal_name = self.sell_under_main(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # The pair is pumped
        sell, signal_name = self.sell_pump_main(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # The pair is descending
        sell, signal_name = self.sell_dec_main(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Trailing
        sell, signal_name = self.sell_trail_main(current_profit, last_candle, max_profit)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Duration based
        sell, signal_name = self.sell_duration_main(current_profit, last_candle, trade, current_time)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Under EMA200, exit with any profit
        sell, signal_name = self.sell_under_min(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Stoplosses
        sell, signal_name = self.sell_stoploss(current_profit, last_candle, previous_candle_1)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Pumped descending pairs
        sell, signal_name = self.sell_pump_dec(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Extra sells for pumped pairs
        sell, signal_name = self.sell_pump_extra(current_profit, last_candle, max_profit)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Extra sells for trades that recovered
        sell, signal_name = self.sell_recover(current_profit, last_candle, max_loss)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Williams %R based sell 1
        sell, signal_name = self.sell_r_1(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Williams %R based sell 2
        sell, signal_name = self.sell_r_2(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Williams %R based sell 3
        sell, signal_name = self.sell_r_3(current_profit, last_candle)
        if sell and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Williams %R based sell 4, plus CTI
        sell, signal_name = self.sell_r_4(current_profit, last_candle)
        if (sell) and (signal_name is not None):
            return signal_name + " ( " + buy_tag + ")"

        # Sell signal 1
        if (
            self.sell_condition_1_enable.value
            and (last_candle["rsi_14"] > self.sell_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 "sell_signal_1_1_1" + " ( " + buy_tag + ")"
            else:
                if current_profit > 0.0:
                    return "sell_signal_1_2_1" + " ( " + buy_tag + ")"
                elif max_loss > 0.25:
                    return "sell_signal_1_2_2" + " ( " + buy_tag + ")"

        # Sell signal 2
        elif (
            (self.sell_condition_2_enable.value)
            and (last_candle["rsi_14"] > self.sell_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 "sell_signal_2_1_1" + " ( " + buy_tag + ")"
            else:
                if current_profit > 0.0:
                    return "sell_signal_2_2_1" + " ( " + buy_tag + ")"
                elif max_loss > 0.25:
                    return "sell_signal_2_2_2" + " ( " + buy_tag + ")"

        # Sell signal 4
        elif (
            self.sell_condition_4_enable.value
            and (last_candle["rsi_14"] > self.sell_dual_rsi_rsi_4.value)
            and (last_candle["rsi_14_1h"] > self.sell_dual_rsi_rsi_1h_4.value)
        ):
            if last_candle["close"] > last_candle["ema_200"]:
                if current_profit > 0.0:
                    return "sell_signal_4_1_1" + " ( " + buy_tag + ")"
            else:
                if current_profit > 0.0:
                    return "sell_signal_4_2_1" + " ( " + buy_tag + ")"
                elif max_loss > 0.25:
                    return "sell_signal_4_2_2" + " ( " + buy_tag + ")"

        # Sell signal 6
        elif (
            self.sell_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.sell_rsi_under_6.value)
        ):
            if current_profit > 0.0:
                return "sell_signal_6_1" + " ( " + buy_tag + ")"
            elif max_loss > 0.25:
                return "sell_signal_6_2" + " ( " + buy_tag + ")"

        # Sell signal 7
        elif (
            self.sell_condition_7_enable.value
            and (last_candle["rsi_14_1h"] > self.sell_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 "sell_signal_7_1_1" + " ( " + buy_tag + ")"
            else:
                if current_profit > 0.0:
                    return "sell_signal_7_2_1" + " ( " + buy_tag + ")"
                elif max_loss > 0.25:
                    return "sell_signal_7_2_2" + " ( " + buy_tag + ")"

        # Sell signal 8
        elif self.sell_condition_8_enable.value and (
            last_candle["close"] > last_candle["bb20_2_upp_1h"] * self.sell_bb_relative_8.value
        ):
            if last_candle["close"] > last_candle["ema_200"]:
                if current_profit > 0.0:
                    return "sell_signal_8_1_1" + " ( " + buy_tag + ")"
            else:
                if current_profit > 0.0:
                    return "sell_signal_8_2_1" + " ( " + buy_tag + ")"
                elif max_loss > 0.25:
                    return "sell_signal_8_2_2" + " ( " + buy_tag + ")"

        return None

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, self.info_timeframe) 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.buy_pump_threshold_10_24.value, self.buy_pump_pull_threshold_10_24.value
        )
        informative_1h["safe_pump_36_10"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_10_36.value, self.buy_pump_pull_threshold_10_36.value
        )
        informative_1h["safe_pump_48_10"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_10_48.value, self.buy_pump_pull_threshold_10_48.value
        )

        informative_1h["safe_pump_24_20"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_20_24.value, self.buy_pump_pull_threshold_20_24.value
        )
        informative_1h["safe_pump_36_20"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_20_36.value, self.buy_pump_pull_threshold_20_36.value
        )
        informative_1h["safe_pump_48_20"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_20_48.value, self.buy_pump_pull_threshold_20_48.value
        )

        informative_1h["safe_pump_24_30"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_30_24.value, self.buy_pump_pull_threshold_30_24.value
        )
        informative_1h["safe_pump_36_30"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_30_36.value, self.buy_pump_pull_threshold_30_36.value
        )
        informative_1h["safe_pump_48_30"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_30_48.value, self.buy_pump_pull_threshold_30_48.value
        )

        informative_1h["safe_pump_24_40"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_40_24.value, self.buy_pump_pull_threshold_40_24.value
        )
        informative_1h["safe_pump_36_40"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_40_36.value, self.buy_pump_pull_threshold_40_36.value
        )
        informative_1h["safe_pump_48_40"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_40_48.value, self.buy_pump_pull_threshold_40_48.value
        )

        informative_1h["safe_pump_24_50"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_50_24.value, self.buy_pump_pull_threshold_50_24.value
        )
        informative_1h["safe_pump_36_50"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_50_36.value, self.buy_pump_pull_threshold_50_36.value
        )
        informative_1h["safe_pump_48_50"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_50_48.value, self.buy_pump_pull_threshold_50_48.value
        )

        informative_1h["safe_pump_24_60"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_60_24.value, self.buy_pump_pull_threshold_60_24.value
        )
        informative_1h["safe_pump_36_60"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_60_36.value, self.buy_pump_pull_threshold_60_36.value
        )
        informative_1h["safe_pump_48_60"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_60_48.value, self.buy_pump_pull_threshold_60_48.value
        )

        informative_1h["safe_pump_24_70"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_70_24.value, self.buy_pump_pull_threshold_70_24.value
        )
        informative_1h["safe_pump_36_70"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_70_36.value, self.buy_pump_pull_threshold_70_36.value
        )
        informative_1h["safe_pump_48_70"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_70_48.value, self.buy_pump_pull_threshold_70_48.value
        )

        informative_1h["safe_pump_24_80"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_80_24.value, self.buy_pump_pull_threshold_80_24.value
        )
        informative_1h["safe_pump_36_80"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_80_36.value, self.buy_pump_pull_threshold_80_36.value
        )
        informative_1h["safe_pump_48_80"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_80_48.value, self.buy_pump_pull_threshold_80_48.value
        )

        informative_1h["safe_pump_24_90"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_90_24.value, self.buy_pump_pull_threshold_90_24.value
        )
        informative_1h["safe_pump_36_90"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_90_36.value, self.buy_pump_pull_threshold_90_36.value
        )
        informative_1h["safe_pump_48_90"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_90_48.value, self.buy_pump_pull_threshold_90_48.value
        )

        informative_1h["safe_pump_24_100"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_100_24.value, self.buy_pump_pull_threshold_100_24.value
        )
        informative_1h["safe_pump_36_100"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_100_36.value, self.buy_pump_pull_threshold_100_36.value
        )
        informative_1h["safe_pump_48_100"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_100_48.value, self.buy_pump_pull_threshold_100_48.value
        )

        informative_1h["safe_pump_24_110"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_110_24.value, self.buy_pump_pull_threshold_110_24.value
        )
        informative_1h["safe_pump_36_110"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_110_36.value, self.buy_pump_pull_threshold_110_36.value
        )
        informative_1h["safe_pump_48_110"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_110_48.value, self.buy_pump_pull_threshold_110_48.value
        )

        informative_1h["safe_pump_24_120"] = self.safe_pump(
            informative_1h, 24, self.buy_pump_threshold_120_24.value, self.buy_pump_pull_threshold_120_24.value
        )
        informative_1h["safe_pump_36_120"] = self.safe_pump(
            informative_1h, 36, self.buy_pump_threshold_120_36.value, self.buy_pump_pull_threshold_120_36.value
        )
        informative_1h["safe_pump_48_120"] = self.safe_pump(
            informative_1h, 48, self.buy_pump_threshold_120_48.value, self.buy_pump_pull_threshold_120_48.value
        )

        informative_1h["safe_dump_10"] = (
            (informative_1h["hl_pct_change_5"] < self.buy_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.buy_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.buy_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.buy_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.buy_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.buy_dump_protection_60_5.value)
            | (informative_1h["close"] < informative_1h["low_5"])
            | (informative_1h["close"] > informative_1h["open"])
        )

        informative_1h["sell_pump_48_1"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_1.value
        informative_1h["sell_pump_48_2"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_2.value
        informative_1h["sell_pump_48_3"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_3.value

        informative_1h["sell_pump_36_1"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_1.value
        informative_1h["sell_pump_36_2"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_2.value
        informative_1h["sell_pump_36_3"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_3.value

        informative_1h["sell_pump_24_1"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_1.value
        informative_1h["sell_pump_24_2"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_2.value
        informative_1h["sell_pump_24_3"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_3.value

        # TD Sequential
        informative_1h["exceed_high"] = False
        informative_1h["exceed_low"] = False

        # count consecutive closes “lower” than the close 4 bars prior.
        informative_1h["seq_buy"] = informative_1h["close"] < informative_1h["close"].shift(4)
        informative_1h["seq_buy"] = informative_1h["seq_buy"] * (
            informative_1h["seq_buy"]
            .groupby((informative_1h["seq_buy"] != informative_1h["seq_buy"].shift()).cumsum())
            .cumcount()
            + 1
        )

        # count consecutive closes “higher” than the close 4 bars prior.
        informative_1h["seq_sell"] = informative_1h["close"] > informative_1h["close"].shift(4)
        informative_1h["seq_sell"] = informative_1h["seq_sell"] * (
            informative_1h["seq_sell"]
            .groupby((informative_1h["seq_sell"] != informative_1h["seq_sell"].shift()).cumsum())
            .cumcount()
            + 1
        )

        for index, row in informative_1h.iterrows():
            # check if the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9.
            seq_b = row["seq_buy"]
            if seq_b == 8:
                informative_1h.loc[index, "exceed_low"] = (row["low"] < informative_1h.loc[index - 2, "low"]) | (
                    row["low"] < informative_1h.loc[index - 1, "low"]
                )
            if seq_b > 8:
                informative_1h.loc[index, "exceed_low"] = (
                    row["low"] < informative_1h.loc[index - 3 - (seq_b - 9), "low"]
                ) | (row["low"] < informative_1h.loc[index - 2 - (seq_b - 9), "low"])
                if seq_b == 9:
                    informative_1h.loc[index, "exceed_low"] = (
                        row["exceed_low"] | informative_1h.loc[index - 1, "exceed_low"]
                    )

            # check if the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9.
            seq_s = row["seq_sell"]
            if seq_s == 8:
                informative_1h.loc[index, "exceed_high"] = (row["high"] > informative_1h.loc[index - 2, "high"]) | (
                    row["high"] > informative_1h.loc[index - 1, "high"]
                )
            if seq_s > 8:
                informative_1h.loc[index, "exceed_high"] = (
                    row["high"] > informative_1h.loc[index - 3 - (seq_s - 9), "high"]
                ) | (row["high"] > informative_1h.loc[index - 2 - (seq_s - 9), "high"])
                if seq_s == 9:
                    informative_1h.loc[index, "exceed_high"] = (
                        row["exceed_high"] | informative_1h.loc[index - 1, "exceed_high"]
                    )

        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 sell checks
        dataframe["crossed_below_ema_12_26"] = qtpylib.crossed_below(dataframe["ema_12"], dataframe["ema_26"])

        # Heiken Ashi
        heikinashi = qtpylib.heikinashi(dataframe)
        heikinashi["volume"] = dataframe["volume"]

        # 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_sell"] = 2 * dataframe["low"] - ta.WMA(dataframe["low"], 2)
        dataframe["hrsi_sell"] = ta.RSI(dataframe["hull_sell"], 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_sell"] = dataframe["lowKF"]
        dataframe["zlema_1_std_sell"] = dataframe["low"]
        dataframe["ema_data_sell"] = dataframe["lowKF"] + (dataframe["lowKF"] - dataframe["lowKF"].shift(2))
        dataframe["zlema_4_sell"] = ta.EMA(dataframe["ema_data_sell"], timeperiod=4)
        dataframe["zlema_4_std_sell"] = 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.buy_dip_threshold_10_1.value,
            self.buy_dip_threshold_10_2.value,
            self.buy_dip_threshold_10_3.value,
            self.buy_dip_threshold_10_4.value,
        )
        dataframe["safe_dips_20"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_20_1.value,
            self.buy_dip_threshold_20_2.value,
            self.buy_dip_threshold_20_3.value,
            self.buy_dip_threshold_20_4.value,
        )
        dataframe["safe_dips_30"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_30_1.value,
            self.buy_dip_threshold_30_2.value,
            self.buy_dip_threshold_30_3.value,
            self.buy_dip_threshold_30_4.value,
        )
        dataframe["safe_dips_40"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_40_1.value,
            self.buy_dip_threshold_40_2.value,
            self.buy_dip_threshold_40_3.value,
            self.buy_dip_threshold_40_4.value,
        )
        dataframe["safe_dips_50"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_50_1.value,
            self.buy_dip_threshold_50_2.value,
            self.buy_dip_threshold_50_3.value,
            self.buy_dip_threshold_50_4.value,
        )
        dataframe["safe_dips_60"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_60_1.value,
            self.buy_dip_threshold_60_2.value,
            self.buy_dip_threshold_60_3.value,
            self.buy_dip_threshold_60_4.value,
        )
        dataframe["safe_dips_70"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_70_1.value,
            self.buy_dip_threshold_70_2.value,
            self.buy_dip_threshold_70_3.value,
            self.buy_dip_threshold_70_4.value,
        )
        dataframe["safe_dips_80"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_80_1.value,
            self.buy_dip_threshold_80_2.value,
            self.buy_dip_threshold_80_3.value,
            self.buy_dip_threshold_80_4.value,
        )
        dataframe["safe_dips_90"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_90_1.value,
            self.buy_dip_threshold_90_2.value,
            self.buy_dip_threshold_90_3.value,
            self.buy_dip_threshold_90_4.value,
        )
        dataframe["safe_dips_100"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_100_1.value,
            self.buy_dip_threshold_100_2.value,
            self.buy_dip_threshold_100_3.value,
            self.buy_dip_threshold_100_4.value,
        )
        dataframe["safe_dips_110"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_110_1.value,
            self.buy_dip_threshold_110_2.value,
            self.buy_dip_threshold_110_3.value,
            self.buy_dip_threshold_110_4.value,
        )
        dataframe["safe_dips_120"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_120_1.value,
            self.buy_dip_threshold_120_2.value,
            self.buy_dip_threshold_120_3.value,
            self.buy_dip_threshold_120_4.value,
        )
        dataframe["safe_dips_130"] = self.safe_dips(
            dataframe,
            self.buy_dip_threshold_130_1.value,
            self.buy_dip_threshold_130_2.value,
            self.buy_dip_threshold_130_3.value,
            self.buy_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)

        """
        --> Informative timeframe
        ___________________________________________________________________________________________
        """
        if self.info_timeframe != "none":
            informative_1h = self.informative_1h_indicators(dataframe, metadata)
            dataframe = merge_informative_pair(
                dataframe, informative_1h, self.timeframe, self.info_timeframe, ffill=True
            )
            drop_columns = [(s + "_" + self.info_timeframe) for s in ["date"]]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)

        """
        --> Resampled to another timeframe
        ___________________________________________________________________________________________
        """
        if self.res_timeframe != "none":
            resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe))
            resampled = self.resampled_tf_indicators(resampled, metadata)
            # Merge resampled info dataframe
            dataframe = resampled_merge(dataframe, resampled, fill_na=True)
            dataframe.rename(
                columns=lambda s: s + "_{}".format(self.res_timeframe) if "resample_" in s else s, inplace=True
            )
            dataframe.rename(
                columns=lambda s: s.replace("resample_{}_".format(self.res_timeframe.replace("m", "")), ""),
                inplace=True,
            )
            drop_columns = [(s + "_" + self.res_timeframe) for s in ["date"]]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)

        """
        --> The indicators for the normal (5m) timeframe
        ___________________________________________________________________________________________
        """
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, "buy_tag"] = ""

        for index in self.buy_protection_params:
            item_buy_protection_list = [True]
            global_buy_protection_params = self.buy_protection_params[index]

            if self.buy_params["buy_condition_" + str(index) + "_enable"]:
                # Standard protections - Common to every condition
                # -----------------------------------------------------------------------------------------
                if global_buy_protection_params["ema_fast"]:
                    item_buy_protection_list.append(
                        dataframe[f"ema_{global_buy_protection_params['ema_fast_len']}"] > dataframe["ema_200"]
                    )
                if global_buy_protection_params["ema_slow"]:
                    item_buy_protection_list.append(
                        dataframe[f"ema_{global_buy_protection_params['ema_slow_len']}_1h"] > dataframe["ema_200_1h"]
                    )
                if global_buy_protection_params["close_above_ema_fast"]:
                    item_buy_protection_list.append(
                        dataframe["close"]
                        > dataframe[f"ema_{global_buy_protection_params['close_above_ema_fast_len']}"]
                    )
                if global_buy_protection_params["close_above_ema_slow"]:
                    item_buy_protection_list.append(
                        dataframe["close"]
                        > dataframe[f"ema_{global_buy_protection_params['close_above_ema_slow_len']}_1h"]
                    )
                if global_buy_protection_params["sma200_rising"]:
                    item_buy_protection_list.append(
                        dataframe["sma_200"]
                        > dataframe["sma_200"].shift(int(global_buy_protection_params["sma200_rising_val"]))
                    )
                if global_buy_protection_params["sma200_1h_rising"]:
                    item_buy_protection_list.append(
                        dataframe["sma_200_1h"]
                        > dataframe["sma_200_1h"].shift(int(global_buy_protection_params["sma200_1h_rising_val"]))
                    )
                if global_buy_protection_params["safe_dips"]:
                    item_buy_protection_list.append(
                        dataframe[f"safe_dips_{global_buy_protection_params['safe_dips_type']}"]
                    )
                if global_buy_protection_params["safe_pump"]:
                    item_buy_protection_list.append(
                        dataframe[
                            f"safe_pump_{global_buy_protection_params['safe_pump_period']}_{global_buy_protection_params['safe_pump_type']}_1h"
                        ]
                    )
                if global_buy_protection_params["btc_1h_not_downtrend"]:
                    item_buy_protection_list.append(dataframe["btc_not_downtrend_1h"])
                if not self.config["runmode"] in ("live", "dry_run"):
                    if self.has_bt_agefilter:
                        item_buy_protection_list.append(dataframe["bt_agefilter_ok"])
                else:
                    if self.has_downtime_protection:
                        item_buy_protection_list.append(dataframe["live_data_ok"])

                # Buy conditions
                # -----------------------------------------------------------------------------------------
                item_buy_logic = []
                item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list))

                # Condition #1
                if index == 1:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(
                        (
                            (dataframe["close"] - dataframe["open"].rolling(36).min())
                            / dataframe["open"].rolling(36).min()
                        )
                        > self.buy_min_inc_1.value
                    )
                    item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_1.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_1.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_1.value)
                    item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_1.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_1.value)

                # Condition #2
                elif index == 2:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["rsi_14"] < dataframe["rsi_14_1h"] - self.buy_rsi_1h_diff_2.value)
                    item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_2.value)
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_2.value))
                    item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_2.value))

                # Condition #3
                elif index == 3:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_3.value))

                    # Logic
                    item_buy_logic.append(dataframe["bb40_2_low"].shift().gt(0))
                    item_buy_logic.append(
                        dataframe["bb40_2_delta"].gt(dataframe["close"] * self.buy_bb40_bbdelta_close_3.value)
                    )
                    item_buy_logic.append(
                        dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close_3.value)
                    )
                    item_buy_logic.append(
                        dataframe["tail"].lt(dataframe["bb40_2_delta"] * self.buy_bb40_tail_bbdelta_3.value)
                    )
                    item_buy_logic.append(dataframe["close"].lt(dataframe["bb40_2_low"].shift()))
                    item_buy_logic.append(dataframe["close"].le(dataframe["close"].shift()))
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_3.value)

                # Condition #4
                elif index == 4:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_50"])
                    item_buy_logic.append(
                        dataframe["close"] < self.buy_bb20_close_bblowerband_4.value * dataframe["bb20_2_low"]
                    )
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_30"].shift(1) * self.buy_bb20_volume_4.value)
                    )
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_4.value)

                # Condition #5
                elif index == 5:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_5.value))

                    # Logic
                    item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
                    item_buy_logic.append(
                        (dataframe["ema_26"] - dataframe["ema_12"])
                        > (dataframe["open"] * self.buy_ema_open_mult_5.value)
                    )
                    item_buy_logic.append(
                        (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
                    )
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_5.value))
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_5.value)
                    item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_5.value))

                # Condition #6
                elif index == 6:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
                    item_buy_logic.append(
                        (dataframe["ema_26"] - dataframe["ema_12"])
                        > (dataframe["open"] * self.buy_ema_open_mult_6.value)
                    )
                    item_buy_logic.append(
                        (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
                    )
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_6.value))

                # Condition #7
                elif index == 7:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
                    item_buy_logic.append(
                        (dataframe["ema_26"] - dataframe["ema_12"])
                        > (dataframe["open"] * self.buy_ema_open_mult_7.value)
                    )
                    item_buy_logic.append(
                        (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
                    )
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_7.value)

                # Condition #8
                elif index == 8:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_96"])
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_8.value)
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_8.value))
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_8.value)
                    item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_8.value))

                # Condition #9
                elif index == 9:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["ema_50"] > dataframe["ema_200"])

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_9.value)
                    item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_bb_offset_9.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_9.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_9.value)
                    item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_9.value)

                # Condition #10
                elif index == 10:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_10.value)
                    item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_bb_offset_10.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_10.value)

                # Condition #11
                elif index == 11:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])

                    # Logic
                    item_buy_logic.append(
                        (
                            (dataframe["close"] - dataframe["open"].rolling(36).min())
                            / dataframe["open"].rolling(36).min()
                        )
                        > self.buy_min_inc_11.value
                    )
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_11.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_11.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_11.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_11.value)
                    item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_11.value)

                # Condition #12
                elif index == 12:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_12.value)
                    item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_12.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_12.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_12.value)

                # Condition #13
                elif index == 13:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_13.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_13.value)
                    item_buy_logic.append(dataframe["ewo"] < self.buy_ewo_13.value)

                # Condition #14
                elif index == 14:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
                    item_buy_logic.append(
                        (dataframe["ema_26"] - dataframe["ema_12"])
                        > (dataframe["open"] * self.buy_ema_open_mult_14.value)
                    )
                    item_buy_logic.append(
                        (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
                    )
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_14.value))
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_14.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_14.value)

                # Condition #15
                elif index == 15:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["close"] > dataframe["ema_200_1h"] * self.buy_ema_rel_15.value)

                    # Logic
                    item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
                    item_buy_logic.append(
                        (dataframe["ema_26"] - dataframe["ema_12"])
                        > (dataframe["open"] * self.buy_ema_open_mult_15.value)
                    )
                    item_buy_logic.append(
                        (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
                    )
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_15.value)
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_15.value)

                # Condition #16
                elif index == 16:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_16.value)
                    item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_16.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_16.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_16.value)

                # Condition #17
                elif index == 17:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_17.value)
                    item_buy_logic.append(dataframe["ewo"] < self.buy_ewo_17.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_17.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_17.value)
                    )

                # Condition #18
                elif index == 18:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["sma_200"] > dataframe["sma_200"].shift(20))
                    item_buy_logic.append(dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(36))

                    # Logic
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_18.value)
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_18.value))
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_18.value)
                    )
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_18.value)

                # Condition #19
                elif index == 19:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"].shift(1) > dataframe["ema_100_1h"])
                    item_buy_logic.append(dataframe["low"] < dataframe["ema_100_1h"])
                    item_buy_logic.append(dataframe["close"] > dataframe["ema_100_1h"])
                    item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_19.value)
                    item_buy_logic.append(dataframe["chop"] < self.buy_chop_max_19.value)
                    item_buy_logic.append(dataframe["moderi_32"] == True)
                    item_buy_logic.append(dataframe["moderi_64"] == True)
                    item_buy_logic.append(dataframe["moderi_96"] == True)

                # Condition #20
                elif index == 20:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_20.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_20.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_20.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_20.value)
                    )

                # Condition #21
                elif index == 21:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_21.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_21.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_cti_21.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_21.value)
                    )

                # Condition #22
                elif index == 22:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe["ema_100_1h"] > dataframe["ema_100_1h"].shift(12))
                    item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(36))

                    # Logic
                    item_buy_logic.append(
                        (dataframe["volume_mean_4"] * self.buy_volume_22.value) > dataframe["volume"]
                    )
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_22.value)
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_22.value))
                    item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_22.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_22.value)

                # Condition #23
                elif index == 23:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_23.value))
                    item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_23.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_23.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_23.value)

                # Condition #24
                elif index == 24:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["ema_12_1h"].shift(12) < dataframe["ema_35_1h"].shift(12))
                    item_buy_logic.append(dataframe["ema_12_1h"] > dataframe["ema_35_1h"])
                    item_buy_logic.append(dataframe["cmf_1h"].shift(12) < 0)
                    item_buy_logic.append(dataframe["cmf_1h"] > 0)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_24_rsi_max.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_24_rsi_1h_min.value)

                # Condition #25
                elif index == 25:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["rsi_20"] < dataframe["rsi_20"].shift())
                    item_buy_logic.append(dataframe["rsi_4"] < self.buy_25_rsi_4.value)
                    item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_26_1h"])
                    item_buy_logic.append(dataframe["close"] < (dataframe["sma_20"] * self.buy_25_ma_offset.value))
                    item_buy_logic.append(dataframe["open"] > (dataframe["sma_20"] * self.buy_25_ma_offset.value))
                    item_buy_logic.append(
                        (dataframe["open"] < dataframe["ema_20_1h"]) & (dataframe["low"] < dataframe["ema_20_1h"])
                        | (dataframe["open"] > dataframe["ema_20_1h"]) & (dataframe["low"] > dataframe["ema_20_1h"])
                    )
                    item_buy_logic.append(dataframe["cti"] < self.buy_25_cti.value)

                # Condition #26
                elif index == 26:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(
                        dataframe["close"] < (dataframe["zema_61"] * self.buy_26_zema_low_offset.value)
                    )
                    item_buy_logic.append(dataframe["cti"] < self.buy_26_cti.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_26_volume.value)
                    )

                # Condition #27
                elif index == 27:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["r_480"] < -self.buy_27_wr_max.value)
                    item_buy_logic.append(dataframe["r_480_1h"] < -self.buy_27_wr_1h_max.value)
                    item_buy_logic.append(dataframe["rsi_14_1h"] + dataframe["rsi_14"] < self.buy_27_rsi_max.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_27_cti.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_27_volume.value)
                    )

                # Condition #28
                elif index == 28:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_64"] == True)
                    item_buy_logic.append(dataframe["close"] < dataframe["hull_75"] * self.buy_28_ma_offset.value)
                    item_buy_logic.append(dataframe["ewo"] > self.buy_28_ewo.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_28_rsi.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_28_cti.value)

                # Condition #29
                elif index == 29:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_64"] == True)
                    item_buy_logic.append(dataframe["close"] < dataframe["hull_75"] * self.buy_29_ma_offset.value)
                    item_buy_logic.append(dataframe["ewo"] < self.buy_29_ewo.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_29_cti.value)

                # Condition #30
                elif index == 30:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_64"] == False)
                    item_buy_logic.append(dataframe["close"] < dataframe["zlema_68"] * self.buy_30_ma_offset.value)
                    item_buy_logic.append(dataframe["ewo"] > self.buy_30_ewo.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_30_rsi.value)
                    item_buy_logic.append(dataframe["cti"] < self.buy_30_cti.value)

                # Condition #31
                elif index == 31:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_64"] == False)
                    item_buy_logic.append(dataframe["close"] < dataframe["zlema_68"] * self.buy_31_ma_offset.value)
                    item_buy_logic.append(dataframe["ewo"] < self.buy_31_ewo.value)
                    item_buy_logic.append(dataframe["r_480"] < self.buy_31_wr.value)

                # Condition #32 - Quick mode buy
                elif index == 32:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_32"])
                    item_buy_logic.append(dataframe["moderi_64"])
                    item_buy_logic.append(dataframe["moderi_96"])
                    item_buy_logic.append(dataframe["cti"] < self.buy_32_cti.value)
                    item_buy_logic.append(dataframe["rsi_20"] < dataframe["rsi_20"].shift(1))
                    item_buy_logic.append(dataframe["rsi_4"] < self.buy_32_rsi.value)
                    item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_25_1h"])
                    item_buy_logic.append(
                        (dataframe["open"] - dataframe["close"]) / dataframe["close"] < self.buy_32_dip.value
                    )
                    item_buy_logic.append(dataframe["close"] < (dataframe["sma_15"] * self.buy_32_ma_offset.value))
                    item_buy_logic.append(
                        ((dataframe["open"] < dataframe["ema_20_1h"]) & (dataframe["low"] < dataframe["ema_20_1h"]))
                        | ((dataframe["open"] > dataframe["ema_20_1h"]) & (dataframe["low"] > dataframe["ema_20_1h"]))
                    )

                # Condition #33 - Quick mode buy
                elif index == 33:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["moderi_96"])
                    item_buy_logic.append(dataframe["cti"] < self.buy_33_cti.value)
                    item_buy_logic.append(dataframe["close"] < (dataframe["ema_13"] * self.buy_33_ma_offset.value))
                    item_buy_logic.append(dataframe["ewo"] > self.buy_33_ewo.value)
                    item_buy_logic.append(dataframe["rsi_14"] < self.buy_33_rsi.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_33_volume.value)
                    )

                # Condition #34 - Quick mode buy
                elif index == 34:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["cti"] < self.buy_34_cti.value)
                    item_buy_logic.append(
                        (dataframe["open"] - dataframe["close"]) / dataframe["close"] < self.buy_34_dip.value
                    )
                    item_buy_logic.append(dataframe["close"] < dataframe["ema_13"] * self.buy_34_ma_offset.value)
                    item_buy_logic.append(dataframe["ewo"] < self.buy_34_ewo.value)
                    item_buy_logic.append(
                        dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_34_volume.value)
                    )

                # Condition #35 - PMAX0 buy
                elif index == 35:
                    # Non-Standard protections

                    # Logic
                    item_buy_logic.append(dataframe["pm"] <= dataframe["pmax_thresh"])
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * 0.984)
                    item_buy_logic.append(dataframe["ewo"] > 9.6)
                    item_buy_logic.append(dataframe["rsi_14"] < 32.0)
                    item_buy_logic.append(dataframe["cti"] < -0.5)

                # Condition #36 - PMAX1 buy
                elif index == 36:
                    # Non-Standard protections (add below)

                    # Logic
                    item_buy_logic.append(dataframe["pm"] <= dataframe["pmax_thresh"])
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * 0.98)
                    item_buy_logic.append(dataframe["ewo"] < -8.8)
                    item_buy_logic.append(dataframe["cti"] < -0.8)

                # Condition #37 - PMAX2 buy
                elif index == 37:
                    # Non-Standard protections (add below)

                    # Logic
                    item_buy_logic.append(dataframe["pm"] > dataframe["pmax_thresh"])
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * 0.98)
                    item_buy_logic.append(dataframe["ewo"] > 9.8)
                    item_buy_logic.append(dataframe["rsi_14"] < 56.0)
                    item_buy_logic.append(dataframe["cti"] < -0.7)
                    item_buy_logic.append(dataframe["safe_dump_50_1h"])

                # Condition #38 - PMAX3 buy
                elif index == 38:
                    # Non-Standard protections (add below)

                    # Logic
                    item_buy_logic.append(dataframe["pm"] > dataframe["pmax_thresh"])
                    item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * 0.7)
                    item_buy_logic.append(dataframe["ewo"] < -2.0)
                    item_buy_logic.append(dataframe["cti"] < -0.86)

                # Condition #39 - Ichimoku
                elif index == 39:
                    # Non-Standard protections (add below)

                    # Logic
                    item_buy_logic.append(dataframe["tenkan_sen_1h"] > dataframe["kijun_sen_1h"])
                    item_buy_logic.append(dataframe["close"] > dataframe["cloud_top_1h"])
                    item_buy_logic.append(
                        dataframe["leading_senkou_span_a_1h"] > dataframe["leading_senkou_span_b_1h"]
                    )
                    item_buy_logic.append(dataframe["chikou_span_1h"] > dataframe["senkou_a_1h"])
                    item_buy_logic.append(dataframe["efi_1h"] > 0)
                    item_buy_logic.append(dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"])
                    item_buy_logic.append(dataframe["close"] < dataframe["ssl_up_1h"])
                    item_buy_logic.append(dataframe["cti"] < -0.73)
                    # Start of trend
                    item_buy_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 buy
                elif index == 40:
                    # Non-Standard protections (add below)

                    # Logic
                    item_buy_logic.append(qtpylib.crossed_above(dataframe["zlema_2"], dataframe["zlema_4"]))
                    item_buy_logic.append(dataframe["hrsi"] < 30)
                    item_buy_logic.append(dataframe["cci"] < -200)
                    item_buy_logic.append(dataframe["rsi_14"] < 30)
                    item_buy_logic.append(dataframe["exceed_low_1h"])
                    item_buy_logic.append(dataframe["seq_buy_1h"] > 8)

                item_buy_logic.append(dataframe["volume"] > 0)
                item_buy = reduce(lambda x, y: x & y, item_buy_logic)
                dataframe.loc[item_buy, "buy_tag"] += str(index) + " "
                conditions.append(item_buy)

        if conditions:
            dataframe.loc[:, "buy"] = reduce(lambda x, y: x | y, conditions)

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, "sell"] = 0

        return dataframe

    def confirm_trade_exit(
        self,
        pair: str,
        trade: "Trade",
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        sell_reason: str,
        **kwargs,
    ) -> bool:
        """
        Called right before placing a regular sell order.
        Timing for this function is critical, so avoid doing heavy computations or
        network requests in this method.

        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

        When not implemented by a strategy, returns True (always confirming).

        :param pair: Pair that's about to be sold.
        :param trade: trade object.
        :param order_type: Order type (as configured in order_types). usually limit or market.
        :param amount: Amount in quote currency.
        :param rate: Rate that's going to be used when using limit orders
        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
        :param sell_reason: Sell reason.
            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
                           'sell_signal', 'force_sell', 'emergency_sell']
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return bool: When True is returned, then the sell-order is placed on the exchange.
            False aborts the process
        """
        # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
        if self.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, sell
                return True

            if trade.id not in self.hold_trade_ids:
                # This pair is not on the list to hold until profit, sell
                return True

            trade_profit_ratio = self.hold_trade_ids[trade.id]
            current_profit_ratio = trade.calc_profit_ratio(rate)
            if sell_reason == "force_sell":
                formatted_profit_ratio = "{}%".format(trade_profit_ratio * 100)
                formatted_current_profit_ratio = "{}%".format(current_profit_ratio * 100)
                log.warning(
                    "Force selling %s even though the current profit of %s < %s",
                    trade,
                    formatted_current_profit_ratio,
                    formatted_profit_ratio,
                )
                return True
            elif current_profit_ratio >= trade_profit_ratio:
                # This pair is on the list to hold, and we reached minimum profit, sell
                return True

            # This pair is on the list to hold, and we haven't reached minimum profit, hold
            return False
        else:
            return True


# Elliot Wave Oscillator
def ewo(dataframe, sma1_length=5, sma2_length=35):
    sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
    sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / dataframe["close"] * 100
    return smadif


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


# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
    of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
    Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
    of its recent trading range.
    The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """

    highest_high = dataframe["high"].rolling(center=False, window=period).max()
    lowest_low = dataframe["low"].rolling(center=False, window=period).min()

    WR = Series(
        (highest_high - dataframe["close"]) / (highest_high - lowest_low),
        name="{0} Williams %R".format(period),
    )

    return WR * -100


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


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


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


# zlhull
def zlhull(dataframe, timeperiod):
    lag = int(math.floor((timeperiod - 1) / 2))
    if isinstance(dataframe, Series):
        wma_data = dataframe + (dataframe - dataframe.shift(lag))
    else:
        wma_data = dataframe["close"] + (dataframe["close"] - dataframe["close"].shift(lag))

    return ta.WMA(
        2 * ta.WMA(wma_data, int(math.floor(timeperiod / 2))) - ta.WMA(wma_data, timeperiod),
        int(round(np.sqrt(timeperiod))),
    )


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


# PMAX
def pmax(df, period, multiplier, length, MAtype, src):

    period = int(period)
    multiplier = int(multiplier)
    length = int(length)
    MAtype = int(MAtype)
    src = int(src)

    mavalue = "MA_" + str(MAtype) + "_" + str(length)
    atr = "ATR_" + str(period)
    pm = "pm_" + str(period) + "_" + str(multiplier) + "_" + str(length) + "_" + str(MAtype)
    pmx = "pmX_" + str(period) + "_" + str(multiplier) + "_" + str(length) + "_" + str(MAtype)

    # MAtype==1 --> EMA
    # MAtype==2 --> DEMA
    # MAtype==3 --> T3
    # MAtype==4 --> SMA
    # MAtype==5 --> VIDYA
    # MAtype==6 --> TEMA
    # MAtype==7 --> WMA
    # MAtype==8 --> VWMA
    # MAtype==9 --> zema
    if src == 1:
        masrc = df["close"]
    elif src == 2:
        masrc = (df["high"] + df["low"]) / 2
    elif src == 3:
        masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4

    if MAtype == 1:
        mavalue = ta.EMA(masrc, timeperiod=length)
    elif MAtype == 2:
        mavalue = ta.DEMA(masrc, timeperiod=length)
    elif MAtype == 3:
        mavalue = ta.T3(masrc, timeperiod=length)
    elif MAtype == 4:
        mavalue = ta.SMA(masrc, timeperiod=length)
    elif MAtype == 5:
        mavalue = VIDYA(df, length=length)
    elif MAtype == 6:
        mavalue = ta.TEMA(masrc, timeperiod=length)
    elif MAtype == 7:
        mavalue = ta.WMA(df, timeperiod=length)
    elif MAtype == 8:
        mavalue = vwma(df, length)
    elif MAtype == 9:
        mavalue = zema(df, period=length)

    df[atr] = ta.ATR(df, timeperiod=period)
    df["basic_ub"] = mavalue + ((multiplier / 10) * df[atr])
    df["basic_lb"] = mavalue - ((multiplier / 10) * df[atr])

    basic_ub = df["basic_ub"].values
    final_ub = np.full(len(df), 0.00)
    basic_lb = df["basic_lb"].values
    final_lb = np.full(len(df), 0.00)

    for i in range(period, len(df)):
        final_ub[i] = (
            basic_ub[i] if (basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]
        )
        final_lb[i] = (
            basic_lb[i] if (basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]
        )

    df["final_ub"] = final_ub
    df["final_lb"] = final_lb

    pm_arr = np.full(len(df), 0.00)
    for i in range(period, len(df)):
        pm_arr[i] = (
            final_ub[i]
            if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i])
            else (
                final_lb[i]
                if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i])
                else (
                    final_lb[i]
                    if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i])
                    else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00
                )
            )
        )

    pm = Series(pm_arr)

    # Mark the trend direction up/down
    pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), "down", "up"), None)

    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"]