Danke

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

class Danke(IStrategy):
    INTERFACE_VERSION = 3
    plot_config = {'main_plot': {}, 'subplots': {'buy tag': {'enter_tag': {'color': 'green'}}}}
    # ROI table:
    minimal_roi = {'0': 10}
    stoploss = -0.99
    # Trailing stoploss (not used)
    trailing_stop = False
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03
    use_custom_stoploss = False
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    res_timeframe = 'none'
    info_timeframe = '1h'
    # BTC informative
    has_BTC_base_tf = False
    has_BTC_info_tf = True
    # Backtest Age Filter emulation
    has_bt_agefilter = False
    bt_min_age_days = 3
    # Exchange Downtime protection
    has_downtime_protection = False
    # Do you want to use the hold feature? (with hold-trades.json)
    holdSupportEnabled = False
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 480
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False}
    #############################################################
    #############
    # Enable/Disable conditions
    #############
    buy_params = {'buy_condition_1_enable': True, 'buy_condition_2_enable': True, 'buy_condition_3_enable': True, 'buy_condition_4_enable': True, 'buy_condition_5_enable': True, 'buy_condition_6_enable': True, 'buy_condition_7_enable': True, 'buy_condition_8_enable': True, 'buy_condition_9_enable': True, 'buy_condition_10_enable': True, 'buy_condition_11_enable': True, 'buy_condition_12_enable': True, 'buy_condition_13_enable': True, 'buy_condition_14_enable': True, 'buy_condition_15_enable': True, 'buy_condition_16_enable': True, 'buy_condition_17_enable': True, 'buy_condition_18_enable': True, 'buy_condition_19_enable': True, 'buy_condition_20_enable': True, 'buy_condition_21_enable': True, 'buy_condition_22_enable': True, 'buy_condition_23_enable': True, 'buy_condition_24_enable': True, 'buy_condition_25_enable': True, 'buy_condition_26_enable': True, 'buy_condition_27_enable': True, 'buy_condition_28_enable': True, 'buy_condition_29_enable': True, 'buy_condition_30_enable': True, 'buy_condition_31_enable': True, 'buy_condition_32_enable': True, 'buy_condition_33_enable': True, 'buy_condition_34_enable': True, 'buy_condition_35_enable': True, 'buy_condition_36_enable': True, 'buy_condition_37_enable': True, 'buy_condition_38_enable': True, 'buy_condition_39_enable': True, 'buy_condition_40_enable': True, 'buy_condition_41_enable': True, 'buy_condition_42_enable': True, 'buy_condition_43_enable': True, 'buy_condition_44_enable': True}
    #############
    # Enable/Disable conditions
    ## Dead fish
    #############
    sell_params = {'sell_condition_1_enable': True, 'sell_condition_2_enable': True, 'sell_condition_3_enable': True, 'sell_condition_4_enable': True, 'sell_condition_5_enable': True, 'sell_condition_6_enable': True, 'sell_condition_7_enable': True, 'sell_condition_8_enable': True, 'base_nb_candles_sell': 22, 'high_offset': 1.014, 'high_offset_2': 1.01, 'sell_deadfish_profit': -0.063, 'sell_deadfish_bb_factor': 0.954, 'sell_deadfish_bb_width': 0.043, 'sell_deadfish_volume_factor': 2.37}
    #############################################################
    buy_protection_params = {1: {'ema_fast': False, 'ema_fast_len': '26', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '28', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '80', 'safe_pump': False, 'safe_pump_type': '70', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 2: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '20', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 3: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': True, 'safe_pump_type': '100', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 4: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 5: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '100', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '30', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 6: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 7: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 8: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 9: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 10: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 11: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 12: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '40', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 13: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '24', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 14: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 15: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 16: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '50', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 17: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 18: {'ema_fast': True, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '44', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '72', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 19: {'ema_fast': True, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '36', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 20: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 21: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '90', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 22: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '50', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 23: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '15', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '24', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '110', 'safe_pump': True, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 24: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': True, 'sma200_1h_rising_val': '36', 'safe_dips': True, 'safe_dips_type': '20', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 25: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '20', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '10', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 26: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': True, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': True, 'safe_pump_type': '20', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 27: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 28: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': True, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 29: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 30: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '110', 'safe_pump_period': '36', 'btc_1h_not_downtrend': False}, 31: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 32: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '120', 'safe_pump': True, 'safe_pump_type': '120', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 33: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': True, 'ema_slow_len': '50', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '100', 'safe_pump': True, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 34: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 35: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 36: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}, 37: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '48', 'btc_1h_not_downtrend': False}, 38: {'ema_fast': False, 'ema_fast_len': '50', 'ema_slow': False, 'ema_slow_len': '100', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '50', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '100', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '50', 'safe_dips': True, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '10', 'safe_pump_period': '36', 'btc_1h_not_downtrend': True}, 39: {'ema_fast': False, 'ema_fast_len': '100', 'ema_slow': True, 'ema_slow_len': '15', 'close_above_ema_fast': True, 'close_above_ema_fast_len': '100', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}, 40: {'ema_fast': True, 'ema_fast_len': '12', 'ema_slow': True, 'ema_slow_len': '25', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': True, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '130', 'safe_pump': False, 'safe_pump_type': '50', 'safe_pump_period': '48', 'btc_1h_not_downtrend': True}, 41: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '50', 'safe_pump': False, 'safe_pump_type': '120', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}, 42: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '110', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}, 43: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': True, 'safe_dips_type': '70', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': True}, 44: {'ema_fast': False, 'ema_fast_len': '12', 'ema_slow': False, 'ema_slow_len': '12', 'close_above_ema_fast': False, 'close_above_ema_fast_len': '200', 'close_above_ema_slow': False, 'close_above_ema_slow_len': '200', 'sma200_rising': False, 'sma200_rising_val': '30', 'sma200_1h_rising': False, 'sma200_1h_rising_val': '20', 'safe_dips': False, 'safe_dips_type': '100', 'safe_pump': False, 'safe_pump_type': '100', 'safe_pump_period': '24', 'btc_1h_not_downtrend': False}}
    # SMAOffset
    #base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    #low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(1.01, 1.02, default=sell_params['high_offset_2'], space='sell', optimize=True)
    # Strict dips - level 10
    buy_dip_threshold_10_1 = 0.015
    buy_dip_threshold_10_2 = 0.1
    buy_dip_threshold_10_3 = 0.24
    buy_dip_threshold_10_4 = 0.42
    # Strict dips - level 20
    buy_dip_threshold_20_1 = 0.016
    buy_dip_threshold_20_2 = 0.11
    buy_dip_threshold_20_3 = 0.26
    buy_dip_threshold_20_4 = 0.44
    # Strict dips - level 30
    buy_dip_threshold_30_1 = 0.018
    buy_dip_threshold_30_2 = 0.12
    buy_dip_threshold_30_3 = 0.28
    buy_dip_threshold_30_4 = 0.46
    # Strict dips - level 40
    buy_dip_threshold_40_1 = 0.019
    buy_dip_threshold_40_2 = 0.13
    buy_dip_threshold_40_3 = 0.3
    buy_dip_threshold_40_4 = 0.48
    # Normal dips - level 50
    buy_dip_threshold_50_1 = 0.02
    buy_dip_threshold_50_2 = 0.14
    buy_dip_threshold_50_3 = 0.32
    buy_dip_threshold_50_4 = 0.5
    # Normal dips - level 60
    buy_dip_threshold_60_1 = 0.022
    buy_dip_threshold_60_2 = 0.18
    buy_dip_threshold_60_3 = 0.34
    buy_dip_threshold_60_4 = 0.56
    # Normal dips - level 70
    buy_dip_threshold_70_1 = 0.023
    buy_dip_threshold_70_2 = 0.2
    buy_dip_threshold_70_3 = 0.36
    buy_dip_threshold_70_4 = 0.6
    # Normal dips - level 80
    buy_dip_threshold_80_1 = 0.024
    buy_dip_threshold_80_2 = 0.22
    buy_dip_threshold_80_3 = 0.38
    buy_dip_threshold_80_4 = 0.66
    # Normal dips - level 70
    buy_dip_threshold_90_1 = 0.025
    buy_dip_threshold_90_2 = 0.23
    buy_dip_threshold_90_3 = 0.4
    buy_dip_threshold_90_4 = 0.7
    # Loose dips - level 100
    buy_dip_threshold_100_1 = 0.026
    buy_dip_threshold_100_2 = 0.24
    buy_dip_threshold_100_3 = 0.42
    buy_dip_threshold_100_4 = 0.8
    # Loose dips - level 110
    buy_dip_threshold_110_1 = 0.027
    buy_dip_threshold_110_2 = 0.26
    buy_dip_threshold_110_3 = 0.44
    buy_dip_threshold_110_4 = 0.84
    # Loose dips - level 120
    buy_dip_threshold_120_1 = 0.028
    buy_dip_threshold_120_2 = 0.28
    buy_dip_threshold_120_3 = 0.46
    buy_dip_threshold_120_4 = 0.86
    # Loose dips - level 130
    buy_dip_threshold_130_1 = 0.028
    buy_dip_threshold_130_2 = 0.3
    buy_dip_threshold_130_3 = 0.48
    buy_dip_threshold_130_4 = 0.9
    dump = DecimalParameter(-1, 0, default=-0.15, space='buy', optimize=True)
    dump_buy = DecimalParameter(0, 0.5, default=0.15, space='buy', optimize=True)
    range_filter_length = IntParameter(10, 200, default=200, space='buy', optimize=False)
    # 24 hours - level 10
    buy_pump_pull_threshold_10_24 = 2.2
    buy_pump_threshold_10_24 = 0.42
    # 36 hours - level 10
    buy_pump_pull_threshold_10_36 = 2.0
    buy_pump_threshold_10_36 = 0.58
    # 48 hours - level 10
    buy_pump_pull_threshold_10_48 = 2.0
    buy_pump_threshold_10_48 = 0.8
    # 24 hours - level 20
    buy_pump_pull_threshold_20_24 = 2.2
    buy_pump_threshold_20_24 = 0.46
    # 36 hours - level 20
    buy_pump_pull_threshold_20_36 = 2.0
    buy_pump_threshold_20_36 = 0.6
    # 48 hours - level 20
    buy_pump_pull_threshold_20_48 = 2.0
    buy_pump_threshold_20_48 = 0.81
    # 24 hours - level 30
    buy_pump_pull_threshold_30_24 = 2.2
    buy_pump_threshold_30_24 = 0.5
    # 36 hours - level 30
    buy_pump_pull_threshold_30_36 = 2.0
    buy_pump_threshold_30_36 = 0.62
    # 48 hours - level 30
    buy_pump_pull_threshold_30_48 = 2.0
    buy_pump_threshold_30_48 = 0.82
    # 24 hours - level 40
    buy_pump_pull_threshold_40_24 = 2.2
    buy_pump_threshold_40_24 = 0.54
    # 36 hours - level 40
    buy_pump_pull_threshold_40_36 = 2.0
    buy_pump_threshold_40_36 = 0.63
    # 48 hours - level 40
    buy_pump_pull_threshold_40_48 = 2.0
    buy_pump_threshold_40_48 = 0.84
    # 24 hours - level 50
    buy_pump_pull_threshold_50_24 = 1.75
    buy_pump_threshold_50_24 = 0.6
    # 36 hours - level 50
    buy_pump_pull_threshold_50_36 = 1.75
    buy_pump_threshold_50_36 = 0.64
    # 48 hours - level 50
    buy_pump_pull_threshold_50_48 = 1.75
    buy_pump_threshold_50_48 = 0.85
    # 24 hours - level 60
    buy_pump_pull_threshold_60_24 = 1.75
    buy_pump_threshold_60_24 = 0.62
    # 36 hours - level 60
    buy_pump_pull_threshold_60_36 = 1.75
    buy_pump_threshold_60_36 = 0.66
    # 48 hours - level 60
    buy_pump_pull_threshold_60_48 = 1.75
    buy_pump_threshold_60_48 = 0.9
    # 24 hours - level 70
    buy_pump_pull_threshold_70_24 = 1.75
    buy_pump_threshold_70_24 = 0.63
    # 36 hours - level 70
    buy_pump_pull_threshold_70_36 = 1.75
    buy_pump_threshold_70_36 = 0.67
    # 48 hours - level 70
    buy_pump_pull_threshold_70_48 = 1.75
    buy_pump_threshold_70_48 = 0.95
    # 24 hours - level 80
    buy_pump_pull_threshold_80_24 = 1.75
    buy_pump_threshold_80_24 = 0.64
    # 36 hours - level 80
    buy_pump_pull_threshold_80_36 = 1.75
    buy_pump_threshold_80_36 = 0.68
    # 48 hours - level 80
    buy_pump_pull_threshold_80_48 = 1.75
    buy_pump_threshold_80_48 = 1.0
    # 24 hours - level 90
    buy_pump_pull_threshold_90_24 = 1.75
    buy_pump_threshold_90_24 = 0.65
    # 36 hours - level 90
    buy_pump_pull_threshold_90_36 = 1.75
    buy_pump_threshold_90_36 = 0.69
    # 48 hours - level 90
    buy_pump_pull_threshold_90_48 = 1.75
    buy_pump_threshold_90_48 = 1.1
    # 24 hours - level 100
    buy_pump_pull_threshold_100_24 = 1.7
    buy_pump_threshold_100_24 = 0.66
    # 36 hours - level 100
    buy_pump_pull_threshold_100_36 = 1.7
    buy_pump_threshold_100_36 = 0.7
    # 48 hours - level 100
    buy_pump_pull_threshold_100_48 = 1.4
    buy_pump_threshold_100_48 = 1.6
    # 24 hours - level 110
    buy_pump_pull_threshold_110_24 = 1.7
    buy_pump_threshold_110_24 = 0.7
    # 36 hours - level 110
    buy_pump_pull_threshold_110_36 = 1.7
    buy_pump_threshold_110_36 = 0.74
    # 48 hours - level 110
    buy_pump_pull_threshold_110_48 = 1.4
    buy_pump_threshold_110_48 = 1.8
    # 24 hours - level 120
    buy_pump_pull_threshold_120_24 = 1.7
    buy_pump_threshold_120_24 = 0.78
    # 36 hours - level 120
    buy_pump_pull_threshold_120_36 = 1.7
    buy_pump_threshold_120_36 = 0.78
    # 48 hours - level 120
    buy_pump_pull_threshold_120_48 = 1.4
    buy_pump_threshold_120_48 = 2.0
    # 5 hours - level 10
    buy_dump_protection_10_5 = 0.4
    # 5 hours - level 20
    buy_dump_protection_20_5 = 0.44
    # 5 hours - level 30
    buy_dump_protection_30_5 = 0.5
    # 5 hours - level 40
    buy_dump_protection_40_5 = 0.58
    # 5 hours - level 50
    buy_dump_protection_50_5 = 0.66
    # 5 hours - level 60
    buy_dump_protection_60_5 = 0.74
    buy_min_inc_1 = 0.022
    buy_rsi_1h_min_1 = 20.0
    buy_rsi_1h_max_1 = 84.0
    buy_rsi_1 = 36.0
    buy_mfi_1 = 50.0
    buy_cti_1 = -0.92
    buy_rsi_1h_min_2 = 32.0
    buy_rsi_1h_max_2 = 84.0
    buy_rsi_1h_diff_2 = 38.8
    buy_mfi_2 = 49.0
    buy_bb_offset_2 = 0.983
    buy_volume_2 = 1.6
    buy_bb40_bbdelta_close_3 = 0.045
    buy_bb40_closedelta_close_3 = 0.023
    buy_bb40_tail_bbdelta_3 = 0.418
    buy_ema_rel_3 = 0.986
    buy_cti_3 = -0.5
    buy_bb20_close_bblowerband_4 = 0.979
    buy_bb20_volume_4 = 10.0
    buy_cti_4 = -0.8
    buy_ema_open_mult_5 = 0.018
    buy_bb_offset_5 = 0.996
    buy_ema_rel_5 = 0.915
    buy_cti_5 = -0.84
    buy_volume_5 = 1.8
    buy_ema_open_mult_6 = 0.021
    buy_bb_offset_6 = 0.976
    buy_ema_open_mult_7 = 0.03
    buy_cti_7 = -0.89
    buy_cti_8 = -0.88
    buy_rsi_8 = 40.0
    buy_bb_offset_8 = 0.99
    buy_rsi_1h_8 = 64.0
    buy_volume_8 = 1.8
    buy_ma_offset_9 = 0.968
    buy_bb_offset_9 = 0.942
    buy_rsi_1h_min_9 = 20.0
    buy_rsi_1h_max_9 = 88.0
    buy_mfi_9 = 50.0
    buy_ma_offset_10 = 0.98
    buy_bb_offset_10 = 0.972
    buy_rsi_1h_10 = 50.0
    buy_ma_offset_11 = 0.946
    buy_min_inc_11 = 0.038
    buy_rsi_1h_min_11 = 46.0
    buy_rsi_1h_max_11 = 84.0
    buy_rsi_11 = 38.0
    buy_mfi_11 = 36.0
    buy_ma_offset_12 = 0.921
    buy_rsi_12 = 28.0
    buy_ewo_12 = 1.8
    buy_cti_12 = -0.7
    buy_ma_offset_13 = 0.99
    buy_cti_13 = -0.82
    buy_ewo_13 = -9.0
    buy_ema_open_mult_14 = 0.014
    buy_bb_offset_14 = 0.988
    buy_ma_offset_14 = 0.945
    buy_cti_14 = -0.86
    buy_ema_open_mult_15 = 0.024
    buy_ma_offset_15 = 0.958
    buy_rsi_15 = 28.0
    buy_ema_rel_15 = 0.974
    buy_ma_offset_16 = 0.953
    buy_rsi_16 = 31.0
    buy_ewo_16 = 2.8
    buy_cti_16 = -0.84
    buy_ma_offset_17 = 0.99
    buy_ewo_17 = -9.4
    buy_cti_17 = -0.96
    buy_volume_17 = 2.0
    buy_rsi_18 = 33.0
    buy_bb_offset_18 = 0.986
    buy_volume_18 = 2.0
    buy_cti_18 = -0.86
    buy_rsi_1h_min_19 = 30.0
    buy_chop_max_19 = 21.3
    buy_rsi_20 = 36.0
    buy_rsi_1h_20 = 16.0
    buy_cti_20 = -0.84
    buy_volume_20 = 2.0
    buy_rsi_21 = 14.0
    buy_rsi_1h_21 = 28.0
    buy_cti_21 = -0.902
    buy_volume_21 = 2.0
    buy_volume_22 = 2.0
    buy_bb_offset_22 = 0.984
    buy_ma_offset_22 = 0.942
    buy_ewo_22 = 5.8
    buy_rsi_22 = 36.0
    buy_23_bb_offset = 0.984
    buy_23_ewo = 7.8
    buy_23_rsi = 32.4
    buy_23_rsi_1h = 80.0
    buy_23_cti = -0.66
    buy_23_r = -80.0
    buy_23_r_1h = -80.0
    buy_24_rsi_max = 50.0
    buy_24_rsi_1h_min = 66.9
    buy_25_ma_offset = 0.922
    buy_25_rsi_4 = 38.0
    buy_25_cti = -0.76
    buy_26_zema_low_offset = 0.94
    buy_26_cti = -0.91
    buy_26_r = -35.0
    buy_26_r_1h = -60.0
    buy_26_volume = 2.0
    buy_27_wr_max = 90.0
    buy_27_wr_1h_max = 90.0
    buy_27_rsi_max = 50
    buy_27_cti = -0.93
    buy_27_volume = 2.0
    buy_28_ma_offset = 0.97
    buy_28_ewo = 7.2
    buy_28_rsi = 32.5
    buy_28_cti = -0.9
    buy_29_ma_offset = 0.94
    buy_29_ewo = -4.0
    buy_29_cti = -0.95
    buy_30_ma_offset = 0.97
    buy_30_ewo = 7.4
    buy_30_rsi = 40.0
    buy_30_cti = -0.88
    buy_31_ma_offset = 0.962
    buy_31_ewo = -10.4
    buy_31_wr = -90.0
    buy_31_cti = -0.89
    buy_32_ma_offset = 0.934
    buy_32_dip = 0.005
    buy_32_rsi = 46.0
    buy_32_cti = -0.8
    buy_33_ma_offset = 0.988
    buy_33_rsi = 32.0
    buy_33_cti = -0.88
    buy_33_ewo = 6.4
    buy_33_volume = 2.0
    buy_34_ma_offset = 0.93
    buy_34_dip = 0.005
    buy_34_ewo = -6.0
    buy_34_cti = -0.88
    buy_34_volume = 2.0
    buy_35_ma_offset = 0.984
    buy_35_ewo = 9.6
    buy_35_rsi = 32.0
    buy_35_cti = -0.5
    buy_36_ma_offset = 0.98
    buy_36_ewo = -8.8
    buy_36_cti = -0.8
    buy_37_ma_offset = 0.98
    buy_37_ewo = 9.8
    buy_37_rsi = 56.0
    buy_37_cti = -0.7
    buy_38_ma_offset = 0.98
    buy_38_ewo = -5.2
    buy_38_cti = -0.96
    buy_39_cti = -0.77
    buy_39_r = -60.0
    buy_39_r_1h = -38.0
    buy_40_hrsi = 30.0
    buy_40_cci = -240.0
    buy_40_rsi = 30.0
    buy_40_cti = -0.8
    buy_40_r = -90.0
    buy_40_r_1h = -90.0
    buy_41_cti_1h = -0.84
    buy_41_r_1h = -42.0
    buy_41_ma_offset = 0.96
    buy_41_cti = -0.8
    buy_41_r = -75.0
    buy_42_cti_1h = 0.5
    buy_42_r_1h = -46.0
    buy_42_ema_open_mult = 0.018
    buy_42_bb_offset = 0.992
    buy_43_cti_1h = 0.5
    buy_43_r_1h = -80.0
    buy_43_bb40_bbdelta_close = 0.046
    buy_43_bb40_closedelta_close = 0.02
    buy_43_bb40_tail_bbdelta = 0.5
    buy_43_cti = -0.6
    buy_43_r = -90.0
    buy_44_ma_offset = 0.982
    buy_44_ewo = -18.143
    buy_44_cti = -0.8
    buy_44_r_1h = -75.0
    # Sell
    sell_condition_1_enable = True
    sell_condition_2_enable = True
    sell_condition_3_enable = True
    sell_condition_4_enable = True
    sell_condition_5_enable = True
    sell_condition_6_enable = True
    sell_condition_7_enable = True
    sell_condition_8_enable = True
    # 48h for pump sell checks
    sell_pump_threshold_48_1 = 0.9
    sell_pump_threshold_48_2 = 0.7
    sell_pump_threshold_48_3 = 0.5
    # 36h for pump sell checks
    sell_pump_threshold_36_1 = 0.72
    sell_pump_threshold_36_2 = 4.0
    sell_pump_threshold_36_3 = 1.0
    # 24h for pump sell checks
    sell_pump_threshold_24_1 = 0.68
    sell_pump_threshold_24_2 = 0.62
    sell_pump_threshold_24_3 = 0.88
    sell_rsi_bb_1 = 79.5
    sell_rsi_bb_2 = 81
    sell_rsi_main_3 = 82
    sell_dual_rsi_rsi_4 = 73.4
    sell_dual_rsi_rsi_1h_4 = 79.6
    sell_ema_relative_5 = 0.024
    sell_rsi_diff_5 = 4.4
    sell_rsi_under_6 = 79.0
    sell_rsi_1h_7 = 81.7
    sell_bb_relative_8 = 1.1
    # Profit over EMA200
    sell_custom_profit_bull_0 = 0.012
    sell_custom_rsi_under_bull_0 = 34.0
    sell_custom_profit_bull_1 = 0.02
    sell_custom_rsi_under_bull_1 = 35.0
    sell_custom_profit_bull_2 = 0.03
    sell_custom_rsi_under_bull_2 = 36.0
    sell_custom_profit_bull_3 = 0.04
    sell_custom_rsi_under_bull_3 = 37.0
    sell_custom_profit_bull_4 = 0.05
    sell_custom_rsi_under_bull_4 = 42.0
    sell_custom_profit_bull_5 = 0.06
    sell_custom_rsi_under_bull_5 = 49.0
    sell_custom_profit_bull_6 = 0.07
    sell_custom_rsi_under_bull_6 = 50.0
    sell_custom_profit_bull_7 = 0.08
    sell_custom_rsi_under_bull_7 = 54.0
    sell_custom_profit_bull_8 = 0.09
    sell_custom_rsi_under_bull_8 = 50.0
    sell_custom_profit_bull_9 = 0.1
    sell_custom_rsi_under_bull_9 = 46.0
    sell_custom_profit_bull_10 = 0.12
    sell_custom_rsi_under_bull_10 = 42.0
    sell_custom_profit_bull_11 = 0.2
    sell_custom_rsi_under_bull_11 = 30.0
    sell_custom_profit_bear_0 = 0.012
    sell_custom_rsi_under_bear_0 = 34.0
    sell_custom_profit_bear_1 = 0.02
    sell_custom_rsi_under_bear_1 = 35.0
    sell_custom_profit_bear_2 = 0.03
    sell_custom_rsi_under_bear_2 = 37.0
    sell_custom_profit_bear_3 = 0.04
    sell_custom_rsi_under_bear_3 = 44.0
    sell_custom_profit_bear_4 = 0.05
    sell_custom_rsi_under_bear_4 = 48.0
    sell_custom_profit_bear_5 = 0.06
    sell_custom_rsi_under_bear_5 = 50.0
    sell_custom_rsi_over_bear_5 = 78.0
    sell_custom_profit_bear_6 = 0.07
    sell_custom_rsi_under_bear_6 = 52.0
    sell_custom_rsi_over_bear_6 = 78.0
    sell_custom_profit_bear_7 = 0.08
    sell_custom_rsi_under_bear_7 = 54.0
    sell_custom_rsi_over_bear_7 = 80.0
    sell_custom_profit_bear_8 = 0.09
    sell_custom_rsi_under_bear_8 = 52.0
    sell_custom_rsi_over_bear_8 = 82.0
    sell_custom_profit_bear_9 = 0.1
    sell_custom_rsi_under_bear_9 = 46.0
    sell_custom_profit_bear_10 = 0.12
    sell_custom_rsi_under_bear_10 = 42.0
    sell_custom_profit_bear_11 = 0.2
    sell_custom_rsi_under_bear_11 = 30.0
    # Profit under EMA200
    sell_custom_under_profit_bull_0 = 0.01
    sell_custom_under_rsi_under_bull_0 = 38.0
    sell_custom_under_profit_bull_1 = 0.02
    sell_custom_under_rsi_under_bull_1 = 46.0
    sell_custom_under_profit_bull_2 = 0.03
    sell_custom_under_rsi_under_bull_2 = 47.0
    sell_custom_under_profit_bull_3 = 0.04
    sell_custom_under_rsi_under_bull_3 = 48.0
    sell_custom_under_profit_bull_4 = 0.05
    sell_custom_under_rsi_under_bull_4 = 49.0
    sell_custom_under_profit_bull_5 = 0.06
    sell_custom_under_rsi_under_bull_5 = 50.0
    sell_custom_under_profit_bull_6 = 0.07
    sell_custom_under_rsi_under_bull_6 = 52.0
    sell_custom_under_profit_bull_7 = 0.08
    sell_custom_under_rsi_under_bull_7 = 54.0
    sell_custom_under_profit_bull_8 = 0.09
    sell_custom_under_rsi_under_bull_8 = 50.0
    sell_custom_under_profit_bull_9 = 0.1
    sell_custom_under_rsi_under_bull_9 = 46.0
    sell_custom_under_profit_bull_10 = 0.12
    sell_custom_under_rsi_under_bull_10 = 42.0
    sell_custom_under_profit_bull_11 = 0.2
    sell_custom_under_rsi_under_bull_11 = 30.0
    sell_custom_under_profit_bear_0 = 0.01
    sell_custom_under_rsi_under_bear_0 = 38.0
    sell_custom_under_profit_bear_1 = 0.02
    sell_custom_under_rsi_under_bear_1 = 56.0
    sell_custom_under_profit_bear_2 = 0.03
    sell_custom_under_rsi_under_bear_2 = 57.0
    sell_custom_under_profit_bear_3 = 0.04
    sell_custom_under_rsi_under_bear_3 = 58.0
    sell_custom_under_profit_bear_4 = 0.05
    sell_custom_under_rsi_under_bear_4 = 57.0
    sell_custom_under_profit_bear_5 = 0.06
    sell_custom_under_rsi_under_bear_5 = 56.0
    sell_custom_under_rsi_over_bear_5 = 78.0
    sell_custom_under_profit_bear_6 = 0.07
    sell_custom_under_rsi_under_bear_6 = 55.0
    sell_custom_under_rsi_over_bear_6 = 78.0
    sell_custom_under_profit_bear_7 = 0.08
    sell_custom_under_rsi_under_bear_7 = 54.0
    sell_custom_under_rsi_over_bear_7 = 80.0
    sell_custom_under_profit_bear_8 = 0.09
    sell_custom_under_rsi_under_bear_8 = 50.0
    sell_custom_under_rsi_over_bear_8 = 82.0
    sell_custom_under_profit_bear_9 = 0.1
    sell_custom_under_rsi_under_bear_9 = 46.0
    sell_custom_under_profit_bear_10 = 0.12
    sell_custom_under_rsi_under_bear_10 = 42.0
    sell_custom_under_profit_bear_11 = 0.2
    sell_custom_under_rsi_under_bear_11 = 30.0
    # Profit targets for pumped pairs 48h 1
    sell_custom_pump_profit_1_1 = 0.01
    sell_custom_pump_rsi_1_1 = 34.0
    sell_custom_pump_profit_1_2 = 0.02
    sell_custom_pump_rsi_1_2 = 40.0
    sell_custom_pump_profit_1_3 = 0.04
    sell_custom_pump_rsi_1_3 = 42.0
    sell_custom_pump_profit_1_4 = 0.1
    sell_custom_pump_rsi_1_4 = 34.0
    sell_custom_pump_profit_1_5 = 0.2
    sell_custom_pump_rsi_1_5 = 30.0
    # Profit targets for pumped pairs 36h 1
    sell_custom_pump_profit_2_1 = 0.01
    sell_custom_pump_rsi_2_1 = 34.0
    sell_custom_pump_profit_2_2 = 0.02
    sell_custom_pump_rsi_2_2 = 40.0
    sell_custom_pump_profit_2_3 = 0.04
    sell_custom_pump_rsi_2_3 = 42.0
    sell_custom_pump_profit_2_4 = 0.1
    sell_custom_pump_rsi_2_4 = 34.0
    sell_custom_pump_profit_2_5 = 0.2
    sell_custom_pump_rsi_2_5 = 30.0
    # Profit targets for pumped pairs 24h 1
    sell_custom_pump_profit_3_1 = 0.01
    sell_custom_pump_rsi_3_1 = 34.0
    sell_custom_pump_profit_3_2 = 0.02
    sell_custom_pump_rsi_3_2 = 40.0
    sell_custom_pump_profit_3_3 = 0.04
    sell_custom_pump_rsi_3_3 = 42.0
    sell_custom_pump_profit_3_4 = 0.1
    sell_custom_pump_rsi_3_4 = 34.0
    sell_custom_pump_profit_3_5 = 0.2
    sell_custom_pump_rsi_3_5 = 30.0
    # SMA descending
    sell_custom_dec_profit_min_1 = 0.05
    sell_custom_dec_profit_max_1 = 0.12
    # Under EMA100
    sell_custom_dec_profit_min_2 = 0.07
    sell_custom_dec_profit_max_2 = 0.16
    # Trail 1
    #sell_trail_profit_min_1 = 0.03
    #sell_trail_profit_max_1 = 0.05
    #sell_trail_down_1 = 0.05
    sell_trail_rsi_min_1 = 10.0
    sell_trail_rsi_max_1 = 20.0
    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True)
    # Trail 2
    #sell_trail_profit_min_2 = 0.1
    #sell_trail_profit_max_2 = 0.4
    #sell_trail_down_2 = 0.03
    sell_trail_rsi_min_2 = 20.0
    sell_trail_rsi_max_2 = 50.0
    # Trail 3
    sell_trail_profit_min_3 = 0.06
    sell_trail_profit_max_3 = 0.2
    sell_trail_down_3 = 0.05
    # Trail 4
    sell_trail_profit_min_4 = 0.03
    sell_trail_profit_max_4 = 0.06
    sell_trail_down_4 = 0.02
    # Under & near EMA200, accept profit
    sell_custom_profit_under_profit_min_1 = 0.001
    sell_custom_profit_under_profit_max_1 = 0.008
    sell_custom_profit_under_rel_1 = 0.024
    sell_custom_profit_under_rsi_diff_1 = 4.4
    sell_custom_profit_under_profit_2 = 0.03
    sell_custom_profit_under_rel_2 = 0.024
    sell_custom_profit_under_rsi_diff_2 = 4.4
    # Under & near EMA200, take the loss
    sell_custom_stoploss_under_rel_1 = 0.002
    sell_custom_stoploss_under_rsi_diff_1 = 10.0
    # Long duration/recover stoploss 1
    sell_custom_stoploss_long_profit_min_1 = -0.08
    sell_custom_stoploss_long_profit_max_1 = -0.04
    sell_custom_stoploss_long_recover_1 = 0.14
    sell_custom_stoploss_long_rsi_diff_1 = 4.0
    # Long duration/recover stoploss 2
    sell_custom_stoploss_long_recover_2 = 0.06
    sell_custom_stoploss_long_rsi_diff_2 = 40.0
    # Pumped, descending SMA
    sell_custom_pump_dec_profit_min_1 = 0.005
    sell_custom_pump_dec_profit_max_1 = 0.05
    sell_custom_pump_dec_profit_min_2 = 0.04
    sell_custom_pump_dec_profit_max_2 = 0.06
    sell_custom_pump_dec_profit_min_3 = 0.06
    sell_custom_pump_dec_profit_max_3 = 0.09
    sell_custom_pump_dec_profit_min_4 = 0.02
    sell_custom_pump_dec_profit_max_4 = 0.04
    # Pumped 48h 1, under EMA200
    sell_custom_pump_under_profit_min_1 = 0.04
    sell_custom_pump_under_profit_max_1 = 0.09
    #Deadfish
    is_optimize_deadfish = False
    sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_bb_factor = DecimalParameter(0.9, 1.2, default=1.0, space='sell', optimize=is_optimize_deadfish)
    sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5, space='sell', optimize=is_optimize_deadfish)
    # Pumped trail 1
    sell_custom_pump_trail_profit_min_1 = 0.05
    sell_custom_pump_trail_profit_max_1 = 0.07
    sell_custom_pump_trail_down_1 = 0.05
    sell_custom_pump_trail_rsi_min_1 = 20.0
    sell_custom_pump_trail_rsi_max_1 = 70.0
    # Stoploss, pumped, 48h 1
    sell_custom_stoploss_pump_max_profit_1 = 0.01
    sell_custom_stoploss_pump_min_1 = -0.02
    sell_custom_stoploss_pump_max_1 = -0.01
    sell_custom_stoploss_pump_ma_offset_1 = 0.94
    # Stoploss, pumped, 48h 1
    sell_custom_stoploss_pump_max_profit_2 = 0.025
    sell_custom_stoploss_pump_loss_2 = -0.05
    sell_custom_stoploss_pump_ma_offset_2 = 0.92
    # Stoploss, pumped, 36h 3
    sell_custom_stoploss_pump_max_profit_3 = 0.008
    sell_custom_stoploss_pump_loss_3 = -0.12
    sell_custom_stoploss_pump_ma_offset_3 = 0.88
    # Recover
    sell_custom_recover_profit_1 = 0.06
    sell_custom_recover_min_loss_1 = 0.12
    sell_custom_recover_profit_min_2 = 0.01
    sell_custom_recover_profit_max_2 = 0.05
    sell_custom_recover_min_loss_2 = 0.06
    sell_custom_recover_rsi_2 = 46.0
    # Profit for long duration trades
    sell_custom_long_profit_min_1 = 0.03
    sell_custom_long_profit_max_1 = 0.04
    sell_custom_long_duration_min_1 = 900
    #############################################################
    hold_trades_cache = None

    @informative('15m')
    def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # # Dynamic RSI
        # dataframe['mom'] = momentum(dataframe, 5)
        # dataframe['corr'] = get_rolling_corr(dataframe['mom'], dataframe, 20)
        # dataframe['dyn_rsi'] = ta.RSI(dataframe['close'], get_rsi_lenght(dataframe['corr']))
        # dataframe['dyn_rsi_3r_mean'] = (dataframe['dyn_rsi']).rolling(3).mean()
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
        return dataframe

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

    def load_hold_trades_config(self):
        if self.hold_trades_cache is None:
            hold_trades_config_file = NostalgiaForInfinity772martinsk2.get_hold_trades_config_file()
            if hold_trades_config_file:
                self.hold_trades_cache = HoldsCache(hold_trades_config_file)
        if self.hold_trades_cache:
            self.hold_trades_cache.load()

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

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

    def sell_over_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['close'] > last_candle['ema_200']:
            if last_candle['moderi_96']:
                if current_profit >= self.sell_custom_profit_bull_11:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_11:
                        return (True, 'signal_profit_o_bull_11')
                elif self.sell_custom_profit_bull_11 > current_profit >= self.sell_custom_profit_bull_10:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_10:
                        return (True, 'signal_profit_o_bull_10')
                elif self.sell_custom_profit_bull_10 > current_profit >= self.sell_custom_profit_bull_9:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_9:
                        return (True, 'signal_profit_o_bull_9')
                elif self.sell_custom_profit_bull_9 > current_profit >= self.sell_custom_profit_bull_8:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_8:
                        return (True, 'signal_profit_o_bull_8')
                elif self.sell_custom_profit_bull_8 > current_profit >= self.sell_custom_profit_bull_7:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_7:
                        return (True, 'signal_profit_o_bull_7')
                elif self.sell_custom_profit_bull_7 > current_profit >= self.sell_custom_profit_bull_6:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_6 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_6')
                elif self.sell_custom_profit_bull_6 > current_profit >= self.sell_custom_profit_bull_5:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_5 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_5')
                elif self.sell_custom_profit_bull_5 > current_profit >= self.sell_custom_profit_bull_4:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_4 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_4')
                elif self.sell_custom_profit_bull_4 > current_profit >= self.sell_custom_profit_bull_3:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_3 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_3')
                elif self.sell_custom_profit_bull_3 > current_profit >= self.sell_custom_profit_bull_2:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_2 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_2')
                elif self.sell_custom_profit_bull_2 > current_profit >= self.sell_custom_profit_bull_1:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_1 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_1')
                elif self.sell_custom_profit_bull_1 > current_profit >= self.sell_custom_profit_bull_0:
                    if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_0 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_o_bull_0')
            elif current_profit >= self.sell_custom_profit_bear_11:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_11:
                    return (True, 'signal_profit_o_bear_11')
            elif self.sell_custom_profit_bear_11 > current_profit >= self.sell_custom_profit_bear_10:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_10:
                    return (True, 'signal_profit_o_bear_10')
            elif self.sell_custom_profit_bear_10 > current_profit >= self.sell_custom_profit_bear_9:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_9:
                    return (True, 'signal_profit_o_bear_9')
            elif self.sell_custom_profit_bear_9 > current_profit >= self.sell_custom_profit_bear_8:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_8:
                    return (True, 'signal_profit_o_bear_8_1')
                elif last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_8:
                    return (True, 'signal_profit_o_bear_8_2')
            elif self.sell_custom_profit_bear_8 > current_profit >= self.sell_custom_profit_bear_7:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_7:
                    return (True, 'signal_profit_o_bear_7_1')
                elif last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_7:
                    return (True, 'signal_profit_o_bear_7_2')
            elif self.sell_custom_profit_bear_7 > current_profit >= self.sell_custom_profit_bear_6:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_6:
                    return (True, 'signal_profit_o_bear_6_1')
                elif last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_6:
                    return (True, 'signal_profit_o_bear_6_2')
            elif self.sell_custom_profit_bear_6 > current_profit >= self.sell_custom_profit_bear_5:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_5:
                    return (True, 'signal_profit_o_bear_5_1')
                elif last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_5:
                    return (True, 'signal_profit_o_bear_5_2')
            elif self.sell_custom_profit_bear_5 > current_profit >= self.sell_custom_profit_bear_4:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_4:
                    return (True, 'signal_profit_o_bear_4')
            elif self.sell_custom_profit_bear_4 > current_profit >= self.sell_custom_profit_bear_3:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_3 and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_3')
            elif self.sell_custom_profit_bear_3 > current_profit >= self.sell_custom_profit_bear_2:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_2 and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_2')
            elif self.sell_custom_profit_bear_2 > current_profit >= self.sell_custom_profit_bear_1:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_1 and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_1')
            elif self.sell_custom_profit_bear_1 > current_profit >= self.sell_custom_profit_bear_0:
                if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_0 and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_o_bear_0')
        return (False, None)

    def sell_under_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['close'] < last_candle['ema_200']:
            if last_candle['moderi_96']:
                if current_profit >= self.sell_custom_under_profit_bull_11:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_11:
                        return (True, 'signal_profit_u_bull_11')
                elif self.sell_custom_under_profit_bull_11 > current_profit >= self.sell_custom_under_profit_bull_10:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_10:
                        return (True, 'signal_profit_u_bull_10')
                elif self.sell_custom_under_profit_bull_10 > current_profit >= self.sell_custom_under_profit_bull_9:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_9:
                        return (True, 'signal_profit_u_bull_9')
                elif self.sell_custom_under_profit_bull_9 > current_profit >= self.sell_custom_under_profit_bull_8:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_8:
                        return (True, 'signal_profit_u_bull_8')
                elif self.sell_custom_under_profit_bull_8 > current_profit >= self.sell_custom_under_profit_bull_7:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_7:
                        return (True, 'signal_profit_u_bull_7')
                elif self.sell_custom_under_profit_bull_7 > current_profit >= self.sell_custom_under_profit_bull_6:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_6:
                        return (True, 'signal_profit_u_bull_6')
                elif self.sell_custom_under_profit_bull_6 > current_profit >= self.sell_custom_under_profit_bull_5:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_5:
                        return (True, 'signal_profit_u_bull_5')
                elif self.sell_custom_under_profit_bull_5 > current_profit >= self.sell_custom_under_profit_bull_4:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_4:
                        return (True, 'signal_profit_u_bull_4')
                elif self.sell_custom_under_profit_bull_4 > current_profit >= self.sell_custom_under_profit_bull_3:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_3:
                        return (True, 'signal_profit_u_bull_3')
                elif self.sell_custom_under_profit_bull_3 > current_profit >= self.sell_custom_under_profit_bull_2:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_2:
                        return (True, 'signal_profit_u_bull_2')
                elif self.sell_custom_under_profit_bull_2 > current_profit >= self.sell_custom_under_profit_bull_1:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_1:
                        return (True, 'signal_profit_u_bull_1')
                elif self.sell_custom_under_profit_bull_1 > current_profit >= self.sell_custom_under_profit_bull_0:
                    if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_0 and last_candle['cmf'] < 0.0:
                        return (True, 'signal_profit_u_bull_0')
            elif current_profit >= self.sell_custom_under_profit_bear_11:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_11:
                    return (True, 'signal_profit_u_bear_11')
            elif self.sell_custom_under_profit_bear_11 > current_profit >= self.sell_custom_under_profit_bear_10:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_10:
                    return (True, 'signal_profit_u_bear_10')
            elif self.sell_custom_under_profit_bear_10 > current_profit >= self.sell_custom_under_profit_bear_9:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_9:
                    return (True, 'signal_profit_u_bear_9')
            elif self.sell_custom_under_profit_bear_9 > current_profit >= self.sell_custom_under_profit_bear_8:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_8:
                    return (True, 'signal_profit_u_bear_8_1')
                elif last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_8:
                    return (True, 'signal_profit_u_bear_8_2')
            elif self.sell_custom_under_profit_bear_8 > current_profit >= self.sell_custom_under_profit_bear_7:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_7:
                    return (True, 'signal_profit_u_bear_7_1')
                elif last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_7:
                    return (True, 'signal_profit_u_bear_7_2')
            elif self.sell_custom_under_profit_bear_7 > current_profit >= self.sell_custom_under_profit_bear_6:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_6:
                    return (True, 'signal_profit_u_bear_6_1')
                elif last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_6:
                    return (True, 'signal_profit_u_bear_6_2')
            elif self.sell_custom_under_profit_bear_6 > current_profit >= self.sell_custom_under_profit_bear_5:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_5:
                    return (True, 'signal_profit_u_bear_5_1')
                elif last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_5:
                    return (True, 'signal_profit_u_bear_5_2')
            elif self.sell_custom_under_profit_bear_5 > current_profit >= self.sell_custom_under_profit_bear_4:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_4:
                    return (True, 'signal_profit_u_bear_4')
            elif self.sell_custom_under_profit_bear_4 > current_profit >= self.sell_custom_under_profit_bear_3:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_3:
                    return (True, 'signal_profit_u_bear_3')
            elif self.sell_custom_under_profit_bear_3 > current_profit >= self.sell_custom_under_profit_bear_2:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_2:
                    return (True, 'signal_profit_u_bear_2')
            elif self.sell_custom_under_profit_bear_2 > current_profit >= self.sell_custom_under_profit_bear_1:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_1:
                    return (True, 'signal_profit_u_bear_1')
            elif self.sell_custom_under_profit_bear_1 > current_profit >= self.sell_custom_under_profit_bear_0:
                if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_0 and last_candle['cmf'] < 0.0:
                    return (True, 'signal_profit_u_bear_0')
        return (False, None)

    def sell_pump_main(self, current_profit: float, last_candle) -> tuple:
        if last_candle['sell_pump_48_1_1h']:
            if current_profit >= self.sell_custom_pump_profit_1_5:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_5:
                    return (True, 'signal_profit_p_1_5')
            elif self.sell_custom_pump_profit_1_5 > current_profit >= self.sell_custom_pump_profit_1_4:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_4:
                    return (True, 'signal_profit_p_1_4')
            elif self.sell_custom_pump_profit_1_4 > current_profit >= self.sell_custom_pump_profit_1_3:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_3:
                    return (True, 'signal_profit_p_1_3')
            elif self.sell_custom_pump_profit_1_3 > current_profit >= self.sell_custom_pump_profit_1_2:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_2:
                    return (True, 'signal_profit_p_1_2')
            elif self.sell_custom_pump_profit_1_2 > current_profit >= self.sell_custom_pump_profit_1_1:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_1:
                    return (True, 'signal_profit_p_1_1')
        elif last_candle['sell_pump_36_1_1h']:
            if current_profit >= self.sell_custom_pump_profit_2_5:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_5:
                    return (True, 'signal_profit_p_2_5')
            elif self.sell_custom_pump_profit_2_5 > current_profit >= self.sell_custom_pump_profit_2_4:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_4:
                    return (True, 'signal_profit_p_2_4')
            elif self.sell_custom_pump_profit_2_4 > current_profit >= self.sell_custom_pump_profit_2_3:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_3:
                    return (True, 'signal_profit_p_2_3')
            elif self.sell_custom_pump_profit_2_3 > current_profit >= self.sell_custom_pump_profit_2_2:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_2:
                    return (True, 'signal_profit_p_2_2')
            elif self.sell_custom_pump_profit_2_2 > current_profit >= self.sell_custom_pump_profit_2_1:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_1:
                    return (True, 'signal_profit_p_2_1')
        elif last_candle['sell_pump_24_1_1h']:
            if current_profit >= self.sell_custom_pump_profit_3_5:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_5:
                    return (True, 'signal_profit_p_3_5')
            elif self.sell_custom_pump_profit_3_5 > current_profit >= self.sell_custom_pump_profit_3_4:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_4:
                    return (True, 'signal_profit_p_3_4')
            elif self.sell_custom_pump_profit_3_4 > current_profit >= self.sell_custom_pump_profit_3_3:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_3:
                    return (True, 'signal_profit_p_3_3')
            elif self.sell_custom_pump_profit_3_3 > current_profit >= self.sell_custom_pump_profit_3_2:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_2:
                    return (True, 'signal_profit_p_3_2')
            elif self.sell_custom_pump_profit_3_2 > current_profit >= self.sell_custom_pump_profit_3_1:
                if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_1:
                    return (True, 'signal_profit_p_3_1')
        return (False, None)

    def sell_dec_main(self, current_profit: float, last_candle) -> tuple:
        if self.sell_custom_dec_profit_max_1 > current_profit >= self.sell_custom_dec_profit_min_1 and last_candle['sma_200_dec_20']:
            return (True, 'signal_profit_d_1')
        elif self.sell_custom_dec_profit_max_2 > current_profit >= self.sell_custom_dec_profit_min_2 and last_candle['close'] < last_candle['ema_100']:
            return (True, 'signal_profit_d_2')
        return (False, None)

    def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        if self.sell_trail_profit_max_1 > current_profit >= self.sell_trail_profit_min_1 and self.sell_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_trail_rsi_max_1 and (max_profit > current_profit + self.sell_trail_down_1) and (last_candle['moderi_96'] == False):
            return (True, 'signal_profit_t_1')
        elif self.sell_trail_profit_max_2 > current_profit >= self.sell_trail_profit_min_2 and self.sell_trail_rsi_min_2 < last_candle['rsi_14'] < self.sell_trail_rsi_max_2 and (max_profit > current_profit + self.sell_trail_down_2) and (last_candle['ema_25'] < last_candle['ema_50']):
            return (True, 'signal_profit_t_2')
        elif self.sell_trail_profit_max_3 > current_profit >= self.sell_trail_profit_min_3 and max_profit > current_profit + self.sell_trail_down_3 and last_candle['sma_200_dec_20_1h']:
            return (True, 'signal_profit_t_3')
        elif self.sell_trail_profit_max_4 > current_profit >= self.sell_trail_profit_min_4 and max_profit > current_profit + self.sell_trail_down_4 and last_candle['sma_200_dec_24'] and (last_candle['cmf'] < 0.0):
            return (True, 'signal_profit_t_4')
        return (False, None)

    def sell_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple:
        # Pumped pair, short duration
        if last_candle['sell_pump_24_1_1h'] and 0.2 > current_profit >= 0.07 and (current_time - timedelta(minutes=30) < trade.open_date_utc):
            return (True, 'signal_profit_p_s_1')
        elif self.sell_custom_long_profit_min_1 < current_profit < self.sell_custom_long_profit_max_1 and current_time - timedelta(minutes=self.sell_custom_long_duration_min_1) > trade.open_date_utc:
            return (True, 'signal_profit_l_1')
        return (False, None)

    def sell_under_min(self, current_profit: float, last_candle) -> tuple:
        if last_candle['moderi_96'] == False:
            # Downtrend
            if self.sell_custom_profit_under_profit_max_1 > current_profit >= self.sell_custom_profit_under_profit_min_1 and last_candle['close'] < last_candle['ema_200'] and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.sell_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_1):
                return (True, 'signal_profit_u_e_1')
        # Uptrend
        elif current_profit >= self.sell_custom_profit_under_profit_2 and last_candle['close'] < last_candle['ema_200'] and ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.sell_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_2):
            return (True, 'signal_profit_u_e_2')
        return (False, None)

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

    def sell_pump_dec(self, current_profit: float, last_candle) -> tuple:
        if self.sell_custom_pump_dec_profit_max_1 > current_profit >= self.sell_custom_pump_dec_profit_min_1 and last_candle['sell_pump_48_1_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_1')
        elif self.sell_custom_pump_dec_profit_max_2 > current_profit >= self.sell_custom_pump_dec_profit_min_2 and last_candle['sell_pump_48_2_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_2')
        elif self.sell_custom_pump_dec_profit_max_3 > current_profit >= self.sell_custom_pump_dec_profit_min_3 and last_candle['sell_pump_48_3_1h'] and last_candle['sma_200_dec_20'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_d_3')
        elif self.sell_custom_pump_dec_profit_max_4 > current_profit >= self.sell_custom_pump_dec_profit_min_4 and last_candle['sma_200_dec_20'] and last_candle['sell_pump_24_2_1h']:
            return (True, 'signal_profit_p_d_4')
        return (False, None)

    def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple:
        # Pumped 48h 1, under EMA200
        if self.sell_custom_pump_under_profit_max_1 > current_profit >= self.sell_custom_pump_under_profit_min_1 and last_candle['sell_pump_48_1_1h'] and (last_candle['close'] < last_candle['ema_200']):
            return (True, 'signal_profit_p_u_1')
        # Pumped 36h 2, trail 1
        elif last_candle['sell_pump_36_2_1h'] and self.sell_custom_pump_trail_profit_max_1 > current_profit >= self.sell_custom_pump_trail_profit_min_1 and (self.sell_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_custom_pump_trail_rsi_max_1) and (max_profit > current_profit + self.sell_custom_pump_trail_down_1):
            return (True, 'signal_profit_p_t_1')
        return (False, None)

    def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple:
        if max_loss > self.sell_custom_recover_min_loss_1 and current_profit >= self.sell_custom_recover_profit_1:
            return (True, 'signal_profit_r_1')
        elif max_loss > self.sell_custom_recover_min_loss_2 and self.sell_custom_recover_profit_max_2 > current_profit >= self.sell_custom_recover_profit_min_2 and (last_candle['rsi_14'] < self.sell_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']):
            return (True, 'signal_profit_r_2')
        return (False, None)

    def sell_r_1(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle['r_480'] > -0.5:
                return (True, 'signal_profit_w_1_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -0.6:
                return (True, 'signal_profit_w_1_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -0.7:
                return (True, 'signal_profit_w_1_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -0.8:
                return (True, 'signal_profit_w_1_4')
        elif 0.06 > current_profit >= 0.05:
            if last_candle['r_480'] > -0.9:
                return (True, 'signal_profit_w_1_5')
        elif 0.07 > current_profit >= 0.06:
            if last_candle['r_480'] > -2.0:
                return (True, 'signal_profit_w_1_6')
        elif 0.08 > current_profit >= 0.07:
            if last_candle['r_480'] > -2.2:
                return (True, 'signal_profit_w_1_7')
        elif 0.09 > current_profit >= 0.08:
            if last_candle['r_480'] > -2.4:
                return (True, 'signal_profit_w_1_8')
        elif 0.1 > current_profit >= 0.09:
            if last_candle['r_480'] > -2.6:
                return (True, 'signal_profit_w_1_9')
        elif 0.12 > current_profit >= 0.1:
            if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 72.0:
                return (True, 'signal_profit_w_1_10')
        elif 0.2 > current_profit >= 0.12:
            if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 78.0:
                return (True, 'signal_profit_w_1_11')
        elif current_profit >= 0.2:
            if last_candle['r_480'] > -1.0 and last_candle['rsi_14'] > 80.0:
                return (True, 'signal_profit_w_1_12')
        return (False, None)

    def sell_r_2(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -4.1 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -4.2 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -4.3 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_4')
        elif 0.06 > current_profit >= 0.05:
            if last_candle['r_480'] > -4.4 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_5')
        elif 0.07 > current_profit >= 0.06:
            if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_6')
        elif 0.08 > current_profit >= 0.07:
            if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 80.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_7')
        elif 0.09 > current_profit >= 0.08:
            if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_8')
        elif 0.1 > current_profit >= 0.09:
            if last_candle['r_480'] > -4.8 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_9')
        elif 0.12 > current_profit >= 0.1:
            if last_candle['r_480'] > -4.4 and last_candle['rsi_14'] > 80.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_10')
        elif 0.2 > current_profit >= 0.12:
            if last_candle['r_480'] > -3.2 and last_candle['rsi_14'] > 81.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_11')
        elif current_profit >= 0.2:
            if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 81.5 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_2_12')
        return (False, None)

    def sell_r_3(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_3_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -3.5 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_3_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 74.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_3_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0):
                return (True, 'signal_profit_w_3_4')
        return (False, None)

    def sell_r_4(self, current_profit: float, last_candle) -> tuple:
        if 0.02 > current_profit >= 0.012:
            if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_1')
        elif 0.03 > current_profit >= 0.02:
            if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_2')
        elif 0.04 > current_profit >= 0.03:
            if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_3')
        elif 0.05 > current_profit >= 0.04:
            if last_candle['r_480'] > -3.5 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_4')
        elif 0.06 > current_profit >= 0.05:
            if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 68.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_5')
        elif 0.07 > current_profit >= 0.06:
            if last_candle['r_480'] > -4.5 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_6')
        elif 0.08 > current_profit >= 0.07:
            if last_candle['r_480'] > -5.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_7')
        elif 0.09 > current_profit >= 0.08:
            if last_candle['r_480'] > -5.5 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_8')
        elif 0.1 > current_profit >= 0.09:
            if last_candle['r_480'] > -4.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_9')
        elif 0.12 > current_profit >= 0.1:
            if last_candle['r_480'] > -3.0 and last_candle['rsi_14'] > 79.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_10')
        elif 0.2 > current_profit >= 0.12:
            if last_candle['r_480'] > -2.5 and last_candle['rsi_14'] > 80.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_11')
        elif current_profit >= 0.2:
            if last_candle['r_480'] > -2.0 and last_candle['rsi_14'] > 80.0 and (last_candle['cti'] > 0.9):
                return (True, 'signal_profit_w_4_12')
        return (False, None)

    def sell_quick_mode(self, current_profit: float, max_profit: float, last_candle, previous_candle_1) -> tuple:
        if 0.06 > current_profit > 0.02 and last_candle['rsi_14'] > 80.0:
            return (True, 'signal_profit_q_1')
        if 0.06 > current_profit > 0.02 and last_candle['cti'] > 0.95:
            return (True, 'signal_profit_q_2')
        if last_candle['close'] < last_candle['atr_high_thresh_q'] and previous_candle_1['close'] > previous_candle_1['atr_high_thresh_q']:
            if 0.05 > current_profit > 0.02:
                return (True, 'signal_profit_q_atr')
            elif current_profit < -0.08:
                return (True, 'signal_stoploss_q_atr')
        if 0.04 > current_profit > 0.02 and last_candle['pm'] <= last_candle['pmax_thresh'] and (last_candle['close'] > last_candle['sma_21'] * 1.1):
            return (True, 'signal_profit_q_pmax_bull')
        if 0.045 > current_profit > 0.003 and last_candle['pm'] > last_candle['pmax_thresh'] and (last_candle['close'] > last_candle['sma_21'] * 1.016):
            return (True, 'signal_profit_q_pmax_bear')
        return (False, None)

    def sell_ichi(self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple:
        if 0.0 < current_profit < 0.05 and current_time - timedelta(minutes=1440) > trade.open_date_utc and (last_candle['rsi_14'] > 78.0):
            return (True, 'signal_profit_ichi_u')
        elif -0.03 < current_profit < -0.0 and current_time - timedelta(minutes=1440) > trade.open_date_utc and (last_candle['rsi_14'] > 75.0):
            return (True, 'signal_stoploss_ichi_u')
        elif max_loss > 0.07 and current_profit > 0.02:
            return (True, 'signal_profit_ichi_r_0')
        elif max_loss > 0.06 and current_profit > 0.03:
            return (True, 'signal_profit_ichi_r_1')
        elif max_loss > 0.05 and current_profit > 0.04:
            return (True, 'signal_profit_ichi_r_2')
        elif max_loss > 0.04 and current_profit > 0.05:
            return (True, 'signal_profit_ichi_r_3')
        elif max_loss > 0.03 and current_profit > 0.06:
            return (True, 'signal_profit_ichi_r_4')
        elif 0.05 < current_profit < 0.1 and current_time - timedelta(minutes=720) > trade.open_date_utc:
            return (True, 'signal_profit_ichi_slow')
        elif 0.07 < current_profit < 0.1 and max_profit - current_profit > 0.025 and (max_profit > 0.1):
            return (True, 'signal_profit_ichi_t')
        elif current_profit < -0.1:
            return (True, 'signal_stoploss_ichi')
        return (False, None)

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        previous_candle_1 = dataframe.iloc[-2]
        previous_candle_2 = dataframe.iloc[-3]
        previous_candle_3 = dataframe.iloc[-4]
        previous_candle_4 = dataframe.iloc[-5]
        previous_candle_5 = dataframe.iloc[-6]
        enter_tag = 'empty'
        if hasattr(trade, 'enter_tag') and trade.buy_tag is not None:
            enter_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]
                enter_tag = buy_signal_candle['enter_tag'] if buy_signal_candle['enter_tag'] != '' else 'empty'
        buy_tags = buy_tag.split()
        max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate
        max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate
        filled_buys = trade.select_filled_orders('entry')
        count_of_buys = len(filled_buys)
        if current_profit < self.sell_deadfish_profit.value and last_candle['close'] < last_candle['ema_200'] and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0):
            return f'sell_stoploss_deadfish( {enter_tag})'
        if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_trail_down_1.value):
            return 'trail_target_1'
        elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & ((trade.max_rate - trade.open_rate) / 100 > current_profit + self.sell_trail_down_2.value):
            return 'trail_target_2'
        elif (current_profit > 3) & (last_candle['rsi'] > 85):
            return 'RSI-85 target'
        if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (last_candle['rsi'] > 50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']):
            return 'sell signal1'
        if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value) & (last_candle['rsi'] > 50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']):
            return 'sell signal1 * 1.01'
        if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value):
            return 'sell signal2'
        if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']):
            return 'sell signal2 * 1.01'
        if (current_profit > 0.05) & (last_candle['rsi'] < 42.0) & (last_candle['cmf'] < -0.4):
            return f'profit exit1( {enter_tag})'
        elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.0) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0):
            return f'profit exit2( {enter_tag})'
        elif (current_profit > 0.05) & (last_candle['rsi'] < 41.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_1h'] < -0.0):
            return f'profit exit3( {enter_tag})'
        elif (current_profit > 0.05) & (last_candle['rsi'] < 44.0) & (last_candle['cmf'] < -0.1) & (last_candle['cmf_15m'] < -0.1) & (last_candle['cmf_1h'] < -0.1):
            return f'profit exit4( {enter_tag})'
        elif (current_profit > 0.05) & (last_candle['rsi'] < 40.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_15m'] < -0.2):
            return f'pprofit exit5( {enter_tag})'
        elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.4) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0):
            return f'profit exit6( {enter_tag})'
        elif (current_profit > 0.02) & (last_candle['rsi'] < 30.0) & (last_candle['cmf'] < -0.4):
            return f'profit exit bear1( {enter_tag})'
        elif (current_profit > 0.03) & (last_candle['rsi'] < 35.0) & (last_candle['cmf'] < -0.4):
            return f'profit exit bear2( {enter_tag})'
        elif (current_profit > 0.01) & (last_candle['rsi'] < 25.0) & (last_candle['cmf'] < -0.6):
            return f'profit exit bear3( {enter_tag})'
        return None

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe)
        # EMA
        informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12)
        informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
        informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20)
        informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25)
        informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26)
        informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35)
        informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
        informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
        informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
        # SMA
        informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
        informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20)
        # RSI
        informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb20_2_low'] = bollinger['lower']
        informative_1h['bb20_2_mid'] = bollinger['mid']
        informative_1h['bb20_2_upp'] = bollinger['upper']
        # Chaikin Money Flow
        informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20)
        # Williams %R
        informative_1h['r_480'] = williams_r(informative_1h, period=480)
        # CTI
        informative_1h['cti'] = pta.cti(informative_1h['close'], length=20)
        # Ichimoku
        ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
        informative_1h['chikou_span'] = ichi['chikou_span']
        informative_1h['tenkan_sen'] = ichi['tenkan_sen']
        informative_1h['kijun_sen'] = ichi['kijun_sen']
        informative_1h['senkou_a'] = ichi['senkou_span_a']
        informative_1h['senkou_b'] = ichi['senkou_span_b']
        informative_1h['leading_senkou_span_a'] = ichi['leading_senkou_span_a']
        informative_1h['leading_senkou_span_b'] = ichi['leading_senkou_span_b']
        informative_1h['chikou_span_greater'] = (informative_1h['chikou_span'] > informative_1h['senkou_a']).shift(30).fillna(False)
        informative_1h.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1)
        # EFI - Elders Force Index
        informative_1h['efi'] = pta.efi(informative_1h['close'], informative_1h['volume'], length=13)
        # SSL
        ssl_down, ssl_up = SSLChannels(informative_1h, 10)
        informative_1h['ssl_down'] = ssl_down
        informative_1h['ssl_up'] = ssl_up
        # Pump protections
        informative_1h['hl_pct_change_48'] = self.range_percent_change(informative_1h, 'HL', 48)
        informative_1h['hl_pct_change_36'] = self.range_percent_change(informative_1h, 'HL', 36)
        informative_1h['hl_pct_change_24'] = self.range_percent_change(informative_1h, 'HL', 24)
        informative_1h['oc_pct_change_48'] = self.range_percent_change(informative_1h, 'OC', 48)
        informative_1h['oc_pct_change_36'] = self.range_percent_change(informative_1h, 'OC', 36)
        informative_1h['oc_pct_change_24'] = self.range_percent_change(informative_1h, 'OC', 24)
        informative_1h['hl_pct_change_5'] = self.range_percent_change(informative_1h, 'HL', 5)
        informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min()
        informative_1h['safe_pump_24_10'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_10_24, self.buy_pump_pull_threshold_10_24)
        informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_10_36, self.buy_pump_pull_threshold_10_36)
        informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_10_48, self.buy_pump_pull_threshold_10_48)
        informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_20_24, self.buy_pump_pull_threshold_20_24)
        informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_20_36, self.buy_pump_pull_threshold_20_36)
        informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_20_48, self.buy_pump_pull_threshold_20_48)
        informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_30_24, self.buy_pump_pull_threshold_30_24)
        informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_30_36, self.buy_pump_pull_threshold_30_36)
        informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_30_48, self.buy_pump_pull_threshold_30_48)
        informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_40_24, self.buy_pump_pull_threshold_40_24)
        informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_40_36, self.buy_pump_pull_threshold_40_36)
        informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_40_48, self.buy_pump_pull_threshold_40_48)
        informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_50_24, self.buy_pump_pull_threshold_50_24)
        informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_50_36, self.buy_pump_pull_threshold_50_36)
        informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_50_48, self.buy_pump_pull_threshold_50_48)
        informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_60_24, self.buy_pump_pull_threshold_60_24)
        informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_60_36, self.buy_pump_pull_threshold_60_36)
        informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_60_48, self.buy_pump_pull_threshold_60_48)
        informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_70_24, self.buy_pump_pull_threshold_70_24)
        informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_70_36, self.buy_pump_pull_threshold_70_36)
        informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_70_48, self.buy_pump_pull_threshold_70_48)
        informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_80_24, self.buy_pump_pull_threshold_80_24)
        informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_80_36, self.buy_pump_pull_threshold_80_36)
        informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_80_48, self.buy_pump_pull_threshold_80_48)
        informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_90_24, self.buy_pump_pull_threshold_90_24)
        informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_90_36, self.buy_pump_pull_threshold_90_36)
        informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_90_48, self.buy_pump_pull_threshold_90_48)
        informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_100_24, self.buy_pump_pull_threshold_100_24)
        informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_100_36, self.buy_pump_pull_threshold_100_36)
        informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_100_48, self.buy_pump_pull_threshold_100_48)
        informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_110_24, self.buy_pump_pull_threshold_110_24)
        informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_110_36, self.buy_pump_pull_threshold_110_36)
        informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_110_48, self.buy_pump_pull_threshold_110_48)
        informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_120_24, self.buy_pump_pull_threshold_120_24)
        informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_120_36, self.buy_pump_pull_threshold_120_36)
        informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_120_48, self.buy_pump_pull_threshold_120_48)
        informative_1h['safe_dump_10'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_10_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_20'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_20_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_30'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_30_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_40'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_40_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_50'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_50_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['safe_dump_60'] = (informative_1h['hl_pct_change_5'] < self.buy_dump_protection_60_5) | (informative_1h['close'] < informative_1h['low_5']) | (informative_1h['close'] > informative_1h['open'])
        informative_1h['sell_pump_48_1'] = informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_1
        informative_1h['sell_pump_48_2'] = informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_2
        informative_1h['sell_pump_48_3'] = informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_3
        informative_1h['sell_pump_36_1'] = informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_1
        informative_1h['sell_pump_36_2'] = informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_2
        informative_1h['sell_pump_36_3'] = informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_3
        informative_1h['sell_pump_24_1'] = informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_1
        informative_1h['sell_pump_24_2'] = informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_2
        informative_1h['sell_pump_24_3'] = informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_3
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # BB 40 - STD2
        bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['bb40_2_low'] = bb_40_std2['lower']
        dataframe['bb40_2_mid'] = bb_40_std2['mid']
        dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
        # BB 20 - STD2
        bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb20_2_low'] = bb_20_std2['lower']
        dataframe['bb20_2_mid'] = bb_20_std2['mid']
        dataframe['bb20_2_upp'] = bb_20_std2['upper']
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['hma_5'] = qtpylib.hull_moving_average(dataframe['close'], window=30)
        dataframe['hma_4'] = qtpylib.hull_moving_average(dataframe['close'], window=15)
        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe = dataframe.copy()
        # EMA 200
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
        dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15)
        dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_35'] = ta.EMA(dataframe, timeperiod=35)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        #pump stregth
        dataframe['zema_30'] = ftt.zema(dataframe, period=30)
        dataframe['zema_200'] = ftt.zema(dataframe, period=100)
        dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']
        # SMA
        dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
        dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        dataframe['sma_200_dec_24'] = dataframe['sma_200'] < dataframe['sma_200'].shift(24)
        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)
        # CMF
        dataframe['cmf'] = chaikin_money_flow(dataframe, 20)
        # EWO
        dataframe['ewo'] = ewo(dataframe, 50, 200)
        # RSI
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
        # Chopiness
        dataframe['chop'] = qtpylib.chopiness(dataframe, 14)
        # Zero-Lag EMA
        dataframe['zema_61'] = zema(dataframe, period=61)
        # Williams %R
        dataframe['r_480'] = williams_r(dataframe, period=480)
        # Stochastic RSI
        stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0)
        dataframe['stochrsi_fastk_96'] = stochrsi['fastk']
        dataframe['stochrsi_fastd_96'] = stochrsi['fastd']
        # Modified Elder Ray Index
        dataframe['moderi_32'] = moderi(dataframe, 32)
        dataframe['moderi_64'] = moderi(dataframe, 64)
        dataframe['moderi_96'] = moderi(dataframe, 96)
        # hull
        dataframe['hull_75'] = hull(dataframe, 75)
        # zlema
        dataframe['zlema_68'] = zlema(dataframe, 68)
        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)
        # For sell checks
        dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26'])
        # Heiken Ashi
        heikinashi = qtpylib.heikinashi(dataframe)
        heikinashi['volume'] = dataframe['volume']
        # Profit Maximizer - PMAX
        dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3)
        dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4
        dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9)
        dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21)
        dataframe['sma_68'] = ta.SMA(dataframe, timeperiod=68)
        dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75)
        # HLC3
        dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
        # HRSI
        dataframe['hull'] = 2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2)
        dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2)
        # ZLEMA
        dataframe['zlema_2'] = pta.zlma(dataframe['hlc3'], length=2)
        dataframe['zlema_4'] = pta.zlma(dataframe['hlc3'], length=4)
        # CCI
        dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20)
        # ATR
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_high_thresh_1'] = dataframe['high'] - dataframe['atr'] * 5.4
        dataframe['atr_high_thresh_2'] = dataframe['high'] - dataframe['atr'] * 5.2
        dataframe['atr_high_thresh_3'] = dataframe['high'] - dataframe['atr'] * 5.0
        dataframe['atr_high_thresh_4'] = dataframe['high'] - dataframe['atr'] * 2.0
        dataframe['atr_high_thresh_q'] = dataframe['high'] - dataframe['atr'] * 3.0
        # Dip protection
        dataframe['tpct_change_0'] = self.top_percent_change(dataframe, 0)
        dataframe['tpct_change_2'] = self.top_percent_change(dataframe, 2)
        dataframe['tpct_change_12'] = self.top_percent_change(dataframe, 12)
        dataframe['tpct_change_144'] = self.top_percent_change(dataframe, 144)
        dataframe['safe_dips_10'] = self.safe_dips(dataframe, self.buy_dip_threshold_10_1, self.buy_dip_threshold_10_2, self.buy_dip_threshold_10_3, self.buy_dip_threshold_10_4)
        dataframe['safe_dips_20'] = self.safe_dips(dataframe, self.buy_dip_threshold_20_1, self.buy_dip_threshold_20_2, self.buy_dip_threshold_20_3, self.buy_dip_threshold_20_4)
        dataframe['safe_dips_30'] = self.safe_dips(dataframe, self.buy_dip_threshold_30_1, self.buy_dip_threshold_30_2, self.buy_dip_threshold_30_3, self.buy_dip_threshold_30_4)
        dataframe['safe_dips_40'] = self.safe_dips(dataframe, self.buy_dip_threshold_40_1, self.buy_dip_threshold_40_2, self.buy_dip_threshold_40_3, self.buy_dip_threshold_40_4)
        dataframe['safe_dips_50'] = self.safe_dips(dataframe, self.buy_dip_threshold_50_1, self.buy_dip_threshold_50_2, self.buy_dip_threshold_50_3, self.buy_dip_threshold_50_4)
        dataframe['safe_dips_60'] = self.safe_dips(dataframe, self.buy_dip_threshold_60_1, self.buy_dip_threshold_60_2, self.buy_dip_threshold_60_3, self.buy_dip_threshold_60_4)
        dataframe['safe_dips_70'] = self.safe_dips(dataframe, self.buy_dip_threshold_70_1, self.buy_dip_threshold_70_2, self.buy_dip_threshold_70_3, self.buy_dip_threshold_70_4)
        dataframe['safe_dips_80'] = self.safe_dips(dataframe, self.buy_dip_threshold_80_1, self.buy_dip_threshold_80_2, self.buy_dip_threshold_80_3, self.buy_dip_threshold_80_4)
        dataframe['safe_dips_90'] = self.safe_dips(dataframe, self.buy_dip_threshold_90_1, self.buy_dip_threshold_90_2, self.buy_dip_threshold_90_3, self.buy_dip_threshold_90_4)
        dataframe['safe_dips_100'] = self.safe_dips(dataframe, self.buy_dip_threshold_100_1, self.buy_dip_threshold_100_2, self.buy_dip_threshold_100_3, self.buy_dip_threshold_100_4)
        dataframe['safe_dips_110'] = self.safe_dips(dataframe, self.buy_dip_threshold_110_1, self.buy_dip_threshold_110_2, self.buy_dip_threshold_110_3, self.buy_dip_threshold_110_4)
        dataframe['safe_dips_120'] = self.safe_dips(dataframe, self.buy_dip_threshold_120_1, self.buy_dip_threshold_120_2, self.buy_dip_threshold_120_3, self.buy_dip_threshold_120_4)
        dataframe['safe_dips_130'] = self.safe_dips(dataframe, self.buy_dip_threshold_130_1, self.buy_dip_threshold_130_2, self.buy_dip_threshold_130_3, self.buy_dip_threshold_130_4)
        # Bollinger bands (hyperopt hard to implement)
        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband2'] = bollinger2['lower']
        dataframe['bb_middleband2'] = bollinger2['mid']
        dataframe['bb_upperband2'] = bollinger2['upper']
        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['bb_lowerband3'] = bollinger3['lower']
        dataframe['bb_middleband3'] = bollinger3['mid']
        dataframe['bb_upperband3'] = bollinger3['upper']
        ### Other checks
        dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']
        dataframe['bb_delta'] = (dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
        # Volume
        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
        dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean()
        dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
        dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)
        dataframe[f'range{self.range_filter_length.value}'] = range_filter(dataframe=dataframe, length=self.range_filter_length.value)
        dataframe['test'] = (dataframe['close'].rolling(48).max() >= dataframe['close'] * 1.125) & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.225)
        if not self.config['runmode'].value in ('live', 'dry_run'):
            # Backtest age filter
            dataframe['bt_agefilter_ok'] = False
            dataframe.loc[dataframe.index > 12 * 24 * self.bt_min_age_days, 'bt_agefilter_ok'] = True
        else:
            # Exchange downtime protection
            dataframe['live_data_ok'] = dataframe['volume'].rolling(window=72, min_periods=72).min() > 0
        return dataframe

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

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

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

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

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        for index in self.buy_protection_params:
            item_buy_protection_list = [True]
            global_buy_protection_params = self.buy_protection_params[index]
            if self.buy_params['buy_condition_' + str(index) + '_enable']:
                # Standard protections - Common to every condition
                # -----------------------------------------------------------------------------------------
                if global_buy_protection_params['ema_fast']:
                    item_buy_protection_list.append(dataframe[f'ema_{global_buy_protection_params['ema_fast_len']}'] > dataframe['ema_200'])
                if global_buy_protection_params['ema_slow']:
                    item_buy_protection_list.append(dataframe[f'ema_{global_buy_protection_params['ema_slow_len']}_1h'] > dataframe['ema_200_1h'])
                if global_buy_protection_params['close_above_ema_fast']:
                    item_buy_protection_list.append(dataframe['close'] > dataframe[f'ema_{global_buy_protection_params['close_above_ema_fast_len']}'])
                if global_buy_protection_params['close_above_ema_slow']:
                    item_buy_protection_list.append(dataframe['close'] > dataframe[f'ema_{global_buy_protection_params['close_above_ema_slow_len']}_1h'])
                if global_buy_protection_params['sma200_rising']:
                    item_buy_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_buy_protection_params['sma200_rising_val'])))
                if global_buy_protection_params['sma200_1h_rising']:
                    item_buy_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_buy_protection_params['sma200_1h_rising_val'])))
                if global_buy_protection_params['safe_dips']:
                    item_buy_protection_list.append(dataframe[f'safe_dips_{global_buy_protection_params['safe_dips_type']}'])
                if global_buy_protection_params['safe_pump']:
                    item_buy_protection_list.append(dataframe[f'safe_pump_{global_buy_protection_params['safe_pump_period']}_{global_buy_protection_params['safe_pump_type']}_1h'])
                if global_buy_protection_params['btc_1h_not_downtrend']:
                    item_buy_protection_list.append(dataframe['btc_not_downtrend_1h'])
                if not self.config['runmode'].value in ('live', 'dry_run'):
                    if self.has_bt_agefilter:
                        item_buy_protection_list.append(dataframe['bt_agefilter_ok'])
                elif self.has_downtime_protection:
                    item_buy_protection_list.append(dataframe['live_data_ok'])
                item_buy_protection_list.append(dataframe['pump_strength'] < 0.15)
                item_buy_protection_list.append(dataframe['pump_strength'] > self.dump.value)
                item_buy_protection_list.append(dataframe['test'])
                item_buy_protection_list.append(dataframe[f'range{self.range_filter_length.value}'] < 1)
                dataframe['pump_strength'] < self.dump.value
                # Buy conditions
                # -----------------------------------------------------------------------------------------
                item_buy_logic = []
                item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list))
                # Condition #1
                if index == 1:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.buy_min_inc_1)
                    item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_1)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_1)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_1)
                    item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_1)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_1)
                # Condition #2
                elif index == 2:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.buy_rsi_1h_diff_2)
                    item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_2)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_2)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_2)
                # Condition #3
                elif index == 3:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_3)
                    # Logic
                    item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0))
                    item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3))
                    item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3))
                    item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_bb40_tail_bbdelta_3))
                    item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
                    item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_3)
                # Condition #4
                elif index == 4:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_50'])
                    item_buy_logic.append(dataframe['close'] < self.buy_bb20_close_bblowerband_4 * dataframe['bb20_2_low'])
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_4)
                # Condition #5
                elif index == 5:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_5)
                    # Logic
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_5)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_5)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_5)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_5)
                # Condition #6
                elif index == 6:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_6)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_6)
                # Condition #7
                elif index == 7:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_7)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_7)
                # Condition #8
                elif index == 8:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_96'])
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_8)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_8)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_8)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_8)
                # Condition #9
                elif index == 9:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['ema_50'] > dataframe['ema_200'])
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_9)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_9)
                    item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_9)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_9)
                    item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_9)
                # Condition #10
                elif index == 10:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_10)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_10)
                # Condition #11
                elif index == 11:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_buy_logic.append((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.buy_min_inc_11)
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11)
                    item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_11)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_11)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_11)
                    item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_11)
                # Condition #12
                elif index == 12:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_12)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_12)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_12)
                # Condition #13
                elif index == 13:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h'])
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_13)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_13)
                # Condition #14
                elif index == 14:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_14)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_14)
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_14)
                # Condition #15
                elif index == 15:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15)
                    # Logic
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_15)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_15)
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15)
                # Condition #16
                elif index == 16:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_16)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_16)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_16)
                # Condition #17
                elif index == 17:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_17)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_17)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_17)
                # Condition #18
                elif index == 18:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20))
                    item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36))
                    # Logic
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_18)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_18)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_18)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_18)
                # Condition #19
                elif index == 19:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h'])
                    item_buy_logic.append(dataframe['low'] < dataframe['ema_100_1h'])
                    item_buy_logic.append(dataframe['close'] > dataframe['ema_100_1h'])
                    item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_19)
                    item_buy_logic.append(dataframe['chop'] < self.buy_chop_max_19)
                    item_buy_logic.append(dataframe['moderi_32'] == True)
                    item_buy_logic.append(dataframe['moderi_64'] == True)
                    item_buy_logic.append(dataframe['moderi_96'] == True)
                # Condition #20
                elif index == 20:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_20)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_20)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_20)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_20)
                # Condition #21
                elif index == 21:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_21)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_21)
                    item_buy_logic.append(dataframe['cti'] < self.buy_cti_21)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_volume_21)
                # Condition #22
                elif index == 22:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12))
                    item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36))
                    # Logic
                    item_buy_logic.append(dataframe['volume_mean_4'] * self.buy_volume_22 > dataframe['volume'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_22)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_22)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_22)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_22)
                # Condition #23
                elif index == 23:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_23_bb_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_23_ewo)
                    item_buy_logic.append(dataframe['cti'] < self.buy_23_cti)
                    item_buy_logic.append(dataframe['r_480'] > self.buy_23_r)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_23_r_1h)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_23_rsi)
                    item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_23_rsi_1h)
                # Condition #24
                elif index == 24:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12))
                    item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h'])
                    item_buy_logic.append(dataframe['cmf_1h'].shift(12) < 0)
                    item_buy_logic.append(dataframe['cmf_1h'] > 0)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_24_rsi_max)
                    item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_24_rsi_1h_min)
                # Condition #25
                elif index == 25:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift())
                    item_buy_logic.append(dataframe['rsi_4'] < self.buy_25_rsi_4)
                    item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_20'] * self.buy_25_ma_offset)
                    item_buy_logic.append(dataframe['open'] > dataframe['sma_20'] * self.buy_25_ma_offset)
                    item_buy_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h']))
                    item_buy_logic.append(dataframe['cti'] < self.buy_25_cti)
                # Condition #26
                elif index == 26:
                    # Non-Standard protections
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'])
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['zema_61'] * self.buy_26_zema_low_offset)
                    item_buy_logic.append(dataframe['cti'] < self.buy_26_cti)
                    item_buy_logic.append(dataframe['r_480'] > self.buy_26_r)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_26_r_1h)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_26_volume)
                # Condition #27
                elif index == 27:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['r_480'] < -self.buy_27_wr_max)
                    item_buy_logic.append(dataframe['r_480_1h'] < -self.buy_27_wr_1h_max)
                    item_buy_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.buy_27_rsi_max)
                    item_buy_logic.append(dataframe['cti'] < self.buy_27_cti)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_27_volume)
                # Condition #28
                elif index == 28:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_64'] == True)
                    item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_28_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_28_ewo)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_28_rsi)
                    item_buy_logic.append(dataframe['cti'] < self.buy_28_cti)
                # Condition #29
                elif index == 29:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_64'] == True)
                    item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_29_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_29_ewo)
                    item_buy_logic.append(dataframe['cti'] < self.buy_29_cti)
                # Condition #30
                elif index == 30:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_64'] == False)
                    item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_30_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_30_ewo)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_30_rsi)
                    item_buy_logic.append(dataframe['cti'] < self.buy_30_cti)
                # Condition #31
                elif index == 31:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_64'] == False)
                    item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_31_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_31_ewo)
                    item_buy_logic.append(dataframe['r_480'] < self.buy_31_wr)
                    item_buy_logic.append(dataframe['cti'] < self.buy_31_cti)
                # Condition #32 - Quick mode buy
                elif index == 32:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_32'])
                    item_buy_logic.append(dataframe['moderi_64'])
                    item_buy_logic.append(dataframe['moderi_96'])
                    item_buy_logic.append(dataframe['cti'] < self.buy_32_cti)
                    item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1))
                    item_buy_logic.append(dataframe['rsi_4'] < self.buy_32_rsi)
                    item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h'])
                    item_buy_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_32_dip)
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_15'] * self.buy_32_ma_offset)
                    item_buy_logic.append((dataframe['open'] < dataframe['ema_20_1h']) & (dataframe['low'] < dataframe['ema_20_1h']) | (dataframe['open'] > dataframe['ema_20_1h']) & (dataframe['low'] > dataframe['ema_20_1h']))
                # Condition #33 - Quick mode buy
                elif index == 33:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['moderi_96'])
                    item_buy_logic.append(dataframe['cti'] < self.buy_33_cti)
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_13'] * self.buy_33_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_33_ewo)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_33_rsi)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_33_volume)
                # Condition #34 - Quick mode buy
                elif index == 34:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['cti'] < self.buy_34_cti)
                    item_buy_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_34_dip)
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_13'] * self.buy_34_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_34_ewo)
                    item_buy_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.buy_34_volume)
                # Condition #35 - PMAX0 buy
                elif index == 35:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_35_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_35_ewo)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_35_rsi)
                    item_buy_logic.append(dataframe['cti'] < self.buy_35_cti)
                # Condition #36 - PMAX1 buy
                elif index == 36:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_36_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_36_ewo)
                    item_buy_logic.append(dataframe['cti'] < self.buy_36_cti)
                # Condition #37 - PMAX2 buy
                elif index == 37:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] > self.buy_37_ewo)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_37_rsi)
                    item_buy_logic.append(dataframe['cti'] < self.buy_37_cti)
                    item_buy_logic.append(dataframe['safe_dump_50_1h'])
                # Condition #38 - PMAX3 buy
                elif index == 38:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['pm'] > dataframe['pmax_thresh'])
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_38_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_38_ewo)
                    item_buy_logic.append(dataframe['cti'] < self.buy_38_cti)
                # Condition #39 - Ichimoku
                elif index == 39:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h'])
                    item_buy_logic.append(dataframe['close'] > dataframe['cloud_top_1h'])
                    item_buy_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h'])
                    item_buy_logic.append(dataframe['chikou_span_greater_1h'])
                    item_buy_logic.append(dataframe['efi_1h'] > 0)
                    item_buy_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h'])
                    item_buy_logic.append(dataframe['close'] < dataframe['ssl_up_1h'])
                    item_buy_logic.append(dataframe['cti'] < self.buy_39_cti)
                    item_buy_logic.append(dataframe['r_480'] > self.buy_39_r)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_39_r_1h)
                    item_buy_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12))
                    # Start of trend
                    item_buy_logic.append((dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) | (dataframe['ssl_up_1h'].shift(12) < dataframe['ssl_down_1h'].shift(12)))
                # Condition #40 - ZLEMA X buy
                elif index == 40:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(qtpylib.crossed_above(dataframe['zlema_2'], dataframe['zlema_4']))
                    item_buy_logic.append(dataframe['hrsi'] < self.buy_40_hrsi)
                    item_buy_logic.append(dataframe['cci'] < self.buy_40_cci)
                    item_buy_logic.append(dataframe['rsi_14'] < self.buy_40_rsi)
                    item_buy_logic.append(dataframe['cti'] < self.buy_40_cti)
                    item_buy_logic.append(dataframe['r_480'] > self.buy_40_r)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_40_r_1h)
                # Condition #41
                elif index == 41:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
                    item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
                    item_buy_logic.append(dataframe['cti_1h'] < self.buy_41_cti_1h)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_41_r_1h)
                    item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_41_ma_offset)
                    item_buy_logic.append(dataframe['cti'] < self.buy_41_cti)
                    item_buy_logic.append(dataframe['r_480'] < self.buy_41_r)
                # Condition #42
                elif index == 42:
                    # Non-Standard protections (add below)
                    # Logic
                    item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
                    item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
                    item_buy_logic.append(dataframe['cti_1h'] < self.buy_42_cti_1h)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_42_r_1h)
                    item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
                    item_buy_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_42_ema_open_mult)
                    item_buy_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100)
                    item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_42_bb_offset)
                # Condition #43
                elif index == 43:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12))
                    item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24))
                    item_buy_logic.append(dataframe['cti_1h'] < self.buy_43_cti_1h)
                    item_buy_logic.append(dataframe['r_480_1h'] > self.buy_43_r_1h)
                    item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0))
                    item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_43_bb40_bbdelta_close))
                    item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_43_bb40_closedelta_close))
                    item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_43_bb40_tail_bbdelta))
                    item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
                    item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
                    item_buy_logic.append(dataframe['cti'] < self.buy_43_cti)
                    item_buy_logic.append(dataframe['r_480'] > self.buy_43_r)
                # Condition #44
                elif index == 44:
                    # Non-Standard protections
                    # Logic
                    item_buy_logic.append(dataframe['close'] < dataframe['ema_16'] * self.buy_44_ma_offset)
                    item_buy_logic.append(dataframe['ewo'] < self.buy_44_ewo)
                    item_buy_logic.append(dataframe['cti'] < self.buy_44_cti)
                    item_buy_logic.append(dataframe['r_480_1h'] < self.buy_44_r_1h)
                item_buy_logic.append(dataframe['volume'] > 0)
                item_buy = reduce(lambda x, y: x & y, item_buy_logic)
                dataframe.loc[item_buy, 'enter_tag'] += str(index) + ' '
                conditions.append(item_buy)
        if conditions:
            dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions)
        return dataframe

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

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

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

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

        :param pair: Pair that's about to be sold.
        :param trade: trade object.
        :param order_type: Order type (as configured in order_types). usually limit or market.
        :param amount: Amount in quote currency.
        :param rate: Rate that's going to be used when using limit orders
        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
        :param sell_reason: Sell reason.
            Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
                           'sell_signal', 'force_sell', 'emergency_sell']
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return bool: When True is returned, then the sell-order is placed on the exchange.
            False aborts the process
        """
        # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
        if self.holdSupportEnabled and self.config['runmode'].value in ('live', 'dry_run'):
            self.load_hold_trades_config()
            if not self.hold_trades_cache:
                # Cache hasn't been setup, likely because the corresponding file does not exist, sell
                return True
            if not self.hold_trades_cache.data:
                # We have no pairs we want to hold until profit, sell
                return True
            if trade.id not in self.hold_trades_cache.data:
                # This pair is not on the list to hold until profit, sell
                return True
            trade_profit_ratio = self.hold_trades_cache.data[trade.id]
            current_profit_ratio = trade.calc_profit_ratio(rate)
            if exit_reason == 'force_exit':
                formatted_profit_ratio = '{}%'.format(trade_profit_ratio * 100)
                formatted_current_profit_ratio = '{}%'.format(current_profit_ratio * 100)
                log.warning('Force selling %s even though the current profit of %s < %s', trade, formatted_current_profit_ratio, formatted_profit_ratio)
                return True
            elif current_profit_ratio >= trade_profit_ratio:
                # This pair is on the list to hold, and we reached minimum profit, sell
                return True
            # This pair is on the list to hold, and we haven't reached minimum profit, hold
            return False
        else:
            return True
    initial_safety_order_trigger = -0.018
    max_safety_orders = 8
    safety_order_step_scale = 1.2
    safety_order_volume_scale = 1.4

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

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

    def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs):
        if current_profit > self.initial_safety_order_trigger:
            return None
        # credits to reinuvader for not blindly executing safety orders
        # Obtain pair dataframe.
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
        # Only buy when it seems it's climbing back up
        last_candle = dataframe.iloc[-1].squeeze()
        #previous_candle = dataframe.iloc[-2].squeeze()
        #previous2_candle = dataframe.iloc[-3].squeeze()
        #if last_candle['close'] / previous_candle['close'] < 1.02 :
        #if last_candle['close'] < previous_candle['close']:
        filled_buys = trade.select_filled_orders('entry')
        count_of_buys = len(filled_buys)
        if last_candle['pump_strength'] < self.dump.value:
            logger.info(f'DCA for {trade.pair} waiting for pump_strength ({last_candle['pump_strength']}) to rise above -0.15')
            return None
        #elif (count_of_buys > 3):
        #    if (last_candle['cmf_1h'] < 0.00) and (last_candle['rsi_1h'] < 28) and (last_candle['close'] < last_candle['open']):
        #         logger.info(f"DCA for {trade.pair} waiting RSI 1h ({last_candle['rsi_1h']}) to rise above 30 and CMF 1h ({last_candle['cmf_1h']}) to rise above 0")
        #    return None
        #if (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30):
        #   logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30")
        #if (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']):
        #   return None
        #count_of_buys = 0
        #for order in trade.orders:
        #    if order.ft_is_open or order.ft_order_side != 'buy':
        #        continue
        #    if order.status == "closed":
        #        count_of_buys += 1
        if 1 <= count_of_buys <= self.max_safety_orders:
            safety_order_trigger = abs(self.initial_safety_order_trigger) * count_of_buys
            if self.safety_order_step_scale > 1:
                safety_order_trigger = abs(self.initial_safety_order_trigger) + abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale, count_of_buys - 1) - 1) / (self.safety_order_step_scale - 1)
            elif self.safety_order_step_scale < 1:
                safety_order_trigger = abs(self.initial_safety_order_trigger) + abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale, count_of_buys - 1)) / (1 - self.safety_order_step_scale)
            if current_profit <= -1 * abs(safety_order_trigger):
                try:
                    # This returns first order stake size
                    stake_amount = filled_buys[0].cost
                    # This then calculates current safety order size
                    stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, count_of_buys - 1)
                    amount = stake_amount / current_rate
                    logger.info(f'Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}')
                    return stake_amount
                except Exception as exception:
                    logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
                    return None
        return None
# Elliot Wave Oscillator

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

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

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

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

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

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

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

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

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

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

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

class Cache:

    def __init__(self, path):
        self.path = path
        self.data = {}
        self._mtime = None
        self._previous_data = {}
        try:
            self.load()
        except FileNotFoundError:
            pass

    def load(self):
        if not self._mtime or self.path.stat().st_mtime_ns != self._mtime:
            self._load()

    def save(self):
        if self.data != self._previous_data:
            self._save()

    def process_loaded_data(self, data):
        return data

    def _load(self):
        # This method only exists to simplify unit testing
        with self.path.open('r') as rfh:
            try:
                data = json_load(rfh)
            except rapidjson.JSONDecodeError as exc:
                log.error('Failed to load JSON from %s: %s', self.path, exc)
            else:
                self.data = self.process_loaded_data(data)
                self._previous_data = copy.deepcopy(self.data)
                self._mtime = self.path.stat().st_mtime_ns

    def _save(self):
        # This method only exists to simplify unit testing
        file_dump_json(self.path, self.data, is_zip=False, log=True)
        self._mtime = self.path.stat().st_mtime
        self._previous_data = copy.deepcopy(self.data)

class HoldsCache(Cache):

    def save(self):
        raise RuntimeError('The holds cache does not allow programatical save')

    def process_loaded_data(self, data):
        trade_ids = data.get('trade_ids')
        if not trade_ids:
            return {}
        rdata = {}
        open_trades = {trade.id: trade for trade in Trade.get_trades_proxy(is_open=True)}
        if isinstance(trade_ids, dict):
            # New syntax
            for trade_id, profit_ratio in trade_ids.items():
                try:
                    trade_id = int(trade_id)
                except ValueError:
                    log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path)
                    continue
                if not isinstance(profit_ratio, float):
                    log.error("The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path)
                if trade_id in open_trades:
                    formatted_profit_ratio = '{}%'.format(profit_ratio * 100)
                    log.warning('The trade %s is configured to HOLD until the profit ratio of %s is met', open_trades[trade_id], formatted_profit_ratio)
                    rdata[trade_id] = profit_ratio
                else:
                    log.warning("The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path)
        else:
            # Initial Syntax
            profit_ratio = data.get('profit_ratio')
            if profit_ratio:
                if not isinstance(profit_ratio, float):
                    log.error("The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path)
            else:
                profit_ratio = 0.005
            formatted_profit_ratio = '{}%'.format(profit_ratio * 100)
            for trade_id in trade_ids:
                if not isinstance(trade_id, int):
                    log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path)
                    continue
                if trade_id in open_trades:
                    log.warning('The trade %s is configured to HOLD until the profit ratio of %s is met', open_trades[trade_id], formatted_profit_ratio)
                    rdata[trade_id] = profit_ratio
                else:
                    log.warning("The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path)
        return rdata

def range_filter(dataframe, length=10, min_rate_of_change=0.01):
    df = dataframe[['date', 'open', 'high', 'low', 'close', 'volume']].copy()
    highest_high = df['high'].rolling(length).max()
    lowest_low = df['low'].rolling(length).min()
    df['range'] = (highest_high - lowest_low) / lowest_low
    return df['range']