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NFI5MOHO_WIP_2

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neozhou2009/freqtrade-strategies/strategies/NFI5MOHO_WIP_2/NFI5MOHO_WIP_2_converted.py · first seen 2026-07-28 · repo updated 2026-04-20

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.1 has minimal roi trailing process only new candles startup candle count: 300 hyperopt hyperopt params: 195
Indicators Bollinger_Bands EMA KAMA MFI RSI SMA talib
Concepts trailing
Methods EWO custom_exit get_ticker_indicator informative_1h_indicators normal_tf_indicators
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
from pandas import DataFrame
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime
###########################################################################################################
##                NostalgiaForInfinityV5 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).                                    ##
##                                                                                                       ##
###########################################################################################################
##               DONATIONS                                                                               ##
##                                                                                                       ##
##   Absolutely not required. However, will be accepted as a token of appreciation.                      ##
##                                                                                                       ##
##   BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk                                                     ##
##   ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91                                             ##
##   BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe                               ##
##                                                                                                       ##
###########################################################################################################
# 20210624
# NostalgiaForInfinityV5 + MultiOffsetLamboV0 + Hyper-optimized some parameters.
# I hope you do enough testing before proceeding.
# Thank you to those who created these strategies.

class NFI5MOHO_WIP_2(IStrategy):
    INTERFACE_VERSION = 3
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False}
    #############################################################
    #############
    # Enable/Disable conditions
    # Hyperopt
    # Multi Offset
    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, '\n\t"base_nb_candles_buy": 42,\n        "buy_chop_min_19": 29.3,\n        "buy_rsi_1h_min_19": 52.4,\n        "ewo_high": 5.262,\n        "ewo_low": -8.164,\n        "low_offset_ema": 0.984,\n        "low_offset_kama": 0.919,\n        "low_offset_sma": 0.97,\n        "low_offset_t3": 0.904,\n        "low_offset_trima": 0.984,\n\tbase_nb_candles_buy': 72, 'buy_chop_min_19': 58.2, 'buy_rsi_1h_min_19': 65.3, 'ewo_high': 3.319, 'ewo_low': -11.101, 'low_offset_ema': 0.929, 'low_offset_kama': 0.972, 'low_offset_sma': 0.955, 'low_offset_t3': 0.975, 'low_offset_trima': 0.949}
    #############
    # Enable/Disable conditions
    #############
    # Hyperopt
    # Multi Offset
    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': 34, 'high_offset_ema': 1.047, 'high_offset_kama': 1.07, 'high_offset_sma': 1.051, 'high_offset_t3': 0.999, 'high_offset_trima': 1.096}
    # ROI table:
    minimal_roi = {'0': 0.08, '10': 0.04, '30': 0.02, '60': 0.01}
    stoploss = -0.1
    # Multi Offset
    base_nb_candles_buy = IntParameter(5, 80, default=20, load=True, space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(5, 80, default=20, load=True, space='sell', optimize=True)
    low_offset_sma = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    high_offset_sma = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=True)
    low_offset_ema = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=True)
    low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    high_offset_trima = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=True)
    low_offset_t3 = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    high_offset_t3 = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=True)
    low_offset_kama = DecimalParameter(0.9, 0.99, default=0.958, load=True, space='buy', optimize=True)
    high_offset_kama = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=True)
    # Protection
    ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, load=True, space='buy', optimize=True)
    ewo_high = DecimalParameter(2.0, 12.0, default=6.0, load=True, space='buy', optimize=True)
    fast_ewo = IntParameter(10, 50, default=50, load=True, space='buy', optimize=True)
    slow_ewo = IntParameter(100, 200, default=200, load=True, space='buy', optimize=True)
    # MA list
    ma_types = ['sma', 'ema', 'trima', 't3', 'kama']
    ma_map = {'sma': {'low_offset': low_offset_sma.value, 'high_offset': high_offset_sma.value, 'calculate': ta.SMA}, 'ema': {'low_offset': low_offset_ema.value, 'high_offset': high_offset_ema.value, 'calculate': ta.EMA}, 'trima': {'low_offset': low_offset_trima.value, 'high_offset': high_offset_trima.value, 'calculate': ta.TRIMA}, 't3': {'low_offset': low_offset_t3.value, 'high_offset': high_offset_t3.value, 'calculate': ta.T3}, 'kama': {'low_offset': low_offset_kama.value, 'high_offset': high_offset_kama.value, 'calculate': ta.KAMA}}
    # Trailing stoploss (not used)
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.04
    use_custom_stoploss = False
    # Optimal timeframe for the strategy.
    timeframe = '5m'
    inf_1h = '1h'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 300
    # plot config
    plot_config = {'main_plot': {'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}}}
    #############################################################
    buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_18_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_19_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_20_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    buy_condition_21_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True)
    # Normal dips
    buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=True, load=True)
    # Strict dips
    buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=True, load=True)
    # Loose dips
    buy_dip_threshold_9 = DecimalParameter(0.001, 0.05, default=0.026, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_10 = DecimalParameter(0.01, 0.2, default=0.24, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_11 = DecimalParameter(0.05, 0.4, default=0.42, space='buy', decimals=3, optimize=True, load=True)
    buy_dip_threshold_12 = DecimalParameter(0.2, 0.5, default=0.66, space='buy', decimals=3, optimize=True, load=True)
    # 24 hours
    buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=True, load=True)
    # 36 hours
    buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=True, load=True)
    # 48 hours
    buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=True, load=True)
    # 24 hours strict
    buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=True, load=True)
    # 36 hours strict
    buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=True, load=True)
    # 48 hours strict
    buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=True, load=True)
    # 24 hours loose
    buy_pump_pull_threshold_7 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_7 = DecimalParameter(0.4, 1.0, default=0.66, space='buy', decimals=3, optimize=True, load=True)
    # 36 hours loose
    buy_pump_pull_threshold_8 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_8 = DecimalParameter(0.4, 1.0, default=0.7, space='buy', decimals=3, optimize=True, load=True)
    # 48 hours loose
    buy_pump_pull_threshold_9 = DecimalParameter(1.5, 3.0, default=1.4, space='buy', decimals=2, optimize=True, load=True)
    buy_pump_threshold_9 = DecimalParameter(0.4, 1.8, default=1.3, space='buy', decimals=3, optimize=True, load=True)
    buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=True, load=True)
    buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.6, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=39.0, space='buy', decimals=1, optimize=True, load=True)
    buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space='buy', decimals=1, optimize=True, load=True)
    buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space='buy', decimals=3, optimize=True, load=True)
    buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=True, load=True)
    buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=True, load=True)
    buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=True, load=True)
    buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=True, load=True)
    buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space='buy', optimize=True, load=True)
    buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space='buy', decimals=2, optimize=True, load=True)
    buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, optimize=True, load=True)
    buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, optimize=True, load=True)
    buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.982, space='buy', decimals=3, optimize=True, load=True)
    buy_ema_open_mult_6 = DecimalParameter(0.02, 0.03, default=0.025, space='buy', decimals=3, optimize=True, load=True)
    buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.984, space='buy', decimals=3, optimize=True, load=True)
    buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ema_rel_7 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=True, load=True)
    buy_volume_8 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_8 = DecimalParameter(36.0, 40.0, default=20.0, space='buy', decimals=1, optimize=True, load=True)
    buy_tail_diff_8 = DecimalParameter(3.0, 10.0, default=3.5, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_9 = DecimalParameter(1.0, 4.0, default=1.0, space='buy', decimals=2, optimize=True, load=True)
    buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.97, space='buy', decimals=3, optimize=True, load=True)
    buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.985, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space='buy', decimals=1, optimize=True, load=True)
    buy_mfi_9 = DecimalParameter(36.0, 65.0, default=30.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_10 = DecimalParameter(1.0, 8.0, default=2.4, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.944, space='buy', decimals=3, optimize=True, load=True)
    buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_1h_10 = DecimalParameter(20.0, 40.0, default=37.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.939, space='buy', decimals=3, optimize=True, load=True)
    buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.022, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=56.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_11 = DecimalParameter(30.0, 48.0, default=48.0, space='buy', decimals=1, optimize=True, load=True)
    buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_12 = DecimalParameter(1.0, 10.0, default=1.7, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_13 = DecimalParameter(1.0, 10.0, default=1.6, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.978, space='buy', decimals=3, optimize=True, load=True)
    buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-10.4, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, optimize=True, load=True)
    buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.986, space='buy', decimals=3, optimize=True, load=True)
    buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.97, space='buy', decimals=3, optimize=True, load=True)
    buy_volume_15 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.018, space='buy', decimals=3, optimize=True, load=True)
    buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.954, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_15 = DecimalParameter(30.0, 50.0, default=28.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True)
    buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.952, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ewo_16 = DecimalParameter(4.0, 8.0, default=2.8, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_17 = DecimalParameter(0.5, 8.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, optimize=True, load=True)
    buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_18 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_18 = DecimalParameter(16.0, 32.0, default=26.0, space='buy', decimals=1, optimize=True, load=True)
    buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.982, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=50.0, space='buy', decimals=1, optimize=True, load=True)
    buy_chop_min_19 = DecimalParameter(20.0, 60.0, default=24.1, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_20 = DecimalParameter(0.5, 6.0, default=1.2, space='buy', decimals=1, optimize=True, load=True)
    #buy_ema_rel_20 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_20 = DecimalParameter(20.0, 36.0, default=26.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=20.0, space='buy', decimals=1, optimize=True, load=True)
    buy_volume_21 = DecimalParameter(0.5, 6.0, default=3.0, space='buy', decimals=1, optimize=True, load=True)
    #buy_ema_rel_21 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True)
    buy_rsi_21 = DecimalParameter(10.0, 28.0, default=23.0, space='buy', decimals=1, optimize=True, load=True)
    buy_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=24.0, space='buy', decimals=1, optimize=True, load=True)
    # Sell
    sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True)
    sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=True, load=True)
    sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=True, load=True)
    sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=True, load=True)
    sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=True, load=True)
    sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=True, load=True)
    sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=True, load=True)
    sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=True, load=True)
    sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=True, load=True)
    sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=True, load=True)
    sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_profit_3 = DecimalParameter(0.06, 0.3, default=0.08, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=True, load=True)
    sell_custom_under_profit_1 = DecimalParameter(0.01, 0.1, default=0.02, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=True, load=True)
    sell_custom_under_profit_2 = DecimalParameter(0.01, 0.1, default=0.04, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=True, load=True)
    sell_custom_under_profit_3 = DecimalParameter(0.01, 0.1, default=0.6, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=True, load=True)
    sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=True, load=True)
    sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=True, load=True)
    sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=True, load=True)
    sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=True, load=True)
    sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=True, load=True)
    sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=True, load=True)
    sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=True, load=True)
    #############################################################

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

    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].squeeze()
        max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate
        if last_candle is not None:
            if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value):
                return 'signal_profit_4'
            elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
                return 'signal_profit_3'
            elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
                return 'signal_profit_2'
            elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
                return 'signal_profit_1'
            elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value):
                return 'signal_profit_0'
            elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']):
                return 'signal_profit_u_1'
            elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']):
                return 'signal_profit_u_2'
            elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']):
                return 'signal_profit_u_3'
            elif (current_profit > self.sell_custom_dec_profit_1.value) & last_candle['sma_200_dec']:
                return 'signal_profit_d_1'
            elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']):
                return 'signal_profit_d_2'
            elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > current_profit + self.sell_trail_down_1.value):
                return 'signal_profit_t_1'
            elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > current_profit + self.sell_trail_down_2.value):
                return 'signal_profit_t_2'
            elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > current_profit + self.sell_trail_down_3.value):
                return 'signal_profit_u_t_1'
            elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value):
                return 'signal_profit_u_e_1'
            elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & ((last_candle['ema_200'] - last_candle['close']) / last_candle['close'] < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value):
                return 'signal_stoploss_u_1'
        return None

    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, '1h') for pair in pairs]
        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.inf_1h)
        # EMA
        informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15)
        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)
        # RSI
        informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
        # BB
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
        informative_1h['bb_lowerband'] = bollinger['lower']
        informative_1h['bb_middleband'] = bollinger['mid']
        informative_1h['bb_upperband'] = bollinger['upper']
        # Pump protections
        informative_1h['safe_pump_24'] = ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min() < self.buy_pump_threshold_1.value) | ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_1.value > informative_1h['close'] - informative_1h['close'].rolling(24).min())
        informative_1h['safe_pump_36'] = ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min() < self.buy_pump_threshold_2.value) | ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_2.value > informative_1h['close'] - informative_1h['close'].rolling(36).min())
        informative_1h['safe_pump_48'] = ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min() < self.buy_pump_threshold_3.value) | ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_3.value > informative_1h['close'] - informative_1h['close'].rolling(48).min())
        informative_1h['safe_pump_24_strict'] = ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min() < self.buy_pump_threshold_4.value) | ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_4.value > informative_1h['close'] - informative_1h['close'].rolling(24).min())
        informative_1h['safe_pump_36_strict'] = ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min() < self.buy_pump_threshold_5.value) | ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_5.value > informative_1h['close'] - informative_1h['close'].rolling(36).min())
        informative_1h['safe_pump_48_strict'] = ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min() < self.buy_pump_threshold_6.value) | ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_6.value > informative_1h['close'] - informative_1h['close'].rolling(48).min())
        informative_1h['safe_pump_24_loose'] = ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min() < self.buy_pump_threshold_7.value) | ((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_7.value > informative_1h['close'] - informative_1h['close'].rolling(24).min())
        informative_1h['safe_pump_36_loose'] = ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min() < self.buy_pump_threshold_8.value) | ((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_8.value > informative_1h['close'] - informative_1h['close'].rolling(36).min())
        informative_1h['safe_pump_48_loose'] = ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min() < self.buy_pump_threshold_9.value) | ((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_9.value > informative_1h['close'] - informative_1h['close'].rolling(48).min())
        return informative_1h

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # BB 40
        bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
        dataframe['lower'] = bb_40['lower']
        dataframe['mid'] = bb_40['mid']
        dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
        dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
        dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
        # BB 20
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        # EMA 200
        dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        # SMA
        dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)
        # EWO
        dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Chopiness
        dataframe['chop'] = qtpylib.chopiness(dataframe, 14)
        # Dip protection
        dataframe['safe_dips'] = ((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_1.value) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_2.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_3.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_4.value)
        dataframe['safe_dips_strict'] = ((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_5.value) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_6.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_7.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_8.value)
        dataframe['safe_dips_loose'] = ((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_9.value) & ((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_10.value) & ((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_11.value) & ((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close'] < self.buy_dip_threshold_12.value)
        # Volume
        dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
        dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean()
        # Offset
        for i in self.ma_types:
            dataframe[f'{i}_offset_buy'] = self.ma_map[f'{i}']['calculate'](dataframe, self.base_nb_candles_buy.value) * self.ma_map[f'{i}']['low_offset']
            dataframe[f'{i}_offset_sell'] = self.ma_map[f'{i}']['calculate'](dataframe, self.base_nb_candles_sell.value) * self.ma_map[f'{i}']['high_offset']
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(self.buy_condition_1_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(50)) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_1h'] & ((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.buy_min_inc_1.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > 0))
        #(dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) &
        #(dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) &
        conditions.append(self.buy_condition_2_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(50)) & dataframe['safe_pump_24_strict_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_2.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_3.value) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & dataframe['safe_pump_36_strict_1h'] & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta_3.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_4_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_1h'] & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband_4.value * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4.value))
        conditions.append(self.buy_condition_5_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_5.value) & dataframe['safe_dips'] & dataframe['safe_pump_36_strict_1h'] & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_5.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_5.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_6_enable.value & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_loose'] & dataframe['safe_pump_36_strict_1h'] & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_6.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_6.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_7_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_strict'] & (dataframe['volume'].rolling(4).mean() * self.buy_volume_7.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_7.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['rsi'] < self.buy_rsi_7.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_8_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_loose'] & dataframe['safe_pump_24_1h'] & (dataframe['rsi'] < self.buy_rsi_8.value) & (dataframe['volume'] > dataframe['volume'].shift(1) * self.buy_volume_8.value) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] - dataframe['low'] > (dataframe['close'] - dataframe['open']) * self.buy_tail_diff_8.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_9_enable.value & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_100'] > dataframe['ema_200']) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_loose_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_9.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_9.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_9.value) & (dataframe['mfi'] < self.buy_mfi_9.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_10_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & dataframe['safe_dips_loose'] & dataframe['safe_pump_24_loose_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_10.value > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_11_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & dataframe['safe_dips_loose'] & dataframe['safe_pump_24_loose_1h'] & dataframe['safe_pump_36_1h'] & dataframe['safe_pump_48_loose_1h'] & ((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min() > self.buy_min_inc_11.value) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_11.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_11.value) & (dataframe['rsi'] < self.buy_rsi_11.value) & (dataframe['mfi'] < self.buy_mfi_11.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_12_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_12.value > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12.value) & (dataframe['ewo'] > self.buy_ewo_12.value) & (dataframe['rsi'] < self.buy_rsi_12.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_13_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_loose_1h'] & dataframe['safe_pump_36_loose_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_13.value > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13.value) & (dataframe['ewo'] < self.buy_ewo_13.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_14_enable.value & (dataframe['sma_200'] > dataframe['sma_200'].shift(30)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(50)) & dataframe['safe_dips_loose'] & dataframe['safe_pump_24_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_14.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_14.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_14.value) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_15_enable.value & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15.value) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips'] & dataframe['safe_pump_36_strict_1h'] & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_open_mult_15.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['rsi'] < self.buy_rsi_15.value) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_16_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_strict_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_16.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16.value) & (dataframe['ewo'] > self.buy_ewo_16.value) & (dataframe['rsi'] < self.buy_rsi_16.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_17_enable.value & dataframe['safe_dips_strict'] & dataframe['safe_pump_24_loose_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_17.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) & (dataframe['ewo'] < self.buy_ewo_17.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_18_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['sma_200'] > dataframe['sma_200'].shift(44)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(72)) & dataframe['safe_dips'] & dataframe['safe_pump_24_strict_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_18.value > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_18.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_18.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_19_enable.value & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(36)) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips'] & dataframe['safe_pump_24_1h'] & (dataframe['close'].shift(1) > dataframe['ema_100_1h']) & (dataframe['low'] < dataframe['ema_100_1h']) & (dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_19.value) & (dataframe['chop'] < self.buy_chop_min_19.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_20_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips'] & dataframe['safe_pump_24_loose_1h'] & (dataframe['volume_mean_4'] * self.buy_volume_20.value > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_20.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_20.value) & (dataframe['volume'] > 0))
        conditions.append(self.buy_condition_21_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & dataframe['safe_dips_strict'] & (dataframe['volume_mean_4'] * self.buy_volume_21.value > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_21.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_21.value) & (dataframe['volume'] > 0))
        for i in self.ma_types:
            conditions.append((dataframe['close'] < dataframe[f'{i}_offset_buy']) & ((dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value)) & (dataframe['volume'] > 0))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(self.sell_condition_1_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_1.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) & (dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) & (dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_2_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_2.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_3_enable.value & (dataframe['rsi'] > self.sell_rsi_main_3.value) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_4_enable.value & (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_6_enable.value & (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > self.sell_rsi_under_6.value) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_7_enable.value & (dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) & qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) & (dataframe['volume'] > 0))
        conditions.append(self.sell_condition_8_enable.value & (dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) & (dataframe['volume'] > 0))
        "\n\tfor i in self.ma_types:\n            conditions.append(\n                (\n                    (dataframe['close'] > dataframe[f'{i}_offset_sell']) &\n                    (dataframe['volume'] > 0)\n                )\n        )\n\t"
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 0
        return dataframe
# Elliot Wave Oscillator

def EWO(dataframe, sma1_length=5, sma2_length=35):
    df = dataframe.copy()
    sma1 = ta.EMA(df, timeperiod=sma1_length)
    sma2 = ta.EMA(df, timeperiod=sma2_length)
    smadif = (sma1 - sma2) / df['close'] * 100
    return smadif