from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
from cachetools import TTLCache
from functools import reduce

## I hope you know what these are already
from pandas import DataFrame, Series
import numpy as np

## Indicator libs
import talib.abstract as ta
from finta import TA as fta

## FT stuffs
from freqtrade.strategy import (
    IStrategy,
    merge_informative_pair,
    stoploss_from_open,
    IntParameter,
    DecimalParameter,
    CategoricalParameter,
)
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from skopt.space import Dimension


class CryptoFrogNFI(IStrategy):
    # Sell hyperspace params:
    sell_params = {
        "cstp_bail_how": "roc",
        "cstp_bail_roc": -0.016,
        "cstp_bail_time": 901,
        "cstp_threshold": 0.0,
        "droi_pullback": False,
        "droi_pullback_amount": 0.008,
        "droi_pullback_respect_table": False,
        "droi_trend_type": "rmi",
    }

    # ROI table - this strat REALLY benefits from roi and trailing hyperopt:
    minimal_roi = {"0": 0.191, "35": 0.025, "77": 0.012, "188": 0}

    # Stoploss:
    stoploss = -0.15

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.278
    trailing_stop_positive_offset = 0.338
    trailing_only_offset_is_reached = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 400

    use_custom_stoploss = True
    custom_stop = {
        # Linear Decay Parameters
        "decay-time": 166,  # minutes to reach end, I find it works well to match this to the final ROI value - default 1080
        "decay-delay": 0,  # minutes to wait before decay starts
        "decay-start": -0.085,  # -0.32118, # -0.07163,     # starting value: should be the same or smaller than initial stoploss - default -0.30
        "decay-end": -0.02,  # ending value - default -0.03
        # Profit and TA
        "cur-min-diff": 0.03,  # diff between current and minimum profit to move stoploss up to min profit point
        "cur-threshold": -0.02,  # how far negative should current profit be before we consider moving it up based on cur/min or roc
        "roc-bail": -0.03,  # value for roc to use for dynamic bailout
        "rmi-trend": 50,  # rmi-slow value to pause stoploss decay
        "bail-how": "immediate",  # set the stoploss to the atr offset below current price, or immediate
        # Positive Trailing
        "pos-trail": True,  # enable trailing once positive
        "pos-threshold": 0.005,  # trail after how far positive
        "pos-trail-dist": 0.015,  # how far behind to place the trail
    }

    # Dynamic ROI
    droi_trend_type = CategoricalParameter(
        ["rmi", "ssl", "candle", "any"], default="any", space="sell", optimize=True
    )
    droi_pullback = CategoricalParameter([True, False], default=True, space="sell", optimize=True)
    droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space="sell")
    droi_pullback_respect_table = CategoricalParameter(
        [True, False], default=False, space="sell", optimize=True
    )

    # Custom Stoploss
    cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space="sell")
    cstp_bail_how = CategoricalParameter(
        ["roc", "time", "any"], default="roc", space="sell", optimize=True
    )
    cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space="sell")
    cstp_bail_time = IntParameter(720, 1440, default=720, space="sell")

    stoploss = custom_stop["decay-start"]

    custom_trade_info = {}
    custom_current_price_cache: TTLCache = TTLCache(maxsize=100, ttl=300)  # 5 minutes

    # run "populate_indicators" only for new candle
    process_only_new_candles = False

    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

    use_dynamic_roi = True

    timeframe = "5m"
    informative_timeframe = "1h"

    # Optional order type mapping
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    plot_config = {
        "main_plot": {
            "Smooth_HA_H": {"color": "orange"},
            "Smooth_HA_L": {"color": "yellow"},
        },
        "subplots": {
            "StochRSI": {
                "srsi_k": {"color": "blue"},
                "srsi_d": {"color": "red"},
            },
            "MFI": {
                "mfi": {"color": "green"},
            },
            "BBEXP": {
                "bbw_expansion": {"color": "orange"},
            },
            "FAST": {
                "fastd": {"color": "red"},
                "fastk": {"color": "blue"},
            },
            "SQZMI": {
                "sqzmi": {"color": "lightgreen"},
            },
            "VFI": {
                "vfi": {"color": "lightblue"},
            },
            "DMI": {
                "dmi_plus": {"color": "orange"},
                "dmi_minus": {"color": "yellow"},
            },
            "EMACO": {
                "emac_1h": {"color": "red"},
                "emao_1h": {"color": "blue"},
            },
        },
    }

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

    buy_params = {
        #############
        # Enable/Disable conditions
        "buy_condition_1_enable": True,
        "buy_condition_2_enable": True,
        "buy_condition_3_enable": True,
        "buy_condition_4_enable": True,
        "buy_condition_5_enable": True,
        "buy_condition_6_enable": True,
        "buy_condition_7_enable": True,
        "buy_condition_8_enable": True,
        "buy_condition_9_enable": True,
        "buy_condition_10_enable": True,
        "buy_condition_11_enable": True,
        "buy_condition_12_enable": True,
        "buy_condition_13_enable": True,
        "buy_condition_14_enable": True,
        "buy_condition_15_enable": True,
        "buy_condition_16_enable": True,
        "buy_condition_17_enable": True,
        "buy_condition_18_enable": True,
        "buy_condition_19_enable": True,
        "buy_condition_20_enable": True,
        "buy_condition_21_enable": True,
        "buy_condition_22_enable": True,
        "buy_condition_23_enable": True,
        "buy_condition_24_enable": True,
        #############
    }

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

    #############################################################
    buy_condition_1_enable = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_01_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="26", space="buy", optimize=True, load=True
    )
    buy_01_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_01_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_01_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_01_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_01_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="36", space="buy", optimize=True, load=True
    )
    buy_01_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_01_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_01_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_01_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_01_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_01_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="36", space="buy", optimize=True, load=True
    )

    buy_condition_2_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_02_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_02_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_02_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_02_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_02_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_02_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_02_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_02_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )
    buy_02_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_02_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_02_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_02_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )

    buy_condition_3_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_03_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_03_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_03_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_03_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_03_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_03_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_03_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_03_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_03_protection__safe_dips = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_03_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_03_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_03_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_03_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="36", space="buy", optimize=True, load=True
    )
    buy_03_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_03_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_03_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_03_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )

    buy_condition_4_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_04_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_04_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_04_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_04_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_04_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_04_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_04_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_04_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_04_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_04_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_04_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_04_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_04_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="48", space="buy", optimize=True, load=True
    )
    buy_04_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_04_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_04_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_04_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="20", space="buy", optimize=True, load=True
    )

    buy_condition_5_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_05_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_05_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_05_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_05_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_05_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_05_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_05_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_05_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_05_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_05_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_05_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_05_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_05_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="36", space="buy", optimize=True, load=True
    )
    buy_05_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_05_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_05_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_05_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )

    buy_condition_6_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_06_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_06_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_06_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_06_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_06_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_06_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_06_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_06_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_06_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_06_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_06_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_06_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_06_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_06_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_06_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_06_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_06_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="36", space="buy", optimize=True, load=True
    )

    buy_condition_7_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_07_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_07_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_07_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_07_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_07_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_07_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_07_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_07_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_07_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_07_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_07_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_07_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_07_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_07_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_07_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_07_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_07_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_8_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_08_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_08_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_08_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_08_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_08_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_08_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_08_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_08_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_08_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_08_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_08_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_08_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_08_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_08_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_08_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_08_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_08_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_9_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_09_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_09_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_09_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_09_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_09_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_09_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_09_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_09_protection__safe_dips = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_09_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_09_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_09_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_10_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_10_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_10_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_10_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_10_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_10_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_10_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_10_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="24", space="buy", optimize=True, load=True
    )
    buy_10_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_10_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_10_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_10_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_10_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_11_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_11_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_11_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_11_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_11_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_11_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_11_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_11_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_11_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_11_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_11_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_11_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_11_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_12_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_12_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_12_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_12_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_12_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_12_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_12_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_12_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="24", space="buy", optimize=True, load=True
    )
    buy_12_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_12_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_12_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_12_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_12_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_13_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_13_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_13_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_13_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_13_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_13_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_13_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_13_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="24", space="buy", optimize=True, load=True
    )
    buy_13_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_13_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_13_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_13_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_13_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_14_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_14_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_14_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_14_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_14_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_14_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_14_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_14_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_14_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_14_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_14_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="30", space="buy", optimize=True, load=True
    )
    buy_14_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_14_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_14_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_14_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_14_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_14_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_14_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_15_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_15_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_15_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_15_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_15_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_15_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_15_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_15_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_15_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_15_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_15_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_15_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_15_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_15_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_15_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_15_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_15_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_15_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="36", space="buy", optimize=True, load=True
    )

    buy_condition_16_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_16_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_16_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_16_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_16_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_16_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_16_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_16_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_16_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_16_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_16_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_16_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_16_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_16_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_16_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_16_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_16_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_16_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_17_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_17_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_17_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_17_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_17_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_17_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_17_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_17_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_17_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_17_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_17_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_17_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_17_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_18_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_18_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_18_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_18_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_18_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_18_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="44", space="buy", optimize=True, load=True
    )
    buy_18_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="72", space="buy", optimize=True, load=True
    )
    buy_18_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_18_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_18_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_18_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_19_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_19_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_19_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_19_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_19_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="100", space="buy", optimize=True, load=True
    )
    buy_19_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_19_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_19_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_19_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_19_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_19_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="36", space="buy", optimize=True, load=True
    )
    buy_19_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_19_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_19_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_19_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_19_protection__safe_pump = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_19_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_19_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_20_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_20_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_20_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_20_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_20_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_20_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_20_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_20_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_20_protection__safe_dips = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_20_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_20_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_20_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_21_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_21_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_21_protection__ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_21_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_21_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_21_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_21_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_21_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_21_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_21_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_21_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_21_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_21_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_22_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_22_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_22_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_22_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_22_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_22_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_22_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_22_protection__safe_dips = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_22_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_22_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_22_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_23_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_23_protection__ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_23_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_23_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_23_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_23_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_23_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_23_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_23_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_23_protection__sma200_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_23_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_23_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_23_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="50", space="buy", optimize=True, load=True
    )
    buy_23_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_23_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="loose", space="buy", optimize=True, load=True
    )
    buy_23_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_23_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_23_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", space="buy", optimize=True, load=True
    )

    buy_condition_24_enable = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_24_protection__ema_fast = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_24_protection__ema_fast_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_24_protection__ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_24_protection__ema_slow_len = CategoricalParameter(
        ["26", "50", "100", "200"], default="50", space="buy", optimize=True, load=True
    )
    buy_24_protection__close_above_ema_fast = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_24_protection__close_above_ema_fast_len = CategoricalParameter(
        ["12", "20", "26", "50", "100", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_24_protection__close_above_ema_slow = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_24_protection__close_above_ema_slow_len = CategoricalParameter(
        ["15", "50", "200"], default="200", space="buy", optimize=True, load=True
    )
    buy_24_protection__sma200_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_24_protection__sma200_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="30", space="buy", optimize=True, load=True
    )
    buy_24_protection__sma200_1h_rising = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_24_protection__sma200_1h_rising_val = CategoricalParameter(
        ["20", "30", "36", "44", "50"], default="36", space="buy", optimize=True, load=True
    )
    buy_24_protection__safe_dips = CategoricalParameter(
        [True, False], default=True, space="buy", optimize=True, load=True
    )
    buy_24_protection__safe_dips_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="strict", space="buy", optimize=True, load=True
    )
    buy_24_protection__safe_pump = CategoricalParameter(
        [True, False], default=False, space="buy", optimize=True, load=True
    )
    buy_24_protection__safe_pump_type = CategoricalParameter(
        ["strict", "normal", "loose"], default="normal", space="buy", optimize=True, load=True
    )
    buy_24_protection__safe_pump_period = CategoricalParameter(
        ["24", "36", "48"], default="24", 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=0.1, 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=0.42, 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=0.8, 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=0.6, 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=0.64, 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=0.42, 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=0.58, 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=0.8, 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(
        1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=True, load=True
    )
    buy_pump_threshold_9 = DecimalParameter(
        0.4, 1.8, default=1.6, 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=36.0, 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=0.018, space="buy", decimals=3, optimize=True, load=True
    )
    buy_bb_offset_5 = DecimalParameter(
        0.98, 1.0, default=0.996, 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=0.024, 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_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_volume_8 = DecimalParameter(
        1.0, 6.0, default=2.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_rsi_8 = DecimalParameter(
        16.0, 30.0, 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_ma_offset_9 = DecimalParameter(
        0.91, 0.94, default=0.922, space="buy", decimals=3, optimize=True, load=True
    )
    buy_bb_offset_9 = DecimalParameter(
        0.96, 0.98, default=0.965, 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, 56.0, default=50.0, space="buy", decimals=1, optimize=True, load=True
    )

    buy_ma_offset_10 = DecimalParameter(
        0.93, 0.97, default=0.948, 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=0.01, 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(
        34.0, 50.0, default=48.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_mfi_11 = DecimalParameter(
        30.0, 46.0, default=36.0, space="buy", decimals=1, optimize=True, load=True
    )

    buy_ma_offset_12 = DecimalParameter(
        0.93, 0.97, default=0.922, 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(
        1.0, 6.0, default=1.8, space="buy", decimals=1, optimize=True, load=True
    )

    buy_ma_offset_13 = DecimalParameter(
        0.93, 0.98, default=0.99, space="buy", decimals=3, optimize=True, load=True
    )
    buy_ewo_13 = DecimalParameter(
        -14.0, -7.0, default=-11.8, 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=0.988, space="buy", decimals=3, optimize=True, load=True
    )
    buy_ma_offset_14 = DecimalParameter(
        0.93, 0.99, default=0.98, space="buy", decimals=3, optimize=True, load=True
    )

    buy_ema_open_mult_15 = DecimalParameter(
        0.01, 0.03, 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(
        20.0, 36.0, 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_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(
        2.0, 6.0, default=2.8, space="buy", decimals=1, optimize=True, load=True
    )

    buy_ma_offset_17 = DecimalParameter(
        0.93, 0.98, default=0.952, 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_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_rsi_20 = DecimalParameter(
        20.0, 36.0, default=27.0, 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_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
    )

    buy_volume_22 = DecimalParameter(
        0.5, 6.0, default=3.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_bb_offset_22 = DecimalParameter(
        0.98, 1.0, default=0.98, space="buy", decimals=3, optimize=True, load=True
    )
    buy_ma_offset_22 = DecimalParameter(
        0.93, 0.98, default=0.94, space="buy", decimals=3, optimize=True, load=True
    )
    buy_ewo_22 = DecimalParameter(
        2.0, 10.0, default=4.2, space="buy", decimals=1, optimize=True, load=True
    )
    buy_rsi_22 = DecimalParameter(
        26.0, 56.0, default=37.0, space="buy", decimals=1, optimize=True, load=True
    )

    buy_bb_offset_23 = DecimalParameter(
        0.97, 1.0, default=0.987, space="buy", decimals=3, optimize=True, load=True
    )
    buy_ewo_23 = DecimalParameter(
        2.0, 10.0, default=7.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_rsi_23 = DecimalParameter(
        20.0, 40.0, default=30.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_rsi_1h_23 = DecimalParameter(
        60.0, 80.0, default=70.0, space="buy", decimals=1, optimize=True, load=True
    )

    buy_24_rsi_max = DecimalParameter(
        26.0, 60.0, default=60.0, space="buy", decimals=1, optimize=True, load=True
    )
    buy_24_rsi_1h_min = DecimalParameter(
        40.0, 90.0, default=66.9, 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=0.02, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_1 = DecimalParameter(
        30.0, 50.0, default=34.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_2 = DecimalParameter(
        0.01, 0.1, default=0.03, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_2 = DecimalParameter(
        30.0, 50.0, default=38.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_3 = DecimalParameter(
        0.01, 0.1, default=0.04, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_3 = DecimalParameter(
        30.0, 50.0, default=42.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_4 = DecimalParameter(
        0.01, 0.1, default=0.05, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_4 = DecimalParameter(
        35.0, 50.0, default=43.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_5 = DecimalParameter(
        0.01, 0.1, default=0.06, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_5 = DecimalParameter(
        35.0, 50.0, default=44.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_6 = DecimalParameter(
        0.01, 0.1, default=0.07, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_6 = DecimalParameter(
        38.0, 55.0, default=49.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_7 = DecimalParameter(
        0.01, 0.1, default=0.08, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_7 = DecimalParameter(
        40.0, 58.0, default=54.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_8 = DecimalParameter(
        0.06, 0.1, default=0.09, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_8 = DecimalParameter(
        40.0, 50.0, default=54.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_9 = DecimalParameter(
        0.05, 0.14, default=0.1, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_9 = DecimalParameter(
        40.0, 60.0, default=50.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_10 = DecimalParameter(
        0.1, 0.14, default=0.12, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_10 = DecimalParameter(
        38.0, 50.0, default=42.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_custom_profit_11 = DecimalParameter(
        0.16, 0.45, default=0.20, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_rsi_11 = DecimalParameter(
        28.0, 40.0, default=34.0, space="sell", decimals=2, optimize=True, load=True
    )

    # Profit under EMA200
    sell_custom_under_profit_0 = DecimalParameter(
        0.01, 0.4, default=0.01, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_0 = DecimalParameter(
        28.0, 40.0, default=33.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_1 = DecimalParameter(
        0.01, 0.10, 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, 0.10, default=0.03, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_2 = DecimalParameter(
        46.0, 66.0, default=57.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_3 = DecimalParameter(
        0.01, 0.10, default=0.04, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_3 = DecimalParameter(
        50.0, 68.0, default=58.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_4 = DecimalParameter(
        0.02, 0.1, default=0.05, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_4 = DecimalParameter(
        50.0, 68.0, default=59.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_5 = DecimalParameter(
        0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_5 = DecimalParameter(
        46.0, 62.0, default=58.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_6 = DecimalParameter(
        0.03, 0.1, default=0.07, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_6 = DecimalParameter(
        44.0, 60.0, default=56.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_7 = DecimalParameter(
        0.04, 0.1, default=0.08, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_7 = DecimalParameter(
        46.0, 60.0, default=54.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_8 = DecimalParameter(
        0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_8 = DecimalParameter(
        40.0, 58.0, default=50.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_9 = DecimalParameter(
        0.08, 0.14, default=0.1, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_9 = DecimalParameter(
        32.0, 48.0, default=44.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_10 = DecimalParameter(
        0.1, 0.16, default=0.12, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_10 = DecimalParameter(
        30.0, 50.0, default=42.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_custom_under_profit_11 = DecimalParameter(
        0.16, 0.3, default=0.2, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_under_rsi_11 = DecimalParameter(
        24.0, 40.0, default=34.0, space="sell", decimals=1, optimize=True, load=True
    )

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

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

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

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

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

    # Trail 1
    sell_trail_profit_min_1 = DecimalParameter(
        0.1, 0.2, default=0.16, space="sell", decimals=2, optimize=True, load=True
    )
    sell_trail_profit_max_1 = DecimalParameter(
        0.4, 0.7, default=0.6, space="sell", decimals=2, optimize=True, load=True
    )
    sell_trail_down_1 = DecimalParameter(
        0.01, 0.08, default=0.03, space="sell", decimals=3, optimize=True, load=True
    )
    sell_trail_rsi_min_1 = DecimalParameter(
        16.0, 36.0, default=20.0, space="sell", decimals=1, optimize=True, load=True
    )
    sell_trail_rsi_max_1 = DecimalParameter(
        30.0, 50.0, default=50.0, space="sell", decimals=1, optimize=True, load=True
    )

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

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

    # Under & near EMA200, accept profit
    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
    )

    # Under & near EMA200, take the loss
    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
    )

    # 48h for pump sell checks
    sell_pump_threshold_1 = DecimalParameter(
        0.5, 1.2, default=0.9, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_2 = DecimalParameter(
        0.4, 0.9, default=0.7, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_3 = DecimalParameter(
        0.3, 0.7, default=0.5, space="sell", decimals=2, optimize=True, load=True
    )

    # 36h for pump sell checks
    sell_pump_threshold_4 = DecimalParameter(
        0.5, 0.9, default=0.72, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_5 = DecimalParameter(
        3.0, 6.0, default=4.0, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_6 = DecimalParameter(
        0.8, 1.6, default=1.0, space="sell", decimals=2, optimize=True, load=True
    )

    # 24h for pump sell checks
    sell_pump_threshold_7 = DecimalParameter(
        0.5, 0.9, default=0.68, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_8 = DecimalParameter(
        0.3, 0.6, default=0.62, space="sell", decimals=2, optimize=True, load=True
    )
    sell_pump_threshold_9 = DecimalParameter(
        0.2, 0.5, default=0.3, space="sell", decimals=2, optimize=True, load=True
    )

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

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

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

    # Stoploss, pumped, 48h 1
    sell_custom_stoploss_pump_max_profit_1 = DecimalParameter(
        0.01, 0.04, default=0.025, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_stoploss_pump_min_1 = DecimalParameter(
        -0.1, -0.01, default=-0.02, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_stoploss_pump_max_1 = DecimalParameter(
        -0.1, -0.01, default=-0.01, space="sell", decimals=3, optimize=True, load=True
    )
    sell_custom_stoploss_pump_ma_offset_1 = DecimalParameter(
        0.7, 0.99, default=0.94, space="sell", decimals=2, optimize=True, load=True
    )

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

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

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

    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_11.value) & (
                last_candle["rsi"] < self.sell_custom_rsi_11.value
            ):
                return "signal_profit_11"
            if (
                self.sell_custom_profit_11.value > current_profit > self.sell_custom_profit_10.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_10.value):
                return "signal_profit_10"
            if (
                self.sell_custom_profit_10.value > current_profit > self.sell_custom_profit_9.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_9.value):
                return "signal_profit_9"
            if (
                self.sell_custom_profit_9.value > current_profit > self.sell_custom_profit_8.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_8.value):
                return "signal_profit_8"
            if (
                self.sell_custom_profit_8.value > current_profit > self.sell_custom_profit_7.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_7.value):
                return "signal_profit_7"
            if (
                self.sell_custom_profit_7.value > current_profit > self.sell_custom_profit_6.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_6.value):
                return "signal_profit_6"
            if (
                self.sell_custom_profit_6.value > current_profit > self.sell_custom_profit_5.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_5.value):
                return "signal_profit_5"
            elif (
                self.sell_custom_profit_5.value > current_profit > self.sell_custom_profit_4.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_4.value):
                return "signal_profit_4"
            elif (
                self.sell_custom_profit_4.value > current_profit > self.sell_custom_profit_3.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_3.value):
                return "signal_profit_3"
            elif (
                self.sell_custom_profit_3.value > current_profit > self.sell_custom_profit_2.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_2.value):
                return "signal_profit_2"
            elif (
                self.sell_custom_profit_2.value > current_profit > self.sell_custom_profit_1.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_1.value):
                return "signal_profit_1"
            elif (
                self.sell_custom_profit_1.value > current_profit > self.sell_custom_profit_0.value
            ) & (last_candle["rsi"] < self.sell_custom_rsi_0.value):
                return "signal_profit_0"

            # check if close is under EMA200
            elif (
                (current_profit > self.sell_custom_under_profit_11.value)
                & (last_candle["rsi"] < self.sell_custom_under_rsi_11.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_11"
            elif (
                (
                    self.sell_custom_under_profit_11.value
                    > current_profit
                    > self.sell_custom_under_profit_10.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_10.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_10"
            elif (
                (
                    self.sell_custom_under_profit_10.value
                    > current_profit
                    > self.sell_custom_under_profit_9.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_9.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_9"
            elif (
                (
                    self.sell_custom_under_profit_9.value
                    > current_profit
                    > self.sell_custom_under_profit_8.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_8.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_8"
            elif (
                (
                    self.sell_custom_under_profit_8.value
                    > current_profit
                    > self.sell_custom_under_profit_7.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_7.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_7"
            elif (
                (
                    self.sell_custom_under_profit_7.value
                    > current_profit
                    > self.sell_custom_under_profit_6.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_6.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_6"
            elif (
                (
                    self.sell_custom_under_profit_6.value
                    > current_profit
                    > self.sell_custom_under_profit_5.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_5.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_5"
            elif (
                (
                    self.sell_custom_under_profit_5.value
                    > current_profit
                    > self.sell_custom_under_profit_4.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_4.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_4"
            elif (
                (
                    self.sell_custom_under_profit_4.value
                    > 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 (
                (
                    self.sell_custom_under_profit_3.value
                    > 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 (
                (
                    self.sell_custom_under_profit_2.value
                    > 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 (
                (
                    self.sell_custom_under_profit_1.value
                    > current_profit
                    > self.sell_custom_under_profit_0.value
                )
                & (last_candle["rsi"] < self.sell_custom_under_rsi_0.value)
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_u_0"

            # check if the pair is "pumped"

            elif (
                (last_candle["sell_pump_48_1_1h"])
                & (current_profit > self.sell_custom_pump_profit_1_5.value)
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_1_5.value)
            ):
                return "signal_profit_p_1_5"
            elif (
                (last_candle["sell_pump_48_1_1h"])
                & (
                    self.sell_custom_pump_profit_1_5.value
                    > current_profit
                    > self.sell_custom_pump_profit_1_4.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_1_4.value)
            ):
                return "signal_profit_p_1_4"
            elif (
                (last_candle["sell_pump_48_1_1h"])
                & (
                    self.sell_custom_pump_profit_1_4.value
                    > current_profit
                    > self.sell_custom_pump_profit_1_3.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_1_3.value)
            ):
                return "signal_profit_p_1_3"
            elif (
                (last_candle["sell_pump_48_1_1h"])
                & (
                    self.sell_custom_pump_profit_1_3.value
                    > current_profit
                    > self.sell_custom_pump_profit_1_2.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_1_2.value)
            ):
                return "signal_profit_p_1_2"
            elif (
                (last_candle["sell_pump_48_1_1h"])
                & (
                    self.sell_custom_pump_profit_1_2.value
                    > current_profit
                    > self.sell_custom_pump_profit_1_1.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_1_1.value)
            ):
                return "signal_profit_p_1_1"

            elif (
                (last_candle["sell_pump_36_1_1h"])
                & (current_profit > self.sell_custom_pump_profit_2_5.value)
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_2_5.value)
            ):
                return "signal_profit_p_2_5"
            elif (
                (last_candle["sell_pump_36_1_1h"])
                & (
                    self.sell_custom_pump_profit_2_5.value
                    > current_profit
                    > self.sell_custom_pump_profit_2_4.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_2_4.value)
            ):
                return "signal_profit_p_2_4"
            elif (
                (last_candle["sell_pump_36_1_1h"])
                & (
                    self.sell_custom_pump_profit_2_4.value
                    > current_profit
                    > self.sell_custom_pump_profit_2_3.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_2_3.value)
            ):
                return "signal_profit_p_2_3"
            elif (
                (last_candle["sell_pump_36_1_1h"])
                & (
                    self.sell_custom_pump_profit_2_3.value
                    > current_profit
                    > self.sell_custom_pump_profit_2_2.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_2_2.value)
            ):
                return "signal_profit_p_2_2"
            elif (
                (last_candle["sell_pump_36_1_1h"])
                & (
                    self.sell_custom_pump_profit_2_2.value
                    > current_profit
                    > self.sell_custom_pump_profit_2_1.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_2_1.value)
            ):
                return "signal_profit_p_2_1"

            elif (
                (last_candle["sell_pump_24_1_1h"])
                & (current_profit > self.sell_custom_pump_profit_3_5.value)
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_3_5.value)
            ):
                return "signal_profit_p_3_5"
            elif (
                (last_candle["sell_pump_24_1_1h"])
                & (
                    self.sell_custom_pump_profit_3_5.value
                    > current_profit
                    > self.sell_custom_pump_profit_3_4.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_3_4.value)
            ):
                return "signal_profit_p_3_4"
            elif (
                (last_candle["sell_pump_24_1_1h"])
                & (
                    self.sell_custom_pump_profit_3_4.value
                    > current_profit
                    > self.sell_custom_pump_profit_3_3.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_3_3.value)
            ):
                return "signal_profit_p_3_3"
            elif (
                (last_candle["sell_pump_24_1_1h"])
                & (
                    self.sell_custom_pump_profit_3_3.value
                    > current_profit
                    > self.sell_custom_pump_profit_3_2.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_3_2.value)
            ):
                return "signal_profit_p_3_2"
            elif (
                (last_candle["sell_pump_24_1_1h"])
                & (
                    self.sell_custom_pump_profit_3_2.value
                    > current_profit
                    > self.sell_custom_pump_profit_3_1.value
                )
                & (last_candle["rsi"] < self.sell_custom_pump_rsi_3_1.value)
            ):
                return "signal_profit_p_3_1"

            elif (
                self.sell_custom_dec_profit_max_1.value
                > current_profit
                > self.sell_custom_dec_profit_min_1.value
            ) & (last_candle["sma_200_dec"]):
                return "signal_profit_d_1"
            elif (
                self.sell_custom_dec_profit_max_2.value
                > current_profit
                > self.sell_custom_dec_profit_min_2.value
            ) & (last_candle["close"] < last_candle["ema_100"]):
                return "signal_profit_d_2"

            # Trailing
            elif (
                (
                    self.sell_trail_profit_max_1.value
                    > current_profit
                    > self.sell_trail_profit_min_1.value
                )
                & (
                    self.sell_trail_rsi_min_1.value
                    < last_candle["rsi"]
                    < self.sell_trail_rsi_max_1.value
                )
                & (max_profit > (current_profit + self.sell_trail_down_1.value))
            ):
                return "signal_profit_t_1"
            elif (
                (
                    self.sell_trail_profit_max_2.value
                    > current_profit
                    > self.sell_trail_profit_min_2.value
                )
                & (
                    self.sell_trail_rsi_min_2.value
                    < last_candle["rsi"]
                    < self.sell_trail_rsi_max_2.value
                )
                & (max_profit > (current_profit + self.sell_trail_down_2.value))
            ):
                return "signal_profit_t_2"
            elif (
                (
                    self.sell_trail_profit_max_3.value
                    > current_profit
                    > self.sell_trail_profit_min_3.value
                )
                & (max_profit > (current_profit + self.sell_trail_down_3.value))
                & (last_candle["sma_200_dec_1h"])
            ):
                return "signal_profit_t_3"

            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"

            elif (
                (
                    self.sell_custom_pump_dec_profit_max_1.value
                    > current_profit
                    > self.sell_custom_pump_dec_profit_min_1.value
                )
                & (last_candle["sell_pump_48_1_1h"])
                & (last_candle["sma_200_dec"])
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_p_d_1"
            elif (
                (
                    self.sell_custom_pump_dec_profit_max_2.value
                    > current_profit
                    > self.sell_custom_pump_dec_profit_min_2.value
                )
                & (last_candle["sell_pump_48_2_1h"])
                & (last_candle["sma_200_dec"])
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_p_d_2"
            elif (
                (
                    self.sell_custom_pump_dec_profit_max_3.value
                    > current_profit
                    > self.sell_custom_pump_dec_profit_min_3.value
                )
                & (last_candle["sell_pump_48_3_1h"])
                & (last_candle["sma_200_dec"])
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_p_d_3"
            elif (
                (
                    self.sell_custom_pump_dec_profit_max_4.value
                    > current_profit
                    > self.sell_custom_pump_dec_profit_min_4.value
                )
                & (last_candle["sma_200_dec"])
                & (last_candle["sell_pump_24_2_1h"])
            ):
                return "signal_profit_p_d_4"

            # Pumped 48h 1, under EMA200
            elif (
                (
                    self.sell_custom_pump_under_profit_max_1.value
                    > current_profit
                    > self.sell_custom_pump_under_profit_min_1.value
                )
                & (last_candle["sell_pump_48_1_1h"])
                & (last_candle["close"] < last_candle["ema_200"])
            ):
                return "signal_profit_p_u_1"

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

            elif (
                (max_profit < self.sell_custom_stoploss_pump_max_profit_1.value)
                & (
                    self.sell_custom_stoploss_pump_min_1.value
                    < current_profit
                    < self.sell_custom_stoploss_pump_max_1.value
                )
                & (last_candle["sell_pump_48_1_1h"])
                & (last_candle["sma_200_dec"])
                & (
                    last_candle["close"]
                    < (last_candle["ema_200"] * self.sell_custom_stoploss_pump_ma_offset_1.value)
                )
            ):
                return "signal_stoploss_p_1"

            elif (
                (max_profit < self.sell_custom_stoploss_pump_max_profit_2.value)
                & (current_profit < self.sell_custom_stoploss_pump_loss_2.value)
                & (last_candle["sell_pump_48_1_1h"])
                & (last_candle["sma_200_dec_1h"])
                & (
                    last_candle["close"]
                    < (last_candle["ema_200"] * self.sell_custom_stoploss_pump_ma_offset_2.value)
                )
            ):
                return "signal_stoploss_p_2"

            elif (
                (max_profit < self.sell_custom_stoploss_pump_max_profit_3.value)
                & (current_profit < self.sell_custom_stoploss_pump_loss_3.value)
                & (last_candle["sell_pump_36_3_1h"])
                & (
                    last_candle["close"]
                    < (last_candle["ema_200"] * self.sell_custom_stoploss_pump_ma_offset_3.value)
                )
            ):
                return "signal_stoploss_p_3"

        return None

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    ## smoothed Heiken Ashi
    def HA(self, dataframe, smoothing=None):
        df = dataframe.copy()

        df["HA_Close"] = (df["open"] + df["high"] + df["low"] + df["close"]) / 4

        df.reset_index(inplace=True)

        ha_open = [(df["open"][0] + df["close"][0]) / 2]
        [ha_open.append((ha_open[i] + df["HA_Close"].values[i]) / 2) for i in range(0, len(df) - 1)]
        df["HA_Open"] = ha_open

        df.set_index("index", inplace=True)

        df["HA_High"] = df[["HA_Open", "HA_Close", "high"]].max(axis=1)
        df["HA_Low"] = df[["HA_Open", "HA_Close", "low"]].min(axis=1)

        if smoothing is not None:
            sml = abs(int(smoothing))
            if sml > 0:
                df["Smooth_HA_O"] = ta.EMA(df["HA_Open"], sml)
                df["Smooth_HA_C"] = ta.EMA(df["HA_Close"], sml)
                df["Smooth_HA_H"] = ta.EMA(df["HA_High"], sml)
                df["Smooth_HA_L"] = ta.EMA(df["HA_Low"], sml)

        return df

    def hansen_HA(self, informative_df, period=6):
        dataframe = informative_df.copy()

        dataframe["hhclose"] = (
            dataframe["open"] + dataframe["high"] + dataframe["low"] + dataframe["close"]
        ) / 4
        dataframe["hhopen"] = (
            dataframe["open"].shift(2) + dataframe["close"].shift(2)
        ) / 2  # it is not the same as real heikin ashi since I found that this is better.
        dataframe["hhhigh"] = dataframe[["open", "close", "high"]].max(axis=1)
        dataframe["hhlow"] = dataframe[["open", "close", "low"]].min(axis=1)

        dataframe["emac"] = ta.SMA(
            dataframe["hhclose"], timeperiod=period
        )  # to smooth out the data and thus less noise.
        dataframe["emao"] = ta.SMA(dataframe["hhopen"], timeperiod=period)

        return {"emac": dataframe["emac"], "emao": dataframe["emao"]}

    ## detect BB width expansion to indicate possible volatility
    def bbw_expansion(self, bbw_rolling, mult=1.1):
        bbw = list(bbw_rolling)

        m = 0.0
        for i in range(len(bbw) - 1):
            if bbw[i] > m:
                m = bbw[i]

        if bbw[-1] > (m * mult):
            return 1
        return 0

    ## do_indicator style a la Obelisk strategies
    def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Stoch fast - mainly due to 5m timeframes
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        # StochRSI for double checking things
        period = 14
        smoothD = 3
        SmoothK = 3
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        stochrsi = (dataframe["rsi"] - dataframe["rsi"].rolling(period).min()) / (
            dataframe["rsi"].rolling(period).max() - dataframe["rsi"].rolling(period).min()
        )
        dataframe["srsi_k"] = stochrsi.rolling(SmoothK).mean() * 100
        dataframe["srsi_d"] = dataframe["srsi_k"].rolling(smoothD).mean()

        # Bollinger Bands because obviously
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]

        # SAR Parabol - probably don't need this
        dataframe["sar"] = ta.SAR(dataframe)

        ## confirm wideboi variance signal with bbw expansion
        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
            "bb_middleband"
        ]
        dataframe["bbw_expansion"] = (
            dataframe["bb_width"].rolling(window=4).apply(self.bbw_expansion)
        )

        # confirm entry and exit on smoothed HA
        dataframe = self.HA(dataframe, 4)

        # thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative
        # https://github.com/hansen1015/freqtrade_strategy
        hansencalc = self.hansen_HA(dataframe, 6)
        dataframe["emac"] = hansencalc["emac"]
        dataframe["emao"] = hansencalc["emao"]

        # money flow index (MFI) for in/outflow of money, like RSI adjusted for vol
        dataframe["mfi"] = fta.MFI(dataframe)

        ## sqzmi to detect quiet periods
        dataframe["sqzmi"] = fta.SQZMI(dataframe)  # , MA=hansencalc['emac'])

        # Volume Flow Indicator (MFI) for volume based on the direction of price movement
        dataframe["vfi"] = fta.VFI(dataframe, period=14)

        dmi = fta.DMI(dataframe, period=14)
        dataframe["dmi_plus"] = dmi["DI+"]
        dataframe["dmi_minus"] = dmi["DI-"]
        dataframe["adx"] = fta.ADX(dataframe, period=14)

        ## for stoploss - all from Solipsis4
        ## simple ATR and ROC for stoploss
        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
        dataframe["roc"] = ta.ROC(dataframe, timeperiod=9)
        dataframe["rmi"] = RMI(dataframe, length=24, mom=5)
        ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
        dataframe["sroc"] = SROC(dataframe, roclen=21, emalen=13, smooth=21)
        dataframe["ssl-dir"] = np.where(sslup > ssldown, "up", "down")
        dataframe["rmi-up"] = np.where(dataframe["rmi"] >= dataframe["rmi"].shift(), 1, 0)
        dataframe["rmi-up-trend"] = np.where(dataframe["rmi-up"].rolling(5).sum() >= 3, 1, 0)
        dataframe["candle-up"] = np.where(dataframe["close"] >= dataframe["close"].shift(), 1, 0)
        dataframe["candle-up-trend"] = np.where(dataframe["candle-up"].rolling(5).sum() >= 3, 1, 0)

        return dataframe

    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.informative_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_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"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(
            20
        )

        # 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"]

        # Chaikin Money Flow
        informative_1h["cmf"] = chaikin_money_flow(informative_1h, 20)

        # Pump protections
        informative_1h["safe_pump_24_normal"] = self.safe_pump(
            informative_1h,
            24,
            self.buy_pump_threshold_1.value,
            self.buy_pump_pull_threshold_1.value,
        )
        informative_1h["safe_pump_36_normal"] = self.safe_pump(
            informative_1h,
            36,
            self.buy_pump_threshold_2.value,
            self.buy_pump_pull_threshold_2.value,
        )
        informative_1h["safe_pump_48_normal"] = self.safe_pump(
            informative_1h,
            48,
            self.buy_pump_threshold_3.value,
            self.buy_pump_pull_threshold_3.value,
        )

        informative_1h["safe_pump_24_strict"] = self.safe_pump(
            informative_1h,
            24,
            self.buy_pump_threshold_4.value,
            self.buy_pump_pull_threshold_4.value,
        )
        informative_1h["safe_pump_36_strict"] = self.safe_pump(
            informative_1h,
            36,
            self.buy_pump_threshold_5.value,
            self.buy_pump_pull_threshold_5.value,
        )
        informative_1h["safe_pump_48_strict"] = self.safe_pump(
            informative_1h,
            48,
            self.buy_pump_threshold_6.value,
            self.buy_pump_pull_threshold_6.value,
        )

        informative_1h["safe_pump_24_loose"] = self.safe_pump(
            informative_1h,
            24,
            self.buy_pump_threshold_7.value,
            self.buy_pump_pull_threshold_7.value,
        )
        informative_1h["safe_pump_36_loose"] = self.safe_pump(
            informative_1h,
            36,
            self.buy_pump_threshold_8.value,
            self.buy_pump_pull_threshold_8.value,
        )
        informative_1h["safe_pump_48_loose"] = self.safe_pump(
            informative_1h,
            48,
            self.buy_pump_threshold_9.value,
            self.buy_pump_pull_threshold_9.value,
        )

        informative_1h["sell_pump_48_1"] = (
            (informative_1h["high"].rolling(48).max() - informative_1h["low"].rolling(48).min())
            / informative_1h["low"].rolling(48).min()
        ) > self.sell_pump_threshold_1.value
        informative_1h["sell_pump_48_2"] = (
            (informative_1h["high"].rolling(48).max() - informative_1h["low"].rolling(48).min())
            / informative_1h["low"].rolling(48).min()
        ) > self.sell_pump_threshold_2.value
        informative_1h["sell_pump_48_3"] = (
            (informative_1h["high"].rolling(48).max() - informative_1h["low"].rolling(48).min())
            / informative_1h["low"].rolling(48).min()
        ) > self.sell_pump_threshold_3.value

        informative_1h["sell_pump_36_1"] = (
            (informative_1h["high"].rolling(36).max() - informative_1h["low"].rolling(36).min())
            / informative_1h["low"].rolling(36).min()
        ) > self.sell_pump_threshold_4.value
        informative_1h["sell_pump_36_2"] = (
            (informative_1h["high"].rolling(36).max() - informative_1h["low"].rolling(36).min())
            / informative_1h["low"].rolling(36).min()
        ) > self.sell_pump_threshold_5.value
        informative_1h["sell_pump_36_3"] = (
            (informative_1h["high"].rolling(36).max() - informative_1h["low"].rolling(36).min())
            / informative_1h["low"].rolling(36).min()
        ) > self.sell_pump_threshold_6.value

        informative_1h["sell_pump_24_1"] = (
            (informative_1h["high"].rolling(24).max() - informative_1h["low"].rolling(24).min())
            / informative_1h["low"].rolling(24).min()
        ) > self.sell_pump_threshold_7.value
        informative_1h["sell_pump_24_2"] = (
            (informative_1h["high"].rolling(24).max() - informative_1h["low"].rolling(24).min())
            / informative_1h["low"].rolling(24).min()
        ) > self.sell_pump_threshold_8.value
        informative_1h["sell_pump_24_3"] = (
            (informative_1h["high"].rolling(24).max() - informative_1h["low"].rolling(24).min())
            / informative_1h["low"].rolling(24).min()
        ) > self.sell_pump_threshold_9.value

        return informative_1h

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

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

    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
        """
        df = dataframe.copy()
        return df["open"].rolling(length).max() - df["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
        """
        df = dataframe.copy()
        return df["close"] - df["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
        """
        df = dataframe.copy()
        return (self.range_percent_change(df, length) < thresh) | (
            self.range_maxgap_adjusted(df, length, pull_thresh) > self.range_height(df, length)
        )

    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, 50, 200)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # Chopiness
        dataframe["chop"] = qtpylib.chopiness(dataframe, 14)

        # Dip protection
        dataframe["safe_dips_normal"] = (
            (
                ((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()

        return dataframe

    ## stolen from Obelisk's Ichi strat code and backtest blog post, and Solipsis4
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # The indicators for the 1h informative timeframe
        informative_1h = self.informative_1h_indicators(dataframe, metadata)

        # Populate/update the trade data if there is any, set trades to false if not live/dry
        self.custom_trade_info[metadata["pair"]] = self.populate_trades(metadata["pair"])

        if self.config["runmode"].value in ("backtest", "hyperopt"):
            assert (
                timeframe_to_minutes(self.timeframe) <= 30
            ), "Backtest this strategy in 5m or 1m timeframe."

        if self.timeframe == self.informative_timeframe:
            dataframe = self.do_indicators(dataframe, metadata)
        else:
            if not self.dp:
                return dataframe

            informative = self.dp.get_pair_dataframe(
                pair=metadata["pair"], timeframe=self.informative_timeframe
            )

            informative = self.do_indicators(informative.copy(), metadata)

            dataframe = merge_informative_pair(
                dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True
            )

            skip_columns = [
                (s + "_" + self.informative_timeframe)
                for s in ["date", "open", "high", "low", "close", "volume", "emac", "emao"]
            ]
            dataframe.rename(
                columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "")
                if (not s in skip_columns)
                else s,
                inplace=True,
            )

        # Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
        if self.dp.runmode.value in ("backtest", "hyperopt"):
            self.custom_trade_info[metadata["pair"]]["roc"] = (
                dataframe[["date", "roc"]].copy().set_index("date")
            )
            self.custom_trade_info[metadata["pair"]]["atr"] = (
                dataframe[["date", "atr"]].copy().set_index("date")
            )
            self.custom_trade_info[metadata["pair"]]["sroc"] = (
                dataframe[["date", "sroc"]].copy().set_index("date")
            )
            self.custom_trade_info[metadata["pair"]]["ssl-dir"] = (
                dataframe[["date", "ssl-dir"]].copy().set_index("date")
            )
            self.custom_trade_info[metadata["pair"]]["rmi-up-trend"] = (
                dataframe[["date", "rmi-up-trend"]].copy().set_index("date")
            )
            self.custom_trade_info[metadata["pair"]]["candle-up-trend"] = (
                dataframe[["date", "candle-up-trend"]].copy().set_index("date")
            )

        dataframe = merge_informative_pair(
            dataframe, informative_1h, self.timeframe, self.informative_timeframe, ffill=True
        )

        # The indicators for the normal (5m) timeframe
        dataframe = self.normal_tf_indicators(dataframe, metadata)

        return dataframe

    ## cryptofrog signals
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    ## close ALWAYS needs to be lower than the heiken low at 5m
                    (dataframe["close"] < dataframe["Smooth_HA_L"])
                    &
                    ## Hansen's HA EMA at informative timeframe
                    (dataframe["emac_1h"] < dataframe["emao_1h"])
                )
                & (
                    (
                        ## potential uptick incoming so buy
                        (dataframe["bbw_expansion"] == 1)
                        & (dataframe["sqzmi"] == False)
                        & ((dataframe["mfi"] < 20) | (dataframe["dmi_minus"] > 30))
                    )
                    | (
                        # this tries to find extra buys in undersold regions
                        (dataframe["close"] < dataframe["sar"])
                        & (
                            (dataframe["srsi_d"] >= dataframe["srsi_k"])
                            & (dataframe["srsi_d"] < 30)
                        )
                        & ((dataframe["fastd"] > dataframe["fastk"]) & (dataframe["fastd"] < 23))
                        & (dataframe["mfi"] < 30)
                    )
                    | (
                        # find smaller temporary dips in sideways
                        (
                            (
                                (dataframe["dmi_minus"] > 30)
                                & qtpylib.crossed_above(
                                    dataframe["dmi_minus"], dataframe["dmi_plus"]
                                )
                            )
                            & (dataframe["close"] < dataframe["bb_lowerband"])
                        )
                        | (
                            ## if nothing else is making a buy signal
                            ## just throw in any old SQZMI shit based fastd
                            ## this needs work!
                            (dataframe["sqzmi"] == True)
                            & (
                                (dataframe["fastd"] > dataframe["fastk"])
                                & (dataframe["fastd"] < 20)
                            )
                        )
                    )
                    ## volume sanity checks
                    & (dataframe["vfi"] < 0.0)
                    & (dataframe["volume"] > 0)
                )
            ),
            "buy",
        ] = 1

        conditions = []
        # Protections
        buy_01_protections = [True]
        if self.buy_01_protection__ema_fast.value:
            buy_01_protections.append(
                dataframe[f"ema_{self.buy_01_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_01_protection__ema_slow.value:
            buy_01_protections.append(
                dataframe[f"ema_{self.buy_01_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_01_protection__close_above_ema_fast.value:
            buy_01_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_01_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_01_protection__close_above_ema_slow.value:
            buy_01_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_01_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_01_protection__sma200_rising.value:
            buy_01_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_01_protection__sma200_rising_val.value))
            )
        if self.buy_01_protection__sma200_1h_rising.value:
            buy_01_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_01_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_01_protection__safe_dips.value:
            buy_01_protections.append(
                dataframe[f"safe_dips_{self.buy_01_protection__safe_dips_type.value}"]
            )
        if self.buy_01_protection__safe_pump.value:
            buy_01_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_01_protection__safe_pump_period.value}_{self.buy_01_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_01_logic = []
        buy_01_logic.append(reduce(lambda x, y: x & y, buy_01_protections))
        buy_01_logic.append(
            (
                (dataframe["close"] - dataframe["open"].rolling(36).min())
                / dataframe["open"].rolling(36).min()
            )
            > self.buy_min_inc_1.value
        )
        buy_01_logic.append(dataframe["rsi_1h"] > self.buy_rsi_1h_min_1.value)
        buy_01_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_max_1.value)
        buy_01_logic.append(dataframe["rsi"] < self.buy_rsi_1.value)
        buy_01_logic.append(dataframe["mfi"] < self.buy_mfi_1.value)
        buy_01_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_01_trigger"] = reduce(lambda x, y: x & y, buy_01_logic)
        if self.buy_condition_1_enable.value:
            conditions.append(dataframe.loc[:, "buy_01_trigger"])

        # Protections
        buy_02_protections = [True]
        if self.buy_02_protection__ema_fast.value:
            buy_02_protections.append(
                dataframe[f"ema_{self.buy_02_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_02_protection__ema_slow.value:
            buy_02_protections.append(
                dataframe[f"ema_{self.buy_02_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_02_protection__close_above_ema_fast.value:
            buy_02_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_02_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_02_protection__close_above_ema_slow.value:
            buy_02_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_02_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_02_protection__sma200_rising.value:
            buy_02_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_02_protection__sma200_rising_val.value))
            )
        if self.buy_02_protection__sma200_1h_rising.value:
            buy_02_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_02_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_02_protection__safe_dips.value:
            buy_02_protections.append(
                dataframe[f"safe_dips_{self.buy_02_protection__safe_dips_type.value}"]
            )
        if self.buy_02_protection__safe_pump.value:
            buy_02_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_02_protection__safe_pump_period.value}_{self.buy_02_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_02_logic = []
        buy_02_logic.append(reduce(lambda x, y: x & y, buy_02_protections))
        # buy_02_logic.append(dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume'])
        buy_02_logic.append(dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_1h_diff_2.value)
        buy_02_logic.append(dataframe["mfi"] < self.buy_mfi_2.value)
        buy_02_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_2.value)
        )
        buy_02_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_02_trigger"] = reduce(lambda x, y: x & y, buy_02_logic)
        if self.buy_condition_2_enable.value:
            conditions.append(dataframe.loc[:, "buy_02_trigger"])

        # Protections
        buy_03_protections = [True]
        if self.buy_03_protection__ema_fast.value:
            buy_03_protections.append(
                dataframe[f"ema_{self.buy_03_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_03_protection__ema_slow.value:
            buy_03_protections.append(
                dataframe[f"ema_{self.buy_03_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_03_protection__close_above_ema_fast.value:
            buy_03_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_03_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_03_protection__close_above_ema_slow.value:
            buy_03_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_03_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_03_protection__sma200_rising.value:
            buy_03_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_03_protection__sma200_rising_val.value))
            )
        if self.buy_03_protection__sma200_1h_rising.value:
            buy_03_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_03_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_03_protection__safe_dips.value:
            buy_03_protections.append(
                dataframe[f"safe_dips_{self.buy_03_protection__safe_dips_type.value}"]
            )
        if self.buy_03_protection__safe_pump.value:
            buy_03_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_03_protection__safe_pump_period.value}_{self.buy_03_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_03_protections.append(
            dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_3.value)
        )

        # Logic
        buy_03_logic = []
        buy_03_logic.append(reduce(lambda x, y: x & y, buy_03_protections))
        buy_03_logic.append(dataframe["lower"].shift().gt(0))
        buy_03_logic.append(
            dataframe["bbdelta"].gt(dataframe["close"] * self.buy_bb40_bbdelta_close_3.value)
        )
        buy_03_logic.append(
            dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close_3.value)
        )
        buy_03_logic.append(
            dataframe["tail"].lt(dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta_3.value)
        )
        buy_03_logic.append(dataframe["close"].lt(dataframe["lower"].shift()))
        buy_03_logic.append(dataframe["close"].le(dataframe["close"].shift()))
        buy_03_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_03_trigger"] = reduce(lambda x, y: x & y, buy_03_logic)
        if self.buy_condition_3_enable.value:
            conditions.append(dataframe.loc[:, "buy_03_trigger"])

        # Protections
        buy_04_protections = [True]
        if self.buy_04_protection__ema_fast.value:
            buy_04_protections.append(
                dataframe[f"ema_{self.buy_04_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_04_protection__ema_slow.value:
            buy_04_protections.append(
                dataframe[f"ema_{self.buy_04_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_04_protection__close_above_ema_fast.value:
            buy_04_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_04_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_04_protection__close_above_ema_slow.value:
            buy_04_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_04_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_04_protection__sma200_rising.value:
            buy_04_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_04_protection__sma200_rising_val.value))
            )
        if self.buy_04_protection__sma200_1h_rising.value:
            buy_04_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_04_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_04_protection__safe_dips.value:
            buy_04_protections.append(
                dataframe[f"safe_dips_{self.buy_04_protection__safe_dips_type.value}"]
            )
        if self.buy_04_protection__safe_pump.value:
            buy_04_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_04_protection__safe_pump_period.value}_{self.buy_04_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_04_logic = []
        buy_04_logic.append(reduce(lambda x, y: x & y, buy_04_protections))
        buy_04_logic.append(dataframe["close"] < dataframe["ema_50"])
        buy_04_logic.append(
            dataframe["close"] < self.buy_bb20_close_bblowerband_4.value * dataframe["bb_lowerband"]
        )
        buy_04_logic.append(
            dataframe["volume"]
            < (dataframe["volume_mean_30"].shift(1) * self.buy_bb20_volume_4.value)
        )
        # Populate
        dataframe.loc[:, "buy_04_trigger"] = reduce(lambda x, y: x & y, buy_04_logic)
        if self.buy_condition_4_enable.value:
            conditions.append(dataframe.loc[:, "buy_04_trigger"])

        # Protections
        buy_05_protections = [True]
        if self.buy_05_protection__ema_fast.value:
            buy_05_protections.append(
                dataframe[f"ema_{self.buy_05_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_05_protection__ema_slow.value:
            buy_05_protections.append(
                dataframe[f"ema_{self.buy_05_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_05_protection__close_above_ema_fast.value:
            buy_05_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_05_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_05_protection__close_above_ema_slow.value:
            buy_05_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_05_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_05_protection__sma200_rising.value:
            buy_05_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_05_protection__sma200_rising_val.value))
            )
        if self.buy_05_protection__sma200_1h_rising.value:
            buy_05_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_05_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_05_protection__safe_dips.value:
            buy_05_protections.append(
                dataframe[f"safe_dips_{self.buy_05_protection__safe_dips_type.value}"]
            )
        if self.buy_05_protection__safe_pump.value:
            buy_05_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_05_protection__safe_pump_period.value}_{self.buy_05_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_05_protections.append(
            dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_5.value)
        )

        # Logic
        buy_05_logic = []
        buy_05_logic.append(reduce(lambda x, y: x & y, buy_05_protections))
        buy_05_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
        buy_05_logic.append(
            (dataframe["ema_26"] - dataframe["ema_12"])
            > (dataframe["open"] * self.buy_ema_open_mult_5.value)
        )
        buy_05_logic.append(
            (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
        )
        buy_05_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_5.value)
        )
        buy_05_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_05_trigger"] = reduce(lambda x, y: x & y, buy_05_logic)
        if self.buy_condition_5_enable.value:
            conditions.append(dataframe.loc[:, "buy_05_trigger"])

        # Protections
        buy_06_protections = [True]
        if self.buy_06_protection__ema_fast.value:
            buy_06_protections.append(
                dataframe[f"ema_{self.buy_06_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_06_protection__ema_slow.value:
            buy_06_protections.append(
                dataframe[f"ema_{self.buy_06_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_06_protection__close_above_ema_fast.value:
            buy_06_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_06_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_06_protection__close_above_ema_slow.value:
            buy_06_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_06_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_06_protection__sma200_rising.value:
            buy_06_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_06_protection__sma200_rising_val.value))
            )
        if self.buy_06_protection__sma200_1h_rising.value:
            buy_06_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_06_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_06_protection__safe_dips.value:
            buy_06_protections.append(
                dataframe[f"safe_dips_{self.buy_06_protection__safe_dips_type.value}"]
            )
        if self.buy_06_protection__safe_pump.value:
            buy_06_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_06_protection__safe_pump_period.value}_{self.buy_06_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_06_logic = []
        buy_06_logic.append(reduce(lambda x, y: x & y, buy_06_protections))
        buy_06_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
        buy_06_logic.append(
            (dataframe["ema_26"] - dataframe["ema_12"])
            > (dataframe["open"] * self.buy_ema_open_mult_6.value)
        )
        buy_06_logic.append(
            (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
        )
        buy_06_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_6.value)
        )
        buy_06_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_06_trigger"] = reduce(lambda x, y: x & y, buy_06_logic)
        if self.buy_condition_6_enable.value:
            conditions.append(dataframe.loc[:, "buy_06_trigger"])

        # Protections
        buy_07_protections = [True]
        if self.buy_07_protection__ema_fast.value:
            buy_07_protections.append(
                dataframe[f"ema_{self.buy_07_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_07_protection__ema_slow.value:
            buy_07_protections.append(
                dataframe[f"ema_{self.buy_07_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_07_protection__close_above_ema_fast.value:
            buy_07_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_07_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_07_protection__close_above_ema_slow.value:
            buy_07_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_07_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_07_protection__sma200_rising.value:
            buy_07_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_07_protection__sma200_rising_val.value))
            )
        if self.buy_07_protection__sma200_1h_rising.value:
            buy_07_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_07_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_07_protection__safe_dips.value:
            buy_07_protections.append(
                dataframe[f"safe_dips_{self.buy_07_protection__safe_dips_type.value}"]
            )
        if self.buy_07_protection__safe_pump.value:
            buy_07_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_07_protection__safe_pump_period.value}_{self.buy_07_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_07_logic = []
        buy_07_logic.append(reduce(lambda x, y: x & y, buy_07_protections))
        buy_07_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
        buy_07_logic.append(
            (dataframe["ema_26"] - dataframe["ema_12"])
            > (dataframe["open"] * self.buy_ema_open_mult_7.value)
        )
        buy_07_logic.append(
            (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
        )
        buy_07_logic.append(dataframe["rsi"] < self.buy_rsi_7.value)
        buy_07_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_07_trigger"] = reduce(lambda x, y: x & y, buy_07_logic)
        if self.buy_condition_7_enable.value:
            conditions.append(dataframe.loc[:, "buy_07_trigger"])

        # Protections
        buy_08_protections = [True]
        if self.buy_08_protection__ema_fast.value:
            buy_08_protections.append(
                dataframe[f"ema_{self.buy_08_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_08_protection__ema_slow.value:
            buy_08_protections.append(
                dataframe[f"ema_{self.buy_08_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_08_protection__close_above_ema_fast.value:
            buy_08_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_08_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_08_protection__close_above_ema_slow.value:
            buy_08_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_08_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_08_protection__sma200_rising.value:
            buy_08_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_08_protection__sma200_rising_val.value))
            )
        if self.buy_08_protection__sma200_1h_rising.value:
            buy_08_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_08_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_08_protection__safe_dips.value:
            buy_08_protections.append(
                dataframe[f"safe_dips_{self.buy_08_protection__safe_dips_type.value}"]
            )
        if self.buy_08_protection__safe_pump.value:
            buy_08_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_08_protection__safe_pump_period.value}_{self.buy_08_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_08_logic = []
        buy_08_logic.append(reduce(lambda x, y: x & y, buy_08_protections))
        buy_08_logic.append(dataframe["rsi"] < self.buy_rsi_8.value)
        buy_08_logic.append(
            dataframe["volume"] > (dataframe["volume"].shift(1) * self.buy_volume_8.value)
        )
        buy_08_logic.append(dataframe["close"] > dataframe["open"])
        buy_08_logic.append(
            (dataframe["close"] - dataframe["low"])
            > ((dataframe["close"] - dataframe["open"]) * self.buy_tail_diff_8.value)
        )
        buy_08_logic.append(dataframe["volume"] > 0)

        # Populate
        dataframe.loc[:, "buy_08_trigger"] = reduce(lambda x, y: x & y, buy_08_logic)
        if self.buy_condition_8_enable.value:
            conditions.append(dataframe.loc[:, "buy_08_trigger"])

        # Protections
        buy_09_protections = [True]
        if self.buy_09_protection__ema_fast.value:
            buy_09_protections.append(
                dataframe[f"ema_{self.buy_09_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_09_protection__ema_slow.value:
            buy_09_protections.append(
                dataframe[f"ema_{self.buy_09_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_09_protection__close_above_ema_fast.value:
            buy_09_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_09_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_09_protection__close_above_ema_slow.value:
            buy_09_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_09_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_09_protection__sma200_rising.value:
            buy_09_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_09_protection__sma200_rising_val.value))
            )
        if self.buy_09_protection__sma200_1h_rising.value:
            buy_09_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_09_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_09_protection__safe_dips.value:
            buy_09_protections.append(
                dataframe[f"safe_dips_{self.buy_09_protection__safe_dips_type.value}"]
            )
        if self.buy_09_protection__safe_pump.value:
            buy_09_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_09_protection__safe_pump_period.value}_{self.buy_09_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_09_protections.append(dataframe["ema_50"] > dataframe["ema_200"])

        # Logic
        buy_09_logic = []
        buy_09_logic.append(reduce(lambda x, y: x & y, buy_09_protections))
        buy_09_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_9.value)
        buy_09_logic.append(
            dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb_offset_9.value
        )
        buy_09_logic.append(dataframe["rsi_1h"] > self.buy_rsi_1h_min_9.value)
        buy_09_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_max_9.value)
        buy_09_logic.append(dataframe["mfi"] < self.buy_mfi_9.value)
        buy_09_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_09_trigger"] = reduce(lambda x, y: x & y, buy_09_logic)
        if self.buy_condition_9_enable.value:
            conditions.append(dataframe.loc[:, "buy_09_trigger"])

        # Protections
        buy_10_protections = [True]
        if self.buy_10_protection__ema_fast.value:
            buy_10_protections.append(
                dataframe[f"ema_{self.buy_10_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_10_protection__ema_slow.value:
            buy_10_protections.append(
                dataframe[f"ema_{self.buy_10_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_10_protection__close_above_ema_fast.value:
            buy_10_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_10_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_10_protection__close_above_ema_slow.value:
            buy_10_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_10_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_10_protection__sma200_rising.value:
            buy_10_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_10_protection__sma200_rising_val.value))
            )
        if self.buy_10_protection__sma200_1h_rising.value:
            buy_10_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_10_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_10_protection__safe_dips.value:
            buy_10_protections.append(
                dataframe[f"safe_dips_{self.buy_10_protection__safe_dips_type.value}"]
            )
        if self.buy_10_protection__safe_pump.value:
            buy_10_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_10_protection__safe_pump_period.value}_{self.buy_10_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_10_protections.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])

        # Logic
        buy_10_logic = []
        buy_10_logic.append(reduce(lambda x, y: x & y, buy_10_protections))
        buy_10_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_10.value)
        buy_10_logic.append(
            dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb_offset_10.value
        )
        buy_10_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_10.value)
        buy_10_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_10_trigger"] = reduce(lambda x, y: x & y, buy_10_logic)
        if self.buy_condition_10_enable.value:
            conditions.append(dataframe.loc[:, "buy_10_trigger"])

        # Protections
        buy_11_protections = [True]
        if self.buy_11_protection__ema_fast.value:
            buy_11_protections.append(
                dataframe[f"ema_{self.buy_11_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_11_protection__ema_slow.value:
            buy_11_protections.append(
                dataframe[f"ema_{self.buy_11_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_11_protection__close_above_ema_fast.value:
            buy_11_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_11_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_11_protection__close_above_ema_slow.value:
            buy_11_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_11_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_11_protection__sma200_rising.value:
            buy_11_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_11_protection__sma200_rising_val.value))
            )
        if self.buy_11_protection__sma200_1h_rising.value:
            buy_11_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_11_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_11_protection__safe_dips.value:
            buy_11_protections.append(
                dataframe[f"safe_dips_{self.buy_11_protection__safe_dips_type.value}"]
            )
        if self.buy_11_protection__safe_pump.value:
            buy_11_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_11_protection__safe_pump_period.value}_{self.buy_11_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_11_protections.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
        buy_11_protections.append(dataframe["safe_pump_36_normal_1h"])
        buy_11_protections.append(dataframe["safe_pump_48_loose_1h"])

        # Logic
        buy_11_logic = []
        buy_11_logic.append(reduce(lambda x, y: x & y, buy_11_protections))
        buy_11_logic.append(
            (
                (dataframe["close"] - dataframe["open"].rolling(36).min())
                / dataframe["open"].rolling(36).min()
            )
            > self.buy_min_inc_11.value
        )
        buy_11_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_11.value)
        buy_11_logic.append(dataframe["rsi_1h"] > self.buy_rsi_1h_min_11.value)
        buy_11_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_max_11.value)
        buy_11_logic.append(dataframe["rsi"] < self.buy_rsi_11.value)
        buy_11_logic.append(dataframe["mfi"] < self.buy_mfi_11.value)
        buy_11_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_11_trigger"] = reduce(lambda x, y: x & y, buy_11_logic)
        if self.buy_condition_11_enable.value:
            conditions.append(dataframe.loc[:, "buy_11_trigger"])

        # Protections
        buy_12_protections = [True]
        if self.buy_12_protection__ema_fast.value:
            buy_12_protections.append(
                dataframe[f"ema_{self.buy_12_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_12_protection__ema_slow.value:
            buy_12_protections.append(
                dataframe[f"ema_{self.buy_12_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_12_protection__close_above_ema_fast.value:
            buy_12_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_12_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_12_protection__close_above_ema_slow.value:
            buy_12_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_12_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_12_protection__sma200_rising.value:
            buy_12_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_12_protection__sma200_rising_val.value))
            )
        if self.buy_12_protection__sma200_1h_rising.value:
            buy_12_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_12_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_12_protection__safe_dips.value:
            buy_12_protections.append(
                dataframe[f"safe_dips_{self.buy_12_protection__safe_dips_type.value}"]
            )
        if self.buy_12_protection__safe_pump.value:
            buy_12_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_12_protection__safe_pump_period.value}_{self.buy_12_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_12_logic = []
        buy_12_logic.append(reduce(lambda x, y: x & y, buy_12_protections))
        buy_12_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_12.value)
        buy_12_logic.append(dataframe["ewo"] > self.buy_ewo_12.value)
        buy_12_logic.append(dataframe["rsi"] < self.buy_rsi_12.value)
        buy_12_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_12_trigger"] = reduce(lambda x, y: x & y, buy_12_logic)
        if self.buy_condition_12_enable.value:
            conditions.append(dataframe.loc[:, "buy_12_trigger"])

        # Protections
        buy_13_protections = [True]
        if self.buy_13_protection__ema_fast.value:
            buy_13_protections.append(
                dataframe[f"ema_{self.buy_13_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_13_protection__ema_slow.value:
            buy_13_protections.append(
                dataframe[f"ema_{self.buy_13_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_13_protection__close_above_ema_fast.value:
            buy_13_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_13_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_13_protection__close_above_ema_slow.value:
            buy_13_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_13_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_13_protection__sma200_rising.value:
            buy_13_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_13_protection__sma200_rising_val.value))
            )
        if self.buy_13_protection__sma200_1h_rising.value:
            buy_13_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_13_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_13_protection__safe_dips.value:
            buy_13_protections.append(
                dataframe[f"safe_dips_{self.buy_13_protection__safe_dips_type.value}"]
            )
        if self.buy_13_protection__safe_pump.value:
            buy_13_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_13_protection__safe_pump_period.value}_{self.buy_13_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_13_protections.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
        # buy_13_protections.append(dataframe['safe_pump_36_loose_1h'])

        # Logic
        buy_13_logic = []
        buy_13_logic.append(reduce(lambda x, y: x & y, buy_13_protections))
        buy_13_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_13.value)
        buy_13_logic.append(dataframe["ewo"] < self.buy_ewo_13.value)
        buy_13_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_13_trigger"] = reduce(lambda x, y: x & y, buy_13_logic)
        if self.buy_condition_13_enable.value:
            conditions.append(dataframe.loc[:, "buy_13_trigger"])

        # Protections
        buy_14_protections = [True]
        if self.buy_14_protection__ema_fast.value:
            buy_14_protections.append(
                dataframe[f"ema_{self.buy_14_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_14_protection__ema_slow.value:
            buy_14_protections.append(
                dataframe[f"ema_{self.buy_14_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_14_protection__close_above_ema_fast.value:
            buy_14_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_14_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_14_protection__close_above_ema_slow.value:
            buy_14_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_14_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_14_protection__sma200_rising.value:
            buy_14_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_14_protection__sma200_rising_val.value))
            )
        if self.buy_14_protection__sma200_1h_rising.value:
            buy_14_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_14_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_14_protection__safe_dips.value:
            buy_14_protections.append(
                dataframe[f"safe_dips_{self.buy_14_protection__safe_dips_type.value}"]
            )
        if self.buy_14_protection__safe_pump.value:
            buy_14_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_14_protection__safe_pump_period.value}_{self.buy_14_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_14_logic = []
        buy_14_logic.append(reduce(lambda x, y: x & y, buy_14_protections))
        buy_14_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
        buy_14_logic.append(
            (dataframe["ema_26"] - dataframe["ema_12"])
            > (dataframe["open"] * self.buy_ema_open_mult_14.value)
        )
        buy_14_logic.append(
            (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
        )
        buy_14_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_14.value)
        )
        buy_14_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_14.value)
        buy_14_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_14_trigger"] = reduce(lambda x, y: x & y, buy_14_logic)
        if self.buy_condition_14_enable.value:
            conditions.append(dataframe.loc[:, "buy_14_trigger"])

        # Protections
        buy_15_protections = [True]
        if self.buy_15_protection__ema_fast.value:
            buy_15_protections.append(
                dataframe[f"ema_{self.buy_15_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_15_protection__ema_slow.value:
            buy_15_protections.append(
                dataframe[f"ema_{self.buy_15_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_15_protection__close_above_ema_fast.value:
            buy_15_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_15_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_15_protection__close_above_ema_slow.value:
            buy_15_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_15_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_15_protection__sma200_rising.value:
            buy_15_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_15_protection__sma200_rising_val.value))
            )
        if self.buy_15_protection__sma200_1h_rising.value:
            buy_15_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_15_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_15_protection__safe_dips.value:
            buy_15_protections.append(
                dataframe[f"safe_dips_{self.buy_15_protection__safe_dips_type.value}"]
            )
        if self.buy_15_protection__safe_pump.value:
            buy_15_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_15_protection__safe_pump_period.value}_{self.buy_15_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_15_protections.append(
            dataframe["close"] > dataframe["ema_200_1h"] * self.buy_ema_rel_15.value
        )

        # Logic
        buy_15_logic = []
        buy_15_logic.append(reduce(lambda x, y: x & y, buy_15_protections))
        buy_15_logic.append(dataframe["ema_26"] > dataframe["ema_12"])
        buy_15_logic.append(
            (dataframe["ema_26"] - dataframe["ema_12"])
            > (dataframe["open"] * self.buy_ema_open_mult_15.value)
        )
        buy_15_logic.append(
            (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)
        )
        buy_15_logic.append(dataframe["rsi"] < self.buy_rsi_15.value)
        buy_15_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_15.value)
        buy_15_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_15_trigger"] = reduce(lambda x, y: x & y, buy_15_logic)
        if self.buy_condition_15_enable.value:
            conditions.append(dataframe.loc[:, "buy_15_trigger"])

        # Protections
        buy_16_protections = [True]
        if self.buy_16_protection__ema_fast.value:
            buy_16_protections.append(
                dataframe[f"ema_{self.buy_16_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_16_protection__ema_slow.value:
            buy_16_protections.append(
                dataframe[f"ema_{self.buy_16_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_16_protection__close_above_ema_fast.value:
            buy_16_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_16_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_16_protection__close_above_ema_slow.value:
            buy_16_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_16_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_16_protection__sma200_rising.value:
            buy_16_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_16_protection__sma200_rising_val.value))
            )
        if self.buy_16_protection__sma200_1h_rising.value:
            buy_16_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_16_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_16_protection__safe_dips.value:
            buy_16_protections.append(
                dataframe[f"safe_dips_{self.buy_16_protection__safe_dips_type.value}"]
            )
        if self.buy_16_protection__safe_pump.value:
            buy_16_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_16_protection__safe_pump_period.value}_{self.buy_16_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_16_logic = []
        buy_16_logic.append(reduce(lambda x, y: x & y, buy_16_protections))
        buy_16_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_16.value)
        buy_16_logic.append(dataframe["ewo"] > self.buy_ewo_16.value)
        buy_16_logic.append(dataframe["rsi"] < self.buy_rsi_16.value)
        buy_16_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_16_trigger"] = reduce(lambda x, y: x & y, buy_16_logic)
        if self.buy_condition_16_enable.value:
            conditions.append(dataframe.loc[:, "buy_16_trigger"])

        # Protections
        buy_17_protections = [True]
        if self.buy_17_protection__ema_fast.value:
            buy_17_protections.append(
                dataframe[f"ema_{self.buy_17_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_17_protection__ema_slow.value:
            buy_17_protections.append(
                dataframe[f"ema_{self.buy_17_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_17_protection__close_above_ema_fast.value:
            buy_17_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_17_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_17_protection__close_above_ema_slow.value:
            buy_17_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_17_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_17_protection__sma200_rising.value:
            buy_17_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_17_protection__sma200_rising_val.value))
            )
        if self.buy_17_protection__sma200_1h_rising.value:
            buy_17_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_17_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_17_protection__safe_dips.value:
            buy_17_protections.append(
                dataframe[f"safe_dips_{self.buy_17_protection__safe_dips_type.value}"]
            )
        if self.buy_17_protection__safe_pump.value:
            buy_17_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_17_protection__safe_pump_period.value}_{self.buy_17_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_17_logic = []
        buy_17_logic.append(reduce(lambda x, y: x & y, buy_17_protections))
        buy_17_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_17.value)
        buy_17_logic.append(dataframe["ewo"] < self.buy_ewo_17.value)
        buy_17_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_17_trigger"] = reduce(lambda x, y: x & y, buy_17_logic)
        if self.buy_condition_17_enable.value:
            conditions.append(dataframe.loc[:, "buy_17_trigger"])

        # Protections
        buy_18_protections = [True]
        if self.buy_18_protection__ema_fast.value:
            buy_18_protections.append(
                dataframe[f"ema_{self.buy_18_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_18_protection__ema_slow.value:
            buy_18_protections.append(
                dataframe[f"ema_{self.buy_18_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_18_protection__close_above_ema_fast.value:
            buy_18_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_18_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_18_protection__close_above_ema_slow.value:
            buy_18_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_18_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_18_protection__sma200_rising.value:
            buy_18_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_18_protection__sma200_rising_val.value))
            )
        if self.buy_18_protection__sma200_1h_rising.value:
            buy_18_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_18_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_18_protection__safe_dips.value:
            buy_18_protections.append(
                dataframe[f"safe_dips_{self.buy_18_protection__safe_dips_type.value}"]
            )
        if self.buy_18_protection__safe_pump.value:
            buy_18_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_18_protection__safe_pump_period.value}_{self.buy_18_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        # buy_18_protections.append(dataframe['ema_100'] > dataframe['ema_200'])
        buy_18_protections.append(dataframe["sma_200"] > dataframe["sma_200"].shift(20))
        buy_18_protections.append(dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(36))

        # Logic
        buy_18_logic = []
        buy_18_logic.append(reduce(lambda x, y: x & y, buy_18_protections))
        buy_18_logic.append(dataframe["rsi"] < self.buy_rsi_18.value)
        buy_18_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_18.value)
        )
        buy_18_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_18_trigger"] = reduce(lambda x, y: x & y, buy_18_logic)
        if self.buy_condition_18_enable.value:
            conditions.append(dataframe.loc[:, "buy_18_trigger"])

        # Protections
        buy_19_protections = [True]
        if self.buy_19_protection__ema_fast.value:
            buy_19_protections.append(
                dataframe[f"ema_{self.buy_19_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_19_protection__ema_slow.value:
            buy_19_protections.append(
                dataframe[f"ema_{self.buy_19_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_19_protection__close_above_ema_fast.value:
            buy_19_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_19_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_19_protection__close_above_ema_slow.value:
            buy_19_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_19_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_19_protection__sma200_rising.value:
            buy_19_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_19_protection__sma200_rising_val.value))
            )
        if self.buy_19_protection__sma200_1h_rising.value:
            buy_19_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_19_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_19_protection__safe_dips.value:
            buy_19_protections.append(
                dataframe[f"safe_dips_{self.buy_19_protection__safe_dips_type.value}"]
            )
        if self.buy_19_protection__safe_pump.value:
            buy_19_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_19_protection__safe_pump_period.value}_{self.buy_19_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_19_protections.append(dataframe["ema_50_1h"] > dataframe["ema_200_1h"])

        # Logic
        buy_19_logic = []
        buy_19_logic.append(reduce(lambda x, y: x & y, buy_19_protections))
        buy_19_logic.append(dataframe["close"].shift(1) > dataframe["ema_100_1h"])
        buy_19_logic.append(dataframe["low"] < dataframe["ema_100_1h"])
        buy_19_logic.append(dataframe["close"] > dataframe["ema_100_1h"])
        buy_19_logic.append(dataframe["rsi_1h"] > self.buy_rsi_1h_min_19.value)
        buy_19_logic.append(dataframe["chop"] < self.buy_chop_min_19.value)
        buy_19_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_19_trigger"] = reduce(lambda x, y: x & y, buy_19_logic)
        if self.buy_condition_19_enable.value:
            conditions.append(dataframe.loc[:, "buy_19_trigger"])

        # Protections
        buy_20_protections = [True]
        if self.buy_20_protection__ema_fast.value:
            buy_20_protections.append(
                dataframe[f"ema_{self.buy_20_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_20_protection__ema_slow.value:
            buy_20_protections.append(
                dataframe[f"ema_{self.buy_20_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_20_protection__close_above_ema_fast.value:
            buy_20_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_20_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_20_protection__close_above_ema_slow.value:
            buy_20_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_20_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_20_protection__sma200_rising.value:
            buy_20_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_20_protection__sma200_rising_val.value))
            )
        if self.buy_20_protection__sma200_1h_rising.value:
            buy_20_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_20_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_20_protection__safe_dips.value:
            buy_20_protections.append(
                dataframe[f"safe_dips_{self.buy_20_protection__safe_dips_type.value}"]
            )
        if self.buy_20_protection__safe_pump.value:
            buy_20_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_20_protection__safe_pump_period.value}_{self.buy_20_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_20_logic = []
        buy_20_logic.append(reduce(lambda x, y: x & y, buy_20_protections))
        buy_20_logic.append(dataframe["rsi"] < self.buy_rsi_20.value)
        buy_20_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_20.value)
        buy_20_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_20_trigger"] = reduce(lambda x, y: x & y, buy_20_logic)
        if self.buy_condition_20_enable.value:
            conditions.append(dataframe.loc[:, "buy_20_trigger"])

        # Protections
        buy_21_protections = [True]
        if self.buy_21_protection__ema_fast.value:
            buy_21_protections.append(
                dataframe[f"ema_{self.buy_21_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_21_protection__ema_slow.value:
            buy_21_protections.append(
                dataframe[f"ema_{self.buy_21_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_21_protection__close_above_ema_fast.value:
            buy_21_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_21_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_21_protection__close_above_ema_slow.value:
            buy_21_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_21_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_21_protection__sma200_rising.value:
            buy_21_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_21_protection__sma200_rising_val.value))
            )
        if self.buy_21_protection__sma200_1h_rising.value:
            buy_21_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_21_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_21_protection__safe_dips.value:
            buy_21_protections.append(
                dataframe[f"safe_dips_{self.buy_21_protection__safe_dips_type.value}"]
            )
        if self.buy_21_protection__safe_pump.value:
            buy_21_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_21_protection__safe_pump_period.value}_{self.buy_21_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_21_logic = []
        buy_21_logic.append(reduce(lambda x, y: x & y, buy_21_protections))
        buy_21_logic.append(dataframe["rsi"] < self.buy_rsi_21.value)
        buy_21_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_21.value)
        buy_21_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_21_trigger"] = reduce(lambda x, y: x & y, buy_21_logic)
        if self.buy_condition_21_enable.value:
            conditions.append(dataframe.loc[:, "buy_21_trigger"])

        # Protections
        buy_22_protections = [True]
        if self.buy_22_protection__ema_fast.value:
            buy_22_protections.append(
                dataframe[f"ema_{self.buy_22_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_22_protection__ema_slow.value:
            buy_22_protections.append(
                dataframe[f"ema_{self.buy_22_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_22_protection__close_above_ema_fast.value:
            buy_22_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_22_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_22_protection__close_above_ema_slow.value:
            buy_22_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_22_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_22_protection__sma200_rising.value:
            buy_22_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_22_protection__sma200_rising_val.value))
            )
        if self.buy_22_protection__sma200_1h_rising.value:
            buy_22_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_22_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_22_protection__safe_dips.value:
            buy_22_protections.append(
                dataframe[f"safe_dips_{self.buy_22_protection__safe_dips_type.value}"]
            )
        if self.buy_22_protection__safe_pump.value:
            buy_22_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_22_protection__safe_pump_period.value}_{self.buy_22_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)
        buy_22_protections.append(dataframe["ema_100_1h"] > dataframe["ema_100_1h"].shift(12))
        buy_22_protections.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(36))

        # Logic
        buy_22_logic = []
        buy_22_logic.append(reduce(lambda x, y: x & y, buy_22_protections))
        buy_22_logic.append(
            (dataframe["volume_mean_4"] * self.buy_volume_22.value) > dataframe["volume"]
        )
        buy_22_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_22.value)
        buy_22_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_22.value)
        )
        buy_22_logic.append(dataframe["ewo"] > self.buy_ewo_22.value)
        buy_22_logic.append(dataframe["rsi"] < self.buy_rsi_22.value)
        buy_22_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_22_trigger"] = reduce(lambda x, y: x & y, buy_22_logic)
        if self.buy_condition_22_enable.value:
            conditions.append(dataframe.loc[:, "buy_22_trigger"])

            # Protections
        buy_23_protections = [True]
        if self.buy_23_protection__ema_fast.value:
            buy_23_protections.append(
                dataframe[f"ema_{self.buy_23_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_23_protection__ema_slow.value:
            buy_23_protections.append(
                dataframe[f"ema_{self.buy_23_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_23_protection__close_above_ema_fast.value:
            buy_23_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_23_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_23_protection__close_above_ema_slow.value:
            buy_23_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_23_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_23_protection__sma200_rising.value:
            buy_23_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_23_protection__sma200_rising_val.value))
            )
        if self.buy_23_protection__sma200_1h_rising.value:
            buy_23_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_23_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_23_protection__safe_dips.value:
            buy_23_protections.append(
                dataframe[f"safe_dips_{self.buy_23_protection__safe_dips_type.value}"]
            )
        if self.buy_23_protection__safe_pump.value:
            buy_23_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_23_protection__safe_pump_period.value}_{self.buy_23_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_23_logic = []
        buy_23_logic.append(reduce(lambda x, y: x & y, buy_23_protections))
        buy_23_logic.append(
            dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_23.value)
        )
        buy_23_logic.append(dataframe["ewo"] > self.buy_ewo_23.value)
        buy_23_logic.append(dataframe["rsi"] < self.buy_rsi_23.value)
        buy_23_logic.append(dataframe["rsi_1h"] < self.buy_rsi_1h_23.value)
        buy_23_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_23_trigger"] = reduce(lambda x, y: x & y, buy_23_logic)
        if self.buy_condition_23_enable.value:
            conditions.append(dataframe.loc[:, "buy_23_trigger"])

        # Protections
        buy_24_protections = [True]
        if self.buy_24_protection__ema_fast.value:
            buy_24_protections.append(
                dataframe[f"ema_{self.buy_24_protection__ema_fast_len.value}"]
                > dataframe["ema_200"]
            )
        if self.buy_24_protection__ema_slow.value:
            buy_24_protections.append(
                dataframe[f"ema_{self.buy_24_protection__ema_slow_len.value}_1h"]
                > dataframe["ema_200_1h"]
            )
        if self.buy_24_protection__close_above_ema_fast.value:
            buy_24_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_24_protection__close_above_ema_fast_len.value}"]
            )
        if self.buy_24_protection__close_above_ema_slow.value:
            buy_24_protections.append(
                dataframe["close"]
                > dataframe[f"ema_{self.buy_24_protection__close_above_ema_slow_len.value}_1h"]
            )
        if self.buy_24_protection__sma200_rising.value:
            buy_24_protections.append(
                dataframe["sma_200"]
                > dataframe["sma_200"].shift(int(self.buy_24_protection__sma200_rising_val.value))
            )
        if self.buy_24_protection__sma200_1h_rising.value:
            buy_24_protections.append(
                dataframe["sma_200_1h"]
                > dataframe["sma_200_1h"].shift(
                    int(self.buy_24_protection__sma200_1h_rising_val.value)
                )
            )
        if self.buy_24_protection__safe_dips.value:
            buy_24_protections.append(
                dataframe[f"safe_dips_{self.buy_24_protection__safe_dips_type.value}"]
            )
        if self.buy_24_protection__safe_pump.value:
            buy_24_protections.append(
                dataframe[
                    f"safe_pump_{self.buy_24_protection__safe_pump_period.value}_{self.buy_24_protection__safe_pump_type.value}_1h"
                ]
            )
        # Non-Standard protections (add below)

        # Logic
        buy_24_logic = []
        buy_24_logic.append(reduce(lambda x, y: x & y, buy_24_protections))
        buy_24_logic.append(dataframe["ema_12_1h"].shift(12) < dataframe["ema_35_1h"].shift(12))
        buy_24_logic.append(dataframe["ema_12_1h"].shift(12) < dataframe["ema_35_1h"].shift(12))
        buy_24_logic.append(dataframe["ema_12_1h"] > dataframe["ema_35_1h"])
        buy_24_logic.append(dataframe["cmf_1h"].shift(12) < 0)
        buy_24_logic.append(dataframe["cmf_1h"] > 0)
        buy_24_logic.append(dataframe["rsi"] < self.buy_24_rsi_max.value)
        buy_24_logic.append(dataframe["rsi_1h"] > self.buy_24_rsi_1h_min.value)
        buy_24_logic.append(dataframe["volume"] > 0)
        # Populate
        dataframe.loc[:, "buy_24_trigger"] = reduce(lambda x, y: x & y, buy_24_logic)
        if self.buy_condition_24_enable.value:
            conditions.append(dataframe.loc[:, "buy_24_trigger"])

        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1

        return dataframe

    ## more going on here
    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)
            )
        )

        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1

        dataframe.loc[
            (
                (
                    ## close ALWAYS needs to be higher than the heiken high at 5m
                    (dataframe["close"] > dataframe["Smooth_HA_H"])
                    &
                    ## Hansen's HA EMA at informative timeframe
                    (dataframe["emac_1h"] > dataframe["emao_1h"])
                )
                & (
                    ## try to find oversold regions with a corresponding BB expansion
                    (
                        (dataframe["bbw_expansion"] == 1)
                        & ((dataframe["mfi"] > 80) | (dataframe["dmi_plus"] > 30))
                    )
                    ## volume sanity checks
                    & (dataframe["vfi"] > 0.0)
                    & (dataframe["volume"] > 0)
                )
            ),
            "sell",
        ] = 1

        return dataframe

    """
    Everything from here completely stolen from the godly work of @werkkrew
    
    Custom Stoploss 
    """

    def custom_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)

        if self.config["runmode"].value in ("live", "dry_run"):
            dataframe, last_updated = self.dp.get_analyzed_dataframe(
                pair=pair, timeframe=self.timeframe
            )
            sroc = dataframe["sroc"].iat[-1]
        # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
        else:
            sroc = self.custom_trade_info[trade.pair]["sroc"].loc[current_time]["sroc"]

        if current_profit < self.cstp_threshold.value:
            if self.cstp_bail_how.value == "roc" or self.cstp_bail_how.value == "any":
                # Dynamic bailout based on rate of change
                if (sroc / 100) <= self.cstp_bail_roc.value:
                    return 0.001
            if self.cstp_bail_how.value == "time" or self.cstp_bail_how.value == "any":
                # Dynamic bailout based on time
                if trade_dur > self.cstp_bail_time.value:
                    return 0.001

        return 1

    """
    Freqtrade ROI Overload for dynamic ROI functionality
    """

    def min_roi_reached_dynamic(
        self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int
    ) -> Tuple[Optional[int], Optional[float]]:
        minimal_roi = self.minimal_roi
        _, table_roi = self.min_roi_reached_entry(trade_dur)

        # see if we have the data we need to do this, otherwise fall back to the standard table
        if self.custom_trade_info and trade and trade.pair in self.custom_trade_info:
            if self.config["runmode"].value in ("live", "dry_run"):
                dataframe, last_updated = self.dp.get_analyzed_dataframe(
                    pair=trade.pair, timeframe=self.timeframe
                )
                rmi_trend = dataframe["rmi-up-trend"].iat[-1]
                candle_trend = dataframe["candle-up-trend"].iat[-1]
                ssl_dir = dataframe["ssl-dir"].iat[-1]
            # If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
            else:
                rmi_trend = self.custom_trade_info[trade.pair]["rmi-up-trend"].loc[current_time][
                    "rmi-up-trend"
                ]
                candle_trend = self.custom_trade_info[trade.pair]["candle-up-trend"].loc[
                    current_time
                ]["candle-up-trend"]
                ssl_dir = self.custom_trade_info[trade.pair]["ssl-dir"].loc[current_time]["ssl-dir"]

            min_roi = table_roi
            max_profit = trade.calc_profit_ratio(trade.max_rate)
            pullback_value = max_profit - self.droi_pullback_amount.value
            in_trend = False

            if self.droi_trend_type.value == "rmi" or self.droi_trend_type.value == "any":
                if rmi_trend == 1:
                    in_trend = True
            if self.droi_trend_type.value == "ssl" or self.droi_trend_type.value == "any":
                if ssl_dir == "up":
                    in_trend = True
            if self.droi_trend_type.value == "candle" or self.droi_trend_type.value == "any":
                if candle_trend == 1:
                    in_trend = True

            # Force the ROI value high if in trend
            if in_trend == True:
                min_roi = 100
                # If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend
                if self.droi_pullback.value == True and (current_profit < pullback_value):
                    if self.droi_pullback_respect_table.value == True:
                        min_roi = table_roi
                    else:
                        min_roi = current_profit / 2

        else:
            min_roi = table_roi

        return trade_dur, min_roi

    # Change here to allow loading of the dynamic_roi settings
    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
        trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)

        if self.use_dynamic_roi:
            _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur)
        else:
            _, roi = self.min_roi_reached_entry(trade_dur)
        if roi is None:
            return False
        else:
            return current_profit > roi

    # Get the current price from the exchange (or local cache)
    def get_current_price(self, pair: str, refresh: bool) -> float:
        if not refresh:
            rate = self.custom_current_price_cache.get(pair)
            # Check if cache has been invalidated
            if rate:
                return rate

        ask_strategy = self.config.get("ask_strategy", {})
        if ask_strategy.get("use_order_book", False):
            ob = self.dp.orderbook(pair, 1)
            rate = ob[f"{ask_strategy['price_side']}s"][0][0]
        else:
            ticker = self.dp.ticker(pair)
            rate = ticker["last"]

        self.custom_current_price_cache[pair] = rate
        return rate

    """
    Stripped down version from Schism, meant only to update the price data a bit
    more frequently than the default instead of getting all sorts of trade information
    """

    def populate_trades(self, pair: str) -> dict:
        # Initialize the trades dict if it doesn't exist, persist it otherwise
        if not pair in self.custom_trade_info:
            self.custom_trade_info[pair] = {}

        # init the temp dicts and set the trade stuff to false
        trade_data = {}
        trade_data["active_trade"] = False

        # active trade stuff only works in live and dry, not backtest
        if self.config["runmode"].value in ("live", "dry_run"):
            # find out if we have an open trade for this pair
            active_trade = Trade.get_trades(
                [
                    Trade.pair == pair,
                    Trade.is_open.is_(True),
                ]
            ).all()

            # if so, get some information
            if active_trade:
                # get current price and update the min/max rate
                current_rate = self.get_current_price(pair, True)
                active_trade[0].adjust_min_max_rates(current_rate, current_rate)

        return trade_data

    # nested hyperopt class
    class HyperOpt:
        # defining as dummy, so that no error is thrown about missing
        # sell indicator space when hyperopting for all spaces
        @staticmethod
        def indicator_space() -> List[Dimension]:
            return []


## goddamnit


def RMI(dataframe, *, length=20, mom=5):
    """
    Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
    """
    df = dataframe.copy()

    df["maxup"] = (df["close"] - df["close"].shift(mom)).clip(lower=0)
    df["maxdown"] = (df["close"].shift(mom) - df["close"]).clip(lower=0)

    df.fillna(0, inplace=True)

    df["emaInc"] = ta.EMA(df, price="maxup", timeperiod=length)
    df["emaDec"] = ta.EMA(df, price="maxdown", timeperiod=length)

    df["RMI"] = np.where(df["emaDec"] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))

    return df["RMI"]


def SSLChannels_ATR(dataframe, length=7):
    """
    SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
    Credit to @JimmyNixx for python
    """
    df = dataframe.copy()

    df["ATR"] = ta.ATR(df, timeperiod=14)
    df["smaHigh"] = df["high"].rolling(length).mean() + df["ATR"]
    df["smaLow"] = df["low"].rolling(length).mean() - df["ATR"]
    df["hlv"] = np.where(
        df["close"] > df["smaHigh"], 1, np.where(df["close"] < df["smaLow"], -1, np.NAN)
    )
    df["hlv"] = df["hlv"].ffill()
    df["sslDown"] = np.where(df["hlv"] < 0, df["smaHigh"], df["smaLow"])
    df["sslUp"] = np.where(df["hlv"] < 0, df["smaLow"], df["smaHigh"])

    return df["sslDown"], df["sslUp"]


def SROC(dataframe, roclen=21, emalen=13, smooth=21):
    df = dataframe.copy()

    roc = ta.ROC(df, timeperiod=roclen)
    ema = ta.EMA(df, timeperiod=emalen)
    sroc = ta.ROC(ema, timeperiod=smooth)

    return sroc


# 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


# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False):
    """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.
    """
    df = dataframe.copy()
    mfv = ((df["close"] - df["low"]) - (df["high"] - df["close"])) / (df["high"] - df["low"])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= df["volume"]
    cmf = mfv.rolling(n, min_periods=0).sum() / df["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")