CryptoFrogNFI2

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 CryptoFrogNFI2(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 = -0.299

    # 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': 188,       # 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': -0.299, # -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 = True

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = False
    sell_profit_offset = 0.01
    ignore_roi_if_buy_signal = False

    use_dynamic_roi = True    
    
    timeframe = '5m'
    informative_timeframe = '1h'

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': '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,
    }

    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=True, space='buy', optimize=False, load=True)
    buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
    buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)

    # Normal dips
    buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=False, load=True)
    # Strict dips
    buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True)
    buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True)

    # 12 hours
    buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.46, space='buy', decimals=3, optimize=False, load=True)
    # 36 hours
    buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True)
    # 48 hours
    buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=False, load=True)

    # 12 hours strict
    buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=False, load=True)
    # 36 hours strict
    buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True)
    # 48 hours strict
    buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=False, load=True)

    buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=80.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_1 = DecimalParameter(20.0, 56.0, default=26.0, space='buy', decimals=1, optimize=False, load=True)

    buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=36.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=90.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=34.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_2 = DecimalParameter(30.0, 65.0, default=56.0, space='buy', decimals=1, optimize=False, load=True)
    buy_bb_offset_2 = DecimalParameter(0.97, 0.99, default=0.983, space='buy', decimals=3, optimize=False, load=True)

    buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=False, load=True)
    buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=False, load=True)
    buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=False, load=True)
    buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True)

    buy_bb20_close_bblowerband_4 = DecimalParameter(0.9, 0.99, default=0.979, space='buy', optimize=False, load=True)
    buy_bb20_volume_4 = IntParameter(16, 35, default=18, space='buy', optimize=False, load=True)

    buy_volume_5 = DecimalParameter(1.0, 10.0, default=6.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, optimize=False, load=True)
    buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.5, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_6 = DecimalParameter(0.03, 0.04, default=0.025, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.995, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, optimize=False, load=True)

    buy_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_rel_8 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_9 = DecimalParameter(1.0, 4.0, default=2.0, space='buy', decimals=2, optimize=False, load=True)
    buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.958, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.984, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=80.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_9 = DecimalParameter(36.0, 65.0, default=56.0, space='buy', decimals=1, optimize=False, load=True)

    buy_volume_10 = DecimalParameter(1.0, 26.0, default=23.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.94, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_10 = DecimalParameter(20.0, 40.0, default=39.0, space='buy', decimals=1, optimize=False, load=True)

    buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.938, space='buy', decimals=3, optimize=False, load=True)
    buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.01, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=55.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=82.0, space='buy', decimals=1, optimize=False, load=True)
    buy_rsi_11 = DecimalParameter(30.0, 48.0, default=46.0, space='buy', decimals=1, optimize=False, load=True)
    buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, optimize=False, load=True)

    buy_volume_12 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.8, space='buy', decimals=1, optimize=False, load=True)

    buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.952, space='buy', decimals=3, optimize=False, load=True)
    buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-7.9, space='buy', decimals=1, optimize=False, load=True)

    buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, optimize=False, load=True)
    buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.992, space='buy', decimals=3, optimize=False, load=True)
    buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.998, space='buy', decimals=3, optimize=False, load=True)

    buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.026, space='buy', decimals=3, optimize=False, load=True)
    buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.985, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_15 = DecimalParameter(30.0, 50.0, default=32.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True)

    buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.95, space='buy', decimals=3, optimize=False, load=True)
    buy_rsi_16 = DecimalParameter(26.0, 50.0, default=38.0, space='buy', decimals=1, optimize=False, load=True)
    buy_ewo_16 = DecimalParameter(4.0, 8.0, default=3.6, space='buy', decimals=1, optimize=False, load=True)

    buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, optimize=False, load=True)
    buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, optimize=False, load=True)

    # Sell

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

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

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

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

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

    sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True)
    sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.382, space='sell', optimize=False, load=True)

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

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

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

    sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=30.0, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=36.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=False, load=True)
    sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=False, load=True)

    sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.035, space='sell', decimals=3, optimize=False, load=True)
    sell_custom_under_profit_3 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True)

    sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True)
    sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True)
    sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True)

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

    def custom_sell(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_buy_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 = []

        conditions.append(
            (
                self.buy_condition_1_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) &

                (dataframe['safe_dips']) &
                (dataframe['safe_pump_48_1h']) &

                (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) &
                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) &
                (dataframe['rsi'] < self.buy_rsi_1.value) &
                (dataframe['mfi'] < self.buy_mfi_1.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_2_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_pump_24_strict_1h']) &

                (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) &

                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) &
                (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) &
                (dataframe['mfi'] < self.buy_mfi_2.value) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_2.value)) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_3_enable.value &

                (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_3.value)) &
                (dataframe['ema_100'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_pump_36_1h']) &

                dataframe['lower'].shift().gt(0) &
                dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3.value) &
                dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3.value) &
                dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta_3.value) &
                dataframe['close'].lt(dataframe['lower'].shift()) &
                dataframe['close'].le(dataframe['close'].shift()) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_4_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_24_1h']) &

                (dataframe['close'] < dataframe['ema_50']) &
                (dataframe['close'] < self.buy_bb20_close_bblowerband_4.value * dataframe['bb_lowerband']) &
                (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4.value))
            )
        )

        conditions.append(
            (
                self.buy_condition_5_enable.value &

                (dataframe['ema_100'] > dataframe['ema_200']) &
                (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_5.value)) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips']) &
                (dataframe['safe_pump_36_strict_1h']) &

                (dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_5.value)) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_6_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips_strict']) &

                (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_6.value)) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_7_enable.value &

                (dataframe['ema_100'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips']) &

                (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['rsi'] < self.buy_rsi_7.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_8_enable.value &

                (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_8.value)) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['close'] > dataframe['open']) &

                (dataframe['close'] > dataframe['smma_lips']) &

                (dataframe['smma_lips'] > dataframe['smma_teeth']) &
                (dataframe['smma_teeth'] > dataframe['smma_jaw']) &
                (dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) &
                (dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) &

                (dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) &
                (dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) &
                (dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) &

                (dataframe['rsi'] < self.buy_rsi_8.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_9_enable.value &

                (dataframe['ema_50'] > dataframe['ema_200']) &
                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &

                (dataframe['safe_dips_strict']) &

                (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) &

                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_9.value) &
                (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) &
                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_9.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_9.value) &
                (dataframe['mfi'] < self.buy_mfi_9.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_10_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips']) &
                (dataframe['safe_pump_24_1h']) &

                ((dataframe['volume_mean_4'] * self.buy_volume_10.value) > dataframe['volume']) &

                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10.value) &
                (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_11_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &

                (dataframe['safe_pump_24_1h']) &

                (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_11.value) &
                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11.value) &
                (dataframe['rsi_1h'] > self.buy_rsi_1h_min_11.value) &
                (dataframe['rsi_1h'] < self.buy_rsi_1h_max_11.value) &
                (dataframe['rsi'] < self.buy_rsi_11.value) &
                (dataframe['mfi'] < self.buy_mfi_11.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_12_enable.value &

                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_24_strict_1h']) &

                ((dataframe['volume_mean_4'] * self.buy_volume_12.value) > dataframe['volume']) &

                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12.value) &
                (dataframe['ewo'] > self.buy_ewo_12.value) &
                (dataframe['rsi'] < self.buy_rsi_12.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_13_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) &
                (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_24_strict_1h']) &

                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13.value) &
                (dataframe['ewo'] < self.buy_ewo_13.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_14_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
                (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_48_1h']) &

                (dataframe['volume_mean_4'] * self.buy_volume_14.value > dataframe['volume']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_14.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_14.value)) &
                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_14.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_15_enable.value &

                (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15.value) &
                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips_strict']) &

                (dataframe['ema_26'] > dataframe['ema_12']) &
                ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_15.value)) &
                ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) &
                (dataframe['rsi'] < self.buy_rsi_15.value) &
                (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_15.value) &

                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_16_enable.value &

                (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &

                (dataframe['safe_dips_strict']) &
                (dataframe['safe_pump_24_strict_1h']) &

                ((dataframe['volume_mean_4'] * self.buy_volume_16.value) > dataframe['volume']) &

                (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16.value) &
                (dataframe['ewo'] > self.buy_ewo_16.value) &
                (dataframe['rsi'] < self.buy_rsi_16.value) &
                (dataframe['volume'] > 0)
            )
        )

        conditions.append(
            (
                self.buy_condition_17_enable.value &

                (dataframe['safe_dips_strict']) &

                (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) &
                (dataframe['ewo'] < self.buy_ewo_17.value) &
                (dataframe['volume'] > 0)
            )
        )

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

        return dataframe
    
    ## more going on here
    def populate_sell_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)

        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')