import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta import pandas as pd import pandas_ta as pta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair from pandas import DataFrame, Series from functools import reduce, partial from freqtrade.persistence import Trade, LocalTrade from datetime import datetime, timedelta import time from typing import Optional import warnings log = logging.getLogger(__name__) #log.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) ############################################################################################################# ## NostalgiaForInfinityX2 by iterativ ## ## https://github.com/iterativv/NostalgiaForInfinity ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ############################################################################################################# ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_exit_signal must set to true (or not set at all). ## ## exit_profit_only must set to false (or not set at all). ## ## ignore_roi_if_entry_signal must set to true (or not set at all). ## ## ## ############################################################################################################# ## DONATIONS ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=C68K26A9 (20% discount on trading fees) ## ## Kucoin: https://www.kucoin.com/r/af/QBSSS5J2 (20% lifetime discount on trading fees) ## ## Gate.io: https://www.gate.io/signup/8054544 (20% discount on trading fees) ## ## OKX: https://www.okx.com/join/11749725931 (20% discount on trading fees) ## ## MEXC: https://promote.mexc.com/a/nfi (10% discount on trading fees) ## ## ByBit: https://partner.bybit.com/b/nfi ## ## Huobi: https://www.huobi.com/en-us/v/register/double-invite/?inviter_id=11345710&invite_code=ubpt2223 ## ## (20% discount on trading fees) ## ## Bitvavo: https://account.bitvavo.com/create?a=D22103A4BC (no fees for the first € 1000) ## ############################################################################################################# class NostalgiaForInfinityX2(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: return "v12.0.377" # ROI table: minimal_roi = { "0": 100.0, } stoploss = -0.99 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' info_timeframes = ['15m','1h','4h','1d'] # BTC informatives btc_info_timeframes = ['5m','15m','1h','4h','1d'] # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Do you want to use the hold feature? (with hold-trades.json) hold_support_enabled = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 800 # Normal mode tags normal_mode_tags = ['force_entry', '1', '2', '3', '4', '5', '6'] # Pump mode tags pump_mode_tags = ['21', '22'] # Quick mode tags quick_mode_tags = ['41', '42', '43', '44'] # Rebuy mode tags rebuy_mode_tags = ['61'] # Long mode tags long_mode_tags = ['81', '82'] normal_mode_name = "normal" pump_mode_name = "pump" quick_mode_name = "quick" rebuy_mode_name = "rebuy" long_mode_name = "long" # Stop thesholds. 0: Doom Bull, 1: Doom Bear, 2: u_e Bull, 3: u_e Bear, 4: u_e mins Bull, 5: u_e mins Bear. # 6: u_e ema % Bull, 7: u_e ema % Bear, 8: u_e RSI diff Bull, 9: u_e RSI diff Bear. # 10: enable Doom Bull, 11: enable Doom Bear, 12: enable u_e Bull, 13: enable u_e Bear. stop_thresholds = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, False, False, True, True] # Rebuy mode minimum number of free slots rebuy_mode_min_free_slots = 2 # Position adjust feature position_adjustment_enable = True # Grinding feature grinding_enable = True # Grinding stakes grinding_stakes = [0.25, 0.25, 0.25, 0.25, 0.25] grinding_stakes_alt_1 = [0.5, 0.5] grinding_stakes_alt_2 = [0.75] # Current total profit grinding_thresholds = [-0.04, -0.08, -0.1, -0.12, -0.14] grinding_thresholds_alt_1 = [-0.06, -0.12] grinding_thresholds_alt_2 = [-0.06] stake_rebuy_mode_multiplier = 0.33 pa_rebuy_mode_max = 2 pa_rebuy_mode_pcts = (-0.02, -0.04, -0.04) pa_rebuy_mode_multi = (1.0, 1.0, 1.0) # Profit max thresholds profit_max_thresholds = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.05, 0.05] # Max allowed buy "slippage", how high to buy on the candle max_slippage = 0.01 ############################################################# # Buy side configuration 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_21_enable": True, "buy_condition_22_enable": True, "buy_condition_41_enable": True, "buy_condition_42_enable": True, "buy_condition_43_enable": True, "buy_condition_44_enable": True, "buy_condition_61_enable": True, "buy_condition_81_enable": True, "buy_condition_82_enable": True, } buy_protection_params = {} ############################################################# # CACHES hold_trades_cache = None target_profit_cache = None ############################################################# def __init__(self, config: dict) -> None: super().__init__(config) if (('exit_profit_only' in self.config and self.config['exit_profit_only']) or ('sell_profit_only' in self.config and self.config['sell_profit_only'])): self.exit_profit_only = True if ('stop_thresholds_normal' in self.config): self.stop_thresholds_normal = self.config['stop_thresholds_normal'] if ('stop_thresholds_pump' in self.config): self.stop_thresholds_pump = self.config['stop_thresholds_pump'] if ('stop_thresholds_quick' in self.config): self.stop_thresholds_quick = self.config['stop_thresholds_quick'] if ('stop_thresholds_rebuy' in self.config): self.stop_thresholds_rebuy = self.config['stop_thresholds_rebuy'] if ('stop_thresholds_long' in self.config): self.stop_thresholds_long = self.config['stop_thresholds_long'] if ('profit_max_thresholds' in self.config): self.profit_max_thresholds = self.config['profit_max_thresholds'] if ('grinding_enable' in self.config): self.grinding_enable = self.config['grinding_enable'] if ('grinding_stakes' in self.config): self.grinding_stakes = self.config['grinding_stakes'] if ('grinding_thresholds' in self.config): self.grinding_thresholds = self.config['grinding_thresholds'] if ('grinding_stakes_alt_1' in self.config): self.grinding_stakes_alt_1 = self.config['grinding_stakes_alt_1'] if ('grinding_thresholds_alt_1' in self.config): self.grinding_thresholds_alt_1 = self.config['grinding_thresholds_alt_1'] if ('grinding_stakes_alt_2' in self.config): self.grinding_stakes_alt_2 = self.config['grinding_stakes_alt_2'] if ('grinding_thresholds_alt_2' in self.config): self.grinding_thresholds_alt_2 = self.config['grinding_thresholds_alt_2'] if ('max_slippage' in self.config): self.max_slippage = self.config['max_slippage'] if self.target_profit_cache is None: bot_name = "" if ('bot_name' in self.config): bot_name = self.config["bot_name"] + "-" self.target_profit_cache = Cache( self.config["user_data_dir"] / ("nfix2-profit_max-" + bot_name + self.config["exchange"]["name"] + "-" + self.config["stake_currency"] + ("-(backtest)" if (self.config['runmode'].value == 'backtest') else "") + ".json") ) # OKX, Kraken provides a lower number of candle data per API call if self.config["exchange"]["name"] in ["okx", "okex"]: self.startup_candle_count = 480 elif self.config["exchange"]["name"] in ["kraken"]: self.startup_candle_count = 710 elif self.config["exchange"]["name"] in ["bybit"]: self.startup_candle_count = 199 # If the cached data hasn't changed, it's a no-op self.target_profit_cache.save() def get_ticker_indicator(self): return int(self.timeframe[:-1]) def exit_normal(self, pair: str, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_signals(self.normal_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_main(self.normal_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_r(self.normal_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_stoploss(self.normal_mode_name, current_rate, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.exit_profit_target(self.normal_mode_name, pair, trade, current_time, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (previous_sell_reason in [f"exit_{self.normal_mode_name}_stoploss_u_e"]): if (profit_ratio > (previous_profit + 0.005)): mark_pair, mark_signal = self.mark_profit_target(self.normal_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) elif (profit_current_stake_ratio > (previous_profit + 0.005)) and (previous_sell_reason not in [f"exit_{self.normal_mode_name}_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.mark_profit_target(self.normal_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (signal_name in [f"exit_{self.normal_mode_name}_stoploss_doom", f"exit_{self.normal_mode_name}_stoploss_u_e"]): mark_pair, mark_signal = self.mark_profit_target(self.normal_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" elif ( (previous_profit is None) or (previous_profit < profit_current_stake_ratio) ): mark_pair, mark_signal = self.mark_profit_target(self.normal_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (profit_current_stake_ratio >= self.profit_max_thresholds[0]) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < profit_current_stake_ratio): mark_signal = f"exit_profit_{self.normal_mode_name}_max" self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) if (signal_name not in [f"exit_profit_{self.normal_mode_name}_max", f"exit_{self.normal_mode_name}_stoploss_doom", f"exit_{self.normal_mode_name}_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def exit_pump(self, pair: str, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_signals(self.pump_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_main(self.pump_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_r(self.pump_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_stoploss(self.pump_mode_name, current_rate, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.exit_profit_target(self.pump_mode_name, pair, trade, current_time, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (previous_sell_reason in [f"exit_{self.pump_mode_name}_stoploss_u_e"]): if (profit_ratio > (previous_profit + 0.005)): mark_pair, mark_signal = self.mark_profit_target(self.pump_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) elif (profit_current_stake_ratio > (previous_profit + 0.005)) and (previous_sell_reason not in [f"exit_{self.pump_mode_name}_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.mark_profit_target(self.pump_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (signal_name in [f"exit_{self.pump_mode_name}_stoploss_doom", f"exit_{self.pump_mode_name}_stoploss_u_e"]): mark_pair, mark_signal = self.mark_profit_target(self.pump_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" elif ( (previous_profit is None) or (previous_profit < profit_current_stake_ratio) ): mark_pair, mark_signal = self.mark_profit_target(self.pump_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (profit_current_stake_ratio >= self.profit_max_thresholds[2]) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < profit_current_stake_ratio): mark_signal = f"exit_profit_{self.pump_mode_name}_max" self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) if (signal_name not in [f"exit_profit_{self.pump_mode_name}_max", f"exit_{self.pump_mode_name}_stoploss_doom", f"exit_{self.pump_mode_name}_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def exit_quick(self, pair: str, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_signals(self.quick_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_main(self.quick_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_r(self.quick_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_stoploss(self.quick_mode_name, current_rate, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Extra sell logic if not sell: if (0.09 >= profit_current_stake_ratio > 0.02) and (last_candle['rsi_14'] > 78.0): sell, signal_name = True, f'exit_{self.quick_mode_name}_q_1' if (0.09 >= profit_current_stake_ratio > 0.02) and (last_candle['cti_20'] > 0.95): sell, signal_name = True, f'exit_{self.quick_mode_name}_q_2' if (0.09 >= profit_current_stake_ratio > 0.02) and (last_candle['r_14'] >= -0.1): sell, signal_name = True, f'exit_{self.quick_mode_name}_q_3' # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.exit_profit_target(self.quick_mode_name, pair, trade, current_time, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (previous_sell_reason in [f"exit_{self.quick_mode_name}_stoploss_u_e"]): if (profit_ratio > (previous_profit + 0.005)): mark_pair, mark_signal = self.mark_profit_target(self.quick_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) elif (profit_current_stake_ratio > (previous_profit + 0.005)) and (previous_sell_reason not in [f"exit_{self.quick_mode_name}_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.mark_profit_target(self.quick_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (signal_name in [f"exit_{self.quick_mode_name}_stoploss_doom", f"exit_{self.quick_mode_name}_stoploss_u_e"]): mark_pair, mark_signal = self.mark_profit_target(self.quick_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" elif ( (previous_profit is None) or (previous_profit < profit_current_stake_ratio) ): mark_pair, mark_signal = self.mark_profit_target(self.quick_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (profit_current_stake_ratio >= self.profit_max_thresholds[4]) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < profit_current_stake_ratio): mark_signal = f"exit_profit_{self.quick_mode_name}_max" self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) if (signal_name not in [f"exit_profit_{self.quick_mode_name}_max", f"exit_{self.quick_mode_name}_stoploss_doom", f"exit_{self.quick_mode_name}_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def exit_rebuy(self, pair: str, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_signals(self.rebuy_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_main(self.rebuy_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_r(self.rebuy_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_stoploss(self.rebuy_mode_name, current_rate, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.exit_profit_target(self.rebuy_mode_name, pair, trade, current_time, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (previous_sell_reason in [f"exit_{self.rebuy_mode_name}_stoploss_u_e"]): if (profit_ratio > (previous_profit + 0.005)): mark_pair, mark_signal = self.mark_profit_target(self.rebuy_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) elif (profit_current_stake_ratio > (previous_profit + 0.005)) and (previous_sell_reason not in [f"exit_{self.rebuy_mode_name}_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.mark_profit_target(self.rebuy_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (signal_name in [f"exit_{self.rebuy_mode_name}_stoploss_doom", f"exit_{self.rebuy_mode_name}_stoploss_u_e"]): mark_pair, mark_signal = self.mark_profit_target(self.rebuy_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" elif ( (previous_profit is None) or (previous_profit < profit_current_stake_ratio) ): mark_pair, mark_signal = self.mark_profit_target(self.rebuy_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (profit_current_stake_ratio >= self.profit_max_thresholds[6]) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < profit_current_stake_ratio): mark_signal = f"exit_profit_{self.rebuy_mode_name}_max" self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) if (signal_name not in [f"exit_profit_{self.rebuy_mode_name}_max", f"exit_{self.rebuy_mode_name}_stoploss_doom", f"exit_{self.rebuy_mode_name}_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def exit_long(self, pair: str, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple: sell = False # Original sell signals sell, signal_name = self.exit_signals(self.long_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Main sell signals if not sell: sell, signal_name = self.exit_main(self.long_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Williams %R based sells if not sell: sell, signal_name = self.exit_r(self.long_mode_name, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Stoplosses if not sell: sell, signal_name = self.exit_stoploss(self.long_mode_name, current_rate, profit_current_stake_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]['rate'] previous_profit = self.target_profit_cache.data[pair]['profit'] previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) sell_max, signal_name_max = self.exit_profit_target(self.long_mode_name, pair, trade, current_time, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) if sell_max and signal_name_max is not None: return True, f"{signal_name_max}_m" if (previous_sell_reason in [f"exit_{self.long_mode_name}_stoploss_u_e"]): if (profit_ratio > (previous_profit + 0.005)): mark_pair, mark_signal = self.mark_profit_target(self.long_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) elif (profit_current_stake_ratio > (previous_profit + 0.005)) and (previous_sell_reason not in [f"exit_{self.long_mode_name}_stoploss_doom"]): # Update the target, raise it. mark_pair, mark_signal = self.mark_profit_target(self.long_mode_name, pair, True, previous_sell_reason, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) # Add the pair to the list, if a sell triggered and conditions met if sell and signal_name is not None: previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (signal_name in [f"exit_{self.long_mode_name}_stoploss_doom", f"exit_{self.long_mode_name}_stoploss_u_e"]): mark_pair, mark_signal = self.mark_profit_target(self.long_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" elif ( (previous_profit is None) or (previous_profit < profit_current_stake_ratio) ): mark_pair, mark_signal = self.mark_profit_target(self.long_mode_name, pair, sell, signal_name, trade, current_time, current_rate, profit_current_stake_ratio, last_candle, previous_candle_1) if mark_pair: self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) else: # Just sell it, without maximize return True, f"{signal_name}" else: if ( (profit_current_stake_ratio >= self.profit_max_thresholds[8]) ): previous_profit = None if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_profit = self.target_profit_cache.data[pair]['profit'] if (previous_profit is None) or (previous_profit < profit_current_stake_ratio): mark_signal = f"exit_profit_{self.long_mode_name}_max" self._set_profit_target(pair, mark_signal, current_rate, profit_current_stake_ratio, current_time) if (signal_name not in [f"exit_profit_{self.long_mode_name}_max", f"exit_{self.long_mode_name}_stoploss_doom", f"exit_{self.long_mode_name}_stoploss_u_e"]): if sell and (signal_name is not None): return True, f"{signal_name}" return False, None def mark_profit_target(self, mode_name: str, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: if sell and (signal_name is not None): return pair, signal_name return None, None def exit_profit_target(self, mode_name: str, pair: str, trade: Trade, current_time: datetime, current_rate: float, profit_stake: float, profit_ratio: float, profit_current_stake_ratio: float, profit_init_ratio: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple: if (previous_sell_reason in [f"exit_{mode_name}_stoploss_doom"]): if (profit_ratio > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (profit_ratio < -0.18): if (profit_ratio < (previous_profit - 0.04)): return True, previous_sell_reason elif (profit_ratio < -0.1): if (profit_ratio < (previous_profit - 0.04)): return True, previous_sell_reason elif (profit_ratio < -0.04): if (profit_ratio < (previous_profit - 0.04)): return True, previous_sell_reason else: if (profit_ratio < (previous_profit - 0.04)): return True, previous_sell_reason elif (previous_sell_reason in [f"exit_{mode_name}_stoploss_u_e"]): if (profit_current_stake_ratio > 0.04): # profit is over the threshold, don't exit self._remove_profit_target(pair) return False, None if (profit_ratio < (previous_profit - (0.14 if trade.realized_profit == 0.0 else 0.26))): return True, previous_sell_reason elif (previous_sell_reason in [f"exit_profit_{mode_name}_max"]): if (profit_current_stake_ratio < -0.08): # profit is under the threshold, cancel it self._remove_profit_target(pair) return False, None if (0.001 <= profit_current_stake_ratio < 0.01): if (profit_current_stake_ratio < (previous_profit - 0.01)): return True, previous_sell_reason elif (0.01 <= profit_current_stake_ratio < 0.02): if (profit_current_stake_ratio < (previous_profit - 0.02)): return True, previous_sell_reason elif (0.02 <= profit_current_stake_ratio < 0.03): if (profit_current_stake_ratio < (previous_profit - 0.025)): return True, previous_sell_reason elif (0.03 <= profit_current_stake_ratio < 0.05): if (profit_current_stake_ratio < (previous_profit - 0.03)): return True, previous_sell_reason elif (0.05 <= profit_current_stake_ratio < 0.08): if (profit_current_stake_ratio < (previous_profit - 0.035)): return True, previous_sell_reason elif (0.08 <= profit_current_stake_ratio < 0.12): if (profit_current_stake_ratio < (previous_profit - 0.04)): return True, previous_sell_reason elif (0.12 <= profit_current_stake_ratio): if (profit_current_stake_ratio < (previous_profit - 0.045)): return True, previous_sell_reason else: return False, None return False, None def exit_signals(self, mode_name: str, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: # Sell signal 1 if (last_candle['rsi_14'] > 79.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']) and (previous_candle_3['close'] > previous_candle_3['bb20_2_upp']) and (previous_candle_4['close'] > previous_candle_4['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_1_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 80.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_2_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 85.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_3_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 78.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_4_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_4_2_1' # Sell signal 6 elif (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > 79.0): if (current_profit > 0.01): return True, f'exit_{mode_name}_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 79.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_7_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.08): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, f'exit_{mode_name}_8_1_1' else: if (current_profit > 0.01): return True, f'exit_{mode_name}_8_2_1' return False, None def exit_main(self, mode_name: str, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if (last_candle['close'] > last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 20.0): return True, f'exit_{mode_name}_o_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 28.0): return True, f'exit_{mode_name}_o_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 30.0): return True, f'exit_{mode_name}_o_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 32.0): return True, f'exit_{mode_name}_o_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 34.0): return True, f'exit_{mode_name}_o_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 36.0): return True, f'exit_{mode_name}_o_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 38.0): return True, f'exit_{mode_name}_o_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 40.0): return True, f'exit_{mode_name}_o_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 42.0): return True, f'exit_{mode_name}_o_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_o_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.0): return True, f'exit_{mode_name}_o_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_o_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 42.0): return True, f'exit_{mode_name}_o_12' elif (last_candle['close'] < last_candle['sma_200_1h']): if 0.01 > current_profit >= 0.001: if (last_candle['rsi_14'] < 22.0): return True, f'exit_{mode_name}_u_0' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 30.0): return True, f'exit_{mode_name}_u_1' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 32.0): return True, f'exit_{mode_name}_u_2' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 34.0): return True, f'exit_{mode_name}_u_3' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 36.0): return True, f'exit_{mode_name}_u_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 38.0): return True, f'exit_{mode_name}_u_5' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 40.0): return True, f'exit_{mode_name}_u_6' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 42.0): return True, f'exit_{mode_name}_u_7' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_u_8' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 46.0): return True, f'exit_{mode_name}_u_9' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 48.0): return True, f'exit_{mode_name}_u_10' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 46.0): return True, f'exit_{mode_name}_u_11' elif current_profit >= 0.2: if (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_u_12' return False, None def exit_r(self, mode_name: str, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: if 0.01 > current_profit >= 0.001: if (last_candle['r_480'] > -0.1): return True, f'exit_{mode_name}_w_0_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, f'exit_{mode_name}_w_0_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_w_0_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_0_4' elif (last_candle['r_14'] >= -1.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_0_5' elif 0.02 > current_profit >= 0.01: if (last_candle['r_480'] > -0.2): return True, f'exit_{mode_name}_w_1_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 78.0): return True, f'exit_{mode_name}_w_1_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 46.0): return True, f'exit_{mode_name}_w_1_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_1_4' elif (last_candle['r_14'] >= -2.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_1_5' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -0.3): return True, f'exit_{mode_name}_w_2_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 77.0): return True, f'exit_{mode_name}_w_2_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 48.0): return True, f'exit_{mode_name}_w_2_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 73.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_2_4' elif (last_candle['r_14'] >= -3.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_2_5' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -0.4): return True, f'exit_{mode_name}_w_3_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 76.0): return True, f'exit_{mode_name}_w_3_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 50.0): return True, f'exit_{mode_name}_w_3_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 72.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_3_4' elif (last_candle['r_14'] >= -4.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_3_5' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -0.5): return True, f'exit_{mode_name}_w_4_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 75.0): return True, f'exit_{mode_name}_w_4_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 52.0): return True, f'exit_{mode_name}_w_4_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 71.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_4_4' elif (last_candle['r_14'] >= -5.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_4_5' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -0.6): return True, f'exit_{mode_name}_w_5_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 74.0): return True, f'exit_{mode_name}_w_5_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 54.0): return True, f'exit_{mode_name}_w_5_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 70.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_5_4' elif (last_candle['r_14'] >= -6.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_5_5' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -0.7): return True, f'exit_{mode_name}_w_6_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 75.0): return True, f'exit_{mode_name}_w_6_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 52.0): return True, f'exit_{mode_name}_w_6_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 71.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_6_4' elif (last_candle['r_14'] >= -5.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_6_5' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -0.8): return True, f'exit_{mode_name}_w_7_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 76.0): return True, f'exit_{mode_name}_w_7_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 50.0): return True, f'exit_{mode_name}_w_7_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 72.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_7_4' elif (last_candle['r_14'] >= -4.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_7_5' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -0.9): return True, f'exit_{mode_name}_w_8_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 77.0): return True, f'exit_{mode_name}_w_8_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 48.0): return True, f'exit_{mode_name}_w_8_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 73.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_8_4' elif (last_candle['r_14'] >= -3.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_8_5' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -1.0): return True, f'exit_{mode_name}_w_9_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 78.0): return True, f'exit_{mode_name}_w_9_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 46.0): return True, f'exit_{mode_name}_w_9_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_9_4' elif (last_candle['r_14'] >= -2.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_9_5' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -1.1): return True, f'exit_{mode_name}_w_10_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0): return True, f'exit_{mode_name}_w_10_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 44.0): return True, f'exit_{mode_name}_w_10_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_10_4' elif (last_candle['r_14'] >= -1.0) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_10_5' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -0.4): return True, f'exit_{mode_name}_w_11_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0): return True, f'exit_{mode_name}_w_11_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 42.0): return True, f'exit_{mode_name}_w_11_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 76.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_11_4' elif (last_candle['r_14'] >= -0.5) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_11_5' elif current_profit >= 0.2: if (last_candle['r_480'] > -0.2): return True, f'exit_{mode_name}_w_12_1' elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 81.0): return True, f'exit_{mode_name}_w_12_2' elif (last_candle['r_14'] >= -2.0) and (last_candle['rsi_14'] < 40.0): return True, f'exit_{mode_name}_w_12_3' elif (last_candle['r_14'] >= -5.0) and (last_candle['rsi_14'] > 77.0) and (last_candle['r_480_1h'] > -25.0): return True, f'exit_{mode_name}_w_12_4' elif (last_candle['r_14'] >= -0.1) and (last_candle['cti_20'] > 0.95): return True, f'exit_{mode_name}_w_12_5' return False, None def exit_stoploss(self, mode_name: str, current_rate: float, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: 'Trade', current_time: 'datetime', buy_tag) -> tuple: is_backtest = self.dp.runmode.value == 'backtest' rel_profit = ((current_rate - trade.open_rate) / trade.open_rate) # Stoploss doom if ( (self.stop_thresholds[10]) and (rel_profit < self.stop_thresholds[0]) ): return True, f'exit_{mode_name}_stoploss_doom' # Under & near EMA200, local uptrend move if ( (self.stop_thresholds[12]) and (rel_profit < self.stop_thresholds[2]) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds[6]) and (last_candle['rsi_14'] > previous_candle_1['rsi_14']) and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds[8])) and (current_time - timedelta(minutes=self.stop_thresholds[4]) > trade.open_date_utc) ): return True, f'exit_{mode_name}_stoploss_u_e' return False, None def calc_total_profit(self, trade: 'Trade', filled_entries: 'Orders', filled_exits: 'Orders', exit_rate: float) -> tuple: """ Calculates the absolute profit for open trades. :param trade: trade object. :param filled_entries: Filled entries list. :param filled_exits: Filled exits list. :param exit_rate: The exit rate. :return tuple: The total profit in stake, ratio, ratio based on current stake, and ratio based on the first entry stake. """ total_stake = 0.0 total_profit = 0.0 for entry in filled_entries: entry_stake = entry.filled * entry.average * (1 + trade.fee_open) total_stake += entry_stake total_profit -= entry_stake for exit in filled_exits: exit_stake = exit.filled * exit.average * (1 - trade.fee_close) total_profit += exit_stake current_stake = (trade.amount * exit_rate * (1 - trade.fee_close)) total_profit += current_stake total_profit_ratio = (total_profit / total_stake) current_profit_ratio = (total_profit / current_stake) init_profit_ratio = (total_profit / filled_entries[0].cost) return total_profit, total_profit_ratio, current_profit_ratio, init_profit_ratio 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() previous_candle_1 = dataframe.iloc[-2].squeeze() previous_candle_2 = dataframe.iloc[-3].squeeze() previous_candle_3 = dataframe.iloc[-4].squeeze() previous_candle_4 = dataframe.iloc[-5].squeeze() previous_candle_5 = dataframe.iloc[-6].squeeze() enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() filled_entries = trade.select_filled_orders(trade.entry_side) filled_exits = trade.select_filled_orders(trade.exit_side) profit_stake = 0.0 profit_ratio = 0.0 profit_current_stake_ratio = 0.0 profit_init_ratio = 0.0 if (trade.realized_profit != 0.0): profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio = self.calc_total_profit(trade, filled_entries, filled_exits, current_rate) else: profit_ratio = current_profit profit_current_stake_ratio = current_profit profit_init_ratio = current_profit max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) count_of_entries = len(filled_entries) if count_of_entries > 1: initial_entry = filled_entries[0] if (initial_entry is not None and initial_entry.average is not None): max_profit = ((trade.max_rate - initial_entry.average) / initial_entry.average) max_loss = ((initial_entry.average - trade.min_rate) / trade.min_rate) # Normal mode if any(c in self.normal_mode_tags for c in enter_tags): sell, signal_name = self.exit_normal(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Pump mode if any(c in self.pump_mode_tags for c in enter_tags): sell, signal_name = self.exit_pump(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Quick mode if any(c in self.quick_mode_tags for c in enter_tags): sell, signal_name = self.exit_quick(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Rebuy mode if all(c in self.rebuy_mode_tags for c in enter_tags): sell, signal_name = self.exit_rebuy(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Long mode if any(c in self.long_mode_tags for c in enter_tags): sell, signal_name = self.exit_long(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" # Trades not opened by X2 if not any(c in (self.normal_mode_tags + self.pump_mode_tags + self.quick_mode_tags + self.rebuy_mode_tags + self.long_mode_tags) for c in enter_tags): # use normal mode for such trades sell, signal_name = self.exit_normal(pair, current_rate, profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, enter_tags) if sell and (signal_name is not None): return f"{signal_name} ( {enter_tag})" return None def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: if (self.position_adjustment_enable == True): enter_tags = entry_tag.split() # Rebuy mode if all(c in self.rebuy_mode_tags for c in enter_tags): return proposed_stake * self.stake_rebuy_mode_multiplier return proposed_stake def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: if (self.position_adjustment_enable == False): return None enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() # Grinding if (any(c in (self.normal_mode_tags + self.pump_mode_tags + self.quick_mode_tags + self.long_mode_tags) for c in enter_tags) or not any(c in (self.normal_mode_tags + self.pump_mode_tags + self.quick_mode_tags + self.rebuy_mode_tags + self.long_mode_tags) for c in enter_tags)): return self.grind_adjust_trade_position(trade, current_time, current_rate, current_profit, min_stake, max_stake, current_entry_rate, current_exit_rate, current_entry_profit, current_exit_profit ) # Rebuy mode if all(c in self.rebuy_mode_tags for c in enter_tags): return self.rebuy_adjust_trade_position(trade, current_time, current_rate, current_profit, min_stake, max_stake, current_entry_rate, current_exit_rate, current_entry_profit, current_exit_profit ) return None def grind_adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: is_backtest = self.dp.runmode.value == 'backtest' if (self.grinding_enable) and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 8, 1) or is_backtest): dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if(len(dataframe) < 2): return None last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() filled_orders = trade.select_filled_orders() filled_entries = trade.select_filled_orders(trade.entry_side) filled_exits = trade.select_filled_orders(trade.exit_side) count_of_entries = trade.nr_of_successful_entries count_of_exits = trade.nr_of_successful_exits if (count_of_entries == 0): return None exit_rate = current_rate if self.dp.runmode.value in ('live', 'dry_run'): ticker = self.dp.ticker(trade.pair) if ('bid' in ticker) and ('ask' in ticker): if (trade.is_short): if (self.config['exit_pricing']['price_side'] in ["ask", "other"]): exit_rate = ticker['ask'] else: if (self.config['exit_pricing']['price_side'] in ["bid", "other"]): exit_rate = ticker['bid'] profit_stake, profit_ratio, profit_current_stake_ratio, profit_init_ratio = self.calc_total_profit(trade, filled_entries, filled_exits, exit_rate) slice_amount = filled_entries[0].cost slice_profit = (exit_rate - filled_orders[-1].average) / filled_orders[-1].average slice_profit_entry = (exit_rate - filled_entries[-1].average) / filled_entries[-1].average slice_profit_exit = ((exit_rate - filled_exits[-1].average) / filled_exits[-1].average) if count_of_exits > 0 else 0.0 current_stake_amount = trade.amount * current_rate # Buy stake_amount_threshold = slice_amount grinding_parts = len(self.grinding_stakes) grinding_thresholds = self.grinding_thresholds grinding_stakes = self.grinding_stakes # Low stakes, on Binance mostly if ((slice_amount * self.grinding_stakes[0]) < min_stake): if ((slice_amount * self.grinding_stakes_alt_1[0]) < min_stake): grinding_parts = len(self.grinding_stakes_alt_2) grinding_thresholds = self.grinding_thresholds_alt_2 grinding_stakes = self.grinding_stakes_alt_2 else: grinding_parts = len(self.grinding_stakes_alt_1) grinding_thresholds = self.grinding_thresholds_alt_1 grinding_stakes = self.grinding_stakes_alt_1 for i in range(grinding_parts): if (current_stake_amount < stake_amount_threshold): if ( (profit_current_stake_ratio < grinding_thresholds[i]) and ( (current_time - timedelta(minutes=30) > filled_entries[-1].order_filled_utc) or (slice_profit_entry < -0.01) ) and ( (last_candle['close_max_12'] < (last_candle['close'] * 1.1)) and (last_candle['close_max_24'] < (last_candle['close'] * 1.12)) and (last_candle['close_max_48'] < (last_candle['close'] * 1.16)) and (last_candle['btc_pct_close_max_72_5m'] < 0.04) and (last_candle['btc_pct_close_max_24_5m'] < 0.03) ) and ( ( (last_candle['rsi_14'] < 36.0) and (last_candle['rsi_3'] > 5.0) and (last_candle['ema_26'] > last_candle['ema_12']) and ((last_candle['ema_26'] - last_candle['ema_12']) > (last_candle['open'] * 0.005)) and ((previous_candle['ema_26'] - previous_candle['ema_12']) > (last_candle['open'] / 100.0)) and (last_candle['rsi_3_1h'] > 10.0) ) or ( (last_candle['rsi_14'] < 40.0) and (last_candle['rsi_3'] > 5.0) and (last_candle['close'] < (last_candle['ema_12'] * 0.99)) and (last_candle['rsi_3_1h'] > 10.0) and (last_candle['not_downtrend_1h']) and (last_candle['not_downtrend_4h']) ) or ( (last_candle['rsi_14'] < 36.0) and (last_candle['rsi_3'] > 5.0) and (last_candle['close'] < (last_candle['bb20_2_low'] * 0.996)) and (last_candle['rsi_3_1h'] > 25.0) and (last_candle['not_downtrend_1h']) and (last_candle['not_downtrend_4h']) ) or ( (last_candle['rsi_14'] < 32.0) and (last_candle['rsi_3'] > 5.0) and (last_candle['ha_close'] > last_candle['ha_open']) and (last_candle['rsi_3_1h'] > 10.0) ) ) ): buy_amount = slice_amount * grinding_stakes[i] if (buy_amount > max_stake): buy_amount = max_stake if (buy_amount < min_stake): return None self.dp.send_msg(f"Grinding entry [{trade.pair}] | Rate: {current_rate} | Stake amount: {buy_amount} | Profit (stake): {profit_stake} | Profit: {(profit_ratio * 100.0):.2f}%") return buy_amount stake_amount_threshold += slice_amount * grinding_stakes[i] # Sell if (count_of_entries > 1): count_of_full_exits = 0 for exit_order in filled_exits: if ((exit_order.remaining * exit_rate) < min_stake): count_of_full_exits += 1 num_buys = 0 num_sells = 0 for order in reversed(filled_orders): if (order.ft_order_side == "buy"): num_buys += 1 elif (order.ft_order_side == "sell"): if ((order.remaining * exit_rate) < min_stake): num_sells += 1 # patial fills on exits if (num_buys == num_sells) and (order.ft_order_side == "sell"): sell_amount = order.remaining * exit_rate grind_profit = (exit_rate - order.average) / order.average if (sell_amount > min_stake): # Test if it's the last exit. Normal exit with partial fill if ((trade.stake_amount - sell_amount) > min_stake): if ( (grind_profit > 0.01) ): self.dp.send_msg(f"Grinding exit (remaining) [{trade.pair}] | Rate: {exit_rate} | Stake amount: {sell_amount} | Coin amount: {order.remaining} | Profit (stake): {profit_stake} | Profit: {(profit_ratio * 100.0):.2f}% | Grind profit: {(grind_profit * 100.0):.2f}%") return -sell_amount elif (count_of_entries > (count_of_full_exits + 1)) and (num_buys > num_sells) and (order.ft_order_side == "buy"): buy_order = order grind_profit = (exit_rate - buy_order.average) / buy_order.average if ( (grind_profit > 0.012) ): sell_amount = buy_order.filled * exit_rate self.dp.send_msg(f"Grinding exit [{trade.pair}] | Rate: {exit_rate} | Stake amount: {sell_amount}| Coin amount: {buy_order.filled} | Profit (stake): {profit_stake} | Profit: {(profit_ratio * 100.0):.2f}% | Grind profit: {(grind_profit * 100.0):.2f}%") return -sell_amount break return None def rebuy_adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if(len(dataframe) < 2): return None last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() filled_orders = trade.select_filled_orders() filled_entries = trade.select_filled_orders(trade.entry_side) filled_exits = trade.select_filled_orders(trade.exit_side) count_of_entries = trade.nr_of_successful_entries count_of_exits = trade.nr_of_successful_exits if (count_of_entries == 0): return None is_rebuy = False if (0 < count_of_entries <= self.pa_rebuy_mode_max): if ( (current_profit < self.pa_rebuy_mode_pcts[count_of_entries - 1]) and ( (last_candle['rsi_3'] > 10.0) and (last_candle['rsi_14'] < 40.0) and (last_candle['rsi_3_1h'] > 10.0) and (last_candle['close_max_48'] < (last_candle['close'] * 1.1)) and (last_candle['btc_pct_close_max_72_5m'] < 0.03) ) ): is_rebuy = True if is_rebuy: # This returns first order stake size stake_amount = filled_entries[0].cost print('rebuying..') stake_amount = stake_amount * self.pa_rebuy_mode_multi[count_of_entries - 1] return stake_amount return None def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [] for info_timeframe in self.info_timeframes: informative_pairs.extend([(pair, info_timeframe) for pair in pairs]) if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.extend([(btc_info_pair, btc_info_timeframe) for btc_info_timeframe in self.btc_info_timeframes]) return informative_pairs def informative_1d_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_1d['rsi_14'] = ta.RSI(informative_1d, timeperiod=14) # EMA informative_1d['ema_200'] = ta.EMA(informative_1d, timeperiod=200) # CTI informative_1d['cti_20'] = pta.cti(informative_1d["close"], length=20) # Pivots informative_1d['pivot'], informative_1d['res1'], informative_1d['res2'], informative_1d['res3'], informative_1d['sup1'], informative_1d['sup2'], informative_1d['sup3'] = pivot_points(informative_1d, mode='fibonacci') # S/R res_series = informative_1d['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_1d['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_1d['res_level'] = Series(np.where(res_series, np.where(informative_1d['close'] > informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill() informative_1d['res_hlevel'] = Series(np.where(res_series, informative_1d['high'], float('NaN'))).ffill() informative_1d['sup_level'] = Series(np.where(sup_series, np.where(informative_1d['close'] < informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill() # Downtrend checks informative_1d['is_downtrend_3'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2))) informative_1d['is_downtrend_5'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2)) & (informative_1d['close'].shift(3) < informative_1d['open'].shift(3)) & (informative_1d['close'].shift(4) < informative_1d['open'].shift(4))) # Wicks informative_1d['top_wick_pct'] = ((informative_1d['high'] - np.maximum(informative_1d['open'], informative_1d['close'])) / np.maximum(informative_1d['open'], informative_1d['close'])) # Candle change informative_1d['change_pct'] = (informative_1d['close'] - informative_1d['open']) / informative_1d['open'] # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_1d def informative_4h_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_4h['rsi_14'] = ta.RSI(informative_4h, timeperiod=14, fillna=True) informative_4h['rsi_14_max_6'] = informative_4h['rsi_14'].rolling(6).max() # EMA informative_4h['ema_12'] = ta.EMA(informative_4h, timeperiod=12) informative_4h['ema_26'] = ta.EMA(informative_4h, timeperiod=26) informative_4h['ema_50'] = ta.EMA(informative_4h, timeperiod=50) informative_4h['ema_100'] = ta.EMA(informative_4h, timeperiod=100) informative_4h['ema_200'] = ta.EMA(informative_4h, timeperiod=200) # SMA informative_4h['sma_12'] = ta.SMA(informative_4h, timeperiod=12) informative_4h['sma_26'] = ta.SMA(informative_4h, timeperiod=26) informative_4h['sma_50'] = ta.SMA(informative_4h, timeperiod=50) informative_4h['sma_200'] = ta.SMA(informative_4h, timeperiod=200) # Williams %R informative_4h['r_14'] = williams_r(informative_4h, period=14) informative_4h['r_480'] = williams_r(informative_4h, period=480) # CTI informative_4h['cti_20'] = pta.cti(informative_4h["close"], length=20) # S/R res_series = informative_4h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_4h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_4h['res_level'] = Series(np.where(res_series, np.where(informative_4h['close'] > informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill() informative_4h['res_hlevel'] = Series(np.where(res_series, informative_4h['high'], float('NaN'))).ffill() informative_4h['sup_level'] = Series(np.where(sup_series, np.where(informative_4h['close'] < informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill() # Downtrend checks informative_4h['not_downtrend'] = ((informative_4h['close'] > informative_4h['close'].shift(2)) | (informative_4h['rsi_14'] > 50.0)) informative_4h['is_downtrend_3'] = ((informative_4h['close'] < informative_4h['open']) & (informative_4h['close'].shift(1) < informative_4h['open'].shift(1)) & (informative_4h['close'].shift(2) < informative_4h['open'].shift(2))) # Wicks informative_4h['top_wick_pct'] = ((informative_4h['high'] - np.maximum(informative_4h['open'], informative_4h['close'])) / np.maximum(informative_4h['open'], informative_4h['close'])) # Candle change informative_4h['change_pct'] = (informative_4h['close'] - informative_4h['open']) / informative_4h['open'] # Max highs informative_4h['high_max_3'] = informative_4h['high'].rolling(3).max() informative_4h['high_max_12'] = informative_4h['high'].rolling(12).max() informative_4h['high_max_24'] = informative_4h['high'].rolling(24).max() informative_4h['high_max_36'] = informative_4h['high'].rolling(36).max() informative_4h['high_max_48'] = informative_4h['high'].rolling(48).max() informative_4h['pct_change_high_max_1_12'] = (informative_4h['high'] - informative_4h['high_max_12']) / informative_4h['high_max_12'] informative_4h['pct_change_high_max_3_12'] = (informative_4h['high_max_3'] - informative_4h['high_max_12']) / informative_4h['high_max_12'] informative_4h['pct_change_high_max_3_24'] = (informative_4h['high_max_3'] - informative_4h['high_max_24']) / informative_4h['high_max_24'] informative_4h['pct_change_high_max_3_36'] = (informative_4h['high_max_3'] - informative_4h['high_max_36']) / informative_4h['high_max_36'] informative_4h['pct_change_high_max_3_48'] = (informative_4h['high_max_3'] - informative_4h['high_max_48']) / informative_4h['high_max_48'] # Volume informative_4h['volume_mean_factor_6'] = informative_4h['volume'] / informative_4h['volume'].rolling(6).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_4h def informative_1h_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_1h['rsi_3'] = ta.RSI(informative_1h, timeperiod=3) informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) # EMA informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) 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_12'] = ta.SMA(informative_1h, timeperiod=12) informative_1h['sma_26'] = ta.SMA(informative_1h, timeperiod=26) informative_1h['sma_50'] = ta.SMA(informative_1h, timeperiod=50) informative_1h['sma_100'] = ta.SMA(informative_1h, timeperiod=100) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb20_2_low'] = bollinger['lower'] informative_1h['bb20_2_mid'] = bollinger['mid'] informative_1h['bb20_2_upp'] = bollinger['upper'] informative_1h['bb20_2_width'] = ((informative_1h['bb20_2_upp'] - informative_1h['bb20_2_low']) / informative_1h['bb20_2_mid']) # Williams %R informative_1h['r_14'] = williams_r(informative_1h, period=14) informative_1h['r_96'] = williams_r(informative_1h, period=96) informative_1h['r_480'] = williams_r(informative_1h, period=480) # CTI informative_1h['cti_20'] = pta.cti(informative_1h["close"], length=20) informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40) # S/R res_series = informative_1h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2) sup_series = informative_1h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2) informative_1h['res_level'] = Series(np.where(res_series, np.where(informative_1h['close'] > informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill() informative_1h['res_hlevel'] = Series(np.where(res_series, informative_1h['high'], float('NaN'))).ffill() informative_1h['sup_level'] = Series(np.where(sup_series, np.where(informative_1h['close'] < informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill() # Pump protections informative_1h['hl_pct_change_48'] = range_percent_change(self, informative_1h, 'HL', 48) informative_1h['hl_pct_change_36'] = range_percent_change(self, informative_1h, 'HL', 36) informative_1h['hl_pct_change_24'] = range_percent_change(self, informative_1h, 'HL', 24) informative_1h['hl_pct_change_12'] = range_percent_change(self, informative_1h, 'HL', 12) informative_1h['hl_pct_change_6'] = range_percent_change(self, informative_1h, 'HL', 6) # Downtrend checks informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi_14'] > 50.0)) informative_1h['is_downtrend_3'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2))) informative_1h['is_downtrend_5'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2)) & (informative_1h['close'].shift(3) < informative_1h['open'].shift(3)) & (informative_1h['close'].shift(4) < informative_1h['open'].shift(4))) # Wicks informative_1h['top_wick_pct'] = ((informative_1h['high'] - np.maximum(informative_1h['open'], informative_1h['close'])) / np.maximum(informative_1h['open'], informative_1h['close'])) # Candle change informative_1h['change_pct'] = (informative_1h['close'] - informative_1h['open']) / informative_1h['open'] # Max highs informative_1h['high_max_3'] = informative_1h['high'].rolling(3).max() informative_1h['high_max_6'] = informative_1h['high'].rolling(6).max() informative_1h['high_max_12'] = informative_1h['high'].rolling(12).max() informative_1h['high_max_24'] = informative_1h['high'].rolling(24).max() informative_1h['high_max_36'] = informative_1h['high'].rolling(36).max() informative_1h['high_max_48'] = informative_1h['high'].rolling(48).max() informative_1h['pct_change_high_max_3_12'] = (informative_1h['high_max_3'] - informative_1h['high_max_12']) / informative_1h['high_max_12'] informative_1h['pct_change_high_max_6_12'] = (informative_1h['high_max_6'] - informative_1h['high_max_12']) / informative_1h['high_max_12'] informative_1h['pct_change_high_max_6_24'] = (informative_1h['high_max_6'] - informative_1h['high_max_24']) / informative_1h['high_max_24'] # Volume informative_1h['volume_mean_factor_12'] = informative_1h['volume'] / informative_1h['volume'].rolling(12).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return informative_1h def informative_15m_indicators(self, metadata: dict, info_timeframe) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI informative_15m['rsi_3'] = ta.RSI(informative_15m, timeperiod=3) informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14) # EMA informative_15m['ema_12'] = ta.EMA(informative_15m, timeperiod=12) informative_15m['ema_26'] = ta.EMA(informative_15m, timeperiod=26) # SMA informative_15m['sma_200'] = ta.SMA(informative_15m, timeperiod=200) # CTI informative_15m['cti_20'] = pta.cti(informative_15m["close"], length=20) # Downtrend check informative_15m['not_downtrend'] = ((informative_15m['close'] > informative_15m['open']) | (informative_15m['close'].shift(1) > informative_15m['open'].shift(1)) | (informative_15m['close'].shift(2) > informative_15m['open'].shift(2)) | (informative_15m['rsi_14'] > 50.0) | (informative_15m['rsi_3'] > 25.0)) # Volume informative_15m['volume_mean_factor_12'] = informative_15m['volume'] / informative_15m['volume'].rolling(12).mean() # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_15m_indicators took: {tok - tik:0.4f} seconds.") return informative_15m # Coin Pair Base Timeframe Indicators # --------------------------------------------------------------------------------------------- def base_tf_5m_indicators(self, metadata: dict, dataframe: DataFrame) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- # RSI dataframe['rsi_3'] = ta.RSI(dataframe, timeperiod=3) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # EMA dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_200_pct_change_144'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(144)) / dataframe['ema_200'].shift(144)) dataframe['ema_200_pct_change_288'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(288)) / dataframe['ema_200'].shift(288)) # SMA dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) # BB 20 - STD2 bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb20_2_low'] = bb_20_std2['lower'] dataframe['bb20_2_mid'] = bb_20_std2['mid'] dataframe['bb20_2_upp'] = bb_20_std2['upper'] # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['bb40_2_low'] = bb_40_std2['lower'] dataframe['bb40_2_mid'] = bb_40_std2['mid'] dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs() dataframe['bb40_2_tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs() # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_480'] = williams_r(dataframe, period=480) # CTI dataframe['cti_20'] = pta.cti(dataframe["close"], length=20) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # Dip protection dataframe['tpct_change_0'] = top_percent_change(self, dataframe, 0) dataframe['tpct_change_2'] = top_percent_change(self, dataframe, 2) # Close max dataframe['close_max_12'] = dataframe['close'].rolling(12).max() dataframe['close_max_24'] = dataframe['close'].rolling(24).max() dataframe['close_max_48'] = dataframe['close'].rolling(48).max() dataframe['pct_close_max_48'] = (dataframe['close_max_48'] - dataframe['close']) / dataframe['close'] # Close delta dataframe['close_delta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # For sell checks dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) # Global protections # ----------------------------------------------------------------------------------------- if not self.config['runmode'].value in ('live', 'dry_run'): # Backtest age filter dataframe['bt_agefilter_ok'] = False dataframe.loc[dataframe.index > (12 * 24 * self.bt_min_age_days),'bt_agefilter_ok'] = True else: # Exchange downtime protection dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) # Performance logging # ----------------------------------------------------------------------------------------- tok = time.perf_counter() log.debug(f"[{metadata['pair']}] base_tf_5m_indicators took: {tok - tik:0.4f} seconds.") return dataframe # Coin Pair Indicator Switch Case # --------------------------------------------------------------------------------------------- def info_switcher(self, metadata: dict, info_timeframe) -> DataFrame: if info_timeframe == '1d': return self.informative_1d_indicators(metadata, info_timeframe) elif info_timeframe == '4h': return self.informative_4h_indicators(metadata, info_timeframe) elif info_timeframe == '1h': return self.informative_1h_indicators(metadata, info_timeframe) elif info_timeframe == '15m': return self.informative_15m_indicators(metadata, info_timeframe) else: raise RuntimeError(f"{info_timeframe} not supported as informative timeframe for BTC pair.") # BTC 1D Indicators # --------------------------------------------------------------------------------------------- def btc_info_1d_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_1d = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- btc_info_1d['rsi_14'] = ta.RSI(btc_info_1d, timeperiod=14) #btc_info_1d['pivot'], btc_info_1d['res1'], btc_info_1d['res2'], btc_info_1d['res3'], btc_info_1d['sup1'], btc_info_1d['sup2'], btc_info_1d['sup3'] = pivot_points(btc_info_1d, mode='fibonacci') # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_1d.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_1d_indicators took: {tok - tik:0.4f} seconds.") return btc_info_1d # BTC 4h Indicators # --------------------------------------------------------------------------------------------- def btc_info_4h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_4h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_4h['rsi_14'] = ta.RSI(btc_info_4h, timeperiod=14) # SMA btc_info_4h['sma_200'] = ta.SMA(btc_info_4h, timeperiod=200) # Bull market or not btc_info_4h['is_bull'] = btc_info_4h['close'] > btc_info_4h['sma_200'] # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_4h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_4h_indicators took: {tok - tik:0.4f} seconds.") return btc_info_4h # BTC 1h Indicators # --------------------------------------------------------------------------------------------- def btc_info_1h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_1h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_1h['rsi_14'] = ta.RSI(btc_info_1h, timeperiod=14) btc_info_1h['not_downtrend'] = ((btc_info_1h['close'] > btc_info_1h['close'].shift(2)) | (btc_info_1h['rsi_14'] > 50)) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_1h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_1h_indicators took: {tok - tik:0.4f} seconds.") return btc_info_1h # BTC 15m Indicators # --------------------------------------------------------------------------------------------- def btc_info_15m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_15m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- btc_info_15m['rsi_14'] = ta.RSI(btc_info_15m, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_15m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_15m_indicators took: {tok - tik:0.4f} seconds.") return btc_info_15m # BTC 5m Indicators # --------------------------------------------------------------------------------------------- def btc_info_5m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: tik = time.perf_counter() btc_info_5m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe) # Indicators # ----------------------------------------------------------------------------------------- # RSI btc_info_5m['rsi_14'] = ta.RSI(btc_info_5m, timeperiod=14) # Close max btc_info_5m['close_max_24'] = btc_info_5m['close'].rolling(24).max() btc_info_5m['close_max_72'] = btc_info_5m['close'].rolling(72).max() btc_info_5m['pct_close_max_24'] = (btc_info_5m['close_max_24'] - btc_info_5m['close']) / btc_info_5m['close'] btc_info_5m['pct_close_max_72'] = (btc_info_5m['close_max_72'] - btc_info_5m['close']) / btc_info_5m['close'] # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date'] btc_info_5m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] btc_info_5m_indicators took: {tok - tik:0.4f} seconds.") return btc_info_5m # BTC Indicator Switch Case # --------------------------------------------------------------------------------------------- def btc_info_switcher(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame: if btc_info_timeframe == '1d': return self.btc_info_1d_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '4h': return self.btc_info_4h_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '1h': return self.btc_info_1h_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '15m': return self.btc_info_15m_indicators(btc_info_pair, btc_info_timeframe, metadata) elif btc_info_timeframe == '5m': return self.btc_info_5m_indicators(btc_info_pair, btc_info_timeframe, metadata) else: raise RuntimeError(f"{btc_info_timeframe} not supported as informative timeframe for BTC pair.") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() ''' --> BTC informative indicators ___________________________________________________________________________________________ ''' if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" for btc_info_timeframe in self.btc_info_timeframes: btc_informative = self.btc_info_switcher(btc_info_pair, btc_info_timeframe, metadata) dataframe = merge_informative_pair(dataframe, btc_informative, self.timeframe, btc_info_timeframe, ffill=True) # Customize what we drop - in case we need to maintain some BTC informative ohlcv data # Default drop all drop_columns = { '1d': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '4h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '1h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '15m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '5m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], }.get(btc_info_timeframe,[f"{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]) drop_columns.append(f"date_{btc_info_timeframe}") dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> Indicators on informative timeframes ___________________________________________________________________________________________ ''' for info_timeframe in self.info_timeframes: info_indicators = self.info_switcher(metadata, info_timeframe) dataframe = merge_informative_pair(dataframe, info_indicators, self.timeframe, info_timeframe, ffill=True) # Customize what we drop - in case we need to maintain some informative timeframe ohlcv data # Default drop all except base timeframe ohlcv data drop_columns = { '1d': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '4h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '1h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']], '15m': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] }.get(info_timeframe,[f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]) dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> The indicators for the base timeframe (5m) ___________________________________________________________________________________________ ''' dataframe = self.base_tf_5m_indicators(metadata, dataframe) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' # the number of free slots current_free_slots = self.config["max_open_trades"] - len(LocalTrade.get_trades_proxy(is_open=True)) protections_global_1 = ( ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) ) protections_global_2 = ( ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) ) ) protections_global_3 = ( ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.75) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) # | (dataframe['r_480_4h'] > -95.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.3)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.3)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.3)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 2.0)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) # KONO & ( (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 60.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['close'] < (dataframe['res3_1d'] * 1.5)) | (dataframe['close'] > (dataframe['sup_level_1h'] * 0.9)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.85) ) ) protections_global_4 = ( ( (dataframe['change_pct_1d'] > -0.3) | (dataframe['top_wick_pct_1d'] < 0.3) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['change_pct_1d'] < 0.25) | (dataframe['top_wick_pct_1d'] < 0.25) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['change_pct_4h'] < 0.5) | (dataframe['top_wick_pct_4h'] < 0.5) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) ) & ( (dataframe['change_pct_1d'] < 0.5) | (dataframe['top_wick_pct_1d'] < 0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['r_480_4h'] > -90.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.25)) | (dataframe['hl_pct_change_48_1h'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['change_pct_1d'] < 2.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < -0.5) ) & ( (dataframe['change_pct_1d'] > -0.16) | (dataframe['change_pct_4h'] < 0.16) | (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.24)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 5.0)) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_40_1h'] < 0.55) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) ) protections_global_5 = ( ( (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_40_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_40_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_40_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 5.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_40_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) ) # BNX & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_40_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['change_pct_1d'] > -0.3) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_40_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_40_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.3)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 15.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) ) protections_global_6 = ( ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['rsi_14_1h'] < 50.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 50.0) ) # & # ( # (dataframe['cti_20_15m'] < -0.5) # | (dataframe['rsi_3_15m'] > 25.0) # | (dataframe['cti_20_1h'] < -0.0) # | (dataframe['cti_20_4h'] < -0.85) # | (dataframe['cti_20_1d'] < -0.0) # | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) # ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['change_pct_4h'] < 0.2) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 80.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 30.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.85) | (dataframe['rsi_14_4h'] < 80.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 80.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['change_pct_1d'] < 0.5) | (dataframe['top_wick_pct_1d'] < 0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) ) protections_global_7 = ( ( (dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) # pumped, still high & ( (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 70.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['hl_pct_change_12_1h'] < 0.75) ) # pumped, coming down & ( (dataframe['change_pct_1d'] < 0.5) | (dataframe['top_wick_pct_1d'] < 0.5) | (dataframe['change_pct_4h'] > 0.01) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_1d'] < 80.0) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 60.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['change_pct_1d'] > -0.1) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 80.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 40.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['rsi_14_1d'] < 80.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) | (dataframe['hl_pct_change_12_1h'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['r_480_1h'] < -25.0) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['change_pct_4h'] < 0.16) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 40.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['rsi_14_4h'] < 50.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 40.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) ) protections_global_8 = ( ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 60.0) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.85) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['r_14_4h'] < -30.0) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['r_14_4h'] < -30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['change_pct_4h'] > -0.1) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['r_14_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 5.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) ) protections_global_9 = ( ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['is_downtrend_3_1h'] == False) | (dataframe['change_pct_4h'].shift(48) < 0.25) | (dataframe['top_wick_pct_4h'].shift(48) < 0.25) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['change_pct_1d'] < 0.25) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['r_14_4h'] < -10.0) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.75) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['change_pct_1d'] < 0.4) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) ) protections_global_10 = ( ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 60.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < 0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] < 40.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.25)) ) & ( (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)) ) & ( (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['r_480_1h'] < -30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['rsi_14_1d'] < 60.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_1h'] == False) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['r_480_1h'] < -30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['r_480_1h'] < -30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 70.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) ) & ( (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.8) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['change_pct_4h'] < 0.05) | (dataframe['top_wick_pct_4h'] < 0.05) | (dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['rsi_3_15m'] > 5.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) ) & ( (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) ) ) for buy_enable in self.buy_params: index = int(buy_enable.split('_')[2]) item_buy_protection_list = [True] if self.buy_params[f'{buy_enable}']: # Buy conditions # ----------------------------------------------------------------------------------------- item_buy_logic = [] item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list)) # Condition #1 - Long mode bull. Uptrend. if index == 1: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['cti_20_1d'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.2))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['rsi_14_4h'] < 30.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] < 40.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_14_1h'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['hl_pct_change_48_1h'] < 0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_4h'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.12)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.999)) # Condition #2 - Normal mode bull. if index == 2: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['cti_20_1d'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.25))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 16.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 16.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < 0.5)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['change_pct_1d'] < 0.25) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_14_1h'] < 40.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.06)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) # Condition #3 - Normal mode bull. if index == 3: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26)) item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['ema_12_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 75.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.85) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_1h'] > 16.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 16.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07))) item_buy_logic.append((dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['close_max_48'] < (dataframe['close'] * 1.16)) | (dataframe['hl_pct_change_24_1h'] < 0.4) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['rsi_14'] < 36.0) item_buy_logic.append(dataframe['ha_close'] > dataframe['ha_open']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) # Condition #4 - Normal mode bull. if index == 4: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['hl_pct_change_12_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['cti_20_1d'] < 0.9) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 16.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) # BNX item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 15.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['r_480_4h'] < -30.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 16.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < 0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #5 - Normal mode bull. if index == 5: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['change_pct_4h'] < 0.32) | (dataframe['top_wick_pct_4h'] < 0.16) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['cti_20_1h'] < 0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.9) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.1))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 16.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5)) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.2)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 5.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['r_14_4h'] < -25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #6 - Normal mode bull. if index == 6: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.5) item_buy_logic.append(dataframe['cti_20_4h'] < 0.75) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3)) | (dataframe['close'] < (dataframe['ema_26'] * 0.9)) | (dataframe['rsi_3'] > 20.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_max_12'] < (dataframe['close'] * 1.12)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_12'] < (dataframe['close'] * 1.12)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['close_max_12'] < (dataframe['close'] * 1.16)) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_12'] < (dataframe['close'] * 1.12)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['close_max_12'] < (dataframe['close'] * 1.12)) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 5.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.89))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_12'] < (dataframe['close'] * 1.12)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close_max_48'] < (dataframe['close'] * 1.26)) | (dataframe['close'] < (dataframe['ema_26'] * 0.86))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.9))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.91))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.94)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) # Condition #21 - Pump mode bull. if index == 21: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) item_buy_logic.append(dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06))) item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['rsi_14_1d'] < 70.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) # CHZ item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_14_1h'] < 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['r_14_4h'] > -50.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['change_pct_4h'] < 0.08) | (dataframe['top_wick_pct_4h'] < 0.08) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_14_1h'] > 60.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['rsi_14_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_4h'] < 0.8) | (dataframe['cti_20_1d'] < -0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 10.0) | (dataframe['rsi_14_1h'] < 10.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 5.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['r_14_4h'] < -25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #22 - Pump mode bull. if index == 22: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['cti_20_4h'] < 0.9) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.92))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < 0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.92))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.9))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['cti_20_15m'] < 0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.92))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['close'] < (dataframe['ema_16'] * 0.94))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['close'] < (dataframe['ema_16'] * 0.96))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close'] < (dataframe['ema_16'] * 0.95))) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * 0.968)) item_buy_logic.append(dataframe['cti_20'] < -0.9) item_buy_logic.append(dataframe['rsi_14'] < 50.0) # Condition #41 - Quick mode bull. if index == 41: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['cti_20_1h'] < 0.5) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.07)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.034))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.03))) item_buy_logic.append((dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.03))) item_buy_logic.append((dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.06)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.07)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.05)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < 0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.05)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.03))) item_buy_logic.append((dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.05)) | (dataframe['close_delta'].gt(dataframe['close'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8)) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.2))) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.036)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.4)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 36.0) # Condition #42 - Quick mode bull. if index == 42: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['cti_20_1h'] < 0.5) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.75) | (dataframe['cti_20_1d'] < 0.75) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.75) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 20.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 30.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['r_14_4h'] < -25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996)) item_buy_logic.append(dataframe['rsi_14'] < 40.0) # Condition #43 - Quick mode bull. if index == 43: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.92))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.93)) | (dataframe['cti_20'] < -0.9)) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < 0.75) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close'] < (dataframe['ema_26'] * 0.88))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8)) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.8) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['cti_20_15m'] < -0.75) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['close'] < (dataframe['ema_26'] * 0.93))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['r_14_4h'] < -25.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close'] < (dataframe['ema_26'] * 0.91)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.938)) item_buy_logic.append(dataframe['cti_20'] < -0.75) item_buy_logic.append(dataframe['r_14'] < -94.0) # Condition #44 - Quick mode bull. if index == 44: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] > (dataframe['close'] * 1.1)) item_buy_logic.append(dataframe['cti_40_1h'] < -0.0) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['rsi_3_15m'] > 15.0) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['cti_20'] < -0.9) | (dataframe['r_14'] < -94.0)) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.75) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['cti_20'] < -0.9)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.9) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['rsi_14_1h'] < 40.0) | (dataframe['rsi_14_4h'] < 40.0) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.5) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.2))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | (dataframe['close_max_48'] < (dataframe['close'] * 1.12))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_14_15m'] < 30.0) | (dataframe['rsi_3_1h'] > 25.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.0)) item_buy_logic.append(((dataframe['not_downtrend_1h']) & (dataframe['not_downtrend_4h'])) | (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_14_15m'] < 20.0) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['rsi_3_1h'] > 20.0) | (dataframe['cti_20_4h'] < -0.8) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))) # Logic item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.132) item_buy_logic.append(dataframe['cti_20'] < -0.8) item_buy_logic.append(dataframe['r_14'] < -90.0) # Condition #61 - Rebuy mode bull. if index == 61: # Protections item_buy_logic.append(current_free_slots >= self.rebuy_mode_min_free_slots) item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36)) item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5) item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 1h downtrend, downtrend 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 1h red, overbought 1h, downtrend 1h, downtrend 1h, drop last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] < 0.16) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, current 4h red, drop last 4h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 5.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['change_pct_4h'] > -0.04) | (dataframe['close_max_48'] < (dataframe['close'] * 1.1))) # downtrend 1d, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red with top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.02) | (dataframe['top_wick_pct_1d'] < 0.02) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d green with top wick, downtrend 4h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.06) | (dataframe['top_wick_pct_1d'] < 0.06) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1h red, overbought 1h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1h'] > -0.02) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h grered, previous 4h green, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['cti_20_1h'] < 0.85) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))) # current 4h red, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['close_max_24'] < (dataframe['close'] * 1.08))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 2.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d item_buy_logic.append((dataframe['change_pct_1d'] > -0.0) | (dataframe['change_pct_1d'].shift(288) > -0.0) | (dataframe['cti_20_1d'] < 0.85)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # overbought 1d item_buy_logic.append((dataframe['cti_20_1d'] < 0.9) | (dataframe['rsi_14_1d'] < 80.0)) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['rsi_14_1d'] < 70.0)) # XAVA item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['rsi_3_15m'] > 25.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_1d'] < -0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close_delta'] > dataframe['close'] * 12.0 / 1000) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #81 - Long mode bull. if index == 81: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12)) item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16)) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24)) item_buy_logic.append(dataframe['cti_20_1h'] < 0.95) item_buy_logic.append(dataframe['cti_20_4h'] < 0.95) item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_14_1h'] < -25.0) item_buy_logic.append(dataframe['r_14_4h'] < -25.0) item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3) item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) item_buy_logic.append(dataframe['not_downtrend_15m']) # current 4h relative long top wick, overbought 1h, downtrend 1h, downtrend 4h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576))) # current 4h relative long top wick, overbought 1d item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 6.0)) | (dataframe['cti_20_1d'] < 0.5)) # current 4h relative long top wick, overbought 1h, downtrend 1h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0)) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['not_downtrend_1h'])) # big drop in last 48h, downtrend 1h item_buy_logic.append((dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)) | (dataframe['not_downtrend_1h'])) # downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # downtrend 1h, overbought 1h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < 0.5)) # downtrend 1h, overbought 4h item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5)) # downtrend 1h, downtrend 4h, overbought 1d item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['not_downtrend_4h']) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, overbought 1d item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['cti_20_1d'] < 0.5)) # downtrend 1d, downtrend 1h item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False) | (dataframe['not_downtrend_1h'])) # current 4h red, previous 4h green, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['change_pct_4h'].shift(48) < 0.06) | (dataframe['cti_20_4h'] < 0.5)) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # current 1d long 1d with top wick, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] < 0.2) | (dataframe['top_wick_pct_1d'] < 0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) # current 1d long red, overbought 1d, downtrend 1h item_buy_logic.append((dataframe['change_pct_1d'] > -0.1) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['not_downtrend_1h'])) item_buy_logic.append((dataframe['cti_20_15m'] < -0.5) | (dataframe['cti_20_1h'] < 0.5) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['cti_20_1d'] < 0.75) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.0) | (dataframe['cti_20_1h'] < -0.0) | (dataframe['cti_20_4h'] < -0.0) | (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) # Logic item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.052)) item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.024)) item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['rsi_14'] < 30.0) # Condition #82 - Long mode bull. if index == 82: # Protections item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03) item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03) item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2)) item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3)) item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) item_buy_logic.append(dataframe['sma_50_1h'] > dataframe['sma_200_1h']) item_buy_logic.append(dataframe['ema_50_4h'] > dataframe['ema_200_4h']) item_buy_logic.append(dataframe['sma_50_4h'] > dataframe['sma_200_4h']) item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0) item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0) item_buy_logic.append(dataframe['r_480_4h'] < -10.0) item_buy_logic.append(protections_global_1) item_buy_logic.append(protections_global_2) item_buy_logic.append(protections_global_3) item_buy_logic.append(protections_global_4) item_buy_logic.append(protections_global_5) item_buy_logic.append(protections_global_6) item_buy_logic.append(protections_global_7) item_buy_logic.append(protections_global_8) item_buy_logic.append(protections_global_9) item_buy_logic.append(protections_global_10) # current 1d long green with long top wick item_buy_logic.append((dataframe['change_pct_1d'] < 0.12) | (dataframe['top_wick_pct_1d'] < 0.12)) # overbought 1d, overbought 4h, downtrend 1h, drop in last 2h item_buy_logic.append((dataframe['rsi_14_1d'] < 70.0) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['not_downtrend_1h']) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red, downtrend 1h, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.06) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_4h'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long red, downtrend 1h, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_4h'] > -0.12) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.8) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, downtrend 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['is_downtrend_3_4h'] == False) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d red, overbought 1d, downtrend 1h, current 4h red, previous 4h green with top wick item_buy_logic.append((dataframe['change_pct_1d'] > -0.08) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['not_downtrend_1h']) | (dataframe['change_pct_4h'] > -0.0) | (dataframe['change_pct_4h'].shift(48) < 0.04) | (dataframe['top_wick_pct_4h'].shift(48) < 0.04)) # current 1d long red with long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.12) | (dataframe['top_wick_pct_1d'] < 0.12) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 1d long red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.16) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h red with top wick, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['cti_20_1d'] < 0.85)) # current 4h green with top wick, overbought 4h item_buy_logic.append((dataframe['change_pct_4h'] < 0.04) | (dataframe['top_wick_pct_4h'] < 0.04) | (dataframe['rsi_14_4h'] < 70.0)) # current 4h red, downtrend 1h, overbought 1d item_buy_logic.append((dataframe['change_pct_4h'] > -0.04) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1d'] < 0.5)) # current 1d long relative top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 4.0)) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h relative long top wick, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0)) | (dataframe['cti_20_1d'] < 0.85) | (dataframe['rsi_14_1d'] < 50.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current and previous 1d red, overbought 1d, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] > -0.04) | (dataframe['change_pct_1d'].shift(288) > -0.04) | (dataframe['cti_20_1d'] < 0.5) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) # current 4h long green, overbought 4h, drop in last 2h item_buy_logic.append((dataframe['change_pct_1d'] < 0.08) | (dataframe['rsi_14_4h'] < 70.0) | (dataframe['close_max_24'] < (dataframe['close'] * 1.1))) item_buy_logic.append((dataframe['not_downtrend_15m']) | (dataframe['not_downtrend_1h']) | (dataframe['cti_20_1h'] < -0.5) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['cti_20_1d'] < -0.0) | (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)) | (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.8) | (dataframe['rsi_3_15m'] > 10.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['not_downtrend_1h']) | (dataframe['cti_20_15m'] < -0.9) | (dataframe['rsi_3_15m'] > 30.0) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['rsi_3_1h'] > 10.0) | (dataframe['cti_20_4h'] < -0.0) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) item_buy_logic.append((dataframe['cti_20_15m'] < -0.9) | (dataframe['cti_20_1h'] < -0.8) | (dataframe['cti_20_4h'] < 0.75) | (dataframe['r_14_4h'] < -25.0) | (dataframe['cti_20_1d'] < 0.5) | ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['cti_20'] < -0.8) item_buy_logic.append(dataframe['volume'] > 0) item_buy = reduce(lambda x, y: x & y, item_buy_logic) dataframe.loc[item_buy, 'enter_tag'] += f"{index} " conditions.append(item_buy) dataframe.loc[:, 'enter_long'] = item_buy if conditions: dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool: # allow force entries if (entry_tag == 'force_entry'): return True dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False dataframe = dataframe.iloc[-1].squeeze() if ((rate > dataframe['close'])): slippage = ((rate / dataframe['close']) - 1.0) if slippage < self.max_slippage: return True else: log.warning(f"Cancelling buy for {pair} due to slippage {(slippage * 100.0):.2f}") return False return True def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # Allow force exits if exit_reason != 'force_exit': if self._should_hold_trade(trade, rate, exit_reason): return False if (exit_reason == 'stop_loss'): return False if self.exit_profit_only: if self.exit_profit_only: profit = 0.0 if (trade.realized_profit != 0.0): profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close) profit = profit + trade.realized_profit profit = profit / trade.stake_amount else: profit = trade.calc_profit_ratio(rate) if (profit < self.exit_profit_offset): return False self._remove_profit_target(pair) return True def bot_loop_start(self, **kwargs) -> None: if self.config["runmode"].value not in ("live", "dry_run"): return super().bot_loop_start(**kwargs) if self.hold_support_enabled: self.load_hold_trades_config() return super().bot_loop_start(**kwargs) def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 5.0 def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_profit: float, current_time: datetime): self.target_profit_cache.data[pair] = { "rate": rate, "profit": current_profit, "sell_reason": sell_reason, "time_profit_reached": current_time.isoformat() } self.target_profit_cache.save() def _remove_profit_target(self, pair: str): if self.target_profit_cache is not None: self.target_profit_cache.data.pop(pair, None) self.target_profit_cache.save() def get_hold_trades_config_file(self): proper_holds_file_path = self.config["user_data_dir"].resolve() / "nfi-hold-trades.json" if proper_holds_file_path.is_file(): return proper_holds_file_path strat_file_path = pathlib.Path(__file__) hold_trades_config_file_resolve = strat_file_path.resolve().parent / "hold-trades.json" if hold_trades_config_file_resolve.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_resolve, proper_holds_file_path, ) return hold_trades_config_file_resolve # The resolved path does not exist, is it a symlink? hold_trades_config_file_absolute = strat_file_path.absolute().parent / "hold-trades.json" if hold_trades_config_file_absolute.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_absolute, proper_holds_file_path, ) return hold_trades_config_file_absolute def load_hold_trades_config(self): if self.hold_trades_cache is None: hold_trades_config_file = self.get_hold_trades_config_file() if hold_trades_config_file: log.warning("Loading hold support data from %s", hold_trades_config_file) self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() def _should_hold_trade(self, trade: "Trade", rate: float, sell_reason: str) -> bool: if self.config['runmode'].value not in ('live', 'dry_run'): return False if not self.hold_support_enabled: return False # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop self.load_hold_trades_config() if not self.hold_trades_cache: # Cache hasn't been setup, likely because the corresponding file does not exist, sell return False if not self.hold_trades_cache.data: # We have no pairs we want to hold until profit, sell return False # By default, no hold should be done hold_trade = False trade_ids: dict = self.hold_trades_cache.data.get("trade_ids") if trade_ids and trade.id in trade_ids: trade_profit_ratio = trade_ids[trade.id] profit = 0.0 if (trade.realized_profit != 0.0): profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close) profit = profit + trade.realized_profit profit = profit / trade.stake_amount else: profit = trade.calc_profit_ratio(rate) current_profit_ratio = profit if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True trade_pairs: dict = self.hold_trades_cache.data.get("trade_pairs") if trade_pairs and trade.pair in trade_pairs: trade_profit_ratio = trade_pairs[trade.pair] profit = 0.0 if (trade.realized_profit != 0.0): profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close) profit = profit + trade.realized_profit profit = profit / trade.stake_amount else: profit = trade.calc_profit_ratio(rate) current_profit_ratio = profit if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True return hold_trade # +---------------------------------------------------------------------------+ # | Custom Indicators | # +---------------------------------------------------------------------------+ # Range midpoint acts as Support def is_support(row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)//2: conditions.append(row_data[row] > row_data[row+1]) else: conditions.append(row_data[row] < row_data[row+1]) result = reduce(lambda x, y: x & y, conditions) return result # Range midpoint acts as Resistance def is_resistance(row_data) -> bool: conditions = [] for row in range(len(row_data)-1): if row < len(row_data)//2: conditions.append(row_data[row] < row_data[row+1]) else: conditions.append(row_data[row] > row_data[row+1]) result = reduce(lambda x, y: x & y, conditions) return result # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf') # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) vwma = vwma.fillna(0, inplace=True) return vwma # Exponential moving average of a volume weighted simple moving average def ema_vwma_osc(dataframe, len_slow_ma): slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma)) return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100 def t3_average(dataframe, length=5): """ T3 Average by HPotter on Tradingview https://www.tradingview.com/script/qzoC9H1I-T3-Average/ """ df = dataframe.copy() df['xe1'] = ta.EMA(df['close'], timeperiod=length) df['xe1'].fillna(0, inplace=True) df['xe2'] = ta.EMA(df['xe1'], timeperiod=length) df['xe2'].fillna(0, inplace=True) df['xe3'] = ta.EMA(df['xe2'], timeperiod=length) df['xe3'].fillna(0, inplace=True) df['xe4'] = ta.EMA(df['xe3'], timeperiod=length) df['xe4'].fillna(0, inplace=True) df['xe5'] = ta.EMA(df['xe4'], timeperiod=length) df['xe5'].fillna(0, inplace=True) df['xe6'] = ta.EMA(df['xe5'], timeperiod=length) df['xe6'].fillna(0, inplace=True) b = 0.7 c1 = -b * b * b c2 = 3 * b * b + 3 * b * b * b c3 = -6 * b * b - 3 * b - 3 * b * b * b c4 = 1 + 3 * b + b * b * b + 3 * b * b df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3'] return df['T3Average'] # Pivot Points - 3 variants - daily recommended def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series: if mode == 'simple': hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 res1 = hlc3_pivot * 2 - dataframe['low'].shift(1) sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1) res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift() sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift() res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift() sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift() return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 elif mode == 'fibonacci': hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 hl_range = (dataframe['high'] - dataframe['low']).shift(1) res1 = hlc3_pivot + 0.382 * hl_range sup1 = hlc3_pivot - 0.382 * hl_range res2 = hlc3_pivot + 0.618 * hl_range sup2 = hlc3_pivot - 0.618 * hl_range res3 = hlc3_pivot + 1 * hl_range sup3 = hlc3_pivot - 1 * hl_range return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 elif mode == 'DeMark': demark_pivot_lt = (dataframe['low'] * 2 + dataframe['high'] + dataframe['close']) demark_pivot_eq = (dataframe['close'] * 2 + dataframe['low'] + dataframe['high']) demark_pivot_gt = (dataframe['high'] * 2 + dataframe['low'] + dataframe['close']) demark_pivot = np.where((dataframe['close'] < dataframe['open']), demark_pivot_lt, np.where((dataframe['close'] > dataframe['open']), demark_pivot_gt, demark_pivot_eq)) dm_pivot = demark_pivot / 4 dm_res = demark_pivot / 2 - dataframe['low'] dm_sup = demark_pivot / 2 - dataframe['high'] return dm_pivot, dm_res, dm_sup # Heikin Ashi candles def heikin_ashi(dataframe, smooth_inputs = False, smooth_outputs = False, length = 10): df = dataframe[['open','close','high','low']].copy().fillna(0) if smooth_inputs: df['open_s'] = ta.EMA(df['open'], timeframe = length) df['high_s'] = ta.EMA(df['high'], timeframe = length) df['low_s'] = ta.EMA(df['low'], timeframe = length) df['close_s'] = ta.EMA(df['close'],timeframe = length) open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2 high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1) low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1) close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s'])/4 else: open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2 high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1) low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1) close_ha = (df['open'] + df['high'] + df['low'] + df['close'])/4 open_ha = open_ha.fillna(0) high_ha = high_ha.fillna(0) low_ha = low_ha.fillna(0) close_ha = close_ha.fillna(0) if smooth_outputs: open_sha = ta.EMA(open_ha, timeframe = length) high_sha = ta.EMA(high_ha, timeframe = length) low_sha = ta.EMA(low_ha, timeframe = length) close_sha = ta.EMA(close_ha, timeframe = length) return open_sha, close_sha, low_sha else: return open_ha, close_ha, low_ha # Peak Percentage Change def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == 'HL': return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min() elif method == 'OC': return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min() else: raise ValueError(f"Method {method} not defined!") # Percentage distance to top peak def top_percent_change(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] # +---------------------------------------------------------------------------+ # | Classes | # +---------------------------------------------------------------------------+ class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass @staticmethod def rapidjson_load_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} @staticmethod def rapidjson_dump_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open("r") as rfh: try: data = rapidjson.load( rfh, **self.rapidjson_load_kwargs() ) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing rapidjson.dump( self.data, self.path.open("w"), **self.rapidjson_dump_kwargs() ) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): @staticmethod def rapidjson_load_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "object_hook": HoldsCache._object_hook, } @staticmethod def rapidjson_dump_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS, } def save(self): raise RuntimeError("The holds cache does not allow programatical save") def process_loaded_data(self, data): trade_ids = data.get("trade_ids") trade_pairs = data.get("trade_pairs") if not trade_ids and not trade_pairs: return data open_trades = {} for trade in Trade.get_trades_proxy(is_open=True): open_trades[trade.id] = open_trades[trade.pair] = trade r_trade_ids = {} if trade_ids: if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path ) if trade_id in open_trades: formatted_profit_ratio = f"{profit_ratio * 100}%" log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) else: # Initial Syntax profit_ratio = data.get("profit_ratio") if profit_ratio: if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path ) else: profit_ratio = 0.005 formatted_profit_ratio = f"{profit_ratio * 100}%" for trade_id in trade_ids: if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if trade_id in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) r_trade_pairs = {} if trade_pairs: for trade_pair, profit_ratio in trade_pairs.items(): if not isinstance(trade_pair, str): log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s is not a string", trade_pair, self.path ) continue if "/" not in trade_pair: log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s does not look like " "a valid '/' formatted pair.", trade_pair, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_pair %s in %s is not a float", profit_ratio, trade_pair, self.path ) formatted_profit_ratio = f"{profit_ratio * 100}%" if trade_pair in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_pair], formatted_profit_ratio ) else: log.warning( "The trade pair %s is configured to HOLD until the profit ratio of %s is met", trade_pair, formatted_profit_ratio ) r_trade_pairs[trade_pair] = profit_ratio r_data = {} if r_trade_ids: r_data["trade_ids"] = r_trade_ids if r_trade_pairs: r_data["trade_pairs"] = r_trade_pairs return r_data @staticmethod def _object_hook(data): _data = {} for key, value in data.items(): try: key = int(key) except ValueError: pass _data[key] = value return _data