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 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/UAARUlhf/20pct?ref_type=103 (20% lifetime 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 ## ## Bitget: https://bonus.bitget.com/nfi (lifetime 20% rebate all & 10% discount on spot fees) ## ## HTX: https://www.htx.com/invite/en-us/1f?invite_code=ubpt2223 ## ## (Welcome Bonus worth 241 USDT upon completion of a deposit and trade) ## ## 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 "v12.0.640" # 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": False, # "buy_condition_81_enable": False, # "buy_condition_82_enable": False, } buy_protection_params = {} ############################################################# # CACHES hold_trades_cache = None target_profit_cache = None ############################################################# def __init__(self, config: dict) -> None: if "ccxt_config" not in config["exchange"]: config["exchange"]["ccxt_config"] = {} if "ccxt_async_config" not in config["exchange"]: config["exchange"]["ccxt_async_config"] = {} options = { "brokerId": None, "broker": {"spot": None, "margin": None, "future": None, "delivery": None}, "partner": {"spot": {"id": None, "key": None}, "future": {"id": None, "key": None}, "id": None, "key": None}, } config["exchange"]["ccxt_config"]["options"] = options config["exchange"]["ccxt_async_config"]["options"] = options 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 - (0.20 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 ( (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 ( (current_time - timedelta(minutes=30) > filled_entries[-1].order_filled_utc) or (slice_profit_entry < -0.01) ) 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 else: # Current order is sell partial fill return None 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) informative_1d["ema_200_dec_4"] = (informative_1d["ema_200"].isnull()) | ( informative_1d["ema_200"] <= informative_1d["ema_200"].shift(4) ) # 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"] # Pump protections informative_1d["hl_pct_change_3"] = range_percent_change(self, informative_1d, "HL", 3) informative_1d["hl_pct_change_6"] = range_percent_change(self, informative_1d, "HL", 6) # Max highs informative_1d["high_max_6"] = informative_1d["high"].rolling(6).max() informative_1d["high_max_12"] = informative_1d["high"].rolling(12).max() # 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_3"] = ta.RSI(informative_4h, timeperiod=3, fillna=True) 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) informative_4h["ema_200_dec_24"] = (informative_4h["ema_200"].isnull()) | ( informative_4h["ema_200"] <= informative_4h["ema_200"].shift(24) ) # 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() # 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) informative_1h["ema_200_dec_48"] = (informative_1h["ema_200"].isnull()) | ( informative_1h["ema_200"] <= informative_1h["ema_200"].shift(48) ) # 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() # 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)) 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["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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.12)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["change_pct_4h"] > -0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 80.0) ) 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_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.1)) | ((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"] < -0.5) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.18)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.06)) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.1) | (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.1) | (dataframe["top_wick_pct_4h"].shift(48) < 0.1) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.02) | (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["top_wick_pct_1h"] < (abs(dataframe["change_pct_1h"]) * 6.0)) | (dataframe["cti_20_1h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.26) | (dataframe["top_wick_pct_4h"] < 0.26) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.26) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.12)) ) item_buy_logic.append( (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_1h"] < (dataframe["close"] * 1.3)) | (dataframe["hl_pct_change_24_1h"] < 0.75) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.25)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["is_downtrend_3_4h"] == False) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["hl_pct_change_24_1h"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 50.0) | (dataframe["cti_20_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["rsi_3_4h"] > 10.0) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (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"] < 0.5) ) item_buy_logic.append( (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < 0.7) | (dataframe["close_max_48"] < (dataframe["close"] * 1.18)) ) # 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["high_max_24_1h"] < (dataframe["close"] * 1.5)) item_buy_logic.append(dataframe["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) ) 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_dec_24_4h"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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"] < -0.8) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.75) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 6.0)) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 6.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.12)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["close_max_48"] < (dataframe["close"] * 1.12)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.25)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["is_downtrend_3_4h"] == False) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1h"] > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.04) | (dataframe["change_pct_4h"].shift(48) < 0.04) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"].shift(48) < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) 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["cti_20_4h"] < -0.8) | (dataframe["rsi_14_4h"] < 30.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["rsi_14_1d"] < 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["rsi_14_4h"] < 30.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 50.0) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["rsi_3_4h"] > 30.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["rsi_3_4h"] > 30.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append((dataframe["change_pct_1d"] > -0.02) | (dataframe["cti_20_1h"] < 0.7)) # 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_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"] * 1.26)) item_buy_logic.append(dataframe["high_max_24_1h"] < (dataframe["close"] * 1.5)) item_buy_logic.append(dataframe["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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)) ) 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"] < -0.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) ) 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"] < 0.5) | (dataframe["rsi_3_1h"] > 25.0) | (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["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_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 10.0) | (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"] > 25.0) | (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"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["hl_pct_change_24_1h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["change_pct_4h"] > -0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 80.0) ) item_buy_logic.append( (dataframe["change_pct_1d"].shift(288) < 0.02) | (dataframe["change_pct_1d"] > -0.06) | (dataframe["cti_20_1d"] < 0.85) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.05) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 65.0) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 30.0) | (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"] * 0.05)) ) 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["rsi_14_1d"] < 65.0) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.3)) ) 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"] < -0.8) | (dataframe["cti_20_1d"] < 0.7) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 4.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.1) | (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["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_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["rsi_14_1h"] < 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 20.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["rsi_14_1d"] < 30.0) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 40.0) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (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_max_24"] < (dataframe["close"] * 1.1)) | ((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"] > 6.0) | (dataframe["rsi_14_1d"] < 50.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 6.0) | (dataframe["rsi_3_4h"] > 6.0) | (dataframe["rsi_14_1d"] < 50.0) | (dataframe["ema_200_dec_4_1d"] == False) ) # 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"] * 1.2)) 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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | ((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"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 10.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 10.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["rsi_3_15m"] > 10.0) | (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["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < 30.0) | (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"] * 0.04)) ) 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"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 8.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) 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"] > 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["change_pct_4h"].shift(48) < 0.02) | (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) 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"] < 0.7) | (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_3_15m"] > 8.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.25)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["is_downtrend_3_4h"] == False) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["ema_200_dec_48_1h"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["rsi_14_1d"] < 50.0) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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["rsi_3_1h"] > 20.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["rsi_3_4h"] > 30.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["hl_pct_change_24_1h"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 50.0) | (dataframe["cti_20_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["rsi_3_4h"] > 10.0) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (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"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3"] > 10.0) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_4_1d"] == False) ) # 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["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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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)) ) 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"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 30.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.05)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (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"] * 0.04)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["hl_pct_change_24_1h"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 8.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) 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"] < -0.5) | (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["change_pct_4h"].shift(48) < 0.06) | (dataframe["change_pct_4h"] > -0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 80.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.18)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.08)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.03) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_12"] < (dataframe["close"] * 1.12)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.12)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 8.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.1) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_48"] < (dataframe["close"] * 1.3)) ) item_buy_logic.append( (dataframe["rsi_3"] > 10.0) | (dataframe["rsi_3_15m"] > 16.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.08) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["close_max_48"] < (dataframe["close"] * 1.24)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.08) | (dataframe["top_wick_pct_1d"] < 0.08) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["top_wick_pct_4h"] < 0.02) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_14_1h"] < 30.0) | (dataframe["cti_20_4h"] < 0.7) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.03) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["rsi_14_1d"] < 60.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 50.0) | (dataframe["cti_20_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (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_max_24"] < (dataframe["close"] * 1.1)) | ((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["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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) 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_1h"]) & (dataframe["not_downtrend_4h"])) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.02) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["change_pct_1d"].shift(288) < 0.02) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 70.0) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (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_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["r_480_4h"] > -95.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.9)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_1h"] < (dataframe["close"] * 1.3)) | (dataframe["close"] < (dataframe["ema_26"] * 0.88)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 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_dec_4_1d"] == False) | (dataframe["close_max_12"] < (dataframe["close"] * 1.12)) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 8.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) 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_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.1)) | (dataframe["close"] < (dataframe["ema_26"] * 0.88)) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.1) | (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.91)) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.88)) ) 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["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.88)) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 4.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.91)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.08) | (dataframe["top_wick_pct_1d"] < 0.08) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["rsi_3_15m"] > 3.0) | (dataframe["high_max_12_1d"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["rsi_3_4h"] > 10.0) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["rsi_3_4h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["rsi_3"] > 5.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3"] > 5.0) | (dataframe["rsi_3_15m"] > 16.0) | (dataframe["rsi_3_1h"] > 26.0) | (dataframe["close"] < (dataframe["ema_26"] * 0.90)) ) # 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["high_max_24_1h"] < (dataframe["close"] * 1.5)) item_buy_logic.append(dataframe["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) item_buy_logic.append(dataframe["ema_200_dec_48_1h"] == False) item_buy_logic.append(dataframe["ema_200_dec_24_4h"] == False) 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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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_dec_4_1d"] == False) | ((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)) ) 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"] < 0.5) | (dataframe["rsi_3_1h"] > 25.0) | (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["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) > -0.06) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.75) | (dataframe["close"] < (dataframe["ema_200_4h"] * 1.1)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["hl_pct_change_24_1h"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 8.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) 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"] < -0.5) | (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["change_pct_1d"].shift(288) < 0.02) | (dataframe["change_pct_1d"] > -0.06) | (dataframe["cti_20_1d"] < 0.85) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1d"] < (dataframe["close"] * 1.8)) ) 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["rsi_14_1d"] < 65.0) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.3)) ) item_buy_logic.append( (dataframe["is_downtrend_3_1h"] == False) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 65.0) | (dataframe["high_max_6_1h"] < (dataframe["close"] * 1.25)) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_4h"] < 0.85) | (dataframe["cti_20_4h"].shift(48) < 0.85) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["rsi_14_1d"] < 50.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["rsi_14_1h"] < 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 20.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["rsi_14_1d"] < 30.0) | ((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["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 40.0) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["rsi_14_4h"] < 30.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["rsi_14_1d"] < 60.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 6.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_4h"] < -0.5) ) # 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["high_max_24_4h"] < (dataframe["close"] * 1.75)) 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["rsi_14_1d"] < 85.0) item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(96)) item_buy_logic.append(dataframe["ema_200_dec_48_1h"] == False) item_buy_logic.append(dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(24)) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.02) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["change_pct_1d"].shift(288) < 0.02) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 70.0) ) 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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (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_dec_24_4h"] == False) | (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_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | (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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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_dec_4_1d"] == False) | (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)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["close"] < (dataframe["ema_16"] * 0.92)) ) 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"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (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"] < 0.75) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_16"] * 0.95)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (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"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_16"] * 0.94)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (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["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (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["cti_20_1d"] < 0.5) | (dataframe["close"] < (dataframe["ema_16"] * 0.92)) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 8.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) 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"] < -0.5) | (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["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.03) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.05) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 65.0) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.03) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.06) | (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["hl_pct_change_24_1h"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_16"] * 0.94)) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_16"] * 0.93)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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_max_24"] < (dataframe["close"] * 1.1)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["hl_pct_change_6_1d"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 6.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_4h"] < -0.5) ) # 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["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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) # pump and now started dumping, still high item_buy_logic.append( (dataframe["change_pct_1d"] > -0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_48_1h"] < 0.4) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.16) | (dataframe["top_wick_pct_1d"] < 0.1) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.02) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["change_pct_1d"].shift(288) < 0.12) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.06) | (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_4h"] < 0.85) | (dataframe["cti_20_4h"].shift(48) < 0.85) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.9) | (dataframe["rsi_14_1d"] < 75.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (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["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 30.0) | (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["ema_200_dec_4_1d"] == False) ) 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"] < 30.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 20.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.75) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.0) | (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) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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_4h"] < -0.0) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.75) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) ) 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["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_1h"] < -0.8) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 30.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1d"] < 0.75) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.75) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (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"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.75) | (dataframe["cti_20_1d"] < -0.75) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_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"] < -0.5) ) 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_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.0) | (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) ) 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"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.0) | (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_200_dec_48_1h"] == False) ) 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"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) ) 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"] < -0.8) | (dataframe["r_14_4h"] < -30.0) | (dataframe["cti_20_1d"] < 0.5) ) 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_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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"] > 20.0) | (dataframe["cti_20_4h"] < -0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < 0.5) ) 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"] < -0.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["cti_20_15m"] < 0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.0) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.8) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.75) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 25.0) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) ) 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"] < -0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_14_4h"] < 70.0) | (dataframe["cti_20_1d"] < 0.5) ) 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["hl_pct_change_48_1h"] < 0.5) ) 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"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (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_200_dec_4_1d"] == False) ) 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"] < -0.0) | (dataframe["rsi_14_4h"] < 70.0) ) 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"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) 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["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) 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) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.0) ) 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_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) ) 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"] < -0.75) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.75) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_48_1h"] < (dataframe["close"] * 1.3)) ) 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"] < -0.75) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["high_max_48_1h"] < (dataframe["close"] * 1.3)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.75) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.75) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.75) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.8) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.75) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.5) ) 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["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (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["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["cti_20_15m"] < 0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < 0.8) | (dataframe["cti_20_1d"] < -0.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.75) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.75) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < 0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (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_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.75) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) 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"] < -0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close_max_12"] < (dataframe["close"] * 1.12)) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) > -0.06) | (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"] < 0.5) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.75) | (dataframe["close"] < (dataframe["ema_200_4h"] * 1.1)) ) 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_1d"] < -0.75) | (dataframe["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["r_480_1h"] > -95.0) | (dataframe["r_480_4h"] > -95.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 6.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1d"] < (dataframe["close"] * 1.8)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["is_downtrend_3_4h"] == False) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1d"] < 0.7) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_4h"] < 0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["cti_20_15m"] < -0.5) | (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["rsi_14_1d"] < 40.0) ) item_buy_logic.append( (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"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["rsi_14_1h"] < 30.0) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["rsi_3_4h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.0) | (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.03) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < 0.7) ) item_buy_logic.append( (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.3)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) # 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["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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) item_buy_logic.append(dataframe["cti_20_1h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_1h"] < 80.0) item_buy_logic.append(dataframe["cti_20_4h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_4h"] < 80.0) 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["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.1) | (dataframe["change_pct_4h"] > -0.04) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 2.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) 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"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | ((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"] > 20.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_3_1h"] > 25.0) | (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_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < -0.75) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 10.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | ((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"] < -0.75) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.75) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_1d"] < -0.0) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.75) | (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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < 0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 20.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < -0.5) | (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["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"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | ((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["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.05)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.16) | (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["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 30.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | ((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["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 20.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_3_15m"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < -0.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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["ema_200_dec_48_1h"] == False) | ((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["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["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["cti_20_15m"] < -0.75) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.75) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] > 10.0) | (dataframe["rsi_3_1h"] > 10.0) | ((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"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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["rsi_3_1h"] > 25.0) | (dataframe["cti_20_1h"] < -0.8) | (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["cti_20_15m"] < -0.75) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.75) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < -0.0) | (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["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"] < -0.8) | (dataframe["ema_200_dec_24_4h"] == False) | ((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["rsi_3_1h"] > 25.0) | ((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"] < -0.8) | (dataframe["rsi_3_1h"] > 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_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_1h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | ((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"] > 10.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -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"] > 20.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.75) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_1h"] < -0.0) | (dataframe["cti_20_4h"] < 0.5) | ((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"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_24_4h"] == False) | ((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"] < 20.0) | (dataframe["cti_20_1h"] < -0.5) | (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["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((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_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.1)) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_3_15m"] > 8.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 4.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["ema_200_dec_48_1h"] == False) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["rsi_3_15m"] > 3.0) | (dataframe["high_max_12_1d"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.03) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 50.0) | (dataframe["cti_20_1d"] < 0.5) | ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.04)) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.01) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_6_1d"] < 0.7) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.06) | (dataframe["top_wick_pct_1d"] < 0.06) | (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"] < 0.5) ) # 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(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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) item_buy_logic.append(dataframe["cti_20_1h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_1h"] < 80.0) item_buy_logic.append(dataframe["cti_20_4h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_4h"] < 80.0) 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["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.05) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) < 0.1) | (dataframe["top_wick_pct_1d"].shift(288) < 0.1) | (dataframe["cti_20_1d"].shift(288) < 0.8) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.1) | (dataframe["change_pct_4h"] > -0.04) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 2.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.75) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) item_buy_logic.append( (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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (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_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.5) | (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["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"] > 25.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) 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_1d"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["close_max_24"] < (dataframe["close"] * 1.12)) | (dataframe["close"] < (dataframe["ema_26"] * 0.91)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.93)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (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["close"] < (dataframe["ema_26"] * 0.9)) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.91)) ) 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"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) 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"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (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["cti_20_1h"] < -0.9) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["close_max_12"] < (dataframe["close"] * 1.1)) | (dataframe["close"] < (dataframe["ema_26"] * 0.89)) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["cti_20_15m"] < -0.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (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"] < -0.8) | (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["close"] < (dataframe["ema_26"] * 0.91)) ) 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"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (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["close"] < (dataframe["ema_26"] * 0.92)) ) 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["rsi_3_1h"] > 30.0) | (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_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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) 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["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.91)) ) 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["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) 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_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["close"] < (dataframe["ema_26"] * 0.92)) ) 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["cti_20_4h"] < 0.5) | (dataframe["close"] < (dataframe["ema_26"] * 0.88)) ) item_buy_logic.append( (dataframe["rsi_3"] > 20.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.8) ) item_buy_logic.append( (dataframe["rsi_3"] > 10.0) | (dataframe["rsi_3_15m"] > 16.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3"] > 10.0) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_3_1h"] > 10.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 4.0)) | (dataframe["change_pct_1h"] > -0.02) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) # 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_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["high_max_24_4h"] < (dataframe["close"] * 1.75)) item_buy_logic.append(dataframe["hl_pct_change_6_1h"] < 0.4) item_buy_logic.append(dataframe["hl_pct_change_12_1h"] < 0.5) item_buy_logic.append(dataframe["hl_pct_change_24_1h"] < 0.75) item_buy_logic.append(dataframe["hl_pct_change_48_1h"] < 0.9) item_buy_logic.append(dataframe["cti_20_1h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_1h"] < 80.0) item_buy_logic.append(dataframe["cti_20_4h"] < 0.8) item_buy_logic.append(dataframe["rsi_14_4h"] < 80.0) item_buy_logic.append(dataframe["close_max_48"] > (dataframe["close"] * 1.1)) # pump and now started dumping, still high item_buy_logic.append( (dataframe["change_pct_1d"] > -0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) | (dataframe["hl_pct_change_48_1h"] < 0.4) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.1) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.02) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["change_pct_1d"].shift(288) < 0.12) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.06) | (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_4h"] < 0.85) | (dataframe["cti_20_4h"].shift(48) < 0.85) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 5.0)) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.1) | (dataframe["change_pct_4h"] > -0.04) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 2.0)) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.16) | (dataframe["top_wick_pct_4h"] < 0.08) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) < 0.1) | (dataframe["top_wick_pct_1d"].shift(288) < 0.1) | (dataframe["cti_20_1d"].shift(288) < 0.8) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.1) | (dataframe["change_pct_1d"].shift(288) < 0.1) | (dataframe["cti_20_1d"].shift(288) < 0.5) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.05) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["rsi_14_1d"] < 80.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.2) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["change_pct_4h"].shift(48) < 0.01) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["top_wick_pct_1h"] < (abs(dataframe["change_pct_1h"]) * 3.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (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["ema_200_dec_4_1d"] == False) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 25.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["cti_20_15m"] < -0.8) | (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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1h"] > -0.08) | (dataframe["change_pct_1h"].shift(12) < 0.08) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (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) ) 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_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_15m"] < -0.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 4.0)) | (dataframe["cti_20_4h"] < 0.5) ) 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["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.9) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.08) | (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_200_dec_48_1h"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.05) | (dataframe["top_wick_pct_1d"] < 0.05) | (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_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) 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["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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"] < 0.5) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["is_downtrend_3_4h"] == False) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.75) | (dataframe["close"] < (dataframe["ema_200_4h"] * 1.1)) ) 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_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_4_1d"] == False) ) 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_200_dec_4_1d"] == False) ) 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_4h"] < 0.5) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 2.0)) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (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["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.03) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.04) | (dataframe["top_wick_pct_1d"] < 0.04) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.5) | (dataframe["rsi_14_1h"] < 50.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < 0.8) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1h"] > -0.03) | (dataframe["change_pct_1h"].shift(12) < 0.03) | (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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (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["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 2.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 70.0) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.04) | (dataframe["top_wick_pct_4h"].shift(48) < 0.04) | (dataframe["change_pct_4h"] > -0.02) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.06) | (dataframe["change_pct_1d"].shift(288) < 0.06) | (dataframe["cti_20_1d"].shift(288) < 0.8) | (dataframe["rsi_14_1d"].shift(288) < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["cti_20_15m"] < -0.0) | (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_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.04) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 50.0) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.16) | (dataframe["top_wick_pct_1d"] < 0.16) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.16) | (dataframe["top_wick_pct_1d"] < 0.16) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["cti_20_1d"] < -0.5) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.16) | (dataframe["top_wick_pct_1d"] < 0.16) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.1) | (dataframe["top_wick_pct_4h"] < 0.1) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 70.0) ) 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["cti_20_4h"] < -0.9) | (dataframe["cti_20_1d"] < 0.5) ) 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"] > 10.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 4.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.08) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.8) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.0) ) item_buy_logic.append( (dataframe["change_pct_1h"] > -0.02) | (dataframe["change_pct_1h"].shift(12) < 0.04) | (dataframe["top_wick_pct_1h"].shift(12) < 0.04) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["bb20_2_width_1h"] > 0.18) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 5.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.26) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.5) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["not_downtrend_15m"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["high_max_24_4h"] < (dataframe["close"] * 1.5)) ) item_buy_logic.append( (dataframe["change_pct_1h"] > -0.06) | (dataframe["change_pct_1h"].shift(12) < 0.06) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_1d"] < (abs(dataframe["change_pct_1d"]) * 3.0)) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1d"] < 0.5) ) 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["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["bb20_2_width_1h"] > 0.18) ) 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_1d"] < 0.5) | (dataframe["bb20_2_width_1h"] > 0.2) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.2) | (dataframe["top_wick_pct_1d"] < 0.05) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1d"] < -0.5) ) 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["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["bb20_2_width_1h"] > 0.17) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.25) | (dataframe["top_wick_pct_1d"] < 0.2) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["cti_20_4h"] < -0.0) ) item_buy_logic.append( (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["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["r_14"] < -96.0) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) 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"] < 0.5) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["bb20_2_width_1h"] > 0.16) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.05) | (dataframe["top_wick_pct_4h"] < 0.05) | (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["cti_20_1d"] < -0.0) | (dataframe["bb20_2_width_1h"] > 0.22) ) item_buy_logic.append( (dataframe["change_pct_1d"] < 0.1) | (dataframe["top_wick_pct_1d"] < 0.06) | (dataframe["cti_20_15m"] < -0.8) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < 0.8) | (dataframe["rsi_14_1d"] < 70.0) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["bb20_2_width_1h"] > 0.17) ) 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["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["is_downtrend_3_1d"] == False) | (dataframe["rsi_3_15m"] > 16.0) | (dataframe["rsi_3_1h"] > 16.0) | (dataframe["cti_20_1d"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["bb20_2_width_1h"] > 0.18) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1h"] < -0.8) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["bb20_2_width_1h"] > 0.16) ) 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"] < -0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["bb20_2_width_1h"] > 0.19) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.06) | (dataframe["change_pct_4h"] > -0.06) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1h"] < -0.0) | (dataframe["rsi_3_1h"] > 12.0) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["bb20_2_width_1h"] > 0.23) ) 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"] > 10.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_4h"]) | (dataframe["cti_20_15m"] < -0.9) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 10.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["high_max_24_4h"] < (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"] > 10.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["bb20_2_width_1h"] > 0.22) ) 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["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["bb20_2_width_1h"] > 0.22) ) item_buy_logic.append( (dataframe["change_pct_4h"].shift(48) < 0.16) | (dataframe["top_wick_pct_4h"].shift(48) < 0.04) | (dataframe["change_pct_4h"] > -0.02) | (dataframe["top_wick_pct_4h"] < 0.04) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["cti_20_1d"] < 0.5) | (dataframe["high_max_6_1d"] < (dataframe["close"] * 1.8)) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 2.0)) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["cti_20_4h"] < -0.8) | (dataframe["cti_20_1d"] < -0.8) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["bb20_2_width_1h"] > 0.25) ) item_buy_logic.append( (dataframe["rsi_3"] > 10.0) | (dataframe["rsi_3_15m"] > 16.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_1h"] < (abs(dataframe["change_pct_1h"]) * 4.0)) | (dataframe["rsi_14_1h"] < 70.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_1h"] > 30.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["high_max_48_1h"] < (dataframe["close"] * 1.25)) ) item_buy_logic.append( (dataframe["is_downtrend_3_1d"] == False) | (dataframe["r_480_1h"] > -90.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_1d"] > -0.04) | (dataframe["top_wick_pct_1d"] < 0.04) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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"] < -0.5) | (dataframe["rsi_3_1h"] > 25.0) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) ) 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["cti_20_4h"] < -0.5) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["rsi_14_15m"] < 30.0) | (dataframe["rsi_3_15m"] > 20.0) | (dataframe["cti_20_1h"] < -0.5) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 20.0) | (dataframe["rsi_3_1h"] > 20.0) | (dataframe["r_480_1h"] > -90.0) | (dataframe["rsi_3_4h"] > 30.0) | (dataframe["r_480_4h"] > -90.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["top_wick_pct_4h"] < 0.02) | (dataframe["cti_20_4h"].shift(48) < 0.5) | (dataframe["rsi_14_4h"].shift(48) < 70.0) ) item_buy_logic.append( (dataframe["cti_20_15m"] < -0.5) | (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_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["rsi_14_4h"] < 60.0) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 2.0)) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["cti_20_4h"] < -0.0) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["top_wick_pct_4h"] < (abs(dataframe["change_pct_4h"]) * 4.0)) | (dataframe["rsi_14_1h"] < 40.0) | (dataframe["rsi_14_4h"] < 40.0) | (dataframe["rsi_14_1d"] < 40.0) | (dataframe["cti_20_1d"] < 0.5) ) item_buy_logic.append( (dataframe["change_pct_4h"] < 0.06) | (dataframe["top_wick_pct_4h"] < 0.06) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["change_pct_4h"] > -0.02) | (dataframe["top_wick_pct_4h"] < 0.02) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["rsi_3_15m"] > 25.0) | (dataframe["cti_20_4h"] < 0.5) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) | (dataframe["hl_pct_change_6_1d"] < 0.5) ) item_buy_logic.append( (dataframe["is_downtrend_3_4h"] == False) | (dataframe["not_downtrend_15m"]) | (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_15m"] > 30.0) | (dataframe["rsi_3_1h"] > 20.0) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_3_1h"] > 6.0) | (dataframe["rsi_3_4h"] > 16.0) | (dataframe["ema_200_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) ) item_buy_logic.append( (dataframe["rsi_3"] > 12.0) | (dataframe["cti_20_15m"] < -0.0) | (dataframe["rsi_14_15m"] < 36.0) | (dataframe["cti_20_1h"] < 0.5) | (dataframe["rsi_14_1h"] < 60.0) | (dataframe["rsi_14_4h"] < 60.0) ) # 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_dec_24_4h"] == False) ) # 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_dec_24_4h"] == False) | (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_dec_24_4h"] == False) ) # 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_dec_24_4h"] == False) | (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_dec_4_1d"] == False) | ((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)) ) item_buy_logic.append( (dataframe["not_downtrend_1h"]) | (dataframe["not_downtrend_4h"]) | (dataframe["rsi_14_15m"] < 30.0) | (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)) ) # 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_dec_4_1d"] == False) ) 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_dec_48_1h"] == False) | (dataframe["ema_200_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) # 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_dec_24_4h"] == False) | (dataframe["ema_200_dec_4_1d"] == False) ) 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