Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
480
Indicators
12
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import copy
import logging
import pathlib
import rapidjson
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas as pd
import pandas_ta as pta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair
from pandas import DataFrame, Series
from functools import reduce, partial
from freqtrade.persistence import Trade, LocalTrade
from datetime import datetime, timedelta
import time
from typing import Optional
import warnings
log = logging.getLogger(__name__)
#log.setLevel(logging.DEBUG)
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
#############################################################################################################
## NostalgiaForInfinityX2 by iterativ ##
## https://github.com/iterativv/NostalgiaForInfinity ##
## ##
## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
## ##
#############################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 40 to 80 pairs. Volume pairlist works well. ##
## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ##
## Ensure that you don't override any variables in you config.json. Especially ##
## the timeframe (must be 5m). ##
## use_exit_signal must set to true (or not set at all). ##
## exit_profit_only must set to false (or not set at all). ##
## ignore_roi_if_entry_signal must set to true (or not set at all). ##
## ##
#############################################################################################################
## DONATIONS ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ##
## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ##
## ##
## REFERRAL LINKS ##
## ##
## Binance: https://accounts.binance.com/en/register?ref=C68K26A9 (20% discount on trading fees) ##
## Kucoin: https://www.kucoin.com/r/af/QBSSS5J2 (20% lifetime discount on trading fees) ##
## Gate.io: https://www.gate.io/signup/8054544 (20% discount on trading fees) ##
## OKX: https://www.okx.com/join/11749725931 (20% discount on trading fees) ##
## MEXC: https://promote.mexc.com/a/nfi (10% discount on trading fees) ##
## ByBit: https://partner.bybit.com/b/nfi ##
## Huobi: https://www.huobi.com/en-us/v/register/double-invite/?inviter_id=11345710&invite_code=ubpt2223 ##
## (20% discount on trading fees) ##
## Bitvavo: https://account.bitvavo.com/create?a=D22103A4BC (no fees for the first € 1000) ##
#############################################################################################################
class NostalgiaForInfinityX2488(IStrategy):
INTERFACE_VERSION = 3
def version(self) -> str:
return "v12.0.488"
# 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)
# CTI
informative_1d['cti_20'] = pta.cti(informative_1d["close"], length=20)
# Pivots
informative_1d['pivot'], informative_1d['res1'], informative_1d['res2'], informative_1d['res3'], informative_1d['sup1'], informative_1d['sup2'], informative_1d['sup3'] = pivot_points(informative_1d, mode='fibonacci')
# S/R
res_series = informative_1d['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2)
sup_series = informative_1d['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2)
informative_1d['res_level'] = Series(np.where(res_series, np.where(informative_1d['close'] > informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill()
informative_1d['res_hlevel'] = Series(np.where(res_series, informative_1d['high'], float('NaN'))).ffill()
informative_1d['sup_level'] = Series(np.where(sup_series, np.where(informative_1d['close'] < informative_1d['open'], informative_1d['close'], informative_1d['open']), float('NaN'))).ffill()
# Downtrend checks
informative_1d['is_downtrend_3'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2)))
informative_1d['is_downtrend_5'] = ((informative_1d['close'] < informative_1d['open']) & (informative_1d['close'].shift(1) < informative_1d['open'].shift(1)) & (informative_1d['close'].shift(2) < informative_1d['open'].shift(2)) & (informative_1d['close'].shift(3) < informative_1d['open'].shift(3)) & (informative_1d['close'].shift(4) < informative_1d['open'].shift(4)))
# Wicks
informative_1d['top_wick_pct'] = ((informative_1d['high'] - np.maximum(informative_1d['open'], informative_1d['close'])) / np.maximum(informative_1d['open'], informative_1d['close']))
# Candle change
informative_1d['change_pct'] = (informative_1d['close'] - informative_1d['open']) / informative_1d['open']
# Performance logging
# -----------------------------------------------------------------------------------------
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.")
return informative_1d
def informative_4h_indicators(self, metadata: dict, info_timeframe) -> DataFrame:
tik = time.perf_counter()
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
informative_4h['rsi_14'] = ta.RSI(informative_4h, timeperiod=14, fillna=True)
informative_4h['rsi_14_max_6'] = informative_4h['rsi_14'].rolling(6).max()
# EMA
informative_4h['ema_12'] = ta.EMA(informative_4h, timeperiod=12)
informative_4h['ema_26'] = ta.EMA(informative_4h, timeperiod=26)
informative_4h['ema_50'] = ta.EMA(informative_4h, timeperiod=50)
informative_4h['ema_100'] = ta.EMA(informative_4h, timeperiod=100)
informative_4h['ema_200'] = ta.EMA(informative_4h, timeperiod=200)
# SMA
informative_4h['sma_12'] = ta.SMA(informative_4h, timeperiod=12)
informative_4h['sma_26'] = ta.SMA(informative_4h, timeperiod=26)
informative_4h['sma_50'] = ta.SMA(informative_4h, timeperiod=50)
informative_4h['sma_200'] = ta.SMA(informative_4h, timeperiod=200)
# Williams %R
informative_4h['r_14'] = williams_r(informative_4h, period=14)
informative_4h['r_480'] = williams_r(informative_4h, period=480)
# CTI
informative_4h['cti_20'] = pta.cti(informative_4h["close"], length=20)
# S/R
res_series = informative_4h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2)
sup_series = informative_4h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2)
informative_4h['res_level'] = Series(np.where(res_series, np.where(informative_4h['close'] > informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill()
informative_4h['res_hlevel'] = Series(np.where(res_series, informative_4h['high'], float('NaN'))).ffill()
informative_4h['sup_level'] = Series(np.where(sup_series, np.where(informative_4h['close'] < informative_4h['open'], informative_4h['close'], informative_4h['open']), float('NaN'))).ffill()
# Downtrend checks
informative_4h['not_downtrend'] = ((informative_4h['close'] > informative_4h['close'].shift(2)) | (informative_4h['rsi_14'] > 50.0))
informative_4h['is_downtrend_3'] = ((informative_4h['close'] < informative_4h['open']) & (informative_4h['close'].shift(1) < informative_4h['open'].shift(1)) & (informative_4h['close'].shift(2) < informative_4h['open'].shift(2)))
# Wicks
informative_4h['top_wick_pct'] = ((informative_4h['high'] - np.maximum(informative_4h['open'], informative_4h['close'])) / np.maximum(informative_4h['open'], informative_4h['close']))
# Candle change
informative_4h['change_pct'] = (informative_4h['close'] - informative_4h['open']) / informative_4h['open']
# Max highs
informative_4h['high_max_3'] = informative_4h['high'].rolling(3).max()
informative_4h['high_max_12'] = informative_4h['high'].rolling(12).max()
informative_4h['high_max_24'] = informative_4h['high'].rolling(24).max()
informative_4h['high_max_36'] = informative_4h['high'].rolling(36).max()
informative_4h['high_max_48'] = informative_4h['high'].rolling(48).max()
informative_4h['pct_change_high_max_1_12'] = (informative_4h['high'] - informative_4h['high_max_12']) / informative_4h['high_max_12']
informative_4h['pct_change_high_max_3_12'] = (informative_4h['high_max_3'] - informative_4h['high_max_12']) / informative_4h['high_max_12']
informative_4h['pct_change_high_max_3_24'] = (informative_4h['high_max_3'] - informative_4h['high_max_24']) / informative_4h['high_max_24']
informative_4h['pct_change_high_max_3_36'] = (informative_4h['high_max_3'] - informative_4h['high_max_36']) / informative_4h['high_max_36']
informative_4h['pct_change_high_max_3_48'] = (informative_4h['high_max_3'] - informative_4h['high_max_48']) / informative_4h['high_max_48']
# Volume
informative_4h['volume_mean_factor_6'] = informative_4h['volume'] / informative_4h['volume'].rolling(6).mean()
# Performance logging
# -----------------------------------------------------------------------------------------
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.")
return informative_4h
def informative_1h_indicators(self, metadata: dict, info_timeframe) -> DataFrame:
tik = time.perf_counter()
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
informative_1h['rsi_3'] = ta.RSI(informative_1h, timeperiod=3)
informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14)
# EMA
informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12)
informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26)
informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100)
informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# SMA
informative_1h['sma_12'] = ta.SMA(informative_1h, timeperiod=12)
informative_1h['sma_26'] = ta.SMA(informative_1h, timeperiod=26)
informative_1h['sma_50'] = ta.SMA(informative_1h, timeperiod=50)
informative_1h['sma_100'] = ta.SMA(informative_1h, timeperiod=100)
informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200)
# BB
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2)
informative_1h['bb20_2_low'] = bollinger['lower']
informative_1h['bb20_2_mid'] = bollinger['mid']
informative_1h['bb20_2_upp'] = bollinger['upper']
informative_1h['bb20_2_width'] = ((informative_1h['bb20_2_upp'] - informative_1h['bb20_2_low']) / informative_1h['bb20_2_mid'])
# Williams %R
informative_1h['r_14'] = williams_r(informative_1h, period=14)
informative_1h['r_96'] = williams_r(informative_1h, period=96)
informative_1h['r_480'] = williams_r(informative_1h, period=480)
# CTI
informative_1h['cti_20'] = pta.cti(informative_1h["close"], length=20)
informative_1h['cti_40'] = pta.cti(informative_1h["close"], length=40)
# S/R
res_series = informative_1h['high'].rolling(window = 5, center=True).apply(lambda row: is_resistance(row), raw=True).shift(2)
sup_series = informative_1h['low'].rolling(window = 5, center=True).apply(lambda row: is_support(row), raw=True).shift(2)
informative_1h['res_level'] = Series(np.where(res_series, np.where(informative_1h['close'] > informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill()
informative_1h['res_hlevel'] = Series(np.where(res_series, informative_1h['high'], float('NaN'))).ffill()
informative_1h['sup_level'] = Series(np.where(sup_series, np.where(informative_1h['close'] < informative_1h['open'], informative_1h['close'], informative_1h['open']), float('NaN'))).ffill()
# Pump protections
informative_1h['hl_pct_change_48'] = range_percent_change(self, informative_1h, 'HL', 48)
informative_1h['hl_pct_change_36'] = range_percent_change(self, informative_1h, 'HL', 36)
informative_1h['hl_pct_change_24'] = range_percent_change(self, informative_1h, 'HL', 24)
informative_1h['hl_pct_change_12'] = range_percent_change(self, informative_1h, 'HL', 12)
informative_1h['hl_pct_change_6'] = range_percent_change(self, informative_1h, 'HL', 6)
# Downtrend checks
informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi_14'] > 50.0))
informative_1h['is_downtrend_3'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2)))
informative_1h['is_downtrend_5'] = ((informative_1h['close'] < informative_1h['open']) & (informative_1h['close'].shift(1) < informative_1h['open'].shift(1)) & (informative_1h['close'].shift(2) < informative_1h['open'].shift(2)) & (informative_1h['close'].shift(3) < informative_1h['open'].shift(3)) & (informative_1h['close'].shift(4) < informative_1h['open'].shift(4)))
# Wicks
informative_1h['top_wick_pct'] = ((informative_1h['high'] - np.maximum(informative_1h['open'], informative_1h['close'])) / np.maximum(informative_1h['open'], informative_1h['close']))
# Candle change
informative_1h['change_pct'] = (informative_1h['close'] - informative_1h['open']) / informative_1h['open']
# Max highs
informative_1h['high_max_3'] = informative_1h['high'].rolling(3).max()
informative_1h['high_max_6'] = informative_1h['high'].rolling(6).max()
informative_1h['high_max_12'] = informative_1h['high'].rolling(12).max()
informative_1h['high_max_24'] = informative_1h['high'].rolling(24).max()
informative_1h['high_max_36'] = informative_1h['high'].rolling(36).max()
informative_1h['high_max_48'] = informative_1h['high'].rolling(48).max()
informative_1h['pct_change_high_max_3_12'] = (informative_1h['high_max_3'] - informative_1h['high_max_12']) / informative_1h['high_max_12']
informative_1h['pct_change_high_max_6_12'] = (informative_1h['high_max_6'] - informative_1h['high_max_12']) / informative_1h['high_max_12']
informative_1h['pct_change_high_max_6_24'] = (informative_1h['high_max_6'] - informative_1h['high_max_24']) / informative_1h['high_max_24']
# Volume
informative_1h['volume_mean_factor_12'] = informative_1h['volume'] / informative_1h['volume'].rolling(12).mean()
# Performance logging
# -----------------------------------------------------------------------------------------
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.")
return informative_1h
def informative_15m_indicators(self, metadata: dict, info_timeframe) -> DataFrame:
tik = time.perf_counter()
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
informative_15m['rsi_3'] = ta.RSI(informative_15m, timeperiod=3)
informative_15m['rsi_14'] = ta.RSI(informative_15m, timeperiod=14)
# EMA
informative_15m['ema_12'] = ta.EMA(informative_15m, timeperiod=12)
informative_15m['ema_26'] = ta.EMA(informative_15m, timeperiod=26)
# SMA
informative_15m['sma_200'] = ta.SMA(informative_15m, timeperiod=200)
# CTI
informative_15m['cti_20'] = pta.cti(informative_15m["close"], length=20)
# Downtrend check
informative_15m['not_downtrend'] = ((informative_15m['close'] > informative_15m['open']) | (informative_15m['close'].shift(1) > informative_15m['open'].shift(1)) | (informative_15m['close'].shift(2) > informative_15m['open'].shift(2)) | (informative_15m['rsi_14'] > 50.0) | (informative_15m['rsi_3'] > 25.0))
# Volume
informative_15m['volume_mean_factor_12'] = informative_15m['volume'] / informative_15m['volume'].rolling(12).mean()
# Performance logging
# -----------------------------------------------------------------------------------------
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] informative_15m_indicators took: {tok - tik:0.4f} seconds.")
return informative_15m
# Coin Pair Base Timeframe Indicators
# ---------------------------------------------------------------------------------------------
def base_tf_5m_indicators(self, metadata: dict, dataframe: DataFrame) -> DataFrame:
tik = time.perf_counter()
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
dataframe['rsi_3'] = ta.RSI(dataframe, timeperiod=3)
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
# EMA
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['ema_200_pct_change_144'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(144)) / dataframe['ema_200'].shift(144))
dataframe['ema_200_pct_change_288'] = ((dataframe['ema_200'] - dataframe['ema_200'].shift(288)) / dataframe['ema_200'].shift(288))
# SMA
dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
# BB 20 - STD2
bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb20_2_low'] = bb_20_std2['lower']
dataframe['bb20_2_mid'] = bb_20_std2['mid']
dataframe['bb20_2_upp'] = bb_20_std2['upper']
# BB 40 - STD2
bb_40_std2 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['bb40_2_low'] = bb_40_std2['lower']
dataframe['bb40_2_mid'] = bb_40_std2['mid']
dataframe['bb40_2_delta'] = (bb_40_std2['mid'] - dataframe['bb40_2_low']).abs()
dataframe['bb40_2_tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs()
# Williams %R
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['r_480'] = williams_r(dataframe, period=480)
# CTI
dataframe['cti_20'] = pta.cti(dataframe["close"], length=20)
# Heiken Ashi
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
# Dip protection
dataframe['tpct_change_0'] = top_percent_change(self, dataframe, 0)
dataframe['tpct_change_2'] = top_percent_change(self, dataframe, 2)
# Close max
dataframe['close_max_12'] = dataframe['close'].rolling(12).max()
dataframe['close_max_24'] = dataframe['close'].rolling(24).max()
dataframe['close_max_48'] = dataframe['close'].rolling(48).max()
dataframe['pct_close_max_48'] = (dataframe['close_max_48'] - dataframe['close']) / dataframe['close']
# Close delta
dataframe['close_delta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
# For sell checks
dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26'])
# Global protections
# -----------------------------------------------------------------------------------------
if not self.config['runmode'].value in ('live', 'dry_run'):
# Backtest age filter
dataframe['bt_agefilter_ok'] = False
dataframe.loc[dataframe.index > (12 * 24 * self.bt_min_age_days),'bt_agefilter_ok'] = True
else:
# Exchange downtime protection
dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)
# Performance logging
# -----------------------------------------------------------------------------------------
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] base_tf_5m_indicators took: {tok - tik:0.4f} seconds.")
return dataframe
# Coin Pair Indicator Switch Case
# ---------------------------------------------------------------------------------------------
def info_switcher(self, metadata: dict, info_timeframe) -> DataFrame:
if info_timeframe == '1d':
return self.informative_1d_indicators(metadata, info_timeframe)
elif info_timeframe == '4h':
return self.informative_4h_indicators(metadata, info_timeframe)
elif info_timeframe == '1h':
return self.informative_1h_indicators(metadata, info_timeframe)
elif info_timeframe == '15m':
return self.informative_15m_indicators(metadata, info_timeframe)
else:
raise RuntimeError(f"{info_timeframe} not supported as informative timeframe for BTC pair.")
# BTC 1D Indicators
# ---------------------------------------------------------------------------------------------
def btc_info_1d_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
tik = time.perf_counter()
btc_info_1d = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
btc_info_1d['rsi_14'] = ta.RSI(btc_info_1d, timeperiod=14)
#btc_info_1d['pivot'], btc_info_1d['res1'], btc_info_1d['res2'], btc_info_1d['res3'], btc_info_1d['sup1'], btc_info_1d['sup2'], btc_info_1d['sup3'] = pivot_points(btc_info_1d, mode='fibonacci')
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date']
btc_info_1d.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] btc_info_1d_indicators took: {tok - tik:0.4f} seconds.")
return btc_info_1d
# BTC 4h Indicators
# ---------------------------------------------------------------------------------------------
def btc_info_4h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
tik = time.perf_counter()
btc_info_4h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
btc_info_4h['rsi_14'] = ta.RSI(btc_info_4h, timeperiod=14)
# SMA
btc_info_4h['sma_200'] = ta.SMA(btc_info_4h, timeperiod=200)
# Bull market or not
btc_info_4h['is_bull'] = btc_info_4h['close'] > btc_info_4h['sma_200']
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date']
btc_info_4h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] btc_info_4h_indicators took: {tok - tik:0.4f} seconds.")
return btc_info_4h
# BTC 1h Indicators
# ---------------------------------------------------------------------------------------------
def btc_info_1h_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
tik = time.perf_counter()
btc_info_1h = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
btc_info_1h['rsi_14'] = ta.RSI(btc_info_1h, timeperiod=14)
btc_info_1h['not_downtrend'] = ((btc_info_1h['close'] > btc_info_1h['close'].shift(2)) | (btc_info_1h['rsi_14'] > 50))
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date']
btc_info_1h.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] btc_info_1h_indicators took: {tok - tik:0.4f} seconds.")
return btc_info_1h
# BTC 15m Indicators
# ---------------------------------------------------------------------------------------------
def btc_info_15m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
tik = time.perf_counter()
btc_info_15m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
btc_info_15m['rsi_14'] = ta.RSI(btc_info_15m, timeperiod=14)
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date']
btc_info_15m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] btc_info_15m_indicators took: {tok - tik:0.4f} seconds.")
return btc_info_15m
# BTC 5m Indicators
# ---------------------------------------------------------------------------------------------
def btc_info_5m_indicators(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
tik = time.perf_counter()
btc_info_5m = self.dp.get_pair_dataframe(btc_info_pair, btc_info_timeframe)
# Indicators
# -----------------------------------------------------------------------------------------
# RSI
btc_info_5m['rsi_14'] = ta.RSI(btc_info_5m, timeperiod=14)
# Close max
btc_info_5m['close_max_24'] = btc_info_5m['close'].rolling(24).max()
btc_info_5m['close_max_72'] = btc_info_5m['close'].rolling(72).max()
btc_info_5m['pct_close_max_24'] = (btc_info_5m['close_max_24'] - btc_info_5m['close']) / btc_info_5m['close']
btc_info_5m['pct_close_max_72'] = (btc_info_5m['close_max_72'] - btc_info_5m['close']) / btc_info_5m['close']
# Add prefix
# -----------------------------------------------------------------------------------------
ignore_columns = ['date']
btc_info_5m.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] btc_info_5m_indicators took: {tok - tik:0.4f} seconds.")
return btc_info_5m
# BTC Indicator Switch Case
# ---------------------------------------------------------------------------------------------
def btc_info_switcher(self, btc_info_pair, btc_info_timeframe, metadata: dict) -> DataFrame:
if btc_info_timeframe == '1d':
return self.btc_info_1d_indicators(btc_info_pair, btc_info_timeframe, metadata)
elif btc_info_timeframe == '4h':
return self.btc_info_4h_indicators(btc_info_pair, btc_info_timeframe, metadata)
elif btc_info_timeframe == '1h':
return self.btc_info_1h_indicators(btc_info_pair, btc_info_timeframe, metadata)
elif btc_info_timeframe == '15m':
return self.btc_info_15m_indicators(btc_info_pair, btc_info_timeframe, metadata)
elif btc_info_timeframe == '5m':
return self.btc_info_5m_indicators(btc_info_pair, btc_info_timeframe, metadata)
else:
raise RuntimeError(f"{btc_info_timeframe} not supported as informative timeframe for BTC pair.")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tik = time.perf_counter()
'''
--> BTC informative indicators
___________________________________________________________________________________________
'''
if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']:
btc_info_pair = f"BTC/{self.config['stake_currency']}"
else:
btc_info_pair = "BTC/USDT"
for btc_info_timeframe in self.btc_info_timeframes:
btc_informative = self.btc_info_switcher(btc_info_pair, btc_info_timeframe, metadata)
dataframe = merge_informative_pair(dataframe, btc_informative, self.timeframe, btc_info_timeframe, ffill=True)
# Customize what we drop - in case we need to maintain some BTC informative ohlcv data
# Default drop all
drop_columns = {
'1d': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'4h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'1h': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'15m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'5m': [f"btc_{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
}.get(btc_info_timeframe,[f"{s}_{btc_info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']])
drop_columns.append(f"date_{btc_info_timeframe}")
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
'''
--> Indicators on informative timeframes
___________________________________________________________________________________________
'''
for info_timeframe in self.info_timeframes:
info_indicators = self.info_switcher(metadata, info_timeframe)
dataframe = merge_informative_pair(dataframe, info_indicators, self.timeframe, info_timeframe, ffill=True)
# Customize what we drop - in case we need to maintain some informative timeframe ohlcv data
# Default drop all except base timeframe ohlcv data
drop_columns = {
'1d': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'4h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'1h': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']],
'15m': [f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
}.get(info_timeframe,[f"{s}_{info_timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']])
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
'''
--> The indicators for the base timeframe (5m)
___________________________________________________________________________________________
'''
dataframe = self.base_tf_5m_indicators(metadata, dataframe)
tok = time.perf_counter()
log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.")
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = ''
# the number of free slots
current_free_slots = self.config["max_open_trades"] - len(LocalTrade.get_trades_proxy(is_open=True))
for buy_enable in self.buy_params:
index = int(buy_enable.split('_')[2])
item_buy_protection_list = [True]
if self.buy_params[f'{buy_enable}']:
# Buy conditions
# -----------------------------------------------------------------------------------------
item_buy_logic = []
item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list))
# Condition #1 - Long mode bull. Uptrend.
if index == 1:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_1d'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append(dataframe['r_14_1h'] < -25.0)
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.2)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_14_1h'] < 30.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['rsi_14_4h'] < 30.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_14_1h'] < 30.0)
| (dataframe['rsi_14_4h'] < 30.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['rsi_14_1h'] < 30.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['rsi_14_4h'] < 30.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_14_1h'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 30.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_14_1h'] < 40.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['rsi_14_4h'] < 40.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_14_1h'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 30.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['hl_pct_change_48_1h'] < 0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_14_4h'] < 30.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append(((dataframe['not_downtrend_1h'])
& (dataframe['not_downtrend_4h']))
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.12))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.12)))
item_buy_logic.append((dataframe['cti_20_1h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3))
| (dataframe['hl_pct_change_24_1h'] < 0.75))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.999))
# Condition #2 - Normal mode bull.
if index == 2:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16))
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24))
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.75)
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_1d'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append(dataframe['r_14_1h'] < -25.0)
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
item_buy_logic.append(((dataframe['not_downtrend_1h'])
& (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.25)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 16.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['r_480_4h'] < -30.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 16.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['r_480_4h'] < -30.0)
| (dataframe['cti_20_1d'] < 0.5))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['change_pct_1d'] < 0.25)
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_14_1h'] < 40.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['rsi_14_4h'] < 40.0)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1d'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.12)))
# Logic
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.06))
item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.02))
item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2))
item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
# Condition #3 - Normal mode bull.
if index == 3:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26))
item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_200_1h'])
item_buy_logic.append(dataframe['ema_12_4h'] > dataframe['ema_200_4h'])
item_buy_logic.append(dataframe['rsi_14_4h'] < 75.0)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.85)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_1h'] > 16.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.8)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 16.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07)))
item_buy_logic.append((dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16))
| (dataframe['hl_pct_change_24_1h'] < 0.4)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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))
# Logic
item_buy_logic.append(dataframe['rsi_14'] < 36.0)
item_buy_logic.append(dataframe['ha_close'] > dataframe['ha_open'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018))
# Condition #4 - Normal mode bull.
if index == 4:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16))
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24))
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.5))
item_buy_logic.append(dataframe['hl_pct_change_12_1h'] < 0.75)
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_1d'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 16.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
# BNX
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 15.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['r_480_4h'] < -30.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 16.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < 0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append(((dataframe['not_downtrend_1h'])
& (dataframe['not_downtrend_4h']))
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['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))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996))
# Condition #5 - Normal mode bull.
if index == 5:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['change_pct_4h'] < 0.32)
| (dataframe['top_wick_pct_4h'] < 0.16)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['cti_20_1h'] < 0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.9)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.9)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.9)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.08)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.25))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.1)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 16.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1d'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append(((dataframe['not_downtrend_1h'])
& (dataframe['not_downtrend_4h']))
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.2))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.09)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 5.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['r_14_4h'] < -25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['change_pct_4h'] < 0.16)
| (dataframe['top_wick_pct_4h'] < 0.08)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.12)))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['rsi_14'] < 36.0)
# Condition #6 - Normal mode bull.
if index == 6:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2))
item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.75)
item_buy_logic.append(dataframe['cti_20_1h'] < 0.5)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.75)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(((dataframe['not_downtrend_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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3))
| (dataframe['close'] < (dataframe['ema_26'] * 0.9))
| (dataframe['rsi_3'] > 20.0))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close_max_12'] < (dataframe['close'] * 1.12))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_12'] < (dataframe['close'] * 1.12))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close_max_12'] < (dataframe['close'] * 1.16))
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close_max_12'] < (dataframe['close'] * 1.12))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['close_max_12'] < (dataframe['close'] * 1.12))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.8)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 5.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.89)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_14_1h'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_12'] < (dataframe['close'] * 1.12))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close_max_48'] < (dataframe['close'] * 1.26))
| (dataframe['close'] < (dataframe['ema_26'] * 0.86)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.88)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.9)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
# Logic
item_buy_logic.append(dataframe['close'] < (dataframe['ema_26'] * 0.94))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996))
# Condition #21 - Pump mode bull.
if index == 21:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.16))
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24))
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
item_buy_logic.append(dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.06)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['rsi_14_1d'] < 70.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
# CHZ
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_14_1h'] < 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.75)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['r_14_4h'] > -50.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['change_pct_4h'] < 0.08)
| (dataframe['top_wick_pct_4h'] < 0.08)
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_14_1h'] > 60.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.8)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['rsi_14_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['cti_20_4h'] < 0.8)
| (dataframe['cti_20_1d'] < -0.75)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_14_15m'] < 10.0)
| (dataframe['rsi_14_1h'] < 10.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.07)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 5.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['r_14_4h'] < -25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.016))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['rsi_14'] < 36.0)
# Condition #22 - Pump mode bull.
if index == 22:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2))
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.24))
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))
item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96))
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24))
item_buy_logic.append((dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.92)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < 0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.75)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.92)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.75)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.75)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.9)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.75)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['cti_20_15m'] < 0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.9)
| (dataframe['close'] < (dataframe['ema_16'] * 0.96)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.75)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.95)))
item_buy_logic.append((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_16'] * 0.94)))
item_buy_logic.append((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['change_pct_1d'] > -0.05)
| (dataframe['cti_20_1d'] < 0.8)
| (dataframe['rsi_14_1d'] < 65.0))
# 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['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_4h'] < -0.75)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['rsi_3_1h'] > 20.0)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.8)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_48_1h'] < (dataframe['close'] * 1.3)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.8)
| (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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
# 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['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_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_1h'] > dataframe['ema_200_1h'].shift(576))
| ((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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| ((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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.05)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| ((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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.75)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.75)
| (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_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_14_15m'] < 20.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['cti_20_15m'] < -0.75)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.8)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.018))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * 0.996))
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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.93)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.91)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['close'] < (dataframe['ema_26'] * 0.92)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['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)))
# 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_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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['r_480_1h'] > -90.0)
| (dataframe['r_480_4h'] > -90.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['not_downtrend_4h'])
| (dataframe['is_downtrend_3_4h'] == False)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_3_1h'] > 25.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.8)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['cti_20_15m'] < -0.0)
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1d'] > dataframe['ema_200_1d'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['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_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_14_15m'] < 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (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_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
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_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['rsi_3_15m'] > 20.0)
| (dataframe['r_480_4h'] > -90.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['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_1d'] > dataframe['ema_200_1d'].shift(1152))
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5)))
# Logic
item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.132)
item_buy_logic.append(dataframe['cti_20'] < -0.8)
item_buy_logic.append(dataframe['r_14'] < -90.0)
# Condition #61 - Rebuy mode bull.
if index == 61:
# Protections
item_buy_logic.append(current_free_slots >= self.rebuy_mode_min_free_slots)
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.12))
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.16))
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2))
item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.24))
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36))
item_buy_logic.append(dataframe['hl_pct_change_24_1h'] < 0.5)
item_buy_logic.append(dataframe['hl_pct_change_48_1h'] < 0.75)
item_buy_logic.append(dataframe['cti_20_1h'] < 0.95)
item_buy_logic.append(dataframe['cti_20_4h'] < 0.95)
item_buy_logic.append(dataframe['rsi_14_1h'] < 85.0)
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append(dataframe['r_14_1h'] < -25.0)
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.3)
item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4)
item_buy_logic.append(protections_global_1)
item_buy_logic.append(protections_global_2)
item_buy_logic.append(protections_global_3)
item_buy_logic.append(protections_global_4)
item_buy_logic.append(protections_global_5)
item_buy_logic.append(protections_global_6)
item_buy_logic.append(protections_global_7)
item_buy_logic.append(protections_global_8)
item_buy_logic.append(protections_global_9)
item_buy_logic.append(protections_global_10)
item_buy_logic.append(dataframe['not_downtrend_15m'])
# current 1h downtrend, downtrend 4h
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
# current 1h red, overbought 1h, downtrend 1h, downtrend 1h, drop last 2h
item_buy_logic.append((dataframe['change_pct_1h'] > -0.04)
| (dataframe['cti_20_1h'] < 0.85)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d green, overbought 1d
item_buy_logic.append((dataframe['change_pct_1d'] < 0.16)
| (dataframe['cti_20_1d'] < 0.5))
# current 1d long relative top wick, overbought 1d, current 4h red, drop last 4h
item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 5.0))
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['change_pct_4h'] > -0.04)
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
# downtrend 1d, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d red with top wick, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.02)
| (dataframe['top_wick_pct_1d'] < 0.02)
| (dataframe['cti_20_1d'] < 0.85)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d green with top wick, downtrend 4h, overbought 4h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] < 0.06)
| (dataframe['top_wick_pct_1d'] < 0.06)
| (dataframe['is_downtrend_3_4h'] == False)
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1h red, overbought 1h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1h'] > -0.02)
| (dataframe['cti_20_1h'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h grered, previous 4h green, overbought 1h, downtrend 1h, downtrend 4h
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
| (dataframe['change_pct_4h'].shift(48) < 0.04)
| (dataframe['cti_20_1h'] < 0.85)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
# current 4h red, downtrend 1h, downtrend 4h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
| (dataframe['not_downtrend_1h'])
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['close_max_24'] < (dataframe['close'] * 1.08)))
# current 1d long red, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.16)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d relative long top wick, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 2.0))
| (dataframe['cti_20_1d'] < 0.85)
| (dataframe['rsi_14_1d'] < 70.0)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current and previous 1d red, overbought 1d
item_buy_logic.append((dataframe['change_pct_1d'] > -0.0)
| (dataframe['change_pct_1d'].shift(288) > -0.0)
| (dataframe['cti_20_1d'] < 0.85))
# downtrend 1d, overbought 1d
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
| (dataframe['cti_20_1d'] < 0.5))
# overbought 1d
item_buy_logic.append((dataframe['cti_20_1d'] < 0.9)
| (dataframe['rsi_14_1d'] < 80.0))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152))
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['cti_20_1d'] < 0.75)
| (dataframe['rsi_14_1d'] < 70.0))
# XAVA
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
| (dataframe['rsi_3_15m'] > 25.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_1d'] < -0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)))
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_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
| (dataframe['cti_20_1h'] < -0.0)
| (dataframe['cti_20_4h'] < -0.0)
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
# Logic
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.052))
item_buy_logic.append(dataframe['close_delta'].gt(dataframe['close'] * 0.024))
item_buy_logic.append(dataframe['bb40_2_tail'].lt(dataframe['bb40_2_delta'] * 0.2))
item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift()))
item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift()))
item_buy_logic.append(dataframe['rsi_14'] < 30.0)
# Condition #82 - Long mode bull.
if index == 82:
# Protections
item_buy_logic.append(dataframe['btc_pct_close_max_24_5m'] < 0.03)
item_buy_logic.append(dataframe['btc_pct_close_max_72_5m'] < 0.03)
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.2))
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3))
item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_200_1h'])
item_buy_logic.append(dataframe['sma_50_1h'] > dataframe['sma_200_1h'])
item_buy_logic.append(dataframe['ema_50_4h'] > dataframe['ema_200_4h'])
item_buy_logic.append(dataframe['sma_50_4h'] > dataframe['sma_200_4h'])
item_buy_logic.append(dataframe['rsi_14_4h'] < 85.0)
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
item_buy_logic.append(dataframe['r_480_4h'] < -10.0)
item_buy_logic.append(protections_global_1)
item_buy_logic.append(protections_global_2)
item_buy_logic.append(protections_global_3)
item_buy_logic.append(protections_global_4)
item_buy_logic.append(protections_global_5)
item_buy_logic.append(protections_global_6)
item_buy_logic.append(protections_global_7)
item_buy_logic.append(protections_global_8)
item_buy_logic.append(protections_global_9)
item_buy_logic.append(protections_global_10)
# current 1d long green with long top wick
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
| (dataframe['top_wick_pct_1d'] < 0.12))
# overbought 1d, overbought 4h, downtrend 1h, drop in last 2h
item_buy_logic.append((dataframe['rsi_14_1d'] < 70.0)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['not_downtrend_1h'])
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h red, downtrend 1h, overbought 4h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_4h'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h long red, downtrend 1h, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_4h'] > -0.12)
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1d'] < 0.8)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d red, overbought 1d, downtrend 1h, downtrend 4h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
| (dataframe['cti_20_1d'] < 0.85)
| (dataframe['not_downtrend_1h'])
| (dataframe['is_downtrend_3_4h'] == False)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d red, overbought 1d, downtrend 1h, current 4h red, previous 4h green with top wick
item_buy_logic.append((dataframe['change_pct_1d'] > -0.08)
| (dataframe['cti_20_1d'] < 0.85)
| (dataframe['not_downtrend_1h'])
| (dataframe['change_pct_4h'] > -0.0)
| (dataframe['change_pct_4h'].shift(48) < 0.04)
| (dataframe['top_wick_pct_4h'].shift(48) < 0.04))
# current 1d long red with long top wick, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
| (dataframe['top_wick_pct_1d'] < 0.12)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 1d long red, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.16)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h red with top wick, overbought 1d
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
| (dataframe['top_wick_pct_4h'] < 0.04)
| (dataframe['cti_20_1d'] < 0.85))
# current 4h green with top wick, overbought 4h
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
| (dataframe['top_wick_pct_4h'] < 0.04)
| (dataframe['rsi_14_4h'] < 70.0))
# current 4h red, downtrend 1h, overbought 1d
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1d'] < 0.5))
# current 1d long relative top wick, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 4.0))
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h relative long top wick, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
| (dataframe['cti_20_1d'] < 0.85)
| (dataframe['rsi_14_1d'] < 50.0)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current and previous 1d red, overbought 1d, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
| (dataframe['change_pct_1d'].shift(288) > -0.04)
| (dataframe['cti_20_1d'] < 0.5)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
# current 4h long green, overbought 4h, drop in last 2h
item_buy_logic.append((dataframe['change_pct_1d'] < 0.08)
| (dataframe['rsi_14_4h'] < 70.0)
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
item_buy_logic.append((dataframe['not_downtrend_15m'])
| (dataframe['not_downtrend_1h'])
| (dataframe['cti_20_1h'] < -0.5)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['cti_20_1d'] < -0.0)
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
| (dataframe['ema_200_1d'] > dataframe['ema_200_1d'].shift(1152)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.8)
| (dataframe['rsi_3_15m'] > 10.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['not_downtrend_1h'])
| (dataframe['cti_20_15m'] < -0.9)
| (dataframe['rsi_3_15m'] > 30.0)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['rsi_3_1h'] > 10.0)
| (dataframe['cti_20_4h'] < -0.0)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
item_buy_logic.append((dataframe['cti_20_15m'] < -0.9)
| (dataframe['cti_20_1h'] < -0.8)
| (dataframe['cti_20_4h'] < 0.75)
| (dataframe['r_14_4h'] < -25.0)
| (dataframe['cti_20_1d'] < 0.5)
| ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.04)))
# Logic
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03))
item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100))
item_buy_logic.append(dataframe['cti_20'] < -0.8)
item_buy_logic.append(dataframe['volume'] > 0)
item_buy = reduce(lambda x, y: x & y, item_buy_logic)
dataframe.loc[item_buy, 'enter_tag'] += f"{index} "
conditions.append(item_buy)
dataframe.loc[:, 'enter_long'] = item_buy
if conditions:
dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions)
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[:, 'exit_long'] = 0
dataframe.loc[:, 'exit_short'] = 0
return dataframe
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
**kwargs) -> bool:
# allow force entries
if (entry_tag == 'force_entry'):
return True
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if(len(dataframe) < 1):
return False
dataframe = dataframe.iloc[-1].squeeze()
if ((rate > dataframe['close'])):
slippage = ((rate / dataframe['close']) - 1.0)
if slippage < self.max_slippage:
return True
else:
log.warning(f"Cancelling buy for {pair} due to slippage {(slippage * 100.0):.2f}%")
return False
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str,
current_time: datetime, **kwargs) -> bool:
# Allow force exits
if exit_reason != 'force_exit':
if self._should_hold_trade(trade, rate, exit_reason):
return False
if (exit_reason == 'stop_loss'):
return False
if self.exit_profit_only:
if self.exit_profit_only:
profit = 0.0
if (trade.realized_profit != 0.0):
profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close)
profit = profit + trade.realized_profit
profit = profit / trade.stake_amount
else:
profit = trade.calc_profit_ratio(rate)
if (profit < self.exit_profit_offset):
return False
self._remove_profit_target(pair)
return True
def bot_loop_start(self, **kwargs) -> None:
if self.config["runmode"].value not in ("live", "dry_run"):
return super().bot_loop_start(**kwargs)
if self.hold_support_enabled:
self.load_hold_trades_config()
return super().bot_loop_start(**kwargs)
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return 5.0
def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_profit: float, current_time: datetime):
self.target_profit_cache.data[pair] = {
"rate": rate,
"profit": current_profit,
"sell_reason": sell_reason,
"time_profit_reached": current_time.isoformat()
}
self.target_profit_cache.save()
def _remove_profit_target(self, pair: str):
if self.target_profit_cache is not None:
self.target_profit_cache.data.pop(pair, None)
self.target_profit_cache.save()
def get_hold_trades_config_file(self):
proper_holds_file_path = self.config["user_data_dir"].resolve() / "nfi-hold-trades.json"
if proper_holds_file_path.is_file():
return proper_holds_file_path
strat_file_path = pathlib.Path(__file__)
hold_trades_config_file_resolve = strat_file_path.resolve().parent / "hold-trades.json"
if hold_trades_config_file_resolve.is_file():
log.warning(
"Please move %s to %s which is now the expected path for the holds file",
hold_trades_config_file_resolve,
proper_holds_file_path,
)
return hold_trades_config_file_resolve
# The resolved path does not exist, is it a symlink?
hold_trades_config_file_absolute = strat_file_path.absolute().parent / "hold-trades.json"
if hold_trades_config_file_absolute.is_file():
log.warning(
"Please move %s to %s which is now the expected path for the holds file",
hold_trades_config_file_absolute,
proper_holds_file_path,
)
return hold_trades_config_file_absolute
def load_hold_trades_config(self):
if self.hold_trades_cache is None:
hold_trades_config_file = self.get_hold_trades_config_file()
if hold_trades_config_file:
log.warning("Loading hold support data from %s", hold_trades_config_file)
self.hold_trades_cache = HoldsCache(hold_trades_config_file)
if self.hold_trades_cache:
self.hold_trades_cache.load()
def _should_hold_trade(self, trade: "Trade", rate: float, sell_reason: str) -> bool:
if self.config['runmode'].value not in ('live', 'dry_run'):
return False
if not self.hold_support_enabled:
return False
# Just to be sure our hold data is loaded, should be a no-op call after the first bot loop
self.load_hold_trades_config()
if not self.hold_trades_cache:
# Cache hasn't been setup, likely because the corresponding file does not exist, sell
return False
if not self.hold_trades_cache.data:
# We have no pairs we want to hold until profit, sell
return False
# By default, no hold should be done
hold_trade = False
trade_ids: dict = self.hold_trades_cache.data.get("trade_ids")
if trade_ids and trade.id in trade_ids:
trade_profit_ratio = trade_ids[trade.id]
profit = 0.0
if (trade.realized_profit != 0.0):
profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close)
profit = profit + trade.realized_profit
profit = profit / trade.stake_amount
else:
profit = trade.calc_profit_ratio(rate)
current_profit_ratio = profit
if sell_reason == "force_sell":
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Force selling %s even though the current profit of %s < %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
elif current_profit_ratio >= trade_profit_ratio:
# This pair is on the list to hold, and we reached minimum profit, sell
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Selling %s because the current profit of %s >= %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
# This pair is on the list to hold, and we haven't reached minimum profit, hold
hold_trade = True
trade_pairs: dict = self.hold_trades_cache.data.get("trade_pairs")
if trade_pairs and trade.pair in trade_pairs:
trade_profit_ratio = trade_pairs[trade.pair]
profit = 0.0
if (trade.realized_profit != 0.0):
profit = ((rate - trade.open_rate) / trade.open_rate) * trade.stake_amount * (1 - trade.fee_close)
profit = profit + trade.realized_profit
profit = profit / trade.stake_amount
else:
profit = trade.calc_profit_ratio(rate)
current_profit_ratio = profit
if sell_reason == "force_sell":
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Force selling %s even though the current profit of %s < %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
elif current_profit_ratio >= trade_profit_ratio:
# This pair is on the list to hold, and we reached minimum profit, sell
formatted_profit_ratio = f"{trade_profit_ratio * 100}%"
formatted_current_profit_ratio = f"{current_profit_ratio * 100}%"
log.warning(
"Selling %s because the current profit of %s >= %s",
trade, formatted_current_profit_ratio, formatted_profit_ratio
)
return False
# This pair is on the list to hold, and we haven't reached minimum profit, hold
hold_trade = True
return hold_trade
# +---------------------------------------------------------------------------+
# | Custom Indicators |
# +---------------------------------------------------------------------------+
# Range midpoint acts as Support
def is_support(row_data) -> bool:
conditions = []
for row in range(len(row_data)-1):
if row < len(row_data)//2:
conditions.append(row_data[row] > row_data[row+1])
else:
conditions.append(row_data[row] < row_data[row+1])
result = reduce(lambda x, y: x & y, conditions)
return result
# Range midpoint acts as Resistance
def is_resistance(row_data) -> bool:
conditions = []
for row in range(len(row_data)-1):
if row < len(row_data)//2:
conditions.append(row_data[row] < row_data[row+1])
else:
conditions.append(row_data[row] > row_data[row+1])
result = reduce(lambda x, y: x & y, conditions)
return result
# Elliot Wave Oscillator
def ewo(dataframe, sma1_length=5, sma2_length=35):
sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
smadif = (sma1 - sma2) / dataframe['close'] * 100
return smadif
# Chaikin Money Flow
def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
"""Chaikin Money Flow (CMF)
It measures the amount of Money Flow Volume over a specific period.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
Args:
dataframe(pandas.Dataframe): dataframe containing ohlcv
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
mfv = mfv.fillna(0.0) # float division by zero
mfv *= dataframe['volume']
cmf = (mfv.rolling(n, min_periods=0).sum()
/ dataframe['volume'].rolling(n, min_periods=0).sum())
if fillna:
cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
return Series(cmf, name='cmf')
# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
"""Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
"""
highest_high = dataframe["high"].rolling(center=False, window=period).max()
lowest_low = dataframe["low"].rolling(center=False, window=period).min()
WR = Series(
(highest_high - dataframe["close"]) / (highest_high - lowest_low),
name=f"{period} Williams %R",
)
return WR * -100
# Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
"""Indicator: Volume Weighted Moving Average (VWMA)"""
# Calculate Result
pv = dataframe['close'] * dataframe['volume']
vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
vwma = vwma.fillna(0, inplace=True)
return vwma
# Exponential moving average of a volume weighted simple moving average
def ema_vwma_osc(dataframe, len_slow_ma):
slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma))
return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100
def t3_average(dataframe, length=5):
"""
T3 Average by HPotter on Tradingview
https://www.tradingview.com/script/qzoC9H1I-T3-Average/
"""
df = dataframe.copy()
df['xe1'] = ta.EMA(df['close'], timeperiod=length)
df['xe1'].fillna(0, inplace=True)
df['xe2'] = ta.EMA(df['xe1'], timeperiod=length)
df['xe2'].fillna(0, inplace=True)
df['xe3'] = ta.EMA(df['xe2'], timeperiod=length)
df['xe3'].fillna(0, inplace=True)
df['xe4'] = ta.EMA(df['xe3'], timeperiod=length)
df['xe4'].fillna(0, inplace=True)
df['xe5'] = ta.EMA(df['xe4'], timeperiod=length)
df['xe5'].fillna(0, inplace=True)
df['xe6'] = ta.EMA(df['xe5'], timeperiod=length)
df['xe6'].fillna(0, inplace=True)
b = 0.7
c1 = -b * b * b
c2 = 3 * b * b + 3 * b * b * b
c3 = -6 * b * b - 3 * b - 3 * b * b * b
c4 = 1 + 3 * b + b * b * b + 3 * b * b
df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3']
return df['T3Average']
# Pivot Points - 3 variants - daily recommended
def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series:
if mode == 'simple':
hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3
res1 = hlc3_pivot * 2 - dataframe['low'].shift(1)
sup1 = hlc3_pivot * 2 - dataframe['high'].shift(1)
res2 = hlc3_pivot + (dataframe['high'] - dataframe['low']).shift()
sup2 = hlc3_pivot - (dataframe['high'] - dataframe['low']).shift()
res3 = hlc3_pivot * 2 + (dataframe['high'] - 2 * dataframe['low']).shift()
sup3 = hlc3_pivot * 2 - (2 * dataframe['high'] - dataframe['low']).shift()
return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3
elif mode == 'fibonacci':
hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3
hl_range = (dataframe['high'] - dataframe['low']).shift(1)
res1 = hlc3_pivot + 0.382 * hl_range
sup1 = hlc3_pivot - 0.382 * hl_range
res2 = hlc3_pivot + 0.618 * hl_range
sup2 = hlc3_pivot - 0.618 * hl_range
res3 = hlc3_pivot + 1 * hl_range
sup3 = hlc3_pivot - 1 * hl_range
return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3
elif mode == 'DeMark':
demark_pivot_lt = (dataframe['low'] * 2 + dataframe['high'] + dataframe['close'])
demark_pivot_eq = (dataframe['close'] * 2 + dataframe['low'] + dataframe['high'])
demark_pivot_gt = (dataframe['high'] * 2 + dataframe['low'] + dataframe['close'])
demark_pivot = np.where((dataframe['close'] < dataframe['open']), demark_pivot_lt, np.where((dataframe['close'] > dataframe['open']), demark_pivot_gt, demark_pivot_eq))
dm_pivot = demark_pivot / 4
dm_res = demark_pivot / 2 - dataframe['low']
dm_sup = demark_pivot / 2 - dataframe['high']
return dm_pivot, dm_res, dm_sup
# Heikin Ashi candles
def heikin_ashi(dataframe, smooth_inputs = False, smooth_outputs = False, length = 10):
df = dataframe[['open','close','high','low']].copy().fillna(0)
if smooth_inputs:
df['open_s'] = ta.EMA(df['open'], timeframe = length)
df['high_s'] = ta.EMA(df['high'], timeframe = length)
df['low_s'] = ta.EMA(df['low'], timeframe = length)
df['close_s'] = ta.EMA(df['close'],timeframe = length)
open_ha = (df['open_s'].shift(1) + df['close_s'].shift(1)) / 2
high_ha = df.loc[:, ['high_s', 'open_s', 'close_s']].max(axis=1)
low_ha = df.loc[:, ['low_s', 'open_s', 'close_s']].min(axis=1)
close_ha = (df['open_s'] + df['high_s'] + df['low_s'] + df['close_s'])/4
else:
open_ha = (df['open'].shift(1) + df['close'].shift(1)) / 2
high_ha = df.loc[:, ['high', 'open', 'close']].max(axis=1)
low_ha = df.loc[:, ['low', 'open', 'close']].min(axis=1)
close_ha = (df['open'] + df['high'] + df['low'] + df['close'])/4
open_ha = open_ha.fillna(0)
high_ha = high_ha.fillna(0)
low_ha = low_ha.fillna(0)
close_ha = close_ha.fillna(0)
if smooth_outputs:
open_sha = ta.EMA(open_ha, timeframe = length)
high_sha = ta.EMA(high_ha, timeframe = length)
low_sha = ta.EMA(low_ha, timeframe = length)
close_sha = ta.EMA(close_ha, timeframe = length)
return open_sha, close_sha, low_sha
else:
return open_ha, close_ha, low_ha
# Peak Percentage Change
def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float:
"""
Rolling Percentage Change Maximum across interval.
:param dataframe: DataFrame The original OHLC dataframe
:param method: High to Low / Open to Close
:param length: int The length to look back
"""
if method == 'HL':
return (dataframe['high'].rolling(length).max() - dataframe['low'].rolling(length).min()) / dataframe['low'].rolling(length).min()
elif method == 'OC':
return (dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min()) / dataframe['close'].rolling(length).min()
else:
raise ValueError(f"Method {method} not defined!")
# Percentage distance to top peak
def top_percent_change(self, dataframe: DataFrame, length: int) -> float:
"""
Percentage change of the current close from the range maximum Open price
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
if length == 0:
return (dataframe['open'] - dataframe['close']) / dataframe['close']
else:
return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']
# +---------------------------------------------------------------------------+
# | Classes |
# +---------------------------------------------------------------------------+
class Cache:
def __init__(self, path):
self.path = path
self.data = {}
self._mtime = None
self._previous_data = {}
try:
self.load()
except FileNotFoundError:
pass
@staticmethod
def rapidjson_load_kwargs():
return {"number_mode": rapidjson.NM_NATIVE}
@staticmethod
def rapidjson_dump_kwargs():
return {"number_mode": rapidjson.NM_NATIVE}
def load(self):
if not self._mtime or self.path.stat().st_mtime_ns != self._mtime:
self._load()
def save(self):
if self.data != self._previous_data:
self._save()
def process_loaded_data(self, data):
return data
def _load(self):
# This method only exists to simplify unit testing
with self.path.open("r") as rfh:
try:
data = rapidjson.load(
rfh,
**self.rapidjson_load_kwargs()
)
except rapidjson.JSONDecodeError as exc:
log.error("Failed to load JSON from %s: %s", self.path, exc)
else:
self.data = self.process_loaded_data(data)
self._previous_data = copy.deepcopy(self.data)
self._mtime = self.path.stat().st_mtime_ns
def _save(self):
# This method only exists to simplify unit testing
rapidjson.dump(
self.data,
self.path.open("w"),
**self.rapidjson_dump_kwargs()
)
self._mtime = self.path.stat().st_mtime
self._previous_data = copy.deepcopy(self.data)
class HoldsCache(Cache):
@staticmethod
def rapidjson_load_kwargs():
return {
"number_mode": rapidjson.NM_NATIVE,
"object_hook": HoldsCache._object_hook,
}
@staticmethod
def rapidjson_dump_kwargs():
return {
"number_mode": rapidjson.NM_NATIVE,
"mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS,
}
def save(self):
raise RuntimeError("The holds cache does not allow programatical save")
def process_loaded_data(self, data):
trade_ids = data.get("trade_ids")
trade_pairs = data.get("trade_pairs")
if not trade_ids and not trade_pairs:
return data
open_trades = {}
for trade in Trade.get_trades_proxy(is_open=True):
open_trades[trade.id] = open_trades[trade.pair] = trade
r_trade_ids = {}
if trade_ids:
if isinstance(trade_ids, dict):
# New syntax
for trade_id, profit_ratio in trade_ids.items():
if not isinstance(trade_id, int):
log.error(
"The trade_id(%s) defined under 'trade_ids' in %s is not an integer",
trade_id, self.path
)
continue
if not isinstance(profit_ratio, float):
log.error(
"The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float",
profit_ratio,
trade_id,
self.path
)
if trade_id in open_trades:
formatted_profit_ratio = f"{profit_ratio * 100}%"
log.warning(
"The trade %s is configured to HOLD until the profit ratio of %s is met",
open_trades[trade_id],
formatted_profit_ratio
)
r_trade_ids[trade_id] = profit_ratio
else:
log.warning(
"The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s",
trade_id,
self.path
)
else:
# Initial Syntax
profit_ratio = data.get("profit_ratio")
if profit_ratio:
if not isinstance(profit_ratio, float):
log.error(
"The 'profit_ratio' config value(%s) in %s is not a float",
profit_ratio,
self.path
)
else:
profit_ratio = 0.005
formatted_profit_ratio = f"{profit_ratio * 100}%"
for trade_id in trade_ids:
if not isinstance(trade_id, int):
log.error(
"The trade_id(%s) defined under 'trade_ids' in %s is not an integer",
trade_id, self.path
)
continue
if trade_id in open_trades:
log.warning(
"The trade %s is configured to HOLD until the profit ratio of %s is met",
open_trades[trade_id],
formatted_profit_ratio
)
r_trade_ids[trade_id] = profit_ratio
else:
log.warning(
"The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s",
trade_id,
self.path
)
r_trade_pairs = {}
if trade_pairs:
for trade_pair, profit_ratio in trade_pairs.items():
if not isinstance(trade_pair, str):
log.error(
"The trade_pair(%s) defined under 'trade_pairs' in %s is not a string",
trade_pair, self.path
)
continue
if "/" not in trade_pair:
log.error(
"The trade_pair(%s) defined under 'trade_pairs' in %s does not look like "
"a valid '<TOKEN_NAME>/<STAKE_CURRENCY>' 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