8391 lines
515 KiB
Python
Executable File
8391 lines
515 KiB
Python
Executable File
import copy
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import logging
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import pathlib
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import rapidjson
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy as np
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import talib.abstract as ta
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import pandas as pd
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import pandas_ta as pta
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from freqtrade.strategy.interface import IStrategy
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from freqtrade.strategy import merge_informative_pair
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from pandas import DataFrame, Series
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from functools import reduce, partial
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from freqtrade.persistence import Trade, LocalTrade
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from datetime import datetime, timedelta
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import time
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from typing import Optional
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import warnings
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log = logging.getLogger(__name__)
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#log.setLevel(logging.DEBUG)
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warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
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#############################################################################################################
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## NostalgiaForInfinityX2 by iterativ ##
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## https://github.com/iterativv/NostalgiaForInfinity ##
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## ##
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## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
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## ##
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#############################################################################################################
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## GENERAL RECOMMENDATIONS ##
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## ##
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## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
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## A pairlist with 40 to 80 pairs. Volume pairlist works well. ##
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## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
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## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ##
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## Ensure that you don't override any variables in you config.json. Especially ##
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## the timeframe (must be 5m). ##
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## use_exit_signal must set to true (or not set at all). ##
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## exit_profit_only must set to false (or not set at all). ##
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## ignore_roi_if_entry_signal must set to true (or not set at all). ##
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## ##
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#############################################################################################################
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## DONATIONS ##
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## ##
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## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
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## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
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## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ##
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## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ##
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## ##
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## REFERRAL LINKS ##
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## ##
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## Binance: https://accounts.binance.com/en/register?ref=C68K26A9 (20% discount on trading fees) ##
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## Kucoin: https://www.kucoin.com/r/af/QBSSS5J2 (20% lifetime discount on trading fees) ##
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## Gate.io: https://www.gate.io/signup/8054544 (20% discount on trading fees) ##
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## OKX: https://www.okx.com/join/11749725931 (20% discount on trading fees) ##
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## ByBit: https://partner.bybit.com/b/nfi ##
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## Huobi: https://www.huobi.com/en-us/v/register/double-invite/?inviter_id=11345710&invite_code=ubpt2223 ##
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## (20% discount on trading fees) ##
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## Bitvavo: https://account.bitvavo.com/create?a=D22103A4BC (no fees for the first € 1000) ##
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#############################################################################################################
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class NostalgiaForInfinityX2(IStrategy):
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INTERFACE_VERSION = 3
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def version(self) -> str:
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return "v12.0.80"
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# ROI table:
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minimal_roi = {
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"0": 100.0,
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}
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stoploss = -0.99
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# Trailing stoploss (not used)
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trailing_stop = False
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trailing_only_offset_is_reached = True
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.03
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use_custom_stoploss = False
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# Optimal timeframe for the strategy.
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timeframe = '5m'
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info_timeframes = ['15m','1h','4h','1d']
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# BTC informatives
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btc_info_timeframes = ['5m','15m','1h','4h','1d']
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# Backtest Age Filter emulation
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has_bt_agefilter = False
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bt_min_age_days = 3
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# Exchange Downtime protection
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has_downtime_protection = False
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# Do you want to use the hold feature? (with hold-trades.json)
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hold_support_enabled = True
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = True
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# These values can be overridden in the "ask_strategy" section in the config.
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = True
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 480
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# Normal mode bull tags
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normal_mode_bull_tags = ['force_entry', '1', '2', '3', '4', '5', '6']
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# Normal mode bear tags
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normal_mode_bear_tags = ['11', '12', '13', '14', '15', '16']
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# Pump mode bull tags
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pump_mode_bull_tags = ['21', '22']
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# Pump mode bear tags
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pump_mode_bear_tags = ['31', '32']
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# Quick mode bull tags
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quick_mode_bull_tags = ['41', '42', '43', '44']
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# Quick mode bear tags
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quick_mode_bear_tags = ['51', '52', '53', '54']
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# Rebuy mode bull tags
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rebuy_mode_bull_tags = ['61']
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# Rebuy mode bear tags
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rebuy_mode_bear_tags = ['71']
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# Long mode bull tags
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long_mode_bull_tags = ['81', '82']
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# Long mode bear tags
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long_mode_bear_tags = ['91', '92']
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# 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.
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# 6: u_e ema % Bull, 7: u_e ema % Bear, 8: u_e RSI diff Bull, 9: u_e RSI diff Bear.
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# 10: enable Doom Bull, 11: enable Doom Bear, 12: enable u_e Bull, 13: enable u_e Bear.
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stop_thresholds_normal = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True]
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stop_thresholds_pump = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True]
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stop_thresholds_quick = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True]
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stop_thresholds_rebuy = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True]
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stop_thresholds_long = [-0.2, -0.2, -0.025, -0.025, 720, 720, 0.016, 0.016, 24.0, 24.0, True, True, True, True]
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# Rebuy mode minimum number of free slots
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rebuy_mode_min_free_slots = 2
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# Position adjust feature
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position_adjustment_enable = True
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stake_rebuy_mode_bull_multiplier = 0.33
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pa_rebuy_mode_bull_max = 2
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pa_rebuy_mode_bull_pcts = (-0.02, -0.04, -0.04)
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pa_rebuy_mode_bull_multi = (1.0, 1.0, 1.0)
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stake_rebuy_mode_bear_multiplier = 0.33
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pa_rebuy_mode_bear_max = 2
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pa_rebuy_mode_bear_pcts = (-0.02, -0.04, -0.04)
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pa_rebuy_mode_bear_multi = (1.0, 1.0, 1.0)
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#############################################################
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# Buy side configuration
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buy_params = {
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# Enable/Disable conditions
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# -------------------------------------------------------
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"buy_condition_1_enable": True,
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"buy_condition_2_enable": True,
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"buy_condition_3_enable": True,
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"buy_condition_4_enable": True,
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"buy_condition_5_enable": True,
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"buy_condition_6_enable": True,
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"buy_condition_11_enable": True,
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"buy_condition_12_enable": True,
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"buy_condition_13_enable": True,
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"buy_condition_14_enable": True,
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"buy_condition_15_enable": True,
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"buy_condition_16_enable": True,
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"buy_condition_21_enable": True,
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"buy_condition_22_enable": True,
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"buy_condition_31_enable": True,
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"buy_condition_32_enable": True,
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"buy_condition_41_enable": True,
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"buy_condition_42_enable": True,
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"buy_condition_43_enable": True,
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"buy_condition_44_enable": True,
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"buy_condition_51_enable": True,
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"buy_condition_52_enable": True,
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"buy_condition_53_enable": True,
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"buy_condition_54_enable": True,
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"buy_condition_61_enable": True,
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"buy_condition_71_enable": True,
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"buy_condition_81_enable": True,
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"buy_condition_82_enable": True,
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"buy_condition_91_enable": True,
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"buy_condition_92_enable": True,
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}
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buy_protection_params = {}
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#############################################################
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# CACHES
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hold_trades_cache = None
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target_profit_cache = None
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#############################################################
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def __init__(self, config: dict) -> None:
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super().__init__(config)
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if (('exit_profit_only' in self.config and self.config['exit_profit_only'])
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or ('sell_profit_only' in self.config and self.config['sell_profit_only'])):
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self.exit_profit_only = True
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if ('stop_thresholds_normal' in self.config):
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self.stop_thresholds_normal = self.config['stop_thresholds_normal']
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if ('stop_thresholds_pump' in self.config):
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self.stop_thresholds_pump = self.config['stop_thresholds_pump']
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if ('stop_thresholds_quick' in self.config):
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self.stop_thresholds_quick = self.config['stop_thresholds_quick']
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if ('stop_thresholds_rebuy' in self.config):
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self.stop_thresholds_rebuy = self.config['stop_thresholds_rebuy']
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if ('stop_thresholds_long' in self.config):
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self.stop_thresholds_long = self.config['stop_thresholds_long']
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if self.target_profit_cache is None:
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bot_name = ""
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if ('bot_name' in self.config):
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bot_name = self.config["bot_name"] + "-"
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self.target_profit_cache = Cache(
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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")
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)
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# If the cached data hasn't changed, it's a no-op
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self.target_profit_cache.save()
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1])
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def exit_normal_bull(self, pair: str, current_rate: float, current_profit: float,
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max_profit: float, max_loss: float,
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last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5,
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trade: 'Trade', current_time: 'datetime', enter_tags) -> tuple:
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sell = False
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# Original sell signals
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sell, signal_name = self.exit_normal_bull_signals(current_profit, 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)
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# Main sell signals
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if not sell:
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sell, signal_name = self.exit_normal_bull_main(current_profit, 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)
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# Williams %R based sells
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if not sell:
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sell, signal_name = self.exit_normal_bull_r(current_profit, 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)
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# Stoplosses
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if not sell:
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sell, signal_name = self.exit_normal_bull_stoploss(current_profit, 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)
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# Profit Target Signal
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# Check if pair exist on target_profit_cache
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if self.target_profit_cache is not None and pair in self.target_profit_cache.data:
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previous_rate = self.target_profit_cache.data[pair]['rate']
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previous_profit = self.target_profit_cache.data[pair]['profit']
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previous_sell_reason = self.target_profit_cache.data[pair]['sell_reason']
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previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached'])
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sell_max, signal_name_max = self.normal_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
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last_candle, previous_candle_1,
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previous_rate, previous_profit, previous_sell_reason,
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previous_time_profit_reached, enter_tags)
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if sell_max and signal_name_max is not None:
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return True, f"{signal_name_max}_m"
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if (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_normal_bull_stoploss_doom"]):
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# Update the target, raise it.
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mark_pair, mark_signal = self.normal_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
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if mark_pair:
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self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
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# Add the pair to the list, if a sell triggered and conditions met
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if sell and signal_name is not None:
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previous_profit = None
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if self.target_profit_cache is not None and pair in self.target_profit_cache.data:
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previous_profit = self.target_profit_cache.data[pair]['profit']
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if (
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(previous_profit is None)
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or (previous_profit < current_profit)
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):
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mark_pair, mark_signal = self.normal_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
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if mark_pair:
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self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
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else:
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# Just sell it, without maximize
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return True, f"{signal_name}"
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else:
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if (
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(current_profit >= 0.01)
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):
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previous_profit = None
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if self.target_profit_cache is not None and pair in self.target_profit_cache.data:
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previous_profit = self.target_profit_cache.data[pair]['profit']
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if (previous_profit is None) or (previous_profit < current_profit):
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mark_signal = "exit_profit_normal_bull_max"
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self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
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if (signal_name not in ["exit_profit_normal_bull_max", "exit_normal_bull_stoploss_doom", "exit_normal_bull_stoploss_u_e"]):
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if sell and (signal_name is not None):
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return True, f"{signal_name}"
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return False, None
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def normal_bull_mark_profit_target(self, pair: str, sell: bool, signal_name: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple:
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if sell and (signal_name is not None):
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return pair, signal_name
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return None, None
|
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|
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def normal_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_profit, previous_sell_reason, previous_time_profit_reached, enter_tags) -> tuple:
|
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if (previous_sell_reason in ["exit_normal_bull_stoploss_doom"]):
|
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if (current_profit > 0.04):
|
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# profit is over the threshold, don't exit
|
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self._remove_profit_target(pair)
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return False, None
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if (current_profit < -0.18):
|
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if (current_profit < (previous_profit - 0.04)):
|
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return True, previous_sell_reason
|
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elif (current_profit < -0.1):
|
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if (current_profit < (previous_profit - 0.04)):
|
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return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
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return True, previous_sell_reason
|
||
else:
|
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if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_normal_bull_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_normal_bull_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel it
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bull_signals(self, 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['rsi_14_4h'] > 75.0) 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, 'exit_normal_bull_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_normal_bull_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_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, 'exit_normal_bull_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, 'exit_normal_bull_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['rsi_14_4h'] > 75.0) and (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, 'exit_normal_bull_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bull_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bull_main(self, 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, 'exit_normal_bull_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 28.0):
|
||
return True, 'exit_normal_bull_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_normal_bull_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_normal_bull_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_normal_bull_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_normal_bull_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_normal_bull_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_normal_bull_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_normal_bull_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bull_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bull_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bull_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_normal_bull_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, 'exit_normal_bull_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_normal_bull_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_normal_bull_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_normal_bull_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_normal_bull_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_normal_bull_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_normal_bull_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_normal_bull_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bull_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bull_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_normal_bull_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bull_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bull_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bull_r(self, 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, 'exit_normal_bull_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_normal_bull_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_normal_bull_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_normal_bull_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_normal_bull_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_normal_bull_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_normal_bull_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_normal_bull_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_normal_bull_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_normal_bull_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_normal_bull_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_normal_bull_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bull_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_normal_bull_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_normal_bull_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bull_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_normal[10])
|
||
and (current_profit < self.stop_thresholds_normal[0])
|
||
):
|
||
return True, 'exit_normal_bull_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_normal[12])
|
||
and (current_profit < self.stop_thresholds_normal[2])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_normal[6])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_normal[8]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_normal[4]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_normal_bull_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bear(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_normal_bear_signals(current_profit, 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_normal_bear_main(current_profit, 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_normal_bear_r(current_profit, 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_normal_bear_stoploss(current_profit, 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.normal_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_normal_bear_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.normal_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.normal_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_normal_bear_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_normal_bear_max", "exit_normal_bear_stoploss_doom", "exit_normal_bear_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def normal_bear_mark_profit_target(self, 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 normal_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_normal_bear_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_normal_bear_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_normal_bear_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel it
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bear_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_normal_bear_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_normal_bear_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_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'] > 78.5):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_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, 'exit_normal_bear_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_normal_bear_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bear_main(self, 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'] < 22.0):
|
||
return True, 'exit_normal_bear_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_normal_bear_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_normal_bear_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_normal_bear_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_normal_bear_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_normal_bear_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_normal_bear_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_normal_bear_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bear_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bear_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_normal_bear_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bear_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bear_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 24.0):
|
||
return True, 'exit_normal_bear_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_normal_bear_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_normal_bear_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_normal_bear_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_normal_bear_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_normal_bear_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_normal_bear_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_normal_bear_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bear_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_normal_bear_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 50.0):
|
||
return True, 'exit_normal_bear_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_normal_bear_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_normal_bear_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bear_r(self, 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, 'exit_normal_bear_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_normal_bear_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_normal_bear_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_normal_bear_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_normal_bear_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_normal_bear_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_normal_bear_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_normal_bear_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_normal_bear_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_normal_bear_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_normal_bear_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_normal_bear_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_normal_bear_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_normal_bear_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_normal_bear_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_normal_bear_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_normal[11])
|
||
and (current_profit < self.stop_thresholds_normal[1])
|
||
):
|
||
return True, 'exit_normal_bear_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_normal[13])
|
||
and (current_profit < self.stop_thresholds_normal[3])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_normal[7])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_normal[9]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_normal[5]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_normal_bear_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bull(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_pump_bull_signals(current_profit, 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_pump_bull_main(current_profit, 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_pump_bull_r(current_profit, 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_pump_bull_stoploss(current_profit, 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.pump_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_pump_bull_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.pump_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.pump_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_pump_bull_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_pump_bull_max", "exit_pump_bull_stoploss_doom"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def pump_bull_mark_profit_target(self, 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 pump_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_pump_bull_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_pump_bull_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_pump_bull_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bull_signals(self, 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['rsi_14_4h'] > 75.0) 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, 'exit_pump_bull_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_pump_bull_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_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, 'exit_pump_bull_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, 'exit_pump_bull_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['rsi_14_4h'] > 75.0) and (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, 'exit_pump_bull_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bull_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bull_main(self, 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, 'exit_pump_bull_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 28.0):
|
||
return True, 'exit_pump_bull_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_pump_bull_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_pump_bull_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_pump_bull_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_pump_bull_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_pump_bull_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_pump_bull_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_pump_bull_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bull_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bull_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bull_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_pump_bull_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, 'exit_pump_bull_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_pump_bull_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_pump_bull_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_pump_bull_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_pump_bull_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_pump_bull_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_pump_bull_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_pump_bull_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bull_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bull_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_pump_bull_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bull_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bull_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bull_r(self, 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, 'exit_pump_bull_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_pump_bull_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_pump_bull_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_pump_bull_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_pump_bull_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_pump_bull_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_pump_bull_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_pump_bull_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_pump_bull_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_pump_bull_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_pump_bull_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_pump_bull_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bull_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_pump_bull_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_pump_bull_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bull_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_pump[10])
|
||
and (current_profit < self.stop_thresholds_pump[0])
|
||
):
|
||
return True, 'exit_pump_bull_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_pump[12])
|
||
and (current_profit < self.stop_thresholds_pump[2])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_pump[6])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_pump[8]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_pump[4]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_pump_bull_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bear(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_pump_bear_signals(current_profit, 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_pump_bear_main(current_profit, 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_pump_bear_r(current_profit, 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_pump_bear_stoploss(current_profit, 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.pump_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_pump_bear_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.pump_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.pump_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_pump_bear_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_pump_bear_max", "exit_pump_bear_stoploss_doom"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def pump_bear_mark_profit_target(self, 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 pump_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_pump_bear_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_pump_bear_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_pump_bear_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bear_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_pump_bear_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_pump_bear_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_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'] > 78.5):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_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, 'exit_pump_bear_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_pump_bear_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bear_main(self, 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'] < 22.0):
|
||
return True, 'exit_pump_bear_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_pump_bear_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_pump_bear_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_pump_bear_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_pump_bear_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_pump_bear_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_pump_bear_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_pump_bear_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bear_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bear_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_pump_bear_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bear_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bear_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 24.0):
|
||
return True, 'exit_pump_bear_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_pump_bear_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_pump_bear_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_pump_bear_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_pump_bear_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_pump_bear_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_pump_bear_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_pump_bear_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bear_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_pump_bear_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 50.0):
|
||
return True, 'exit_pump_bear_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_pump_bear_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_pump_bear_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bear_r(self, 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, 'exit_pump_bear_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_pump_bear_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_pump_bear_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_pump_bear_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_pump_bear_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_pump_bear_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_pump_bear_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_pump_bear_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_pump_bear_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_pump_bear_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_pump_bear_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_pump_bear_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_pump_bear_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_pump_bear_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_pump_bear_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_pump_bear_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_pump[11])
|
||
and (current_profit < self.stop_thresholds_pump[1])
|
||
):
|
||
return True, 'exit_pump_bear_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_pump[13])
|
||
and (current_profit < self.stop_thresholds_pump[3])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_pump[7])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_pump[9]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_pump[5]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_pump_bear_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bull(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_quick_bull_signals(current_profit, 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_quick_bull_main(current_profit, 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_quick_bull_r(current_profit, 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_quick_bull_stoploss(current_profit, 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 >= current_profit > 0.02) and (last_candle['rsi_14'] > 78.0):
|
||
sell, signal_name = True, 'exit_quick_bull_q_1'
|
||
|
||
if (0.09 >= current_profit > 0.02) and (last_candle['cti_20'] > 0.95):
|
||
sell, signal_name = True, 'exit_quick_bull_q_2'
|
||
|
||
if (0.09 >= current_profit > 0.02) and (last_candle['r_14'] >= -0.1):
|
||
sell, signal_name = True, 'exit_quick_bull_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.quick_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_quick_bull_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.quick_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.quick_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_quick_bull_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_quick_bull_max", "exit_quick_bull_stoploss_doom", "exit_quick_bull_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def quick_bull_mark_profit_target(self, 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 quick_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_quick_bull_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_quick_bull_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_quick_bull_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bull_signals(self, 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['rsi_14_4h'] > 75.0) 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, 'exit_quick_bull_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_quick_bull_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_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, 'exit_quick_bull_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, 'exit_quick_bull_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['rsi_14_4h'] > 75.0) and (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, 'exit_quick_bull_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bull_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bull_main(self, 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, 'exit_quick_bull_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 28.0):
|
||
return True, 'exit_quick_bull_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_quick_bull_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_quick_bull_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_quick_bull_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_quick_bull_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_quick_bull_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_quick_bull_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_quick_bull_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bull_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bull_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bull_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_quick_bull_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, 'exit_quick_bull_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_quick_bull_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_quick_bull_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_quick_bull_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_quick_bull_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_quick_bull_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_quick_bull_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_quick_bull_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bull_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bull_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_quick_bull_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bull_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bull_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bull_r(self, 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, 'exit_quick_bull_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_quick_bull_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_quick_bull_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_quick_bull_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_quick_bull_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_quick_bull_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_quick_bull_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_quick_bull_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_quick_bull_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_quick_bull_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_quick_bull_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_quick_bull_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bull_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_quick_bull_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_quick_bull_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bull_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_quick[10])
|
||
and (current_profit < self.stop_thresholds_quick[0])
|
||
):
|
||
return True, 'exit_quick_bull_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_quick[12])
|
||
and (current_profit < self.stop_thresholds_quick[2])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_quick[6])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_quick[8]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_quick[4]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_quick_bull_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bear(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_quick_bear_signals(current_profit, 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_quick_bear_main(current_profit, 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_quick_bear_r(current_profit, 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_quick_bear_stoploss(current_profit, 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 >= current_profit > 0.02) and (last_candle['rsi_14'] > 78.0):
|
||
sell, signal_name = True, 'exit_quick_bear_q_1'
|
||
|
||
if (0.09 >= current_profit > 0.02) and (last_candle['cti_20'] > 0.95):
|
||
sell, signal_name = True, 'exit_quick_bear_q_2'
|
||
|
||
if (0.09 >= current_profit > 0.02) and (last_candle['r_14'] >= -0.1):
|
||
sell, signal_name = True, 'exit_quick_bear_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.quick_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_quick_bear_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.quick_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.quick_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_quick_bear_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_quick_bear_max", "exit_quick_bear_stoploss_doom", "exit_quick_bear_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def quick_bear_mark_profit_target(self, 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 quick_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_quick_bear_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_quick_bear_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_quick_bear_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bear_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_quick_bear_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_quick_bear_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_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'] > 78.5):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_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, 'exit_quick_bear_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_quick_bear_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bear_main(self, 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'] < 22.0):
|
||
return True, 'exit_quick_bear_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_quick_bear_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_quick_bear_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_quick_bear_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_quick_bear_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_quick_bear_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_quick_bear_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_quick_bear_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bear_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bear_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_quick_bear_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bear_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bear_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 24.0):
|
||
return True, 'exit_quick_bear_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_quick_bear_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_quick_bear_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_quick_bear_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_quick_bear_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_quick_bear_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_quick_bear_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_quick_bear_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bear_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_quick_bear_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 50.0):
|
||
return True, 'exit_quick_bear_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_quick_bear_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_quick_bear_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bear_r(self, 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, 'exit_quick_bear_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_quick_bear_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_quick_bear_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_quick_bear_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_quick_bear_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_quick_bear_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_quick_bear_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_quick_bear_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_quick_bear_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_quick_bear_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_quick_bear_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_quick_bear_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_quick_bear_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_quick_bear_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_quick_bear_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_quick_bear_stoploss(self, 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'
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_quick[11])
|
||
and (current_profit < self.stop_thresholds_quick[1])
|
||
):
|
||
return True, 'exit_quick_bear_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_quick[13])
|
||
and (current_profit < self.stop_thresholds_quick[3])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_quick[7])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_quick[9]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_quick[5]) > trade.open_date_utc)
|
||
# temporary
|
||
and (trade.open_date_utc.replace(tzinfo=None) >= datetime(2022, 12, 25) or is_backtest)
|
||
):
|
||
return True, 'exit_quick_bear_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bull(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_rebuy_bull_signals(current_profit, 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_rebuy_bull_main(current_profit, 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_rebuy_bull_r(current_profit, 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_rebuy_bull_stoploss(current_profit, 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.rebuy_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_rebuy_bull_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.rebuy_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.rebuy_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_rebuy_bull_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_rebuy_bull_max", "exit_rebuy_bull_stoploss_doom", "exit_rebuy_bull_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def rebuy_bull_mark_profit_target(self, 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 rebuy_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_rebuy_bull_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_rebuy_bull_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_rebuy_bull_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bull_signals(self, 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['rsi_14_4h'] > 75.0) 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, 'exit_rebuy_bull_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_rebuy_bull_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 85.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_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, 'exit_rebuy_bull_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, 'exit_rebuy_bull_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['rsi_14_4h'] > 75.0) and (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, 'exit_rebuy_bull_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bull_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bull_main(self, 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, 'exit_rebuy_bull_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 28.0):
|
||
return True, 'exit_rebuy_bull_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_rebuy_bull_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_rebuy_bull_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_rebuy_bull_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_rebuy_bull_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_rebuy_bull_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_rebuy_bull_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_rebuy_bull_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bull_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bull_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bull_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_rebuy_bull_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, 'exit_rebuy_bull_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_rebuy_bull_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_rebuy_bull_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_rebuy_bull_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_rebuy_bull_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_rebuy_bull_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_rebuy_bull_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_rebuy_bull_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bull_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bull_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_rebuy_bull_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bull_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bull_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bull_r(self, 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, 'exit_rebuy_bull_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_rebuy_bull_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_rebuy_bull_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_rebuy_bull_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_rebuy_bull_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_rebuy_bull_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_rebuy_bull_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_rebuy_bull_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_rebuy_bull_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_rebuy_bull_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_rebuy_bull_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_rebuy_bull_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bull_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_rebuy_bull_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_rebuy_bull_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bull_stoploss(self, 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:
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_rebuy[10])
|
||
and (current_profit < self.stop_thresholds_rebuy[0])
|
||
):
|
||
return True, 'exit_rebuy_bull_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_rebuy[12])
|
||
and (current_profit < self.stop_thresholds_rebuy[2])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
#and (last_candle['cmf_20'] < -0.0)
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_rebuy[6])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_rebuy[8]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_rebuy[4]) > trade.open_date_utc)
|
||
):
|
||
return True, 'exit_rebuy_bull_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bear(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_rebuy_bear_signals(current_profit, 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_rebuy_bear_main(current_profit, 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_rebuy_bear_r(current_profit, 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_rebuy_bear_stoploss(current_profit, 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.rebuy_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_rebuy_bear_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.rebuy_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.rebuy_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.01)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_rebuy_bear_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_rebuy_bear_max", "exit_rebuy_bear_stoploss_doom", "exit_rebuy_bear_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def rebuy_bear_mark_profit_target(self, 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 rebuy_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_rebuy_bear_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_rebuy_bear_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_rebuy_bear_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bear_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_rebuy_bear_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0) 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, 'exit_rebuy_bear_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 84.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_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'] > 78.5):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_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, 'exit_rebuy_bear_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 75.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_rebuy_bear_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bear_main(self, 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'] < 22.0):
|
||
return True, 'exit_rebuy_bear_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_rebuy_bear_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_rebuy_bear_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_rebuy_bear_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_rebuy_bear_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_rebuy_bear_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_rebuy_bear_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_rebuy_bear_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bear_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bear_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_rebuy_bear_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bear_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bear_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 24.0):
|
||
return True, 'exit_rebuy_bear_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_rebuy_bear_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_rebuy_bear_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_rebuy_bear_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_rebuy_bear_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_rebuy_bear_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_rebuy_bear_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_rebuy_bear_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bear_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_rebuy_bear_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 50.0):
|
||
return True, 'exit_rebuy_bear_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_rebuy_bear_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_rebuy_bear_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bear_r(self, 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, 'exit_rebuy_bear_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_rebuy_bear_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_rebuy_bear_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_rebuy_bear_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_rebuy_bear_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_rebuy_bear_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_rebuy_bear_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_rebuy_bear_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_rebuy_bear_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_rebuy_bear_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_rebuy_bear_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_rebuy_bear_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_rebuy_bear_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_rebuy_bear_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_rebuy_bear_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_rebuy_bear_stoploss(self, 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:
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_rebuy[11])
|
||
and (current_profit < self.stop_thresholds_rebuy[1])
|
||
):
|
||
return True, 'exit_rebuy_bear_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_rebuy[13])
|
||
and (current_profit < self.stop_thresholds_rebuy[3])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_rebuy[7])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_rebuy[9]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_rebuy[5]) > trade.open_date_utc)
|
||
):
|
||
return True, 'exit_rebuy_bear_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bull(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_long_bull_signals(current_profit, 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_long_bull_main(current_profit, 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_long_bull_r(current_profit, 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_long_bull_stoploss(current_profit, 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.long_bull_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_long_bull_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.long_bull_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.long_bull_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.05)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_long_bull_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_long_bull_max", "exit_long_bull_stoploss_doom", "exit_long_bull_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def long_bull_mark_profit_target(self, 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 long_bull_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_long_bull_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_long_bull_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_long_bull_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel it
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_long_bull_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 50.0) 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, 'exit_long_bull_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0) 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, 'exit_long_bull_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 81.0) and (last_candle['rsi_14_4h'] > 50.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 80.0) and (last_candle['rsi_14_1h'] > 80.0) and (last_candle['rsi_14_4h'] > 50.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_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, 'exit_long_bull_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, 'exit_long_bull_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['rsi_14_4h'] > 50.0) and (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, 'exit_long_bull_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bull_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bull_main(self, 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'] < 10.0):
|
||
return True, 'exit_long_bull_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 28.0):
|
||
return True, 'exit_long_bull_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_long_bull_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_long_bull_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_long_bull_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_long_bull_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_long_bull_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_long_bull_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_long_bull_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bull_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bull_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bull_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_long_bull_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 12.0):
|
||
return True, 'exit_long_bull_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_long_bull_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_long_bull_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_long_bull_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_long_bull_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_long_bull_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_long_bull_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_long_bull_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bull_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bull_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_long_bull_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bull_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bull_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bull_r(self, 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, 'exit_long_bull_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_long_bull_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_long_bull_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_long_bull_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_long_bull_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_long_bull_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_long_bull_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_long_bull_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_long_bull_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_long_bull_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_long_bull_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_long_bull_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bull_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_long_bull_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_long_bull_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bull_stoploss(self, 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:
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_long[10])
|
||
and (current_profit < self.stop_thresholds_long[0])
|
||
):
|
||
return True, 'exit_long_bull_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_long[12])
|
||
and (current_profit < self.stop_thresholds_long[2])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
#and (last_candle['cmf_20'] < -0.0)
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_long[6])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_long[8]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_long[4]) > trade.open_date_utc)
|
||
):
|
||
return True, 'exit_long_bull_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bear(self, pair: 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', enter_tags) -> tuple:
|
||
sell = False
|
||
|
||
# Original sell signals
|
||
sell, signal_name = self.exit_long_bear_signals(current_profit, 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_long_bear_main(current_profit, 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_long_bear_r(current_profit, 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_long_bear_stoploss(current_profit, 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.long_bear_exit_profit_target(pair, trade, current_time, current_rate, current_profit,
|
||
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 (current_profit > (previous_profit + 0.005)) and (previous_sell_reason not in ["exit_long_bear_stoploss_doom"]):
|
||
# Update the target, raise it.
|
||
mark_pair, mark_signal = self.long_bear_mark_profit_target(pair, True, previous_sell_reason, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, 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 (
|
||
(previous_profit is None)
|
||
or (previous_profit < current_profit)
|
||
):
|
||
mark_pair, mark_signal = self.long_bear_mark_profit_target(pair, sell, signal_name, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1)
|
||
if mark_pair:
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
else:
|
||
# Just sell it, without maximize
|
||
return True, f"{signal_name}"
|
||
else:
|
||
if (
|
||
(current_profit >= 0.05)
|
||
):
|
||
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 < current_profit):
|
||
mark_signal = "exit_profit_long_bear_max"
|
||
self._set_profit_target(pair, mark_signal, current_rate, current_profit, current_time)
|
||
|
||
if (signal_name not in ["exit_profit_long_bear_max", "exit_long_bear_stoploss_doom", "exit_long_bear_stoploss_u_e"]):
|
||
if sell and (signal_name is not None):
|
||
return True, f"{signal_name}"
|
||
|
||
return False, None
|
||
|
||
def long_bear_mark_profit_target(self, 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 long_bear_exit_profit_target(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: 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 ["exit_long_bear_stoploss_doom"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < -0.18):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.1):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (current_profit < -0.04):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_long_bear_stoploss_u_e"]):
|
||
if (current_profit > 0.04):
|
||
# profit is over the threshold, don't exit
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (previous_sell_reason in ["exit_profit_long_bear_max"]):
|
||
if (current_profit < -0.08):
|
||
# profit is under the threshold, cancel it
|
||
self._remove_profit_target(pair)
|
||
return False, None
|
||
if (0.001 <= current_profit < 0.01):
|
||
if (current_profit < (previous_profit - 0.01)):
|
||
return True, previous_sell_reason
|
||
elif (0.01 <= current_profit < 0.02):
|
||
if (current_profit < (previous_profit - 0.02)):
|
||
return True, previous_sell_reason
|
||
elif (0.02 <= current_profit < 0.03):
|
||
if (current_profit < (previous_profit - 0.03)):
|
||
return True, previous_sell_reason
|
||
elif (0.03 <= current_profit < 0.05):
|
||
if (current_profit < (previous_profit - 0.04)):
|
||
return True, previous_sell_reason
|
||
elif (0.05 <= current_profit < 0.08):
|
||
if (current_profit < (previous_profit - 0.05)):
|
||
return True, previous_sell_reason
|
||
elif (0.08 <= current_profit < 0.12):
|
||
if (current_profit < (previous_profit - 0.06)):
|
||
return True, previous_sell_reason
|
||
elif (0.12 <= current_profit):
|
||
if (current_profit < (previous_profit - 0.07)):
|
||
return True, previous_sell_reason
|
||
else:
|
||
return False, None
|
||
|
||
return False, None
|
||
|
||
def exit_long_bear_signals(self, 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'] > 78.0) and (last_candle['rsi_14_4h'] > 50.0) 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, 'exit_long_bear_1_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_1_2_1'
|
||
|
||
# Sell signal 2
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0) 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, 'exit_long_bear_2_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_2_2_1'
|
||
|
||
# Sell signal 3
|
||
elif (last_candle['rsi_14'] > 81.0) and (last_candle['rsi_14_4h'] > 50.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_3_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_3_2_1'
|
||
|
||
# Sell signal 4
|
||
elif (last_candle['rsi_14'] > 79.0) and (last_candle['rsi_14_1h'] > 79.0) and (last_candle['rsi_14_4h'] > 50.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_4_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_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'] > 78.5):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_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, 'exit_long_bear_7_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_7_2_1'
|
||
|
||
# Sell signal 8
|
||
elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.07) and (last_candle['rsi_14_4h'] > 50.0):
|
||
if (last_candle['close'] > last_candle['ema_200']):
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_8_1_1'
|
||
else:
|
||
if (current_profit > 0.01):
|
||
return True, 'exit_long_bear_8_2_1'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bear_main(self, 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'] < 12.0):
|
||
return True, 'exit_long_bear_o_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 30.0):
|
||
return True, 'exit_long_bear_o_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_long_bear_o_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_long_bear_o_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_long_bear_o_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_long_bear_o_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_long_bear_o_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_long_bear_o_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bear_o_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bear_o_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_long_bear_o_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bear_o_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bear_o_12'
|
||
elif (last_candle['close'] < last_candle['sma_200_1h']):
|
||
if 0.01 > current_profit >= 0.001:
|
||
if (last_candle['rsi_14'] < 14.0):
|
||
return True, 'exit_long_bear_u_0'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['rsi_14'] < 32.0):
|
||
return True, 'exit_long_bear_u_1'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['rsi_14'] < 34.0):
|
||
return True, 'exit_long_bear_u_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['rsi_14'] < 36.0):
|
||
return True, 'exit_long_bear_u_3'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['rsi_14'] < 38.0):
|
||
return True, 'exit_long_bear_u_4'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['rsi_14'] < 40.0):
|
||
return True, 'exit_long_bear_u_5'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['rsi_14'] < 42.0):
|
||
return True, 'exit_long_bear_u_6'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['rsi_14'] < 44.0):
|
||
return True, 'exit_long_bear_u_7'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bear_u_8'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_long_bear_u_9'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['rsi_14'] < 50.0):
|
||
return True, 'exit_long_bear_u_10'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['rsi_14'] < 48.0):
|
||
return True, 'exit_long_bear_u_11'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['rsi_14'] < 46.0):
|
||
return True, 'exit_long_bear_u_12'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bear_r(self, 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, 'exit_long_bear_w_0_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_0_2'
|
||
elif 0.02 > current_profit >= 0.01:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_long_bear_w_1_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_1_2'
|
||
elif 0.03 > current_profit >= 0.02:
|
||
if (last_candle['r_480'] > -0.3):
|
||
return True, 'exit_long_bear_w_2_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_2_2'
|
||
elif 0.04 > current_profit >= 0.03:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_long_bear_w_3_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_3_2'
|
||
elif 0.05 > current_profit >= 0.04:
|
||
if (last_candle['r_480'] > -0.5):
|
||
return True, 'exit_long_bear_w_4_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_4_2'
|
||
elif 0.06 > current_profit >= 0.05:
|
||
if (last_candle['r_480'] > -0.6):
|
||
return True, 'exit_long_bear_w_5_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_5_2'
|
||
elif 0.07 > current_profit >= 0.06:
|
||
if (last_candle['r_480'] > -0.7):
|
||
return True, 'exit_long_bear_w_6_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_6_2'
|
||
elif 0.08 > current_profit >= 0.07:
|
||
if (last_candle['r_480'] > -0.8):
|
||
return True, 'exit_long_bear_w_7_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_7_2'
|
||
elif 0.09 > current_profit >= 0.08:
|
||
if (last_candle['r_480'] > -0.9):
|
||
return True, 'exit_long_bear_w_8_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_8_2'
|
||
elif 0.1 > current_profit >= 0.09:
|
||
if (last_candle['r_480'] > -1.0):
|
||
return True, 'exit_long_bear_w_9_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_9_2'
|
||
elif 0.12 > current_profit >= 0.1:
|
||
if (last_candle['r_480'] > -1.1):
|
||
return True, 'exit_long_bear_w_10_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_10_2'
|
||
elif 0.2 > current_profit >= 0.12:
|
||
if (last_candle['r_480'] > -0.4):
|
||
return True, 'exit_long_bear_w_11_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 79.0):
|
||
return True, 'exit_long_bear_w_11_2'
|
||
elif current_profit >= 0.2:
|
||
if (last_candle['r_480'] > -0.2):
|
||
return True, 'exit_long_bear_w_12_1'
|
||
elif (last_candle['r_14'] >= -1.0) and (last_candle['rsi_14'] > 80.0):
|
||
return True, 'exit_long_bear_w_12_2'
|
||
|
||
return False, None
|
||
|
||
def exit_long_bear_stoploss(self, 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:
|
||
# Stoploss doom
|
||
if (
|
||
(self.stop_thresholds_long[11])
|
||
and (current_profit < self.stop_thresholds_long[1])
|
||
):
|
||
return True, 'exit_long_bear_stoploss_doom'
|
||
|
||
# Under & near EMA200, local uptrend move
|
||
if (
|
||
(self.stop_thresholds_long[13])
|
||
and (current_profit < self.stop_thresholds_long[3])
|
||
and (last_candle['close'] < last_candle['ema_200'])
|
||
and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.stop_thresholds_long[7])
|
||
and (last_candle['rsi_14'] > previous_candle_1['rsi_14'])
|
||
and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.stop_thresholds_long[9]))
|
||
and (current_time - timedelta(minutes=self.stop_thresholds_long[5]) > trade.open_date_utc)
|
||
):
|
||
return True, 'exit_long_bear_stoploss_u_e'
|
||
|
||
return False, None
|
||
|
||
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()
|
||
|
||
profit = current_profit
|
||
|
||
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
|
||
max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate)
|
||
|
||
if hasattr(trade, 'select_filled_orders'):
|
||
filled_entries = trade.select_filled_orders('enter_long')
|
||
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, bull
|
||
if any(c in self.normal_mode_bull_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_normal_bull(pair, current_rate, profit, 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})"
|
||
|
||
# Normal mode, bear
|
||
if any(c in self.normal_mode_bear_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_normal_bear(pair, current_rate, profit, 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 mpde, bull
|
||
if any(c in self.pump_mode_bull_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_pump_bull(pair, current_rate, profit, 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, bear
|
||
if any(c in self.pump_mode_bear_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_pump_bear(pair, current_rate, profit, 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, bull
|
||
if any(c in self.quick_mode_bull_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_quick_bull(pair, current_rate, profit, 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, bear
|
||
if any(c in self.quick_mode_bear_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_quick_bear(pair, current_rate, profit, 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, bull
|
||
if all(c in self.rebuy_mode_bull_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_rebuy_bull(pair, current_rate, profit, 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, bear
|
||
if all(c in self.rebuy_mode_bear_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_rebuy_bear(pair, current_rate, profit, 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, bull
|
||
if any(c in self.long_mode_bull_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_long_bull(pair, current_rate, profit, 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, bear
|
||
if any(c in self.long_mode_bear_tags for c in enter_tags):
|
||
sell, signal_name = self.exit_long_bear(pair, current_rate, profit, 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()
|
||
if all(c in self.rebuy_mode_bull_tags for c in enter_tags):
|
||
return proposed_stake * self.stake_rebuy_mode_bull_multiplier
|
||
# Rebuy mode, bear
|
||
if all(c in self.rebuy_mode_bear_tags for c in enter_tags):
|
||
return proposed_stake * self.stake_rebuy_mode_bear_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()
|
||
|
||
# Rebuy mode, bull
|
||
if all(c in self.rebuy_mode_bull_tags for c in enter_tags):
|
||
return self.rebuy_bull_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, bear
|
||
if all(c in self.rebuy_mode_bear_tags for c in enter_tags):
|
||
return self.rebuy_bear_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 rebuy_bull_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_bull_max):
|
||
if (
|
||
(current_profit < self.pa_rebuy_mode_bull_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'] < 1.03)
|
||
)
|
||
):
|
||
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_bull_multi[count_of_entries - 1]
|
||
return stake_amount
|
||
|
||
return None
|
||
|
||
def rebuy_bear_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_bear_max):
|
||
if (
|
||
(current_profit < self.pa_rebuy_mode_bear_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'] < 1.03)
|
||
)
|
||
):
|
||
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_bear_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)
|
||
|
||
# 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)
|
||
|
||
# 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_is_bull_4h'])
|
||
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_48'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5))
|
||
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['ema_50_1h'] > dataframe['ema_200_1h'])
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.95)
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_14_1h'] < -25.0)
|
||
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
|
||
|
||
# curent 4h long red, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.08)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 1d red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d long green, current 4h red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
# current 4h red with top wick, drop in last 4 hours
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.08)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# current 4h long red, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 4h green with top wick, current 1d red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 4h green with top wick, previous high is higher than current high
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.01)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 3.0))
|
||
| (dataframe['pct_change_high_max_3_24_4h'] > -0.1))
|
||
# current 4h with relative long top wick, drop in the last 4h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
#current 4h red, current higher lower than previous high, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['pct_change_high_max_3_24_4h'] > -0.12)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h red, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.12)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16))
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# current 1d long green with top long wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 1d red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['cti_20_1d'] < 0.9))
|
||
# current 1d long red, overbought 1d, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 1d red, previous 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.04)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.5))
|
||
# current 1d red, previous 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.02)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.02)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.02)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['is_downtrend_3_1h'] == False))
|
||
# current 1d red, 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'] < 0.9))
|
||
# current 1d green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# drop while near there was overbought 4h
|
||
item_buy_logic.append((dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current 4h downtrend, drop in last 24h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.2)))
|
||
# current 4h downtrend, overbought 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['change_pct_1d'] < 0.12))
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d long top wick
|
||
item_buy_logic.append(dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 10.0))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (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_4h'] < 0.5))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1d'] < 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'] * 0.016))
|
||
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_is_bull_4h'])
|
||
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['tpct_change_0'] < 0.034))
|
||
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26))
|
||
item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(48))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.5)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.95)
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04))
|
||
# current 4h red, overbought, 4h downtrend
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < 0.02)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# 3 4h red
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current and previous 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.0)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.0)
|
||
| (dataframe['rsi_14_4h'] < 50.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
item_buy_logic.append((dataframe['hl_pct_change_48_1h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# currend 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# currend 1d green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# currend 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d green with top wick, current 4h red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 1d red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.05)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 1d red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.04))
|
||
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_is_bull_4h'])
|
||
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['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h green with wick, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.1)
|
||
| (dataframe['top_wick_pct_4h'] < 0.16)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h long green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.12)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > 0.0)
|
||
| (dataframe['top_wick_pct_4h'] < 0.1)
|
||
| (dataframe['ema_12_4h'] > dataframe['ema_200_4h']))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['rsi_14_max_6_4h'] < 85.0))
|
||
# current 4h very long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['rsi_14_4h'] < 70.00))
|
||
# current 1d long red, previous 1d long green with long top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.2)
|
||
| (dataframe['change_pct_1d'].shift(288) < 0.2)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.2))
|
||
# current 1d green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 1d long green with long green wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['top_wick_pct_1d'] < 0.2))
|
||
# current 1d long green, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
|
||
|
||
# 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_is_bull_4h'])
|
||
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'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_480_1h'] < -25.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -4.0)
|
||
item_buy_logic.append(dataframe['rsi_3_15m'] > 14.0)
|
||
item_buy_logic.append(dataframe['rsi_3_1h'] > 16.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h red with wick, previous 4h green with wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.08)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.08))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.02)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0))
|
||
| (dataframe['high_max_6_1h'] < (dataframe['close'] * 1.2)))
|
||
# current and previous 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.05)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.05)
|
||
| (dataframe['rsi_14_max_6_4h'] < 85.0))
|
||
# current 4h red, previous 4h long green with long top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.16)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.16)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 4h long green and descending
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.2)
|
||
| (dataframe['r_14_4h'] < -20.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['pct_change_high_max_3_12_4h'] > -0.05))
|
||
# current 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['pct_change_high_max_1_12_4h'] > -0.05))
|
||
# current 4h red with top wick, previous 4h green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.06)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3)))
|
||
item_buy_logic.append((dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['pct_change_high_max_3_12_4h'] > -0.1))
|
||
# current 4h red with long top wick, downtrend
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 5.0))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
|
||
# 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_is_bull_4h'])
|
||
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_24'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
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['cti_20_4h'] < 0.9)
|
||
| (dataframe['r_14_4h'] < -30.0))
|
||
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h']))
|
||
# downtrend 15m, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 1h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_12'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 4h long green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.08)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h with relative long top wick, drop in the last 4h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
# current 4h red, overbought 4h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
# current 1d very long green with very long top wick, drop in last 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['top_wick_pct_1d'] < 0.3)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h green with top wick, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h red with top wick, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# 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 1h long red, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1h'] > -0.08)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
|
||
# 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_is_bull_4h'])
|
||
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['tpct_change_0'] < 0.03))
|
||
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.9))
|
||
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
|
||
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((dataframe['not_downtrend_15m']))
|
||
# downtrend 1h, drop in the last 1h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_12'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 1h, downtrend 4h, drop in the last 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)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
|
||
# 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 green with top wick, downtrend 1h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (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 4h green with top wick, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 4h red with top wick, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['rsi_14_1h'] < 70.0)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# downtrend 1d, downtrend 15m, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)))
|
||
# # downtrend 1d, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 1d, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['cti_20_1d'] < 0.8)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 1d, overbought 1d, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (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 4h red, overbought 1h, drop in the last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 1d very long green with very long top wick, drop in last 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['top_wick_pct_1d'] < 0.3)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 1d long green with long top wick, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.16)
|
||
| (dataframe['top_wick_pct_1d'] < 0.16)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['rsi_14_1d'] < 75.0)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# overbought 1d, overbought 4h
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current 1d long green, down 15m, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 4h, mild overbought 4h, drop in last 2h and 4h
|
||
item_buy_logic.append((dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
|
||
# 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 #11 - Normal mode bear.
|
||
if index == 11:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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_48'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.5))
|
||
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['ema_50_1h'] > dataframe['ema_200_1h'])
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.95)
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_14_1h'] < -25.0)
|
||
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
|
||
|
||
# curent 4h long red, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.08)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 1d red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d long green, current 4h red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
# current 4h red with top wick, drop in last 4 hours
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.08)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# current 4h long red, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 4h green with top wick, current 1d red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 4h green with top wick, previous high is higher than current high
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.01)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 3.0))
|
||
| (dataframe['pct_change_high_max_3_24_4h'] > -0.1))
|
||
# current 4h with relative long top wick, drop in the last 4h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
#current 4h red, current higher lower than previous high, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['pct_change_high_max_3_24_4h'] > -0.12)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h red, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.12)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16))
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# current 1d long green with top long wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 1d red with top wick, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['cti_20_1d'] < 0.9))
|
||
# current 1d long red, overbought 1d, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 1d red, previous 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.04)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.04)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.5))
|
||
# current 1d red, previous 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.02)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.02)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.02)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['is_downtrend_3_1h'] == False))
|
||
# current 1d red, 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'] < 0.9))
|
||
# current 1d green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# drop while near there was overbought 4h
|
||
item_buy_logic.append((dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current 4h downtrend, drop in last 24h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.2)))
|
||
# current 4h downtrend, overbought 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['change_pct_1d'] < 0.12))
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d long top wick
|
||
item_buy_logic.append(dataframe['top_wick_pct_1d'] < (abs(dataframe['change_pct_1d']) * 10.0))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (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_4h'] < 0.5))
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1d'] < 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'] * 0.016))
|
||
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 #12 - Normal mode bear.
|
||
if index == 12:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['tpct_change_0'] < 0.034))
|
||
item_buy_logic.append(dataframe['close_max_12'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.26))
|
||
item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(48))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.5)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.95)
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_14_4h'] < -25.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04))
|
||
# current 4h red, overbought, 4h downtrend
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < 0.02)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# 3 4h red
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current and previous 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.0)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.0)
|
||
| (dataframe['rsi_14_4h'] < 50.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
item_buy_logic.append((dataframe['hl_pct_change_48_1h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# currend 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# currend 1d green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['top_wick_pct_1d'] < 0.12))
|
||
# currend 1d red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# current 1d green with top wick, current 4h red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.06)
|
||
| (dataframe['top_wick_pct_1d'] < 0.06)
|
||
| (dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 1d red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.05)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# current 1d red
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * 0.04))
|
||
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 #13 - Normal mode bear.
|
||
if index == 13:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h green with wick, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.1)
|
||
| (dataframe['top_wick_pct_4h'] < 0.16)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h long green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.12)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > 0.0)
|
||
| (dataframe['top_wick_pct_4h'] < 0.1)
|
||
| (dataframe['ema_12_4h'] > dataframe['ema_200_4h']))
|
||
# current 4h long red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.1)
|
||
| (dataframe['rsi_14_max_6_4h'] < 85.0))
|
||
# current 4h very long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['rsi_14_4h'] < 70.00))
|
||
# current 1d long red, previous 1d long green with long top wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.2)
|
||
| (dataframe['change_pct_1d'].shift(288) < 0.2)
|
||
| (dataframe['top_wick_pct_1d'].shift(288) < 0.2))
|
||
# current 1d green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 1d long green with long green wick
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['top_wick_pct_1d'] < 0.2))
|
||
# current 1d long green, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.12)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_1d'] < 0.8))
|
||
|
||
|
||
# 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 #14 - Normal mode bear.
|
||
if index == 14:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_480_1h'] < -25.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -4.0)
|
||
item_buy_logic.append(dataframe['rsi_3_15m'] > 14.0)
|
||
item_buy_logic.append(dataframe['rsi_3_1h'] > 16.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h red with wick, previous 4h green with wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.08)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.08))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.02)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0))
|
||
| (dataframe['high_max_6_1h'] < (dataframe['close'] * 1.2)))
|
||
# current and previous 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.05)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.05)
|
||
| (dataframe['rsi_14_max_6_4h'] < 85.0))
|
||
# current 4h red, previous 4h long green with long top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.16)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.16)
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
# current 4h long green and descending
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.2)
|
||
| (dataframe['r_14_4h'] < -20.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['pct_change_high_max_3_12_4h'] > -0.05))
|
||
# current 4h red
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['pct_change_high_max_1_12_4h'] > -0.05))
|
||
# current 4h red with top wick, previous 4h green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.06)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3)))
|
||
item_buy_logic.append((dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['pct_change_high_max_3_12_4h'] > -0.1))
|
||
# current 4h red with long top wick, downtrend
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 5.0))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
|
||
# 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 #15 - Normal mode bear.
|
||
if index == 15:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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_24'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_6_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
|
||
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['cti_20_4h'] < 0.9)
|
||
| (dataframe['r_14_4h'] < -30.0))
|
||
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h']))
|
||
# downtrend 15m, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 1h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_12'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
# current 4h long green, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.08)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# current 4h with relative long top wick, drop in the last 4h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
# current 4h red, overbought 4h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.8)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.1)))
|
||
# current 1d very long green with very long top wick, drop in last 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['top_wick_pct_1d'] < 0.3)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h green with top wick, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 4h red with top wick, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# 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 1h long red, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1h'] > -0.08)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
|
||
# 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 #16 - Normal mode bear.
|
||
if index == 16:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['tpct_change_0'] < 0.03))
|
||
item_buy_logic.append(dataframe['close_max_24'] < (dataframe['close'] * 1.2))
|
||
item_buy_logic.append(dataframe['close_max_48'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_4h'] < (dataframe['close'] * 1.9))
|
||
|
||
item_buy_logic.append(dataframe['rsi_14_1d'] < 85.0)
|
||
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((dataframe['not_downtrend_15m']))
|
||
# downtrend 1h, drop in the last 1h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_12'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 1h, downtrend 4h, drop in the last 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)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 1h, drop in the last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
|
||
# 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 green with top wick, downtrend 1h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (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 4h green with top wick, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 4h red with top wick, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['rsi_14_1h'] < 70.0)
|
||
| (dataframe['rsi_14_4h'] < 70.0))
|
||
# downtrend 1d, downtrend 15m, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288)))
|
||
# # downtrend 1d, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(288))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 1d, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['cti_20_1d'] < 0.8)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 1d, overbought 1d, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1d'] == False)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (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 4h red, overbought 1h, drop in the last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 1d very long green with very long top wick, drop in last 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['top_wick_pct_1d'] < 0.3)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 1d long green with long top wick, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.16)
|
||
| (dataframe['top_wick_pct_1d'] < 0.16)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['rsi_14_1d'] < 75.0)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# overbought 1d, overbought 4h
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# current 1d long green, down 15m, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.2)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 4h, mild overbought 4h, drop in last 2h and 4h
|
||
item_buy_logic.append((dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.12)))
|
||
|
||
# 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_is_bull_4h'])
|
||
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_6_1h'] < (dataframe['close'] * 1.24))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3))
|
||
|
||
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96))
|
||
item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24))
|
||
item_buy_logic.append(dataframe['sma_200_4h'] > dataframe['sma_200_4h'].shift(96))
|
||
|
||
item_buy_logic.append(dataframe['close'] > dataframe['ema_200'])
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.88)
|
||
item_buy_logic.append(dataframe['rsi_14_1h'] < 80.0)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -5.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h red with top wick, previous 4h long green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0))
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.12))
|
||
# current 4h red, previous 4h green with wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.18)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.08)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
item_buy_logic.append((dataframe['cti_20_4h'] < 0.7)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3)))
|
||
# 4h long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
# current 4h red, previous 4h red, 2nd previous 4h big green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.02)
|
||
| (dataframe['change_pct_4h'].shift(96) < 0.2)
|
||
| (dataframe['hl_pct_change_24_1h'] < 0.5))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.01)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['pct_change_high_max_3_36_4h'] > -0.1))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.1)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
|
||
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.022))
|
||
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_is_bull_4h'])
|
||
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['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4))
|
||
|
||
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['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
|
||
item_buy_logic.append(dataframe['rsi_14_1h'] < 70.0)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 70.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -25.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.2)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.01)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_24_4h'] > -0.05)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_48_4h'] > -0.1)
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['r_480_1h'] < -25.0))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.05)
|
||
| (dataframe['top_wick_pct_4h'] < 0.05)
|
||
| (dataframe['cti_20_1h'] < 0.8))
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.05)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['r_14_4h'] < -16.0))
|
||
# current 4h long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * 0.968))
|
||
item_buy_logic.append(dataframe['cti_20'] < -0.8)
|
||
item_buy_logic.append(dataframe['rsi_14'] < 50.0)
|
||
|
||
# Condition #31 - Pump mode bear.
|
||
if index == 31:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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_6_1h'] < (dataframe['close'] * 1.24))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.3))
|
||
|
||
item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(96))
|
||
item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24))
|
||
item_buy_logic.append(dataframe['sma_200_4h'] > dataframe['sma_200_4h'].shift(96))
|
||
|
||
item_buy_logic.append(dataframe['close'] > dataframe['ema_200'])
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.88)
|
||
item_buy_logic.append(dataframe['rsi_14_1h'] < 80.0)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 80.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -5.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
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)
|
||
# current 4h red with top wick, previous 4h long green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 2.0))
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.12))
|
||
# current 4h red, previous 4h green with wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) < 0.18)
|
||
| (dataframe['top_wick_pct_4h'].shift(48) < 0.08)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
item_buy_logic.append((dataframe['cti_20_4h'] < 0.7)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.3)))
|
||
# 4h long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(576)))
|
||
# current 4h red, previous 4h red, 2nd previous 4h big green
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.02)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.02)
|
||
| (dataframe['change_pct_4h'].shift(96) < 0.2)
|
||
| (dataframe['hl_pct_change_24_1h'] < 0.5))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.01)
|
||
| (dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['pct_change_high_max_3_36_4h'] > -0.1))
|
||
# current 4h red with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.06)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0))
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.1)
|
||
| (dataframe['top_wick_pct_4h'] < 0.08))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12'])
|
||
item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.022))
|
||
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 #32 - Pump mode bear.
|
||
if index == 32:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['high_max_24_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.4))
|
||
|
||
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['cti_20_1h'] < 0.9)
|
||
item_buy_logic.append(dataframe['cti_20_4h'] < 0.9)
|
||
item_buy_logic.append(dataframe['rsi_14_1h'] < 70.0)
|
||
item_buy_logic.append(dataframe['rsi_14_4h'] < 70.0)
|
||
item_buy_logic.append(dataframe['r_480_4h'] < -25.0)
|
||
|
||
item_buy_logic.append(dataframe['not_downtrend_15m'])
|
||
item_buy_logic.append(dataframe['not_downtrend_1h'])
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append(dataframe['not_downtrend_4h'])
|
||
item_buy_logic.append(dataframe['pct_change_high_max_6_24_1h'] > -0.2)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.01)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_24_4h'] > -0.05)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_48_4h'] > -0.1)
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['r_480_1h'] < -25.0))
|
||
# current 4h green with top wick
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.05)
|
||
| (dataframe['top_wick_pct_4h'] < 0.05)
|
||
| (dataframe['cti_20_1h'] < 0.8))
|
||
item_buy_logic.append((dataframe['change_pct_4h'] < 0.05)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['r_14_4h'] < -16.0))
|
||
# current 4h long top wick
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 10.0))
|
||
| (dataframe['cti_20_4h'] < 0.8))
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * 0.968))
|
||
item_buy_logic.append(dataframe['cti_20'] < -0.8)
|
||
item_buy_logic.append(dataframe['rsi_14'] < 50.0)
|
||
|
||
# Condition #41 - Quick mode bull.
|
||
if index == 41:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'])
|
||
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['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < -0.75)
|
||
|
||
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)
|
||
# current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.16)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# downtrend 4h, downtrend 1h, downtrend 15, drop in last 4 days
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.24)))
|
||
# downtrend4h, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 4h, downtrend 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# downtrend 4h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# downtrend 1h, downtrend 4h
|
||
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)))
|
||
# current 1d red, previous 1d red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.02)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.02)
|
||
| (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)))
|
||
# current 1d relative long top wick, overbought 1d, downtrend 1h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['not_downtrend_1h']))
|
||
# current 1d red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 1d red, overbought 1d, current 4h red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h']))
|
||
# current 1d green with top wick, overbought 1d, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 4h green, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 85.0))
|
||
|
||
# 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_is_bull_4h'])
|
||
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'] * 1.24))
|
||
item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < -0.0)
|
||
|
||
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)
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, drop in last 4 days
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, CTI 1h not low enough
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# downtrend 1h, CTI 1h not low enough, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['rsi_14_1d'] < 75.0))
|
||
# current and previous 4h red, downtrend 4h, downtrend 1h, downtrend 15m
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.04)
|
||
| (dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m']))
|
||
# downtrend 4h, current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.75)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 4h, overbought 4h, downtrend 15m, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 4h, overbought 4h, overbought 1d, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_1h']))
|
||
# current 4h long relative top wick, overbought 1d, downtrend 15m
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 4h long relative top wick, overbought 4h. overbought 15m
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 1h long red, overbought 4h, drop 15m, rapid drop in RSI 1h
|
||
item_buy_logic.append((dataframe['change_pct_1h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (((dataframe['rsi_14_1h']) / (dataframe['rsi_14_1h'].shift(12))) > 0.5))
|
||
# current 4h red, downtrend 1h, downtrend 4h, drop in last 48h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# current 4h red with top wick, overbought 4h, overbought 1d, downtrend 1h, downtrend 15m
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 4h red with top wick, downtrend 1h, downtrend 2h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 1d red, downtrend 15m, downtrend 1h, downtrend 4h, CTI 4h not low enough. drop in last 4 days
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3)))
|
||
# downtrend 1h, downtrend 4h, CTI 1h not low enough, drop in last 4 days
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3)))
|
||
# overbought 1d, overbought 4h, downtrend 15m
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# currend 1d very long green, overbought 1d, overbought 4h, downtrend 1h, CTI 1h not low enouigh
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
|
||
# 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_is_bull_4h'])
|
||
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['pct_change_high_max_6_24_1h'] > -0.3)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4)
|
||
|
||
# overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['rsi_14_1h'] < 50.0)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['rsi_14_4h'] < 50.0))
|
||
# downtrend 15m, overbought 15m, overbought 15m, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 1h, overbought 15m, overbought 4h,downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['rsi_14_1h'] < 36.0)
|
||
| (dataframe['cti_20_4h'] < -0.85)
|
||
| (dataframe['cti_20_1d'] < -0.0)
|
||
| (dataframe['rsi_14_4h'] < 40.0)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 48h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 75.0))
|
||
# downtrend 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (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))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.24)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.24)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.92)
|
||
| (dataframe['cti_20_4h'] < -0.9)
|
||
| (dataframe['cti_20_1d'] < -0.0))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['cti_20_4h'] < 0.70))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < -0.9)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.8)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, big drop in last 24h
|
||
item_buy_logic.append((dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.25)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (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)))
|
||
# overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.25)
|
||
| (dataframe['cti_20_4h'] < -0.25)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1d'] < -0.0)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['cti_20_1d'] < 0.5))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.8)
|
||
| (dataframe['cti_20_4h'] < -0.95)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.25)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.85)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
|
||
# current 1d long green with long top wick, downtrend 15m, overbought 15m, overbought 1h, overbought 4hdowntrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.16)
|
||
| (dataframe['top_wick_pct_1d'] < 0.16)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
|
||
# 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_is_bull_4h'])
|
||
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['high_max_6_1h'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_36_1h'] < (dataframe['close'] * 1.46))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
item_buy_logic.append(dataframe['high_max_36_4h'] < (dataframe['close'] * 1.7))
|
||
|
||
item_buy_logic.append(dataframe['close_max_48'] > (dataframe['close'] * 1.1))
|
||
|
||
item_buy_logic.append(dataframe['cti_40_1h'] < -0.8)
|
||
item_buy_logic.append(dataframe['r_96_1h'] < -70.0)
|
||
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['r_480_1h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['r_480_4h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['r_480_1h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['r_480_4h'] > -95.0))
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_36_4h'] > -0.5)
|
||
item_buy_logic.append((dataframe['pct_change_high_max_3_36_4h'] > -0.2)
|
||
| (dataframe['r_480_4h'] > -80.0))
|
||
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.156)
|
||
item_buy_logic.append(dataframe['cti_20'] < -0.88)
|
||
item_buy_logic.append(dataframe['r_14'] < -50.0)
|
||
|
||
# Condition #51 - Quick mode bear.
|
||
if index == 51:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < -0.75)
|
||
|
||
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)
|
||
# current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.16)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# downtrend 4h, downtrend 1h, downtrend 15, drop in last 4 days
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.24)))
|
||
# downtrend4h, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 4h, downtrend 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# downtrend 4h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
# downtrend 1h, downtrend 4h
|
||
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)))
|
||
# current 1d red, previous 1d red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.02)
|
||
| (dataframe['change_pct_1d'].shift(288) > -0.02)
|
||
| (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)))
|
||
# current 1d relative long top wick, overbought 1d, downtrend 1h
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['not_downtrend_1h']))
|
||
# current 1d red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# current 1d red, overbought 1d, current 4h red, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.06)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h']))
|
||
# current 1d green with top wick, overbought 1d, overbought 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.04)
|
||
| (dataframe['top_wick_pct_1d'] < 0.04)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85))
|
||
# current 4h green, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 85.0))
|
||
|
||
# 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 #52 - Quick mode bear.
|
||
if index == 52:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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'] * 1.24))
|
||
item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
|
||
item_buy_logic.append(dataframe['cti_20_1h'] < -0.0)
|
||
|
||
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)
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, drop in last 4 days
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, CTI 1h not low enough
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# downtrend 1h, CTI 1h not low enough, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_1d'] < 0.9)
|
||
| (dataframe['rsi_14_1d'] < 75.0))
|
||
# current and previous 4h red, downtrend 4h, downtrend 1h, downtrend 15m
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['change_pct_4h'].shift(48) > -0.04)
|
||
| (dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m']))
|
||
# downtrend 4h, current 1d long red, overbought 1d
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['cti_20_1d'] < 0.75)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 4h, overbought 4h, downtrend 15m, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 4h, overbought 4h, overbought 1d, downtrend 1h
|
||
item_buy_logic.append((dataframe['is_downtrend_3_4h'] == False)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_1h']))
|
||
# current 4h long relative top wick, overbought 1d, downtrend 15m
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 4h long relative top wick, overbought 4h. overbought 15m
|
||
item_buy_logic.append((dataframe['top_wick_pct_4h'] < (abs(dataframe['change_pct_4h']) * 4.0))
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 1h long red, overbought 4h, drop 15m, rapid drop in RSI 1h
|
||
item_buy_logic.append((dataframe['change_pct_1h'] > -0.06)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (((dataframe['rsi_14_1h']) / (dataframe['rsi_14_1h'].shift(12))) > 0.5))
|
||
# current 4h red, downtrend 1h, downtrend 4h, drop in last 48h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.06)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# current 4h red with top wick, overbought 4h, overbought 1d, downtrend 1h, downtrend 15m
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_15m']))
|
||
# current 4h red with top wick, downtrend 1h, downtrend 2h, drop in last 2h
|
||
item_buy_logic.append((dataframe['change_pct_4h'] > -0.04)
|
||
| (dataframe['top_wick_pct_4h'] < 0.04)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# current 1d red, downtrend 15m, downtrend 1h, downtrend 4h, CTI 4h not low enough. drop in last 4 days
|
||
item_buy_logic.append((dataframe['change_pct_1d'] > -0.12)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3)))
|
||
# downtrend 1h, downtrend 4h, CTI 1h not low enough, drop in last 4 days
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['high_max_24_4h'] < (dataframe['close'] * 1.3)))
|
||
# overbought 1d, overbought 4h, downtrend 15m
|
||
item_buy_logic.append((dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['not_downtrend_15m']))
|
||
# currend 1d very long green, overbought 1d, overbought 4h, downtrend 1h, CTI 1h not low enouigh
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.3)
|
||
| (dataframe['rsi_14_1d'] < 70.0)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < -0.5))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_1d'] < 0.85))
|
||
|
||
# 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 #53 - Quick mode bear.
|
||
if index == 53:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['pct_change_high_max_6_24_1h'] > -0.3)
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_12_4h'] > -0.4)
|
||
|
||
# overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['rsi_14_1h'] < 50.0)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['rsi_14_4h'] < 50.0))
|
||
# downtrend 15m, overbought 15m, overbought 15m, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 1h, overbought 15m, overbought 4h,downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['rsi_14_1h'] < 36.0)
|
||
| (dataframe['cti_20_4h'] < -0.85)
|
||
| (dataframe['cti_20_1d'] < -0.0)
|
||
| (dataframe['rsi_14_4h'] < 40.0)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 48h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.8)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 75.0))
|
||
# downtrend 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (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))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < 0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.24)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.24)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.92)
|
||
| (dataframe['cti_20_4h'] < -0.9)
|
||
| (dataframe['cti_20_1d'] < -0.0))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.75)
|
||
| (dataframe['cti_20_4h'] < 0.70))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < -0.9)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 4h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['rsi_14_4h'] < 70.0)
|
||
| (dataframe['rsi_14_1d'] < 80.0))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, downtrend 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.8)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.85)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.5)
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, big drop in last 24h
|
||
item_buy_logic.append((dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.25)
|
||
| (dataframe['rsi_14_max_6_4h'] < 80.0)
|
||
| (dataframe['high_max_24_1h'] < (dataframe['close'] * 1.5)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (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)))
|
||
# overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.5)
|
||
| (dataframe['cti_20_1h'] < -0.25)
|
||
| (dataframe['cti_20_4h'] < -0.25)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152))
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.16)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, drop in last 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.75)
|
||
| (dataframe['close_max_48'] < (dataframe['close'] * 1.2)))
|
||
# downtrend 15m, downtrend 1h, downtrend 4h, overbought 15m, overbought 1d, drop in last 2h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1d'] < -0.0)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['close_max_24'] < (dataframe['close'] * 1.1)))
|
||
# downtrend 15m, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < -0.0)
|
||
| (dataframe['cti_20_1d'] < 0.5))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.0)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
# downtrend 1h, downtrend 4h, overbought 15m, overbought 1h, overbought 4h, downtrend 1h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['not_downtrend_4h'])
|
||
| (dataframe['cti_20_15m'] < -0.75)
|
||
| (dataframe['cti_20_1h'] < -0.8)
|
||
| (dataframe['cti_20_4h'] < -0.95)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576))
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h, downtrend 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.25)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 15m, downtrend 1h, overbought 15m, overbought 1h, overbought 4h
|
||
item_buy_logic.append((dataframe['not_downtrend_15m'])
|
||
| (dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.5)
|
||
| (dataframe['cti_20_4h'] < -0.85)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(576)))
|
||
# current 1d long green with long top wick, downtrend 15m, overbought 15m, overbought 1h, overbought 4hdowntrend 4h
|
||
item_buy_logic.append((dataframe['change_pct_1d'] < 0.16)
|
||
| (dataframe['top_wick_pct_1d'] < 0.16)
|
||
| (dataframe['not_downtrend_15m'])
|
||
| (dataframe['cti_20_15m'] < -0.9)
|
||
| (dataframe['cti_20_1h'] < -0.75)
|
||
| (dataframe['cti_20_4h'] < 0.5)
|
||
| (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1152)))
|
||
# downtrend 1h, overbought 15m, overbought 1h, overbought 4h, overbought 1d
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['cti_20_15m'] < -0.0)
|
||
| (dataframe['cti_20_1h'] < -0.9)
|
||
| (dataframe['cti_20_4h'] < 0.85)
|
||
| (dataframe['cti_20_1d'] < 0.5)
|
||
| (dataframe['rsi_14_1d'] < 70.0))
|
||
|
||
# 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 #54 - Quick mode bear.
|
||
if index == 54:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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['high_max_6_1h'] < (dataframe['close'] * 1.3))
|
||
item_buy_logic.append(dataframe['high_max_12_1h'] < (dataframe['close'] * 1.36))
|
||
item_buy_logic.append(dataframe['high_max_24_1h'] < (dataframe['close'] * 1.4))
|
||
item_buy_logic.append(dataframe['high_max_36_1h'] < (dataframe['close'] * 1.46))
|
||
item_buy_logic.append(dataframe['high_max_48_1h'] < (dataframe['close'] * 1.5))
|
||
item_buy_logic.append(dataframe['high_max_24_4h'] < (dataframe['close'] * 1.5))
|
||
item_buy_logic.append(dataframe['high_max_36_4h'] < (dataframe['close'] * 1.7))
|
||
|
||
item_buy_logic.append(dataframe['close_max_48'] > (dataframe['close'] * 1.1))
|
||
|
||
item_buy_logic.append(dataframe['cti_40_1h'] < -0.8)
|
||
item_buy_logic.append(dataframe['r_96_1h'] < -70.0)
|
||
|
||
item_buy_logic.append((dataframe['is_downtrend_3_1h'] == False)
|
||
| (dataframe['rsi_3_1h'] > 20.0))
|
||
item_buy_logic.append(dataframe['is_downtrend_5_1h'] == False)
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['r_480_1h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_1h'])
|
||
| (dataframe['r_480_4h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['r_480_1h'] > -95.0))
|
||
item_buy_logic.append((dataframe['not_downtrend_4h'])
|
||
| (dataframe['r_480_4h'] > -95.0))
|
||
item_buy_logic.append(dataframe['pct_change_high_max_3_36_4h'] > -0.5)
|
||
item_buy_logic.append((dataframe['pct_change_high_max_3_36_4h'] > -0.2)
|
||
| (dataframe['r_480_4h'] > -80.0))
|
||
|
||
|
||
# Logic
|
||
item_buy_logic.append(dataframe['bb20_2_width_1h'] > 0.156)
|
||
item_buy_logic.append(dataframe['cti_20'] < -0.88)
|
||
item_buy_logic.append(dataframe['r_14'] < -50.0)
|
||
|
||
# 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_is_bull_4h'])
|
||
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(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))
|
||
|
||
# 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 #71 - Rebuy mode bear.
|
||
if index == 71:
|
||
# Protections
|
||
item_buy_logic.append(current_free_slots >= self.rebuy_mode_min_free_slots)
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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(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))
|
||
|
||
# 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_is_bull_4h'])
|
||
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(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']))
|
||
|
||
# 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_is_bull_4h'])
|
||
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)
|
||
|
||
# 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)))
|
||
|
||
# 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)
|
||
|
||
# Condition #91 - Long mode bear.
|
||
if index == 91:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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(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']))
|
||
|
||
# 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 #92 - Long mode bear.
|
||
if index == 92:
|
||
# Protections
|
||
item_buy_logic.append(dataframe['btc_is_bull_4h'] == False)
|
||
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)
|
||
|
||
# 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)))
|
||
|
||
# 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 < 0.038:
|
||
return True
|
||
else:
|
||
log.warning(
|
||
"Cancelling buy for %s due to slippage %s",
|
||
pair, slippage
|
||
)
|
||
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 self.exit_profit_only:
|
||
current_profit = ((rate - trade.open_rate) / trade.open_rate)
|
||
if (current_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 _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]
|
||
current_profit_ratio = trade.calc_profit_ratio(rate)
|
||
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]
|
||
current_profit_ratio = trade.calc_profit_ratio(rate)
|
||
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
|