# source: https://raw.githubusercontent.com/Ph3nol/FT-Trading-Bot/209777f0dae424f0d65d2a655d79809af2c51098/strategies/NostalgiaForInfinityNext.py import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes from freqtrade.exchange import timeframe_to_prev_date from freqtrade.data.dataprovider import DataProvider from pandas import DataFrame, Series, concat from functools import reduce import math from typing import Dict from freqtrade.persistence import Trade from datetime import datetime, timedelta from technical.util import resample_to_interval, resampled_merge from technical.indicators import zema, VIDYA, ichimoku import time log = logging.getLogger(__name__) #log.setLevel(logging.DEBUG) try: import pandas_ta as pta except ImportError: log.error( "IMPORTANT - please install the pandas_ta python module which is needed for this strategy. " "If you're running Docker, add RUN pip install pandas_ta to your Dockerfile, otherwise run: " "pip install pandas_ta" ) else: log.info("pandas_ta successfully imported") ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## https://github.com/iterativv/NostalgiaForInfinity ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_sell_signal must set to true (or not set at all). ## ## sell_profit_only must set to false (or not set at all). ## ## ignore_roi_if_buy_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## HOLD SUPPORT ## ## ## ## -------- SPECIFIC TRADES ---------------------------------------------------------------------------- ## ## In case you want to have SOME of the trades to only be sold when on profit, add a file named ## ## "nfi-hold-trades.json" in the user_data directory ## ## ## ## The contents should be similar to: ## ## ## ## {"trade_ids": [1, 3, 7], "profit_ratio": 0.005} ## ## ## ## Or, for individual profit ratios(Notice the trade ID's as strings: ## ## ## ## {"trade_ids": {"1": 0.001, "3": -0.005, "7": 0.05}} ## ## ## ## NOTE: ## ## * `trade_ids` is a list of integers, the trade ID's, which you can get from the logs or from the ## ## output of the telegram status command. ## ## * Regardless of the defined profit ratio(s), the strategy MUST still produce a SELL signal for the ## ## HOLD support logic to run ## ## * This feature can be completely disabled with the holdSupportEnabled class attribute ## ## ## ## -------- SPECIFIC PAIRS ----------------------------------------------------------------------------- ## ## In case you want to have some pairs to always be on held until a specific profit, using the same ## ## "hold-trades.json" file add something like: ## ## ## ## {"trade_pairs": {"BTC/USDT": 0.001, "ETH/USDT": -0.005}} ## ## ## ## -------- SPECIFIC TRADES AND PAIRS ------------------------------------------------------------------ ## ## It is also valid to include specific trades and pairs on the holds file, for example: ## ## ## ## {"trade_ids": {"1": 0.001}, "trade_pairs": {"BTC/USDT": 0.001}} ## ########################################################################################################### ## DONATIONS ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=EAZC47FM (5% discount on trading fees) ## ## Kucoin: https://www.kucoin.com/r/QBSSSPYV (5% discount on trading fees) ## ## Gate.io: https://www.gate.io/signup/8054544 ## ## Huobi: https://www.huobi.com/en-us/topic/double-reward/?invite_code=ubpt2223 ## ########################################################################################################### class github_Ph3nol_FT_Trading_Bot__NostalgiaForInfinityNext__20220103_065818(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 10, } stoploss = -0.50 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' res_timeframe = 'none' info_timeframe_1h = '1h' info_timeframe_1d = '1d' # BTC informative has_BTC_base_tf = False has_BTC_info_tf = True has_BTC_daily_tf = False # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Do you want to use the hold feature? (with hold-trades.json) holdSupportEnabled = True # Coin Metrics coin_metrics = {} coin_metrics['top_traded_enabled'] = False coin_metrics['top_traded_updated'] = False coin_metrics['top_traded_len'] = 10 coin_metrics['tt_dataframe'] = DataFrame() coin_metrics['top_grossing_enabled'] = False coin_metrics['top_grossing_updated'] = False coin_metrics['top_grossing_len'] = 20 coin_metrics['tg_dataframe'] = DataFrame() coin_metrics['current_whitelist'] = [] # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_17_enable": True, "buy_condition_18_enable": True, "buy_condition_19_enable": True, "buy_condition_20_enable": True, "buy_condition_21_enable": True, "buy_condition_22_enable": True, "buy_condition_23_enable": True, "buy_condition_24_enable": True, "buy_condition_25_enable": True, "buy_condition_26_enable": True, "buy_condition_27_enable": True, "buy_condition_28_enable": True, "buy_condition_29_enable": True, "buy_condition_30_enable": True, "buy_condition_31_enable": True, "buy_condition_32_enable": True, "buy_condition_33_enable": True, "buy_condition_34_enable": True, "buy_condition_35_enable": False, "buy_condition_36_enable": False, "buy_condition_37_enable": True, "buy_condition_38_enable": True, "buy_condition_39_enable": True, "buy_condition_40_enable": True, "buy_condition_41_enable": True, "buy_condition_42_enable": True, "buy_condition_43_enable": True, "buy_condition_44_enable": True, "buy_condition_45_enable": True, "buy_condition_46_enable": True, "buy_condition_47_enable": True, "buy_condition_48_enable": True, ############# } sell_params = { ############# # Enable/Disable conditions "sell_condition_1_enable": True, "sell_condition_2_enable": True, "sell_condition_3_enable": True, "sell_condition_4_enable": True, "sell_condition_5_enable": True, "sell_condition_6_enable": True, "sell_condition_7_enable": True, "sell_condition_8_enable": True, ############# } profit_target_params = { ############# # Enable/Disable conditions "profit_target_1_enable": False, ############# } ############################################################# buy_protection_params = { 1: { "ema_fast" : False, "ema_fast_len" : "26", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "28", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "70", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 2: { "ema_fast" : True, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "20", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "48", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "20", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.4 }, 3: { "ema_fast" : False, "ema_fast_len" : "100", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "36", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : True, "safe_pump_type" : "110", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 4: { "ema_fast" : True, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "110", "safe_pump_period" : "48", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 5: { "ema_fast" : False, "ema_fast_len" : "100", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "100", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 6: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 7: { "ema_fast" : True, "ema_fast_len" : "100", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "80", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 8: { "ema_fast" : True, "ema_fast_len" : "12", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : True, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "36", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.016, "safe_dips_threshold_2" : 0.11, "safe_dips_threshold_12" : 0.26, "safe_dips_threshold_144" : 0.44, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.05 }, 9: { "ema_fast" : True, "ema_fast_len" : "100", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.1 }, 10: { "ema_fast" : True, "ema_fast_len" : "35", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "24", "safe_dips_threshold_0" : 0.016, "safe_dips_threshold_2" : 0.11, "safe_dips_threshold_12" : 0.26, "safe_dips_threshold_144" : 0.44, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.6 }, 11: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "20", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "24", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "36", "safe_dips_threshold_0" : 0.022, "safe_dips_threshold_2" : 0.18, "safe_dips_threshold_12" : 0.34, "safe_dips_threshold_144" : 0.56, "safe_pump" : False, "safe_pump_type" : "120", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 12: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "24", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.3 }, 13: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "24", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 14: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : True, "sma200_rising_val" : "30", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.5 }, 15: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "80", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 16: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "50", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.027, "safe_dips_threshold_2" : 0.26, "safe_dips_threshold_12" : 0.44, "safe_dips_threshold_144" : 0.84, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 17: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 18: { "ema_fast" : True, "ema_fast_len" : "100", "ema_slow" : True, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : True, "close_above_ema_slow_len" : "200", "sma200_rising" : True, "sma200_rising_val" : "44", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "72", "safe_dips_threshold_0" : 0.026, "safe_dips_threshold_2" : 0.24, "safe_dips_threshold_12" : 0.42, "safe_dips_threshold_144" : 0.8, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 19: { "ema_fast" : True, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "36", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "36", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 20: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 21: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.025, "safe_dips_threshold_2" : 0.23, "safe_dips_threshold_12" : 0.4, "safe_dips_threshold_144" : 0.7, "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 22: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "50", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "110", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.6 }, 23: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "15", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : True, "sma200_rising_val" : "24", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.022, "safe_dips_threshold_2" : 0.1, "safe_dips_threshold_12" : 0.3, "safe_dips_threshold_144" : 0.84, "safe_pump" : True, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 24: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "50", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "36", "safe_dips_threshold_0" : 0.016, "safe_dips_threshold_2" : 0.11, "safe_dips_threshold_12" : 0.26, "safe_dips_threshold_144" : 0.44, "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 25: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "20", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "36", "safe_dips_threshold_0" : 0.024, "safe_dips_threshold_2" : 0.22, "safe_dips_threshold_12" : 0.38, "safe_dips_threshold_144" : 0.66, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 0.98, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.4 }, 26: { "ema_fast" : False, "ema_fast_len" : "100", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.016, "safe_dips_threshold_2" : 0.1, "safe_dips_threshold_12" : 0.11, "safe_dips_threshold_144" : 0.22, "safe_pump" : True, "safe_pump_type" : "100", "safe_pump_period" : "36", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.35 }, 27: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "50", "safe_pump_period" : "36", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 28: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 0.99, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.32 }, 29: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "110", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.01 }, 30: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "110", "safe_pump_period" : "36", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 31: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.02, "safe_dips_threshold_2" : 0.14, "safe_dips_threshold_12" : 0.32, "safe_dips_threshold_144" : 0.5, "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "48", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "sup3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 0.98, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 32: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "80", "safe_pump_period" : "48", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 33: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 34: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 0.99, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 35: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.1 }, 36: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 37: { "ema_fast" : True, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "48", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.5 }, 38: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : False, "ema_slow_len" : "100", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "50", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "36", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 39: { "ema_fast" : False, "ema_fast_len" : "100", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "100", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "48", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 40: { "ema_fast" : True, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : True, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : True, "safe_pump_type" : "100", "safe_pump_period" : "48", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.2 }, 41: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.015, "safe_dips_threshold_2" : 0.1, "safe_dips_threshold_12" : 0.24, "safe_dips_threshold_144" : 0.42, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 42: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.027, "safe_dips_threshold_2" : 0.26, "safe_dips_threshold_12" : 0.44, "safe_dips_threshold_144" : 0.84, "safe_pump" : True, "safe_pump_type" : "10", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 43: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.024, "safe_dips_threshold_2" : 0.22, "safe_dips_threshold_12" : 0.38, "safe_dips_threshold_144" : 0.66, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 44: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 45: { "ema_fast" : True, "ema_fast_len" : "15", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "20", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.3, "safe_dips_threshold_12" : 0.48, "safe_dips_threshold_144" : 0.9, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 46: { "ema_fast" : False, "ema_fast_len" : "50", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips_threshold_0" : 0.028, "safe_dips_threshold_2" : 0.06, "safe_dips_threshold_12" : 0.25, "safe_dips_threshold_144" : 0.26, "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 2.0 }, 47: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "24", "safe_dips_threshold_0" : 0.025, "safe_dips_threshold_2" : 0.05, "safe_dips_threshold_12" : 0.25, "safe_dips_threshold_144" : 0.5, "safe_pump" : True, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 }, 48: { "ema_fast" : True, "ema_fast_len" : "12", "ema_slow" : True, "ema_slow_len" : "12", "close_above_ema_fast" : True, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : True, "close_above_ema_slow_len" : "200", "sma200_rising" : True, "sma200_rising_val" : "30", "sma200_1h_rising" : True, "sma200_1h_rising_val" : "24", "safe_dips_threshold_0" : None, "safe_dips_threshold_2" : None, "safe_dips_threshold_12" : None, "safe_dips_threshold_144" : None, "safe_pump" : False, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False, "close_over_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset" : 1.0, "close_under_pivot_type" : "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset" : 1.0 } } # 24 hours - level 10 buy_pump_pull_threshold_10_24 = 2.2 buy_pump_threshold_10_24 = 0.42 # 36 hours - level 10 buy_pump_pull_threshold_10_36 = 2.0 buy_pump_threshold_10_36 = 0.58 # 48 hours - level 10 buy_pump_pull_threshold_10_48 = 2.0 buy_pump_threshold_10_48 = 0.8 # 24 hours - level 20 buy_pump_pull_threshold_20_24 = 2.2 buy_pump_threshold_20_24 = 0.46 # 36 hours - level 20 buy_pump_pull_threshold_20_36 = 2.0 buy_pump_threshold_20_36 = 0.6 # 48 hours - level 20 buy_pump_pull_threshold_20_48 = 2.0 buy_pump_threshold_20_48 = 0.81 # 24 hours - level 30 buy_pump_pull_threshold_30_24 = 2.2 buy_pump_threshold_30_24 = 0.5 # 36 hours - level 30 buy_pump_pull_threshold_30_36 = 2.0 buy_pump_threshold_30_36 = 0.62 # 48 hours - level 30 buy_pump_pull_threshold_30_48 = 2.0 buy_pump_threshold_30_48 = 0.82 # 24 hours - level 40 buy_pump_pull_threshold_40_24 = 2.2 buy_pump_threshold_40_24 = 0.54 # 36 hours - level 40 buy_pump_pull_threshold_40_36 = 2.0 buy_pump_threshold_40_36 = 0.63 # 48 hours - level 40 buy_pump_pull_threshold_40_48 = 2.0 buy_pump_threshold_40_48 = 0.84 # 24 hours - level 50 buy_pump_pull_threshold_50_24 = 1.75 buy_pump_threshold_50_24 = 0.6 # 36 hours - level 50 buy_pump_pull_threshold_50_36 = 1.75 buy_pump_threshold_50_36 = 0.64 # 48 hours - level 50 buy_pump_pull_threshold_50_48 = 1.75 buy_pump_threshold_50_48 = 0.85 # 24 hours - level 60 buy_pump_pull_threshold_60_24 = 1.75 buy_pump_threshold_60_24 = 0.62 # 36 hours - level 60 buy_pump_pull_threshold_60_36 = 1.75 buy_pump_threshold_60_36 = 0.66 # 48 hours - level 60 buy_pump_pull_threshold_60_48 = 1.75 buy_pump_threshold_60_48 = 0.9 # 24 hours - level 70 buy_pump_pull_threshold_70_24 = 1.75 buy_pump_threshold_70_24 = 0.63 # 36 hours - level 70 buy_pump_pull_threshold_70_36 = 1.75 buy_pump_threshold_70_36 = 0.67 # 48 hours - level 70 buy_pump_pull_threshold_70_48 = 1.75 buy_pump_threshold_70_48 = 0.95 # 24 hours - level 80 buy_pump_pull_threshold_80_24 = 1.75 buy_pump_threshold_80_24 = 0.64 # 36 hours - level 80 buy_pump_pull_threshold_80_36 = 1.75 buy_pump_threshold_80_36 = 0.68 # 48 hours - level 80 buy_pump_pull_threshold_80_48 = 1.75 buy_pump_threshold_80_48 = 1.0 # 24 hours - level 90 buy_pump_pull_threshold_90_24 = 1.75 buy_pump_threshold_90_24 = 0.65 # 36 hours - level 90 buy_pump_pull_threshold_90_36 = 1.75 buy_pump_threshold_90_36 = 0.69 # 48 hours - level 90 buy_pump_pull_threshold_90_48 = 1.75 buy_pump_threshold_90_48 = 1.1 # 24 hours - level 100 buy_pump_pull_threshold_100_24 = 1.7 buy_pump_threshold_100_24 = 0.66 # 36 hours - level 100 buy_pump_pull_threshold_100_36 = 1.7 buy_pump_threshold_100_36 = 0.7 # 48 hours - level 100 buy_pump_pull_threshold_100_48 = 1.4 buy_pump_threshold_100_48 = 1.6 # 24 hours - level 110 buy_pump_pull_threshold_110_24 = 1.7 buy_pump_threshold_110_24 = 0.7 # 36 hours - level 110 buy_pump_pull_threshold_110_36 = 1.7 buy_pump_threshold_110_36 = 0.74 # 48 hours - level 110 buy_pump_pull_threshold_110_48 = 1.4 buy_pump_threshold_110_48 = 1.8 # 24 hours - level 120 buy_pump_pull_threshold_120_24 = 1.7 buy_pump_threshold_120_24 = 0.78 # 36 hours - level 120 buy_pump_pull_threshold_120_36 = 1.7 buy_pump_threshold_120_36 = 0.78 # 48 hours - level 120 buy_pump_pull_threshold_120_48 = 1.4 buy_pump_threshold_120_48 = 2.0 # 5 hours - level 10 buy_dump_protection_10_5 = 0.4 # 5 hours - level 20 buy_dump_protection_20_5 = 0.44 # 5 hours - level 30 buy_dump_protection_30_5 = 0.50 # 5 hours - level 40 buy_dump_protection_40_5 = 0.58 # 5 hours - level 50 buy_dump_protection_50_5 = 0.66 # 5 hours - level 60 buy_dump_protection_60_5 = 0.74 buy_1_min_inc = 0.022 buy_1_rsi_max = 32.0 buy_2_r_14_max = -75.0 buy_1_mfi_max = 46.0 buy_1_rsi_1h_min = 30.0 buy_1_rsi_1h_max = 84.0 buy_2_rsi_1h_diff = 39.0 buy_2_mfi = 49.0 buy_2_cti_max = -0.9 buy_2_r_480_min = -95.0 buy_2_r_480_max = -46.0 buy_2_cti_1h_max = 0.9 buy_2_volume = 2.0 buy_3_bb40_bbdelta_close = 0.057 buy_3_bb40_closedelta_close = 0.023 buy_3_bb40_tail_bbdelta = 0.418 buy_3_cti_max = -0.5 buy_3_cci_36_osc_min = -0.25 buy_3_crsi_1h_min = 20.0 buy_3_r_480_1h_min = -48.0 buy_3_cti_1h_max = 0.82 buy_4_bb20_close_bblowerband = 0.98 buy_4_bb20_volume = 10.0 buy_4_cti_max = -0.8 buy_5_ema_rel = 0.84 buy_5_ema_open_mult = 0.02 buy_5_bb_offset = 0.999 buy_5_cti_max = -0.5 buy_5_r_14_max = -94.0 buy_5_rsi_14_min = 25.0 buy_5_mfi_min = 18.0 buy_5_crsi_1h_min = 12.0 buy_5_volume = 1.6 buy_6_ema_open_mult = 0.019 buy_6_bb_offset = 0.984 buy_6_r_14_max = -85.0 buy_6_crsi_1h_min = 15.0 buy_6_cti_1h_min = 0.0 buy_7_ema_open_mult = 0.031 buy_7_ma_offset = 0.978 buy_7_cti_max = -0.9 buy_7_rsi_max = 45.0 buy_8_bb_offset = 0.986 buy_8_r_14_max = -98.0 buy_8_cti_1h_max = 0.95 buy_8_r_480_1h_max = -18.0 buy_8_volume = 1.8 buy_9_ma_offset = 0.968 buy_9_bb_offset = 0.982 buy_9_mfi_max = 50.0 buy_9_cti_max = -0.85 buy_9_r_14_max = -94.0 buy_9_rsi_1h_min = 20.0 buy_9_rsi_1h_max = 88.0 buy_9_crsi_1h_min = 21.0 buy_10_ma_offset_high = 0.94 buy_10_bb_offset = 0.984 buy_10_r_14_max = -88.0 buy_10_cti_1h_min = -0.5 buy_10_cti_1h_max = 0.94 buy_11_ma_offset = 0.956 buy_11_min_inc = 0.022 buy_11_rsi_max = 37.0 buy_11_mfi_max = 46.0 buy_11_cci_max = -120.0 buy_11_r_480_max = -32.0 buy_11_rsi_1h_min = 30.0 buy_11_rsi_1h_max = 84.0 buy_11_cti_1h_max = 0.91 buy_11_r_480_1h_max = -25.0 buy_11_crsi_1h_min = 26.0 buy_12_ma_offset = 0.927 buy_12_ewo_min = 2.0 buy_12_rsi_max = 32.0 buy_12_cti_max = -0.9 buy_13_ma_offset = 0.99 buy_13_cti_max = -0.92 buy_13_ewo_max = -6.0 buy_13_cti_1h_max = -0.88 buy_13_crsi_1h_min = 10.0 buy_14_ema_open_mult = 0.014 buy_14_bb_offset = 0.989 buy_14_ma_offset = 0.945 buy_14_cti_max = -0.85 buy_15_ema_open_mult = 0.0238 buy_15_ma_offset = 0.958 buy_15_rsi_min = 28.0 buy_15_cti_1h_min = -0.2 buy_16_ma_offset = 0.942 buy_16_ewo_min = 2.0 buy_16_rsi_max = 36.0 buy_16_cti_max = -0.9 buy_17_ma_offset = 0.999 buy_17_ewo_max = -7.0 buy_17_cti_max = -0.96 buy_17_crsi_1h_min = 12.0 buy_17_volume = 2.0 buy_18_bb_offset = 0.986 buy_18_rsi_max = 33.5 buy_18_cti_max = -0.85 buy_18_cti_1h_max = 0.91 buy_18_volume = 2.0 buy_19_rsi_1h_min = 30.0 buy_19_chop_max = 21.3 buy_20_rsi_14_max = 36.0 buy_20_rsi_14_1h_max = 16.0 buy_20_cti_max = -0.84 buy_20_volume = 2.0 buy_21_rsi_14_max = 14.0 buy_21_rsi_14_1h_max = 28.0 buy_21_cti_max = -0.902 buy_21_volume = 2.0 buy_22_volume = 2.0 buy_22_bb_offset = 0.984 buy_22_ma_offset = 0.98 buy_22_ewo_min = 5.6 buy_22_rsi_14_max = 36.0 buy_22_cti_max = -0.54 buy_22_r_480_max = -40.0 buy_22_cti_1h_min = -0.5 buy_23_bb_offset = 0.984 buy_23_ewo_min = 3.4 buy_23_rsi_14_max = 28.0 buy_23_cti_max = -0.74 buy_23_rsi_14_1h_max = 80.0 buy_23_r_480_1h_min = -95.0 buy_23_cti_1h_max = 0.92 buy_24_rsi_14_max = 50.0 buy_24_rsi_14_1h_min = 66.9 buy_25_ma_offset = 0.953 buy_25_rsi_4_max = 30.0 buy_25_cti_max = -0.78 buy_25_cci_max = -200.0 buy_26_zema_low_offset = 0.9405 buy_26_cti_max = -0.72 buy_26_cci_max = -166.0 buy_26_r_14_max = -98.0 buy_26_cti_1h_max = 0.95 buy_26_volume = 2.0 buy_27_wr_max = -95.0 buy_27_r_14 = -100.0 buy_27_wr_1h_max = -90.0 buy_27_rsi_max = 46.0 buy_27_volume = 2.0 buy_28_ma_offset = 0.928 buy_28_ewo_min = 2.0 buy_28_rsi_14_max = 33.4 buy_28_cti_max = -0.84 buy_28_r_14_max = -97.0 buy_28_cti_1h_max = 0.95 buy_29_ma_offset = 0.984 buy_29_ewo_max = -4.2 buy_29_cti_max = -0.96 buy_30_ma_offset = 0.962 buy_30_ewo_min = 6.4 buy_30_rsi_14_max = 34.0 buy_30_cti_max = -0.87 buy_30_r_14_max = -97.0 buy_31_ma_offset = 0.962 buy_31_ewo_max = -5.2 buy_31_r_14_max = -94.0 buy_31_cti_max = -0.9 buy_32_ma_offset = 0.942 buy_32_rsi_4_max = 46.0 buy_32_cti_max = -0.86 buy_32_rsi_14_min = 19.0 buy_32_crsi_1h_min = 10.0 buy_32_crsi_1h_max = 60.0 buy_33_ma_offset = 0.988 buy_33_ewo_min = 9.0 buy_33_rsi_max = 32.0 buy_33_cti_max = -0.88 buy_33_r_14_max = -98.0 buy_33_cti_1h_max = 0.92 buy_33_volume = 2.0 buy_34_ma_offset = 0.97 buy_34_ewo_max = -4.0 buy_34_cti_max = -0.95 buy_34_r_14_max = -99.9 buy_34_crsi_1h_min = 8.0 buy_34_volume = 2.0 buy_35_ma_offset = 0.984 buy_35_ewo_min = 7.8 buy_35_rsi_max = 32.0 buy_35_cti_max = -0.8 buy_35_r_14_max = -95.0 buy_36_ma_offset = 0.98 buy_36_ewo_max = -5.0 buy_36_cti_max = -0.82 buy_36_r_14_max = -97.0 buy_36_crsi_1h_min = 12.0 buy_37_ma_offset = 0.984 buy_37_ewo_min = 8.3 buy_37_ewo_max = 11.1 buy_37_rsi_14_min = 26.0 buy_37_rsi_14_max = 46.0 buy_37_crsi_1h_min = 12.0 buy_37_crsi_1h_max = 56.0 buy_37_cti_max = -0.85 buy_37_cti_1h_max = 0.92 buy_37_r_14_max = -97.0 buy_37_close_1h_max = 0.1 buy_38_ma_offset = 0.98 buy_38_ewo_max = -4.4 buy_38_cti_max = -0.95 buy_38_r_14_max = -97.0 buy_38_crsi_1h_min = 0.5 buy_39_cti_max = -0.1 buy_39_r_1h_max = -22.0 buy_39_cti_1h_min = -0.1 buy_39_cti_1h_max = 0.4 buy_40_cci_max = -150.0 buy_40_rsi_max = 30.0 buy_40_r_14_max = -99.9 buy_40_cti_max = -0.8 buy_41_ma_offset_high = 0.95 buy_41_cti_max = -0.95 buy_41_cci_max = -178.0 buy_41_ewo_1h_min = 0.5 buy_41_r_480_1h_max = -14.0 buy_41_crsi_1h_min = 14.0 buy_42_ema_open_mult = 0.018 buy_42_bb_offset = 0.992 buy_42_ewo_1h_min = 2.8 buy_42_cti_1h_min = -0.5 buy_42_cti_1h_max = 0.88 buy_42_r_480_1h_max = -12.0 buy_43_bb40_bbdelta_close = 0.045 buy_43_bb40_closedelta_close = 0.02 buy_43_bb40_tail_bbdelta = 0.5 buy_43_cti_max = -0.75 buy_43_r_480_min = -94.0 buy_43_cti_1h_min = -0.75 buy_43_cti_1h_max = 0.45 buy_43_r_480_1h_min = -80.0 buy_44_ma_offset = 0.982 buy_44_ewo_max = -18.0 buy_44_cti_max = -0.73 buy_44_crsi_1h_min = 8.0 buy_45_bb40_bbdelta_close = 0.039 buy_45_bb40_closedelta_close = 0.0231 buy_45_bb40_tail_bbdelta = 0.24 buy_45_ma_offset = 0.948 buy_45_ewo_min = 2.0 buy_45_ewo_1h_min = 2.0 buy_45_cti_1h_max = 0.76 buy_45_r_480_1h_max = -20.0 buy_46_ema_open_mult = 0.0332 buy_46_ewo_1h_min = 0.5 buy_46_cti_1h_min = -0.9 buy_46_cti_1h_max = 0.5 buy_47_ewo_min = 3.2 buy_47_ma_offset = 0.952 buy_47_rsi_14_max = 46.0 buy_47_cti_max = -0.93 buy_47_r_14_max = -97.0 buy_47_ewo_1h_min = 2.0 buy_47_cti_1h_min = -0.9 buy_47_cti_1h_max = 0.3 buy_48_ewo_min = 8.5 buy_48_ewo_1h_min = 14.0 buy_48_r_480_min = -25.0 buy_48_r_480_1h_min = -50.0 buy_48_r_480_1h_max = -10.0 buy_48_cti_1h_min = 0.5 buy_48_crsi_1h_min = 10.0 # Sell sell_condition_1_enable = True sell_condition_2_enable = True sell_condition_3_enable = True sell_condition_4_enable = True sell_condition_5_enable = True sell_condition_6_enable = True sell_condition_7_enable = True sell_condition_8_enable = True # 48h for pump sell checks sell_pump_threshold_48_1 = 0.9 sell_pump_threshold_48_2 = 0.7 sell_pump_threshold_48_3 = 0.5 # 36h for pump sell checks sell_pump_threshold_36_1 = 0.72 sell_pump_threshold_36_2 = 4.0 sell_pump_threshold_36_3 = 1.0 # 24h for pump sell checks sell_pump_threshold_24_1 = 0.68 sell_pump_threshold_24_2 = 0.62 sell_pump_threshold_24_3 = 0.88 sell_rsi_bb_1 = 79.0 sell_rsi_bb_2 = 80.0 sell_rsi_main_3 = 83.0 sell_dual_rsi_rsi_4 = 73.4 sell_dual_rsi_rsi_1h_4 = 79.6 sell_ema_relative_5 = 0.024 sell_rsi_diff_5 = 4.4 sell_rsi_under_6 = 79.0 sell_rsi_1h_7 = 81.7 sell_bb_relative_8 = 1.1 # Profit over EMA200 sell_custom_profit_bull_0 = 0.012 sell_custom_rsi_under_bull_0 = 34.0 sell_custom_profit_bull_1 = 0.02 sell_custom_rsi_under_bull_1 = 35.0 sell_custom_profit_bull_2 = 0.03 sell_custom_rsi_under_bull_2 = 36.0 sell_custom_profit_bull_3 = 0.04 sell_custom_rsi_under_bull_3 = 44.0 sell_custom_profit_bull_4 = 0.05 sell_custom_rsi_under_bull_4 = 45.0 sell_custom_profit_bull_5 = 0.06 sell_custom_rsi_under_bull_5 = 49.0 sell_custom_profit_bull_6 = 0.07 sell_custom_rsi_under_bull_6 = 50.0 sell_custom_profit_bull_7 = 0.08 sell_custom_rsi_under_bull_7 = 57.0 sell_custom_profit_bull_8 = 0.09 sell_custom_rsi_under_bull_8 = 50.0 sell_custom_profit_bull_9 = 0.1 sell_custom_rsi_under_bull_9 = 46.0 sell_custom_profit_bull_10 = 0.12 sell_custom_rsi_under_bull_10 = 42.0 sell_custom_profit_bull_11 = 0.20 sell_custom_rsi_under_bull_11 = 30.0 sell_custom_profit_bear_0 = 0.012 sell_custom_rsi_under_bear_0 = 34.0 sell_custom_profit_bear_1 = 0.02 sell_custom_rsi_under_bear_1 = 35.0 sell_custom_profit_bear_2 = 0.03 sell_custom_rsi_under_bear_2 = 37.0 sell_custom_profit_bear_3 = 0.04 sell_custom_rsi_under_bear_3 = 44.0 sell_custom_profit_bear_4 = 0.05 sell_custom_rsi_under_bear_4 = 48.0 sell_custom_profit_bear_5 = 0.06 sell_custom_rsi_under_bear_5 = 50.0 sell_custom_rsi_over_bear_5 = 78.0 sell_custom_profit_bear_6 = 0.07 sell_custom_rsi_under_bear_6 = 52.0 sell_custom_rsi_over_bear_6 = 78.0 sell_custom_profit_bear_7 = 0.08 sell_custom_rsi_under_bear_7 = 57.0 sell_custom_rsi_over_bear_7 = 77.0 sell_custom_profit_bear_8 = 0.09 sell_custom_rsi_under_bear_8 = 55.0 sell_custom_rsi_over_bear_8 = 75.5 sell_custom_profit_bear_9 = 0.1 sell_custom_rsi_under_bear_9 = 46.0 sell_custom_profit_bear_10 = 0.12 sell_custom_rsi_under_bear_10 = 42.0 sell_custom_profit_bear_11 = 0.20 sell_custom_rsi_under_bear_11 = 30.0 # Profit under EMA200 sell_custom_under_profit_bull_0 = 0.01 sell_custom_under_rsi_under_bull_0 = 38.0 sell_custom_under_profit_bull_1 = 0.02 sell_custom_under_rsi_under_bull_1 = 46.0 sell_custom_under_profit_bull_2 = 0.03 sell_custom_under_rsi_under_bull_2 = 47.0 sell_custom_under_profit_bull_3 = 0.04 sell_custom_under_rsi_under_bull_3 = 48.0 sell_custom_under_profit_bull_4 = 0.05 sell_custom_under_rsi_under_bull_4 = 49.0 sell_custom_under_profit_bull_5 = 0.06 sell_custom_under_rsi_under_bull_5 = 50.0 sell_custom_under_profit_bull_6 = 0.07 sell_custom_under_rsi_under_bull_6 = 52.0 sell_custom_under_profit_bull_7 = 0.08 sell_custom_under_rsi_under_bull_7 = 57.0 sell_custom_under_profit_bull_8 = 0.09 sell_custom_under_rsi_under_bull_8 = 50.0 sell_custom_under_profit_bull_9 = 0.1 sell_custom_under_rsi_under_bull_9 = 46.0 sell_custom_under_profit_bull_10 = 0.12 sell_custom_under_rsi_under_bull_10 = 42.0 sell_custom_under_profit_bull_11 = 0.2 sell_custom_under_rsi_under_bull_11 = 30.0 sell_custom_under_profit_bear_0 = 0.01 sell_custom_under_rsi_under_bear_0 = 38.0 sell_custom_under_profit_bear_1 = 0.02 sell_custom_under_rsi_under_bear_1 = 56.0 sell_custom_under_profit_bear_2 = 0.03 sell_custom_under_rsi_under_bear_2 = 57.0 sell_custom_under_profit_bear_3 = 0.04 sell_custom_under_rsi_under_bear_3 = 57.0 sell_custom_under_profit_bear_4 = 0.05 sell_custom_under_rsi_under_bear_4 = 57.0 sell_custom_under_profit_bear_5 = 0.06 sell_custom_under_rsi_under_bear_5 = 57.0 sell_custom_under_rsi_over_bear_5 = 78.0 sell_custom_under_profit_bear_6 = 0.07 sell_custom_under_rsi_under_bear_6 = 57.0 sell_custom_under_rsi_over_bear_6 = 78.0 sell_custom_under_profit_bear_7 = 0.08 sell_custom_under_rsi_under_bear_7 = 57.0 sell_custom_under_rsi_over_bear_7 = 80.0 sell_custom_under_profit_bear_8 = 0.09 sell_custom_under_rsi_under_bear_8 = 50.0 sell_custom_under_rsi_over_bear_8 = 82.0 sell_custom_under_profit_bear_9 = 0.1 sell_custom_under_rsi_under_bear_9 = 46.0 sell_custom_under_profit_bear_10 = 0.12 sell_custom_under_rsi_under_bear_10 = 42.0 sell_custom_under_profit_bear_11 = 0.2 sell_custom_under_rsi_under_bear_11 = 30.0 # SMA descending sell_custom_dec_profit_min_1 = 0.05 sell_custom_dec_profit_max_1 = 0.12 # Under EMA100 sell_custom_dec_profit_min_2 = 0.07 sell_custom_dec_profit_max_2 = 0.16 # Trail 1 sell_trail_profit_min_1 = 0.03 sell_trail_profit_max_1 = 0.05 sell_trail_down_1 = 0.05 sell_trail_rsi_min_1 = 10.0 sell_trail_rsi_max_1 = 20.0 # Trail 2 sell_trail_profit_min_2 = 0.1 sell_trail_profit_max_2 = 0.4 sell_trail_down_2 = 0.03 sell_trail_rsi_min_2 = 20.0 sell_trail_rsi_max_2 = 50.0 # Trail 3 sell_trail_profit_min_3 = 0.06 sell_trail_profit_max_3 = 0.2 sell_trail_down_3 = 0.05 # Trail 4 sell_trail_profit_min_4 = 0.03 sell_trail_profit_max_4 = 0.06 sell_trail_down_4 = 0.02 # Under & near EMA200, accept profit sell_custom_profit_under_profit_min_1 = 0.001 sell_custom_profit_under_profit_max_1 = 0.008 sell_custom_profit_under_rel_1 = 0.024 sell_custom_profit_under_rsi_diff_1 = 4.4 sell_custom_profit_under_profit_2 = 0.03 sell_custom_profit_under_rel_2 = 0.024 sell_custom_profit_under_rsi_diff_2 = 4.4 # Under & near EMA200, take the loss sell_custom_stoploss_under_rel_1 = 0.002 sell_custom_stoploss_under_rsi_diff_1 = 10.0 # Long duration/recover stoploss 1 sell_custom_stoploss_long_profit_min_1 = -0.08 sell_custom_stoploss_long_profit_max_1 = -0.04 sell_custom_stoploss_long_recover_1 = 0.14 sell_custom_stoploss_long_rsi_diff_1 = 4.0 # Long duration/recover stoploss 2 sell_custom_stoploss_long_recover_2 = 0.06 sell_custom_stoploss_long_rsi_diff_2 = 40.0 # Pumped 48h 1, under EMA200 sell_custom_pump_under_profit_min_1 = 0.04 sell_custom_pump_under_profit_max_1 = 0.09 # Pumped trail 1 sell_custom_pump_trail_profit_min_1 = 0.05 sell_custom_pump_trail_profit_max_1 = 0.07 sell_custom_pump_trail_down_1 = 0.05 sell_custom_pump_trail_rsi_min_1 = 20.0 sell_custom_pump_trail_rsi_max_1 = 70.0 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_1 = 0.01 sell_custom_stoploss_pump_min_1 = -0.02 sell_custom_stoploss_pump_max_1 = -0.01 sell_custom_stoploss_pump_ma_offset_1 = 0.94 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_2 = 0.025 sell_custom_stoploss_pump_loss_2 = -0.05 sell_custom_stoploss_pump_ma_offset_2 = 0.92 # Stoploss, pumped, 36h 3 sell_custom_stoploss_pump_max_profit_3 = 0.008 sell_custom_stoploss_pump_loss_3 = -0.12 sell_custom_stoploss_pump_ma_offset_3 = 0.88 # Recover sell_custom_recover_profit_1 = 0.06 sell_custom_recover_min_loss_1 = 0.12 sell_custom_recover_profit_min_2 = 0.01 sell_custom_recover_profit_max_2 = 0.05 sell_custom_recover_min_loss_2 = 0.06 sell_custom_recover_rsi_2 = 46.0 # Profit for long duration trades sell_custom_long_profit_min_1 = 0.03 sell_custom_long_profit_max_1 = 0.04 sell_custom_long_duration_min_1 = 900 # Profit Target Signal profit_target_1_enable = False ############################################################# plot_config = { 'main_plot': { 'ema_12_1h': { 'color': 'rgba(200,200,100,0.4)' }, 'ema_15_1h': { 'color': 'rgba(200,180,100,0.4)' }, 'ema_20_1h': { 'color': 'rgba(200,160,100,0.4)' }, 'ema_25_1h': { 'color': 'rgba(200,140,100,0.4)' }, 'ema_26_1h': { 'color': 'rgba(200,120,100,0.4)' }, 'ema_35_1h': { 'color': 'rgba(200,100,100,0.4)' }, 'ema_50_1h': { 'color': 'rgba(200,80,100,0.4)' }, 'ema_100_1h': { 'color': 'rgba(200,60,100,0.4)' }, 'ema_200_1h': { 'color': 'rgba(200,40,100,0.4)' }, 'sma_200_1h': { 'color': 'rgba(150,20,100,0.4)' }, 'pm': { 'color': 'rgba(100,20,100,0.5)' } }, 'subplots': { 'buy tag': { 'buy_tag': {'color': 'green'} }, 'RSI/BTC': { 'btc_not_downtrend_1h': { 'color': 'yellow' }, 'btc_rsi_14_1h': { 'color': 'green' }, 'rsi_14_1h': { 'color': '#f41cd1' }, 'crsi': {'color': 'blue' } }, 'pump': { 'cti_1h': {'color': 'pink' }, 'safe_pump_24_10_1h': {'color': '#481110' }, 'safe_pump_24_20_1h': {'color': '#481120' }, 'safe_pump_24_30_1h': {'color': '#481130' }, 'safe_pump_24_40_1h': {'color': '#481140' }, 'safe_pump_24_50_1h': {'color': '#481150' }, 'safe_pump_24_60_1h': {'color': '#481160' }, 'safe_pump_24_70_1h': {'color': '#481170' }, 'safe_pump_24_80_1h': {'color': '#481180' }, 'safe_pump_24_90_1h': {'color': '#481190' }, 'safe_pump_24_100_1h': {'color': '#4811A0' }, 'safe_pump_24_120_1h': {'color': '#4811C0' }, 'safe_pump_36_10_1h': {'color': '#721110' }, 'safe_pump_36_20_1h': {'color': '#721120' }, 'safe_pump_36_30_1h': {'color': '#721130' }, 'safe_pump_36_40_1h': {'color': '#721140' }, 'safe_pump_36_50_1h': {'color': '#721150' }, 'safe_pump_36_60_1h': {'color': '#721160' }, 'safe_pump_36_70_1h': {'color': '#721170' }, 'safe_pump_36_80_1h': {'color': '#721180' }, 'safe_pump_36_90_1h': {'color': '#721190' }, 'safe_pump_36_100_1h': {'color': '#7211A0' }, 'safe_pump_36_120_1h': {'color': '#7211C0' }, 'safe_pump_48_10_1h': {'color': '#961110' }, 'safe_pump_48_20_1h': {'color': '#961120' }, 'safe_pump_48_30_1h': {'color': '#961130' }, 'safe_pump_48_40_1h': {'color': '#961140' }, 'safe_pump_48_50_1h': {'color': '#961150' }, 'safe_pump_48_60_1h': {'color': '#961160' }, 'safe_pump_48_70_1h': {'color': '#961170' }, 'safe_pump_48_80_1h': {'color': '#961180' }, 'safe_pump_48_90_1h': {'color': '#961190' }, 'safe_pump_48_100_1h': {'color': '#9611A0' }, 'safe_pump_48_120_1h': {'color': '#9611C0' } } } } ############################################################# # CACHES hold_trades_cache = None target_profit_cache = None ############################################################# def __init__(self, config: dict) -> None: super().__init__(config) #self.dp = DataProvider(config, config['exchange']) if self.target_profit_cache is None: self.target_profit_cache = Cache( self.config["user_data_dir"] / "data-nfi-profit_target_by_pair.json" ) # If the cached data hasn't changed, it's a no-op 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 whitelist_tracker(self): if sorted(self.coin_metrics['current_whitelist']) != sorted(self.dp.current_whitelist()): log.info("Whitelist has changed...") self.coin_metrics['top_traded_updated'] = False self.coin_metrics['top_grossing_updated'] = False # Update pairlist self.coin_metrics['current_whitelist'] = self.dp.current_whitelist() # Move up BTC for largest data footprint self.coin_metrics['current_whitelist'].insert(0, self.coin_metrics['current_whitelist'].pop(self.coin_metrics['current_whitelist'].index(f"BTC/{self.config['stake_currency']}"))) def top_traded_list(self): log.info("Updating top traded pairlist...") tik = time.perf_counter() self.coin_metrics['tt_dataframe'] = DataFrame() # Build traded volume dataframe for coin_pair in self.coin_metrics['current_whitelist']: coin = coin_pair.split('/')[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index('date') if self.config['runmode'].value in ('live', 'dry_run'): pair_dataframe = pair_dataframe.iloc[-7:,:] # Set the date index of the self.coin_metrics['tt_dataframe'] once if not 'date' in self.coin_metrics['tt_dataframe']: self.coin_metrics['tt_dataframe']['date'] = pair_dataframe['date'] self.coin_metrics['tt_dataframe'].set_index('date') # Calculate daily traded volume pair_dataframe[coin] = pair_dataframe['volume'] * qtpylib.typical_price(pair_dataframe) # Drop the columns we don't need pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True) # Merge it in on the date key self.coin_metrics['tt_dataframe'] = self.coin_metrics['tt_dataframe'].merge(pair_dataframe, on='date', how='left') # Forward fill empty cells (due to different df shapes) self.coin_metrics['tt_dataframe'].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics['tt_dataframe']['date'] self.coin_metrics['tt_dataframe'].drop(columns=['date'], inplace=True) # Build columns and top traded coins column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_traded_len'] + 1)] self.coin_metrics['tt_dataframe'][column_names] = self.coin_metrics['tt_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_traded_len']).index.values, axis=1, result_type='expand') self.coin_metrics['tt_dataframe'].drop(columns=[col for col in self.coin_metrics['tt_dataframe'] if col not in column_names], inplace=True) # Re-add stored date column self.coin_metrics['tt_dataframe'].insert(loc = 0, column = 'date', value = pair_dates) self.coin_metrics['tt_dataframe'].set_index('date') self.coin_metrics['top_traded_updated'] = True log.info("Updated top traded pairlist (tail-5):") log.info(f"\n{self.coin_metrics['tt_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f"Updating top traded pairlist took {tok - tik:0.4f} seconds...") def top_grossing_list(self): log.info("Updating top grossing pairlist...") tik = time.perf_counter() self.coin_metrics['tg_dataframe'] = DataFrame() # Build grossing volume dataframe for coin_pair in self.coin_metrics['current_whitelist']: coin = coin_pair.split('/')[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index('date') if self.config['runmode'].value in ('live', 'dry_run'): pair_dataframe = pair_dataframe.iloc[-7:,:] # Set the date index of the self.coin_metrics['tg_dataframe'] once if not 'date' in self.coin_metrics['tg_dataframe']: self.coin_metrics['tg_dataframe']['date'] = pair_dataframe['date'] self.coin_metrics['tg_dataframe'].set_index('date') # Calculate daily grossing rate pair_dataframe[coin] = pair_dataframe['close'].pct_change() * 100 # Drop the columns we don't need pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True) # Merge it in on the date key self.coin_metrics['tg_dataframe'] = self.coin_metrics['tg_dataframe'].merge(pair_dataframe, on='date', how='left') # Forward fill empty cells (due to different df shapes) self.coin_metrics['tg_dataframe'].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics['tg_dataframe']['date'] self.coin_metrics['tg_dataframe'].drop(columns=['date'], inplace=True) # Build columns and top grossing coins column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_grossing_len'] + 1)] self.coin_metrics['tg_dataframe'][column_names] = self.coin_metrics['tg_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_grossing_len']).index.values, axis=1, result_type='expand') self.coin_metrics['tg_dataframe'].drop(columns=[col for col in self.coin_metrics['tg_dataframe'] if col not in column_names], inplace=True) # Re-add stored date column self.coin_metrics['tg_dataframe'].insert(loc = 0, column = 'date', value = pair_dates) self.coin_metrics['tg_dataframe'].set_index('date') self.coin_metrics['top_grossing_updated'] = True log.info("Updated top grossing pairlist (tail-5):") log.info(f"\n{self.coin_metrics['tg_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f"Updating top grossing pairlist took {tok - tik:0.4f} seconds...") def is_top_coin(self, coin_pair, row_data, top_length) -> bool: return coin_pair.split('/')[0] in row_data.loc['Coin #1':f"Coin #{top_length}"].values def bot_loop_start(self, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Coin metrics mechanism if self.coin_metrics['top_traded_enabled'] or self.coin_metrics['top_grossing_enabled']: self.whitelist_tracker() if self.coin_metrics['top_traded_enabled'] and not self.coin_metrics['top_traded_updated']: self.top_traded_list() if self.coin_metrics['top_grossing_enabled'] and not self.coin_metrics['top_grossing_updated']: self.top_grossing_list() if self.config["runmode"].value not in ("live", "dry_run"): return super().bot_loop_start(**kwargs) if self.holdSupportEnabled: self.load_hold_trades_config() return super().bot_loop_start(**kwargs) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def sell_over_main(self, current_profit: float, last_candle) -> tuple: if last_candle['close'] > last_candle['ema_200']: if (last_candle['moderi_96']): if current_profit >= 0.20: if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_12_1' elif (last_candle['rsi_14'] < 27.0): return True, 'signal_profit_o_bull_12_9' elif 0.20 > current_profit >= 0.12: if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_11_1' elif (last_candle['rsi_14'] < 39.0): return True, 'signal_profit_o_bull_11_9' elif 0.12 > current_profit >= 0.1: if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_10_1' elif (last_candle['rsi_14'] < 48.0): return True, 'signal_profit_o_bull_10_9' elif 0.1 > current_profit >= 0.09: if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_9_1' elif (last_candle['rsi_14'] < 49.0): return True, 'signal_profit_o_bull_9_9' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_8_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_8_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_8_4' elif (last_candle['rsi_14'] < 48.0): return True, 'signal_profit_o_bull_8_9' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_7_1' if last_candle['rsi_14'] > 83.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_7_2' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_7_3' elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_7_4' elif (last_candle['rsi_14'] < 45.0): return True, 'signal_profit_o_bull_7_9' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_6_1' if last_candle['rsi_14'] > 82.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_6_2' elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_6_3' elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_6_4' elif (last_candle['cti'] > 0.95): return True, 'signal_profit_o_bull_6_5' elif (last_candle['rsi_14'] < 42.0): return True, 'signal_profit_o_bull_6_9' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_5_1' if last_candle['rsi_14'] > 80.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_5_2' elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_5_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_5_4' elif (last_candle['cti'] > 0.952): return True, 'signal_profit_o_bull_5_5' elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_5_6' elif (last_candle['rsi_14'] < 41.0): return True, 'signal_profit_o_bull_5_9' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 45.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_4_1' elif (last_candle['rsi_14'] < 48.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_4_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_4_4' elif (last_candle['cti'] > 0.954): return True, 'signal_profit_o_bull_4_5' elif (last_candle['rsi_14'] < 48.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_4_6' elif (last_candle['rsi_14'] < 40.0): return True, 'signal_profit_o_bull_4_9' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_3_1' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_3_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_3_4' elif (last_candle['cti'] > 0.956): return True, 'signal_profit_o_bull_3_5' elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_3_6' elif (last_candle['rsi_14'] < 35.0): return True, 'signal_profit_o_bull_3_9' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_2_1' elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_2_3' elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_2_4' elif (last_candle['cti'] > 0.958): return True, 'signal_profit_o_bull_2_5' elif (last_candle['rsi_14'] < 42.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_2_6' elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_o_bull_2_7' elif last_candle['rsi_14'] < 40.0 and (last_candle['cmf'] < -0.25): return True, 'signal_profit_o_bull_2_8' elif (last_candle['rsi_14'] < 34.0): return True, 'signal_profit_o_bull_2_9' elif 0.02 > current_profit >= 0.012: if (last_candle['rsi_14'] < 34.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_1_1' elif (last_candle['rsi_14'] < 41.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bull_1_3' elif (last_candle['rsi_14'] < 44.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bull_1_4' elif (last_candle['cti'] > 0.96): return True, 'signal_profit_o_bull_1_5' elif (last_candle['rsi_14'] < 41.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_1_6' elif (last_candle['rsi_14'] < 41.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_o_bull_1_7' elif last_candle['rsi_14'] < 39.0 and (last_candle['cmf'] < -0.25): return True, 'signal_profit_o_bull_1_8' elif (last_candle['rsi_14'] < 32.0): return True, 'signal_profit_o_bull_1_9' else: if current_profit >= 0.20: if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_12_1' elif (last_candle['rsi_14'] < 28.0): return True, 'signal_profit_o_bear_12_9' elif 0.20 > current_profit >= 0.12: if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_11_1' elif (last_candle['rsi_14'] < 40.0): return True, 'signal_profit_o_bear_11_9' elif 0.12 > current_profit >= 0.10: if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_10_1' elif (last_candle['rsi_14'] < 49.0): return True, 'signal_profit_o_bear_10_9' elif 0.10 > current_profit >= 0.09: if last_candle['rsi_14'] < 55.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_9_1' elif (last_candle['rsi_14'] > 75.5): return True, 'signal_profit_o_bear_9_2' elif (last_candle['rsi_14'] < 50.0): return True, 'signal_profit_o_bear_9_9' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_8_1' elif (last_candle['rsi_14'] > 77.0): return True, 'signal_profit_o_bear_8_2' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_8_3' elif (last_candle['rsi_14'] < 59.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_8_4' elif (last_candle['rsi_14'] < 49.0): return True, 'signal_profit_o_bear_8_9' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_7_1' elif (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_o_bear_7_2' elif (last_candle['rsi_14'] < 55.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_7_3' elif (last_candle['rsi_14'] < 57.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_7_4' elif (last_candle['rsi_14'] < 46.0): return True, 'signal_profit_o_bear_7_9' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_6_1' elif (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_o_bear_6_2' elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_6_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_6_4' elif (last_candle['cti'] > 0.94): return True, 'signal_profit_o_bear_6_5' elif (last_candle['rsi_14'] < 43.0): return True, 'signal_profit_o_bear_6_9' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 49.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_5_1' elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_5_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_5_4' elif (last_candle['cti'] > 0.942): return True, 'signal_profit_o_bear_5_5' elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_5_6' elif (last_candle['rsi_14'] < 42.0): return True, 'signal_profit_o_bear_5_9' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_4_1' elif (last_candle['rsi_14'] < 48.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_4_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_4_4' elif (last_candle['cti'] > 0.944): return True, 'signal_profit_o_bear_4_5' elif (last_candle['rsi_14'] < 48.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_4_6' elif (last_candle['rsi_14'] < 41.0): return True, 'signal_profit_o_bear_4_9' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_3_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_3_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_3_4' elif (last_candle['cti'] > 0.946): return True, 'signal_profit_o_bear_3_5' elif (last_candle['rsi_14'] < 44.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_3_6' elif (last_candle['rsi_14'] < 36.0): return True, 'signal_profit_o_bear_3_9' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_2_1' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_o_bear_2_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_2_4' elif (last_candle['cti'] > 0.948): return True, 'signal_profit_o_bear_2_5' elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_2_6' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_o_bear_2_7' elif (last_candle['rsi_14'] < 35.0): return True, 'signal_profit_o_bear_2_9' elif 0.02 > current_profit >= 0.012: if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_1_1' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_o_bear_1_3' elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_o_bear_1_4' elif (last_candle['cti'] > 0.95): return True, 'signal_profit_o_bear_1_5' elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_1_6' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_o_bear_1_7' elif (last_candle['rsi_14'] < 33.0): return True, 'signal_profit_o_bear_1_9' return False, None def sell_under_main(self, current_profit: float, last_candle) -> tuple: if last_candle['close'] < last_candle['ema_200']: if (last_candle['moderi_96']): if current_profit >= 0.20: if last_candle['rsi_14'] < 30.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_12_1' elif (last_candle['rsi_14'] < 28.0): return True, 'signal_profit_u_bull_12_9' elif 0.20 > current_profit >= 0.12: if last_candle['rsi_14'] < 42.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_11_1' elif (last_candle['rsi_14'] < 43.0): return True, 'signal_profit_u_bull_11_9' elif 0.12 > current_profit >= 0.10: if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_10_1' elif (last_candle['rsi_14'] < 49.0): return True, 'signal_profit_u_bull_10_9' elif 0.10 > current_profit >= 0.09: if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_9_1' elif (last_candle['rsi_14'] < 50.0): return True, 'signal_profit_u_bull_9_9' elif 0.09 > current_profit >= 0.08: if last_candle['rsi_14'] < 57.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_8_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_8_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_8_4' elif (last_candle['rsi_14'] < 49.0): return True, 'signal_profit_u_bull_8_9' elif 0.08 > current_profit >= 0.07: if last_candle['rsi_14'] < 52.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_7_1' if last_candle['rsi_14'] > 83.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_7_2' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_7_3' elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_7_4' elif (last_candle['rsi_14'] < 46.0): return True, 'signal_profit_u_bull_7_9' elif 0.07 > current_profit >= 0.06: if last_candle['rsi_14'] < 50.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_6_1' if last_candle['rsi_14'] > 82.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_6_2' elif (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_6_3' elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_6_4' elif (last_candle['cti'] > 0.95): return True, 'signal_profit_u_bull_6_5' elif (last_candle['rsi_14'] < 43.0): return True, 'signal_profit_u_bull_6_9' elif 0.06 > current_profit >= 0.05: if last_candle['rsi_14'] < 48.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_5_1' if last_candle['rsi_14'] > 80.0 and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_5_2' elif (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_5_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_5_4' elif (last_candle['cti'] > 0.952): return True, 'signal_profit_u_bull_5_5' elif (last_candle['rsi_14'] < 51.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_5_6' elif (last_candle['rsi_14'] < 42.0): return True, 'signal_profit_u_bull_5_9' elif 0.05 > current_profit >= 0.04: if last_candle['rsi_14'] < 47.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_4_1' elif (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_4_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_4_4' elif (last_candle['cti'] > 0.954): return True, 'signal_profit_u_bull_4_5' elif (last_candle['rsi_14'] < 50.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_4_6' elif (last_candle['rsi_14'] < 41.0): return True, 'signal_profit_u_bull_4_9' elif 0.04 > current_profit >= 0.03: if last_candle['rsi_14'] < 46.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_3_1' elif (last_candle['rsi_14'] < 49.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_3_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_3_4' elif (last_candle['cti'] > 0.956): return True, 'signal_profit_u_bull_3_5' elif (last_candle['rsi_14'] < 49.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_3_6' elif (last_candle['rsi_14'] < 36.0): return True, 'signal_profit_u_bull_3_9' elif 0.03 > current_profit >= 0.02: if last_candle['rsi_14'] < 45.0 and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_2_1' elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_2_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_2_4' elif (last_candle['cti'] > 0.958): return True, 'signal_profit_u_bull_2_5' elif (last_candle['rsi_14'] < 46.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_2_6' elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_u_bull_2_7' elif last_candle['rsi_14'] < 41.0 and (last_candle['cmf'] < -0.25): return True, 'signal_profit_u_bull_2_8' elif (last_candle['rsi_14'] < 35.0): return True, 'signal_profit_u_bull_2_9' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 37.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_1_1' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf'] < -0.4): return True, 'signal_profit_u_bull_1_3' elif (last_candle['rsi_14'] < 47.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bull_1_4' elif (last_candle['cti'] > 0.96): return True, 'signal_profit_u_bull_1_5' elif (last_candle['rsi_14'] < 43.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_1_6' elif (last_candle['rsi_14'] < 43.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_u_bull_1_7' elif last_candle['rsi_14'] < 40.0 and (last_candle['cmf'] < -0.25): return True, 'signal_profit_u_bull_1_8' elif (last_candle['rsi_14'] < 33.0): return True, 'signal_profit_u_bull_1_9' else: if current_profit >= 0.20: if last_candle['rsi_14'] < 30.0: return True, 'signal_profit_u_bear_12_1' elif 0.20 > current_profit >= 0.12: if last_candle['rsi_14'] < 42.0: return True, 'signal_profit_u_bear_11_1' elif 0.12 > current_profit >= 0.10: if last_candle['rsi_14'] < 46.0: return True, 'signal_profit_u_bear_10_1' elif 0.10 > current_profit >= 0.09: if last_candle['rsi_14'] < 50.0: return True, 'signal_profit_u_bear_9_1' elif (last_candle['rsi_14'] > 82.0): return True, 'signal_profit_u_bear_9_2' elif 0.09 > current_profit >= 0.08: if last_candle['rsi_14'] < 57.0: return True, 'signal_profit_u_bear_8_1' elif (last_candle['rsi_14'] > 80.0): return True, 'signal_profit_u_bear_8_2' elif 0.08 > current_profit >= 0.07: if last_candle['rsi_14'] < 56.0: return True, 'signal_profit_u_bear_7_1' elif (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_u_bear_7_2' elif 0.07 > current_profit >= 0.06: if last_candle['rsi_14'] < 54.0: return True, 'signal_profit_u_bear_6_1' elif (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_u_bear_6_2' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_u_bear_6_3' elif (last_candle['cti'] > 0.94): return True, 'signal_profit_u_bear_6_5' elif 0.06 > current_profit >= 0.05: if last_candle['rsi_14'] < 52.0: return True, 'signal_profit_u_bear_5_1' elif (last_candle['rsi_14'] < 57.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_u_bear_5_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bear_5_4' elif (last_candle['cti'] > 0.942): return True, 'signal_profit_u_bear_5_5' elif (last_candle['rsi_14'] < 57.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_5_6' elif 0.05 > current_profit >= 0.04: if last_candle['rsi_14'] < 50.0: return True, 'signal_profit_u_bear_4_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.05): return True, 'signal_profit_u_bear_4_3' elif (last_candle['rsi_14'] < 57.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bear_4_4' elif (last_candle['cti'] > 0.944): return True, 'signal_profit_u_bear_4_5' elif (last_candle['rsi_14'] < 56.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_4_6' elif 0.04 > current_profit >= 0.03: if last_candle['rsi_14'] < 48.0: return True, 'signal_profit_u_bear_3_1' elif (last_candle['rsi_14'] < 55.0) and (last_candle['cmf'] < -0.05): return True, 'signal_profit_u_bear_3_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bear_3_4' elif (last_candle['cti'] > 0.946): return True, 'signal_profit_u_bear_3_5' elif (last_candle['rsi_14'] < 55.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_3_6' elif 0.03 > current_profit >= 0.02: if last_candle['rsi_14'] < 55.0: #46 return True, 'signal_profit_u_bear_2_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.05): return True, 'signal_profit_u_bear_2_3' elif (last_candle['rsi_14'] < 55.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bear_2_4' elif (last_candle['cti'] > 0.948): return True, 'signal_profit_u_bear_2_5' elif (last_candle['rsi_14'] < 54.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_2_6' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_u_bear_2_7' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_1_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.05): return True, 'signal_profit_u_bear_1_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0.0): return True, 'signal_profit_u_bear_1_4' elif (last_candle['cti'] > 0.95): return True, 'signal_profit_u_bear_1_5' elif (last_candle['rsi_14'] < 44.0) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bear_1_6' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf_1h'] < -0.05) and (last_candle['cti_1h'] > 0.85): return True, 'signal_profit_u_bear_1_7' elif (last_candle['rsi_14'] < 34.0): return True, 'signal_profit_u_bear_1_9' return False, None def sell_pump_main(self, current_profit: float, last_candle) -> tuple: if last_candle['sell_pump_48_1_1h']: if (last_candle['moderi_96']): if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_6_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_5_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_4_3' elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_3_1' elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_3_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_2_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_2_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_48_1_1' elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bull_48_1_3' elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_48_1_4' else: if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_6_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_5_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_4_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_3_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_3_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_2_1' elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_2_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_48_1_1' elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.12): return True, 'signal_profit_p_bear_48_1_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_48_1_4' elif last_candle['sell_pump_36_1_1h']: if (last_candle['moderi_96']): if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_6_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_5_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_4_3' elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_3_1' elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_3_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_2_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_2_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_36_1_1' elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bull_36_1_3' elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_36_1_4' else: if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_6_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_5_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_4_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_3_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_3_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_2_1' elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_2_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_36_1_1' elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.2): return True, 'signal_profit_p_bear_36_1_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_36_1_4' elif last_candle['sell_pump_24_1_1h']: if (last_candle['moderi_96']): if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 51.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_6_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_5_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_4_3' elif (last_candle['rsi_14'] < 53.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_3_1' elif (last_candle['rsi_14'] < 46.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_3_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_2_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_2_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 35.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bull_24_1_1' elif (last_candle['rsi_14'] < 38.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bull_24_1_3' elif (last_candle['rsi_14'] < 46.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bull_24_1_4' else: if current_profit >= 0.2: if (last_candle['rsi_14'] < 30.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_12_1' elif 0.2 > current_profit >= 0.12: if (last_candle['rsi_14'] < 42.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_11_1' elif 0.12 > current_profit >= 0.1: if (last_candle['rsi_14'] < 46.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_10_1' elif 0.1 > current_profit >= 0.09: if (last_candle['rsi_14'] < 50.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_9_1' elif 0.09 > current_profit >= 0.08: if (last_candle['rsi_14'] < 57.5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_8_1' elif 0.08 > current_profit >= 0.07: if (last_candle['rsi_14'] < 53.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_7_1' elif 0.07 > current_profit >= 0.06: if (last_candle['rsi_14'] < 52.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_6_1' elif (last_candle['rsi_14'] < 58.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_6_3' elif (last_candle['rsi_14'] < 58.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_6_4' elif 0.06 > current_profit >= 0.05: if (last_candle['rsi_14'] < 50.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_5_1' elif (last_candle['rsi_14'] < 56.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_5_3' elif (last_candle['rsi_14'] < 56.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_5_4' elif 0.05 > current_profit >= 0.04: if (last_candle['rsi_14'] < 47.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_4_1' elif (last_candle['rsi_14'] < 54.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_4_3' elif (last_candle['rsi_14'] < 54.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_4_4' elif 0.04 > current_profit >= 0.03: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_3_1' elif (last_candle['rsi_14'] < 44.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_3_3' elif (last_candle['rsi_14'] < 52.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_3_4' elif 0.03 > current_profit >= 0.02: if (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_2_1' elif (last_candle['rsi_14'] < 42.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_2_3' elif (last_candle['rsi_14'] < 50.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_2_4' elif 0.02 > current_profit >= 0.01: if (last_candle['rsi_14'] < 36.0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_p_bear_24_1_1' elif (last_candle['rsi_14'] < 40.0) and (last_candle['cmf'] < -0.3): return True, 'signal_profit_p_bear_24_1_3' elif (last_candle['rsi_14'] < 48.0) and (last_candle['r_14'] == 0): return True, 'signal_profit_p_bear_24_1_4' return False, None def sell_dec_main(self, current_profit: float, last_candle) -> tuple: if (self.sell_custom_dec_profit_max_1 > current_profit >= self.sell_custom_dec_profit_min_1) and (last_candle['sma_200_dec_20']): return True, 'signal_profit_d_1' elif (self.sell_custom_dec_profit_max_2 > current_profit >= self.sell_custom_dec_profit_min_2) and (last_candle['close'] < last_candle['ema_100']): return True, 'signal_profit_d_2' return False, None def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple: if (self.sell_trail_profit_max_1 > current_profit >= self.sell_trail_profit_min_1) and (self.sell_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_trail_down_1)) and (last_candle['moderi_96'] == False): return True, 'signal_profit_t_1' elif (self.sell_trail_profit_max_2 > current_profit >= self.sell_trail_profit_min_2) and (self.sell_trail_rsi_min_2 < last_candle['rsi_14'] < self.sell_trail_rsi_max_2) and (max_profit > (current_profit + self.sell_trail_down_2)) and (last_candle['ema_25'] < last_candle['ema_50']): return True, 'signal_profit_t_2' elif (self.sell_trail_profit_max_3 > current_profit >= self.sell_trail_profit_min_3) and (max_profit > (current_profit + self.sell_trail_down_3)) and (last_candle['sma_200_dec_20_1h']): return True, 'signal_profit_t_3' elif (self.sell_trail_profit_max_4 > current_profit >= self.sell_trail_profit_min_4) and (max_profit > (current_profit + self.sell_trail_down_4)) and (last_candle['sma_200_dec_24']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_t_4' return False, None def sell_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple: # Pumped pair, short duration if (last_candle['sell_pump_24_1_1h']) and (0.2 > current_profit >= 0.07) and (current_time - timedelta(minutes=30) < trade.open_date_utc): return True, 'signal_profit_p_s_1' elif (self.sell_custom_long_profit_min_1 < current_profit < self.sell_custom_long_profit_max_1) and (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1) > trade.open_date_utc): return True, 'signal_profit_l_1' return False, None def sell_under_min(self, current_profit: float, last_candle) -> tuple: if ((last_candle['moderi_96']) == False): # Downtrend if (self.sell_custom_profit_under_profit_max_1 > current_profit >= self.sell_custom_profit_under_profit_min_1) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_1): return True, 'signal_profit_u_e_1' else: # Uptrend if (current_profit >= self.sell_custom_profit_under_profit_2) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_2): return True, 'signal_profit_u_e_2' return False, None def sell_stoploss(self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple: # Under & near EMA200, local uptrend move if ( (current_profit < -0.05) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['cmf'] < 0.0) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < 0.004) and last_candle['rsi_14'] > previous_candle_1['rsi_14'] and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + 10.0)) and (last_candle['sma_200_dec_24']) and (current_time - timedelta(minutes=2880) > trade.open_date_utc) ): return True, 'signal_stoploss_u_e_1' # Under EMA200, local strong uptrend move if ( (current_profit < -0.08) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['cmf'] < 0.0) and last_candle['rsi_14'] > previous_candle_1['rsi_14'] and (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + 24.0)) and (last_candle['sma_200_dec_20']) and (last_candle['sma_200_dec_24']) and (current_time - timedelta(minutes=2880) > trade.open_date_utc) ): return True, 'signal_stoploss_u_e_2' # Under EMA200, pair negative, low max rate if ( (current_profit < -0.08) and (max_profit < 0.04) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['ema_25'] < last_candle['ema_50']) and (last_candle['sma_200_dec_20']) and (last_candle['sma_200_dec_24']) and (last_candle['sma_200_dec_20_1h']) and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (last_candle['cmf'] < -0.0) and (last_candle['cmf_1h'] < -0.0) and (last_candle['btc_not_downtrend_1h'] == False) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) ): return True, 'signal_stoploss_u_e_doom' # Under EMA200, pair and BTC negative, low max rate if ( (-0.05 > current_profit > -0.09) and (last_candle['btc_not_downtrend_1h'] == False) and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (max_profit < 0.005) and (max_loss < 0.09) and (last_candle['sma_200_dec_24']) and (last_candle['cmf'] < -0.0) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['ema_25'] < last_candle['ema_50']) and (last_candle['cti'] < -0.8) and (last_candle['r_480'] < -50.0) ): return True, 'signal_stoploss_u_e_b_1' # Under EMA200, pair and BTC negative, CTI, Elder Ray Index negative, normal max rate elif ( (-0.1 > current_profit > -0.2) and (last_candle['btc_not_downtrend_1h'] == False) and (last_candle['ema_vwma_osc_32'] < 0.0) and (last_candle['ema_vwma_osc_64'] < 0.0) and (last_candle['ema_vwma_osc_96'] < 0.0) and (max_profit < 0.05) and (max_loss < 0.2) and (last_candle['sma_200_dec_24']) and (last_candle['sma_200_dec_20_1h']) and (last_candle['cmf'] < -0.45) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['ema_25'] < last_candle['ema_50']) and (last_candle['cti'] < -0.8) and (last_candle['r_480'] < -97.0) ): return True, 'signal_stoploss_u_e_b_2' return False, None def sell_pump_dec(self, current_profit: float, last_candle) -> tuple: if (0.03 > current_profit >= 0.005) and (last_candle['sell_pump_48_1_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']): return True, 'signal_profit_p_d_1' elif (0.06 > current_profit >= 0.04) and (last_candle['sell_pump_48_2_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']): return True, 'signal_profit_p_d_2' elif (0.09 > current_profit >= 0.06) and (last_candle['sell_pump_48_3_1h']) and (last_candle['sma_200_dec_20']) and (last_candle['close'] < last_candle['ema_200']): return True, 'signal_profit_p_d_3' elif (0.04 > current_profit >= 0.02) and (last_candle['sma_200_dec_20']) and (last_candle['sell_pump_24_2_1h']): return True, 'signal_profit_p_d_4' return False, None def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple: # Pumped 48h 1, under EMA200 if (self.sell_custom_pump_under_profit_max_1 > current_profit >= self.sell_custom_pump_under_profit_min_1) and (last_candle['sell_pump_48_1_1h']) and (last_candle['close'] < last_candle['ema_200']): return True, 'signal_profit_p_u_1' # Pumped 36h 2, trail 1 elif (last_candle['sell_pump_36_2_1h']) and (self.sell_custom_pump_trail_profit_max_1 > current_profit >= self.sell_custom_pump_trail_profit_min_1) and (self.sell_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_custom_pump_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1)): return True, 'signal_profit_p_t_1' return False, None def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple: if (max_loss > self.sell_custom_recover_min_loss_1) and (current_profit >= self.sell_custom_recover_profit_1): return True, 'signal_profit_r_1' elif (max_loss > self.sell_custom_recover_min_loss_2) and (self.sell_custom_recover_profit_max_2 > current_profit >= self.sell_custom_recover_profit_min_2) and (last_candle['rsi_14'] < self.sell_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']): return True, 'signal_profit_r_2' return False, None def sell_r_1(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -0.4: return True, 'signal_profit_w_1_1' elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -0.5: return True, 'signal_profit_w_1_2' elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -0.6: return True, 'signal_profit_w_1_3' elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -0.7: return True, 'signal_profit_w_1_4' elif 0.06 > current_profit >= 0.05: if last_candle['r_480'] > -1.0: return True, 'signal_profit_w_1_5' elif 0.07 > current_profit >= 0.06: if last_candle['r_480'] > -2.0: return True, 'signal_profit_w_1_6' elif 0.08 > current_profit >= 0.07: if last_candle['r_480'] > -2.2: return True, 'signal_profit_w_1_7' elif 0.09 > current_profit >= 0.08: if last_candle['r_480'] > -2.4: return True, 'signal_profit_w_1_8' elif 0.1 > current_profit >= 0.09: if last_candle['r_480'] > -2.6: return True, 'signal_profit_w_1_9' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 72.0): return True, 'signal_profit_w_1_10' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_w_1_11' elif current_profit >= 0.2: if (last_candle['r_480'] > -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'signal_profit_w_1_12' return False, None def sell_r_2(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_1' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -4.1) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -4.2) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_3' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -4.3) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_4' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_5' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_6' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_7' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_8' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -4.8) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_9' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_10' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -3.2) and (last_candle['rsi_14'] > 81.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_11' elif current_profit >= 0.2: if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 81.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_12' return False, None def sell_r_3(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_1' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_3' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_4' return False, None def sell_r_4(self, current_profit: float, last_candle) -> tuple: if (0.02 > current_profit >= 0.012): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 78.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_1' elif (0.03 > current_profit >= 0.02): if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 78.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_2' elif (0.04 > current_profit >= 0.03): if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 78.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_3' elif (0.05 > current_profit >= 0.04): if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 78.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_4' elif (0.06 > current_profit >= 0.05): if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 78.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_5' elif (0.07 > current_profit >= 0.06): if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_6' elif (0.08 > current_profit >= 0.07): if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_7' elif (0.09 > current_profit >= 0.08): if (last_candle['r_480'] > -5.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_8' elif (0.1 > current_profit >= 0.09): if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_9' elif (0.12 > current_profit >= 0.1): if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_10' elif (0.2 > current_profit >= 0.12): if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_11' elif (current_profit >= 0.2): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_12' return False, None def sell_r_5(self, current_profit: float, last_candle) -> tuple: if (0.02 > current_profit >= 0.012): if (last_candle['r_480'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_1' elif (0.03 > current_profit >= 0.02): if (last_candle['r_480'] > -1.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_2' elif (0.04 > current_profit >= 0.03): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_3' elif (0.05 > current_profit >= 0.04): if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_4' elif (0.06 > current_profit >= 0.05): if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_5' elif (0.07 > current_profit >= 0.06): if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_6' elif (0.08 > current_profit >= 0.07): if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_7' elif (0.09 > current_profit >= 0.08): if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_8' elif (0.1 > current_profit >= 0.09): if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_9' elif (0.12 > current_profit >= 0.1): if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_10' elif (0.2 > current_profit >= 0.12): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_11' elif (current_profit >= 0.2): if (last_candle['r_480'] > -1.5) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti_1h'] > 0.92): return True, 'signal_profit_w_5_12' return False, None def sell_r_6(self, current_profit: float, last_candle) -> tuple: if (0.02 > current_profit >= 0.012): if (last_candle['r_14'] > -0.1) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_1' elif (0.03 > current_profit >= 0.02): if (last_candle['r_14'] > -0.2) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_2' elif (0.04 > current_profit >= 0.03): if (last_candle['r_14'] > -0.3) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_3' elif (0.05 > current_profit >= 0.04): if (last_candle['r_14'] > -0.4) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_4' elif (0.06 > current_profit >= 0.05): if (last_candle['r_14'] > -0.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_5' elif (0.07 > current_profit >= 0.06): if (last_candle['r_14'] > -0.6) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_6' elif (0.08 > current_profit >= 0.07): if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_7' elif (0.09 > current_profit >= 0.08): if (last_candle['r_14'] > -1.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_8' elif (0.1 > current_profit >= 0.09): if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_9' elif (0.12 > current_profit >= 0.1): if (last_candle['r_14'] > -0.75) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_10' elif (0.2 > current_profit >= 0.12): if (last_candle['r_14'] > -0.5) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_11' elif (current_profit >= 0.2): if (last_candle['r_14'] > -0.1) and (last_candle['rsi_14'] > 75.0) and (last_candle['cti'] > 0.8) and (last_candle['cci'] > 200.0): return True, 'signal_profit_w_6_12' return False, None def mark_profit_target(self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1) -> tuple: # if self.profit_target_1_enable: # if (current_profit > 0) and (last_candle['zlema_4_lowKF'] > last_candle['lowKF']) and (previous_candle_1['zlema_4_lowKF'] < previous_candle_1['lowKF']) and (last_candle['cci'] > -100) and (last_candle['hrsi'] > 70): # return pair, "mark_profit_target_01" return None, None def sell_profit_target(self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached) -> tuple: # if self.profit_target_1_enable and previous_sell_reason == "mark_profit_target_01": # if (current_profit > 0) and (current_rate < (previous_rate - 0.005)): # return True, 'sell_profit_target_01' return False, None def sell_quick_mode(self, current_profit: float, max_profit:float, last_candle, previous_candle_1) -> tuple: if (0.06 > current_profit > 0.02) and (last_candle['rsi_14'] > 80.0): return True, 'signal_profit_q_1' if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95): return True, 'signal_profit_q_2' if (0.04 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1): return True, 'signal_profit_q_pmax_bull' if (0.045 > current_profit > 0.005) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016): return True, 'signal_profit_q_pmax_bear' if (last_candle['momdiv_sell_1h'] == True) and (current_profit > 0.02): return True, 'signal_profit_q_momdiv_1h' if (last_candle['momdiv_sell'] == True) and (current_profit > 0.02): return True, 'signal_profit_q_momdiv' if (last_candle['momdiv_coh'] == True) and (current_profit > 0.02): return True, 'signal_profit_q_momdiv_coh' return False, None def sell_ichi(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple: if (0.0 < current_profit < 0.05) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_ichi_u' elif (max_loss > 0.07) and (current_profit > 0.02): return True, 'signal_profit_ichi_r_0' elif (max_loss > 0.06) and (current_profit > 0.03): return True, 'signal_profit_ichi_r_1' elif (max_loss > 0.05) and (current_profit > 0.04): return True, 'signal_profit_ichi_r_2' elif (max_loss > 0.04) and (current_profit > 0.05): return True, 'signal_profit_ichi_r_3' elif (max_loss > 0.03) and (current_profit > 0.06): return True, 'signal_profit_ichi_r_4' elif (0.05 < current_profit < 0.1) and (current_time - timedelta(minutes=720) > trade.open_date_utc): return True, 'signal_profit_ichi_slow' elif (0.07 < current_profit < 0.1) and (max_profit-current_profit > 0.025) and (max_profit > 0.1): return True, 'signal_profit_ichi_t' return False, None def sell_long_mode(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['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']) and (previous_candle_5['close'] > previous_candle_5['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_1_1_1' else: if (current_profit > 0.01): return True, 'sell_long_1_2_1' # Sell signal 2 elif (last_candle['rsi_14'] > 79.0) and (last_candle['close'] > last_candle['bb20_2_upp']) and (previous_candle_1['close'] > previous_candle_1['bb20_2_upp']) and (previous_candle_2['close'] > previous_candle_2['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_2_1_1' else: if (current_profit > 0.01): return True, 'sell_long_2_2_1' # Sell signal 3 elif (last_candle['rsi_14'] > 82.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_3_1_1' else: if (current_profit > 0.01): return True, 'sell_long_3_2_1' # Sell signal 4 elif (last_candle['rsi_14'] > 78.0) and (last_candle['rsi_14_1h'] > 80.0): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_4_1_1' else: if (current_profit > 0.01): return True, 'sell_long_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'] > 79.5): if (current_profit > 0.01): return True, 'sell_long_6_1' # Sell signal 7 elif (last_candle['rsi_14_1h'] > 82.0) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_7_1_1' else: if (current_profit > 0.01): return True, 'sell_long_7_2_1' # Sell signal 8 elif (last_candle['close'] > last_candle['bb20_2_upp_1h'] * 1.05): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return True, 'sell_long_8_1_1' else: if (current_profit > 0.01): return True, 'sell_long_8_2_1' elif (0.02 < current_profit <= 0.06) and (max_profit - current_profit > 0.04) and (last_candle['cmf'] < 0.0) and (last_candle['sma_200_dec_24']): return True, 'sell_long_t_1' elif (0.06 < current_profit <= 0.12) and (max_profit - current_profit > 0.06) and (last_candle['cmf'] < 0.0): return True, 'sell_long_t_2' elif (0.12 < current_profit <= 0.24) and (max_profit - current_profit > 0.08) and (last_candle['cmf'] < 0.0): return True, 'sell_long_t_3' elif (0.24 < current_profit <= 0.5) and (max_profit - current_profit > 0.09) and (last_candle['cmf'] < 0.0): return True, 'sell_long_t_4' elif (0.5 < current_profit <= 0.9) and (max_profit - current_profit > 0.1) and (last_candle['cmf'] < 0.0): return True, 'sell_long_t_5' elif (0.03 < current_profit <= 0.06) and (current_time - timedelta(minutes=720) > trade.open_date_utc) and (last_candle['r_480'] > -20.0): return True, 'sell_long_l_1' return self.sell_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) def sell_pivot(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple: if (last_candle['close'] > (last_candle['res3_1d'] * 2.2)): if (0.02 > current_profit >= 0.012): if (last_candle['r_14'] >= -0.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['r_480'] > -3.0): return True, 'signal_profit_pv_1_1_1' elif (0.03 > current_profit >= 0.02): if (last_candle['r_14'] > -0.4) and (last_candle['rsi_14'] > 76.0) and (last_candle['r_480'] > -5.0): return True, 'signal_profit_pv_1_2_1' elif (0.04 > current_profit >= 0.03): if (last_candle['r_14'] > -0.8) and (last_candle['rsi_14'] > 74.0) and (last_candle['r_480'] > -10.0): return True, 'signal_profit_pv_1_3_1' elif (0.05 > current_profit >= 0.04): if (last_candle['r_14'] > -1.0) and (last_candle['rsi_14'] > 70.0) and (last_candle['r_480'] > -15.0): return True, 'signal_profit_pv_1_4_1' elif (0.06 > current_profit >= 0.05): if (last_candle['r_14'] > -1.2) and (last_candle['rsi_14'] > 66.0) and (last_candle['r_480'] > -20.0): return True, 'signal_profit_pv_1_5_1' elif (0.07 > current_profit >= 0.06): if (last_candle['r_14'] > -1.6) and (last_candle['rsi_14'] > 60.0) and (last_candle['r_480'] > -25.0): return True, 'signal_profit_pv_1_6_1' elif (0.08 > current_profit >= 0.07): if (last_candle['r_14'] > -2.0) and (last_candle['rsi_14'] > 56.0) and (last_candle['r_480'] > -30.0): return True, 'signal_profit_pv_1_7_1' elif (last_candle['close'] > (last_candle['res3_1d'] * 1.3)): if (0.02 > current_profit >= 0.012): if (last_candle['rsi_14'] > 80.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_1_1' elif (last_candle['rsi_14'] > 79.0) and (last_candle['r_14'] > -1.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_1_2' elif (0.03 > current_profit >= 0.02): if (last_candle['rsi_14'] > 78.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_2_1' elif (last_candle['rsi_14'] > 77.0) and (last_candle['r_14'] > -3.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_2_2' elif (0.04 > current_profit >= 0.03): if (last_candle['rsi_14'] > 76.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_3_1' elif (last_candle['rsi_14'] > 75.0) and (last_candle['r_14'] > -5.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_3_2' elif (0.05 > current_profit >= 0.04): if (last_candle['rsi_14'] > 72.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_4_1' elif (last_candle['rsi_14'] > 71.0) and (last_candle['r_14'] > -7.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_4_2' elif (0.06 > current_profit >= 0.05): if (last_candle['rsi_14'] > 68.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_5_1' elif (last_candle['rsi_14'] > 67.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_5_2' elif (0.07 > current_profit >= 0.06): if (last_candle['rsi_14'] > 60.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_6_1' elif (last_candle['rsi_14'] > 59.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_6_2' elif (0.08 > current_profit >= 0.07): if (last_candle['rsi_14'] > 58.0) and (last_candle['cti_1h'] > 0.84) and (last_candle['cmf'] < 0.0) and (last_candle['cci'] > 200.0): return True, 'signal_profit_pv_2_7_1' elif (last_candle['rsi_14'] > 57.0) and (last_candle['r_14'] > -9.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_pv_2_7_2' return False, None def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] previous_candle_3 = dataframe.iloc[-4] previous_candle_4 = dataframe.iloc[-5] previous_candle_5 = dataframe.iloc[-6] buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) max_loss = ((trade.open_rate - trade.min_rate) / trade.min_rate) # Long mode if all(c in ['45', '46', '47'] for c in buy_tags): sell, signal_name = self.sell_long_mode(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, buy_tag) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Skip remaining sell logic for long mode return None # Quick sell mode if all(c in ['empty', '32', '33', '34', '35', '36', '37', '38', '40'] for c in buy_tags): sell, signal_name = self.sell_quick_mode(current_profit, max_profit, last_candle, previous_candle_1) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Ichi Trade management if all(c in ['39'] for c in buy_tags): sell, signal_name = self.sell_ichi(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Over EMA200, main profit targets sell, signal_name = self.sell_over_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Under EMA200, main profit targets sell, signal_name = self.sell_under_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # The pair is pumped sell, signal_name = self.sell_pump_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # The pair is descending sell, signal_name = self.sell_dec_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Trailing sell, signal_name = self.sell_trail_main(current_profit, last_candle, max_profit) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Duration based sell, signal_name = self.sell_duration_main(current_profit, last_candle, trade, current_time) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Under EMA200, exit with any profit sell, signal_name = self.sell_under_min(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Stoplosses sell, signal_name = self.sell_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Pumped descending pairs sell, signal_name = self.sell_pump_dec(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Extra sells for pumped pairs sell, signal_name = self.sell_pump_extra(current_profit, last_candle, max_profit) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Extra sells for trades that recovered sell, signal_name = self.sell_recover(current_profit, last_candle, max_loss) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 1 sell, signal_name = self.sell_r_1(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 2 sell, signal_name = self.sell_r_2(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 3 sell, signal_name = self.sell_r_3(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 4, plus CTI sell, signal_name = self.sell_r_4(current_profit, last_candle) if (sell) and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 5, plus RSI and CTI 1h sell, signal_name = self.sell_r_5(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 6, plus RSI, CTI, CCI sell, signal_name = self.sell_r_6(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Pivot points based sells sell, signal_name = self.sell_pivot(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # 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_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, signal_name = self.sell_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached) if sell and signal_name is not None: return f"{signal_name} ( {buy_tag})" pair, mark_signal = self.mark_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1) if pair: self._set_profit_target(pair, mark_signal, current_rate, current_time) # Sell signal 1 if self.sell_condition_1_enable and (last_candle['rsi_14'] > self.sell_rsi_bb_1) 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']) and (previous_candle_5['close'] > previous_candle_5['bb20_2_upp']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return f"sell_signal_1_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_1_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_1_2_2 ( {buy_tag})" # Sell signal 2 elif (self.sell_condition_2_enable) and (last_candle['rsi_14'] > self.sell_rsi_bb_2) 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 f"sell_signal_2_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_2_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_2_2_2 ( {buy_tag})" # Sell signal 3 elif (self.sell_condition_3_enable) and (last_candle['rsi_14'] > self.sell_rsi_main_3): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return f"sell_signal_3_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_3_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_3_2_2 ( {buy_tag})" # Sell signal 4 elif self.sell_condition_4_enable and (last_candle['rsi_14'] > self.sell_dual_rsi_rsi_4) and (last_candle['rsi_14_1h'] > self.sell_dual_rsi_rsi_1h_4): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return f"sell_signal_4_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_4_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_4_2_2 ( {buy_tag})" # Sell signal 6 elif self.sell_condition_6_enable and (last_candle['close'] < last_candle['ema_200']) and (last_candle['close'] > last_candle['ema_50']) and (last_candle['rsi_14'] > self.sell_rsi_under_6): if (current_profit > 0.01): return f"sell_signal_6_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_6_2 ( {buy_tag})" # Sell signal 7 elif self.sell_condition_7_enable and (last_candle['rsi_14_1h'] > self.sell_rsi_1h_7) and (last_candle['crossed_below_ema_12_26']): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return f"sell_signal_7_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_7_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_7_2_2 ( {buy_tag})" # Sell signal 8 elif self.sell_condition_8_enable and (last_candle['close'] > last_candle['bb20_2_upp_1h'] * self.sell_bb_relative_8): if (last_candle['close'] > last_candle['ema_200']): if (current_profit > 0.01): return f"sell_signal_8_1_1 ( {buy_tag})" else: if (current_profit > 0.01): return f"sell_signal_8_2_1 ( {buy_tag})" elif (max_loss > 0.5): return f"sell_signal_8_2_2 ( {buy_tag})" return None 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!") 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'] def range_maxgap(self, dataframe: DataFrame, length: int) -> float: """ Maximum Price Gap across interval. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe['open'].rolling(length).max() - dataframe['close'].rolling(length).min() def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float: """ Maximum Price Gap across interval adjusted. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param adjustment: int The adjustment to be applied """ return self.range_maxgap(dataframe, length) / adjustment def range_height(self, dataframe: DataFrame, length: int) -> float: """ Current close distance to range bottom. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe['close'] - dataframe['close'].rolling(length).min() def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool: """ Determine if entry after a pump is safe. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param thresh: int Maximum percentage change threshold :param pull_thresh: int Pullback from interval maximum threshold """ return (dataframe[f'oc_pct_change_{length}'] < thresh) | (self.range_maxgap_adjusted(dataframe, length, pull_thresh) > self.range_height(dataframe, length)) def safe_dips(self, dataframe: DataFrame, thresh_0, thresh_2, thresh_12, thresh_144) -> bool: """ Determine if dip is safe to enter. :param dataframe: DataFrame The original OHLC dataframe :param thresh_0: Threshold value for 0 length top pct change :param thresh_2: Threshold value for 2 length top pct change :param thresh_12: Threshold value for 12 length top pct change :param thresh_144: Threshold value for 144 length top pct change """ return ((dataframe['tpct_change_0'] < thresh_0) & (dataframe['tpct_change_2'] < thresh_2) & (dataframe['tpct_change_12'] < thresh_12) & (dataframe['tpct_change_144'] < thresh_144)) 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 = [(pair, self.info_timeframe_1h) for pair in pairs] informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs]) if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP', 'TRY', 'BRL', 'USDT:USDT', 'BUSD:BUSD', 'USDC:USDC', 'DAI:DAI', 'TUSD:TUSD', 'PAX:PAX', 'USD:USD', 'EUR:EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.append((btc_info_pair, self.timeframe)) informative_pairs.append((btc_info_pair, self.info_timeframe_1h)) informative_pairs.append((btc_info_pair, self.info_timeframe_1d)) return informative_pairs def informative_1d_indicators(self, dataframe: DataFrame, metadata: dict) -> 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=self.info_timeframe_1d) # Top traded coins if self.coin_metrics['top_traded_enabled']: informative_1d = informative_1d.merge(self.coin_metrics['tt_dataframe'], on='date', how='left') informative_1d['is_top_traded'] = informative_1d.apply(lambda row: self.is_top_coin(metadata['pair'], row, self.coin_metrics['top_traded_len']), axis=1) column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_traded_len'] + 1)] informative_1d.drop(columns = column_names, inplace=True) # Top grossing coins if self.coin_metrics['top_grossing_enabled']: informative_1d = informative_1d.merge(self.coin_metrics['tg_dataframe'], on='date', how='left') informative_1d['is_top_grossing'] = informative_1d.apply(lambda row: self.is_top_coin(metadata['pair'], row, self.coin_metrics['top_grossing_len']), axis=1) column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics['top_grossing_len'] + 1)] informative_1d.drop(columns = column_names, inplace=True) # 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') # Smoothed Heikin-Ashi informative_1d['open_sha'], informative_1d['close_sha'], informative_1d['low_sha'] = HeikinAshi(informative_1d, smooth_inputs=True, smooth_outputs=False, length=10) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_1d def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> 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=self.info_timeframe_1h) # EMA informative_1h['ema_12'] = ta.EMA(informative_1h, timeperiod=12) informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15) informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20) informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['ema_26'] = ta.EMA(informative_1h, timeperiod=26) informative_1h['ema_35'] = ta.EMA(informative_1h, timeperiod=35) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['sma_200_dec_20'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # RSI informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) # EWO informative_1h['ewo_sma'] = ewo_sma(informative_1h, 50, 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'] # Chaikin Money Flow informative_1h['cmf'] = chaikin_money_flow(informative_1h, 20) # Williams %R informative_1h['r_480'] = williams_r(informative_1h, period=480) # CTI informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) # CRSI (3, 2, 100) crsi_closechange = informative_1h['close'] / informative_1h['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative_1h['crsi'] = (ta.RSI(informative_1h['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h['close'], 100)) / 3 # Ichimoku ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) informative_1h['chikou_span'] = ichi['chikou_span'] informative_1h['tenkan_sen'] = ichi['tenkan_sen'] informative_1h['kijun_sen'] = ichi['kijun_sen'] informative_1h['senkou_a'] = ichi['senkou_span_a'] informative_1h['senkou_b'] = ichi['senkou_span_b'] informative_1h['leading_senkou_span_a'] = ichi['leading_senkou_span_a'] informative_1h['leading_senkou_span_b'] = ichi['leading_senkou_span_b'] informative_1h['chikou_span_greater'] = (informative_1h['chikou_span'] > informative_1h['senkou_a']).shift(30).fillna(False) informative_1h.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1) # SSL ssl_down, ssl_up = SSLChannels(informative_1h, 10) informative_1h['ssl_down'] = ssl_down informative_1h['ssl_up'] = ssl_up # MOMDIV mom = momdiv(informative_1h) informative_1h['momdiv_buy'] = mom['momdiv_buy'] informative_1h['momdiv_sell'] = mom['momdiv_sell'] informative_1h['momdiv_coh'] = mom['momdiv_coh'] informative_1h['momdiv_col'] = mom['momdiv_col'] # Pump protections informative_1h['hl_pct_change_48'] = self.range_percent_change(informative_1h, 'HL', 48) informative_1h['hl_pct_change_36'] = self.range_percent_change(informative_1h, 'HL', 36) informative_1h['hl_pct_change_24'] = self.range_percent_change(informative_1h, 'HL', 24) informative_1h['oc_pct_change_48'] = self.range_percent_change(informative_1h, 'OC', 48) informative_1h['oc_pct_change_36'] = self.range_percent_change(informative_1h, 'OC', 36) informative_1h['oc_pct_change_24'] = self.range_percent_change(informative_1h, 'OC', 24) informative_1h['hl_pct_change_5'] = self.range_percent_change(informative_1h, 'HL', 5) informative_1h['low_5'] = informative_1h['low'].shift().rolling(5).min() informative_1h['safe_pump_24_10'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_10_24, self.buy_pump_pull_threshold_10_24) informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_10_36, self.buy_pump_pull_threshold_10_36) informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_10_48, self.buy_pump_pull_threshold_10_48) informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_20_24, self.buy_pump_pull_threshold_20_24) informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_20_36, self.buy_pump_pull_threshold_20_36) informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_20_48, self.buy_pump_pull_threshold_20_48) informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_30_24, self.buy_pump_pull_threshold_30_24) informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_30_36, self.buy_pump_pull_threshold_30_36) informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_30_48, self.buy_pump_pull_threshold_30_48) informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_40_24, self.buy_pump_pull_threshold_40_24) informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_40_36, self.buy_pump_pull_threshold_40_36) informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_40_48, self.buy_pump_pull_threshold_40_48) informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_50_24, self.buy_pump_pull_threshold_50_24) informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_50_36, self.buy_pump_pull_threshold_50_36) informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_50_48, self.buy_pump_pull_threshold_50_48) informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_60_24, self.buy_pump_pull_threshold_60_24) informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_60_36, self.buy_pump_pull_threshold_60_36) informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_60_48, self.buy_pump_pull_threshold_60_48) informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_70_24, self.buy_pump_pull_threshold_70_24) informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_70_36, self.buy_pump_pull_threshold_70_36) informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_70_48, self.buy_pump_pull_threshold_70_48) informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_80_24, self.buy_pump_pull_threshold_80_24) informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_80_36, self.buy_pump_pull_threshold_80_36) informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_80_48, self.buy_pump_pull_threshold_80_48) informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_90_24, self.buy_pump_pull_threshold_90_24) informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_90_36, self.buy_pump_pull_threshold_90_36) informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_90_48, self.buy_pump_pull_threshold_90_48) informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_100_24, self.buy_pump_pull_threshold_100_24) informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_100_36, self.buy_pump_pull_threshold_100_36) informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_100_48, self.buy_pump_pull_threshold_100_48) informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_110_24, self.buy_pump_pull_threshold_110_24) informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_110_36, self.buy_pump_pull_threshold_110_36) informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_110_48, self.buy_pump_pull_threshold_110_48) informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_120_24, self.buy_pump_pull_threshold_120_24) informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_120_36, self.buy_pump_pull_threshold_120_36) informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_120_48, self.buy_pump_pull_threshold_120_48) informative_1h['sell_pump_48_1'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_1) informative_1h['sell_pump_48_2'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_2) informative_1h['sell_pump_48_3'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_3) informative_1h['sell_pump_36_1'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_1) informative_1h['sell_pump_36_2'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_2) informative_1h['sell_pump_36_3'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_3) informative_1h['sell_pump_24_1'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_1) informative_1h['sell_pump_24_2'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_2) informative_1h['sell_pump_24_3'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_3) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # 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['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['bb40_2_low']).abs() # 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'] # EMA 200 dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_35'] = ta.EMA(dataframe, timeperiod=35) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec_20'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) dataframe['sma_200_dec_24'] = dataframe['sma_200'] < dataframe['sma_200'].shift(24) # MFI dataframe['mfi'] = ta.MFI(dataframe) # CMF dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # EWO dataframe['ewo_sma'] = ewo_sma(dataframe, 50, 200) # RSI dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) # Chopiness dataframe['chop']= qtpylib.chopiness(dataframe, 14) # Zero-Lag EMA dataframe['zema_61'] = zema(dataframe, period=61) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_480'] = williams_r(dataframe, period=480) # Stochastic RSI stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0) dataframe['stochrsi_fastk_96'] = stochrsi['fastk'] dataframe['stochrsi_fastd_96'] = stochrsi['fastd'] # Modified Elder Ray Index dataframe['moderi_32'] = moderi(dataframe, 32) dataframe['moderi_64'] = moderi(dataframe, 64) dataframe['moderi_96'] = moderi(dataframe, 96) # EMA of VWMA Oscillator dataframe['ema_vwma_osc_32'] = ema_vwma_osc(dataframe, 32) dataframe['ema_vwma_osc_64'] = ema_vwma_osc(dataframe, 64) dataframe['ema_vwma_osc_96'] = ema_vwma_osc(dataframe, 96) # hull dataframe['hull_75'] = hull(dataframe, 75) # CRSI (3, 2, 100) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3 # zlema dataframe['zlema_68'] = zlema(dataframe, 68) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # For sell checks dataframe['crossed_below_ema_12_26'] = qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_68'] = ta.SMA(dataframe, timeperiod=68) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) # HLC3 dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 # CCI dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20) # CCI Oscillator cci_36 = ta.CCI(dataframe, timeperiod=36) cci_36_max = cci_36.rolling(self.startup_candle_count).max() cci_36_min = cci_36.rolling(self.startup_candle_count).min() dataframe['cci_36_osc'] = (cci_36 / cci_36_max).where(cci_36 > 0, -cci_36 / cci_36_min) # MOMDIV mom = momdiv(dataframe) dataframe['momdiv_buy'] = mom['momdiv_buy'] dataframe['momdiv_sell'] = mom['momdiv_sell'] dataframe['momdiv_coh'] = mom['momdiv_coh'] dataframe['momdiv_col'] = mom['momdiv_col'] # Dip protection dataframe['tpct_change_0'] = self.top_percent_change(dataframe,0) dataframe['tpct_change_2'] = self.top_percent_change(dataframe,2) dataframe['tpct_change_12'] = self.top_percent_change(dataframe,12) dataframe['tpct_change_144'] = self.top_percent_change(dataframe,144) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() 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) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] normal_tf_indicators took: {tok - tik:0.4f} seconds.") return dataframe def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) return dataframe def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.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']}] base_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi_14'] > 50)) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.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']}] info_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def daily_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- dataframe['pivot'], dataframe['res1'], dataframe['res2'], dataframe['res3'], dataframe['sup1'], dataframe['sup2'], dataframe['sup3'] = pivot_points(dataframe, mode='fibonacci') # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.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']}] daily_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() ''' --> BTC informative (5m/1h) ___________________________________________________________________________________________ ''' if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP', 'TRY', 'BRL', 'USDT:USDT', 'BUSD:BUSD', 'USDC:USDC', 'DAI:DAI', 'TUSD:TUSD', 'PAX:PAX', 'USD:USD', 'EUR:EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" if self.has_BTC_daily_tf: btc_daily_tf = self.dp.get_pair_dataframe(btc_info_pair, '1d') btc_daily_tf = self.daily_tf_btc_indicators(btc_daily_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_daily_tf, self.timeframe, '1d', ffill=True) drop_columns = [f"{s}_1d" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.has_BTC_info_tf: btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.info_timeframe_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.info_timeframe_1h, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.has_BTC_base_tf: btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> Informative timeframe ___________________________________________________________________________________________ ''' if self.info_timeframe_1d != 'none': informative_1d = self.informative_1d_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, self.info_timeframe_1d, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1d}" for s in ['date','open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.info_timeframe_1h != 'none': informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe_1h, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1h}" for s in ['date']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> Resampled to another timeframe ___________________________________________________________________________________________ ''' if self.res_timeframe != 'none': resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe)) resampled = self.resampled_tf_indicators(resampled, metadata) # Merge resampled info dataframe dataframe = resampled_merge(dataframe, resampled, fill_na=True) dataframe.rename(columns=lambda s: f"{s}_{self.res_timeframe}" if "resample_" in s else s, inplace=True) dataframe.rename(columns=lambda s: s.replace("resample_{}_".format(self.res_timeframe.replace("m","")), ""), inplace=True) drop_columns = [f"{s}_{self.res_timeframe}" for s in ['date']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) ''' --> The indicators for the normal (5m) timeframe ___________________________________________________________________________________________ ''' dataframe = self.normal_tf_indicators(dataframe, metadata) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.") return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' for index in self.buy_protection_params: item_buy_protection_list = [True] global_buy_protection_params = self.buy_protection_params[index] if self.buy_params[f"buy_condition_{index}_enable"]: # Standard protections - Common to every condition # ----------------------------------------------------------------------------------------- if global_buy_protection_params["ema_fast"]: item_buy_protection_list.append(dataframe[f"ema_{global_buy_protection_params['ema_fast_len']}"] > dataframe['ema_200']) if global_buy_protection_params["ema_slow"]: item_buy_protection_list.append(dataframe[f"ema_{global_buy_protection_params['ema_slow_len']}_1h"] > dataframe['ema_200_1h']) if global_buy_protection_params["close_above_ema_fast"]: item_buy_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_fast_len']}"]) if global_buy_protection_params["close_above_ema_slow"]: item_buy_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_slow_len']}_1h"]) if global_buy_protection_params["sma200_rising"]: item_buy_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_buy_protection_params['sma200_rising_val']))) if global_buy_protection_params["sma200_1h_rising"]: item_buy_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_buy_protection_params['sma200_1h_rising_val']))) if global_buy_protection_params["safe_dips_threshold_0"] is not None: item_buy_protection_list.append(dataframe['tpct_change_0'] < global_buy_protection_params["safe_dips_threshold_0"]) if global_buy_protection_params["safe_dips_threshold_2"] is not None: item_buy_protection_list.append(dataframe['tpct_change_2'] < global_buy_protection_params["safe_dips_threshold_2"]) if global_buy_protection_params["safe_dips_threshold_12"] is not None: item_buy_protection_list.append(dataframe['tpct_change_12'] < global_buy_protection_params["safe_dips_threshold_12"]) if global_buy_protection_params["safe_dips_threshold_144"] is not None: item_buy_protection_list.append(dataframe['tpct_change_144'] < global_buy_protection_params["safe_dips_threshold_144"]) if global_buy_protection_params["safe_pump"]: item_buy_protection_list.append(dataframe[f"safe_pump_{global_buy_protection_params['safe_pump_period']}_{global_buy_protection_params['safe_pump_type']}_1h"]) if global_buy_protection_params['btc_1h_not_downtrend']: item_buy_protection_list.append(dataframe['btc_not_downtrend_1h']) if global_buy_protection_params['close_over_pivot_type'] != 'none': item_buy_protection_list.append(dataframe['close'] > dataframe[f"{global_buy_protection_params['close_over_pivot_type']}_1d"] * global_buy_protection_params['close_over_pivot_offset']) if global_buy_protection_params['close_under_pivot_type'] != 'none': item_buy_protection_list.append(dataframe['close'] < dataframe[f"{global_buy_protection_params['close_under_pivot_type']}_1d"] * global_buy_protection_params['close_under_pivot_offset']) if not self.config['runmode'].value in ('live', 'dry_run'): if self.has_bt_agefilter: item_buy_protection_list.append(dataframe['bt_agefilter_ok']) else: if self.has_downtime_protection: item_buy_protection_list.append(dataframe['live_data_ok']) # Buy conditions # ----------------------------------------------------------------------------------------- item_buy_logic = [] item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list)) # Condition #1 if index == 1: # Non-Standard protections # Logic item_buy_logic.append(((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > self.buy_1_min_inc) item_buy_logic.append(dataframe['rsi_14'] < self.buy_1_rsi_max) item_buy_logic.append(dataframe['r_14'] < self.buy_2_r_14_max) item_buy_logic.append(dataframe['mfi'] < self.buy_1_mfi_max) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_1_rsi_1h_min) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_1_rsi_1h_max) # Condition #2 elif index == 2: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.buy_2_rsi_1h_diff) item_buy_logic.append(dataframe['mfi'] < self.buy_2_mfi) item_buy_logic.append(dataframe['cti'] < self.buy_2_cti_max) item_buy_logic.append(dataframe['r_480'] > self.buy_2_r_480_min) item_buy_logic.append(dataframe['r_480'] < self.buy_2_r_480_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_2_cti_1h_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_2_volume)) # Condition #3 elif index == 3: # Non-Standard protections # Logic item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_3_bb40_bbdelta_close)) item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_3_bb40_closedelta_close)) item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_3_bb40_tail_bbdelta)) 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['cci_36_osc'] > self.buy_3_cci_36_osc_min) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_3_crsi_1h_min) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_3_r_480_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_3_cti_1h_max) # Condition #4 elif index == 4: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_50']) item_buy_logic.append(dataframe['close'] < self.buy_4_bb20_close_bblowerband * dataframe['bb20_2_low']) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_4_bb20_volume)) item_buy_logic.append(dataframe['cti'] < self.buy_4_cti_max) # Condition #5 elif index == 5: # Non-Standard protections item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_5_ema_rel)) # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_5_ema_open_mult)) 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'] * self.buy_5_bb_offset)) item_buy_logic.append(dataframe['cti'] < self.buy_5_cti_max) item_buy_logic.append(dataframe['rsi_14'] > self.buy_5_rsi_14_min) item_buy_logic.append(dataframe['mfi'] > self.buy_5_mfi_min) item_buy_logic.append(dataframe['r_14'] < self.buy_5_r_14_max) item_buy_logic.append(dataframe['r_14'].shift(1) < self.buy_5_r_14_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_5_crsi_1h_min) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_5_volume)) # Condition #6 elif index == 6: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_6_ema_open_mult)) 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'] * self.buy_6_bb_offset)) item_buy_logic.append(dataframe['r_14'] < self.buy_6_r_14_max) item_buy_logic.append(dataframe['cti_1h'] > self.buy_6_cti_1h_min) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_6_crsi_1h_min) # Condition #7 elif index == 7: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_7_ema_open_mult)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_7_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_7_cti_max) item_buy_logic.append(dataframe['rsi_14'] < self.buy_7_rsi_max) # Condition #8 elif index == 8: # Non-Standard protections item_buy_logic.append(dataframe['ema_20'] > dataframe['ema_50']) item_buy_logic.append(dataframe['ema_15'] > dataframe['ema_100']) item_buy_logic.append(dataframe['ema_200'] > dataframe['sma_200']) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_8_bb_offset)) item_buy_logic.append(dataframe['r_14'] < self.buy_8_r_14_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_8_cti_1h_max) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_8_r_480_1h_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_8_volume)) # Condition #9 elif index == 9: # Non-Standard protections item_buy_logic.append(dataframe['ema_50'] > dataframe['ema_200']) # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_9_ma_offset) item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_9_bb_offset) item_buy_logic.append(dataframe['mfi'] < self.buy_9_mfi_max) item_buy_logic.append(dataframe['cti'] < self.buy_9_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_9_r_14_max) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_9_rsi_1h_min) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_9_rsi_1h_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_9_crsi_1h_min) # Condition #10 elif index == 10: # Non-Standard protections item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_10_ma_offset_high) item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_10_bb_offset) item_buy_logic.append(dataframe['r_14'] < self.buy_10_r_14_max) item_buy_logic.append(dataframe['cti_1h'] > self.buy_10_cti_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_10_cti_1h_max) # Condition #11 elif index == 11: # Non-Standard protections # Logic item_buy_logic.append(((dataframe['close'] - dataframe['open'].rolling(6).min()) / dataframe['open'].rolling(6).min()) > self.buy_11_min_inc) item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_11_ma_offset) item_buy_logic.append(dataframe['rsi_14'] < self.buy_11_rsi_max) item_buy_logic.append(dataframe['mfi'] < self.buy_11_mfi_max) item_buy_logic.append(dataframe['cci'] < self.buy_11_cci_max) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_11_rsi_1h_min) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_11_rsi_1h_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_11_cti_1h_max) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_11_r_480_1h_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_11_crsi_1h_min) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_12_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_12_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_12_rsi_max) item_buy_logic.append(dataframe['cti'] < self.buy_12_cti_max) # Condition #13 elif index == 13: # Non-Standard protections item_buy_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_13_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_13_cti_max) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_13_ewo_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_13_cti_1h_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_13_crsi_1h_min) # Condition #14 elif index == 14: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_14_ema_open_mult)) 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'] * self.buy_14_bb_offset)) item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_14_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_14_cti_max) # Condition #15 elif index == 15: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_15_ema_open_mult)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['rsi_14'] < self.buy_15_rsi_min) item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_15_ma_offset) item_buy_logic.append(dataframe['cti_1h'] > self.buy_15_cti_1h_min) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_16_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_16_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_16_rsi_max) item_buy_logic.append(dataframe['cti'] < self.buy_16_cti_max) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_17_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_17_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_17_cti_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_17_crsi_1h_min) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_17_volume)) # Condition #18 elif index == 18: # Non-Standard protections item_buy_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) item_buy_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36)) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_18_bb_offset)) item_buy_logic.append(dataframe['rsi_14'] < self.buy_18_rsi_max) item_buy_logic.append(dataframe['cti'] < self.buy_18_cti_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_18_cti_1h_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_18_volume)) # Condition #19 elif index == 19: # Non-Standard protections item_buy_logic.append(dataframe['moderi_32'] == True) item_buy_logic.append(dataframe['moderi_64'] == True) item_buy_logic.append(dataframe['moderi_96'] == True) # Logic item_buy_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h']) item_buy_logic.append(dataframe['low'] < dataframe['ema_100_1h']) item_buy_logic.append(dataframe['close'] > dataframe['ema_100_1h']) item_buy_logic.append(dataframe['chop'] < self.buy_19_chop_max) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_19_rsi_1h_min) # Condition #20 elif index == 20: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_14'] < self.buy_20_rsi_14_max) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_20_rsi_14_1h_max) item_buy_logic.append(dataframe['cti'] < self.buy_20_cti_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_20_volume)) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_14'] < self.buy_21_rsi_14_max) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_21_rsi_14_1h_max) item_buy_logic.append(dataframe['cti'] < self.buy_21_cti_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_21_volume)) # Condition #22 elif index == 22: # Non-Standard protections item_buy_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36)) # Logic item_buy_logic.append((dataframe['volume_mean_4'] * self.buy_22_volume) > dataframe['volume']) item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_22_ma_offset) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_22_bb_offset)) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_22_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_22_rsi_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_22_cti_max) item_buy_logic.append(dataframe['r_480'] < self.buy_22_r_480_max) item_buy_logic.append(dataframe['cti_1h'] > self.buy_22_cti_1h_min) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_23_bb_offset)) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_23_ewo_min) item_buy_logic.append(dataframe['cti'] < self.buy_23_cti_max) item_buy_logic.append(dataframe['rsi_14'] < self.buy_23_rsi_14_max) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_23_rsi_14_1h_max) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_23_r_480_1h_min) item_buy_logic.append(dataframe['cti_1h'] < 0.92) # Condition #24 elif index == 24: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12)) item_buy_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h']) item_buy_logic.append(dataframe['cmf_1h'].shift(12) < 0) item_buy_logic.append(dataframe['cmf_1h'] > 0) item_buy_logic.append(dataframe['rsi_14'] < self.buy_24_rsi_14_max) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_24_rsi_14_1h_min) # Condition #25 elif index == 25: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift()) item_buy_logic.append(dataframe['rsi_4'] < self.buy_25_rsi_4_max) item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h']) item_buy_logic.append(dataframe['close'] < dataframe['sma_15'] * self.buy_25_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_25_cti_max) item_buy_logic.append(dataframe['cci'] < self.buy_25_cci_max) # Condition #26 elif index == 26: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['zema_61'] * self.buy_26_zema_low_offset)) item_buy_logic.append(dataframe['cti'] < self.buy_26_cti_max) item_buy_logic.append(dataframe['cci'] < self.buy_26_cci_max) item_buy_logic.append(dataframe['r_14'] < self.buy_26_r_14_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_26_cti_1h_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_26_volume)) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_buy_logic.append(dataframe['r_480'] < self.buy_27_wr_max) item_buy_logic.append(dataframe['r_14'] == self.buy_27_r_14) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_27_wr_1h_max) item_buy_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.buy_27_rsi_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_27_volume)) # Condition #28 elif index == 28: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == True) item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_28_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_28_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_28_rsi_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_28_cti_max) item_buy_logic.append(dataframe['cti'].shift(1) < self.buy_28_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_28_r_14_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_28_cti_1h_max) # Condition #29 elif index == 29: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == True) item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_29_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_29_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_29_cti_max) # Condition #30 elif index == 30: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == False) item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_30_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_30_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_30_rsi_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_30_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_30_r_14_max) # Condition #31 elif index == 31: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == False) item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_31_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_31_ewo_max) item_buy_logic.append(dataframe['r_14'] < self.buy_31_r_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_31_cti_max) # Condition #32 - Quick mode buy elif index == 32: # Non-Standard protections item_buy_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h']) # Logic item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) item_buy_logic.append(dataframe['rsi_4'] < self.buy_32_rsi_4_max) item_buy_logic.append(dataframe['rsi_14'] > self.buy_32_rsi_14_min) item_buy_logic.append(dataframe['close'] < dataframe['sma_15'] * self.buy_32_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_32_cti_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_32_crsi_1h_min) item_buy_logic.append(dataframe['crsi_1h'] < self.buy_32_crsi_1h_max) # Condition #33 - Quick mode buy elif index == 33: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_13'] * self.buy_33_ma_offset)) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_33_ewo_min) item_buy_logic.append(dataframe['cti'] < self.buy_33_cti_max) item_buy_logic.append(dataframe['rsi_14'] < self.buy_33_rsi_max) item_buy_logic.append(dataframe['r_14'] < self.buy_33_r_14_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_33_cti_1h_max) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_33_volume)) # Condition #34 - Quick mode buy elif index == 34: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_13'] * self.buy_34_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_34_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_34_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_34_r_14_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_34_crsi_1h_min) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_34_volume)) # Condition #35 - PMAX0 buy elif index == 35: # Non-Standard protections # Logic item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_35_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_35_ewo_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_35_rsi_max) item_buy_logic.append(dataframe['cti'] < self.buy_35_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_35_r_14_max) # Condition #36 - PMAX1 buy elif index == 36: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_36_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_36_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_36_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_36_r_14_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_36_crsi_1h_min) # Condition #37 - Quick mode buy elif index == 37: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset) item_buy_logic.append(((dataframe['close_1h'].shift(12) - dataframe['close_1h']) / dataframe['close_1h']) < self.buy_37_close_1h_max) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_37_ewo_min) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_37_ewo_max) item_buy_logic.append(dataframe['rsi_14'] > self.buy_37_rsi_14_min) item_buy_logic.append(dataframe['rsi_14'] < self.buy_37_rsi_14_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_37_crsi_1h_min) item_buy_logic.append(dataframe['crsi_1h'] < self.buy_37_crsi_1h_max) item_buy_logic.append(dataframe['cti'] < self.buy_37_cti_max) item_buy_logic.append(dataframe['cti_1h'] < self.buy_37_cti_1h_max) item_buy_logic.append(dataframe['r_14'] < self.buy_37_r_14_max) # Condition #38 - PMAX3 buy elif index == 38: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['pm'] > dataframe['pmax_thresh']) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_38_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_38_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_38_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_38_r_14_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_38_crsi_1h_min) # Condition #39 - Ichimoku elif index == 39: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h']) item_buy_logic.append(dataframe['close'] > dataframe['cloud_top_1h']) item_buy_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h']) item_buy_logic.append(dataframe['chikou_span_greater_1h']) item_buy_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) item_buy_logic.append(dataframe['close'] < dataframe['ssl_up_1h']) item_buy_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12)) item_buy_logic.append(dataframe['cti'] < self.buy_39_cti_max) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_39_r_1h_max) item_buy_logic.append(dataframe['cti_1h'] > self.buy_39_cti_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_39_cti_1h_max) # Start of trend item_buy_logic.append( (dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) ) # Condition #40 elif index == 40: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['momdiv_buy_1h'] == True) item_buy_logic.append(dataframe['cci'] < self.buy_40_cci_max) item_buy_logic.append(dataframe['rsi_14'] < self.buy_40_rsi_max) item_buy_logic.append(dataframe['r_14'] < self.buy_40_r_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_40_cti_max) # Condition #41 elif index == 41: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_41_ma_offset_high) item_buy_logic.append(dataframe['cti'] < self.buy_41_cti_max) item_buy_logic.append(dataframe['cci'] < self.buy_41_cci_max) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_41_ewo_1h_min) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_41_r_480_1h_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_41_crsi_1h_min) # Condition #42 elif index == 42: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_42_ema_open_mult)) 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'] * self.buy_42_bb_offset)) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_42_ewo_1h_min) item_buy_logic.append(dataframe['cti_1h'] > self.buy_42_cti_1h_min) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_42_r_480_1h_max) # Condition #43 elif index == 43: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_43_bb40_bbdelta_close)) item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_43_bb40_closedelta_close)) item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_43_bb40_tail_bbdelta)) 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['cti'] < self.buy_43_cti_max) item_buy_logic.append(dataframe['r_480'] > self.buy_43_r_480_min) item_buy_logic.append(dataframe['cti_1h'] > self.buy_43_cti_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_43_cti_1h_max) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_43_r_480_1h_min) # Condition #44 elif index == 44: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset)) item_buy_logic.append(dataframe['ewo_sma'] < self.buy_44_ewo_max) item_buy_logic.append(dataframe['cti'] < self.buy_44_cti_max) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_44_crsi_1h_min) # Condition #45 - Long mode elif index == 45: # Non-Standard protections # Logic item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_45_bb40_bbdelta_close)) item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_45_bb40_closedelta_close)) item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_45_bb40_tail_bbdelta)) 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['close'] < dataframe['sma_30'] * self.buy_45_ma_offset) item_buy_logic.append(dataframe['ewo_sma'] > self.buy_45_ewo_min) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_45_ewo_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_45_cti_1h_max) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_45_r_480_1h_max) # Condition #46 - Long mode elif index == 46: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_46_ema_open_mult)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_46_ewo_1h_min) item_buy_logic.append(dataframe['cti_1h'] > self.buy_46_cti_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_46_cti_1h_max) # Condition #47 - Long mode elif index == 47: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ewo_sma'] > self.buy_47_ewo_min) item_buy_logic.append(dataframe['close'] < (dataframe['sma_30'] * self.buy_47_ma_offset)) item_buy_logic.append(dataframe['rsi_14'] < self.buy_47_rsi_14_max) item_buy_logic.append(dataframe['cti'] < self.buy_47_cti_max) item_buy_logic.append(dataframe['r_14'] < self.buy_47_r_14_max) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_47_ewo_1h_min) item_buy_logic.append(dataframe['cti_1h'] > self.buy_47_cti_1h_min) item_buy_logic.append(dataframe['cti_1h'] < self.buy_47_cti_1h_max) # Condition #48 - Uptrend mode elif index == 48: # Non-Standard protections item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['moderi_32']) item_buy_logic.append(dataframe['moderi_64']) item_buy_logic.append(dataframe['moderi_96']) # Logic item_buy_logic.append(dataframe['ewo_sma'] > self.buy_48_ewo_min) item_buy_logic.append(dataframe['ewo_sma_1h'] > self.buy_48_ewo_1h_min) item_buy_logic.append(dataframe['r_480'] > self.buy_48_r_480_min) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_48_r_480_1h_min) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_48_r_480_1h_max) item_buy_logic.append(dataframe['r_480_1h'] > dataframe['r_480_1h'].shift(12)) item_buy_logic.append(dataframe['cti_1h'] > self.buy_48_cti_1h_min) item_buy_logic.append(dataframe['crsi_1h'] > self.buy_48_crsi_1h_min) item_buy_logic.append(dataframe['cti'].shift(1).rolling(12).min() < -0.5) item_buy_logic.append(dataframe['cti'].shift(1).rolling(12).max() < 0.0) item_buy_logic.append(dataframe['cti'].shift(1) < 0.0) item_buy_logic.append(dataframe['cti'] > 0.0) item_buy_logic.append(dataframe['volume'] > 0) item_buy = reduce(lambda x, y: x & y, item_buy_logic) dataframe.loc[item_buy, 'buy_tag'] += f"{index} " conditions.append(item_buy) if conditions: dataframe.loc[:, 'buy'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe def confirm_trade_exit(self, pair: str, trade: "Trade", order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: """ Called right before placing a regular sell order. Timing for this function is critical, so avoid doing heavy computations or network requests in this method. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns True (always confirming). :param pair: Pair that's about to be sold. :param trade: trade object. :param order_type: Order type (as configured in order_types). usually limit or market. :param amount: Amount in quote currency. :param rate: Rate that's going to be used when using limit orders :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param sell_reason: Sell reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'sell_signal', 'force_sell', 'emergency_sell'] :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True is returned, then the sell-order is placed on the exchange. False aborts the process """ if self._should_hold_trade(trade, rate, sell_reason): return False self._remove_profit_target(pair) return True def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_time: "datetime"): self.target_profit_cache.data[pair] = { "rate": rate, "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 _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.holdSupportEnabled: 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 # 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 def ewo_sma(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.SMA(dataframe, timeperiod=sma1_length) sma2 = ta.SMA(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)) return vwma # Modified Elder Ray Index def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend # 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 # zlema def zlema(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2) ) if isinstance(dataframe, Series): ema_data = dataframe + (dataframe - dataframe.shift(lag)) else: ema_data = dataframe['close'] + (dataframe['close'] - dataframe['close'] .shift(lag)) return ta.EMA(ema_data, timeperiod = timeperiod) # zlhull def zlhull(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2) ) if isinstance(dataframe, Series): wma_data = dataframe + (dataframe - dataframe.shift(lag)) else: wma_data = dataframe['close'] + (dataframe['close'] - dataframe['close'] .shift(lag)) return ta.WMA( 2 * ta.WMA(wma_data, int(math.floor(timeperiod/2))) - ta.WMA(wma_data, timeperiod), int(round(np.sqrt(timeperiod))) ) # hull def hull(dataframe, timeperiod): if isinstance(dataframe, Series): return ta.WMA( 2 * ta.WMA(dataframe, int(math.floor(timeperiod/2))) - ta.WMA(dataframe, timeperiod), int(round(np.sqrt(timeperiod))) ) else: return ta.WMA( 2 * ta.WMA(dataframe['close'], int(math.floor(timeperiod/2))) - ta.WMA(dataframe['close'], timeperiod), int(round(np.sqrt(timeperiod))) ) # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx def calc_streaks(series: Series): # logic tables geq = series >= series.shift(1) # True if rising eq = series == series.shift(1) # True if equal logic_table = concat([geq, eq], axis=1) streaks = [0] # holds the streak duration, starts with 0 for row in logic_table.iloc[1:].itertuples(): # iterate through logic table if row[2]: # same value as before streaks.append(0) continue last_value = streaks[-1] if row[1]: # higher value than before streaks.append(last_value + 1 if last_value >= 0 else 1) # increase or reset to +1 else: # lower value than before streaks.append(last_value - 1 if last_value < 0 else -1) # decrease or reset to -1 return streaks # SSL Channels def SSLChannels(dataframe, length = 7): ATR = ta.ATR(dataframe, timeperiod=14) smaHigh = dataframe['high'].rolling(length).mean() + ATR smaLow = dataframe['low'].rolling(length).mean() - ATR hlv = Series(np.where(dataframe['close'] > smaHigh, 1, np.where(dataframe['close'] < smaLow, -1, np.NAN))) hlv = hlv.ffill() sslDown = np.where(hlv < 0, smaHigh, smaLow) sslUp = np.where(hlv < 0, smaLow, smaHigh) return sslDown, sslUp def pivot_points(dataframe: DataFrame, mode = 'fibonacci') -> Series: hlc3_pivot = (dataframe['high'] + dataframe['low'] + dataframe['close']).shift(1) / 3 hl_range = (dataframe['high'] - dataframe['low']).shift(1) if mode == 'simple': 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() elif mode == 'fibonacci': 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 def HeikinAshi(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 # Mom DIV def momdiv(dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe['high'].rolling(lookback).max() ll = dataframe['low'].rolling(lookback).min() coh = dataframe['high'] >= hh col = dataframe['low'] <= ll df = DataFrame({ "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe['close'].index) return df class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass @staticmethod def rapidjson_load_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} @staticmethod def rapidjson_dump_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open("r") as rfh: try: data = rapidjson.load( rfh, **self.rapidjson_load_kwargs() ) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing rapidjson.dump( self.data, self.path.open("w"), **self.rapidjson_dump_kwargs() ) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): @staticmethod def rapidjson_load_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "object_hook": HoldsCache._object_hook, } @staticmethod def rapidjson_dump_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS, } def save(self): raise RuntimeError("The holds cache does not allow programatical save") def process_loaded_data(self, data): trade_ids = data.get("trade_ids") trade_pairs = data.get("trade_pairs") if not trade_ids and not trade_pairs: return data open_trades = {} for trade in Trade.get_trades_proxy(is_open=True): open_trades[trade.id] = open_trades[trade.pair] = trade r_trade_ids = {} if trade_ids: if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path ) if trade_id in open_trades: formatted_profit_ratio = f"{profit_ratio * 100}%" log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) else: # Initial Syntax profit_ratio = data.get("profit_ratio") if profit_ratio: if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path ) else: profit_ratio = 0.005 formatted_profit_ratio = f"{profit_ratio * 100}%" for trade_id in trade_ids: if not isinstance(trade_id, int): log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path ) continue if trade_id in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) r_trade_pairs = {} if trade_pairs: for trade_pair, profit_ratio in trade_pairs.items(): if not isinstance(trade_pair, str): log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s is not a string", trade_pair, self.path ) continue if "/" not in trade_pair: log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s does not look like " "a valid '/' formatted pair.", trade_pair, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_pair %s in %s is not a float", profit_ratio, trade_pair, self.path ) formatted_profit_ratio = f"{profit_ratio * 100}%" if trade_pair in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_pair], formatted_profit_ratio ) else: log.warning( "The trade pair %s is configured to HOLD until the profit ratio of %s is met", trade_pair, formatted_profit_ratio ) r_trade_pairs[trade_pair] = profit_ratio r_data = {} if r_trade_ids: r_data["trade_ids"] = r_trade_ids if r_trade_pairs: r_data["trade_pairs"] = r_trade_pairs return r_data @staticmethod def _object_hook(data): _data = {} for key, value in data.items(): try: key = int(key) except ValueError: pass _data[key] = value return _data