# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-futures-k8s-namespace/nfiv7155.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 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_exit_signal must set to true (or not set at all). ## ## exit_profit_only must set to false (or not set at all). ## ## ignore_roi_if_entry_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## 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 ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=37365811 ## ## Kucoin: https://www.kucoin.com/ucenter/signup?rcode=rJTLZ9K ## ## Huobi: https://www.huobi.com/en-us/topic/double-reward/?invite_code=ubpt2223 ## ########################################################################################################### class Github_DerSalvador_freqtrade_helm_chart__nfiv7155__20260416_224245(IStrategy): INTERFACE_VERSION = 3 # ROI table: minimal_roi = {'0': 10} stoploss = -0.5 # 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_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = {'entry': 'limit', 'exit': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99} ############################################################# ############# # Enable/Disable conditions ############# entry_params = {'entry_condition_1_enable': True, 'entry_condition_2_enable': True, 'entry_condition_3_enable': True, 'entry_condition_4_enable': True, 'entry_condition_5_enable': True, 'entry_condition_6_enable': True, 'entry_condition_7_enable': True, 'entry_condition_8_enable': True, 'entry_condition_9_enable': True, 'entry_condition_10_enable': True, 'entry_condition_11_enable': True, 'entry_condition_12_enable': True, 'entry_condition_13_enable': True, 'entry_condition_14_enable': True, 'entry_condition_15_enable': True, 'entry_condition_16_enable': True, 'entry_condition_17_enable': True, 'entry_condition_18_enable': True, 'entry_condition_19_enable': True, 'entry_condition_20_enable': True, 'entry_condition_21_enable': True, 'entry_condition_22_enable': True, 'entry_condition_23_enable': True, 'entry_condition_24_enable': True, 'entry_condition_25_enable': True, 'entry_condition_26_enable': True, 'entry_condition_27_enable': True, 'entry_condition_28_enable': True, 'entry_condition_29_enable': True, 'entry_condition_30_enable': True, 'entry_condition_31_enable': True, 'entry_condition_32_enable': True, 'entry_condition_33_enable': True, 'entry_condition_34_enable': True, 'entry_condition_35_enable': False, 'entry_condition_36_enable': False, 'entry_condition_37_enable': True, 'entry_condition_38_enable': True, 'entry_condition_39_enable': True, 'entry_condition_40_enable': True, 'entry_condition_41_enable': True, 'entry_condition_42_enable': True, 'entry_condition_43_enable': True, 'entry_condition_44_enable': True, 'entry_condition_45_enable': True, 'entry_condition_46_enable': True, 'entry_condition_47_enable': True, 'entry_condition_48_enable': True} ############# # Enable/Disable conditions ############# exit_params = {'exit_condition_1_enable': True, 'exit_condition_2_enable': True, 'exit_condition_3_enable': True, 'exit_condition_4_enable': True, 'exit_condition_5_enable': True, 'exit_condition_6_enable': True, 'exit_condition_7_enable': True, 'exit_condition_8_enable': True} ############# # Enable/Disable conditions ############# profit_target_params = {'profit_target_1_enable': False} ############################################################# # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 # pivot, sup1, sup2, sup3, res1, res2, res3 entry_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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'res3', 'close_under_pivot_offset': 1.3}, 13: {'ema_fast': False, 'ema_fast_len': '50', 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'safe_pump_period': '24', 'btc_1h_not_downtrend': False, 'close_over_pivot_type': 'none', 'close_over_pivot_offset': 0.99, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', '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', 'close_over_pivot_offset': 1.0, 'close_under_pivot_type': 'none', 'close_under_pivot_offset': 1.0}} # 24 hours - level 10 entry_pump_pull_threshold_10_24 = 2.2 entry_pump_threshold_10_24 = 0.42 # 36 hours - level 10 entry_pump_pull_threshold_10_36 = 2.0 entry_pump_threshold_10_36 = 0.58 # 48 hours - level 10 entry_pump_pull_threshold_10_48 = 2.0 entry_pump_threshold_10_48 = 0.8 # 24 hours - level 20 entry_pump_pull_threshold_20_24 = 2.2 entry_pump_threshold_20_24 = 0.46 # 36 hours - level 20 entry_pump_pull_threshold_20_36 = 2.0 entry_pump_threshold_20_36 = 0.6 # 48 hours - level 20 entry_pump_pull_threshold_20_48 = 2.0 entry_pump_threshold_20_48 = 0.81 # 24 hours - level 30 entry_pump_pull_threshold_30_24 = 2.2 entry_pump_threshold_30_24 = 0.5 # 36 hours - level 30 entry_pump_pull_threshold_30_36 = 2.0 entry_pump_threshold_30_36 = 0.62 # 48 hours - level 30 entry_pump_pull_threshold_30_48 = 2.0 entry_pump_threshold_30_48 = 0.82 # 24 hours - level 40 entry_pump_pull_threshold_40_24 = 2.2 entry_pump_threshold_40_24 = 0.54 # 36 hours - level 40 entry_pump_pull_threshold_40_36 = 2.0 entry_pump_threshold_40_36 = 0.63 # 48 hours - level 40 entry_pump_pull_threshold_40_48 = 2.0 entry_pump_threshold_40_48 = 0.84 # 24 hours - level 50 entry_pump_pull_threshold_50_24 = 1.75 entry_pump_threshold_50_24 = 0.6 # 36 hours - level 50 entry_pump_pull_threshold_50_36 = 1.75 entry_pump_threshold_50_36 = 0.64 # 48 hours - level 50 entry_pump_pull_threshold_50_48 = 1.75 entry_pump_threshold_50_48 = 0.85 # 24 hours - level 60 entry_pump_pull_threshold_60_24 = 1.75 entry_pump_threshold_60_24 = 0.62 # 36 hours - level 60 entry_pump_pull_threshold_60_36 = 1.75 entry_pump_threshold_60_36 = 0.66 # 48 hours - level 60 entry_pump_pull_threshold_60_48 = 1.75 entry_pump_threshold_60_48 = 0.9 # 24 hours - level 70 entry_pump_pull_threshold_70_24 = 1.75 entry_pump_threshold_70_24 = 0.63 # 36 hours - level 70 entry_pump_pull_threshold_70_36 = 1.75 entry_pump_threshold_70_36 = 0.67 # 48 hours - level 70 entry_pump_pull_threshold_70_48 = 1.75 entry_pump_threshold_70_48 = 0.95 # 24 hours - level 80 entry_pump_pull_threshold_80_24 = 1.75 entry_pump_threshold_80_24 = 0.64 # 36 hours - level 80 entry_pump_pull_threshold_80_36 = 1.75 entry_pump_threshold_80_36 = 0.68 # 48 hours - level 80 entry_pump_pull_threshold_80_48 = 1.75 entry_pump_threshold_80_48 = 1.0 # 24 hours - level 90 entry_pump_pull_threshold_90_24 = 1.75 entry_pump_threshold_90_24 = 0.65 # 36 hours - level 90 entry_pump_pull_threshold_90_36 = 1.75 entry_pump_threshold_90_36 = 0.69 # 48 hours - level 90 entry_pump_pull_threshold_90_48 = 1.75 entry_pump_threshold_90_48 = 1.1 # 24 hours - level 100 entry_pump_pull_threshold_100_24 = 1.7 entry_pump_threshold_100_24 = 0.66 # 36 hours - level 100 entry_pump_pull_threshold_100_36 = 1.7 entry_pump_threshold_100_36 = 0.7 # 48 hours - level 100 entry_pump_pull_threshold_100_48 = 1.4 entry_pump_threshold_100_48 = 1.6 # 24 hours - level 110 entry_pump_pull_threshold_110_24 = 1.7 entry_pump_threshold_110_24 = 0.7 # 36 hours - level 110 entry_pump_pull_threshold_110_36 = 1.7 entry_pump_threshold_110_36 = 0.74 # 48 hours - level 110 entry_pump_pull_threshold_110_48 = 1.4 entry_pump_threshold_110_48 = 1.8 # 24 hours - level 120 entry_pump_pull_threshold_120_24 = 1.7 entry_pump_threshold_120_24 = 0.78 # 36 hours - level 120 entry_pump_pull_threshold_120_36 = 1.7 entry_pump_threshold_120_36 = 0.78 # 48 hours - level 120 entry_pump_pull_threshold_120_48 = 1.4 entry_pump_threshold_120_48 = 2.0 # 5 hours - level 10 entry_dump_protection_10_5 = 0.4 # 5 hours - level 20 entry_dump_protection_20_5 = 0.44 # 5 hours - level 30 entry_dump_protection_30_5 = 0.5 # 5 hours - level 40 entry_dump_protection_40_5 = 0.58 # 5 hours - level 50 entry_dump_protection_50_5 = 0.66 # 5 hours - level 60 entry_dump_protection_60_5 = 0.74 entry_1_min_inc = 0.022 entry_1_rsi_max = 32.0 entry_2_r_14_max = -75.0 entry_1_mfi_max = 46.0 entry_1_rsi_1h_min = 30.0 entry_1_rsi_1h_max = 84.0 entry_2_rsi_1h_diff = 39.0 entry_2_mfi = 49.0 entry_2_cti_max = -0.9 entry_2_r_480_min = -95.0 entry_2_r_480_max = -46.0 entry_2_cti_1h_max = 0.9 entry_2_volume = 2.0 entry_3_bb40_bbdelta_close = 0.057 entry_3_bb40_closedelta_close = 0.023 entry_3_bb40_tail_bbdelta = 0.418 entry_3_cti_max = -0.5 entry_3_cci_36_osc_min = -0.25 entry_3_crsi_1h_min = 20.0 entry_3_r_480_1h_min = -48.0 entry_3_cti_1h_max = 0.82 entry_4_bb20_close_bblowerband = 0.98 entry_4_bb20_volume = 10.0 entry_4_cti_max = -0.8 entry_5_ema_rel = 0.84 entry_5_ema_open_mult = 0.02 entry_5_bb_offset = 0.999 entry_5_cti_max = -0.5 entry_5_r_14_max = -94.0 entry_5_rsi_14_min = 25.0 entry_5_mfi_min = 18.0 entry_5_crsi_1h_min = 12.0 entry_5_volume = 1.6 entry_6_ema_open_mult = 0.019 entry_6_bb_offset = 0.984 entry_6_r_14_max = -85.0 entry_6_crsi_1h_min = 15.0 entry_6_cti_1h_min = 0.0 entry_7_ema_open_mult = 0.031 entry_7_ma_offset = 0.978 entry_7_cti_max = -0.9 entry_7_rsi_max = 45.0 entry_8_bb_offset = 0.986 entry_8_r_14_max = -98.0 entry_8_cti_1h_max = 0.95 entry_8_r_480_1h_max = -18.0 entry_8_volume = 1.8 entry_9_ma_offset = 0.968 entry_9_bb_offset = 0.982 entry_9_mfi_max = 50.0 entry_9_cti_max = -0.85 entry_9_r_14_max = -94.0 entry_9_rsi_1h_min = 20.0 entry_9_rsi_1h_max = 88.0 entry_9_crsi_1h_min = 21.0 entry_10_ma_offset_high = 0.94 entry_10_bb_offset = 0.984 entry_10_r_14_max = -88.0 entry_10_cti_1h_min = -0.5 entry_10_cti_1h_max = 0.94 entry_11_ma_offset = 0.956 entry_11_min_inc = 0.022 entry_11_rsi_max = 37.0 entry_11_mfi_max = 46.0 entry_11_cci_max = -120.0 entry_11_r_480_max = -32.0 entry_11_rsi_1h_min = 30.0 entry_11_rsi_1h_max = 84.0 entry_11_cti_1h_max = 0.91 entry_11_r_480_1h_max = -25.0 entry_11_crsi_1h_min = 26.0 entry_12_ma_offset = 0.927 entry_12_ewo_min = 2.0 entry_12_rsi_max = 32.0 entry_12_cti_max = -0.9 entry_13_ma_offset = 0.99 entry_13_cti_max = -0.92 entry_13_ewo_max = -6.0 entry_13_cti_1h_max = -0.88 entry_13_crsi_1h_min = 10.0 entry_14_ema_open_mult = 0.014 entry_14_bb_offset = 0.989 entry_14_ma_offset = 0.945 entry_14_cti_max = -0.85 entry_15_ema_open_mult = 0.0238 entry_15_ma_offset = 0.958 entry_15_rsi_min = 28.0 entry_15_cti_1h_min = -0.2 entry_16_ma_offset = 0.942 entry_16_ewo_min = 2.0 entry_16_rsi_max = 36.0 entry_16_cti_max = -0.9 entry_17_ma_offset = 0.999 entry_17_ewo_max = -7.0 entry_17_cti_max = -0.96 entry_17_crsi_1h_min = 12.0 entry_17_volume = 2.0 entry_18_bb_offset = 0.986 entry_18_rsi_max = 33.5 entry_18_cti_max = -0.85 entry_18_cti_1h_max = 0.91 entry_18_volume = 2.0 entry_19_rsi_1h_min = 30.0 entry_19_chop_max = 21.3 entry_20_rsi_14_max = 36.0 entry_20_rsi_14_1h_max = 16.0 entry_20_cti_max = -0.84 entry_20_volume = 2.0 entry_21_rsi_14_max = 14.0 entry_21_rsi_14_1h_max = 28.0 entry_21_cti_max = -0.902 entry_21_volume = 2.0 entry_22_volume = 2.0 entry_22_bb_offset = 0.984 entry_22_ma_offset = 0.98 entry_22_ewo_min = 5.6 entry_22_rsi_14_max = 36.0 entry_22_cti_max = -0.54 entry_22_r_480_max = -40.0 entry_22_cti_1h_min = -0.5 entry_23_bb_offset = 0.984 entry_23_ewo_min = 3.4 entry_23_rsi_14_max = 28.0 entry_23_cti_max = -0.74 entry_23_rsi_14_1h_max = 80.0 entry_23_r_480_1h_min = -95.0 entry_23_cti_1h_max = 0.92 entry_24_rsi_14_max = 50.0 entry_24_rsi_14_1h_min = 66.9 entry_25_ma_offset = 0.953 entry_25_rsi_4_max = 30.0 entry_25_cti_max = -0.78 entry_25_cci_max = -200.0 entry_26_zema_low_offset = 0.9405 entry_26_cti_max = -0.72 entry_26_cci_max = -166.0 entry_26_r_14_max = -98.0 entry_26_cti_1h_max = 0.95 entry_26_volume = 2.0 entry_27_wr_max = -95.0 entry_27_r_14 = -100.0 entry_27_wr_1h_max = -90.0 entry_27_rsi_max = 46.0 entry_27_volume = 2.0 entry_28_ma_offset = 0.928 entry_28_ewo_min = 2.0 entry_28_rsi_14_max = 33.4 entry_28_cti_max = -0.84 entry_28_r_14_max = -97.0 entry_28_cti_1h_max = 0.95 entry_29_ma_offset = 0.984 entry_29_ewo_max = -4.2 entry_29_cti_max = -0.96 entry_30_ma_offset = 0.962 entry_30_ewo_min = 6.4 entry_30_rsi_14_max = 34.0 entry_30_cti_max = -0.87 entry_30_r_14_max = -97.0 entry_31_ma_offset = 0.962 entry_31_ewo_max = -5.2 entry_31_r_14_max = -94.0 entry_31_cti_max = -0.9 entry_32_ma_offset = 0.942 entry_32_rsi_4_max = 46.0 entry_32_cti_max = -0.86 entry_32_rsi_14_min = 19.0 entry_32_crsi_1h_min = 10.0 entry_32_crsi_1h_max = 60.0 entry_33_ma_offset = 0.988 entry_33_ewo_min = 9.0 entry_33_rsi_max = 32.0 entry_33_cti_max = -0.88 entry_33_r_14_max = -98.0 entry_33_cti_1h_max = 0.92 entry_33_volume = 2.0 entry_34_ma_offset = 0.97 entry_34_ewo_max = -4.0 entry_34_cti_max = -0.95 entry_34_r_14_max = -99.9 entry_34_crsi_1h_min = 8.0 entry_34_volume = 2.0 entry_35_ma_offset = 0.984 entry_35_ewo_min = 7.8 entry_35_rsi_max = 32.0 entry_35_cti_max = -0.8 entry_35_r_14_max = -95.0 entry_36_ma_offset = 0.98 entry_36_ewo_max = -5.0 entry_36_cti_max = -0.82 entry_36_r_14_max = -97.0 entry_36_crsi_1h_min = 12.0 entry_37_ma_offset = 0.984 entry_37_ewo_min = 8.3 entry_37_ewo_max = 11.1 entry_37_rsi_14_min = 26.0 entry_37_rsi_14_max = 46.0 entry_37_crsi_1h_min = 12.0 entry_37_crsi_1h_max = 56.0 entry_37_cti_max = -0.85 entry_37_cti_1h_max = 0.92 entry_37_r_14_max = -97.0 entry_37_close_1h_max = 0.1 entry_38_ma_offset = 0.98 entry_38_ewo_max = -4.4 entry_38_cti_max = -0.95 entry_38_r_14_max = -97.0 entry_38_crsi_1h_min = 0.5 entry_39_cti_max = -0.1 entry_39_r_1h_max = -22.0 entry_39_cti_1h_min = -0.1 entry_39_cti_1h_max = 0.4 entry_40_cci_max = -150.0 entry_40_rsi_max = 30.0 entry_40_r_14_max = -99.9 entry_40_cti_max = -0.8 entry_41_ma_offset_high = 0.95 entry_41_cti_max = -0.95 entry_41_cci_max = -178.0 entry_41_ewo_1h_min = 0.5 entry_41_r_480_1h_max = -14.0 entry_41_crsi_1h_min = 14.0 entry_42_ema_open_mult = 0.018 entry_42_bb_offset = 0.992 entry_42_ewo_1h_min = 2.8 entry_42_cti_1h_min = -0.5 entry_42_cti_1h_max = 0.88 entry_42_r_480_1h_max = -12.0 entry_43_bb40_bbdelta_close = 0.045 entry_43_bb40_closedelta_close = 0.02 entry_43_bb40_tail_bbdelta = 0.5 entry_43_cti_max = -0.75 entry_43_r_480_min = -94.0 entry_43_cti_1h_min = -0.75 entry_43_cti_1h_max = 0.45 entry_43_r_480_1h_min = -80.0 entry_44_ma_offset = 0.982 entry_44_ewo_max = -18.0 entry_44_cti_max = -0.73 entry_44_crsi_1h_min = 8.0 entry_45_bb40_bbdelta_close = 0.039 entry_45_bb40_closedelta_close = 0.0231 entry_45_bb40_tail_bbdelta = 0.24 entry_45_ma_offset = 0.948 entry_45_ewo_min = 2.0 entry_45_ewo_1h_min = 2.0 entry_45_cti_1h_max = 0.76 entry_45_r_480_1h_max = -20.0 entry_46_ema_open_mult = 0.0332 entry_46_ewo_1h_min = 0.5 entry_46_cti_1h_min = -0.9 entry_46_cti_1h_max = 0.5 entry_47_ewo_min = 3.2 entry_47_ma_offset = 0.952 entry_47_rsi_14_max = 46.0 entry_47_cti_max = -0.93 entry_47_r_14_max = -97.0 entry_47_ewo_1h_min = 2.0 entry_47_cti_1h_min = -0.9 entry_47_cti_1h_max = 0.3 entry_48_ewo_min = 8.5 entry_48_ewo_1h_min = 14.0 entry_48_r_480_min = -25.0 entry_48_r_480_1h_min = -50.0 entry_48_r_480_1h_max = -10.0 entry_48_cti_1h_min = 0.5 entry_48_crsi_1h_min = 10.0 # Sell exit_condition_1_enable = True exit_condition_2_enable = True exit_condition_3_enable = True exit_condition_4_enable = True exit_condition_5_enable = True exit_condition_6_enable = True exit_condition_7_enable = True exit_condition_8_enable = True # 48h for pump exit checks exit_pump_threshold_48_1 = 0.9 exit_pump_threshold_48_2 = 0.7 exit_pump_threshold_48_3 = 0.5 # 36h for pump exit checks exit_pump_threshold_36_1 = 0.72 exit_pump_threshold_36_2 = 4.0 exit_pump_threshold_36_3 = 1.0 # 24h for pump exit checks exit_pump_threshold_24_1 = 0.68 exit_pump_threshold_24_2 = 0.62 exit_pump_threshold_24_3 = 0.88 exit_rsi_bb_1 = 79.0 exit_rsi_bb_2 = 80.0 exit_rsi_main_3 = 83.0 exit_dual_rsi_rsi_4 = 73.4 exit_dual_rsi_rsi_1h_4 = 79.6 exit_ema_relative_5 = 0.024 exit_rsi_diff_5 = 4.4 exit_rsi_under_6 = 79.0 exit_rsi_1h_7 = 81.7 exit_bb_relative_8 = 1.1 # Profit over EMA200 exit_custom_profit_bull_0 = 0.012 exit_custom_rsi_under_bull_0 = 34.0 exit_custom_profit_bull_1 = 0.02 exit_custom_rsi_under_bull_1 = 35.0 exit_custom_profit_bull_2 = 0.03 exit_custom_rsi_under_bull_2 = 36.0 exit_custom_profit_bull_3 = 0.04 exit_custom_rsi_under_bull_3 = 44.0 exit_custom_profit_bull_4 = 0.05 exit_custom_rsi_under_bull_4 = 45.0 exit_custom_profit_bull_5 = 0.06 exit_custom_rsi_under_bull_5 = 49.0 exit_custom_profit_bull_6 = 0.07 exit_custom_rsi_under_bull_6 = 50.0 exit_custom_profit_bull_7 = 0.08 exit_custom_rsi_under_bull_7 = 57.0 exit_custom_profit_bull_8 = 0.09 exit_custom_rsi_under_bull_8 = 50.0 exit_custom_profit_bull_9 = 0.1 exit_custom_rsi_under_bull_9 = 46.0 exit_custom_profit_bull_10 = 0.12 exit_custom_rsi_under_bull_10 = 42.0 exit_custom_profit_bull_11 = 0.2 exit_custom_rsi_under_bull_11 = 30.0 exit_custom_profit_bear_0 = 0.012 exit_custom_rsi_under_bear_0 = 34.0 exit_custom_profit_bear_1 = 0.02 exit_custom_rsi_under_bear_1 = 35.0 exit_custom_profit_bear_2 = 0.03 exit_custom_rsi_under_bear_2 = 37.0 exit_custom_profit_bear_3 = 0.04 exit_custom_rsi_under_bear_3 = 44.0 exit_custom_profit_bear_4 = 0.05 exit_custom_rsi_under_bear_4 = 48.0 exit_custom_profit_bear_5 = 0.06 exit_custom_rsi_under_bear_5 = 50.0 exit_custom_rsi_over_bear_5 = 78.0 exit_custom_profit_bear_6 = 0.07 exit_custom_rsi_under_bear_6 = 52.0 exit_custom_rsi_over_bear_6 = 78.0 exit_custom_profit_bear_7 = 0.08 exit_custom_rsi_under_bear_7 = 57.0 exit_custom_rsi_over_bear_7 = 77.0 exit_custom_profit_bear_8 = 0.09 exit_custom_rsi_under_bear_8 = 55.0 exit_custom_rsi_over_bear_8 = 75.5 exit_custom_profit_bear_9 = 0.1 exit_custom_rsi_under_bear_9 = 46.0 exit_custom_profit_bear_10 = 0.12 exit_custom_rsi_under_bear_10 = 42.0 exit_custom_profit_bear_11 = 0.2 exit_custom_rsi_under_bear_11 = 30.0 # Profit under EMA200 exit_custom_under_profit_bull_0 = 0.01 exit_custom_under_rsi_under_bull_0 = 38.0 exit_custom_under_profit_bull_1 = 0.02 exit_custom_under_rsi_under_bull_1 = 46.0 exit_custom_under_profit_bull_2 = 0.03 exit_custom_under_rsi_under_bull_2 = 47.0 exit_custom_under_profit_bull_3 = 0.04 exit_custom_under_rsi_under_bull_3 = 48.0 exit_custom_under_profit_bull_4 = 0.05 exit_custom_under_rsi_under_bull_4 = 49.0 exit_custom_under_profit_bull_5 = 0.06 exit_custom_under_rsi_under_bull_5 = 50.0 exit_custom_under_profit_bull_6 = 0.07 exit_custom_under_rsi_under_bull_6 = 52.0 exit_custom_under_profit_bull_7 = 0.08 exit_custom_under_rsi_under_bull_7 = 57.0 exit_custom_under_profit_bull_8 = 0.09 exit_custom_under_rsi_under_bull_8 = 50.0 exit_custom_under_profit_bull_9 = 0.1 exit_custom_under_rsi_under_bull_9 = 46.0 exit_custom_under_profit_bull_10 = 0.12 exit_custom_under_rsi_under_bull_10 = 42.0 exit_custom_under_profit_bull_11 = 0.2 exit_custom_under_rsi_under_bull_11 = 30.0 exit_custom_under_profit_bear_0 = 0.01 exit_custom_under_rsi_under_bear_0 = 38.0 exit_custom_under_profit_bear_1 = 0.02 exit_custom_under_rsi_under_bear_1 = 56.0 exit_custom_under_profit_bear_2 = 0.03 exit_custom_under_rsi_under_bear_2 = 57.0 exit_custom_under_profit_bear_3 = 0.04 exit_custom_under_rsi_under_bear_3 = 57.0 exit_custom_under_profit_bear_4 = 0.05 exit_custom_under_rsi_under_bear_4 = 57.0 exit_custom_under_profit_bear_5 = 0.06 exit_custom_under_rsi_under_bear_5 = 57.0 exit_custom_under_rsi_over_bear_5 = 78.0 exit_custom_under_profit_bear_6 = 0.07 exit_custom_under_rsi_under_bear_6 = 57.0 exit_custom_under_rsi_over_bear_6 = 78.0 exit_custom_under_profit_bear_7 = 0.08 exit_custom_under_rsi_under_bear_7 = 57.0 exit_custom_under_rsi_over_bear_7 = 80.0 exit_custom_under_profit_bear_8 = 0.09 exit_custom_under_rsi_under_bear_8 = 50.0 exit_custom_under_rsi_over_bear_8 = 82.0 exit_custom_under_profit_bear_9 = 0.1 exit_custom_under_rsi_under_bear_9 = 46.0 exit_custom_under_profit_bear_10 = 0.12 exit_custom_under_rsi_under_bear_10 = 42.0 exit_custom_under_profit_bear_11 = 0.2 exit_custom_under_rsi_under_bear_11 = 30.0 # SMA descending exit_custom_dec_profit_min_1 = 0.05 exit_custom_dec_profit_max_1 = 0.12 # Under EMA100 exit_custom_dec_profit_min_2 = 0.07 exit_custom_dec_profit_max_2 = 0.16 # Trail 1 exit_trail_profit_min_1 = 0.03 exit_trail_profit_max_1 = 0.05 exit_trail_down_1 = 0.05 exit_trail_rsi_min_1 = 10.0 exit_trail_rsi_max_1 = 20.0 # Trail 2 exit_trail_profit_min_2 = 0.1 exit_trail_profit_max_2 = 0.4 exit_trail_down_2 = 0.03 exit_trail_rsi_min_2 = 20.0 exit_trail_rsi_max_2 = 50.0 # Trail 3 exit_trail_profit_min_3 = 0.06 exit_trail_profit_max_3 = 0.2 exit_trail_down_3 = 0.05 # Trail 4 exit_trail_profit_min_4 = 0.03 exit_trail_profit_max_4 = 0.06 exit_trail_down_4 = 0.02 # Under & near EMA200, accept profit exit_custom_profit_under_profit_min_1 = 0.001 exit_custom_profit_under_profit_max_1 = 0.008 exit_custom_profit_under_rel_1 = 0.024 exit_custom_profit_under_rsi_diff_1 = 4.4 exit_custom_profit_under_profit_2 = 0.03 exit_custom_profit_under_rel_2 = 0.024 exit_custom_profit_under_rsi_diff_2 = 4.4 # Under & near EMA200, take the loss exit_custom_stoploss_under_rel_1 = 0.002 exit_custom_stoploss_under_rsi_diff_1 = 10.0 # Long duration/recover stoploss 1 exit_custom_stoploss_long_profit_min_1 = -0.08 exit_custom_stoploss_long_profit_max_1 = -0.04 exit_custom_stoploss_long_recover_1 = 0.14 exit_custom_stoploss_long_rsi_diff_1 = 4.0 # Long duration/recover stoploss 2 exit_custom_stoploss_long_recover_2 = 0.06 exit_custom_stoploss_long_rsi_diff_2 = 40.0 # Pumped 48h 1, under EMA200 exit_custom_pump_under_profit_min_1 = 0.04 exit_custom_pump_under_profit_max_1 = 0.09 # Pumped trail 1 exit_custom_pump_trail_profit_min_1 = 0.05 exit_custom_pump_trail_profit_max_1 = 0.07 exit_custom_pump_trail_down_1 = 0.05 exit_custom_pump_trail_rsi_min_1 = 20.0 exit_custom_pump_trail_rsi_max_1 = 70.0 # Stoploss, pumped, 48h 1 exit_custom_stoploss_pump_max_profit_1 = 0.01 exit_custom_stoploss_pump_min_1 = -0.02 exit_custom_stoploss_pump_max_1 = -0.01 exit_custom_stoploss_pump_ma_offset_1 = 0.94 # Stoploss, pumped, 48h 1 exit_custom_stoploss_pump_max_profit_2 = 0.025 exit_custom_stoploss_pump_loss_2 = -0.05 exit_custom_stoploss_pump_ma_offset_2 = 0.92 # Stoploss, pumped, 36h 3 exit_custom_stoploss_pump_max_profit_3 = 0.008 exit_custom_stoploss_pump_loss_3 = -0.12 exit_custom_stoploss_pump_ma_offset_3 = 0.88 # Recover exit_custom_recover_profit_1 = 0.06 exit_custom_recover_min_loss_1 = 0.12 exit_custom_recover_profit_min_2 = 0.01 exit_custom_recover_profit_max_2 = 0.05 exit_custom_recover_min_loss_2 = 0.06 exit_custom_recover_rsi_2 = 46.0 # Profit for long duration trades exit_custom_long_profit_min_1 = 0.03 exit_custom_long_profit_max_1 = 0.04 exit_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': {'entry tag': {'enter_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) 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) self.coin_metrics['tg_dataframe'].to_html('pct_df.html') # 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 exit_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 >= 0.2: 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 0.2 > 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') elif current_profit >= 0.2: 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 0.2 > 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 >= 0.1: 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 0.1 > 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 exit_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 >= 0.2: 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 0.2 > 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 >= 0.1: 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 0.1 > 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') elif current_profit >= 0.2: if last_candle['rsi_14'] < 30.0: return (True, 'signal_profit_u_bear_12_1') elif 0.2 > current_profit >= 0.12: if last_candle['rsi_14'] < 42.0: return (True, 'signal_profit_u_bear_11_1') elif 0.12 > current_profit >= 0.1: if last_candle['rsi_14'] < 46.0: return (True, 'signal_profit_u_bear_10_1') elif 0.1 > 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 exit_pump_main(self, current_profit: float, last_candle) -> tuple: if last_candle['exit_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') elif 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['exit_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') elif 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['exit_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') elif 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 exit_dec_main(self, current_profit: float, last_candle) -> tuple: if self.exit_custom_dec_profit_max_1 > current_profit >= self.exit_custom_dec_profit_min_1 and last_candle['sma_200_dec_20']: return (True, 'signal_profit_d_1') elif self.exit_custom_dec_profit_max_2 > current_profit >= self.exit_custom_dec_profit_min_2 and last_candle['close'] < last_candle['ema_100']: return (True, 'signal_profit_d_2') return (False, None) def exit_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple: if self.exit_trail_profit_max_1 > current_profit >= self.exit_trail_profit_min_1 and self.exit_trail_rsi_min_1 < last_candle['rsi_14'] < self.exit_trail_rsi_max_1 and (max_profit > current_profit + self.exit_trail_down_1) and (last_candle['moderi_96'] == False): return (True, 'signal_profit_t_1') elif self.exit_trail_profit_max_2 > current_profit >= self.exit_trail_profit_min_2 and self.exit_trail_rsi_min_2 < last_candle['rsi_14'] < self.exit_trail_rsi_max_2 and (max_profit > current_profit + self.exit_trail_down_2) and (last_candle['ema_25'] < last_candle['ema_50']): return (True, 'signal_profit_t_2') elif self.exit_trail_profit_max_3 > current_profit >= self.exit_trail_profit_min_3 and max_profit > current_profit + self.exit_trail_down_3 and last_candle['sma_200_dec_20_1h']: return (True, 'signal_profit_t_3') elif self.exit_trail_profit_max_4 > current_profit >= self.exit_trail_profit_min_4 and max_profit > current_profit + self.exit_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 exit_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple: # Pumped pair, short duration if last_candle['exit_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.exit_custom_long_profit_min_1 < current_profit < self.exit_custom_long_profit_max_1 and current_time - timedelta(minutes=self.exit_custom_long_duration_min_1) > trade.open_date_utc: return (True, 'signal_profit_l_1') return (False, None) def exit_under_min(self, current_profit: float, last_candle) -> tuple: if last_candle['moderi_96'] == False: # Downtrend if self.exit_custom_profit_under_profit_max_1 > current_profit >= self.exit_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.exit_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_1): return (True, 'signal_profit_u_e_1') # Uptrend elif current_profit >= self.exit_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.exit_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.exit_custom_profit_under_rsi_diff_2): return (True, 'signal_profit_u_e_2') return (False, None) def exit_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 exit_pump_dec(self, current_profit: float, last_candle) -> tuple: if 0.03 > current_profit >= 0.005 and last_candle['exit_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['exit_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['exit_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['exit_pump_24_2_1h']: return (True, 'signal_profit_p_d_4') return (False, None) def exit_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple: # Pumped 48h 1, under EMA200 if self.exit_custom_pump_under_profit_max_1 > current_profit >= self.exit_custom_pump_under_profit_min_1 and last_candle['exit_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['exit_pump_36_2_1h'] and self.exit_custom_pump_trail_profit_max_1 > current_profit >= self.exit_custom_pump_trail_profit_min_1 and (self.exit_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.exit_custom_pump_trail_rsi_max_1) and (max_profit > current_profit + self.exit_custom_pump_trail_down_1): return (True, 'signal_profit_p_t_1') return (False, None) def exit_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple: if max_loss > self.exit_custom_recover_min_loss_1 and current_profit >= self.exit_custom_recover_profit_1: return (True, 'signal_profit_r_1') elif max_loss > self.exit_custom_recover_min_loss_2 and self.exit_custom_recover_profit_max_2 > current_profit >= self.exit_custom_recover_profit_min_2 and (last_candle['rsi_14'] < self.exit_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']): return (True, 'signal_profit_r_2') return (False, None) def exit_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 exit_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 exit_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 exit_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 exit_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 exit_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 exit_profit_target(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_exit_reason, previous_time_profit_reached) -> tuple: # if self.profit_target_1_enable and previous_exit_reason == "mark_profit_target_01": # if (current_profit > 0) and (current_rate < (previous_rate - 0.005)): # return True, 'exit_profit_target_01' return (False, None) def exit_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_exit_1h'] == True and current_profit > 0.02: return (True, 'signal_profit_q_momdiv_1h') if last_candle['momdiv_exit'] == 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 exit_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 exit_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', enter_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, 'exit_long_1_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_long_2_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_long_3_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_long_4_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_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, 'exit_long_7_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_long_8_1_1') elif current_profit > 0.01: return (True, 'exit_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, 'exit_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, 'exit_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, 'exit_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, 'exit_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, 'exit_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, 'exit_long_l_1') return self.exit_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) def exit_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_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] 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] enter_tag = 'empty' if hasattr(trade, 'enter_tag') and trade.entry_tag is not None: enter_tag = trade.entry_tag entry_tags = entry_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 entry_tags)): exit_long, signal_name = self.exit_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, enter_tag) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Skip remaining exit logic for long mode return None # Quick exit mode if all((c in ['empty', '32', '33', '34', '35', '36', '37', '38', '40'] for c in entry_tags)): exit_long, signal_name = self.exit_quick_mode(current_profit, max_profit, last_candle, previous_candle_1) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Ichi Trade management if all((c in ['39'] for c in entry_tags)): exit_long, signal_name = self.exit_ichi(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Over EMA200, main profit targets exit_long, signal_name = self.exit_over_main(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Under EMA200, main profit targets exit_long, signal_name = self.exit_under_main(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # The pair is pumped exit_long, signal_name = self.exit_pump_main(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # The pair is descending exit_long, signal_name = self.exit_dec_main(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Trailing exit_long, signal_name = self.exit_trail_main(current_profit, last_candle, max_profit) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Duration based exit_long, signal_name = self.exit_duration_main(current_profit, last_candle, trade, current_time) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Under EMA200, exit with any profit exit_long, signal_name = self.exit_under_min(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Stoplosses exit_long, signal_name = self.exit_stoploss(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Pumped descending pairs exit_long, signal_name = self.exit_pump_dec(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Extra exits for pumped pairs exit_long, signal_name = self.exit_pump_extra(current_profit, last_candle, max_profit) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Extra exits for trades that recovered exit_long, signal_name = self.exit_recover(current_profit, last_candle, max_loss) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 1 exit_long, signal_name = self.exit_r_1(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 2 exit_long, signal_name = self.exit_r_2(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 3 exit_long, signal_name = self.exit_r_3(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 4, plus CTI exit_long, signal_name = self.exit_r_4(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 5, plus RSI and CTI 1h exit_long, signal_name = self.exit_r_5(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Williams %R based exit 6, plus RSI, CTI, CCI exit_long, signal_name = self.exit_r_6(current_profit, last_candle) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_tag})' # Pivot points based exits exit_long, signal_name = self.exit_pivot(current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_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_exit_reason = self.target_profit_cache.data[pair]['exit_reason'] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]['time_profit_reached']) exit_long, signal_name = self.exit_profit_target(pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_exit_reason, previous_time_profit_reached) if exit_long and signal_name is not None: return f'{signal_name} ( {enter_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.exit_condition_1_enable and last_candle['rsi_14'] > self.exit_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'exit_signal_1_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_1_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_1_2_2 ( {enter_tag})' # Sell signal 2 elif self.exit_condition_2_enable and last_candle['rsi_14'] > self.exit_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'exit_signal_2_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_2_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_2_2_2 ( {enter_tag})' # Sell signal 3 elif self.exit_condition_3_enable and last_candle['rsi_14'] > self.exit_rsi_main_3: if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.01: return f'exit_signal_3_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_3_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_3_2_2 ( {enter_tag})' # Sell signal 4 elif self.exit_condition_4_enable and last_candle['rsi_14'] > self.exit_dual_rsi_rsi_4 and (last_candle['rsi_14_1h'] > self.exit_dual_rsi_rsi_1h_4): if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.01: return f'exit_signal_4_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_4_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_4_2_2 ( {enter_tag})' # Sell signal 6 elif self.exit_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.exit_rsi_under_6): if current_profit > 0.01: return f'exit_signal_6_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_6_2 ( {enter_tag})' # Sell signal 7 elif self.exit_condition_7_enable and last_candle['rsi_14_1h'] > self.exit_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'exit_signal_7_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_7_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_7_2_2 ( {enter_tag})' # Sell signal 8 elif self.exit_condition_8_enable and last_candle['close'] > last_candle['bb20_2_upp_1h'] * self.exit_bb_relative_8: if last_candle['close'] > last_candle['ema_200']: if current_profit > 0.01: return f'exit_signal_8_1_1 ( {enter_tag})' elif current_profit > 0.01: return f'exit_signal_8_2_1 ( {enter_tag})' elif max_loss > 0.5: return f'exit_signal_8_2_2 ( {enter_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']: 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_entry'] = mom['momdiv_entry'] informative_1h['momdiv_exit'] = mom['momdiv_exit'] 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.entry_pump_threshold_10_24, self.entry_pump_pull_threshold_10_24) informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_10_36, self.entry_pump_pull_threshold_10_36) informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_10_48, self.entry_pump_pull_threshold_10_48) informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_20_24, self.entry_pump_pull_threshold_20_24) informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_20_36, self.entry_pump_pull_threshold_20_36) informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_20_48, self.entry_pump_pull_threshold_20_48) informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_30_24, self.entry_pump_pull_threshold_30_24) informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_30_36, self.entry_pump_pull_threshold_30_36) informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_30_48, self.entry_pump_pull_threshold_30_48) informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_40_24, self.entry_pump_pull_threshold_40_24) informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_40_36, self.entry_pump_pull_threshold_40_36) informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_40_48, self.entry_pump_pull_threshold_40_48) informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_50_24, self.entry_pump_pull_threshold_50_24) informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_50_36, self.entry_pump_pull_threshold_50_36) informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_50_48, self.entry_pump_pull_threshold_50_48) informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_60_24, self.entry_pump_pull_threshold_60_24) informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_60_36, self.entry_pump_pull_threshold_60_36) informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_60_48, self.entry_pump_pull_threshold_60_48) informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_70_24, self.entry_pump_pull_threshold_70_24) informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_70_36, self.entry_pump_pull_threshold_70_36) informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_70_48, self.entry_pump_pull_threshold_70_48) informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_80_24, self.entry_pump_pull_threshold_80_24) informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_80_36, self.entry_pump_pull_threshold_80_36) informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_80_48, self.entry_pump_pull_threshold_80_48) informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_90_24, self.entry_pump_pull_threshold_90_24) informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_90_36, self.entry_pump_pull_threshold_90_36) informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_90_48, self.entry_pump_pull_threshold_90_48) informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_100_24, self.entry_pump_pull_threshold_100_24) informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_100_36, self.entry_pump_pull_threshold_100_36) informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_100_48, self.entry_pump_pull_threshold_100_48) informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_110_24, self.entry_pump_pull_threshold_110_24) informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_110_36, self.entry_pump_pull_threshold_110_36) informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_110_48, self.entry_pump_pull_threshold_110_48) informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.entry_pump_threshold_120_24, self.entry_pump_pull_threshold_120_24) informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.entry_pump_threshold_120_36, self.entry_pump_pull_threshold_120_36) informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.entry_pump_threshold_120_48, self.entry_pump_pull_threshold_120_48) informative_1h['exit_pump_48_1'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_1 informative_1h['exit_pump_48_2'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_2 informative_1h['exit_pump_48_3'] = informative_1h['hl_pct_change_48'] > self.exit_pump_threshold_48_3 informative_1h['exit_pump_36_1'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_1 informative_1h['exit_pump_36_2'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_2 informative_1h['exit_pump_36_3'] = informative_1h['hl_pct_change_36'] > self.exit_pump_threshold_36_3 informative_1h['exit_pump_24_1'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_1 informative_1h['exit_pump_24_2'] = informative_1h['hl_pct_change_24'] > self.exit_pump_threshold_24_2 informative_1h['exit_pump_24_3'] = informative_1h['hl_pct_change_24'] > self.exit_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 exit 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_entry'] = mom['momdiv_entry'] dataframe['momdiv_exit'] = mom['momdiv_exit'] 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() '\n --> BTC informative (5m/1h)\n ___________________________________________________________________________________________\n ' if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = 'BTC/USDT' 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) '\n --> Informative timeframe\n ___________________________________________________________________________________________\n ' 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) '\n --> Resampled to another timeframe\n ___________________________________________________________________________________________\n ' 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) '\n --> The indicators for the normal (5m) timeframe\n ___________________________________________________________________________________________\n ' 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' for index in self.entry_protection_params: item_entry_protection_list = [True] global_entry_protection_params = self.entry_protection_params[index] if self.entry_params[f'entry_condition_{index}_enable']: # Standard protections - Common to every condition # ----------------------------------------------------------------------------------------- if global_entry_protection_params['ema_fast']: item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_fast_len']}"] > dataframe['ema_200']) if global_entry_protection_params['ema_slow']: item_entry_protection_list.append(dataframe[f"ema_{global_entry_protection_params['ema_slow_len']}_1h"] > dataframe['ema_200_1h']) if global_entry_protection_params['close_above_ema_fast']: item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_fast_len']}"]) if global_entry_protection_params['close_above_ema_slow']: item_entry_protection_list.append(dataframe['close'] > dataframe[f"ema_{global_entry_protection_params['close_above_ema_slow_len']}_1h"]) if global_entry_protection_params['sma200_rising']: item_entry_protection_list.append(dataframe['sma_200'] > dataframe['sma_200'].shift(int(global_entry_protection_params['sma200_rising_val']))) if global_entry_protection_params['sma200_1h_rising']: item_entry_protection_list.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(int(global_entry_protection_params['sma200_1h_rising_val']))) if global_entry_protection_params['safe_dips_threshold_0'] is not None: item_entry_protection_list.append(dataframe['tpct_change_0'] < global_entry_protection_params['safe_dips_threshold_0']) if global_entry_protection_params['safe_dips_threshold_2'] is not None: item_entry_protection_list.append(dataframe['tpct_change_2'] < global_entry_protection_params['safe_dips_threshold_2']) if global_entry_protection_params['safe_dips_threshold_12'] is not None: item_entry_protection_list.append(dataframe['tpct_change_12'] < global_entry_protection_params['safe_dips_threshold_12']) if global_entry_protection_params['safe_dips_threshold_144'] is not None: item_entry_protection_list.append(dataframe['tpct_change_144'] < global_entry_protection_params['safe_dips_threshold_144']) if global_entry_protection_params['safe_pump']: item_entry_protection_list.append(dataframe[f"safe_pump_{global_entry_protection_params['safe_pump_period']}_{global_entry_protection_params['safe_pump_type']}_1h"]) if global_entry_protection_params['btc_1h_not_downtrend']: item_entry_protection_list.append(dataframe['btc_not_downtrend_1h']) if global_entry_protection_params['close_over_pivot_type'] != 'none': item_entry_protection_list.append(dataframe['close'] > dataframe[f"{global_entry_protection_params['close_over_pivot_type']}_1d"] * global_entry_protection_params['close_over_pivot_offset']) if global_entry_protection_params['close_under_pivot_type'] != 'none': item_entry_protection_list.append(dataframe['close'] < dataframe[f"{global_entry_protection_params['close_under_pivot_type']}_1d"] * global_entry_protection_params['close_under_pivot_offset']) if not self.config['runmode'].value in ('live', 'dry_run'): if self.has_bt_agefilter: item_entry_protection_list.append(dataframe['bt_agefilter_ok']) elif self.has_downtime_protection: item_entry_protection_list.append(dataframe['live_data_ok']) # Buy conditions # ----------------------------------------------------------------------------------------- item_entry_logic = [] item_entry_logic.append(reduce(lambda x, y: x & y, item_entry_protection_list)) # Condition #1 if index == 1: # Non-Standard protections # Logic item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min() > self.entry_1_min_inc) item_entry_logic.append(dataframe['rsi_14'] < self.entry_1_rsi_max) item_entry_logic.append(dataframe['r_14'] < self.entry_2_r_14_max) item_entry_logic.append(dataframe['mfi'] < self.entry_1_mfi_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_1_rsi_1h_min) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_1_rsi_1h_max) # Condition #2 elif index == 2: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.entry_2_rsi_1h_diff) item_entry_logic.append(dataframe['mfi'] < self.entry_2_mfi) item_entry_logic.append(dataframe['cti'] < self.entry_2_cti_max) item_entry_logic.append(dataframe['r_480'] > self.entry_2_r_480_min) item_entry_logic.append(dataframe['r_480'] < self.entry_2_r_480_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_2_cti_1h_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_2_volume) # Condition #3 elif index == 3: # Non-Standard protections # Logic item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_3_bb40_bbdelta_close)) item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_3_bb40_closedelta_close)) item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_3_bb40_tail_bbdelta)) item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_entry_logic.append(dataframe['cci_36_osc'] > self.entry_3_cci_36_osc_min) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_3_crsi_1h_min) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_3_r_480_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_3_cti_1h_max) # Condition #4 elif index == 4: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_50']) item_entry_logic.append(dataframe['close'] < self.entry_4_bb20_close_bblowerband * dataframe['bb20_2_low']) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_30'].shift(1) * self.entry_4_bb20_volume) item_entry_logic.append(dataframe['cti'] < self.entry_4_cti_max) # Condition #5 elif index == 5: # Non-Standard protections item_entry_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.entry_5_ema_rel) # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_5_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_5_bb_offset) item_entry_logic.append(dataframe['cti'] < self.entry_5_cti_max) item_entry_logic.append(dataframe['rsi_14'] > self.entry_5_rsi_14_min) item_entry_logic.append(dataframe['mfi'] > self.entry_5_mfi_min) item_entry_logic.append(dataframe['r_14'] < self.entry_5_r_14_max) item_entry_logic.append(dataframe['r_14'].shift(1) < self.entry_5_r_14_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_5_crsi_1h_min) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_5_volume) # Condition #6 elif index == 6: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_6_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_6_bb_offset) item_entry_logic.append(dataframe['r_14'] < self.entry_6_r_14_max) item_entry_logic.append(dataframe['cti_1h'] > self.entry_6_cti_1h_min) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_6_crsi_1h_min) # Condition #7 elif index == 7: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_7_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_7_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_7_cti_max) item_entry_logic.append(dataframe['rsi_14'] < self.entry_7_rsi_max) # Condition #8 elif index == 8: # Non-Standard protections item_entry_logic.append(dataframe['ema_20'] > dataframe['ema_50']) item_entry_logic.append(dataframe['ema_15'] > dataframe['ema_100']) item_entry_logic.append(dataframe['ema_200'] > dataframe['sma_200']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_8_bb_offset) item_entry_logic.append(dataframe['r_14'] < self.entry_8_r_14_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_8_cti_1h_max) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_8_r_480_1h_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_8_volume) # Condition #9 elif index == 9: # Non-Standard protections item_entry_logic.append(dataframe['ema_50'] > dataframe['ema_200']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_9_ma_offset) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_9_bb_offset) item_entry_logic.append(dataframe['mfi'] < self.entry_9_mfi_max) item_entry_logic.append(dataframe['cti'] < self.entry_9_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_9_r_14_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_9_rsi_1h_min) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_9_rsi_1h_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_9_crsi_1h_min) # Condition #10 elif index == 10: # Non-Standard protections item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_10_ma_offset_high) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_10_bb_offset) item_entry_logic.append(dataframe['r_14'] < self.entry_10_r_14_max) item_entry_logic.append(dataframe['cti_1h'] > self.entry_10_cti_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_10_cti_1h_max) # Condition #11 elif index == 11: # Non-Standard protections # Logic item_entry_logic.append((dataframe['close'] - dataframe['open'].rolling(6).min()) / dataframe['open'].rolling(6).min() > self.entry_11_min_inc) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_11_ma_offset) item_entry_logic.append(dataframe['rsi_14'] < self.entry_11_rsi_max) item_entry_logic.append(dataframe['mfi'] < self.entry_11_mfi_max) item_entry_logic.append(dataframe['cci'] < self.entry_11_cci_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_11_rsi_1h_min) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_11_rsi_1h_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_11_cti_1h_max) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_11_r_480_1h_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_11_crsi_1h_min) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_12_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_12_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_12_rsi_max) item_entry_logic.append(dataframe['cti'] < self.entry_12_cti_max) # Condition #13 elif index == 13: # Non-Standard protections item_entry_logic.append(dataframe['ema_50_1h'] > dataframe['ema_100_1h']) # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_13_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_13_cti_max) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_13_ewo_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_13_cti_1h_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_13_crsi_1h_min) # Condition #14 elif index == 14: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_14_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_14_bb_offset) item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_14_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_14_cti_max) # Condition #15 elif index == 15: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_15_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['rsi_14'] < self.entry_15_rsi_min) item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_15_ma_offset) item_entry_logic.append(dataframe['cti_1h'] > self.entry_15_cti_1h_min) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_16_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_16_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_16_rsi_max) item_entry_logic.append(dataframe['cti'] < self.entry_16_cti_max) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_20'] * self.entry_17_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_17_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_17_cti_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_17_crsi_1h_min) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_17_volume) # Condition #18 elif index == 18: # Non-Standard protections item_entry_logic.append(dataframe['sma_200'] > dataframe['sma_200'].shift(20)) item_entry_logic.append(dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36)) # Logic item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_18_bb_offset) item_entry_logic.append(dataframe['rsi_14'] < self.entry_18_rsi_max) item_entry_logic.append(dataframe['cti'] < self.entry_18_cti_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_18_cti_1h_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_18_volume) # Condition #19 elif index == 19: # Non-Standard protections item_entry_logic.append(dataframe['moderi_32'] == True) item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['moderi_96'] == True) # Logic item_entry_logic.append(dataframe['close'].shift(1) > dataframe['ema_100_1h']) item_entry_logic.append(dataframe['low'] < dataframe['ema_100_1h']) item_entry_logic.append(dataframe['close'] > dataframe['ema_100_1h']) item_entry_logic.append(dataframe['chop'] < self.entry_19_chop_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_19_rsi_1h_min) # Condition #20 elif index == 20: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < self.entry_20_rsi_14_max) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_20_rsi_14_1h_max) item_entry_logic.append(dataframe['cti'] < self.entry_20_cti_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_20_volume) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_14'] < self.entry_21_rsi_14_max) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_21_rsi_14_1h_max) item_entry_logic.append(dataframe['cti'] < self.entry_21_cti_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_21_volume) # Condition #22 elif index == 22: # Non-Standard protections item_entry_logic.append(dataframe['ema_100_1h'] > dataframe['ema_100_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(36)) # Logic item_entry_logic.append(dataframe['volume_mean_4'] * self.entry_22_volume > dataframe['volume']) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_22_ma_offset) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_22_bb_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_22_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_22_rsi_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_22_cti_max) item_entry_logic.append(dataframe['r_480'] < self.entry_22_r_480_max) item_entry_logic.append(dataframe['cti_1h'] > self.entry_22_cti_1h_min) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_23_bb_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_23_ewo_min) item_entry_logic.append(dataframe['cti'] < self.entry_23_cti_max) item_entry_logic.append(dataframe['rsi_14'] < self.entry_23_rsi_14_max) item_entry_logic.append(dataframe['rsi_14_1h'] < self.entry_23_rsi_14_1h_max) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_23_r_480_1h_min) item_entry_logic.append(dataframe['cti_1h'] < 0.92) # Condition #24 elif index == 24: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_12_1h'].shift(12) < dataframe['ema_35_1h'].shift(12)) item_entry_logic.append(dataframe['ema_12_1h'] > dataframe['ema_35_1h']) item_entry_logic.append(dataframe['cmf_1h'].shift(12) < 0) item_entry_logic.append(dataframe['cmf_1h'] > 0) item_entry_logic.append(dataframe['rsi_14'] < self.entry_24_rsi_14_max) item_entry_logic.append(dataframe['rsi_14_1h'] > self.entry_24_rsi_14_1h_min) # Condition #25 elif index == 25: # Non-Standard protections # Logic item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift()) item_entry_logic.append(dataframe['rsi_4'] < self.entry_25_rsi_4_max) item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_26_1h']) item_entry_logic.append(dataframe['close'] < dataframe['sma_15'] * self.entry_25_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_25_cti_max) item_entry_logic.append(dataframe['cci'] < self.entry_25_cci_max) # Condition #26 elif index == 26: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['zema_61'] * self.entry_26_zema_low_offset) item_entry_logic.append(dataframe['cti'] < self.entry_26_cti_max) item_entry_logic.append(dataframe['cci'] < self.entry_26_cci_max) item_entry_logic.append(dataframe['r_14'] < self.entry_26_r_14_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_26_cti_1h_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_26_volume) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_entry_logic.append(dataframe['r_480'] < self.entry_27_wr_max) item_entry_logic.append(dataframe['r_14'] == self.entry_27_r_14) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_27_wr_1h_max) item_entry_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.entry_27_rsi_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_27_volume) # Condition #28 elif index == 28: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_28_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_28_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_28_rsi_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_28_cti_max) item_entry_logic.append(dataframe['cti'].shift(1) < self.entry_28_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_28_r_14_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_28_cti_1h_max) # Condition #29 elif index == 29: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == True) item_entry_logic.append(dataframe['close'] < dataframe['hull_75'] * self.entry_29_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_29_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_29_cti_max) # Condition #30 elif index == 30: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == False) item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_30_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_30_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_30_rsi_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_30_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_30_r_14_max) # Condition #31 elif index == 31: # Non-Standard protections # Logic item_entry_logic.append(dataframe['moderi_64'] == False) item_entry_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.entry_31_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_31_ewo_max) item_entry_logic.append(dataframe['r_14'] < self.entry_31_r_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_31_cti_max) # Condition #32 - Quick mode entry elif index == 32: # Non-Standard protections item_entry_logic.append(dataframe['ema_20_1h'] > dataframe['ema_25_1h']) # Logic item_entry_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) item_entry_logic.append(dataframe['rsi_4'] < self.entry_32_rsi_4_max) item_entry_logic.append(dataframe['rsi_14'] > self.entry_32_rsi_14_min) item_entry_logic.append(dataframe['close'] < dataframe['sma_15'] * self.entry_32_ma_offset) item_entry_logic.append(dataframe['cti'] < self.entry_32_cti_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_32_crsi_1h_min) item_entry_logic.append(dataframe['crsi_1h'] < self.entry_32_crsi_1h_max) # Condition #33 - Quick mode entry elif index == 33: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_33_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_33_ewo_min) item_entry_logic.append(dataframe['cti'] < self.entry_33_cti_max) item_entry_logic.append(dataframe['rsi_14'] < self.entry_33_rsi_max) item_entry_logic.append(dataframe['r_14'] < self.entry_33_r_14_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_33_cti_1h_max) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_33_volume) # Condition #34 - Quick mode entry elif index == 34: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_13'] * self.entry_34_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_34_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_34_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_34_r_14_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_34_crsi_1h_min) item_entry_logic.append(dataframe['volume'] < dataframe['volume_mean_4'] * self.entry_34_volume) # Condition #35 - PMAX0 entry elif index == 35: # Non-Standard protections # Logic item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_35_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_35_ewo_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_35_rsi_max) item_entry_logic.append(dataframe['cti'] < self.entry_35_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_35_r_14_max) # Condition #36 - PMAX1 entry elif index == 36: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_36_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_36_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_36_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_36_r_14_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_36_crsi_1h_min) # Condition #37 - Quick mode entry elif index == 37: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_37_ma_offset) item_entry_logic.append((dataframe['close_1h'].shift(12) - dataframe['close_1h']) / dataframe['close_1h'] < self.entry_37_close_1h_max) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_37_ewo_min) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_37_ewo_max) item_entry_logic.append(dataframe['rsi_14'] > self.entry_37_rsi_14_min) item_entry_logic.append(dataframe['rsi_14'] < self.entry_37_rsi_14_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_37_crsi_1h_min) item_entry_logic.append(dataframe['crsi_1h'] < self.entry_37_crsi_1h_max) item_entry_logic.append(dataframe['cti'] < self.entry_37_cti_max) item_entry_logic.append(dataframe['cti_1h'] < self.entry_37_cti_1h_max) item_entry_logic.append(dataframe['r_14'] < self.entry_37_r_14_max) # Condition #38 - PMAX3 entry elif index == 38: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['pm'] > dataframe['pmax_thresh']) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_38_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_38_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_38_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_38_r_14_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_38_crsi_1h_min) # Condition #39 - Ichimoku elif index == 39: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h']) item_entry_logic.append(dataframe['close'] > dataframe['cloud_top_1h']) item_entry_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h']) item_entry_logic.append(dataframe['chikou_span_greater_1h']) item_entry_logic.append(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) item_entry_logic.append(dataframe['close'] < dataframe['ssl_up_1h']) item_entry_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12)) item_entry_logic.append(dataframe['cti'] < self.entry_39_cti_max) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_39_r_1h_max) item_entry_logic.append(dataframe['cti_1h'] > self.entry_39_cti_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_39_cti_1h_max) # Start of trend item_entry_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_entry_logic.append(dataframe['momdiv_entry_1h'] == True) item_entry_logic.append(dataframe['cci'] < self.entry_40_cci_max) item_entry_logic.append(dataframe['rsi_14'] < self.entry_40_rsi_max) item_entry_logic.append(dataframe['r_14'] < self.entry_40_r_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_40_cti_max) # Condition #41 elif index == 41: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['close'] < dataframe['sma_75'] * self.entry_41_ma_offset_high) item_entry_logic.append(dataframe['cti'] < self.entry_41_cti_max) item_entry_logic.append(dataframe['cci'] < self.entry_41_cci_max) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_41_ewo_1h_min) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_41_r_480_1h_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_41_crsi_1h_min) # Condition #42 elif index == 42: # Non-Standard protections (add below) # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_42_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.entry_42_bb_offset) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_42_ewo_1h_min) item_entry_logic.append(dataframe['cti_1h'] > self.entry_42_cti_1h_min) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_42_r_480_1h_max) # Condition #43 elif index == 43: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_43_bb40_bbdelta_close)) item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_43_bb40_closedelta_close)) item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_43_bb40_tail_bbdelta)) item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_entry_logic.append(dataframe['cti'] < self.entry_43_cti_max) item_entry_logic.append(dataframe['r_480'] > self.entry_43_r_480_min) item_entry_logic.append(dataframe['cti_1h'] > self.entry_43_cti_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_43_cti_1h_max) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_43_r_480_1h_min) # Condition #44 elif index == 44: # Non-Standard protections # Logic item_entry_logic.append(dataframe['close'] < dataframe['ema_16'] * self.entry_44_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] < self.entry_44_ewo_max) item_entry_logic.append(dataframe['cti'] < self.entry_44_cti_max) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_44_crsi_1h_min) # Condition #45 - Long mode elif index == 45: # Non-Standard protections # Logic item_entry_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_entry_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.entry_45_bb40_bbdelta_close)) item_entry_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.entry_45_bb40_closedelta_close)) item_entry_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.entry_45_bb40_tail_bbdelta)) item_entry_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_entry_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_45_ma_offset) item_entry_logic.append(dataframe['ewo_sma'] > self.entry_45_ewo_min) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_45_ewo_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_45_cti_1h_max) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_45_r_480_1h_max) # Condition #46 - Long mode elif index == 46: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_entry_logic.append(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.entry_46_ema_open_mult) item_entry_logic.append(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_46_ewo_1h_min) item_entry_logic.append(dataframe['cti_1h'] > self.entry_46_cti_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_46_cti_1h_max) # Condition #47 - Long mode elif index == 47: # Non-Standard protections # Logic item_entry_logic.append(dataframe['ewo_sma'] > self.entry_47_ewo_min) item_entry_logic.append(dataframe['close'] < dataframe['sma_30'] * self.entry_47_ma_offset) item_entry_logic.append(dataframe['rsi_14'] < self.entry_47_rsi_14_max) item_entry_logic.append(dataframe['cti'] < self.entry_47_cti_max) item_entry_logic.append(dataframe['r_14'] < self.entry_47_r_14_max) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_47_ewo_1h_min) item_entry_logic.append(dataframe['cti_1h'] > self.entry_47_cti_1h_min) item_entry_logic.append(dataframe['cti_1h'] < self.entry_47_cti_1h_max) # Condition #48 - Uptrend mode elif index == 48: # Non-Standard protections item_entry_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_entry_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_entry_logic.append(dataframe['moderi_32']) item_entry_logic.append(dataframe['moderi_64']) item_entry_logic.append(dataframe['moderi_96']) # Logic item_entry_logic.append(dataframe['ewo_sma'] > self.entry_48_ewo_min) item_entry_logic.append(dataframe['ewo_sma_1h'] > self.entry_48_ewo_1h_min) item_entry_logic.append(dataframe['r_480'] > self.entry_48_r_480_min) item_entry_logic.append(dataframe['r_480_1h'] > self.entry_48_r_480_1h_min) item_entry_logic.append(dataframe['r_480_1h'] < self.entry_48_r_480_1h_max) item_entry_logic.append(dataframe['r_480_1h'] > dataframe['r_480_1h'].shift(12)) item_entry_logic.append(dataframe['cti_1h'] > self.entry_48_cti_1h_min) item_entry_logic.append(dataframe['crsi_1h'] > self.entry_48_crsi_1h_min) item_entry_logic.append(dataframe['cti'].shift(1).rolling(12).min() < -0.5) item_entry_logic.append(dataframe['cti'].shift(1).rolling(12).max() < 0.0) item_entry_logic.append(dataframe['cti'].shift(1) < 0.0) item_entry_logic.append(dataframe['cti'] > 0.0) item_entry_logic.append(dataframe['volume'] > 0) item_entry = reduce(lambda x, y: x & y, item_entry_logic) dataframe.loc[item_entry, 'enter_tag'] += f'{index} ' conditions.append(item_entry) if conditions: dataframe.loc[:, 'enter_long'] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 return dataframe def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, **kwargs) -> bool: """ Called right before placing a regular exit 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 exit_reason: Sell reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'exit_signal', 'force_exit', 'emergency_exit'] :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True is returned, then the exit-order is placed on the exchange. False aborts the process """ if self._should_hold_trade(trade, rate, exit_reason): return False self._remove_profit_target(pair) return True def _set_profit_target(self, pair: str, exit_reason: str, rate: float, current_time: 'datetime'): self.target_profit_cache.data[pair] = {'rate': rate, 'exit_reason': exit_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, exit_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, exit return False if not self.hold_trades_cache.data: # We have no pairs we want to hold until profit, exit 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 exit_reason == 'force_exit': formatted_profit_ratio = f'{trade_profit_ratio * 100}%' formatted_current_profit_ratio = f'{current_profit_ratio * 100}%' log.warning('Force exiting %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, exit 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 exit_reason == 'force_exit': formatted_profit_ratio = f'{trade_profit_ratio * 100}%' formatted_current_profit_ratio = f'{current_profit_ratio * 100}%' log.warning('Force exiting %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, exit 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), 0.0) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.0) 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), 0.0) 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 0.0 pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where(pm_arr > 0.0, 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) enter_long = qtpylib.crossed_below(mom, lowerband) exit_long = 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_entry': enter_long, 'momdiv_exit': exit_long, '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