# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/NostalgiaForInfinityNext.py import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes from freqtrade.exchange import timeframe_to_prev_date from freqtrade.data.dataprovider import DataProvider from pandas import DataFrame, Series, concat from functools import reduce import math from typing import Dict from freqtrade.persistence import Trade from datetime import datetime, timedelta from technical.util import resample_to_interval, resampled_merge from technical.indicators import zema, VIDYA, ichimoku import time log = logging.getLogger(__name__) #log.setLevel(logging.DEBUG) try: import pandas_ta as pta except ImportError: log.error("IMPORTANT - please install the pandas_ta python module which is needed for this strategy. If you're running Docker, add RUN pip install pandas_ta to your Dockerfile, otherwise run: pip install pandas_ta") else: log.info('pandas_ta successfully imported') ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## https://github.com/iterativv/NostalgiaForInfinity ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_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 ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (USDT, ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## TRC20/TRON (USDT, TRON, ...): TTAa9MX6zMLXNgWMhg7tkNormVHWCoq8Xk ## ## ## ## REFERRAL LINKS ## ## ## ## Binance: https://accounts.binance.com/en/register?ref=EAZC47FM (5% discount on trading fees) ## ## Kucoin: https://www.kucoin.com/r/QBSSSPYV (5% discount on trading fees) ## ## Gate.io: https://www.gate.io/signup/8054544 (10% discount on trading fees) ## ## OKEx: https://www.okex.com/join/11749725760 (5% discount on trading fees) ## ## Huobi: https://www.huobi.com/en-us/topic/double-reward/?invite_code=ubpt2223 ## ########################################################################################################### class Github_DerSalvador_freqtrade_helm_chart__NostalgiaForInfinityNext__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) #self.dp = DataProvider(config, config['exchange']) if self.target_profit_cache is None: self.target_profit_cache = Cache(self.config['user_data_dir'] / 'data-nfi-profit_target_by_pair.json') # If the cached data hasn't changed, it's a no-op self.target_profit_cache.save() def get_hold_trades_config_file(self): proper_holds_file_path = self.config['user_data_dir'].resolve() / 'nfi-hold-trades.json' if proper_holds_file_path.is_file(): return proper_holds_file_path strat_file_path = pathlib.Path(__file__) hold_trades_config_file_resolve = strat_file_path.resolve().parent / 'hold-trades.json' if hold_trades_config_file_resolve.is_file(): log.warning('Please move %s to %s which is now the expected path for the holds file', hold_trades_config_file_resolve, proper_holds_file_path) return hold_trades_config_file_resolve # The resolved path does not exist, is it a symlink? hold_trades_config_file_absolute = strat_file_path.absolute().parent / 'hold-trades.json' if hold_trades_config_file_absolute.is_file(): log.warning('Please move %s to %s which is now the expected path for the holds file', hold_trades_config_file_absolute, proper_holds_file_path) return hold_trades_config_file_absolute def load_hold_trades_config(self): if self.hold_trades_cache is None: hold_trades_config_file = self.get_hold_trades_config_file() if hold_trades_config_file: log.warning('Loading hold support data from %s', hold_trades_config_file) self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() def whitelist_tracker(self): if sorted(self.coin_metrics['current_whitelist']) != sorted(self.dp.current_whitelist()): log.info('Whitelist has changed...') self.coin_metrics['top_traded_updated'] = False self.coin_metrics['top_grossing_updated'] = False # Update pairlist self.coin_metrics['current_whitelist'] = self.dp.current_whitelist() # Move up BTC for largest data footprint self.coin_metrics['current_whitelist'].insert(0, self.coin_metrics['current_whitelist'].pop(self.coin_metrics['current_whitelist'].index(f"BTC/{self.config['stake_currency']}"))) def top_traded_list(self): log.info('Updating top traded pairlist...') tik = time.perf_counter() self.coin_metrics['tt_dataframe'] = DataFrame() # Build traded volume dataframe for coin_pair in self.coin_metrics['current_whitelist']: coin = coin_pair.split('/')[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index('date') if self.config['runmode'].value in ('live', 'dry_run'): pair_dataframe = pair_dataframe.iloc[-7:, :] # Set the date index of the self.coin_metrics['tt_dataframe'] once if not 'date' in self.coin_metrics['tt_dataframe']: self.coin_metrics['tt_dataframe']['date'] = pair_dataframe['date'] self.coin_metrics['tt_dataframe'].set_index('date') # Calculate daily traded volume pair_dataframe[coin] = pair_dataframe['volume'] * qtpylib.typical_price(pair_dataframe) # Drop the columns we don't need pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True) # Merge it in on the date key self.coin_metrics['tt_dataframe'] = self.coin_metrics['tt_dataframe'].merge(pair_dataframe, on='date', how='left') # Forward fill empty cells (due to different df shapes) self.coin_metrics['tt_dataframe'].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics['tt_dataframe']['date'] self.coin_metrics['tt_dataframe'].drop(columns=['date'], inplace=True) # Build columns and top traded coins column_names = [f'Coin #{i}' for i in range(1, self.coin_metrics['top_traded_len'] + 1)] self.coin_metrics['tt_dataframe'][column_names] = self.coin_metrics['tt_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_traded_len']).index.values, axis=1, result_type='expand') self.coin_metrics['tt_dataframe'].drop(columns=[col for col in self.coin_metrics['tt_dataframe'] if col not in column_names], inplace=True) # Re-add stored date column self.coin_metrics['tt_dataframe'].insert(loc=0, column='date', value=pair_dates) self.coin_metrics['tt_dataframe'].set_index('date') self.coin_metrics['top_traded_updated'] = True log.info('Updated top traded pairlist (tail-5):') log.info(f"\n{self.coin_metrics['tt_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f'Updating top traded pairlist took {tok - tik:0.4f} seconds...') def top_grossing_list(self): log.info('Updating top grossing pairlist...') tik = time.perf_counter() self.coin_metrics['tg_dataframe'] = DataFrame() # Build grossing volume dataframe for coin_pair in self.coin_metrics['current_whitelist']: coin = coin_pair.split('/')[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index('date') if self.config['runmode'].value in ('live', 'dry_run'): pair_dataframe = pair_dataframe.iloc[-7:, :] # Set the date index of the self.coin_metrics['tg_dataframe'] once if not 'date' in self.coin_metrics['tg_dataframe']: self.coin_metrics['tg_dataframe']['date'] = pair_dataframe['date'] self.coin_metrics['tg_dataframe'].set_index('date') # Calculate daily grossing rate pair_dataframe[coin] = pair_dataframe['close'].pct_change() * 100 # Drop the columns we don't need pair_dataframe.drop(columns=['open', 'high', 'low', 'close', 'volume'], inplace=True) # Merge it in on the date key self.coin_metrics['tg_dataframe'] = self.coin_metrics['tg_dataframe'].merge(pair_dataframe, on='date', how='left') # Forward fill empty cells (due to different df shapes) self.coin_metrics['tg_dataframe'].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics['tg_dataframe']['date'] self.coin_metrics['tg_dataframe'].drop(columns=['date'], inplace=True) # Build columns and top grossing coins column_names = [f'Coin #{i}' for i in range(1, self.coin_metrics['top_grossing_len'] + 1)] self.coin_metrics['tg_dataframe'][column_names] = self.coin_metrics['tg_dataframe'].apply(lambda x: x.nlargest(self.coin_metrics['top_grossing_len']).index.values, axis=1, result_type='expand') self.coin_metrics['tg_dataframe'].drop(columns=[col for col in self.coin_metrics['tg_dataframe'] if col not in column_names], inplace=True) # Re-add stored date column self.coin_metrics['tg_dataframe'].insert(loc=0, column='date', value=pair_dates) self.coin_metrics['tg_dataframe'].set_index('date') self.coin_metrics['top_grossing_updated'] = True log.info('Updated top grossing pairlist (tail-5):') log.info(f"\n{self.coin_metrics['tg_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f'Updating top grossing pairlist took {tok - tik:0.4f} seconds...') def is_top_coin(self, coin_pair, row_data, top_length) -> bool: return coin_pair.split('/')[0] in row_data.loc['Coin #1':f'Coin #{top_length}'].values def bot_loop_start(self, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Coin metrics mechanism if self.coin_metrics['top_traded_enabled'] or self.coin_metrics['top_grossing_enabled']: self.whitelist_tracker() if self.coin_metrics['top_traded_enabled'] and (not self.coin_metrics['top_traded_updated']): self.top_traded_list() if self.coin_metrics['top_grossing_enabled'] and (not self.coin_metrics['top_grossing_updated']): self.top_grossing_list() if self.config['runmode'].value not in ('live', 'dry_run'): return super().bot_loop_start(**kwargs) if self.holdSupportEnabled: self.load_hold_trades_config() return super().bot_loop_start(**kwargs) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def 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