# source: https://raw.githubusercontent.com/iterativv/NostalgiaForInfinity/843d095f4508eb7b46fdf7ef11470842c407188b/legacy/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 pandas import DataFrame, Series, concat from functools import reduce import math from freqtrade.persistence import Trade from datetime import datetime, timedelta from technical.util import resample_to_interval, resampled_merge from technical.indicators import zema, VIDYA, ichimoku import time log = logging.getLogger(__name__) # log.setLevel(logging.DEBUG) try: import pandas_ta as pta except ImportError: log.error( "IMPORTANT - please install the pandas_ta python module which is needed for this strategy. " "If you're running Docker, add RUN pip install pandas_ta to your Dockerfile, otherwise run: " "pip install pandas_ta" ) else: log.info("pandas_ta successfully imported") ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## https://github.com/iterativv/NostalgiaForInfinity ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_sell_signal must set to true (or not set at all). ## ## sell_profit_only must set to false (or not set at all). ## ## ignore_roi_if_buy_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## HOLD SUPPORT ## ## ## ## -------- SPECIFIC TRADES ---------------------------------------------------------------------------- ## ## In case you want to have SOME of the trades to only be sold when on profit, add a file named ## ## "nfi-hold-trades.json" in the user_data directory ## ## ## ## The contents should be similar to: ## ## ## ## {"trade_ids": [1, 3, 7], "profit_ratio": 0.005} ## ## ## ## Or, for individual profit ratios(Notice the trade ID's as strings: ## ## ## ## {"trade_ids": {"1": 0.001, "3": -0.005, "7": 0.05}} ## ## ## ## NOTE: ## ## * `trade_ids` is a list of integers, the trade ID's, which you can get from the logs or from the ## ## output of the telegram status command. ## ## * Regardless of the defined profit ratio(s), the strategy MUST still produce a SELL signal for the ## ## HOLD support logic to run ## ## * This feature can be completely disabled with the holdSupportEnabled class attribute ## ## ## ## -------- SPECIFIC PAIRS ----------------------------------------------------------------------------- ## ## In case you want to have some pairs to always be on held until a specific profit, using the same ## ## "hold-trades.json" file add something like: ## ## ## ## {"trade_pairs": {"BTC/USDT": 0.001, "ETH/USDT": -0.005}} ## ## ## ## -------- SPECIFIC TRADES AND PAIRS ------------------------------------------------------------------ ## ## It is also valid to include specific trades and pairs on the holds file, for example: ## ## ## ## {"trade_ids": {"1": 0.001}, "trade_pairs": {"BTC/USDT": 0.001}} ## ########################################################################################################### ## DONATIONS ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (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_iterativv_NostalgiaForInfinity__NostalgiaForInfinityNext__20240106_181624(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 10, } stoploss = -0.50 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = "5m" res_timeframe = "none" info_timeframe_1h = "1h" info_timeframe_1d = "1d" # BTC informative has_BTC_base_tf = False has_BTC_info_tf = True has_BTC_daily_tf = False # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Do you want to use the hold feature? (with hold-trades.json) holdSupportEnabled = True # Coin Metrics coin_metrics = {} coin_metrics["top_traded_enabled"] = False coin_metrics["top_traded_updated"] = False coin_metrics["top_traded_len"] = 10 coin_metrics["tt_dataframe"] = DataFrame() coin_metrics["top_grossing_enabled"] = False coin_metrics["top_grossing_updated"] = False coin_metrics["top_grossing_len"] = 20 coin_metrics["tg_dataframe"] = DataFrame() coin_metrics["current_whitelist"] = [] # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = { "buy": "limit", "sell": "limit", "trailing_stop_loss": "limit", "stoploss": "limit", "stoploss_on_exchange": False, "stoploss_on_exchange_interval": 60, "stoploss_on_exchange_limit_ratio": 0.99, } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_17_enable": True, "buy_condition_18_enable": True, "buy_condition_19_enable": True, "buy_condition_20_enable": True, "buy_condition_21_enable": True, "buy_condition_22_enable": True, "buy_condition_23_enable": True, "buy_condition_24_enable": True, "buy_condition_25_enable": True, "buy_condition_26_enable": True, "buy_condition_27_enable": True, "buy_condition_28_enable": True, "buy_condition_29_enable": True, "buy_condition_30_enable": True, "buy_condition_31_enable": True, "buy_condition_32_enable": True, "buy_condition_33_enable": True, "buy_condition_34_enable": True, "buy_condition_35_enable": False, "buy_condition_36_enable": False, "buy_condition_37_enable": True, "buy_condition_38_enable": True, "buy_condition_39_enable": True, "buy_condition_40_enable": True, "buy_condition_41_enable": True, "buy_condition_42_enable": True, "buy_condition_43_enable": True, "buy_condition_44_enable": True, "buy_condition_45_enable": True, "buy_condition_46_enable": True, "buy_condition_47_enable": True, "buy_condition_48_enable": True, ############# } sell_params = { ############# # Enable/Disable conditions "sell_condition_1_enable": True, "sell_condition_2_enable": True, "sell_condition_3_enable": True, "sell_condition_4_enable": True, "sell_condition_5_enable": True, "sell_condition_6_enable": True, "sell_condition_7_enable": True, "sell_condition_8_enable": True, ############# } profit_target_params = { ############# # Enable/Disable conditions "profit_target_1_enable": False, ############# } ############################################################# buy_protection_params = { 1: { "ema_fast": False, "ema_fast_len": "26", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "28", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "70", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 2: { "ema_fast": True, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "20", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": True, "sma200_1h_rising_val": "48", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "20", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.4, }, 3: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "36", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": True, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 4: { "ema_fast": True, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "48", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 5: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "100", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 6: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 7: { "ema_fast": True, "ema_fast_len": "100", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "80", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 8: { "ema_fast": True, "ema_fast_len": "12", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": True, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "36", "sma200_1h_rising": True, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.016, "safe_dips_threshold_2": 0.11, "safe_dips_threshold_12": 0.26, "safe_dips_threshold_144": 0.44, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.05, }, 9: { "ema_fast": True, "ema_fast_len": "100", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.1, }, 10: { "ema_fast": True, "ema_fast_len": "35", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "24", "safe_dips_threshold_0": 0.016, "safe_dips_threshold_2": 0.11, "safe_dips_threshold_12": 0.26, "safe_dips_threshold_144": 0.44, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.6, }, 11: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "20", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "24", "sma200_1h_rising": True, "sma200_1h_rising_val": "36", "safe_dips_threshold_0": 0.022, "safe_dips_threshold_2": 0.18, "safe_dips_threshold_12": 0.34, "safe_dips_threshold_144": 0.56, "safe_pump": False, "safe_pump_type": "120", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 12: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": True, "sma200_1h_rising_val": "24", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.3, }, 13: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": True, "sma200_1h_rising_val": "24", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 14: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": True, "sma200_rising_val": "30", "sma200_1h_rising": True, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.5, }, 15: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "80", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 16: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "50", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.027, "safe_dips_threshold_2": 0.26, "safe_dips_threshold_12": 0.44, "safe_dips_threshold_144": 0.84, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 17: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 18: { "ema_fast": True, "ema_fast_len": "100", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": True, "close_above_ema_slow_len": "200", "sma200_rising": True, "sma200_rising_val": "44", "sma200_1h_rising": True, "sma200_1h_rising_val": "72", "safe_dips_threshold_0": 0.026, "safe_dips_threshold_2": 0.24, "safe_dips_threshold_12": 0.42, "safe_dips_threshold_144": 0.8, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 19: { "ema_fast": True, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "36", "sma200_1h_rising": False, "sma200_1h_rising_val": "36", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 20: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 21: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.025, "safe_dips_threshold_2": 0.23, "safe_dips_threshold_12": 0.4, "safe_dips_threshold_144": 0.7, "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 22: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "110", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.6, }, 23: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "15", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": True, "sma200_rising_val": "24", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.022, "safe_dips_threshold_2": 0.1, "safe_dips_threshold_12": 0.3, "safe_dips_threshold_144": 0.84, "safe_pump": True, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 24: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": True, "sma200_1h_rising_val": "36", "safe_dips_threshold_0": 0.016, "safe_dips_threshold_2": 0.11, "safe_dips_threshold_12": 0.26, "safe_dips_threshold_144": 0.44, "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 25: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "20", "sma200_1h_rising": True, "sma200_1h_rising_val": "36", "safe_dips_threshold_0": 0.024, "safe_dips_threshold_2": 0.22, "safe_dips_threshold_12": 0.38, "safe_dips_threshold_144": 0.66, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 0.98, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.4, }, 26: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.016, "safe_dips_threshold_2": 0.1, "safe_dips_threshold_12": 0.11, "safe_dips_threshold_144": 0.22, "safe_pump": True, "safe_pump_type": "100", "safe_pump_period": "36", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.35, }, 27: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "50", "safe_pump_period": "36", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 28: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 0.99, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.32, }, 29: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "pivot", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.01, }, 30: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 31: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.02, "safe_dips_threshold_2": 0.14, "safe_dips_threshold_12": 0.32, "safe_dips_threshold_144": 0.5, "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "48", "btc_1h_not_downtrend": True, "close_over_pivot_type": "sup3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 0.98, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 32: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": True, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "80", "safe_pump_period": "48", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 33: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 34: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 0.99, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 35: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.1, }, 36: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 37: { "ema_fast": True, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "48", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.5, }, 38: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "100", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "100", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "36", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 39: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "100", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "48", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 40: { "ema_fast": True, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": True, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": True, "safe_pump_type": "100", "safe_pump_period": "48", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.2, }, 41: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.015, "safe_dips_threshold_2": 0.1, "safe_dips_threshold_12": 0.24, "safe_dips_threshold_144": 0.42, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 42: { "ema_fast": False, "ema_fast_len": "12", "ema_slow": False, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.027, "safe_dips_threshold_2": 0.26, "safe_dips_threshold_12": 0.44, "safe_dips_threshold_144": 0.84, "safe_pump": True, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 43: { "ema_fast": False, "ema_fast_len": "12", "ema_slow": False, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.024, "safe_dips_threshold_2": 0.22, "safe_dips_threshold_12": 0.38, "safe_dips_threshold_144": 0.66, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 44: { "ema_fast": False, "ema_fast_len": "12", "ema_slow": False, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 45: { "ema_fast": True, "ema_fast_len": "15", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "20", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.3, "safe_dips_threshold_12": 0.48, "safe_dips_threshold_144": 0.9, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 46: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips_threshold_0": 0.028, "safe_dips_threshold_2": 0.06, "safe_dips_threshold_12": 0.25, "safe_dips_threshold_144": 0.26, "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": True, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "res3", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 2.0, }, 47: { "ema_fast": False, "ema_fast_len": "12", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": False, "close_above_ema_fast_len": "200", "close_above_ema_slow": False, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "24", "safe_dips_threshold_0": 0.025, "safe_dips_threshold_2": 0.05, "safe_dips_threshold_12": 0.25, "safe_dips_threshold_144": 0.5, "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, 48: { "ema_fast": True, "ema_fast_len": "12", "ema_slow": True, "ema_slow_len": "12", "close_above_ema_fast": True, "close_above_ema_fast_len": "200", "close_above_ema_slow": True, "close_above_ema_slow_len": "200", "sma200_rising": True, "sma200_rising_val": "30", "sma200_1h_rising": True, "sma200_1h_rising_val": "24", "safe_dips_threshold_0": None, "safe_dips_threshold_2": None, "safe_dips_threshold_12": None, "safe_dips_threshold_144": None, "safe_pump": False, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, "close_over_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_over_pivot_offset": 1.0, "close_under_pivot_type": "none", # pivot, sup1, sup2, sup3, res1, res2, res3 "close_under_pivot_offset": 1.0, }, } # 24 hours - level 10 buy_pump_pull_threshold_10_24 = 2.2 buy_pump_threshold_10_24 = 0.42 # 36 hours - level 10 buy_pump_pull_threshold_10_36 = 2.0 buy_pump_threshold_10_36 = 0.58 # 48 hours - level 10 buy_pump_pull_threshold_10_48 = 2.0 buy_pump_threshold_10_48 = 0.8 # 24 hours - level 20 buy_pump_pull_threshold_20_24 = 2.2 buy_pump_threshold_20_24 = 0.46 # 36 hours - level 20 buy_pump_pull_threshold_20_36 = 2.0 buy_pump_threshold_20_36 = 0.6 # 48 hours - level 20 buy_pump_pull_threshold_20_48 = 2.0 buy_pump_threshold_20_48 = 0.81 # 24 hours - level 30 buy_pump_pull_threshold_30_24 = 2.2 buy_pump_threshold_30_24 = 0.5 # 36 hours - level 30 buy_pump_pull_threshold_30_36 = 2.0 buy_pump_threshold_30_36 = 0.62 # 48 hours - level 30 buy_pump_pull_threshold_30_48 = 2.0 buy_pump_threshold_30_48 = 0.82 # 24 hours - level 40 buy_pump_pull_threshold_40_24 = 2.2 buy_pump_threshold_40_24 = 0.54 # 36 hours - level 40 buy_pump_pull_threshold_40_36 = 2.0 buy_pump_threshold_40_36 = 0.63 # 48 hours - level 40 buy_pump_pull_threshold_40_48 = 2.0 buy_pump_threshold_40_48 = 0.84 # 24 hours - level 50 buy_pump_pull_threshold_50_24 = 1.75 buy_pump_threshold_50_24 = 0.6 # 36 hours - level 50 buy_pump_pull_threshold_50_36 = 1.75 buy_pump_threshold_50_36 = 0.64 # 48 hours - level 50 buy_pump_pull_threshold_50_48 = 1.75 buy_pump_threshold_50_48 = 0.85 # 24 hours - level 60 buy_pump_pull_threshold_60_24 = 1.75 buy_pump_threshold_60_24 = 0.62 # 36 hours - level 60 buy_pump_pull_threshold_60_36 = 1.75 buy_pump_threshold_60_36 = 0.66 # 48 hours - level 60 buy_pump_pull_threshold_60_48 = 1.75 buy_pump_threshold_60_48 = 0.9 # 24 hours - level 70 buy_pump_pull_threshold_70_24 = 1.75 buy_pump_threshold_70_24 = 0.63 # 36 hours - level 70 buy_pump_pull_threshold_70_36 = 1.75 buy_pump_threshold_70_36 = 0.67 # 48 hours - level 70 buy_pump_pull_threshold_70_48 = 1.75 buy_pump_threshold_70_48 = 0.95 # 24 hours - level 80 buy_pump_pull_threshold_80_24 = 1.75 buy_pump_threshold_80_24 = 0.64 # 36 hours - level 80 buy_pump_pull_threshold_80_36 = 1.75 buy_pump_threshold_80_36 = 0.68 # 48 hours - level 80 buy_pump_pull_threshold_80_48 = 1.75 buy_pump_threshold_80_48 = 1.0 # 24 hours - level 90 buy_pump_pull_threshold_90_24 = 1.75 buy_pump_threshold_90_24 = 0.65 # 36 hours - level 90 buy_pump_pull_threshold_90_36 = 1.75 buy_pump_threshold_90_36 = 0.69 # 48 hours - level 90 buy_pump_pull_threshold_90_48 = 1.75 buy_pump_threshold_90_48 = 1.1 # 24 hours - level 100 buy_pump_pull_threshold_100_24 = 1.7 buy_pump_threshold_100_24 = 0.66 # 36 hours - level 100 buy_pump_pull_threshold_100_36 = 1.7 buy_pump_threshold_100_36 = 0.7 # 48 hours - level 100 buy_pump_pull_threshold_100_48 = 1.4 buy_pump_threshold_100_48 = 1.6 # 24 hours - level 110 buy_pump_pull_threshold_110_24 = 1.7 buy_pump_threshold_110_24 = 0.7 # 36 hours - level 110 buy_pump_pull_threshold_110_36 = 1.7 buy_pump_threshold_110_36 = 0.74 # 48 hours - level 110 buy_pump_pull_threshold_110_48 = 1.4 buy_pump_threshold_110_48 = 1.8 # 24 hours - level 120 buy_pump_pull_threshold_120_24 = 1.7 buy_pump_threshold_120_24 = 0.78 # 36 hours - level 120 buy_pump_pull_threshold_120_36 = 1.7 buy_pump_threshold_120_36 = 0.78 # 48 hours - level 120 buy_pump_pull_threshold_120_48 = 1.4 buy_pump_threshold_120_48 = 2.0 # 5 hours - level 10 buy_dump_protection_10_5 = 0.4 # 5 hours - level 20 buy_dump_protection_20_5 = 0.44 # 5 hours - level 30 buy_dump_protection_30_5 = 0.50 # 5 hours - level 40 buy_dump_protection_40_5 = 0.58 # 5 hours - level 50 buy_dump_protection_50_5 = 0.66 # 5 hours - level 60 buy_dump_protection_60_5 = 0.74 buy_1_min_inc = 0.022 buy_1_rsi_max = 32.0 buy_2_r_14_max = -75.0 buy_1_mfi_max = 46.0 buy_1_rsi_1h_min = 30.0 buy_1_rsi_1h_max = 84.0 buy_2_rsi_1h_diff = 39.0 buy_2_mfi = 49.0 buy_2_cti_max = -0.9 buy_2_r_480_min = -95.0 buy_2_r_480_max = -46.0 buy_2_cti_1h_max = 0.9 buy_2_volume = 2.0 buy_3_bb40_bbdelta_close = 0.057 buy_3_bb40_closedelta_close = 0.023 buy_3_bb40_tail_bbdelta = 0.418 buy_3_cti_max = -0.5 buy_3_cci_36_osc_min = -0.25 buy_3_crsi_1h_min = 20.0 buy_3_r_480_1h_min = -48.0 buy_3_cti_1h_max = 0.82 buy_4_bb20_close_bblowerband = 0.98 buy_4_bb20_volume = 10.0 buy_4_cti_max = -0.8 buy_5_ema_rel = 0.84 buy_5_ema_open_mult = 0.02 buy_5_bb_offset = 0.999 buy_5_cti_max = -0.5 buy_5_r_14_max = -94.0 buy_5_rsi_14_min = 25.0 buy_5_mfi_min = 18.0 buy_5_crsi_1h_min = 12.0 buy_5_volume = 1.6 buy_6_ema_open_mult = 0.019 buy_6_bb_offset = 0.984 buy_6_r_14_max = -85.0 buy_6_crsi_1h_min = 15.0 buy_6_cti_1h_min = 0.0 buy_7_ema_open_mult = 0.031 buy_7_ma_offset = 0.978 buy_7_cti_max = -0.9 buy_7_rsi_max = 45.0 buy_8_bb_offset = 0.986 buy_8_r_14_max = -98.0 buy_8_cti_1h_max = 0.95 buy_8_r_480_1h_max = -18.0 buy_8_volume = 1.8 buy_9_ma_offset = 0.968 buy_9_bb_offset = 0.982 buy_9_mfi_max = 50.0 buy_9_cti_max = -0.85 buy_9_r_14_max = -94.0 buy_9_rsi_1h_min = 20.0 buy_9_rsi_1h_max = 88.0 buy_9_crsi_1h_min = 21.0 buy_10_ma_offset_high = 0.94 buy_10_bb_offset = 0.984 buy_10_r_14_max = -88.0 buy_10_cti_1h_min = -0.5 buy_10_cti_1h_max = 0.94 buy_11_ma_offset = 0.956 buy_11_min_inc = 0.022 buy_11_rsi_max = 37.0 buy_11_mfi_max = 46.0 buy_11_cci_max = -120.0 buy_11_r_480_max = -32.0 buy_11_rsi_1h_min = 30.0 buy_11_rsi_1h_max = 84.0 buy_11_cti_1h_max = 0.91 buy_11_r_480_1h_max = -25.0 buy_11_crsi_1h_min = 26.0 buy_12_ma_offset = 0.927 buy_12_ewo_min = 2.0 buy_12_rsi_max = 32.0 buy_12_cti_max = -0.9 buy_13_ma_offset = 0.99 buy_13_cti_max = -0.92 buy_13_ewo_max = -6.0 buy_13_cti_1h_max = -0.88 buy_13_crsi_1h_min = 10.0 buy_14_ema_open_mult = 0.014 buy_14_bb_offset = 0.989 buy_14_ma_offset = 0.945 buy_14_cti_max = -0.85 buy_15_ema_open_mult = 0.0238 buy_15_ma_offset = 0.958 buy_15_rsi_min = 28.0 buy_15_cti_1h_min = -0.2 buy_16_ma_offset = 0.942 buy_16_ewo_min = 2.0 buy_16_rsi_max = 36.0 buy_16_cti_max = -0.9 buy_17_ma_offset = 0.999 buy_17_ewo_max = -7.0 buy_17_cti_max = -0.96 buy_17_crsi_1h_min = 12.0 buy_17_volume = 2.0 buy_18_bb_offset = 0.986 buy_18_rsi_max = 33.5 buy_18_cti_max = -0.85 buy_18_cti_1h_max = 0.91 buy_18_volume = 2.0 buy_19_rsi_1h_min = 30.0 buy_19_chop_max = 21.3 buy_20_rsi_14_max = 36.0 buy_20_rsi_14_1h_max = 16.0 buy_20_cti_max = -0.84 buy_20_volume = 2.0 buy_21_rsi_14_max = 14.0 buy_21_rsi_14_1h_max = 28.0 buy_21_cti_max = -0.902 buy_21_volume = 2.0 buy_22_volume = 2.0 buy_22_bb_offset = 0.984 buy_22_ma_offset = 0.98 buy_22_ewo_min = 5.6 buy_22_rsi_14_max = 36.0 buy_22_cti_max = -0.54 buy_22_r_480_max = -40.0 buy_22_cti_1h_min = -0.5 buy_23_bb_offset = 0.984 buy_23_ewo_min = 3.4 buy_23_rsi_14_max = 28.0 buy_23_cti_max = -0.74 buy_23_rsi_14_1h_max = 80.0 buy_23_r_480_1h_min = -95.0 buy_23_cti_1h_max = 0.92 buy_24_rsi_14_max = 50.0 buy_24_rsi_14_1h_min = 66.9 buy_25_ma_offset = 0.953 buy_25_rsi_4_max = 30.0 buy_25_cti_max = -0.78 buy_25_cci_max = -200.0 buy_26_zema_low_offset = 0.9405 buy_26_cti_max = -0.72 buy_26_cci_max = -166.0 buy_26_r_14_max = -98.0 buy_26_cti_1h_max = 0.95 buy_26_volume = 2.0 buy_27_wr_max = -95.0 buy_27_r_14 = -100.0 buy_27_wr_1h_max = -90.0 buy_27_rsi_max = 46.0 buy_27_volume = 2.0 buy_28_ma_offset = 0.928 buy_28_ewo_min = 2.0 buy_28_rsi_14_max = 33.4 buy_28_cti_max = -0.84 buy_28_r_14_max = -97.0 buy_28_cti_1h_max = 0.95 buy_29_ma_offset = 0.984 buy_29_ewo_max = -4.2 buy_29_cti_max = -0.96 buy_30_ma_offset = 0.962 buy_30_ewo_min = 6.4 buy_30_rsi_14_max = 34.0 buy_30_cti_max = -0.87 buy_30_r_14_max = -97.0 buy_31_ma_offset = 0.962 buy_31_ewo_max = -5.2 buy_31_r_14_max = -94.0 buy_31_cti_max = -0.9 buy_32_ma_offset = 0.942 buy_32_rsi_4_max = 46.0 buy_32_cti_max = -0.86 buy_32_rsi_14_min = 19.0 buy_32_crsi_1h_min = 10.0 buy_32_crsi_1h_max = 60.0 buy_33_ma_offset = 0.988 buy_33_ewo_min = 9.0 buy_33_rsi_max = 32.0 buy_33_cti_max = -0.88 buy_33_r_14_max = -98.0 buy_33_cti_1h_max = 0.92 buy_33_volume = 2.0 buy_34_ma_offset = 0.97 buy_34_ewo_max = -4.0 buy_34_cti_max = -0.95 buy_34_r_14_max = -99.9 buy_34_crsi_1h_min = 8.0 buy_34_volume = 2.0 buy_35_ma_offset = 0.984 buy_35_ewo_min = 7.8 buy_35_rsi_max = 32.0 buy_35_cti_max = -0.8 buy_35_r_14_max = -95.0 buy_36_ma_offset = 0.98 buy_36_ewo_max = -5.0 buy_36_cti_max = -0.82 buy_36_r_14_max = -97.0 buy_36_crsi_1h_min = 12.0 buy_37_ma_offset = 0.984 buy_37_ewo_min = 8.3 buy_37_ewo_max = 11.1 buy_37_rsi_14_min = 26.0 buy_37_rsi_14_max = 46.0 buy_37_crsi_1h_min = 12.0 buy_37_crsi_1h_max = 56.0 buy_37_cti_max = -0.85 buy_37_cti_1h_max = 0.92 buy_37_r_14_max = -97.0 buy_37_close_1h_max = 0.1 buy_38_ma_offset = 0.98 buy_38_ewo_max = -4.4 buy_38_cti_max = -0.95 buy_38_r_14_max = -97.0 buy_38_crsi_1h_min = 0.5 buy_39_cti_max = -0.1 buy_39_r_1h_max = -22.0 buy_39_cti_1h_min = -0.1 buy_39_cti_1h_max = 0.4 buy_40_cci_max = -150.0 buy_40_rsi_max = 30.0 buy_40_r_14_max = -99.9 buy_40_cti_max = -0.8 buy_41_ma_offset_high = 0.95 buy_41_cti_max = -0.95 buy_41_cci_max = -178.0 buy_41_ewo_1h_min = 0.5 buy_41_r_480_1h_max = -14.0 buy_41_crsi_1h_min = 14.0 buy_42_ema_open_mult = 0.018 buy_42_bb_offset = 0.992 buy_42_ewo_1h_min = 2.8 buy_42_cti_1h_min = -0.5 buy_42_cti_1h_max = 0.88 buy_42_r_480_1h_max = -12.0 buy_43_bb40_bbdelta_close = 0.045 buy_43_bb40_closedelta_close = 0.02 buy_43_bb40_tail_bbdelta = 0.5 buy_43_cti_max = -0.75 buy_43_r_480_min = -94.0 buy_43_cti_1h_min = -0.75 buy_43_cti_1h_max = 0.45 buy_43_r_480_1h_min = -80.0 buy_44_ma_offset = 0.982 buy_44_ewo_max = -18.0 buy_44_cti_max = -0.73 buy_44_crsi_1h_min = 8.0 buy_45_bb40_bbdelta_close = 0.039 buy_45_bb40_closedelta_close = 0.0231 buy_45_bb40_tail_bbdelta = 0.24 buy_45_ma_offset = 0.948 buy_45_ewo_min = 2.0 buy_45_ewo_1h_min = 2.0 buy_45_cti_1h_max = 0.76 buy_45_r_480_1h_max = -20.0 buy_46_ema_open_mult = 0.0332 buy_46_ewo_1h_min = 0.5 buy_46_cti_1h_min = -0.9 buy_46_cti_1h_max = 0.5 buy_47_ewo_min = 3.2 buy_47_ma_offset = 0.952 buy_47_rsi_14_max = 46.0 buy_47_cti_max = -0.93 buy_47_r_14_max = -97.0 buy_47_ewo_1h_min = 2.0 buy_47_cti_1h_min = -0.9 buy_47_cti_1h_max = 0.3 buy_48_ewo_min = 8.5 buy_48_ewo_1h_min = 14.0 buy_48_r_480_min = -25.0 buy_48_r_480_1h_min = -50.0 buy_48_r_480_1h_max = -10.0 buy_48_cti_1h_min = 0.5 buy_48_crsi_1h_min = 10.0 # Sell sell_condition_1_enable = True sell_condition_2_enable = True sell_condition_3_enable = True sell_condition_4_enable = True sell_condition_5_enable = True sell_condition_6_enable = True sell_condition_7_enable = True sell_condition_8_enable = True # 48h for pump sell checks sell_pump_threshold_48_1 = 0.9 sell_pump_threshold_48_2 = 0.7 sell_pump_threshold_48_3 = 0.5 # 36h for pump sell checks sell_pump_threshold_36_1 = 0.72 sell_pump_threshold_36_2 = 4.0 sell_pump_threshold_36_3 = 1.0 # 24h for pump sell checks sell_pump_threshold_24_1 = 0.68 sell_pump_threshold_24_2 = 0.62 sell_pump_threshold_24_3 = 0.88 sell_rsi_bb_1 = 79.0 sell_rsi_bb_2 = 80.0 sell_rsi_main_3 = 83.0 sell_dual_rsi_rsi_4 = 73.4 sell_dual_rsi_rsi_1h_4 = 79.6 sell_ema_relative_5 = 0.024 sell_rsi_diff_5 = 4.4 sell_rsi_under_6 = 79.0 sell_rsi_1h_7 = 81.7 sell_bb_relative_8 = 1.1 # Profit over EMA200 sell_custom_profit_bull_0 = 0.012 sell_custom_rsi_under_bull_0 = 34.0 sell_custom_profit_bull_1 = 0.02 sell_custom_rsi_under_bull_1 = 35.0 sell_custom_profit_bull_2 = 0.03 sell_custom_rsi_under_bull_2 = 36.0 sell_custom_profit_bull_3 = 0.04 sell_custom_rsi_under_bull_3 = 44.0 sell_custom_profit_bull_4 = 0.05 sell_custom_rsi_under_bull_4 = 45.0 sell_custom_profit_bull_5 = 0.06 sell_custom_rsi_under_bull_5 = 49.0 sell_custom_profit_bull_6 = 0.07 sell_custom_rsi_under_bull_6 = 50.0 sell_custom_profit_bull_7 = 0.08 sell_custom_rsi_under_bull_7 = 57.0 sell_custom_profit_bull_8 = 0.09 sell_custom_rsi_under_bull_8 = 50.0 sell_custom_profit_bull_9 = 0.1 sell_custom_rsi_under_bull_9 = 46.0 sell_custom_profit_bull_10 = 0.12 sell_custom_rsi_under_bull_10 = 42.0 sell_custom_profit_bull_11 = 0.20 sell_custom_rsi_under_bull_11 = 30.0 sell_custom_profit_bear_0 = 0.012 sell_custom_rsi_under_bear_0 = 34.0 sell_custom_profit_bear_1 = 0.02 sell_custom_rsi_under_bear_1 = 35.0 sell_custom_profit_bear_2 = 0.03 sell_custom_rsi_under_bear_2 = 37.0 sell_custom_profit_bear_3 = 0.04 sell_custom_rsi_under_bear_3 = 44.0 sell_custom_profit_bear_4 = 0.05 sell_custom_rsi_under_bear_4 = 48.0 sell_custom_profit_bear_5 = 0.06 sell_custom_rsi_under_bear_5 = 50.0 sell_custom_rsi_over_bear_5 = 78.0 sell_custom_profit_bear_6 = 0.07 sell_custom_rsi_under_bear_6 = 52.0 sell_custom_rsi_over_bear_6 = 78.0 sell_custom_profit_bear_7 = 0.08 sell_custom_rsi_under_bear_7 = 57.0 sell_custom_rsi_over_bear_7 = 77.0 sell_custom_profit_bear_8 = 0.09 sell_custom_rsi_under_bear_8 = 55.0 sell_custom_rsi_over_bear_8 = 75.5 sell_custom_profit_bear_9 = 0.1 sell_custom_rsi_under_bear_9 = 46.0 sell_custom_profit_bear_10 = 0.12 sell_custom_rsi_under_bear_10 = 42.0 sell_custom_profit_bear_11 = 0.20 sell_custom_rsi_under_bear_11 = 30.0 # Profit under EMA200 sell_custom_under_profit_bull_0 = 0.01 sell_custom_under_rsi_under_bull_0 = 38.0 sell_custom_under_profit_bull_1 = 0.02 sell_custom_under_rsi_under_bull_1 = 46.0 sell_custom_under_profit_bull_2 = 0.03 sell_custom_under_rsi_under_bull_2 = 47.0 sell_custom_under_profit_bull_3 = 0.04 sell_custom_under_rsi_under_bull_3 = 48.0 sell_custom_under_profit_bull_4 = 0.05 sell_custom_under_rsi_under_bull_4 = 49.0 sell_custom_under_profit_bull_5 = 0.06 sell_custom_under_rsi_under_bull_5 = 50.0 sell_custom_under_profit_bull_6 = 0.07 sell_custom_under_rsi_under_bull_6 = 52.0 sell_custom_under_profit_bull_7 = 0.08 sell_custom_under_rsi_under_bull_7 = 57.0 sell_custom_under_profit_bull_8 = 0.09 sell_custom_under_rsi_under_bull_8 = 50.0 sell_custom_under_profit_bull_9 = 0.1 sell_custom_under_rsi_under_bull_9 = 46.0 sell_custom_under_profit_bull_10 = 0.12 sell_custom_under_rsi_under_bull_10 = 42.0 sell_custom_under_profit_bull_11 = 0.2 sell_custom_under_rsi_under_bull_11 = 30.0 sell_custom_under_profit_bear_0 = 0.01 sell_custom_under_rsi_under_bear_0 = 38.0 sell_custom_under_profit_bear_1 = 0.02 sell_custom_under_rsi_under_bear_1 = 56.0 sell_custom_under_profit_bear_2 = 0.03 sell_custom_under_rsi_under_bear_2 = 57.0 sell_custom_under_profit_bear_3 = 0.04 sell_custom_under_rsi_under_bear_3 = 57.0 sell_custom_under_profit_bear_4 = 0.05 sell_custom_under_rsi_under_bear_4 = 57.0 sell_custom_under_profit_bear_5 = 0.06 sell_custom_under_rsi_under_bear_5 = 57.0 sell_custom_under_rsi_over_bear_5 = 78.0 sell_custom_under_profit_bear_6 = 0.07 sell_custom_under_rsi_under_bear_6 = 57.0 sell_custom_under_rsi_over_bear_6 = 78.0 sell_custom_under_profit_bear_7 = 0.08 sell_custom_under_rsi_under_bear_7 = 57.0 sell_custom_under_rsi_over_bear_7 = 80.0 sell_custom_under_profit_bear_8 = 0.09 sell_custom_under_rsi_under_bear_8 = 50.0 sell_custom_under_rsi_over_bear_8 = 82.0 sell_custom_under_profit_bear_9 = 0.1 sell_custom_under_rsi_under_bear_9 = 46.0 sell_custom_under_profit_bear_10 = 0.12 sell_custom_under_rsi_under_bear_10 = 42.0 sell_custom_under_profit_bear_11 = 0.2 sell_custom_under_rsi_under_bear_11 = 30.0 # SMA descending sell_custom_dec_profit_min_1 = 0.05 sell_custom_dec_profit_max_1 = 0.12 # Under EMA100 sell_custom_dec_profit_min_2 = 0.07 sell_custom_dec_profit_max_2 = 0.16 # Trail 1 sell_trail_profit_min_1 = 0.03 sell_trail_profit_max_1 = 0.05 sell_trail_down_1 = 0.05 sell_trail_rsi_min_1 = 10.0 sell_trail_rsi_max_1 = 20.0 # Trail 2 sell_trail_profit_min_2 = 0.1 sell_trail_profit_max_2 = 0.4 sell_trail_down_2 = 0.03 sell_trail_rsi_min_2 = 20.0 sell_trail_rsi_max_2 = 50.0 # Trail 3 sell_trail_profit_min_3 = 0.06 sell_trail_profit_max_3 = 0.2 sell_trail_down_3 = 0.05 # Trail 4 sell_trail_profit_min_4 = 0.03 sell_trail_profit_max_4 = 0.06 sell_trail_down_4 = 0.02 # Under & near EMA200, accept profit sell_custom_profit_under_profit_min_1 = 0.001 sell_custom_profit_under_profit_max_1 = 0.008 sell_custom_profit_under_rel_1 = 0.024 sell_custom_profit_under_rsi_diff_1 = 4.4 sell_custom_profit_under_profit_2 = 0.03 sell_custom_profit_under_rel_2 = 0.024 sell_custom_profit_under_rsi_diff_2 = 4.4 # Under & near EMA200, take the loss sell_custom_stoploss_under_rel_1 = 0.002 sell_custom_stoploss_under_rsi_diff_1 = 10.0 # Long duration/recover stoploss 1 sell_custom_stoploss_long_profit_min_1 = -0.08 sell_custom_stoploss_long_profit_max_1 = -0.04 sell_custom_stoploss_long_recover_1 = 0.14 sell_custom_stoploss_long_rsi_diff_1 = 4.0 # Long duration/recover stoploss 2 sell_custom_stoploss_long_recover_2 = 0.06 sell_custom_stoploss_long_rsi_diff_2 = 40.0 # Pumped 48h 1, under EMA200 sell_custom_pump_under_profit_min_1 = 0.04 sell_custom_pump_under_profit_max_1 = 0.09 # Pumped trail 1 sell_custom_pump_trail_profit_min_1 = 0.05 sell_custom_pump_trail_profit_max_1 = 0.07 sell_custom_pump_trail_down_1 = 0.05 sell_custom_pump_trail_rsi_min_1 = 20.0 sell_custom_pump_trail_rsi_max_1 = 70.0 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_1 = 0.01 sell_custom_stoploss_pump_min_1 = -0.02 sell_custom_stoploss_pump_max_1 = -0.01 sell_custom_stoploss_pump_ma_offset_1 = 0.94 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_2 = 0.025 sell_custom_stoploss_pump_loss_2 = -0.05 sell_custom_stoploss_pump_ma_offset_2 = 0.92 # Stoploss, pumped, 36h 3 sell_custom_stoploss_pump_max_profit_3 = 0.008 sell_custom_stoploss_pump_loss_3 = -0.12 sell_custom_stoploss_pump_ma_offset_3 = 0.88 # Recover sell_custom_recover_profit_1 = 0.06 sell_custom_recover_min_loss_1 = 0.12 sell_custom_recover_profit_min_2 = 0.01 sell_custom_recover_profit_max_2 = 0.05 sell_custom_recover_min_loss_2 = 0.06 sell_custom_recover_rsi_2 = 46.0 # Profit for long duration trades sell_custom_long_profit_min_1 = 0.03 sell_custom_long_profit_max_1 = 0.04 sell_custom_long_duration_min_1 = 900 # Profit Target Signal profit_target_1_enable = False ############################################################# plot_config = { "main_plot": { "ema_12_1h": {"color": "rgba(200,200,100,0.4)"}, "ema_15_1h": {"color": "rgba(200,180,100,0.4)"}, "ema_20_1h": {"color": "rgba(200,160,100,0.4)"}, "ema_25_1h": {"color": "rgba(200,140,100,0.4)"}, "ema_26_1h": {"color": "rgba(200,120,100,0.4)"}, "ema_35_1h": {"color": "rgba(200,100,100,0.4)"}, "ema_50_1h": {"color": "rgba(200,80,100,0.4)"}, "ema_100_1h": {"color": "rgba(200,60,100,0.4)"}, "ema_200_1h": {"color": "rgba(200,40,100,0.4)"}, "sma_200_1h": {"color": "rgba(150,20,100,0.4)"}, "pm": {"color": "rgba(100,20,100,0.5)"}, }, "subplots": { "buy tag": {"buy_tag": {"color": "green"}}, "RSI/BTC": { "btc_not_downtrend_1h": {"color": "yellow"}, "btc_rsi_14_1h": {"color": "green"}, "rsi_14_1h": {"color": "#f41cd1"}, "crsi": {"color": "blue"}, }, "pump": { "cti_1h": {"color": "pink"}, "safe_pump_24_10_1h": {"color": "#481110"}, "safe_pump_24_20_1h": {"color": "#481120"}, "safe_pump_24_30_1h": {"color": "#481130"}, "safe_pump_24_40_1h": {"color": "#481140"}, "safe_pump_24_50_1h": {"color": "#481150"}, "safe_pump_24_60_1h": {"color": "#481160"}, "safe_pump_24_70_1h": {"color": "#481170"}, "safe_pump_24_80_1h": {"color": "#481180"}, "safe_pump_24_90_1h": {"color": "#481190"}, "safe_pump_24_100_1h": {"color": "#4811A0"}, "safe_pump_24_120_1h": {"color": "#4811C0"}, "safe_pump_36_10_1h": {"color": "#721110"}, "safe_pump_36_20_1h": {"color": "#721120"}, "safe_pump_36_30_1h": {"color": "#721130"}, "safe_pump_36_40_1h": {"color": "#721140"}, "safe_pump_36_50_1h": {"color": "#721150"}, "safe_pump_36_60_1h": {"color": "#721160"}, "safe_pump_36_70_1h": {"color": "#721170"}, "safe_pump_36_80_1h": {"color": "#721180"}, "safe_pump_36_90_1h": {"color": "#721190"}, "safe_pump_36_100_1h": {"color": "#7211A0"}, "safe_pump_36_120_1h": {"color": "#7211C0"}, "safe_pump_48_10_1h": {"color": "#961110"}, "safe_pump_48_20_1h": {"color": "#961120"}, "safe_pump_48_30_1h": {"color": "#961130"}, "safe_pump_48_40_1h": {"color": "#961140"}, "safe_pump_48_50_1h": {"color": "#961150"}, "safe_pump_48_60_1h": {"color": "#961160"}, "safe_pump_48_70_1h": {"color": "#961170"}, "safe_pump_48_80_1h": {"color": "#961180"}, "safe_pump_48_90_1h": {"color": "#961190"}, "safe_pump_48_100_1h": {"color": "#9611A0"}, "safe_pump_48_120_1h": {"color": "#9611C0"}, }, }, } ############################################################# # CACHES hold_trades_cache = None target_profit_cache = None ############################################################# def __init__(self, config: dict) -> None: super().__init__(config) # self.dp = DataProvider(config, config['exchange']) if self.target_profit_cache is None: self.target_profit_cache = Cache(self.config["user_data_dir"] / "data-nfi-profit_target_by_pair.json") # If the cached data hasn't changed, it's a no-op self.target_profit_cache.save() def get_hold_trades_config_file(self): proper_holds_file_path = self.config["user_data_dir"].resolve() / "nfi-hold-trades.json" if proper_holds_file_path.is_file(): return proper_holds_file_path strat_file_path = pathlib.Path(__file__) hold_trades_config_file_resolve = strat_file_path.resolve().parent / "hold-trades.json" if hold_trades_config_file_resolve.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_resolve, proper_holds_file_path, ) return hold_trades_config_file_resolve # The resolved path does not exist, is it a symlink? hold_trades_config_file_absolute = strat_file_path.absolute().parent / "hold-trades.json" if hold_trades_config_file_absolute.is_file(): log.warning( "Please move %s to %s which is now the expected path for the holds file", hold_trades_config_file_absolute, proper_holds_file_path, ) return hold_trades_config_file_absolute def load_hold_trades_config(self): if self.hold_trades_cache is None: hold_trades_config_file = self.get_hold_trades_config_file() if hold_trades_config_file: log.warning("Loading hold support data from %s", hold_trades_config_file) self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() def whitelist_tracker(self): if sorted(self.coin_metrics["current_whitelist"]) != sorted(self.dp.current_whitelist()): log.info("Whitelist has changed...") self.coin_metrics["top_traded_updated"] = False self.coin_metrics["top_grossing_updated"] = False # Update pairlist self.coin_metrics["current_whitelist"] = self.dp.current_whitelist() # Move up BTC for largest data footprint self.coin_metrics["current_whitelist"].insert( 0, self.coin_metrics["current_whitelist"].pop( self.coin_metrics["current_whitelist"].index(f"BTC/{self.config['stake_currency']}") ), ) def top_traded_list(self): log.info("Updating top traded pairlist...") tik = time.perf_counter() self.coin_metrics["tt_dataframe"] = DataFrame() # Build traded volume dataframe for coin_pair in self.coin_metrics["current_whitelist"]: coin = coin_pair.split("/")[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index("date") if self.config["runmode"].value in ("live", "dry_run"): pair_dataframe = pair_dataframe.iloc[-7:, :] # Set the date index of the self.coin_metrics['tt_dataframe'] once if not "date" in self.coin_metrics["tt_dataframe"]: self.coin_metrics["tt_dataframe"]["date"] = pair_dataframe["date"] self.coin_metrics["tt_dataframe"].set_index("date") # Calculate daily traded volume pair_dataframe[coin] = pair_dataframe["volume"] * qtpylib.typical_price(pair_dataframe) # Drop the columns we don't need pair_dataframe.drop(columns=["open", "high", "low", "close", "volume"], inplace=True) # Merge it in on the date key self.coin_metrics["tt_dataframe"] = self.coin_metrics["tt_dataframe"].merge( pair_dataframe, on="date", how="left" ) # Forward fill empty cells (due to different df shapes) self.coin_metrics["tt_dataframe"].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics["tt_dataframe"]["date"] self.coin_metrics["tt_dataframe"].drop(columns=["date"], inplace=True) # Build columns and top traded coins column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics["top_traded_len"] + 1)] self.coin_metrics["tt_dataframe"][column_names] = self.coin_metrics["tt_dataframe"].apply( lambda x: x.nlargest(self.coin_metrics["top_traded_len"]).index.values, axis=1, result_type="expand" ) self.coin_metrics["tt_dataframe"].drop( columns=[col for col in self.coin_metrics["tt_dataframe"] if col not in column_names], inplace=True ) # Re-add stored date column self.coin_metrics["tt_dataframe"].insert(loc=0, column="date", value=pair_dates) self.coin_metrics["tt_dataframe"].set_index("date") self.coin_metrics["top_traded_updated"] = True log.info("Updated top traded pairlist (tail-5):") log.info(f"\n{self.coin_metrics['tt_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f"Updating top traded pairlist took {tok - tik:0.4f} seconds...") def top_grossing_list(self): log.info("Updating top grossing pairlist...") tik = time.perf_counter() self.coin_metrics["tg_dataframe"] = DataFrame() # Build grossing volume dataframe for coin_pair in self.coin_metrics["current_whitelist"]: coin = coin_pair.split("/")[0] # Get the volume for the daily informative timeframe and name the column for the coin pair_dataframe = self.dp.get_pair_dataframe(pair=coin_pair, timeframe=self.info_timeframe_1d) pair_dataframe.set_index("date") if self.config["runmode"].value in ("live", "dry_run"): pair_dataframe = pair_dataframe.iloc[-7:, :] # Set the date index of the self.coin_metrics['tg_dataframe'] once if not "date" in self.coin_metrics["tg_dataframe"]: self.coin_metrics["tg_dataframe"]["date"] = pair_dataframe["date"] self.coin_metrics["tg_dataframe"].set_index("date") # Calculate daily grossing rate pair_dataframe[coin] = pair_dataframe["close"].pct_change() * 100 # Drop the columns we don't need pair_dataframe.drop(columns=["open", "high", "low", "close", "volume"], inplace=True) # Merge it in on the date key self.coin_metrics["tg_dataframe"] = self.coin_metrics["tg_dataframe"].merge( pair_dataframe, on="date", how="left" ) # Forward fill empty cells (due to different df shapes) self.coin_metrics["tg_dataframe"].fillna(0, inplace=True) # Store and drop date column for value sorting pair_dates = self.coin_metrics["tg_dataframe"]["date"] self.coin_metrics["tg_dataframe"].drop(columns=["date"], inplace=True) # Build columns and top grossing coins column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics["top_grossing_len"] + 1)] self.coin_metrics["tg_dataframe"][column_names] = self.coin_metrics["tg_dataframe"].apply( lambda x: x.nlargest(self.coin_metrics["top_grossing_len"]).index.values, axis=1, result_type="expand" ) self.coin_metrics["tg_dataframe"].drop( columns=[col for col in self.coin_metrics["tg_dataframe"] if col not in column_names], inplace=True ) # Re-add stored date column self.coin_metrics["tg_dataframe"].insert(loc=0, column="date", value=pair_dates) self.coin_metrics["tg_dataframe"].set_index("date") self.coin_metrics["top_grossing_updated"] = True log.info("Updated top grossing pairlist (tail-5):") log.info(f"\n{self.coin_metrics['tg_dataframe'].tail(5)}") tok = time.perf_counter() log.info(f"Updating top grossing pairlist took {tok - tik:0.4f} seconds...") def is_top_coin(self, coin_pair, row_data, top_length) -> bool: return coin_pair.split("/")[0] in row_data.loc["Coin #1" : f"Coin #{top_length}"].values def bot_loop_start(self, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Coin metrics mechanism if self.coin_metrics["top_traded_enabled"] or self.coin_metrics["top_grossing_enabled"]: self.whitelist_tracker() if self.coin_metrics["top_traded_enabled"] and not self.coin_metrics["top_traded_updated"]: self.top_traded_list() if self.coin_metrics["top_grossing_enabled"] and not self.coin_metrics["top_grossing_updated"]: self.top_grossing_list() if self.config["runmode"].value not in ("live", "dry_run"): return super().bot_loop_start(**kwargs) if self.holdSupportEnabled: self.load_hold_trades_config() return super().bot_loop_start(**kwargs) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def sell_over_main(self, current_profit: float, last_candle) -> tuple: if last_candle["close"] > last_candle["ema_200"]: if last_candle["moderi_96"]: if current_profit >= 0.20: if last_candle["rsi_14"] < 30.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_12_1" elif last_candle["rsi_14"] < 27.0: return True, "signal_profit_o_bull_12_9" elif 0.20 > current_profit >= 0.12: if last_candle["rsi_14"] < 42.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_11_1" elif last_candle["rsi_14"] < 39.0: return True, "signal_profit_o_bull_11_9" elif 0.12 > current_profit >= 0.1: if last_candle["rsi_14"] < 46.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_10_1" elif last_candle["rsi_14"] < 48.0: return True, "signal_profit_o_bull_10_9" elif 0.1 > current_profit >= 0.09: if last_candle["rsi_14"] < 50.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_9_1" elif last_candle["rsi_14"] < 49.0: return True, "signal_profit_o_bull_9_9" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_8_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_8_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_8_4" elif last_candle["rsi_14"] < 48.0: return True, "signal_profit_o_bull_8_9" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_7_1" if last_candle["rsi_14"] > 83.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_7_2" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_7_3" elif (last_candle["rsi_14"] < 55.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_7_4" elif last_candle["rsi_14"] < 45.0: return True, "signal_profit_o_bull_7_9" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_6_1" if last_candle["rsi_14"] > 82.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_6_2" elif (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_6_3" elif (last_candle["rsi_14"] < 53.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_6_4" elif last_candle["cti"] > 0.95: return True, "signal_profit_o_bull_6_5" elif last_candle["rsi_14"] < 42.0: return True, "signal_profit_o_bull_6_9" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_5_1" if last_candle["rsi_14"] > 80.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_5_2" elif (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_5_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_5_4" elif last_candle["cti"] > 0.952: return True, "signal_profit_o_bull_5_5" elif (last_candle["rsi_14"] < 50.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_5_6" elif last_candle["rsi_14"] < 41.0: return True, "signal_profit_o_bull_5_9" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 45.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_4_1" elif (last_candle["rsi_14"] < 48.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_4_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_4_4" elif last_candle["cti"] > 0.954: return True, "signal_profit_o_bull_4_5" elif (last_candle["rsi_14"] < 48.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_4_6" elif last_candle["rsi_14"] < 40.0: return True, "signal_profit_o_bull_4_9" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 37.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_3_1" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_3_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_3_4" elif last_candle["cti"] > 0.956: return True, "signal_profit_o_bull_3_5" elif (last_candle["rsi_14"] < 43.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_3_6" elif last_candle["rsi_14"] < 35.0: return True, "signal_profit_o_bull_3_9" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 36.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_2_1" elif (last_candle["rsi_14"] < 42.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_2_3" elif (last_candle["rsi_14"] < 46.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_2_4" elif last_candle["cti"] > 0.958: return True, "signal_profit_o_bull_2_5" elif (last_candle["rsi_14"] < 42.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_2_6" elif (last_candle["rsi_14"] < 42.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_o_bull_2_7" elif last_candle["rsi_14"] < 40.0 and (last_candle["cmf"] < -0.25): return True, "signal_profit_o_bull_2_8" elif last_candle["rsi_14"] < 34.0: return True, "signal_profit_o_bull_2_9" elif 0.02 > current_profit >= 0.012: if (last_candle["rsi_14"] < 34.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_1_1" elif (last_candle["rsi_14"] < 41.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bull_1_3" elif (last_candle["rsi_14"] < 44.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bull_1_4" elif last_candle["cti"] > 0.96: return True, "signal_profit_o_bull_1_5" elif (last_candle["rsi_14"] < 41.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bull_1_6" elif (last_candle["rsi_14"] < 41.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_o_bull_1_7" elif last_candle["rsi_14"] < 39.0 and (last_candle["cmf"] < -0.25): return True, "signal_profit_o_bull_1_8" elif last_candle["rsi_14"] < 32.0: return True, "signal_profit_o_bull_1_9" else: if current_profit >= 0.20: if last_candle["rsi_14"] < 30.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_12_1" elif last_candle["rsi_14"] < 28.0: return True, "signal_profit_o_bear_12_9" elif 0.20 > current_profit >= 0.12: if last_candle["rsi_14"] < 42.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_11_1" elif last_candle["rsi_14"] < 40.0: return True, "signal_profit_o_bear_11_9" elif 0.12 > current_profit >= 0.10: if last_candle["rsi_14"] < 46.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_10_1" elif last_candle["rsi_14"] < 49.0: return True, "signal_profit_o_bear_10_9" elif 0.10 > current_profit >= 0.09: if last_candle["rsi_14"] < 55.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_9_1" elif last_candle["rsi_14"] > 75.5: return True, "signal_profit_o_bear_9_2" elif last_candle["rsi_14"] < 50.0: return True, "signal_profit_o_bear_9_9" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_8_1" elif last_candle["rsi_14"] > 77.0: return True, "signal_profit_o_bear_8_2" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_8_3" elif (last_candle["rsi_14"] < 59.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_8_4" elif last_candle["rsi_14"] < 49.0: return True, "signal_profit_o_bear_8_9" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_7_1" elif last_candle["rsi_14"] > 78.0: return True, "signal_profit_o_bear_7_2" elif (last_candle["rsi_14"] < 55.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_7_3" elif (last_candle["rsi_14"] < 57.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_7_4" elif last_candle["rsi_14"] < 46.0: return True, "signal_profit_o_bear_7_9" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_6_1" elif last_candle["rsi_14"] > 78.0: return True, "signal_profit_o_bear_6_2" elif (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_6_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_6_4" elif last_candle["cti"] > 0.94: return True, "signal_profit_o_bear_6_5" elif last_candle["rsi_14"] < 43.0: return True, "signal_profit_o_bear_6_9" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 49.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_5_1" elif (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_5_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_5_4" elif last_candle["cti"] > 0.942: return True, "signal_profit_o_bear_5_5" elif (last_candle["rsi_14"] < 50.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_5_6" elif last_candle["rsi_14"] < 42.0: return True, "signal_profit_o_bear_5_9" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_4_1" elif (last_candle["rsi_14"] < 48.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_4_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_4_4" elif last_candle["cti"] > 0.944: return True, "signal_profit_o_bear_4_5" elif (last_candle["rsi_14"] < 48.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_4_6" elif last_candle["rsi_14"] < 41.0: return True, "signal_profit_o_bear_4_9" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_3_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_3_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_3_4" elif last_candle["cti"] > 0.946: return True, "signal_profit_o_bear_3_5" elif (last_candle["rsi_14"] < 44.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_3_6" elif last_candle["rsi_14"] < 36.0: return True, "signal_profit_o_bear_3_9" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 37.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_2_1" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_o_bear_2_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_2_4" elif last_candle["cti"] > 0.948: return True, "signal_profit_o_bear_2_5" elif (last_candle["rsi_14"] < 43.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_2_6" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_o_bear_2_7" elif last_candle["rsi_14"] < 35.0: return True, "signal_profit_o_bear_2_9" elif 0.02 > current_profit >= 0.012: if (last_candle["rsi_14"] < 35.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_1_1" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_o_bear_1_3" elif (last_candle["rsi_14"] < 46.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_o_bear_1_4" elif last_candle["cti"] > 0.95: return True, "signal_profit_o_bear_1_5" elif (last_candle["rsi_14"] < 43.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_o_bear_1_6" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_o_bear_1_7" elif last_candle["rsi_14"] < 33.0: return True, "signal_profit_o_bear_1_9" return False, None def sell_under_main(self, current_profit: float, last_candle) -> tuple: if last_candle["close"] < last_candle["ema_200"]: if last_candle["moderi_96"]: if current_profit >= 0.20: if last_candle["rsi_14"] < 30.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_12_1" elif last_candle["rsi_14"] < 28.0: return True, "signal_profit_u_bull_12_9" elif 0.20 > current_profit >= 0.12: if last_candle["rsi_14"] < 42.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_11_1" elif last_candle["rsi_14"] < 43.0: return True, "signal_profit_u_bull_11_9" elif 0.12 > current_profit >= 0.10: if last_candle["rsi_14"] < 46.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_10_1" elif last_candle["rsi_14"] < 49.0: return True, "signal_profit_u_bull_10_9" elif 0.10 > current_profit >= 0.09: if last_candle["rsi_14"] < 50.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_9_1" elif last_candle["rsi_14"] < 50.0: return True, "signal_profit_u_bull_9_9" elif 0.09 > current_profit >= 0.08: if last_candle["rsi_14"] < 57.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_8_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_8_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_8_4" elif last_candle["rsi_14"] < 49.0: return True, "signal_profit_u_bull_8_9" elif 0.08 > current_profit >= 0.07: if last_candle["rsi_14"] < 52.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_7_1" if last_candle["rsi_14"] > 83.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_7_2" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_7_3" elif (last_candle["rsi_14"] < 55.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_7_4" elif last_candle["rsi_14"] < 46.0: return True, "signal_profit_u_bull_7_9" elif 0.07 > current_profit >= 0.06: if last_candle["rsi_14"] < 50.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_6_1" if last_candle["rsi_14"] > 82.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_6_2" elif (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_6_3" elif (last_candle["rsi_14"] < 53.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_6_4" elif last_candle["cti"] > 0.95: return True, "signal_profit_u_bull_6_5" elif last_candle["rsi_14"] < 43.0: return True, "signal_profit_u_bull_6_9" elif 0.06 > current_profit >= 0.05: if last_candle["rsi_14"] < 48.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_5_1" if last_candle["rsi_14"] > 80.0 and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_5_2" elif (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_5_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_5_4" elif last_candle["cti"] > 0.952: return True, "signal_profit_u_bull_5_5" elif (last_candle["rsi_14"] < 51.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_5_6" elif last_candle["rsi_14"] < 42.0: return True, "signal_profit_u_bull_5_9" elif 0.05 > current_profit >= 0.04: if last_candle["rsi_14"] < 47.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_4_1" elif (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_4_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_4_4" elif last_candle["cti"] > 0.954: return True, "signal_profit_u_bull_4_5" elif (last_candle["rsi_14"] < 50.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_4_6" elif last_candle["rsi_14"] < 41.0: return True, "signal_profit_u_bull_4_9" elif 0.04 > current_profit >= 0.03: if last_candle["rsi_14"] < 46.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_3_1" elif (last_candle["rsi_14"] < 49.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_3_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_3_4" elif last_candle["cti"] > 0.956: return True, "signal_profit_u_bull_3_5" elif (last_candle["rsi_14"] < 49.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_3_6" elif last_candle["rsi_14"] < 36.0: return True, "signal_profit_u_bull_3_9" elif 0.03 > current_profit >= 0.02: if last_candle["rsi_14"] < 45.0 and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_2_1" elif (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_2_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_2_4" elif last_candle["cti"] > 0.958: return True, "signal_profit_u_bull_2_5" elif (last_candle["rsi_14"] < 46.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_2_6" elif (last_candle["rsi_14"] < 46.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_u_bull_2_7" elif last_candle["rsi_14"] < 41.0 and (last_candle["cmf"] < -0.25): return True, "signal_profit_u_bull_2_8" elif last_candle["rsi_14"] < 35.0: return True, "signal_profit_u_bull_2_9" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 37.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_1_1" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf"] < -0.4): return True, "signal_profit_u_bull_1_3" elif (last_candle["rsi_14"] < 47.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bull_1_4" elif last_candle["cti"] > 0.96: return True, "signal_profit_u_bull_1_5" elif (last_candle["rsi_14"] < 43.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bull_1_6" elif (last_candle["rsi_14"] < 43.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_u_bull_1_7" elif last_candle["rsi_14"] < 40.0 and (last_candle["cmf"] < -0.25): return True, "signal_profit_u_bull_1_8" elif last_candle["rsi_14"] < 33.0: return True, "signal_profit_u_bull_1_9" else: if current_profit >= 0.20: if last_candle["rsi_14"] < 30.0: return True, "signal_profit_u_bear_12_1" elif 0.20 > current_profit >= 0.12: if last_candle["rsi_14"] < 42.0: return True, "signal_profit_u_bear_11_1" elif 0.12 > current_profit >= 0.10: if last_candle["rsi_14"] < 46.0: return True, "signal_profit_u_bear_10_1" elif 0.10 > current_profit >= 0.09: if last_candle["rsi_14"] < 50.0: return True, "signal_profit_u_bear_9_1" elif last_candle["rsi_14"] > 82.0: return True, "signal_profit_u_bear_9_2" elif 0.09 > current_profit >= 0.08: if last_candle["rsi_14"] < 57.0: return True, "signal_profit_u_bear_8_1" elif last_candle["rsi_14"] > 80.0: return True, "signal_profit_u_bear_8_2" elif 0.08 > current_profit >= 0.07: if last_candle["rsi_14"] < 56.0: return True, "signal_profit_u_bear_7_1" elif last_candle["rsi_14"] > 78.0: return True, "signal_profit_u_bear_7_2" elif 0.07 > current_profit >= 0.06: if last_candle["rsi_14"] < 54.0: return True, "signal_profit_u_bear_6_1" elif last_candle["rsi_14"] > 78.0: return True, "signal_profit_u_bear_6_2" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_u_bear_6_3" elif last_candle["cti"] > 0.94: return True, "signal_profit_u_bear_6_5" elif 0.06 > current_profit >= 0.05: if last_candle["rsi_14"] < 52.0: return True, "signal_profit_u_bear_5_1" elif (last_candle["rsi_14"] < 57.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_u_bear_5_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bear_5_4" elif last_candle["cti"] > 0.942: return True, "signal_profit_u_bear_5_5" elif (last_candle["rsi_14"] < 57.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_5_6" elif 0.05 > current_profit >= 0.04: if last_candle["rsi_14"] < 50.0: return True, "signal_profit_u_bear_4_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.05): return True, "signal_profit_u_bear_4_3" elif (last_candle["rsi_14"] < 57.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bear_4_4" elif last_candle["cti"] > 0.944: return True, "signal_profit_u_bear_4_5" elif (last_candle["rsi_14"] < 56.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_4_6" elif 0.04 > current_profit >= 0.03: if last_candle["rsi_14"] < 48.0: return True, "signal_profit_u_bear_3_1" elif (last_candle["rsi_14"] < 55.0) and (last_candle["cmf"] < -0.05): return True, "signal_profit_u_bear_3_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bear_3_4" elif last_candle["cti"] > 0.946: return True, "signal_profit_u_bear_3_5" elif (last_candle["rsi_14"] < 55.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_3_6" elif 0.03 > current_profit >= 0.02: if last_candle["rsi_14"] < 55.0: # 46 return True, "signal_profit_u_bear_2_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.05): return True, "signal_profit_u_bear_2_3" elif (last_candle["rsi_14"] < 55.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bear_2_4" elif last_candle["cti"] > 0.948: return True, "signal_profit_u_bear_2_5" elif (last_candle["rsi_14"] < 54.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_2_6" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_u_bear_2_7" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_1_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.05): return True, "signal_profit_u_bear_1_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0.0): return True, "signal_profit_u_bear_1_4" elif last_candle["cti"] > 0.95: return True, "signal_profit_u_bear_1_5" elif (last_candle["rsi_14"] < 44.0) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < 0.0): return True, "signal_profit_u_bear_1_6" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf_1h"] < -0.05) and (last_candle["cti_1h"] > 0.85): return True, "signal_profit_u_bear_1_7" elif last_candle["rsi_14"] < 34.0: return True, "signal_profit_u_bear_1_9" return False, None def sell_pump_main(self, current_profit: float, last_candle) -> tuple: if last_candle["sell_pump_48_1_1h"]: if last_candle["moderi_96"]: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_6_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_5_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_4_3" elif (last_candle["rsi_14"] < 53.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_3_1" elif (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_3_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_2_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_2_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 35.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_48_1_1" elif (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bull_48_1_3" elif (last_candle["rsi_14"] < 46.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_48_1_4" else: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 53.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_6_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_5_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_4_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_3_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_3_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_2_1" elif (last_candle["rsi_14"] < 42.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_2_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 36.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_48_1_1" elif (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < -0.12): return True, "signal_profit_p_bear_48_1_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_48_1_4" elif last_candle["sell_pump_36_1_1h"]: if last_candle["moderi_96"]: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_6_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_5_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_4_3" elif (last_candle["rsi_14"] < 53.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_3_1" elif (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_3_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_2_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_2_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 35.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_36_1_1" elif (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bull_36_1_3" elif (last_candle["rsi_14"] < 46.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_36_1_4" else: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 53.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_6_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_5_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_4_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_3_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_3_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_2_1" elif (last_candle["rsi_14"] < 42.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_2_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 36.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_36_1_1" elif (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < -0.2): return True, "signal_profit_p_bear_36_1_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_36_1_4" elif last_candle["sell_pump_24_1_1h"]: if last_candle["moderi_96"]: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 51.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_6_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_5_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_4_3" elif (last_candle["rsi_14"] < 53.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_3_1" elif (last_candle["rsi_14"] < 46.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_3_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_2_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_2_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 35.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bull_24_1_1" elif (last_candle["rsi_14"] < 38.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bull_24_1_3" elif (last_candle["rsi_14"] < 46.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bull_24_1_4" else: if current_profit >= 0.2: if (last_candle["rsi_14"] < 30.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_12_1" elif 0.2 > current_profit >= 0.12: if (last_candle["rsi_14"] < 42.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_11_1" elif 0.12 > current_profit >= 0.1: if (last_candle["rsi_14"] < 46.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_10_1" elif 0.1 > current_profit >= 0.09: if (last_candle["rsi_14"] < 50.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_9_1" elif 0.09 > current_profit >= 0.08: if (last_candle["rsi_14"] < 57.5) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_8_1" elif 0.08 > current_profit >= 0.07: if (last_candle["rsi_14"] < 53.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_7_1" elif 0.07 > current_profit >= 0.06: if (last_candle["rsi_14"] < 52.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_6_1" elif (last_candle["rsi_14"] < 58.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_6_3" elif (last_candle["rsi_14"] < 58.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_6_4" elif 0.06 > current_profit >= 0.05: if (last_candle["rsi_14"] < 50.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_5_1" elif (last_candle["rsi_14"] < 56.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_5_3" elif (last_candle["rsi_14"] < 56.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_5_4" elif 0.05 > current_profit >= 0.04: if (last_candle["rsi_14"] < 47.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_4_1" elif (last_candle["rsi_14"] < 54.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_4_3" elif (last_candle["rsi_14"] < 54.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_4_4" elif 0.04 > current_profit >= 0.03: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_3_1" elif (last_candle["rsi_14"] < 44.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_3_3" elif (last_candle["rsi_14"] < 52.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_3_4" elif 0.03 > current_profit >= 0.02: if (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_2_1" elif (last_candle["rsi_14"] < 42.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_2_3" elif (last_candle["rsi_14"] < 50.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_2_4" elif 0.02 > current_profit >= 0.01: if (last_candle["rsi_14"] < 36.0) and (last_candle["cmf"] < 0.0): return True, "signal_profit_p_bear_24_1_1" elif (last_candle["rsi_14"] < 40.0) and (last_candle["cmf"] < -0.3): return True, "signal_profit_p_bear_24_1_3" elif (last_candle["rsi_14"] < 48.0) and (last_candle["r_14"] == 0): return True, "signal_profit_p_bear_24_1_4" return False, None def sell_dec_main(self, current_profit: float, last_candle) -> tuple: if (self.sell_custom_dec_profit_max_1 > current_profit >= self.sell_custom_dec_profit_min_1) and ( last_candle["sma_200_dec_20"] ): return True, "signal_profit_d_1" elif (self.sell_custom_dec_profit_max_2 > current_profit >= self.sell_custom_dec_profit_min_2) and ( last_candle["close"] < last_candle["ema_100"] ): return True, "signal_profit_d_2" return False, None def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple: if ( (self.sell_trail_profit_max_1 > current_profit >= self.sell_trail_profit_min_1) and (self.sell_trail_rsi_min_1 < last_candle["rsi_14"] < self.sell_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_trail_down_1)) and (last_candle["moderi_96"] == False) ): return True, "signal_profit_t_1" elif ( (self.sell_trail_profit_max_2 > current_profit >= self.sell_trail_profit_min_2) and (self.sell_trail_rsi_min_2 < last_candle["rsi_14"] < self.sell_trail_rsi_max_2) and (max_profit > (current_profit + self.sell_trail_down_2)) and (last_candle["ema_25"] < last_candle["ema_50"]) ): return True, "signal_profit_t_2" elif ( (self.sell_trail_profit_max_3 > current_profit >= self.sell_trail_profit_min_3) and (max_profit > (current_profit + self.sell_trail_down_3)) and (last_candle["sma_200_dec_20_1h"]) ): return True, "signal_profit_t_3" elif ( (self.sell_trail_profit_max_4 > current_profit >= self.sell_trail_profit_min_4) and (max_profit > (current_profit + self.sell_trail_down_4)) and (last_candle["sma_200_dec_24"]) and (last_candle["cmf"] < 0.0) ): return True, "signal_profit_t_4" return False, None def sell_duration_main(self, current_profit: float, last_candle, trade: "Trade", current_time: "datetime") -> tuple: # Pumped pair, short duration if ( (last_candle["sell_pump_24_1_1h"]) and (0.2 > current_profit >= 0.07) and (current_time - timedelta(minutes=30) < trade.open_date_utc) ): return True, "signal_profit_p_s_1" elif (self.sell_custom_long_profit_min_1 < current_profit < self.sell_custom_long_profit_max_1) and ( current_time - timedelta(minutes=self.sell_custom_long_duration_min_1) > trade.open_date_utc ): return True, "signal_profit_l_1" return False, None def sell_under_min(self, current_profit: float, last_candle) -> tuple: if (last_candle["moderi_96"]) == False: # Downtrend if ( (self.sell_custom_profit_under_profit_max_1 > current_profit >= self.sell_custom_profit_under_profit_min_1) and (last_candle["close"] < last_candle["ema_200"]) and ( ((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < self.sell_custom_profit_under_rel_1 ) and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_1) ): return True, "signal_profit_u_e_1" else: # Uptrend if ( (current_profit >= self.sell_custom_profit_under_profit_2) and (last_candle["close"] < last_candle["ema_200"]) and ( ((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < self.sell_custom_profit_under_rel_2 ) and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_2) ): return True, "signal_profit_u_e_2" return False, None def sell_stoploss( self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: "Trade", current_time: "datetime", ) -> tuple: # Under & near EMA200, local uptrend move if ( (current_profit < -0.05) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["cmf"] < 0.0) and (((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < 0.004) and last_candle["rsi_14"] > previous_candle_1["rsi_14"] and (last_candle["rsi_14"] > (last_candle["rsi_14_1h"] + 10.0)) and (last_candle["sma_200_dec_24"]) and (current_time - timedelta(minutes=2880) > trade.open_date_utc) ): return True, "signal_stoploss_u_e_1" # Under EMA200, local strong uptrend move if ( (current_profit < -0.08) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["cmf"] < 0.0) and last_candle["rsi_14"] > previous_candle_1["rsi_14"] and (last_candle["rsi_14"] > (last_candle["rsi_14_1h"] + 24.0)) and (last_candle["sma_200_dec_20"]) and (last_candle["sma_200_dec_24"]) and (current_time - timedelta(minutes=2880) > trade.open_date_utc) ): return True, "signal_stoploss_u_e_2" # Under EMA200, pair negative, low max rate if ( (current_profit < -0.08) and (max_profit < 0.04) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["ema_25"] < last_candle["ema_50"]) and (last_candle["sma_200_dec_20"]) and (last_candle["sma_200_dec_24"]) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["ema_vwma_osc_32"] < 0.0) and (last_candle["ema_vwma_osc_64"] < 0.0) and (last_candle["ema_vwma_osc_96"] < 0.0) and (last_candle["cmf"] < -0.0) and (last_candle["cmf_1h"] < -0.0) and (last_candle["btc_not_downtrend_1h"] == False) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) ): return True, "signal_stoploss_u_e_doom" # Under EMA200, pair and BTC negative, low max rate if ( (-0.05 > current_profit > -0.09) and (last_candle["btc_not_downtrend_1h"] == False) and (last_candle["ema_vwma_osc_32"] < 0.0) and (last_candle["ema_vwma_osc_64"] < 0.0) and (max_profit < 0.005) and (max_loss < 0.09) and (last_candle["sma_200_dec_24"]) and (last_candle["cmf"] < -0.0) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["ema_25"] < last_candle["ema_50"]) and (last_candle["cti"] < -0.8) and (last_candle["r_480"] < -50.0) ): return True, "signal_stoploss_u_e_b_1" # Under EMA200, pair and BTC negative, CTI, Elder Ray Index negative, normal max rate elif ( (-0.1 > current_profit > -0.2) and (last_candle["btc_not_downtrend_1h"] == False) and (last_candle["ema_vwma_osc_32"] < 0.0) and (last_candle["ema_vwma_osc_64"] < 0.0) and (last_candle["ema_vwma_osc_96"] < 0.0) and (max_profit < 0.05) and (max_loss < 0.2) and (last_candle["sma_200_dec_24"]) and (last_candle["sma_200_dec_20_1h"]) and (last_candle["cmf"] < -0.45) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["ema_25"] < last_candle["ema_50"]) and (last_candle["cti"] < -0.8) and (last_candle["r_480"] < -97.0) ): return True, "signal_stoploss_u_e_b_2" return False, None def sell_pump_dec(self, current_profit: float, last_candle) -> tuple: if ( (0.03 > current_profit >= 0.005) and (last_candle["sell_pump_48_1_1h"]) and (last_candle["sma_200_dec_20"]) and (last_candle["close"] < last_candle["ema_200"]) ): return True, "signal_profit_p_d_1" elif ( (0.06 > current_profit >= 0.04) and (last_candle["sell_pump_48_2_1h"]) and (last_candle["sma_200_dec_20"]) and (last_candle["close"] < last_candle["ema_200"]) ): return True, "signal_profit_p_d_2" elif ( (0.09 > current_profit >= 0.06) and (last_candle["sell_pump_48_3_1h"]) and (last_candle["sma_200_dec_20"]) and (last_candle["close"] < last_candle["ema_200"]) ): return True, "signal_profit_p_d_3" elif (0.04 > current_profit >= 0.02) and (last_candle["sma_200_dec_20"]) and (last_candle["sell_pump_24_2_1h"]): return True, "signal_profit_p_d_4" return False, None def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple: # Pumped 48h 1, under EMA200 if ( (self.sell_custom_pump_under_profit_max_1 > current_profit >= self.sell_custom_pump_under_profit_min_1) and (last_candle["sell_pump_48_1_1h"]) and (last_candle["close"] < last_candle["ema_200"]) ): return True, "signal_profit_p_u_1" # Pumped 36h 2, trail 1 elif ( (last_candle["sell_pump_36_2_1h"]) and (self.sell_custom_pump_trail_profit_max_1 > current_profit >= self.sell_custom_pump_trail_profit_min_1) and (self.sell_custom_pump_trail_rsi_min_1 < last_candle["rsi_14"] < self.sell_custom_pump_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1)) ): return True, "signal_profit_p_t_1" return False, None def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple: if (max_loss > self.sell_custom_recover_min_loss_1) and (current_profit >= self.sell_custom_recover_profit_1): return True, "signal_profit_r_1" elif ( (max_loss > self.sell_custom_recover_min_loss_2) and (self.sell_custom_recover_profit_max_2 > current_profit >= self.sell_custom_recover_profit_min_2) and (last_candle["rsi_14"] < self.sell_custom_recover_rsi_2) and (last_candle["ema_25"] < last_candle["ema_50"]) ): return True, "signal_profit_r_2" return False, None def sell_r_1(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle["r_480"] > -0.4: return True, "signal_profit_w_1_1" elif 0.03 > current_profit >= 0.02: if last_candle["r_480"] > -0.5: return True, "signal_profit_w_1_2" elif 0.04 > current_profit >= 0.03: if last_candle["r_480"] > -0.6: return True, "signal_profit_w_1_3" elif 0.05 > current_profit >= 0.04: if last_candle["r_480"] > -0.7: return True, "signal_profit_w_1_4" elif 0.06 > current_profit >= 0.05: if last_candle["r_480"] > -1.0: return True, "signal_profit_w_1_5" elif 0.07 > current_profit >= 0.06: if last_candle["r_480"] > -2.0: return True, "signal_profit_w_1_6" elif 0.08 > current_profit >= 0.07: if last_candle["r_480"] > -2.2: return True, "signal_profit_w_1_7" elif 0.09 > current_profit >= 0.08: if last_candle["r_480"] > -2.4: return True, "signal_profit_w_1_8" elif 0.1 > current_profit >= 0.09: if last_candle["r_480"] > -2.6: return True, "signal_profit_w_1_9" elif 0.12 > current_profit >= 0.1: if (last_candle["r_480"] > -2.5) and (last_candle["rsi_14"] > 72.0): return True, "signal_profit_w_1_10" elif 0.2 > current_profit >= 0.12: if (last_candle["r_480"] > -2.0) and (last_candle["rsi_14"] > 78.0): return True, "signal_profit_w_1_11" elif current_profit >= 0.2: if (last_candle["r_480"] > -1.0) and (last_candle["rsi_14"] > 80.0): return True, "signal_profit_w_1_12" return False, None def sell_r_2(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if ( (last_candle["r_480"] > -4.0) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_1" elif 0.03 > current_profit >= 0.02: if ( (last_candle["r_480"] > -4.1) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_2" elif 0.04 > current_profit >= 0.03: if ( (last_candle["r_480"] > -4.2) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_3" elif 0.05 > current_profit >= 0.04: if ( (last_candle["r_480"] > -4.3) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_4" elif 0.06 > current_profit >= 0.05: if ( (last_candle["r_480"] > -4.4) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_5" elif 0.07 > current_profit >= 0.06: if ( (last_candle["r_480"] > -4.5) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_6" elif 0.08 > current_profit >= 0.07: if ( (last_candle["r_480"] > -5.0) and (last_candle["rsi_14"] > 80.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_7" elif 0.09 > current_profit >= 0.08: if ( (last_candle["r_480"] > -5.0) and (last_candle["rsi_14"] > 80.5) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_8" elif 0.1 > current_profit >= 0.09: if ( (last_candle["r_480"] > -4.8) and (last_candle["rsi_14"] > 80.5) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_9" elif 0.12 > current_profit >= 0.1: if ( (last_candle["r_480"] > -4.4) and (last_candle["rsi_14"] > 80.5) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_10" elif 0.2 > current_profit >= 0.12: if ( (last_candle["r_480"] > -3.2) and (last_candle["rsi_14"] > 81.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_11" elif current_profit >= 0.2: if ( (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 81.5) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_2_12" return False, None def sell_r_3(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if ( (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 74.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_3_1" elif 0.03 > current_profit >= 0.02: if ( (last_candle["r_480"] > -3.5) and (last_candle["rsi_14"] > 74.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_3_2" elif 0.04 > current_profit >= 0.03: if ( (last_candle["r_480"] > -4.0) and (last_candle["rsi_14"] > 74.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_3_3" elif 0.05 > current_profit >= 0.04: if ( (last_candle["r_480"] > -4.5) and (last_candle["rsi_14"] > 79.0) and (last_candle["stochrsi_fastk_96"] > 99.0) and (last_candle["stochrsi_fastd_96"] > 99.0) ): return True, "signal_profit_w_3_4" return False, None def sell_r_4(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle["r_480"] > -2.0) and (last_candle["rsi_14"] > 78.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_1" elif 0.03 > current_profit >= 0.02: if (last_candle["r_480"] > -2.5) and (last_candle["rsi_14"] > 78.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_2" elif 0.04 > current_profit >= 0.03: if (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 78.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_3" elif 0.05 > current_profit >= 0.04: if (last_candle["r_480"] > -3.5) and (last_candle["rsi_14"] > 78.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_4" elif 0.06 > current_profit >= 0.05: if (last_candle["r_480"] > -4.0) and (last_candle["rsi_14"] > 78.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_5" elif 0.07 > current_profit >= 0.06: if (last_candle["r_480"] > -4.5) and (last_candle["rsi_14"] > 79.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_6" elif 0.08 > current_profit >= 0.07: if (last_candle["r_480"] > -5.0) and (last_candle["rsi_14"] > 79.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_7" elif 0.09 > current_profit >= 0.08: if (last_candle["r_480"] > -5.5) and (last_candle["rsi_14"] > 79.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_8" elif 0.1 > current_profit >= 0.09: if (last_candle["r_480"] > -4.0) and (last_candle["rsi_14"] > 79.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_9" elif 0.12 > current_profit >= 0.1: if (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 79.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_10" elif 0.2 > current_profit >= 0.12: if (last_candle["r_480"] > -2.5) and (last_candle["rsi_14"] > 80.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_11" elif current_profit >= 0.2: if (last_candle["r_480"] > -2.0) and (last_candle["rsi_14"] > 80.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_w_4_12" return False, None def sell_r_5(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle["r_480"] > -1.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_1" elif 0.03 > current_profit >= 0.02: if (last_candle["r_480"] > -1.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_2" elif 0.04 > current_profit >= 0.03: if (last_candle["r_480"] > -2.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_3" elif 0.05 > current_profit >= 0.04: if (last_candle["r_480"] > -2.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_4" elif 0.06 > current_profit >= 0.05: if (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_5" elif 0.07 > current_profit >= 0.06: if (last_candle["r_480"] > -3.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_6" elif 0.08 > current_profit >= 0.07: if (last_candle["r_480"] > -4.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_7" elif 0.09 > current_profit >= 0.08: if (last_candle["r_480"] > -4.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_8" elif 0.1 > current_profit >= 0.09: if (last_candle["r_480"] > -3.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_9" elif 0.12 > current_profit >= 0.1: if (last_candle["r_480"] > -2.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_10" elif 0.2 > current_profit >= 0.12: if (last_candle["r_480"] > -2.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_11" elif current_profit >= 0.2: if (last_candle["r_480"] > -1.5) and (last_candle["rsi_14"] > 80.0) and (last_candle["cti_1h"] > 0.92): return True, "signal_profit_w_5_12" return False, None def sell_r_6(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if ( (last_candle["r_14"] > -0.1) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_1" elif 0.03 > current_profit >= 0.02: if ( (last_candle["r_14"] > -0.2) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_2" elif 0.04 > current_profit >= 0.03: if ( (last_candle["r_14"] > -0.3) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_3" elif 0.05 > current_profit >= 0.04: if ( (last_candle["r_14"] > -0.4) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_4" elif 0.06 > current_profit >= 0.05: if ( (last_candle["r_14"] > -0.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_5" elif 0.07 > current_profit >= 0.06: if ( (last_candle["r_14"] > -0.6) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_6" elif 0.08 > current_profit >= 0.07: if ( (last_candle["r_14"] > -1.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_7" elif 0.09 > current_profit >= 0.08: if ( (last_candle["r_14"] > -1.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_8" elif 0.1 > current_profit >= 0.09: if ( (last_candle["r_14"] > -1.0) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_9" elif 0.12 > current_profit >= 0.1: if ( (last_candle["r_14"] > -0.75) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_10" elif 0.2 > current_profit >= 0.12: if ( (last_candle["r_14"] > -0.5) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_11" elif current_profit >= 0.2: if ( (last_candle["r_14"] > -0.1) and (last_candle["rsi_14"] > 75.0) and (last_candle["cti"] > 0.8) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_w_6_12" return False, None def mark_profit_target( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1, ) -> tuple: # if self.profit_target_1_enable: # if (current_profit > 0) and (last_candle['zlema_4_lowKF'] > last_candle['lowKF']) and (previous_candle_1['zlema_4_lowKF'] < previous_candle_1['lowKF']) and (last_candle['cci'] > -100) and (last_candle['hrsi'] > 70): # return pair, "mark_profit_target_01" return None, None def sell_profit_target( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached, ) -> tuple: # if self.profit_target_1_enable and previous_sell_reason == "mark_profit_target_01": # if (current_profit > 0) and (current_rate < (previous_rate - 0.005)): # return True, 'sell_profit_target_01' return False, None def sell_quick_mode(self, current_profit: float, max_profit: float, last_candle, previous_candle_1) -> tuple: if (0.06 > current_profit > 0.02) and (last_candle["rsi_14"] > 80.0): return True, "signal_profit_q_1" if (0.06 > current_profit > 0.02) and (last_candle["cti"] > 0.95): return True, "signal_profit_q_2" if ( (0.04 > current_profit > 0.02) and (last_candle["pm"] <= last_candle["pmax_thresh"]) and (last_candle["close"] > last_candle["sma_21"] * 1.1) ): return True, "signal_profit_q_pmax_bull" if ( (0.045 > current_profit > 0.005) and (last_candle["pm"] > last_candle["pmax_thresh"]) and (last_candle["close"] > last_candle["sma_21"] * 1.016) ): return True, "signal_profit_q_pmax_bear" if (last_candle["momdiv_sell_1h"] == True) and (current_profit > 0.02): return True, "signal_profit_q_momdiv_1h" if (last_candle["momdiv_sell"] == True) and (current_profit > 0.02): return True, "signal_profit_q_momdiv" if (last_candle["momdiv_coh"] == True) and (current_profit > 0.02): return True, "signal_profit_q_momdiv_coh" return False, None def sell_ichi( self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: "Trade", current_time: "datetime", ) -> tuple: if ( (0.0 < current_profit < 0.05) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle["rsi_14"] > 78.0) ): return True, "signal_profit_ichi_u" elif (max_loss > 0.07) and (current_profit > 0.02): return True, "signal_profit_ichi_r_0" elif (max_loss > 0.06) and (current_profit > 0.03): return True, "signal_profit_ichi_r_1" elif (max_loss > 0.05) and (current_profit > 0.04): return True, "signal_profit_ichi_r_2" elif (max_loss > 0.04) and (current_profit > 0.05): return True, "signal_profit_ichi_r_3" elif (max_loss > 0.03) and (current_profit > 0.06): return True, "signal_profit_ichi_r_4" elif (0.05 < current_profit < 0.1) and (current_time - timedelta(minutes=720) > trade.open_date_utc): return True, "signal_profit_ichi_slow" elif (0.07 < current_profit < 0.1) and (max_profit - current_profit > 0.025) and (max_profit > 0.1): return True, "signal_profit_ichi_t" return False, None def sell_long_mode( self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade: "Trade", current_time: "datetime", buy_tag, ) -> tuple: # Sell signal 1 if ( (last_candle["rsi_14"] > 78.0) and (last_candle["close"] > last_candle["bb20_2_upp"]) and (previous_candle_1["close"] > previous_candle_1["bb20_2_upp"]) and (previous_candle_2["close"] > previous_candle_2["bb20_2_upp"]) and (previous_candle_3["close"] > previous_candle_3["bb20_2_upp"]) and (previous_candle_4["close"] > previous_candle_4["bb20_2_upp"]) and (previous_candle_5["close"] > previous_candle_5["bb20_2_upp"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_1_1_1" else: if current_profit > 0.01: return True, "sell_long_1_2_1" # Sell signal 2 elif ( (last_candle["rsi_14"] > 79.0) and (last_candle["close"] > last_candle["bb20_2_upp"]) and (previous_candle_1["close"] > previous_candle_1["bb20_2_upp"]) and (previous_candle_2["close"] > previous_candle_2["bb20_2_upp"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_2_1_1" else: if current_profit > 0.01: return True, "sell_long_2_2_1" # Sell signal 3 elif last_candle["rsi_14"] > 82.0: if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_3_1_1" else: if current_profit > 0.01: return True, "sell_long_3_2_1" # Sell signal 4 elif (last_candle["rsi_14"] > 78.0) and (last_candle["rsi_14_1h"] > 80.0): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_4_1_1" else: if current_profit > 0.01: return True, "sell_long_4_2_1" # Sell signal 6 elif ( (last_candle["close"] < last_candle["ema_200"]) and (last_candle["close"] > last_candle["ema_50"]) and (last_candle["rsi_14"] > 79.5) ): if current_profit > 0.01: return True, "sell_long_6_1" # Sell signal 7 elif (last_candle["rsi_14_1h"] > 82.0) and (last_candle["crossed_below_ema_12_26"]): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_7_1_1" else: if current_profit > 0.01: return True, "sell_long_7_2_1" # Sell signal 8 elif last_candle["close"] > last_candle["bb20_2_upp_1h"] * 1.05: if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return True, "sell_long_8_1_1" else: if current_profit > 0.01: return True, "sell_long_8_2_1" elif ( (0.02 < current_profit <= 0.06) and (max_profit - current_profit > 0.04) and (last_candle["cmf"] < 0.0) and (last_candle["sma_200_dec_24"]) ): return True, "sell_long_t_1" elif (0.06 < current_profit <= 0.12) and (max_profit - current_profit > 0.06) and (last_candle["cmf"] < 0.0): return True, "sell_long_t_2" elif (0.12 < current_profit <= 0.24) and (max_profit - current_profit > 0.08) and (last_candle["cmf"] < 0.0): return True, "sell_long_t_3" elif (0.24 < current_profit <= 0.5) and (max_profit - current_profit > 0.09) and (last_candle["cmf"] < 0.0): return True, "sell_long_t_4" elif (0.5 < current_profit <= 0.9) and (max_profit - current_profit > 0.1) and (last_candle["cmf"] < 0.0): return True, "sell_long_t_5" elif ( (0.03 < current_profit <= 0.06) and (current_time - timedelta(minutes=720) > trade.open_date_utc) and (last_candle["r_480"] > -20.0) ): return True, "sell_long_l_1" return self.sell_stoploss( current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time ) def sell_pivot( self, current_profit: float, max_profit: float, max_loss: float, last_candle, previous_candle_1, trade: "Trade", current_time: "datetime", ) -> tuple: if last_candle["close"] > (last_candle["res3_1d"] * 2.2): if 0.02 > current_profit >= 0.012: if (last_candle["r_14"] >= -0.0) and (last_candle["rsi_14"] > 79.0) and (last_candle["r_480"] > -3.0): return True, "signal_profit_pv_1_1_1" elif 0.03 > current_profit >= 0.02: if (last_candle["r_14"] > -0.4) and (last_candle["rsi_14"] > 76.0) and (last_candle["r_480"] > -5.0): return True, "signal_profit_pv_1_2_1" elif 0.04 > current_profit >= 0.03: if (last_candle["r_14"] > -0.8) and (last_candle["rsi_14"] > 74.0) and (last_candle["r_480"] > -10.0): return True, "signal_profit_pv_1_3_1" elif 0.05 > current_profit >= 0.04: if (last_candle["r_14"] > -1.0) and (last_candle["rsi_14"] > 70.0) and (last_candle["r_480"] > -15.0): return True, "signal_profit_pv_1_4_1" elif 0.06 > current_profit >= 0.05: if (last_candle["r_14"] > -1.2) and (last_candle["rsi_14"] > 66.0) and (last_candle["r_480"] > -20.0): return True, "signal_profit_pv_1_5_1" elif 0.07 > current_profit >= 0.06: if (last_candle["r_14"] > -1.6) and (last_candle["rsi_14"] > 60.0) and (last_candle["r_480"] > -25.0): return True, "signal_profit_pv_1_6_1" elif 0.08 > current_profit >= 0.07: if (last_candle["r_14"] > -2.0) and (last_candle["rsi_14"] > 56.0) and (last_candle["r_480"] > -30.0): return True, "signal_profit_pv_1_7_1" elif last_candle["close"] > (last_candle["res3_1d"] * 1.3): if 0.02 > current_profit >= 0.012: if ( (last_candle["rsi_14"] > 80.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_1_1" elif (last_candle["rsi_14"] > 79.0) and (last_candle["r_14"] > -1.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_1_2" elif 0.03 > current_profit >= 0.02: if ( (last_candle["rsi_14"] > 78.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_2_1" elif (last_candle["rsi_14"] > 77.0) and (last_candle["r_14"] > -3.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_2_2" elif 0.04 > current_profit >= 0.03: if ( (last_candle["rsi_14"] > 76.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_3_1" elif (last_candle["rsi_14"] > 75.0) and (last_candle["r_14"] > -5.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_3_2" elif 0.05 > current_profit >= 0.04: if ( (last_candle["rsi_14"] > 72.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_4_1" elif (last_candle["rsi_14"] > 71.0) and (last_candle["r_14"] > -7.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_4_2" elif 0.06 > current_profit >= 0.05: if ( (last_candle["rsi_14"] > 68.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_5_1" elif (last_candle["rsi_14"] > 67.0) and (last_candle["r_14"] > -9.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_5_2" elif 0.07 > current_profit >= 0.06: if ( (last_candle["rsi_14"] > 60.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_6_1" elif (last_candle["rsi_14"] > 59.0) and (last_candle["r_14"] > -9.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_6_2" elif 0.08 > current_profit >= 0.07: if ( (last_candle["rsi_14"] > 58.0) and (last_candle["cti_1h"] > 0.84) and (last_candle["cmf"] < 0.0) and (last_candle["cci"] > 200.0) ): return True, "signal_profit_pv_2_7_1" elif (last_candle["rsi_14"] > 57.0) and (last_candle["r_14"] > -9.0) and (last_candle["cti"] > 0.9): return True, "signal_profit_pv_2_7_2" return False, None def custom_sell( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, **kwargs ): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] previous_candle_3 = dataframe.iloc[-4] previous_candle_4 = dataframe.iloc[-5] previous_candle_5 = dataframe.iloc[-6] buy_tag = "empty" if hasattr(trade, "buy_tag") and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate # Long mode if all(c in ["45", "46", "47"] for c in buy_tags): sell, signal_name = self.sell_long_mode( current_profit, max_profit, max_loss, last_candle, previous_candle_1, previous_candle_2, previous_candle_3, previous_candle_4, previous_candle_5, trade, current_time, buy_tag, ) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Skip remaining sell logic for long mode return None # Quick sell mode if all(c in ["empty", "32", "33", "34", "35", "36", "37", "38", "40"] for c in buy_tags): sell, signal_name = self.sell_quick_mode(current_profit, max_profit, last_candle, previous_candle_1) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Ichi Trade management if all(c in ["39"] for c in buy_tags): sell, signal_name = self.sell_ichi( current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time ) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Over EMA200, main profit targets sell, signal_name = self.sell_over_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Under EMA200, main profit targets sell, signal_name = self.sell_under_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # The pair is pumped sell, signal_name = self.sell_pump_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # The pair is descending sell, signal_name = self.sell_dec_main(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Trailing sell, signal_name = self.sell_trail_main(current_profit, last_candle, max_profit) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Duration based sell, signal_name = self.sell_duration_main(current_profit, last_candle, trade, current_time) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Under EMA200, exit with any profit sell, signal_name = self.sell_under_min(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Stoplosses sell, signal_name = self.sell_stoploss( current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time ) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Pumped descending pairs sell, signal_name = self.sell_pump_dec(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Extra sells for pumped pairs sell, signal_name = self.sell_pump_extra(current_profit, last_candle, max_profit) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Extra sells for trades that recovered sell, signal_name = self.sell_recover(current_profit, last_candle, max_loss) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 1 sell, signal_name = self.sell_r_1(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 2 sell, signal_name = self.sell_r_2(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 3 sell, signal_name = self.sell_r_3(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 4, plus CTI sell, signal_name = self.sell_r_4(current_profit, last_candle) if (sell) and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 5, plus RSI and CTI 1h sell, signal_name = self.sell_r_5(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Williams %R based sell 6, plus RSI, CTI, CCI sell, signal_name = self.sell_r_6(current_profit, last_candle) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Pivot points based sells sell, signal_name = self.sell_pivot( current_profit, max_profit, max_loss, last_candle, previous_candle_1, trade, current_time ) if sell and (signal_name is not None): return f"{signal_name} ( {buy_tag})" # Profit Target Signal # Check if pair exist on target_profit_cache if self.target_profit_cache is not None and pair in self.target_profit_cache.data: previous_rate = self.target_profit_cache.data[pair]["rate"] previous_sell_reason = self.target_profit_cache.data[pair]["sell_reason"] previous_time_profit_reached = datetime.fromisoformat(self.target_profit_cache.data[pair]["time_profit_reached"]) sell, signal_name = self.sell_profit_target( pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1, previous_rate, previous_sell_reason, previous_time_profit_reached, ) if sell and signal_name is not None: return f"{signal_name} ( {buy_tag})" pair, mark_signal = self.mark_profit_target( pair, trade, current_time, current_rate, current_profit, last_candle, previous_candle_1 ) if pair: self._set_profit_target(pair, mark_signal, current_rate, current_time) # Sell signal 1 if ( self.sell_condition_1_enable and (last_candle["rsi_14"] > self.sell_rsi_bb_1) and (last_candle["close"] > last_candle["bb20_2_upp"]) and (previous_candle_1["close"] > previous_candle_1["bb20_2_upp"]) and (previous_candle_2["close"] > previous_candle_2["bb20_2_upp"]) and (previous_candle_3["close"] > previous_candle_3["bb20_2_upp"]) and (previous_candle_4["close"] > previous_candle_4["bb20_2_upp"]) and (previous_candle_5["close"] > previous_candle_5["bb20_2_upp"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_1_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_1_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_1_2_2 ( {buy_tag})" # Sell signal 2 elif ( (self.sell_condition_2_enable) and (last_candle["rsi_14"] > self.sell_rsi_bb_2) and (last_candle["close"] > last_candle["bb20_2_upp"]) and (previous_candle_1["close"] > previous_candle_1["bb20_2_upp"]) and (previous_candle_2["close"] > previous_candle_2["bb20_2_upp"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_2_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_2_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_2_2_2 ( {buy_tag})" # Sell signal 3 elif (self.sell_condition_3_enable) and (last_candle["rsi_14"] > self.sell_rsi_main_3): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_3_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_3_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_3_2_2 ( {buy_tag})" # Sell signal 4 elif ( self.sell_condition_4_enable and (last_candle["rsi_14"] > self.sell_dual_rsi_rsi_4) and (last_candle["rsi_14_1h"] > self.sell_dual_rsi_rsi_1h_4) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_4_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_4_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_4_2_2 ( {buy_tag})" # Sell signal 6 elif ( self.sell_condition_6_enable and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["close"] > last_candle["ema_50"]) and (last_candle["rsi_14"] > self.sell_rsi_under_6) ): if current_profit > 0.01: return f"sell_signal_6_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_6_2 ( {buy_tag})" # Sell signal 7 elif ( self.sell_condition_7_enable and (last_candle["rsi_14_1h"] > self.sell_rsi_1h_7) and (last_candle["crossed_below_ema_12_26"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_7_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_7_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_7_2_2 ( {buy_tag})" # Sell signal 8 elif self.sell_condition_8_enable and ( last_candle["close"] > last_candle["bb20_2_upp_1h"] * self.sell_bb_relative_8 ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.01: return f"sell_signal_8_1_1 ( {buy_tag})" else: if current_profit > 0.01: return f"sell_signal_8_2_1 ( {buy_tag})" elif max_loss > 0.5: return f"sell_signal_8_2_2 ( {buy_tag})" return None def range_percent_change(self, dataframe: DataFrame, method, length: int) -> float: """ Rolling Percentage Change Maximum across interval. :param dataframe: DataFrame The original OHLC dataframe :param method: High to Low / Open to Close :param length: int The length to look back """ if method == "HL": return (dataframe["high"].rolling(length).max() - dataframe["low"].rolling(length).min()) / dataframe[ "low" ].rolling(length).min() elif method == "OC": return (dataframe["open"].rolling(length).max() - dataframe["close"].rolling(length).min()) / dataframe[ "close" ].rolling(length).min() else: raise ValueError(f"Method {method} not defined!") def top_percent_change(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe["open"] - dataframe["close"]) / dataframe["close"] else: return (dataframe["open"].rolling(length).max() - dataframe["close"]) / dataframe["close"] def range_maxgap(self, dataframe: DataFrame, length: int) -> float: """ Maximum Price Gap across interval. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe["open"].rolling(length).max() - dataframe["close"].rolling(length).min() def range_maxgap_adjusted(self, dataframe: DataFrame, length: int, adjustment: float) -> float: """ Maximum Price Gap across interval adjusted. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param adjustment: int The adjustment to be applied """ return self.range_maxgap(dataframe, length) / adjustment def range_height(self, dataframe: DataFrame, length: int) -> float: """ Current close distance to range bottom. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ return dataframe["close"] - dataframe["close"].rolling(length).min() def safe_pump(self, dataframe: DataFrame, length: int, thresh: float, pull_thresh: float) -> bool: """ Determine if entry after a pump is safe. :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back :param thresh: int Maximum percentage change threshold :param pull_thresh: int Pullback from interval maximum threshold """ return (dataframe[f"oc_pct_change_{length}"] < thresh) | ( self.range_maxgap_adjusted(dataframe, length, pull_thresh) > self.range_height(dataframe, length) ) def safe_dips(self, dataframe: DataFrame, thresh_0, thresh_2, thresh_12, thresh_144) -> bool: """ Determine if dip is safe to enter. :param dataframe: DataFrame The original OHLC dataframe :param thresh_0: Threshold value for 0 length top pct change :param thresh_2: Threshold value for 2 length top pct change :param thresh_12: Threshold value for 12 length top pct change :param thresh_144: Threshold value for 144 length top pct change """ return ( (dataframe["tpct_change_0"] < thresh_0) & (dataframe["tpct_change_2"] < thresh_2) & (dataframe["tpct_change_12"] < thresh_12) & (dataframe["tpct_change_144"] < thresh_144) ) def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, self.info_timeframe_1h) for pair in pairs] informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs]) if self.config["stake_currency"] in ["USDT", "BUSD", "USDC", "DAI", "TUSD", "PAX", "USD", "EUR", "GBP"]: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.append((btc_info_pair, self.timeframe)) informative_pairs.append((btc_info_pair, self.info_timeframe_1h)) informative_pairs.append((btc_info_pair, self.info_timeframe_1d)) return informative_pairs def informative_1d_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1d = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.info_timeframe_1d) # Top traded coins if self.coin_metrics["top_traded_enabled"]: informative_1d = informative_1d.merge(self.coin_metrics["tt_dataframe"], on="date", how="left") informative_1d["is_top_traded"] = informative_1d.apply( lambda row: self.is_top_coin(metadata["pair"], row, self.coin_metrics["top_traded_len"]), axis=1 ) column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics["top_traded_len"] + 1)] informative_1d.drop(columns=column_names, inplace=True) # Top grossing coins if self.coin_metrics["top_grossing_enabled"]: informative_1d = informative_1d.merge(self.coin_metrics["tg_dataframe"], on="date", how="left") informative_1d["is_top_grossing"] = informative_1d.apply( lambda row: self.is_top_coin(metadata["pair"], row, self.coin_metrics["top_grossing_len"]), axis=1 ) column_names = [f"Coin #{i}" for i in range(1, self.coin_metrics["top_grossing_len"] + 1)] informative_1d.drop(columns=column_names, inplace=True) # Pivots ( informative_1d["pivot"], informative_1d["res1"], informative_1d["res2"], informative_1d["res3"], informative_1d["sup1"], informative_1d["sup2"], informative_1d["sup3"], ) = pivot_points(informative_1d, mode="fibonacci") # Smoothed Heikin-Ashi informative_1d["open_sha"], informative_1d["close_sha"], informative_1d["low_sha"] = HeikinAshi( informative_1d, smooth_inputs=True, smooth_outputs=False, length=10 ) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1d_indicators took: {tok - tik:0.4f} seconds.") return informative_1d def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.info_timeframe_1h) # EMA informative_1h["ema_12"] = ta.EMA(informative_1h, timeperiod=12) informative_1h["ema_15"] = ta.EMA(informative_1h, timeperiod=15) informative_1h["ema_20"] = ta.EMA(informative_1h, timeperiod=20) informative_1h["ema_25"] = ta.EMA(informative_1h, timeperiod=25) informative_1h["ema_26"] = ta.EMA(informative_1h, timeperiod=26) informative_1h["ema_35"] = ta.EMA(informative_1h, timeperiod=35) informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50) informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100) informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200) informative_1h["sma_200_dec_20"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(20) # RSI informative_1h["rsi_14"] = ta.RSI(informative_1h, timeperiod=14) # EWO informative_1h["ewo_sma"] = ewo_sma(informative_1h, 50, 200) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h["bb20_2_low"] = bollinger["lower"] informative_1h["bb20_2_mid"] = bollinger["mid"] informative_1h["bb20_2_upp"] = bollinger["upper"] # Chaikin Money Flow informative_1h["cmf"] = chaikin_money_flow(informative_1h, 20) # Williams %R informative_1h["r_480"] = williams_r(informative_1h, period=480) # CTI informative_1h["cti"] = pta.cti(informative_1h["close"], length=20) # CRSI (3, 2, 100) crsi_closechange = informative_1h["close"] / informative_1h["close"].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) informative_1h["crsi"] = ( ta.RSI(informative_1h["close"], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h["close"], 100) ) / 3 # Ichimoku ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) informative_1h["chikou_span"] = ichi["chikou_span"] informative_1h["tenkan_sen"] = ichi["tenkan_sen"] informative_1h["kijun_sen"] = ichi["kijun_sen"] informative_1h["senkou_a"] = ichi["senkou_span_a"] informative_1h["senkou_b"] = ichi["senkou_span_b"] informative_1h["leading_senkou_span_a"] = ichi["leading_senkou_span_a"] informative_1h["leading_senkou_span_b"] = ichi["leading_senkou_span_b"] informative_1h["chikou_span_greater"] = ( (informative_1h["chikou_span"] > informative_1h["senkou_a"]).shift(30).fillna(False) ) informative_1h.loc[:, "cloud_top"] = informative_1h.loc[:, ["senkou_a", "senkou_b"]].max(axis=1) # SSL ssl_down, ssl_up = SSLChannels(informative_1h, 10) informative_1h["ssl_down"] = ssl_down informative_1h["ssl_up"] = ssl_up # MOMDIV mom = momdiv(informative_1h) informative_1h["momdiv_buy"] = mom["momdiv_buy"] informative_1h["momdiv_sell"] = mom["momdiv_sell"] informative_1h["momdiv_coh"] = mom["momdiv_coh"] informative_1h["momdiv_col"] = mom["momdiv_col"] # Pump protections informative_1h["hl_pct_change_48"] = self.range_percent_change(informative_1h, "HL", 48) informative_1h["hl_pct_change_36"] = self.range_percent_change(informative_1h, "HL", 36) informative_1h["hl_pct_change_24"] = self.range_percent_change(informative_1h, "HL", 24) informative_1h["oc_pct_change_48"] = self.range_percent_change(informative_1h, "OC", 48) informative_1h["oc_pct_change_36"] = self.range_percent_change(informative_1h, "OC", 36) informative_1h["oc_pct_change_24"] = self.range_percent_change(informative_1h, "OC", 24) informative_1h["hl_pct_change_5"] = self.range_percent_change(informative_1h, "HL", 5) informative_1h["low_5"] = informative_1h["low"].shift().rolling(5).min() informative_1h["safe_pump_24_10"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_10_24, self.buy_pump_pull_threshold_10_24 ) informative_1h["safe_pump_36_10"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_10_36, self.buy_pump_pull_threshold_10_36 ) informative_1h["safe_pump_48_10"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_10_48, self.buy_pump_pull_threshold_10_48 ) informative_1h["safe_pump_24_20"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_20_24, self.buy_pump_pull_threshold_20_24 ) informative_1h["safe_pump_36_20"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_20_36, self.buy_pump_pull_threshold_20_36 ) informative_1h["safe_pump_48_20"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_20_48, self.buy_pump_pull_threshold_20_48 ) informative_1h["safe_pump_24_30"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_30_24, self.buy_pump_pull_threshold_30_24 ) informative_1h["safe_pump_36_30"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_30_36, self.buy_pump_pull_threshold_30_36 ) informative_1h["safe_pump_48_30"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_30_48, self.buy_pump_pull_threshold_30_48 ) informative_1h["safe_pump_24_40"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_40_24, self.buy_pump_pull_threshold_40_24 ) informative_1h["safe_pump_36_40"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_40_36, self.buy_pump_pull_threshold_40_36 ) informative_1h["safe_pump_48_40"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_40_48, self.buy_pump_pull_threshold_40_48 ) informative_1h["safe_pump_24_50"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_50_24, self.buy_pump_pull_threshold_50_24 ) informative_1h["safe_pump_36_50"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_50_36, self.buy_pump_pull_threshold_50_36 ) informative_1h["safe_pump_48_50"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_50_48, self.buy_pump_pull_threshold_50_48 ) informative_1h["safe_pump_24_60"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_60_24, self.buy_pump_pull_threshold_60_24 ) informative_1h["safe_pump_36_60"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_60_36, self.buy_pump_pull_threshold_60_36 ) informative_1h["safe_pump_48_60"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_60_48, self.buy_pump_pull_threshold_60_48 ) informative_1h["safe_pump_24_70"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_70_24, self.buy_pump_pull_threshold_70_24 ) informative_1h["safe_pump_36_70"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_70_36, self.buy_pump_pull_threshold_70_36 ) informative_1h["safe_pump_48_70"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_70_48, self.buy_pump_pull_threshold_70_48 ) informative_1h["safe_pump_24_80"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_80_24, self.buy_pump_pull_threshold_80_24 ) informative_1h["safe_pump_36_80"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_80_36, self.buy_pump_pull_threshold_80_36 ) informative_1h["safe_pump_48_80"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_80_48, self.buy_pump_pull_threshold_80_48 ) informative_1h["safe_pump_24_90"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_90_24, self.buy_pump_pull_threshold_90_24 ) informative_1h["safe_pump_36_90"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_90_36, self.buy_pump_pull_threshold_90_36 ) informative_1h["safe_pump_48_90"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_90_48, self.buy_pump_pull_threshold_90_48 ) informative_1h["safe_pump_24_100"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_100_24, self.buy_pump_pull_threshold_100_24 ) informative_1h["safe_pump_36_100"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_100_36, self.buy_pump_pull_threshold_100_36 ) informative_1h["safe_pump_48_100"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_100_48, self.buy_pump_pull_threshold_100_48 ) informative_1h["safe_pump_24_110"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_110_24, self.buy_pump_pull_threshold_110_24 ) informative_1h["safe_pump_36_110"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_110_36, self.buy_pump_pull_threshold_110_36 ) informative_1h["safe_pump_48_110"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_110_48, self.buy_pump_pull_threshold_110_48 ) informative_1h["safe_pump_24_120"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_120_24, self.buy_pump_pull_threshold_120_24 ) informative_1h["safe_pump_36_120"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_120_36, self.buy_pump_pull_threshold_120_36 ) informative_1h["safe_pump_48_120"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_120_48, self.buy_pump_pull_threshold_120_48 ) informative_1h["sell_pump_48_1"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_1 informative_1h["sell_pump_48_2"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_2 informative_1h["sell_pump_48_3"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_3 informative_1h["sell_pump_36_1"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_1 informative_1h["sell_pump_36_2"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_2 informative_1h["sell_pump_36_3"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_3 informative_1h["sell_pump_24_1"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_1 informative_1h["sell_pump_24_2"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_2 informative_1h["sell_pump_24_3"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_3 tok = time.perf_counter() log.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # BB 40 - STD2 bb_40_std2 = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2) dataframe["bb40_2_low"] = bb_40_std2["lower"] dataframe["bb40_2_mid"] = bb_40_std2["mid"] dataframe["bb40_2_delta"] = (bb_40_std2["mid"] - dataframe["bb40_2_low"]).abs() dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs() dataframe["tail"] = (dataframe["close"] - dataframe["bb40_2_low"]).abs() # BB 20 - STD2 bb_20_std2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb20_2_low"] = bb_20_std2["lower"] dataframe["bb20_2_mid"] = bb_20_std2["mid"] dataframe["bb20_2_upp"] = bb_20_std2["upper"] # EMA 200 dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) dataframe["ema_13"] = ta.EMA(dataframe, timeperiod=13) dataframe["ema_15"] = ta.EMA(dataframe, timeperiod=15) dataframe["ema_16"] = ta.EMA(dataframe, timeperiod=16) dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_25"] = ta.EMA(dataframe, timeperiod=25) dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26) dataframe["ema_35"] = ta.EMA(dataframe, timeperiod=35) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_100"] = ta.EMA(dataframe, timeperiod=100) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe["sma_5"] = ta.SMA(dataframe, timeperiod=5) dataframe["sma_15"] = ta.SMA(dataframe, timeperiod=15) dataframe["sma_20"] = ta.SMA(dataframe, timeperiod=20) dataframe["sma_30"] = ta.SMA(dataframe, timeperiod=30) dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["sma_200_dec_20"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20) dataframe["sma_200_dec_24"] = dataframe["sma_200"] < dataframe["sma_200"].shift(24) # MFI dataframe["mfi"] = ta.MFI(dataframe) # CMF dataframe["cmf"] = chaikin_money_flow(dataframe, 20) # EWO dataframe["ewo_sma"] = ewo_sma(dataframe, 50, 200) # RSI dataframe["rsi_4"] = ta.RSI(dataframe, timeperiod=4) dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_20"] = ta.RSI(dataframe, timeperiod=20) # Chopiness dataframe["chop"] = qtpylib.chopiness(dataframe, 14) # Zero-Lag EMA dataframe["zema_61"] = zema(dataframe, period=61) # Williams %R dataframe["r_14"] = williams_r(dataframe, period=14) dataframe["r_480"] = williams_r(dataframe, period=480) # Stochastic RSI stochrsi = ta.STOCHRSI(dataframe, timeperiod=96, fastk_period=3, fastd_period=3, fastd_matype=0) dataframe["stochrsi_fastk_96"] = stochrsi["fastk"] dataframe["stochrsi_fastd_96"] = stochrsi["fastd"] # Modified Elder Ray Index dataframe["moderi_32"] = moderi(dataframe, 32) dataframe["moderi_64"] = moderi(dataframe, 64) dataframe["moderi_96"] = moderi(dataframe, 96) # EMA of VWMA Oscillator dataframe["ema_vwma_osc_32"] = ema_vwma_osc(dataframe, 32) dataframe["ema_vwma_osc_64"] = ema_vwma_osc(dataframe, 64) dataframe["ema_vwma_osc_96"] = ema_vwma_osc(dataframe, 96) # hull dataframe["hull_75"] = hull(dataframe, 75) # CRSI (3, 2, 100) crsi_closechange = dataframe["close"] / dataframe["close"].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe["crsi"] = ( ta.RSI(dataframe["close"], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe["close"], 100) ) / 3 # zlema dataframe["zlema_68"] = zlema(dataframe, 68) # CTI dataframe["cti"] = pta.cti(dataframe["close"], length=20) # For sell checks dataframe["crossed_below_ema_12_26"] = qtpylib.crossed_below(dataframe["ema_12"], dataframe["ema_26"]) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe["pm"], dataframe["pmx"] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe["source"] = (dataframe["high"] + dataframe["low"] + dataframe["open"] + dataframe["close"]) / 4 dataframe["pmax_thresh"] = ta.EMA(dataframe["source"], timeperiod=9) dataframe["sma_21"] = ta.SMA(dataframe, timeperiod=21) dataframe["sma_68"] = ta.SMA(dataframe, timeperiod=68) dataframe["sma_75"] = ta.SMA(dataframe, timeperiod=75) # HLC3 dataframe["hlc3"] = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3 # CCI dataframe["cci"] = ta.CCI(dataframe, source="hlc3", timeperiod=20) # CCI Oscillator cci_36 = ta.CCI(dataframe, timeperiod=36) cci_36_max = cci_36.rolling(self.startup_candle_count).max() cci_36_min = cci_36.rolling(self.startup_candle_count).min() dataframe["cci_36_osc"] = (cci_36 / cci_36_max).where(cci_36 > 0, -cci_36 / cci_36_min) # MOMDIV mom = momdiv(dataframe) dataframe["momdiv_buy"] = mom["momdiv_buy"] dataframe["momdiv_sell"] = mom["momdiv_sell"] dataframe["momdiv_coh"] = mom["momdiv_coh"] dataframe["momdiv_col"] = mom["momdiv_col"] # Dip protection dataframe["tpct_change_0"] = self.top_percent_change(dataframe, 0) dataframe["tpct_change_2"] = self.top_percent_change(dataframe, 2) dataframe["tpct_change_12"] = self.top_percent_change(dataframe, 12) dataframe["tpct_change_144"] = self.top_percent_change(dataframe, 144) # Volume dataframe["volume_mean_4"] = dataframe["volume"].rolling(4).mean().shift(1) dataframe["volume_mean_30"] = dataframe["volume"].rolling(30).mean() if not self.config["runmode"].value in ("live", "dry_run"): # Backtest age filter dataframe["bt_agefilter_ok"] = False dataframe.loc[dataframe.index > (12 * 24 * self.bt_min_age_days), "bt_agefilter_ok"] = True else: # Exchange downtime protection dataframe["live_data_ok"] = dataframe["volume"].rolling(window=72, min_periods=72).min() > 0 tok = time.perf_counter() log.debug(f"[{metadata['pair']}] normal_tf_indicators took: {tok - tik:0.4f} seconds.") return dataframe def resampled_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) return dataframe def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ["date", "open", "high", "low", "close", "volume"] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] base_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) dataframe["not_downtrend"] = (dataframe["close"] > dataframe["close"].shift(2)) | (dataframe["rsi_14"] > 50) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ["date", "open", "high", "low", "close", "volume"] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] info_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def daily_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() # Indicators # ----------------------------------------------------------------------------------------- ( dataframe["pivot"], dataframe["res1"], dataframe["res2"], dataframe["res3"], dataframe["sup1"], dataframe["sup2"], dataframe["sup3"], ) = pivot_points(dataframe, mode="fibonacci") # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ["date", "open", "high", "low", "close", "volume"] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] daily_tf_btc_indicators took: {tok - tik:0.4f} seconds.") return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() """ --> BTC informative (5m/1h) ___________________________________________________________________________________________ """ if self.config["stake_currency"] in ["USDT", "BUSD", "USDC", "DAI", "TUSD", "PAX", "USD", "EUR", "GBP"]: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" if self.has_BTC_daily_tf: btc_daily_tf = self.dp.get_pair_dataframe(btc_info_pair, "1d") btc_daily_tf = self.daily_tf_btc_indicators(btc_daily_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_daily_tf, self.timeframe, "1d", ffill=True) drop_columns = [f"{s}_1d" for s in ["date", "open", "high", "low", "close", "volume"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.has_BTC_info_tf: btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.info_timeframe_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.info_timeframe_1h, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1h}" for s in ["date", "open", "high", "low", "close", "volume"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.has_BTC_base_tf: btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ["date", "open", "high", "low", "close", "volume"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) """ --> Informative timeframe ___________________________________________________________________________________________ """ if self.info_timeframe_1d != "none": informative_1d = self.informative_1d_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, self.info_timeframe_1d, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1d}" for s in ["date", "open", "high", "low", "close", "volume"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) if self.info_timeframe_1h != "none": informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.info_timeframe_1h, ffill=True) drop_columns = [f"{s}_{self.info_timeframe_1h}" for s in ["date"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) """ --> Resampled to another timeframe ___________________________________________________________________________________________ """ if self.res_timeframe != "none": resampled = resample_to_interval(dataframe, timeframe_to_minutes(self.res_timeframe)) resampled = self.resampled_tf_indicators(resampled, metadata) # Merge resampled info dataframe dataframe = resampled_merge(dataframe, resampled, fill_na=True) dataframe.rename(columns=lambda s: f"{s}_{self.res_timeframe}" if "resample_" in s else s, inplace=True) dataframe.rename( columns=lambda s: s.replace("resample_{}_".format(self.res_timeframe.replace("m", "")), ""), inplace=True ) drop_columns = [f"{s}_{self.res_timeframe}" for s in ["date"]] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) """ --> The indicators for the normal (5m) timeframe ___________________________________________________________________________________________ """ dataframe = self.normal_tf_indicators(dataframe, metadata) tok = time.perf_counter() log.debug(f"[{metadata['pair']}] Populate indicators took a total of: {tok - tik:0.4f} seconds.") return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, "buy_tag"] = "" for index in self.buy_protection_params: item_buy_protection_list = [True] global_buy_protection_params = self.buy_protection_params[index] if self.buy_params[f"buy_condition_{index}_enable"]: # Standard protections - Common to every condition # ----------------------------------------------------------------------------------------- if global_buy_protection_params["ema_fast"]: item_buy_protection_list.append( dataframe[f"ema_{global_buy_protection_params['ema_fast_len']}"] > dataframe["ema_200"] ) if global_buy_protection_params["ema_slow"]: item_buy_protection_list.append( dataframe[f"ema_{global_buy_protection_params['ema_slow_len']}_1h"] > dataframe["ema_200_1h"] ) if global_buy_protection_params["close_above_ema_fast"]: item_buy_protection_list.append( dataframe["close"] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_fast_len']}"] ) if global_buy_protection_params["close_above_ema_slow"]: item_buy_protection_list.append( dataframe["close"] > dataframe[f"ema_{global_buy_protection_params['close_above_ema_slow_len']}_1h"] ) if global_buy_protection_params["sma200_rising"]: item_buy_protection_list.append( dataframe["sma_200"] > dataframe["sma_200"].shift(int(global_buy_protection_params["sma200_rising_val"])) ) if global_buy_protection_params["sma200_1h_rising"]: item_buy_protection_list.append( dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(int(global_buy_protection_params["sma200_1h_rising_val"])) ) if global_buy_protection_params["safe_dips_threshold_0"] is not None: item_buy_protection_list.append( dataframe["tpct_change_0"] < global_buy_protection_params["safe_dips_threshold_0"] ) if global_buy_protection_params["safe_dips_threshold_2"] is not None: item_buy_protection_list.append( dataframe["tpct_change_2"] < global_buy_protection_params["safe_dips_threshold_2"] ) if global_buy_protection_params["safe_dips_threshold_12"] is not None: item_buy_protection_list.append( dataframe["tpct_change_12"] < global_buy_protection_params["safe_dips_threshold_12"] ) if global_buy_protection_params["safe_dips_threshold_144"] is not None: item_buy_protection_list.append( dataframe["tpct_change_144"] < global_buy_protection_params["safe_dips_threshold_144"] ) if global_buy_protection_params["safe_pump"]: item_buy_protection_list.append( dataframe[ f"safe_pump_{global_buy_protection_params['safe_pump_period']}_{global_buy_protection_params['safe_pump_type']}_1h" ] ) if global_buy_protection_params["btc_1h_not_downtrend"]: item_buy_protection_list.append(dataframe["btc_not_downtrend_1h"]) if global_buy_protection_params["close_over_pivot_type"] != "none": item_buy_protection_list.append( dataframe["close"] > dataframe[f"{global_buy_protection_params['close_over_pivot_type']}_1d"] * global_buy_protection_params["close_over_pivot_offset"] ) if global_buy_protection_params["close_under_pivot_type"] != "none": item_buy_protection_list.append( dataframe["close"] < dataframe[f"{global_buy_protection_params['close_under_pivot_type']}_1d"] * global_buy_protection_params["close_under_pivot_offset"] ) if not self.config["runmode"].value in ("live", "dry_run"): if self.has_bt_agefilter: item_buy_protection_list.append(dataframe["bt_agefilter_ok"]) else: if self.has_downtime_protection: item_buy_protection_list.append(dataframe["live_data_ok"]) # Buy conditions # ----------------------------------------------------------------------------------------- item_buy_logic = [] item_buy_logic.append(reduce(lambda x, y: x & y, item_buy_protection_list)) # Condition #1 if index == 1: # Non-Standard protections # Logic item_buy_logic.append( ((dataframe["close"] - dataframe["open"].rolling(12).min()) / dataframe["open"].rolling(12).min()) > self.buy_1_min_inc ) item_buy_logic.append(dataframe["rsi_14"] < self.buy_1_rsi_max) item_buy_logic.append(dataframe["r_14"] < self.buy_2_r_14_max) item_buy_logic.append(dataframe["mfi"] < self.buy_1_mfi_max) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_1_rsi_1h_min) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_1_rsi_1h_max) # Condition #2 elif index == 2: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < dataframe["rsi_14_1h"] - self.buy_2_rsi_1h_diff) item_buy_logic.append(dataframe["mfi"] < self.buy_2_mfi) item_buy_logic.append(dataframe["cti"] < self.buy_2_cti_max) item_buy_logic.append(dataframe["r_480"] > self.buy_2_r_480_min) item_buy_logic.append(dataframe["r_480"] < self.buy_2_r_480_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_2_cti_1h_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_2_volume)) # Condition #3 elif index == 3: # Non-Standard protections # Logic item_buy_logic.append(dataframe["bb40_2_low"].shift().gt(0)) item_buy_logic.append(dataframe["bb40_2_delta"].gt(dataframe["close"] * self.buy_3_bb40_bbdelta_close)) item_buy_logic.append(dataframe["closedelta"].gt(dataframe["close"] * self.buy_3_bb40_closedelta_close)) item_buy_logic.append(dataframe["tail"].lt(dataframe["bb40_2_delta"] * self.buy_3_bb40_tail_bbdelta)) item_buy_logic.append(dataframe["close"].lt(dataframe["bb40_2_low"].shift())) item_buy_logic.append(dataframe["close"].le(dataframe["close"].shift())) item_buy_logic.append(dataframe["cci_36_osc"] > self.buy_3_cci_36_osc_min) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_3_crsi_1h_min) item_buy_logic.append(dataframe["r_480_1h"] > self.buy_3_r_480_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_3_cti_1h_max) # Condition #4 elif index == 4: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_50"]) item_buy_logic.append(dataframe["close"] < self.buy_4_bb20_close_bblowerband * dataframe["bb20_2_low"]) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_30"].shift(1) * self.buy_4_bb20_volume)) item_buy_logic.append(dataframe["cti"] < self.buy_4_cti_max) # Condition #5 elif index == 5: # Non-Standard protections item_buy_logic.append(dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_5_ema_rel)) # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_5_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_5_bb_offset)) item_buy_logic.append(dataframe["cti"] < self.buy_5_cti_max) item_buy_logic.append(dataframe["rsi_14"] > self.buy_5_rsi_14_min) item_buy_logic.append(dataframe["mfi"] > self.buy_5_mfi_min) item_buy_logic.append(dataframe["r_14"] < self.buy_5_r_14_max) item_buy_logic.append(dataframe["r_14"].shift(1) < self.buy_5_r_14_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_5_crsi_1h_min) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_5_volume)) # Condition #6 elif index == 6: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_6_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_6_bb_offset)) item_buy_logic.append(dataframe["r_14"] < self.buy_6_r_14_max) item_buy_logic.append(dataframe["cti_1h"] > self.buy_6_cti_1h_min) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_6_crsi_1h_min) # Condition #7 elif index == 7: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_7_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_7_ma_offset) item_buy_logic.append(dataframe["cti"] < self.buy_7_cti_max) item_buy_logic.append(dataframe["rsi_14"] < self.buy_7_rsi_max) # Condition #8 elif index == 8: # Non-Standard protections item_buy_logic.append(dataframe["ema_20"] > dataframe["ema_50"]) item_buy_logic.append(dataframe["ema_15"] > dataframe["ema_100"]) item_buy_logic.append(dataframe["ema_200"] > dataframe["sma_200"]) # Logic item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_8_bb_offset)) item_buy_logic.append(dataframe["r_14"] < self.buy_8_r_14_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_8_cti_1h_max) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_8_r_480_1h_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_8_volume)) # Condition #9 elif index == 9: # Non-Standard protections item_buy_logic.append(dataframe["ema_50"] > dataframe["ema_200"]) # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_9_ma_offset) item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_9_bb_offset) item_buy_logic.append(dataframe["mfi"] < self.buy_9_mfi_max) item_buy_logic.append(dataframe["cti"] < self.buy_9_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_9_r_14_max) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_9_rsi_1h_min) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_9_rsi_1h_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_9_crsi_1h_min) # Condition #10 elif index == 10: # Non-Standard protections item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) # Logic item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_10_ma_offset_high) item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_10_bb_offset) item_buy_logic.append(dataframe["r_14"] < self.buy_10_r_14_max) item_buy_logic.append(dataframe["cti_1h"] > self.buy_10_cti_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_10_cti_1h_max) # Condition #11 elif index == 11: # Non-Standard protections # Logic item_buy_logic.append( ((dataframe["close"] - dataframe["open"].rolling(6).min()) / dataframe["open"].rolling(6).min()) > self.buy_11_min_inc ) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_11_ma_offset) item_buy_logic.append(dataframe["rsi_14"] < self.buy_11_rsi_max) item_buy_logic.append(dataframe["mfi"] < self.buy_11_mfi_max) item_buy_logic.append(dataframe["cci"] < self.buy_11_cci_max) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_11_rsi_1h_min) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_11_rsi_1h_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_11_cti_1h_max) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_11_r_480_1h_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_11_crsi_1h_min) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_12_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_12_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_12_rsi_max) item_buy_logic.append(dataframe["cti"] < self.buy_12_cti_max) # Condition #13 elif index == 13: # Non-Standard protections item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) # Logic item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_13_ma_offset) item_buy_logic.append(dataframe["cti"] < self.buy_13_cti_max) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_13_ewo_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_13_cti_1h_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_13_crsi_1h_min) # Condition #14 elif index == 14: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_14_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_14_bb_offset)) item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_14_ma_offset) item_buy_logic.append(dataframe["cti"] < self.buy_14_cti_max) # Condition #15 elif index == 15: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_15_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["rsi_14"] < self.buy_15_rsi_min) item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_15_ma_offset) item_buy_logic.append(dataframe["cti_1h"] > self.buy_15_cti_1h_min) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_16_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_16_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_16_rsi_max) item_buy_logic.append(dataframe["cti"] < self.buy_16_cti_max) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_17_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_17_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_17_cti_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_17_crsi_1h_min) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_17_volume)) # Condition #18 elif index == 18: # Non-Standard protections item_buy_logic.append(dataframe["sma_200"] > dataframe["sma_200"].shift(20)) item_buy_logic.append(dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(36)) # Logic item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_18_bb_offset)) item_buy_logic.append(dataframe["rsi_14"] < self.buy_18_rsi_max) item_buy_logic.append(dataframe["cti"] < self.buy_18_cti_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_18_cti_1h_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_18_volume)) # Condition #19 elif index == 19: # Non-Standard protections item_buy_logic.append(dataframe["moderi_32"] == True) item_buy_logic.append(dataframe["moderi_64"] == True) item_buy_logic.append(dataframe["moderi_96"] == True) # Logic item_buy_logic.append(dataframe["close"].shift(1) > dataframe["ema_100_1h"]) item_buy_logic.append(dataframe["low"] < dataframe["ema_100_1h"]) item_buy_logic.append(dataframe["close"] > dataframe["ema_100_1h"]) item_buy_logic.append(dataframe["chop"] < self.buy_19_chop_max) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_19_rsi_1h_min) # Condition #20 elif index == 20: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < self.buy_20_rsi_14_max) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_20_rsi_14_1h_max) item_buy_logic.append(dataframe["cti"] < self.buy_20_cti_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_20_volume)) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < self.buy_21_rsi_14_max) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_21_rsi_14_1h_max) item_buy_logic.append(dataframe["cti"] < self.buy_21_cti_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_21_volume)) # Condition #22 elif index == 22: # Non-Standard protections item_buy_logic.append(dataframe["ema_100_1h"] > dataframe["ema_100_1h"].shift(12)) item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(36)) # Logic item_buy_logic.append((dataframe["volume_mean_4"] * self.buy_22_volume) > dataframe["volume"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_22_ma_offset) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_22_bb_offset)) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_22_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_22_rsi_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_22_cti_max) item_buy_logic.append(dataframe["r_480"] < self.buy_22_r_480_max) item_buy_logic.append(dataframe["cti_1h"] > self.buy_22_cti_1h_min) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_23_bb_offset)) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_23_ewo_min) item_buy_logic.append(dataframe["cti"] < self.buy_23_cti_max) item_buy_logic.append(dataframe["rsi_14"] < self.buy_23_rsi_14_max) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_23_rsi_14_1h_max) item_buy_logic.append(dataframe["r_480_1h"] > self.buy_23_r_480_1h_min) item_buy_logic.append(dataframe["cti_1h"] < 0.92) # Condition #24 elif index == 24: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_12_1h"].shift(12) < dataframe["ema_35_1h"].shift(12)) item_buy_logic.append(dataframe["ema_12_1h"] > dataframe["ema_35_1h"]) item_buy_logic.append(dataframe["cmf_1h"].shift(12) < 0) item_buy_logic.append(dataframe["cmf_1h"] > 0) item_buy_logic.append(dataframe["rsi_14"] < self.buy_24_rsi_14_max) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_24_rsi_14_1h_min) # Condition #25 elif index == 25: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_20"] < dataframe["rsi_20"].shift()) item_buy_logic.append(dataframe["rsi_4"] < self.buy_25_rsi_4_max) item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_26_1h"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_15"] * self.buy_25_ma_offset) item_buy_logic.append(dataframe["cti"] < self.buy_25_cti_max) item_buy_logic.append(dataframe["cci"] < self.buy_25_cci_max) # Condition #26 elif index == 26: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < (dataframe["zema_61"] * self.buy_26_zema_low_offset)) item_buy_logic.append(dataframe["cti"] < self.buy_26_cti_max) item_buy_logic.append(dataframe["cci"] < self.buy_26_cci_max) item_buy_logic.append(dataframe["r_14"] < self.buy_26_r_14_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_26_cti_1h_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_26_volume)) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_buy_logic.append(dataframe["r_480"] < self.buy_27_wr_max) item_buy_logic.append(dataframe["r_14"] == self.buy_27_r_14) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_27_wr_1h_max) item_buy_logic.append(dataframe["rsi_14_1h"] + dataframe["rsi_14"] < self.buy_27_rsi_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_27_volume)) # Condition #28 elif index == 28: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_64"] == True) item_buy_logic.append(dataframe["close"] < dataframe["hull_75"] * self.buy_28_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_28_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_28_rsi_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_28_cti_max) item_buy_logic.append(dataframe["cti"].shift(1) < self.buy_28_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_28_r_14_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_28_cti_1h_max) # Condition #29 elif index == 29: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_64"] == True) item_buy_logic.append(dataframe["close"] < dataframe["hull_75"] * self.buy_29_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_29_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_29_cti_max) # Condition #30 elif index == 30: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_64"] == False) item_buy_logic.append(dataframe["close"] < dataframe["zlema_68"] * self.buy_30_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_30_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_30_rsi_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_30_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_30_r_14_max) # Condition #31 elif index == 31: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_64"] == False) item_buy_logic.append(dataframe["close"] < dataframe["zlema_68"] * self.buy_31_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_31_ewo_max) item_buy_logic.append(dataframe["r_14"] < self.buy_31_r_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_31_cti_max) # Condition #32 - Quick mode buy elif index == 32: # Non-Standard protections item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_25_1h"]) # Logic item_buy_logic.append(dataframe["rsi_20"] < dataframe["rsi_20"].shift(1)) item_buy_logic.append(dataframe["rsi_4"] < self.buy_32_rsi_4_max) item_buy_logic.append(dataframe["rsi_14"] > self.buy_32_rsi_14_min) item_buy_logic.append(dataframe["close"] < dataframe["sma_15"] * self.buy_32_ma_offset) item_buy_logic.append(dataframe["cti"] < self.buy_32_cti_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_32_crsi_1h_min) item_buy_logic.append(dataframe["crsi_1h"] < self.buy_32_crsi_1h_max) # Condition #33 - Quick mode buy elif index == 33: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < (dataframe["ema_13"] * self.buy_33_ma_offset)) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_33_ewo_min) item_buy_logic.append(dataframe["cti"] < self.buy_33_cti_max) item_buy_logic.append(dataframe["rsi_14"] < self.buy_33_rsi_max) item_buy_logic.append(dataframe["r_14"] < self.buy_33_r_14_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_33_cti_1h_max) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_33_volume)) # Condition #34 - Quick mode buy elif index == 34: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_13"] * self.buy_34_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_34_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_34_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_34_r_14_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_34_crsi_1h_min) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_34_volume)) # Condition #35 - PMAX0 buy elif index == 35: # Non-Standard protections # Logic item_buy_logic.append(dataframe["pm"] <= dataframe["pmax_thresh"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * self.buy_35_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_35_ewo_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_35_rsi_max) item_buy_logic.append(dataframe["cti"] < self.buy_35_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_35_r_14_max) # Condition #36 - PMAX1 buy elif index == 36: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["pm"] <= dataframe["pmax_thresh"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * self.buy_36_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_36_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_36_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_36_r_14_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_36_crsi_1h_min) # Condition #37 - Quick mode buy elif index == 37: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * self.buy_37_ma_offset) item_buy_logic.append( ((dataframe["close_1h"].shift(12) - dataframe["close_1h"]) / dataframe["close_1h"]) < self.buy_37_close_1h_max ) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_37_ewo_min) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_37_ewo_max) item_buy_logic.append(dataframe["rsi_14"] > self.buy_37_rsi_14_min) item_buy_logic.append(dataframe["rsi_14"] < self.buy_37_rsi_14_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_37_crsi_1h_min) item_buy_logic.append(dataframe["crsi_1h"] < self.buy_37_crsi_1h_max) item_buy_logic.append(dataframe["cti"] < self.buy_37_cti_max) item_buy_logic.append(dataframe["cti_1h"] < self.buy_37_cti_1h_max) item_buy_logic.append(dataframe["r_14"] < self.buy_37_r_14_max) # Condition #38 - PMAX3 buy elif index == 38: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["pm"] > dataframe["pmax_thresh"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * self.buy_38_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_38_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_38_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_38_r_14_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_38_crsi_1h_min) # Condition #39 - Ichimoku elif index == 39: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["tenkan_sen_1h"] > dataframe["kijun_sen_1h"]) item_buy_logic.append(dataframe["close"] > dataframe["cloud_top_1h"]) item_buy_logic.append(dataframe["leading_senkou_span_a_1h"] > dataframe["leading_senkou_span_b_1h"]) item_buy_logic.append(dataframe["chikou_span_greater_1h"]) item_buy_logic.append(dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"]) item_buy_logic.append(dataframe["close"] < dataframe["ssl_up_1h"]) item_buy_logic.append(dataframe["rsi_14_1h"] > dataframe["rsi_14_1h"].shift(12)) item_buy_logic.append(dataframe["cti"] < self.buy_39_cti_max) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_39_r_1h_max) item_buy_logic.append(dataframe["cti_1h"] > self.buy_39_cti_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_39_cti_1h_max) # Start of trend item_buy_logic.append( (dataframe["leading_senkou_span_a_1h"].shift(12) < dataframe["leading_senkou_span_b_1h"].shift(12)) ) # Condition #40 elif index == 40: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["momdiv_buy_1h"] == True) item_buy_logic.append(dataframe["cci"] < self.buy_40_cci_max) item_buy_logic.append(dataframe["rsi_14"] < self.buy_40_rsi_max) item_buy_logic.append(dataframe["r_14"] < self.buy_40_r_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_40_cti_max) # Condition #41 elif index == 41: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) item_buy_logic.append(dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * self.buy_41_ma_offset_high) item_buy_logic.append(dataframe["cti"] < self.buy_41_cti_max) item_buy_logic.append(dataframe["cci"] < self.buy_41_cci_max) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_41_ewo_1h_min) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_41_r_480_1h_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_41_crsi_1h_min) # Condition #42 elif index == 42: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) item_buy_logic.append(dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_42_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_42_bb_offset)) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_42_ewo_1h_min) item_buy_logic.append(dataframe["cti_1h"] > self.buy_42_cti_1h_min) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_42_r_480_1h_max) # Condition #43 elif index == 43: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) item_buy_logic.append(dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) item_buy_logic.append(dataframe["bb40_2_low"].shift().gt(0)) item_buy_logic.append(dataframe["bb40_2_delta"].gt(dataframe["close"] * self.buy_43_bb40_bbdelta_close)) item_buy_logic.append(dataframe["closedelta"].gt(dataframe["close"] * self.buy_43_bb40_closedelta_close)) item_buy_logic.append(dataframe["tail"].lt(dataframe["bb40_2_delta"] * self.buy_43_bb40_tail_bbdelta)) item_buy_logic.append(dataframe["close"].lt(dataframe["bb40_2_low"].shift())) item_buy_logic.append(dataframe["close"].le(dataframe["close"].shift())) item_buy_logic.append(dataframe["cti"] < self.buy_43_cti_max) item_buy_logic.append(dataframe["r_480"] > self.buy_43_r_480_min) item_buy_logic.append(dataframe["cti_1h"] > self.buy_43_cti_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_43_cti_1h_max) item_buy_logic.append(dataframe["r_480_1h"] > self.buy_43_r_480_1h_min) # Condition #44 elif index == 44: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < (dataframe["ema_16"] * self.buy_44_ma_offset)) item_buy_logic.append(dataframe["ewo_sma"] < self.buy_44_ewo_max) item_buy_logic.append(dataframe["cti"] < self.buy_44_cti_max) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_44_crsi_1h_min) # Condition #45 - Long mode elif index == 45: # Non-Standard protections # Logic item_buy_logic.append(dataframe["bb40_2_low"].shift().gt(0)) item_buy_logic.append(dataframe["bb40_2_delta"].gt(dataframe["close"] * self.buy_45_bb40_bbdelta_close)) item_buy_logic.append(dataframe["closedelta"].gt(dataframe["close"] * self.buy_45_bb40_closedelta_close)) item_buy_logic.append(dataframe["tail"].lt(dataframe["bb40_2_delta"] * self.buy_45_bb40_tail_bbdelta)) item_buy_logic.append(dataframe["close"].lt(dataframe["bb40_2_low"].shift())) item_buy_logic.append(dataframe["close"].le(dataframe["close"].shift())) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_45_ma_offset) item_buy_logic.append(dataframe["ewo_sma"] > self.buy_45_ewo_min) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_45_ewo_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_45_cti_1h_max) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_45_r_480_1h_max) # Condition #46 - Long mode elif index == 46: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ema_26"] > dataframe["ema_12"]) item_buy_logic.append( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_46_ema_open_mult) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_46_ewo_1h_min) item_buy_logic.append(dataframe["cti_1h"] > self.buy_46_cti_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_46_cti_1h_max) # Condition #47 - Long mode elif index == 47: # Non-Standard protections # Logic item_buy_logic.append(dataframe["ewo_sma"] > self.buy_47_ewo_min) item_buy_logic.append(dataframe["close"] < (dataframe["sma_30"] * self.buy_47_ma_offset)) item_buy_logic.append(dataframe["rsi_14"] < self.buy_47_rsi_14_max) item_buy_logic.append(dataframe["cti"] < self.buy_47_cti_max) item_buy_logic.append(dataframe["r_14"] < self.buy_47_r_14_max) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_47_ewo_1h_min) item_buy_logic.append(dataframe["cti_1h"] > self.buy_47_cti_1h_min) item_buy_logic.append(dataframe["cti_1h"] < self.buy_47_cti_1h_max) # Condition #48 - Uptrend mode elif index == 48: # Non-Standard protections item_buy_logic.append(dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) item_buy_logic.append(dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) item_buy_logic.append(dataframe["moderi_32"]) item_buy_logic.append(dataframe["moderi_64"]) item_buy_logic.append(dataframe["moderi_96"]) # Logic item_buy_logic.append(dataframe["ewo_sma"] > self.buy_48_ewo_min) item_buy_logic.append(dataframe["ewo_sma_1h"] > self.buy_48_ewo_1h_min) item_buy_logic.append(dataframe["r_480"] > self.buy_48_r_480_min) item_buy_logic.append(dataframe["r_480_1h"] > self.buy_48_r_480_1h_min) item_buy_logic.append(dataframe["r_480_1h"] < self.buy_48_r_480_1h_max) item_buy_logic.append(dataframe["r_480_1h"] > dataframe["r_480_1h"].shift(12)) item_buy_logic.append(dataframe["cti_1h"] > self.buy_48_cti_1h_min) item_buy_logic.append(dataframe["crsi_1h"] > self.buy_48_crsi_1h_min) item_buy_logic.append(dataframe["cti"].shift(1).rolling(12).min() < -0.5) item_buy_logic.append(dataframe["cti"].shift(1).rolling(12).max() < 0.0) item_buy_logic.append(dataframe["cti"].shift(1) < 0.0) item_buy_logic.append(dataframe["cti"] > 0.0) item_buy_logic.append(dataframe["volume"] > 0) item_buy = reduce(lambda x, y: x & y, item_buy_logic) dataframe.loc[item_buy, "buy_tag"] += f"{index} " conditions.append(item_buy) if conditions: dataframe.loc[:, "buy"] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, "sell"] = 0 return dataframe def confirm_trade_exit( self, pair: str, trade: "Trade", order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs, ) -> bool: """ Called right before placing a regular sell order. Timing for this function is critical, so avoid doing heavy computations or network requests in this method. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns True (always confirming). :param pair: Pair that's about to be sold. :param trade: trade object. :param order_type: Order type (as configured in order_types). usually limit or market. :param amount: Amount in quote currency. :param rate: Rate that's going to be used when using limit orders :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param sell_reason: Sell reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'sell_signal', 'force_sell', 'emergency_sell'] :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True is returned, then the sell-order is placed on the exchange. False aborts the process """ if self._should_hold_trade(trade, rate, sell_reason): return False self._remove_profit_target(pair) return True def _set_profit_target(self, pair: str, sell_reason: str, rate: float, current_time: "datetime"): self.target_profit_cache.data[pair] = { "rate": rate, "sell_reason": sell_reason, "time_profit_reached": current_time.isoformat(), } self.target_profit_cache.save() def _remove_profit_target(self, pair: str): if self.target_profit_cache is not None: self.target_profit_cache.data.pop(pair, None) self.target_profit_cache.save() def _should_hold_trade(self, trade: "Trade", rate: float, sell_reason: str) -> bool: if self.config["runmode"].value not in ("live", "dry_run"): return False if not self.holdSupportEnabled: return False # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop self.load_hold_trades_config() if not self.hold_trades_cache: # Cache hasn't been setup, likely because the corresponding file does not exist, sell return False if not self.hold_trades_cache.data: # We have no pairs we want to hold until profit, sell return False # By default, no hold should be done hold_trade = False trade_ids: dict = self.hold_trades_cache.data.get("trade_ids") if trade_ids and trade.id in trade_ids: trade_profit_ratio = trade_ids[trade.id] current_profit_ratio = trade.calc_profit_ratio(rate) if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio, ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio, ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True trade_pairs: dict = self.hold_trades_cache.data.get("trade_pairs") if trade_pairs and trade.pair in trade_pairs: trade_profit_ratio = trade_pairs[trade.pair] current_profit_ratio = trade.calc_profit_ratio(rate) if sell_reason == "force_sell": formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Force selling %s even though the current profit of %s < %s", trade, formatted_current_profit_ratio, formatted_profit_ratio, ) return False elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell formatted_profit_ratio = f"{trade_profit_ratio * 100}%" formatted_current_profit_ratio = f"{current_profit_ratio * 100}%" log.warning( "Selling %s because the current profit of %s >= %s", trade, formatted_current_profit_ratio, formatted_profit_ratio, ) return False # This pair is on the list to hold, and we haven't reached minimum profit, hold hold_trade = True return hold_trade # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe["close"] * 100 return smadif def ewo_sma(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.SMA(dataframe, timeperiod=sma1_length) sma2 = ta.SMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe["close"] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe["close"] - dataframe["low"]) - (dataframe["high"] - dataframe["close"])) / ( dataframe["high"] - dataframe["low"] ) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe["volume"] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe["volume"].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name="cmf") # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 # Volume Weighted Moving Average def vwma(dataframe: DataFrame, length: int = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe["close"] * dataframe["volume"] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe["volume"], timeperiod=length)) return vwma # Modified Elder Ray Index def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma)) return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend # Exponential moving average of a volume weighted simple moving average def ema_vwma_osc(dataframe, len_slow_ma): slow_ema = Series(ta.EMA(vwma(dataframe, len_slow_ma), len_slow_ma)) return ((slow_ema - slow_ema.shift(1)) / slow_ema.shift(1)) * 100 # zlema def zlema(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2)) if isinstance(dataframe, Series): ema_data = dataframe + (dataframe - dataframe.shift(lag)) else: ema_data = dataframe["close"] + (dataframe["close"] - dataframe["close"].shift(lag)) return ta.EMA(ema_data, timeperiod=timeperiod) # zlhull def zlhull(dataframe, timeperiod): lag = int(math.floor((timeperiod - 1) / 2)) if isinstance(dataframe, Series): wma_data = dataframe + (dataframe - dataframe.shift(lag)) else: wma_data = dataframe["close"] + (dataframe["close"] - dataframe["close"].shift(lag)) return ta.WMA( 2 * ta.WMA(wma_data, int(math.floor(timeperiod / 2))) - ta.WMA(wma_data, timeperiod), int(round(np.sqrt(timeperiod))), ) # hull def hull(dataframe, timeperiod): if isinstance(dataframe, Series): return ta.WMA( 2 * ta.WMA(dataframe, int(math.floor(timeperiod / 2))) - ta.WMA(dataframe, timeperiod), int(round(np.sqrt(timeperiod))), ) else: return ta.WMA( 2 * ta.WMA(dataframe["close"], int(math.floor(timeperiod / 2))) - ta.WMA(dataframe["close"], timeperiod), int(round(np.sqrt(timeperiod))), ) # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f"MA_{MAtype}_{length}" atr = f"ATR_{period}" pm = f"pm_{period}_{multiplier}_{length}_{MAtype}" pmx = f"pmX_{period}_{multiplier}_{length}_{MAtype}" # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df["basic_ub"] = mavalue + ((multiplier / 10) * df[atr]) df["basic_lb"] = mavalue - ((multiplier / 10) * df[atr]) basic_ub = df["basic_ub"].values final_ub = np.full(len(df), 0.00) basic_lb = df["basic_lb"].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = ( basic_ub[i] if (basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] ) final_lb[i] = ( basic_lb[i] if (basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] ) df["final_ub"] = final_ub df["final_lb"] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00 ) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), "down", "up"), np.NaN) return pm, pmx def calc_streaks(series: Series): # logic tables geq = series >= series.shift(1) # True if rising eq = series == series.shift(1) # True if equal logic_table = concat([geq, eq], axis=1) streaks = [0] # holds the streak duration, starts with 0 for row in logic_table.iloc[1:].itertuples(): # iterate through logic table if row[2]: # same value as before streaks.append(0) continue last_value = streaks[-1] if row[1]: # higher value than before streaks.append(last_value + 1 if last_value >= 0 else 1) # increase or reset to +1 else: # lower value than before streaks.append(last_value - 1 if last_value < 0 else -1) # decrease or reset to -1 return streaks # SSL Channels def SSLChannels(dataframe, length=7): ATR = ta.ATR(dataframe, timeperiod=14) smaHigh = dataframe["high"].rolling(length).mean() + ATR smaLow = dataframe["low"].rolling(length).mean() - ATR hlv = Series(np.where(dataframe["close"] > smaHigh, 1, np.where(dataframe["close"] < smaLow, -1, np.NAN))) hlv = hlv.ffill() sslDown = np.where(hlv < 0, smaHigh, smaLow) sslUp = np.where(hlv < 0, smaLow, smaHigh) return sslDown, sslUp def pivot_points(dataframe: DataFrame, mode="fibonacci") -> Series: hlc3_pivot = (dataframe["high"] + dataframe["low"] + dataframe["close"]).shift(1) / 3 hl_range = (dataframe["high"] - dataframe["low"]).shift(1) if mode == "simple": res1 = hlc3_pivot * 2 - dataframe["low"].shift(1) sup1 = hlc3_pivot * 2 - dataframe["high"].shift(1) res2 = hlc3_pivot + (dataframe["high"] - dataframe["low"]).shift() sup2 = hlc3_pivot - (dataframe["high"] - dataframe["low"]).shift() res3 = hlc3_pivot * 2 + (dataframe["high"] - 2 * dataframe["low"]).shift() sup3 = hlc3_pivot * 2 - (2 * dataframe["high"] - dataframe["low"]).shift() elif mode == "fibonacci": res1 = hlc3_pivot + 0.382 * hl_range sup1 = hlc3_pivot - 0.382 * hl_range res2 = hlc3_pivot + 0.618 * hl_range sup2 = hlc3_pivot - 0.618 * hl_range res3 = hlc3_pivot + 1 * hl_range sup3 = hlc3_pivot - 1 * hl_range return hlc3_pivot, res1, res2, res3, sup1, sup2, sup3 def HeikinAshi(dataframe, smooth_inputs=False, smooth_outputs=False, length=10): df = dataframe[["open", "close", "high", "low"]].copy().fillna(0) if smooth_inputs: df["open_s"] = ta.EMA(df["open"], timeframe=length) df["high_s"] = ta.EMA(df["high"], timeframe=length) df["low_s"] = ta.EMA(df["low"], timeframe=length) df["close_s"] = ta.EMA(df["close"], timeframe=length) open_ha = (df["open_s"].shift(1) + df["close_s"].shift(1)) / 2 high_ha = df.loc[:, ["high_s", "open_s", "close_s"]].max(axis=1) low_ha = df.loc[:, ["low_s", "open_s", "close_s"]].min(axis=1) close_ha = (df["open_s"] + df["high_s"] + df["low_s"] + df["close_s"]) / 4 else: open_ha = (df["open"].shift(1) + df["close"].shift(1)) / 2 high_ha = df.loc[:, ["high", "open", "close"]].max(axis=1) low_ha = df.loc[:, ["low", "open", "close"]].min(axis=1) close_ha = (df["open"] + df["high"] + df["low"] + df["close"]) / 4 open_ha = open_ha.fillna(0) high_ha = high_ha.fillna(0) low_ha = low_ha.fillna(0) close_ha = close_ha.fillna(0) if smooth_outputs: open_sha = ta.EMA(open_ha, timeframe=length) high_sha = ta.EMA(high_ha, timeframe=length) low_sha = ta.EMA(low_ha, timeframe=length) close_sha = ta.EMA(close_ha, timeframe=length) return open_sha, close_sha, low_sha else: return open_ha, close_ha, low_ha # Mom DIV def momdiv( dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30 ) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS(mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe["high"].rolling(lookback).max() ll = dataframe["low"].rolling(lookback).min() coh = dataframe["high"] >= hh col = dataframe["low"] <= ll df = DataFrame( { "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe["close"].index, ) return df class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass @staticmethod def rapidjson_load_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} @staticmethod def rapidjson_dump_kwargs(): return {"number_mode": rapidjson.NM_NATIVE} def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open("r") as rfh: try: data = rapidjson.load(rfh, **self.rapidjson_load_kwargs()) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing rapidjson.dump(self.data, self.path.open("w"), **self.rapidjson_dump_kwargs()) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): @staticmethod def rapidjson_load_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "object_hook": HoldsCache._object_hook, } @staticmethod def rapidjson_dump_kwargs(): return { "number_mode": rapidjson.NM_NATIVE, "mapping_mode": rapidjson.MM_COERCE_KEYS_TO_STRINGS, } def save(self): raise RuntimeError("The holds cache does not allow programatical save") def process_loaded_data(self, data): trade_ids = data.get("trade_ids") trade_pairs = data.get("trade_pairs") if not trade_ids and not trade_pairs: return data open_trades = {} for trade in Trade.get_trades_proxy(is_open=True): open_trades[trade.id] = open_trades[trade.pair] = trade r_trade_ids = {} if trade_ids: if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): if not isinstance(trade_id, int): log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path, ) if trade_id in open_trades: formatted_profit_ratio = f"{profit_ratio * 100}%" log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio, ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) else: # Initial Syntax profit_ratio = data.get("profit_ratio") if profit_ratio: if not isinstance(profit_ratio, float): log.error("The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path) else: profit_ratio = 0.005 formatted_profit_ratio = f"{profit_ratio * 100}%" for trade_id in trade_ids: if not isinstance(trade_id, int): log.error("The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, self.path) continue if trade_id in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio, ) r_trade_ids[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) r_trade_pairs = {} if trade_pairs: for trade_pair, profit_ratio in trade_pairs.items(): if not isinstance(trade_pair, str): log.error("The trade_pair(%s) defined under 'trade_pairs' in %s is not a string", trade_pair, self.path) continue if "/" not in trade_pair: log.error( "The trade_pair(%s) defined under 'trade_pairs' in %s does not look like " "a valid '/' formatted pair.", trade_pair, self.path, ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_pair %s in %s is not a float", profit_ratio, trade_pair, self.path, ) formatted_profit_ratio = f"{profit_ratio * 100}%" if trade_pair in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_pair], formatted_profit_ratio, ) else: log.warning( "The trade pair %s is configured to HOLD until the profit ratio of %s is met", trade_pair, formatted_profit_ratio, ) r_trade_pairs[trade_pair] = profit_ratio r_data = {} if r_trade_ids: r_data["trade_ids"] = r_trade_ids if r_trade_pairs: r_data["trade_pairs"] = r_trade_pairs return r_data @staticmethod def _object_hook(data): _data = {} for key, value in data.items(): try: key = int(key) except ValueError: pass _data[key] = value return _data