# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/8a40429c1eec29ed9169fb245ff836b9cfa907d3/user_data/strategies/NostalgiaForInfinityNext_ChangeToTower/NostalgiaForInfinityNext_ChangeToTower_V6.py # -*- coding: utf-8 -*- import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.misc import json_load from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter from freqtrade.exchange import timeframe_to_prev_date 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 pandas_ta as pta log = logging.getLogger(__name__) ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_exit_signal must set to true (or not set at all). ## ## exit_profit_only must set to false (or not set at all). ## ## ignore_roi_if_entry_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## HOLD SUPPORT ## ## In case you want to have SOME of the trades to only be sold when on profit, add a file named ## ## "hold-trades.json" in the same directory as this strategy. ## ## ## ## 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 ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## ## ########################################################################################################### class Github_vaskosmihaylov_nfi_custom_strategies__NostalgiaForInfinityNext_ChangeToTower_V6__20260422_193409(IStrategy): INTERFACE_VERSION = 2 plot_config = { "main_plot": {}, "subplots": { "buy tag": {"buy_tag": {"color": "green"}}, }, } # ROI table: tightened for faster profit capture & reduced exposure minimal_roi = { "0": 0.10, "20": 0.05, "45": 0.03, "90": 0.01, } stoploss = -0.07 # Tightened from -0.10, moderate compromise # Trailing stoploss - moderately tighter than original trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.008 # Moderate: between original 0.01 and previous 0.005 trailing_stop_positive_offset = 0.02 # Moderate: between original 0.03 and previous 0.015 use_custom_stoploss = True # Enable tiered custom stoploss # Optimal timeframe for the strategy. timeframe = "5m" res_timeframe = "none" info_timeframe = "1h" # BTC informative has_BTC_base_tf = False has_BTC_info_tf = True # Backtest Age Filter emulation has_bt_agefilter = False bt_min_age_days = 3 # Exchange Downtime protection has_downtime_protection = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = True # Restored: strategy relies on hold-for-recovery behavior ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = { "buy": "limit", "sell": "limit", "trailing_stop_loss": "limit", "stoploss": "limit", "stoploss_on_exchange": False, } ############################################################# 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": False, # Disabled: minimal protection filters "buy_condition_10_enable": True, "buy_condition_11_enable": False, # Disabled: weak protection, RSI-only "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": False, # Disabled: unprotected entries "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, # Disabled: no safe_dips/pump/sma200 "buy_condition_36_enable": False, # Disabled: no safe_dips/pump/sma200 "buy_condition_37_enable": False, # Disabled: no safe_dips/pump/sma200 "buy_condition_38_enable": False, # Disabled: no safe_dips/pump/sma200 "buy_condition_39_enable": False, # Disabled: no safe_dips/pump/sma200 "buy_condition_40_enable": False, # Disabled: no safe_dips/pump/sma200 ############# } 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, ############# } ############################################################# buy_protection_params = { 1: { "ema_fast": False, "ema_fast_len": "26", "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": True, "sma200_rising_val": "28", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips": False, "safe_dips_type": "80", "safe_pump": False, "safe_pump_type": "70", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 2: { "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": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips": True, "safe_dips_type": "50", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 3: { "ema_fast": True, "ema_fast_len": "100", "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": True, "safe_dips_type": "70", "safe_pump": True, "safe_pump_type": "100", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 4: { "ema_fast": True, "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": "20", "safe_dips": True, "safe_dips_type": "50", "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "48", "btc_1h_not_downtrend": False, }, 5: { "ema_fast": True, "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": True, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "30", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "50", "safe_pump": True, "safe_pump_type": "20", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "130", "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 8: { "ema_fast": False, "ema_fast_len": "50", "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": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips": True, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "10", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 10: { "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": "24", "safe_dips": True, "safe_dips_type": "120", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 11: { "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": False, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "130", "safe_pump": True, "safe_pump_type": "40", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "20", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "120", "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "130", "safe_pump": True, "safe_pump_type": "20", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "10", "safe_pump": True, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "120", "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": "50", "safe_dips": True, "safe_dips_type": "130", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "10", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "90", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "130", "safe_pump": True, "safe_pump_type": "110", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 23: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": True, "close_above_ema_fast_len": "200", "close_above_ema_slow": True, "close_above_ema_slow_len": "200", "sma200_rising": False, "sma200_rising_val": "50", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips": True, "safe_dips_type": "50", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "20", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": True, "sma200_rising_val": "20", "sma200_1h_rising": False, "sma200_1h_rising_val": "50", "safe_dips": False, "safe_dips_type": "10", "safe_pump": True, "safe_pump_type": "20", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 26: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "100", "close_above_ema_fast": True, "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": True, "safe_dips_type": "60", "safe_pump": True, "safe_pump_type": "100", "safe_pump_period": "48", "btc_1h_not_downtrend": False, }, 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": True, "safe_dips_type": "130", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "36", "btc_1h_not_downtrend": True, }, 28: { "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": False, "safe_dips_type": "50", "safe_pump": True, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": True, }, 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": False, "safe_dips_type": "50", "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 30: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "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": False, "safe_dips_type": "50", "safe_pump": False, "safe_pump_type": "110", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "110", "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "48", "btc_1h_not_downtrend": False, }, 32: { "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": True, "safe_dips_type": "120", "safe_pump": True, "safe_pump_type": "120", "safe_pump_period": "48", "btc_1h_not_downtrend": False, }, 33: { "ema_fast": False, "ema_fast_len": "50", "ema_slow": True, "ema_slow_len": "50", "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": True, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 35: { "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": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "24", "btc_1h_not_downtrend": False, }, 37: { "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": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "100", "safe_pump_period": "48", "btc_1h_not_downtrend": False, }, 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": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "10", "safe_pump_period": "36", "btc_1h_not_downtrend": False, }, 39: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": True, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": True, "close_above_ema_slow_len": "50", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips": False, "safe_dips_type": "100", "safe_pump": True, "safe_pump_type": "50", "safe_pump_period": "48", "btc_1h_not_downtrend": True, }, 40: { "ema_fast": False, "ema_fast_len": "100", "ema_slow": False, "ema_slow_len": "50", "close_above_ema_fast": False, "close_above_ema_fast_len": "50", "close_above_ema_slow": False, "close_above_ema_slow_len": "50", "sma200_rising": False, "sma200_rising_val": "30", "sma200_1h_rising": False, "sma200_1h_rising_val": "20", "safe_dips": False, "safe_dips_type": "100", "safe_pump": False, "safe_pump_type": "50", "safe_pump_period": "48", "btc_1h_not_downtrend": True, }, } # Strict dips - level 10 buy_dip_threshold_10_1 = DecimalParameter( 0.001, 0.05, default=0.015, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_10_2 = DecimalParameter( 0.01, 0.2, default=0.1, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_10_3 = DecimalParameter( 0.1, 0.3, default=0.24, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_10_4 = DecimalParameter( 0.3, 0.5, default=0.42, space="buy", decimals=3, optimize=False, load=True ) # Strict dips - level 20 buy_dip_threshold_20_1 = DecimalParameter( 0.001, 0.05, default=0.016, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_20_2 = DecimalParameter( 0.01, 0.2, default=0.11, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_20_3 = DecimalParameter( 0.1, 0.4, default=0.26, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_20_4 = DecimalParameter( 0.36, 0.56, default=0.44, space="buy", decimals=3, optimize=False, load=True ) # Strict dips - level 30 buy_dip_threshold_30_1 = DecimalParameter( 0.001, 0.05, default=0.018, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_30_2 = DecimalParameter( 0.01, 0.2, default=0.12, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_30_3 = DecimalParameter( 0.1, 0.4, default=0.28, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_30_4 = DecimalParameter( 0.36, 0.56, default=0.46, space="buy", decimals=3, optimize=False, load=True ) # Strict dips - level 40 buy_dip_threshold_40_1 = DecimalParameter( 0.001, 0.05, default=0.019, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_40_2 = DecimalParameter( 0.01, 0.2, default=0.13, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_40_3 = DecimalParameter( 0.1, 0.4, default=0.3, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_40_4 = DecimalParameter( 0.36, 0.56, default=0.48, space="buy", decimals=3, optimize=False, load=True ) # Normal dips - level 50 buy_dip_threshold_50_1 = DecimalParameter( 0.001, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_50_2 = DecimalParameter( 0.01, 0.2, default=0.14, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_50_3 = DecimalParameter( 0.05, 0.4, default=0.32, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_50_4 = DecimalParameter( 0.2, 0.5, default=0.5, space="buy", decimals=3, optimize=False, load=True ) # Normal dips - level 60 buy_dip_threshold_60_1 = DecimalParameter( 0.001, 0.05, default=0.022, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_60_2 = DecimalParameter( 0.1, 0.22, default=0.18, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_60_3 = DecimalParameter( 0.2, 0.4, default=0.34, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_60_4 = DecimalParameter( 0.4, 0.6, default=0.56, space="buy", decimals=3, optimize=False, load=True ) # Normal dips - level 70 buy_dip_threshold_70_1 = DecimalParameter( 0.001, 0.05, default=0.023, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_70_2 = DecimalParameter( 0.16, 0.28, default=0.2, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_70_3 = DecimalParameter( 0.2, 0.4, default=0.36, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_70_4 = DecimalParameter( 0.5, 0.7, default=0.6, space="buy", decimals=3, optimize=False, load=True ) # Normal dips - level 80 buy_dip_threshold_80_1 = DecimalParameter( 0.001, 0.05, default=0.024, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_80_2 = DecimalParameter( 0.16, 0.28, default=0.22, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_80_3 = DecimalParameter( 0.2, 0.4, default=0.38, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_80_4 = DecimalParameter( 0.5, 0.7, default=0.66, space="buy", decimals=3, optimize=False, load=True ) # Normal dips - level 70 buy_dip_threshold_90_1 = DecimalParameter( 0.001, 0.05, default=0.025, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_90_2 = DecimalParameter( 0.16, 0.28, default=0.23, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_90_3 = DecimalParameter( 0.3, 0.5, default=0.4, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_90_4 = DecimalParameter( 0.6, 0.8, default=0.7, space="buy", decimals=3, optimize=False, load=True ) # Loose dips - level 100 buy_dip_threshold_100_1 = DecimalParameter( 0.001, 0.05, default=0.026, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_100_2 = DecimalParameter( 0.16, 0.3, default=0.24, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_100_3 = DecimalParameter( 0.3, 0.5, default=0.42, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_100_4 = DecimalParameter( 0.6, 1.0, default=0.8, space="buy", decimals=3, optimize=False, load=True ) # Loose dips - level 110 buy_dip_threshold_110_1 = DecimalParameter( 0.001, 0.05, default=0.027, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_110_2 = DecimalParameter( 0.16, 0.3, default=0.26, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_110_3 = DecimalParameter( 0.3, 0.5, default=0.44, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_110_4 = DecimalParameter( 0.6, 1.0, default=0.84, space="buy", decimals=3, optimize=False, load=True ) # Loose dips - level 120 buy_dip_threshold_120_1 = DecimalParameter( 0.001, 0.05, default=0.028, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_120_2 = DecimalParameter( 0.16, 0.3, default=0.28, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_120_3 = DecimalParameter( 0.3, 0.5, default=0.46, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_120_4 = DecimalParameter( 0.6, 1.0, default=0.86, space="buy", decimals=3, optimize=False, load=True ) # Loose dips - level 130 buy_dip_threshold_130_1 = DecimalParameter( 0.001, 0.05, default=0.028, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_130_2 = DecimalParameter( 0.16, 0.34, default=0.3, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_130_3 = DecimalParameter( 0.36, 0.56, default=0.48, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_130_4 = DecimalParameter( 0.6, 1.0, default=0.9, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 10 buy_pump_pull_threshold_10_24 = DecimalParameter( 1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_10_24 = DecimalParameter( 0.4, 1.0, default=0.42, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 10 buy_pump_pull_threshold_10_36 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_10_36 = DecimalParameter( 0.4, 1.0, default=0.58, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 10 buy_pump_pull_threshold_10_48 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_10_48 = DecimalParameter( 0.4, 1.0, default=0.8, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 20 buy_pump_pull_threshold_20_24 = DecimalParameter( 1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_20_24 = DecimalParameter( 0.4, 1.0, default=0.46, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 20 buy_pump_pull_threshold_20_36 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_20_36 = DecimalParameter( 0.4, 1.0, default=0.6, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 20 buy_pump_pull_threshold_20_48 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_20_48 = DecimalParameter( 0.4, 1.0, default=0.81, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 30 buy_pump_pull_threshold_30_24 = DecimalParameter( 1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_30_24 = DecimalParameter( 0.4, 1.0, default=0.5, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 30 buy_pump_pull_threshold_30_36 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_30_36 = DecimalParameter( 0.4, 1.0, default=0.62, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 30 buy_pump_pull_threshold_30_48 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_30_48 = DecimalParameter( 0.4, 1.0, default=0.82, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 40 buy_pump_pull_threshold_40_24 = DecimalParameter( 1.5, 3.0, default=2.2, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_40_24 = DecimalParameter( 0.4, 1.0, default=0.54, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 40 buy_pump_pull_threshold_40_36 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_40_36 = DecimalParameter( 0.4, 1.0, default=0.63, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 40 buy_pump_pull_threshold_40_48 = DecimalParameter( 1.5, 3.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_40_48 = DecimalParameter( 0.4, 1.0, default=0.84, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 50 buy_pump_pull_threshold_50_24 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_50_24 = DecimalParameter( 0.4, 1.0, default=0.6, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 50 buy_pump_pull_threshold_50_36 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_50_36 = DecimalParameter( 0.4, 1.0, default=0.64, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 50 buy_pump_pull_threshold_50_48 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_50_48 = DecimalParameter( 0.4, 1.0, default=0.85, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 60 buy_pump_pull_threshold_60_24 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_60_24 = DecimalParameter( 0.4, 1.0, default=0.62, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 60 buy_pump_pull_threshold_60_36 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_60_36 = DecimalParameter( 0.4, 1.0, default=0.66, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 60 buy_pump_pull_threshold_60_48 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_60_48 = DecimalParameter( 0.4, 1.0, default=0.9, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 70 buy_pump_pull_threshold_70_24 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_70_24 = DecimalParameter( 0.4, 1.0, default=0.63, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 70 buy_pump_pull_threshold_70_36 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_70_36 = DecimalParameter( 0.4, 1.0, default=0.67, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 70 buy_pump_pull_threshold_70_48 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_70_48 = DecimalParameter( 0.4, 1.0, default=0.95, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 80 buy_pump_pull_threshold_80_24 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_80_24 = DecimalParameter( 0.4, 1.0, default=0.64, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 80 buy_pump_pull_threshold_80_36 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_80_36 = DecimalParameter( 0.4, 1.0, default=0.68, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 80 buy_pump_pull_threshold_80_48 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_80_48 = DecimalParameter( 0.8, 1.1, default=1.0, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 90 buy_pump_pull_threshold_90_24 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_90_24 = DecimalParameter( 0.4, 1.0, default=0.65, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 90 buy_pump_pull_threshold_90_36 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_90_36 = DecimalParameter( 0.4, 1.0, default=0.69, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 90 buy_pump_pull_threshold_90_48 = DecimalParameter( 1.5, 3.0, default=1.75, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_90_48 = DecimalParameter( 0.8, 1.2, default=1.1, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 100 buy_pump_pull_threshold_100_24 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_100_24 = DecimalParameter( 0.4, 1.0, default=0.66, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 100 buy_pump_pull_threshold_100_36 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_100_36 = DecimalParameter( 0.4, 1.0, default=0.7, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 100 buy_pump_pull_threshold_100_48 = DecimalParameter( 1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_100_48 = DecimalParameter( 0.4, 1.8, default=1.6, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 110 buy_pump_pull_threshold_110_24 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_110_24 = DecimalParameter( 0.4, 1.0, default=0.7, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 110 buy_pump_pull_threshold_110_36 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_110_36 = DecimalParameter( 0.4, 1.0, default=0.74, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 110 buy_pump_pull_threshold_110_48 = DecimalParameter( 1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_110_48 = DecimalParameter( 1.4, 2.0, default=1.8, space="buy", decimals=3, optimize=False, load=True ) # 24 hours - level 120 buy_pump_pull_threshold_120_24 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_120_24 = DecimalParameter( 0.4, 1.0, default=0.78, space="buy", decimals=3, optimize=False, load=True ) # 36 hours - level 120 buy_pump_pull_threshold_120_36 = DecimalParameter( 1.5, 3.0, default=1.7, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_120_36 = DecimalParameter( 0.4, 1.0, default=0.78, space="buy", decimals=3, optimize=False, load=True ) # 48 hours - level 120 buy_pump_pull_threshold_120_48 = DecimalParameter( 1.3, 2.0, default=1.4, space="buy", decimals=2, optimize=False, load=True ) buy_pump_threshold_120_48 = DecimalParameter( 1.4, 2.8, default=2.0, space="buy", decimals=3, optimize=False, load=True ) # 5 hours - level 10 buy_dump_protection_10_5 = DecimalParameter( 0.3, 0.8, default=0.4, space="buy", decimals=2, optimize=False, load=True ) # 5 hours - level 20 buy_dump_protection_20_5 = DecimalParameter( 0.3, 0.8, default=0.44, space="buy", decimals=2, optimize=False, load=True ) # 5 hours - level 30 buy_dump_protection_30_5 = DecimalParameter( 0.3, 0.8, default=0.50, space="buy", decimals=2, optimize=False, load=True ) # 5 hours - level 40 buy_dump_protection_40_5 = DecimalParameter( 0.3, 0.8, default=0.58, space="buy", decimals=2, optimize=False, load=True ) # 5 hours - level 50 buy_dump_protection_50_5 = DecimalParameter( 0.3, 0.8, default=0.66, space="buy", decimals=2, optimize=False, load=True ) # 5 hours - level 60 buy_dump_protection_60_5 = DecimalParameter( 0.3, 0.8, default=0.74, space="buy", decimals=2, optimize=False, load=True ) buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space="buy", decimals=3, optimize=False, load=True) buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space="buy", decimals=1, optimize=False, load=True) buy_mfi_1 = DecimalParameter(20.0, 40.0, default=50.0, space="buy", decimals=1, optimize=False, load=True) buy_cti_1 = DecimalParameter(-0.99, -0.5, default=-0.92, space="buy", decimals=2, optimize=False, load=True) buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=38.8, space="buy", decimals=1, optimize=False, load=True) buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space="buy", decimals=1, optimize=False, load=True) buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space="buy", decimals=3, optimize=False, load=True) buy_volume_2 = DecimalParameter(0.6, 6.0, default=1.6, space="buy", decimals=1, optimize=False, load=True) buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.045, space="buy", optimize=False, load=True) buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space="buy", optimize=False, load=True) buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space="buy", optimize=False, load=True) buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space="buy", decimals=3, optimize=False, load=True) buy_cti_3 = DecimalParameter(-0.99, -0.5, default=-0.5, space="buy", decimals=2, optimize=False, load=True) buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space="buy", optimize=False, load=True) buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space="buy", decimals=2, optimize=False, load=True) buy_cti_4 = DecimalParameter(-0.99, -0.5, default=-0.8, space="buy", decimals=2, optimize=False, load=True) buy_ema_open_mult_5 = DecimalParameter( 0.016, 0.03, default=0.018, space="buy", decimals=3, optimize=False, load=True ) buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.996, space="buy", decimals=3, optimize=False, load=True) buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.915, space="buy", decimals=3, optimize=False, load=True) buy_cti_5 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True) buy_volume_5 = DecimalParameter(0.6, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True) buy_ema_open_mult_6 = DecimalParameter( 0.02, 0.03, default=0.021, space="buy", decimals=3, optimize=False, load=True ) buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.976, space="buy", decimals=3, optimize=False, load=True) buy_ema_open_mult_7 = DecimalParameter( 0.02, 0.04, default=0.030, space="buy", decimals=3, optimize=False, load=True ) buy_cti_7 = DecimalParameter(-0.99, -0.5, default=-0.89, space="buy", decimals=2, optimize=False, load=True) buy_cti_8 = DecimalParameter(-0.99, -0.5, default=-0.88, space="buy", decimals=2, optimize=False, load=True) buy_rsi_8 = DecimalParameter(20.0, 50.0, default=40.0, space="buy", decimals=1, optimize=False, load=True) buy_bb_offset_8 = DecimalParameter(0.98, 1.0, default=0.99, space="buy", decimals=3, optimize=False, load=True) buy_rsi_1h_8 = DecimalParameter(40.0, 66.0, default=64.0, space="buy", decimals=1, optimize=False, load=True) buy_volume_8 = DecimalParameter(0.6, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True) buy_ma_offset_9 = DecimalParameter(0.91, 0.94, default=0.968, space="buy", decimals=3, optimize=False, load=True) buy_bb_offset_9 = DecimalParameter(0.96, 0.98, default=0.942, space="buy", decimals=3, optimize=False, load=True) buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space="buy", decimals=1, optimize=False, load=True) buy_mfi_9 = DecimalParameter(36.0, 56.0, default=50.0, space="buy", decimals=1, optimize=False, load=True) buy_ma_offset_10 = DecimalParameter(0.94, 0.99, default=0.98, space="buy", decimals=3, optimize=False, load=True) buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.972, space="buy", decimals=3, optimize=False, load=True) buy_rsi_1h_10 = DecimalParameter(30.0, 60.0, default=50.0, space="buy", decimals=1, optimize=False, load=True) buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.946, space="buy", decimals=3, optimize=False, load=True) buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.038, space="buy", decimals=3, optimize=False, load=True) buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=46.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_11 = DecimalParameter(34.0, 50.0, default=38.0, space="buy", decimals=1, optimize=False, load=True) buy_mfi_11 = DecimalParameter(30.0, 46.0, default=36.0, space="buy", decimals=1, optimize=False, load=True) buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.921, space="buy", decimals=3, optimize=False, load=True) buy_rsi_12 = DecimalParameter(26.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True) buy_ewo_12 = DecimalParameter(1.0, 6.0, default=1.8, space="buy", decimals=1, optimize=False, load=True) buy_cti_12 = DecimalParameter(-0.99, -0.5, default=-0.7, space="buy", decimals=2, optimize=False, load=True) buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.99, space="buy", decimals=3, optimize=False, load=True) buy_cti_13 = DecimalParameter(-0.99, -0.5, default=-0.82, space="buy", decimals=2, optimize=False, load=True) buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-9.0, space="buy", decimals=1, optimize=False, load=True) buy_ema_open_mult_14 = DecimalParameter( 0.01, 0.03, default=0.014, space="buy", decimals=3, optimize=False, load=True ) buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.988, space="buy", decimals=3, optimize=False, load=True) buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.945, space="buy", decimals=3, optimize=False, load=True) buy_cti_14 = DecimalParameter(-0.99, -0.5, default=-0.86, space="buy", decimals=2, optimize=False, load=True) buy_ema_open_mult_15 = DecimalParameter( 0.01, 0.03, default=0.024, space="buy", decimals=3, optimize=False, load=True ) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.958, space="buy", decimals=3, optimize=False, load=True) buy_rsi_15 = DecimalParameter(20.0, 36.0, default=28.0, space="buy", decimals=1, optimize=False, load=True) buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.974, space="buy", decimals=3, optimize=False, load=True) buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.953, space="buy", decimals=3, optimize=False, load=True) buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space="buy", decimals=1, optimize=False, load=True) buy_ewo_16 = DecimalParameter(2.0, 6.0, default=2.8, space="buy", decimals=1, optimize=False, load=True) buy_cti_16 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True) buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.99, space="buy", decimals=3, optimize=False, load=True) buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-9.4, space="buy", decimals=1, optimize=False, load=True) buy_cti_17 = DecimalParameter(-0.99, -0.5, default=-0.96, space="buy", decimals=2, optimize=False, load=True) buy_volume_17 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_18 = DecimalParameter(20.0, 36.0, default=33.0, space="buy", decimals=1, optimize=False, load=True) buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.986, space="buy", decimals=3, optimize=False, load=True) buy_volume_18 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_cti_18 = DecimalParameter(-0.99, -0.5, default=-0.86, space="buy", decimals=2, optimize=False, load=True) buy_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=30.0, space="buy", decimals=1, optimize=False, load=True) buy_chop_max_19 = DecimalParameter(20.0, 60.0, default=21.3, space="buy", decimals=1, optimize=False, load=True) buy_rsi_20 = DecimalParameter(20.0, 36.0, default=36.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=16.0, space="buy", decimals=1, optimize=False, load=True) buy_cti_20 = DecimalParameter(-0.99, -0.5, default=-0.84, space="buy", decimals=2, optimize=False, load=True) buy_volume_20 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_21 = DecimalParameter(10.0, 28.0, default=14.0, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True) buy_cti_21 = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True) buy_volume_21 = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_volume_22 = DecimalParameter(0.5, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_bb_offset_22 = DecimalParameter(0.98, 1.0, default=0.984, space="buy", decimals=3, optimize=False, load=True) buy_ma_offset_22 = DecimalParameter(0.93, 0.98, default=0.942, space="buy", decimals=3, optimize=False, load=True) buy_ewo_22 = DecimalParameter(2.0, 10.0, default=5.8, space="buy", decimals=1, optimize=False, load=True) buy_rsi_22 = DecimalParameter(26.0, 56.0, default=36.0, space="buy", decimals=1, optimize=False, load=True) buy_bb_offset_23 = DecimalParameter(0.97, 1.0, default=0.985, space="buy", decimals=3, optimize=False, load=True) buy_ewo_23 = DecimalParameter(2.0, 10.0, default=6.2, space="buy", decimals=1, optimize=False, load=True) buy_rsi_23 = DecimalParameter(20.0, 40.0, default=32.4, space="buy", decimals=1, optimize=False, load=True) buy_rsi_1h_23 = DecimalParameter(60.0, 80.0, default=70.0, space="buy", decimals=1, optimize=False, load=True) buy_24_rsi_max = DecimalParameter(26.0, 60.0, default=50.0, space="buy", decimals=1, optimize=False, load=True) buy_24_rsi_1h_min = DecimalParameter(40.0, 90.0, default=66.9, space="buy", decimals=1, optimize=False, load=True) buy_25_ma_offset = DecimalParameter(0.90, 0.99, default=0.922, space="buy", optimize=False, load=True) buy_25_rsi_4 = DecimalParameter(26.0, 40.0, default=38.0, space="buy", decimals=1, optimize=False, load=True) buy_25_cti = DecimalParameter(-0.99, -0.4, default=-0.76, space="buy", decimals=2, optimize=False, load=True) buy_26_zema_low_offset = DecimalParameter(0.90, 0.99, default=0.932, space="buy", optimize=False, load=True) buy_26_cti = DecimalParameter(-0.99, -0.4, default=-0.82, space="buy", decimals=2, optimize=False, load=True) buy_26_volume = DecimalParameter(0.6, 6.0, default=1.2, space="buy", decimals=1, optimize=False, load=True) buy_27_wr_max = DecimalParameter(90, 99, default=90.0, space="buy", decimals=1, optimize=False, load=True) buy_27_wr_1h_max = DecimalParameter(90, 99, default=90.0, space="buy", decimals=1, optimize=False, load=True) buy_27_rsi_max = DecimalParameter(40, 70, default=50, space="buy", decimals=0, optimize=False, load=True) buy_27_cti = DecimalParameter(-0.99, -0.4, default=-0.93, space="buy", decimals=2, optimize=False, load=True) buy_27_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_28_ma_offset = DecimalParameter(0.90, 0.99, default=0.97, space="buy", optimize=False, load=True) buy_28_ewo = DecimalParameter(2.0, 14.0, default=7.2, space="buy", decimals=1, optimize=False, load=True) buy_28_rsi = DecimalParameter(24.0, 44.0, default=32.5, space="buy", decimals=1, optimize=False, load=True) buy_28_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True) buy_29_ma_offset = DecimalParameter(0.90, 0.99, default=0.94, space="buy", optimize=False, load=True) buy_29_ewo = DecimalParameter(-14.0, -2.0, default=-4.0, space="buy", decimals=1, optimize=False, load=True) buy_29_cti = DecimalParameter(-0.99, -0.4, default=-0.95, space="buy", decimals=2, optimize=False, load=True) buy_30_ma_offset = DecimalParameter(0.90, 0.99, default=0.97, space="buy", optimize=False, load=True) buy_30_ewo = DecimalParameter(2.0, 14.0, default=7.4, space="buy", decimals=1, optimize=False, load=True) buy_30_rsi = DecimalParameter(24.0, 48.0, default=40.0, space="buy", decimals=1, optimize=False, load=True) buy_30_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space="buy", decimals=2, optimize=False, load=True) buy_31_ma_offset = DecimalParameter(0.90, 0.99, default=0.94, space="buy", optimize=False, load=True) buy_31_ewo = DecimalParameter(-22.0, -8.0, default=-19.0, space="buy", decimals=1, optimize=False, load=True) buy_31_wr = DecimalParameter(-99.9, -95.0, default=-98.4, space="buy", decimals=1, optimize=False, load=True) buy_32_ma_offset = DecimalParameter(0.90, 0.99, default=0.934, space="buy", optimize=False, load=True) buy_32_dip = DecimalParameter(0.001, 0.02, default=0.005, space="buy", decimals=3, optimize=False, load=True) buy_32_rsi = DecimalParameter(24.0, 50.0, default=46.0, space="buy", decimals=1, optimize=False, load=True) buy_32_cti = DecimalParameter(-0.99, -0.4, default=-0.8, space="buy", decimals=2, optimize=False, load=True) buy_33_ma_offset = DecimalParameter(0.90, 0.99, default=0.988, space="buy", optimize=False, load=True) buy_33_rsi = DecimalParameter(24.0, 50.0, default=32.0, space="buy", decimals=1, optimize=False, load=True) buy_33_cti = DecimalParameter(-0.99, -0.4, default=-0.9, space="buy", decimals=2, optimize=False, load=True) buy_33_ewo = DecimalParameter(2.0, 14.0, default=6.5, space="buy", decimals=1, optimize=False, load=True) buy_33_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) buy_34_ma_offset = DecimalParameter(0.90, 0.99, default=0.93, space="buy", optimize=False, load=True) buy_34_dip = DecimalParameter(0.001, 0.02, default=0.005, space="buy", decimals=3, optimize=False, load=True) buy_34_ewo = DecimalParameter(-24.0, -5.0, default=-6.0, space="buy", decimals=1, optimize=False, load=True) buy_34_cti = DecimalParameter(-0.99, -0.4, default=-0.88, space="buy", decimals=2, optimize=False, load=True) buy_34_volume = DecimalParameter(0.6, 6.0, default=2.0, space="buy", decimals=1, optimize=False, load=True) # Sell sell_condition_1_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_2_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_3_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_4_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_5_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_6_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_7_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) sell_condition_8_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) # 48h for pump sell checks sell_pump_threshold_48_1 = DecimalParameter( 0.5, 1.2, default=0.9, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_48_2 = DecimalParameter( 0.4, 0.9, default=0.7, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_48_3 = DecimalParameter( 0.3, 0.7, default=0.5, space="sell", decimals=2, optimize=False, load=True ) # 36h for pump sell checks sell_pump_threshold_36_1 = DecimalParameter( 0.5, 0.9, default=0.72, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_36_2 = DecimalParameter( 3.0, 6.0, default=4.0, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_36_3 = DecimalParameter( 0.8, 1.6, default=1.0, space="sell", decimals=2, optimize=False, load=True ) # 24h for pump sell checks sell_pump_threshold_24_1 = DecimalParameter( 0.5, 0.9, default=0.68, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_24_2 = DecimalParameter( 0.3, 0.6, default=0.62, space="sell", decimals=2, optimize=False, load=True ) sell_pump_threshold_24_3 = DecimalParameter( 0.2, 0.5, default=0.88, space="sell", decimals=2, optimize=False, load=True ) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space="sell", decimals=1, optimize=False, load=True) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space="sell", decimals=1, optimize=False, load=True) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space="sell", decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_4 = DecimalParameter( 72.0, 84.0, default=73.4, space="sell", decimals=1, optimize=False, load=True ) sell_dual_rsi_rsi_1h_4 = DecimalParameter( 78.0, 92.0, default=79.6, space="sell", decimals=1, optimize=False, load=True ) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space="sell", optimize=False, load=True) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space="sell", optimize=False, load=True) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space="sell", decimals=1, optimize=False, load=True) sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space="sell", decimals=1, optimize=False, load=True) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space="sell", decimals=3, optimize=False, load=True) # Profit over EMA200 sell_custom_profit_bull_0 = DecimalParameter( 0.01, 0.1, default=0.012, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_0 = DecimalParameter( 30.0, 40.0, default=34.0, space="sell", decimals=3, optimize=False, load=True ) sell_custom_profit_bull_1 = DecimalParameter( 0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_1 = DecimalParameter( 30.0, 50.0, default=35.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_2 = DecimalParameter( 0.01, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_2 = DecimalParameter( 30.0, 50.0, default=36.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_3 = DecimalParameter( 0.01, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_3 = DecimalParameter( 30.0, 50.0, default=37.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_4 = DecimalParameter( 0.01, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_4 = DecimalParameter( 35.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_5 = DecimalParameter( 0.01, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_5 = DecimalParameter( 35.0, 50.0, default=49.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_6 = DecimalParameter( 0.01, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_6 = DecimalParameter( 38.0, 55.0, default=50.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_7 = DecimalParameter( 0.01, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_7 = DecimalParameter( 40.0, 58.0, default=54.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_8 = DecimalParameter( 0.06, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_8 = DecimalParameter( 40.0, 50.0, default=50.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_9 = DecimalParameter( 0.05, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_9 = DecimalParameter( 40.0, 60.0, default=46.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_10 = DecimalParameter( 0.1, 0.14, default=0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_10 = DecimalParameter( 38.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bull_11 = DecimalParameter( 0.16, 0.45, default=0.20, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bull_11 = DecimalParameter( 28.0, 40.0, default=30.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_0 = DecimalParameter( 0.01, 0.1, default=0.012, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_0 = DecimalParameter( 30.0, 40.0, default=34.0, space="sell", decimals=3, optimize=False, load=True ) sell_custom_profit_bear_1 = DecimalParameter( 0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_1 = DecimalParameter( 30.0, 50.0, default=35.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_2 = DecimalParameter( 0.01, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_2 = DecimalParameter( 30.0, 50.0, default=37.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_3 = DecimalParameter( 0.01, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_3 = DecimalParameter( 30.0, 50.0, default=44.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_4 = DecimalParameter( 0.01, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_4 = DecimalParameter( 35.0, 50.0, default=48.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_5 = DecimalParameter( 0.01, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_5 = DecimalParameter( 35.0, 50.0, default=50.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_rsi_over_bear_5 = DecimalParameter( 70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_6 = DecimalParameter( 0.01, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_6 = DecimalParameter( 38.0, 55.0, default=52.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_rsi_over_bear_6 = DecimalParameter( 70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_7 = DecimalParameter( 0.01, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_7 = DecimalParameter( 40.0, 58.0, default=54.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_rsi_over_bear_7 = DecimalParameter( 70.0, 85.0, default=80.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_8 = DecimalParameter( 0.06, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_8 = DecimalParameter( 40.0, 50.0, default=52.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_rsi_over_bear_8 = DecimalParameter( 70.0, 85.0, default=82.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_9 = DecimalParameter( 0.05, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_9 = DecimalParameter( 40.0, 60.0, default=50.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_10 = DecimalParameter( 0.1, 0.14, default=0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_10 = DecimalParameter( 38.0, 50.0, default=42.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_profit_bear_11 = DecimalParameter( 0.16, 0.45, default=0.20, space="sell", decimals=3, optimize=False, load=True ) sell_custom_rsi_under_bear_11 = DecimalParameter( 28.0, 40.0, default=30.0, space="sell", decimals=2, optimize=False, load=True ) # Profit under EMA200 sell_custom_under_profit_bull_0 = DecimalParameter( 0.01, 0.4, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_0 = DecimalParameter( 28.0, 40.0, default=38.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_1 = DecimalParameter( 0.01, 0.10, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_1 = DecimalParameter( 36.0, 60.0, default=54.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_2 = DecimalParameter( 0.01, 0.10, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_2 = DecimalParameter( 46.0, 66.0, default=55.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_3 = DecimalParameter( 0.01, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_3 = DecimalParameter( 50.0, 68.0, default=56.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_4 = DecimalParameter( 0.02, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_4 = DecimalParameter( 50.0, 68.0, default=57.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_5 = DecimalParameter( 0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_5 = DecimalParameter( 46.0, 62.0, default=58.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_6 = DecimalParameter( 0.03, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_6 = DecimalParameter( 44.0, 60.0, default=48.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_7 = DecimalParameter( 0.04, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_7 = DecimalParameter( 46.0, 60.0, default=44.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_8 = DecimalParameter( 0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_8 = DecimalParameter( 40.0, 58.0, default=42.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_9 = DecimalParameter( 0.08, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_9 = DecimalParameter( 40.0, 60.0, default=38.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_10 = DecimalParameter( 0.1, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_10 = DecimalParameter( 30.0, 50.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bull_11 = DecimalParameter( 0.16, 0.3, default=0.2, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bull_11 = DecimalParameter( 24.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_0 = DecimalParameter( 0.01, 0.4, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_0 = DecimalParameter( 28.0, 40.0, default=38.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_1 = DecimalParameter( 0.01, 0.10, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_1 = DecimalParameter( 36.0, 60.0, default=59.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_2 = DecimalParameter( 0.01, 0.10, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_2 = DecimalParameter( 46.0, 66.0, default=60.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_3 = DecimalParameter( 0.01, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_3 = DecimalParameter( 50.0, 68.0, default=61.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_4 = DecimalParameter( 0.02, 0.1, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_4 = DecimalParameter( 50.0, 68.0, default=60.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_5 = DecimalParameter( 0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_5 = DecimalParameter( 46.0, 62.0, default=58.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_rsi_over_bear_5 = DecimalParameter( 70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_under_profit_bear_6 = DecimalParameter( 0.03, 0.1, default=0.07, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_6 = DecimalParameter( 44.0, 60.0, default=50.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_rsi_over_bear_6 = DecimalParameter( 70.0, 85.0, default=78.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_under_profit_bear_7 = DecimalParameter( 0.04, 0.1, default=0.08, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_7 = DecimalParameter( 46.0, 60.0, default=46.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_rsi_over_bear_7 = DecimalParameter( 70.0, 85.0, default=80.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_under_profit_bear_8 = DecimalParameter( 0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_8 = DecimalParameter( 40.0, 58.0, default=42.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_rsi_over_bear_8 = DecimalParameter( 70.0, 85.0, default=82.0, space="sell", decimals=2, optimize=False, load=True ) sell_custom_under_profit_bear_9 = DecimalParameter( 0.08, 0.14, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_9 = DecimalParameter( 40.0, 60.0, default=36.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_10 = DecimalParameter( 0.1, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_10 = DecimalParameter( 30.0, 50.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_under_profit_bear_11 = DecimalParameter( 0.16, 0.3, default=0.2, space="sell", decimals=3, optimize=False, load=True ) sell_custom_under_rsi_under_bear_11 = DecimalParameter( 24.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True ) # Profit targets for pumped pairs 48h 1 sell_custom_pump_profit_1_1 = DecimalParameter( 0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_1_1 = DecimalParameter( 26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_1_2 = DecimalParameter( 0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_1_2 = DecimalParameter( 36.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_1_3 = DecimalParameter( 0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_1_3 = DecimalParameter( 38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_1_4 = DecimalParameter( 0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_1_4 = DecimalParameter( 36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_1_5 = DecimalParameter( 0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_1_5 = DecimalParameter( 20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True ) # Profit targets for pumped pairs 36h 1 sell_custom_pump_profit_2_1 = DecimalParameter( 0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_2_1 = DecimalParameter( 26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_2_2 = DecimalParameter( 0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_2_2 = DecimalParameter( 36.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_2_3 = DecimalParameter( 0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_2_3 = DecimalParameter( 38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_2_4 = DecimalParameter( 0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_2_4 = DecimalParameter( 36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_2_5 = DecimalParameter( 0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_2_5 = DecimalParameter( 20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True ) # Profit targets for pumped pairs 24h 1 sell_custom_pump_profit_3_1 = DecimalParameter( 0.01, 0.03, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_3_1 = DecimalParameter( 26.0, 40.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_3_2 = DecimalParameter( 0.01, 0.6, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_3_2 = DecimalParameter( 34.0, 50.0, default=40.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_3_3 = DecimalParameter( 0.02, 0.10, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_3_3 = DecimalParameter( 38.0, 50.0, default=42.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_3_4 = DecimalParameter( 0.06, 0.12, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_3_4 = DecimalParameter( 36.0, 48.0, default=34.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_profit_3_5 = DecimalParameter( 0.14, 0.24, default=0.2, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_rsi_3_5 = DecimalParameter( 20.0, 40.0, default=30.0, space="sell", decimals=1, optimize=False, load=True ) # SMA descending sell_custom_dec_profit_min_1 = DecimalParameter( 0.01, 0.10, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_dec_profit_max_1 = DecimalParameter( 0.06, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True ) # Under EMA100 sell_custom_dec_profit_min_2 = DecimalParameter( 0.05, 0.12, default=0.07, space="sell", decimals=3, optimize=False, load=True ) sell_custom_dec_profit_max_2 = DecimalParameter( 0.06, 0.2, default=0.16, space="sell", decimals=3, optimize=False, load=True ) # Trail 1 sell_trail_profit_min_1 = DecimalParameter( 0.1, 0.2, default=0.03, space="sell", decimals=2, optimize=False, load=True ) sell_trail_profit_max_1 = DecimalParameter( 0.4, 0.7, default=0.05, space="sell", decimals=2, optimize=False, load=True ) sell_trail_down_1 = DecimalParameter(0.01, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True) sell_trail_rsi_min_1 = DecimalParameter( 16.0, 36.0, default=10.0, space="sell", decimals=1, optimize=False, load=True ) sell_trail_rsi_max_1 = DecimalParameter( 30.0, 50.0, default=20.0, space="sell", decimals=1, optimize=False, load=True ) # Trail 2 sell_trail_profit_min_2 = DecimalParameter( 0.08, 0.16, default=0.1, space="sell", decimals=3, optimize=False, load=True ) sell_trail_profit_max_2 = DecimalParameter( 0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=False, load=True ) sell_trail_down_2 = DecimalParameter(0.02, 0.08, default=0.03, space="sell", decimals=3, optimize=False, load=True) sell_trail_rsi_min_2 = DecimalParameter( 16.0, 36.0, default=20.0, space="sell", decimals=1, optimize=False, load=True ) sell_trail_rsi_max_2 = DecimalParameter( 30.0, 50.0, default=50.0, space="sell", decimals=1, optimize=False, load=True ) # Trail 3 sell_trail_profit_min_3 = DecimalParameter( 0.01, 0.12, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_trail_profit_max_3 = DecimalParameter( 0.1, 0.3, default=0.2, space="sell", decimals=2, optimize=False, load=True ) sell_trail_down_3 = DecimalParameter(0.01, 0.06, default=0.05, space="sell", decimals=3, optimize=False, load=True) # Trail 4 sell_trail_profit_min_4 = DecimalParameter( 0.01, 0.12, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_trail_profit_max_4 = DecimalParameter( 0.02, 0.1, default=0.06, space="sell", decimals=2, optimize=False, load=True ) sell_trail_down_4 = DecimalParameter(0.01, 0.06, default=0.02, space="sell", decimals=3, optimize=False, load=True) # Under & near EMA200, accept profit sell_custom_profit_under_profit_min_1 = DecimalParameter( 0.0, 0.01, default=0.0, space="sell", optimize=False, load=True ) sell_custom_profit_under_profit_max_1 = DecimalParameter( 0.0, 0.05, default=0.02, space="sell", optimize=False, load=True ) sell_custom_profit_under_rel_1 = DecimalParameter( 0.01, 0.04, default=0.024, space="sell", optimize=False, load=True ) sell_custom_profit_under_rsi_diff_1 = DecimalParameter( 0.0, 20.0, default=4.4, space="sell", optimize=False, load=True ) sell_custom_profit_under_profit_2 = DecimalParameter( 0.0, 0.05, default=0.03, space="sell", optimize=False, load=True ) sell_custom_profit_under_rel_2 = DecimalParameter( 0.01, 0.04, default=0.024, space="sell", optimize=False, load=True ) sell_custom_profit_under_rsi_diff_2 = DecimalParameter( 0.0, 20.0, default=4.4, space="sell", optimize=False, load=True ) # Under & near EMA200, take the loss sell_custom_stoploss_under_rel_1 = DecimalParameter( 0.001, 0.02, default=0.002, space="sell", optimize=False, load=True ) sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter( 0.0, 20.0, default=10.0, space="sell", optimize=False, load=True ) # Long duration/recover stoploss 1 sell_custom_stoploss_long_profit_min_1 = DecimalParameter( -0.1, -0.02, default=-0.08, space="sell", optimize=False, load=True ) sell_custom_stoploss_long_profit_max_1 = DecimalParameter( -0.06, -0.01, default=-0.04, space="sell", optimize=False, load=True ) sell_custom_stoploss_long_recover_1 = DecimalParameter( 0.05, 0.15, default=0.14, space="sell", optimize=False, load=True ) sell_custom_stoploss_long_rsi_diff_1 = DecimalParameter( 0.0, 20.0, default=4.0, space="sell", optimize=False, load=True ) # Long duration/recover stoploss 2 sell_custom_stoploss_long_recover_2 = DecimalParameter( 0.03, 0.15, default=0.06, space="sell", optimize=False, load=True ) sell_custom_stoploss_long_rsi_diff_2 = DecimalParameter( 30.0, 50.0, default=40.0, space="sell", optimize=False, load=True ) # Pumped, descending SMA sell_custom_pump_dec_profit_min_1 = DecimalParameter( 0.001, 0.04, default=0.005, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_max_1 = DecimalParameter( 0.03, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_min_2 = DecimalParameter( 0.01, 0.08, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_max_2 = DecimalParameter( 0.04, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_min_3 = DecimalParameter( 0.02, 0.1, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_max_3 = DecimalParameter( 0.06, 0.12, default=0.09, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_min_4 = DecimalParameter( 0.01, 0.05, default=0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_dec_profit_max_4 = DecimalParameter( 0.02, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True ) # Pumped 48h 1, under EMA200 sell_custom_pump_under_profit_min_1 = DecimalParameter( 0.02, 0.06, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_under_profit_max_1 = DecimalParameter( 0.04, 0.1, default=0.09, space="sell", decimals=3, optimize=False, load=True ) # Pumped trail 1 sell_custom_pump_trail_profit_min_1 = DecimalParameter( 0.01, 0.12, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_trail_profit_max_1 = DecimalParameter( 0.06, 0.16, default=0.07, space="sell", decimals=2, optimize=False, load=True ) sell_custom_pump_trail_down_1 = DecimalParameter( 0.01, 0.06, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_pump_trail_rsi_min_1 = DecimalParameter( 16.0, 36.0, default=20.0, space="sell", decimals=1, optimize=False, load=True ) sell_custom_pump_trail_rsi_max_1 = DecimalParameter( 30.0, 50.0, default=70.0, space="sell", decimals=1, optimize=False, load=True ) # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_1 = DecimalParameter( 0.01, 0.04, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_min_1 = DecimalParameter( -0.1, -0.01, default=-0.02, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_max_1 = DecimalParameter( -0.1, -0.01, default=-0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_ma_offset_1 = DecimalParameter( 0.7, 0.99, default=0.94, space="sell", decimals=2, optimize=False, load=True ) # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_2 = DecimalParameter( 0.01, 0.04, default=0.025, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_loss_2 = DecimalParameter( -0.1, -0.01, default=-0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_ma_offset_2 = DecimalParameter( 0.7, 0.99, default=0.92, space="sell", decimals=2, optimize=False, load=True ) # Stoploss, pumped, 36h 3 sell_custom_stoploss_pump_max_profit_3 = DecimalParameter( 0.01, 0.04, default=0.008, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_loss_3 = DecimalParameter( -0.16, -0.06, default=-0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_stoploss_pump_ma_offset_3 = DecimalParameter( 0.7, 0.99, default=0.88, space="sell", decimals=2, optimize=False, load=True ) # Recover sell_custom_recover_profit_1 = DecimalParameter( 0.01, 0.06, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_recover_min_loss_1 = DecimalParameter( 0.06, 0.16, default=0.12, space="sell", decimals=3, optimize=False, load=True ) sell_custom_recover_profit_min_2 = DecimalParameter( 0.01, 0.04, default=0.01, space="sell", decimals=3, optimize=False, load=True ) sell_custom_recover_profit_max_2 = DecimalParameter( 0.02, 0.08, default=0.05, space="sell", decimals=3, optimize=False, load=True ) sell_custom_recover_min_loss_2 = DecimalParameter( 0.04, 0.16, default=0.06, space="sell", decimals=3, optimize=False, load=True ) sell_custom_recover_rsi_2 = DecimalParameter( 32.0, 52.0, default=46.0, space="sell", decimals=1, optimize=False, load=True ) # Profit for long duration trades sell_custom_long_profit_min_1 = DecimalParameter( 0.01, 0.04, default=0.03, space="sell", decimals=3, optimize=False, load=True ) sell_custom_long_profit_max_1 = DecimalParameter( 0.02, 0.08, default=0.04, space="sell", decimals=3, optimize=False, load=True ) sell_custom_long_duration_min_1 = IntParameter(700, 2000, default=900, space="sell", optimize=False, load=True) ############################################################# hold_trade_ids = None @staticmethod def get_hold_trades_config_file(): 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(): 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(): return hold_trades_config_file_absolute if hold_trades_config_file_resolve != hold_trades_config_file_absolute: looked_in = f"'{hold_trades_config_file_resolve}' and '{hold_trades_config_file_absolute}'" else: looked_in = f"'{hold_trades_config_file_resolve}'" log.warning("The 'hold-trades.json' file was not found. Looked in %s. HOLD support disabled.", looked_in) def load_hold_trades_config(self): if self.hold_trade_ids is not None: # Already loaded return # Default Values self.hold_trade_ids = {} # Update values from config file, if it exists hold_trades_config_file = Github_vaskosmihaylov_nfi_custom_strategies__NostalgiaForInfinityNext_ChangeToTower_V6__20260422_193409.get_hold_trades_config_file() if not hold_trades_config_file: return with hold_trades_config_file.open("r") as f: trade_ids = None hold_trades_config = None try: hold_trades_config = json_load(f) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", hold_trades_config_file, exc) else: trade_ids = hold_trades_config.get("trade_ids") if not trade_ids: return open_trades = {trade.id: trade for trade in Trade.get_trades_proxy(is_open=True)} if isinstance(trade_ids, dict): # New syntax for trade_id, profit_ratio in trade_ids.items(): try: trade_id = int(trade_id) except ValueError: log.error( "The trade_id(%s) defined under 'trade_ids' in %s is not an integer", trade_id, hold_trades_config_file, ) 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, hold_trades_config_file, ) if trade_id in open_trades: formatted_profit_ratio = "{}%".format(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, ) self.hold_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, hold_trades_config_file, ) else: # Initial Syntax profit_ratio = hold_trades_config.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, hold_trades_config_file, ) else: profit_ratio = 0.005 formatted_profit_ratio = "{}%".format(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, hold_trades_config_file, ) 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, ) self.hold_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, hold_trades_config_file, ) 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. """ if self.config["runmode"].value in ("live", "dry_run"): 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 >= self.sell_custom_profit_bull_11.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_11.value: return True, "signal_profit_o_bull_11" elif self.sell_custom_profit_bull_11.value > current_profit >= self.sell_custom_profit_bull_10.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_10.value: return True, "signal_profit_o_bull_10" elif self.sell_custom_profit_bull_10.value > current_profit >= self.sell_custom_profit_bull_9.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_9.value: return True, "signal_profit_o_bull_9" elif self.sell_custom_profit_bull_9.value > current_profit >= self.sell_custom_profit_bull_8.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_8.value: return True, "signal_profit_o_bull_8" elif self.sell_custom_profit_bull_8.value > current_profit >= self.sell_custom_profit_bull_7.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_7.value: return True, "signal_profit_o_bull_7" elif self.sell_custom_profit_bull_7.value > current_profit >= self.sell_custom_profit_bull_6.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_6.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_6" elif self.sell_custom_profit_bull_6.value > current_profit >= self.sell_custom_profit_bull_5.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_5.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_5" elif self.sell_custom_profit_bull_5.value > current_profit >= self.sell_custom_profit_bull_4.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_4.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_4" elif self.sell_custom_profit_bull_4.value > current_profit >= self.sell_custom_profit_bull_3.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_3.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_3" elif self.sell_custom_profit_bull_3.value > current_profit >= self.sell_custom_profit_bull_2.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_2.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_2" elif self.sell_custom_profit_bull_2.value > current_profit >= self.sell_custom_profit_bull_1.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_1.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_1" elif self.sell_custom_profit_bull_1.value > current_profit >= self.sell_custom_profit_bull_0.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bull_0.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bull_0" else: if current_profit >= self.sell_custom_profit_bear_11.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_11.value: return True, "signal_profit_o_bear_11" elif self.sell_custom_profit_bear_11.value > current_profit >= self.sell_custom_profit_bear_10.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_10.value: return True, "signal_profit_o_bear_10" elif self.sell_custom_profit_bear_10.value > current_profit >= self.sell_custom_profit_bear_9.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_9.value: return True, "signal_profit_o_bear_9" elif self.sell_custom_profit_bear_9.value > current_profit >= self.sell_custom_profit_bear_8.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_8.value: return True, "signal_profit_o_bear_8_1" elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_8.value: return True, "signal_profit_o_bear_8_2" elif self.sell_custom_profit_bear_8.value > current_profit >= self.sell_custom_profit_bear_7.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_7.value: return True, "signal_profit_o_bear_7_1" elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_7.value: return True, "signal_profit_o_bear_7_2" elif self.sell_custom_profit_bear_7.value > current_profit >= self.sell_custom_profit_bear_6.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_6.value: return True, "signal_profit_o_bear_6_1" elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_6.value: return True, "signal_profit_o_bear_6_2" elif self.sell_custom_profit_bear_6.value > current_profit >= self.sell_custom_profit_bear_5.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_5.value: return True, "signal_profit_o_bear_5_1" elif last_candle["rsi_14"] > self.sell_custom_rsi_over_bear_5.value: return True, "signal_profit_o_bear_5_2" elif self.sell_custom_profit_bear_5.value > current_profit >= self.sell_custom_profit_bear_4.value: if last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_4.value: return True, "signal_profit_o_bear_4" elif self.sell_custom_profit_bear_4.value > current_profit >= self.sell_custom_profit_bear_3.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_3.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bear_3" elif self.sell_custom_profit_bear_3.value > current_profit >= self.sell_custom_profit_bear_2.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_2.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bear_2" elif self.sell_custom_profit_bear_2.value > current_profit >= self.sell_custom_profit_bear_1.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_1.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bear_1" elif self.sell_custom_profit_bear_1.value > current_profit >= self.sell_custom_profit_bear_0.value: if (last_candle["rsi_14"] < self.sell_custom_rsi_under_bear_0.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_o_bear_0" 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 >= self.sell_custom_under_profit_bull_11.value: if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_11.value: return True, "signal_profit_u_bull_11" elif ( self.sell_custom_under_profit_bull_11.value > current_profit >= self.sell_custom_under_profit_bull_10.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_10.value: return True, "signal_profit_u_bull_10" elif ( self.sell_custom_under_profit_bull_10.value > current_profit >= self.sell_custom_under_profit_bull_9.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_9.value: return True, "signal_profit_u_bull_9" elif ( self.sell_custom_under_profit_bull_9.value > current_profit >= self.sell_custom_under_profit_bull_8.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_8.value: return True, "signal_profit_u_bull_8" elif ( self.sell_custom_under_profit_bull_8.value > current_profit >= self.sell_custom_under_profit_bull_7.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_7.value: return True, "signal_profit_u_bull_7" elif ( self.sell_custom_under_profit_bull_7.value > current_profit >= self.sell_custom_under_profit_bull_6.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_6.value: return True, "signal_profit_u_bull_6" elif ( self.sell_custom_under_profit_bull_6.value > current_profit >= self.sell_custom_under_profit_bull_5.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_5.value: return True, "signal_profit_u_bull_5" elif ( self.sell_custom_under_profit_bull_5.value > current_profit >= self.sell_custom_under_profit_bull_4.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_4.value: return True, "signal_profit_u_bull_4" elif ( self.sell_custom_under_profit_bull_4.value > current_profit >= self.sell_custom_under_profit_bull_3.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_3.value: return True, "signal_profit_u_bull_3" elif ( self.sell_custom_under_profit_bull_3.value > current_profit >= self.sell_custom_under_profit_bull_2.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_2.value: return True, "signal_profit_u_bull_2" elif ( self.sell_custom_under_profit_bull_2.value > current_profit >= self.sell_custom_under_profit_bull_1.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_1.value: return True, "signal_profit_u_bull_1" elif ( self.sell_custom_under_profit_bull_1.value > current_profit >= self.sell_custom_under_profit_bull_0.value ): if (last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bull_0.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_u_bull_0" else: if current_profit >= self.sell_custom_under_profit_bear_11.value: if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_11.value: return True, "signal_profit_u_bear_11" elif ( self.sell_custom_under_profit_bear_11.value > current_profit >= self.sell_custom_under_profit_bear_10.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_10.value: return True, "signal_profit_u_bear_10" elif ( self.sell_custom_under_profit_bear_10.value > current_profit >= self.sell_custom_under_profit_bear_9.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_9.value: return True, "signal_profit_u_bear_9" elif ( self.sell_custom_under_profit_bear_9.value > current_profit >= self.sell_custom_under_profit_bear_8.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_8.value: return True, "signal_profit_u_bear_8_1" elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_8.value: return True, "signal_profit_u_bear_8_2" elif ( self.sell_custom_under_profit_bear_8.value > current_profit >= self.sell_custom_under_profit_bear_7.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_7.value: return True, "signal_profit_u_bear_7_1" elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_7.value: return True, "signal_profit_u_bear_7_2" elif ( self.sell_custom_under_profit_bear_7.value > current_profit >= self.sell_custom_under_profit_bear_6.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_6.value: return True, "signal_profit_u_bear_6_1" elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_6.value: return True, "signal_profit_u_bear_6_2" elif ( self.sell_custom_under_profit_bear_6.value > current_profit >= self.sell_custom_under_profit_bear_5.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_5.value: return True, "signal_profit_u_bear_5_1" elif last_candle["rsi_14"] > self.sell_custom_under_rsi_over_bear_5.value: return True, "signal_profit_u_bear_5_2" elif ( self.sell_custom_under_profit_bear_5.value > current_profit >= self.sell_custom_under_profit_bear_4.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_4.value: return True, "signal_profit_u_bear_4" elif ( self.sell_custom_under_profit_bear_4.value > current_profit >= self.sell_custom_under_profit_bear_3.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_3.value: return True, "signal_profit_u_bear_3" elif ( self.sell_custom_under_profit_bear_3.value > current_profit >= self.sell_custom_under_profit_bear_2.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_2.value: return True, "signal_profit_u_bear_2" elif ( self.sell_custom_under_profit_bear_2.value > current_profit >= self.sell_custom_under_profit_bear_1.value ): if last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_1.value: return True, "signal_profit_u_bear_1" elif ( self.sell_custom_under_profit_bear_1.value > current_profit >= self.sell_custom_under_profit_bear_0.value ): if (last_candle["rsi_14"] < self.sell_custom_under_rsi_under_bear_0.value) and ( last_candle["cmf"] < 0.0 ): return True, "signal_profit_u_bear_0" return False, None def sell_pump_main(self, current_profit: float, last_candle) -> tuple: if last_candle["sell_pump_48_1_1h"]: if current_profit >= self.sell_custom_pump_profit_1_5.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_5.value: return True, "signal_profit_p_1_5" elif self.sell_custom_pump_profit_1_5.value > current_profit >= self.sell_custom_pump_profit_1_4.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_4.value: return True, "signal_profit_p_1_4" elif self.sell_custom_pump_profit_1_4.value > current_profit >= self.sell_custom_pump_profit_1_3.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_3.value: return True, "signal_profit_p_1_3" elif self.sell_custom_pump_profit_1_3.value > current_profit >= self.sell_custom_pump_profit_1_2.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_2.value: return True, "signal_profit_p_1_2" elif self.sell_custom_pump_profit_1_2.value > current_profit >= self.sell_custom_pump_profit_1_1.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_1_1.value: return True, "signal_profit_p_1_1" elif last_candle["sell_pump_36_1_1h"]: if current_profit >= self.sell_custom_pump_profit_2_5.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_5.value: return True, "signal_profit_p_2_5" elif self.sell_custom_pump_profit_2_5.value > current_profit >= self.sell_custom_pump_profit_2_4.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_4.value: return True, "signal_profit_p_2_4" elif self.sell_custom_pump_profit_2_4.value > current_profit >= self.sell_custom_pump_profit_2_3.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_3.value: return True, "signal_profit_p_2_3" elif self.sell_custom_pump_profit_2_3.value > current_profit >= self.sell_custom_pump_profit_2_2.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_2.value: return True, "signal_profit_p_2_2" elif self.sell_custom_pump_profit_2_2.value > current_profit >= self.sell_custom_pump_profit_2_1.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_2_1.value: return True, "signal_profit_p_2_1" elif last_candle["sell_pump_24_1_1h"]: if current_profit >= self.sell_custom_pump_profit_3_5.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_5.value: return True, "signal_profit_p_3_5" elif self.sell_custom_pump_profit_3_5.value > current_profit >= self.sell_custom_pump_profit_3_4.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_4.value: return True, "signal_profit_p_3_4" elif self.sell_custom_pump_profit_3_4.value > current_profit >= self.sell_custom_pump_profit_3_3.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_3.value: return True, "signal_profit_p_3_3" elif self.sell_custom_pump_profit_3_3.value > current_profit >= self.sell_custom_pump_profit_3_2.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_2.value: return True, "signal_profit_p_3_2" elif self.sell_custom_pump_profit_3_2.value > current_profit >= self.sell_custom_pump_profit_3_1.value: if last_candle["rsi_14"] < self.sell_custom_pump_rsi_3_1.value: return True, "signal_profit_p_3_1" return False, None def sell_dec_main(self, current_profit: float, last_candle) -> tuple: if (self.sell_custom_dec_profit_max_1.value > current_profit >= self.sell_custom_dec_profit_min_1.value) and ( last_candle["sma_200_dec_20"] ): return True, "signal_profit_d_1" elif ( self.sell_custom_dec_profit_max_2.value > current_profit >= self.sell_custom_dec_profit_min_2.value ) 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.value > current_profit >= self.sell_trail_profit_min_1.value) and (self.sell_trail_rsi_min_1.value < last_candle["rsi_14"] < self.sell_trail_rsi_max_1.value) and (max_profit > (current_profit + self.sell_trail_down_1.value)) and (last_candle["moderi_96"] == False) ): return True, "signal_profit_t_1" elif ( (self.sell_trail_profit_max_2.value > current_profit >= self.sell_trail_profit_min_2.value) and (self.sell_trail_rsi_min_2.value < last_candle["rsi_14"] < self.sell_trail_rsi_max_2.value) and (max_profit > (current_profit + self.sell_trail_down_2.value)) and (last_candle["ema_25"] < last_candle["ema_50"]) ): return True, "signal_profit_t_2" elif ( (self.sell_trail_profit_max_3.value > current_profit >= self.sell_trail_profit_min_3.value) and (max_profit > (current_profit + self.sell_trail_down_3.value)) and (last_candle["sma_200_dec_20_1h"]) ): return True, "signal_profit_t_3" elif ( (self.sell_trail_profit_max_4.value > current_profit >= self.sell_trail_profit_min_4.value) and (max_profit > (current_profit + self.sell_trail_down_4.value)) 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.value < current_profit < self.sell_custom_long_profit_max_1.value ) and (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1.value) > 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.value > current_profit >= self.sell_custom_profit_under_profit_min_1.value ) 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.value ) and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_1.value) ): return True, "signal_profit_u_e_1" else: # Uptrend if ( (current_profit >= self.sell_custom_profit_under_profit_2.value) 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.value ) and (last_candle["rsi_14"] > last_candle["rsi_14_1h"] + self.sell_custom_profit_under_rsi_diff_2.value) ): return True, "signal_profit_u_e_2" return False, None def sell_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple: # ATR-based stoplosses: tightened moderately from original (-8/-12/-16/-20) if -0.06 <= current_profit < -0.03: if (last_candle["close"] < last_candle["atr_high_thresh_1"]) and ( previous_candle_1["close"] > previous_candle_1["atr_high_thresh_1"] ): return True, "signal_stoploss_atr_1" elif -0.10 <= current_profit < -0.06: if (last_candle["close"] < last_candle["atr_high_thresh_2"]) and ( previous_candle_1["close"] > previous_candle_1["atr_high_thresh_2"] ): return True, "signal_stoploss_atr_2" elif -0.14 <= current_profit < -0.10: if (last_candle["close"] < last_candle["atr_high_thresh_3"]) and ( previous_candle_1["close"] > previous_candle_1["atr_high_thresh_3"] ): return True, "signal_stoploss_atr_3" elif current_profit < -0.14: if (last_candle["close"] < last_candle["atr_high_thresh_4"]) and ( previous_candle_1["close"] > previous_candle_1["atr_high_thresh_4"] ): return True, "signal_stoploss_atr_4" return False, None def sell_pump_dec(self, current_profit: float, last_candle) -> tuple: if ( ( self.sell_custom_pump_dec_profit_max_1.value > current_profit >= self.sell_custom_pump_dec_profit_min_1.value ) 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 ( ( self.sell_custom_pump_dec_profit_max_2.value > current_profit >= self.sell_custom_pump_dec_profit_min_2.value ) 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 ( ( self.sell_custom_pump_dec_profit_max_3.value > current_profit >= self.sell_custom_pump_dec_profit_min_3.value ) 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 ( ( self.sell_custom_pump_dec_profit_max_4.value > current_profit >= self.sell_custom_pump_dec_profit_min_4.value ) 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.value > current_profit >= self.sell_custom_pump_under_profit_min_1.value ) 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.value > current_profit >= self.sell_custom_pump_trail_profit_min_1.value ) and ( self.sell_custom_pump_trail_rsi_min_1.value < last_candle["rsi_14"] < self.sell_custom_pump_trail_rsi_max_1.value ) and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1.value)) ): 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.value) and ( current_profit >= self.sell_custom_recover_profit_1.value ): return True, "signal_profit_r_1" elif ( (max_loss > self.sell_custom_recover_min_loss_2.value) and ( self.sell_custom_recover_profit_max_2.value > current_profit >= self.sell_custom_recover_profit_min_2.value ) and (last_candle["rsi_14"] < self.sell_custom_recover_rsi_2.value) 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"] > -0.1: return True, "signal_profit_w_1_1" elif 0.03 > current_profit >= 0.02: if last_candle["r_480"] > -0.2: return True, "signal_profit_w_1_2" elif 0.04 > current_profit >= 0.03: if last_candle["r_480"] > -0.4: return True, "signal_profit_w_1_3" elif 0.05 > current_profit >= 0.04: if last_candle["r_480"] > -0.6: return True, "signal_profit_w_1_4" elif 0.06 > current_profit >= 0.05: if last_candle["r_480"] > -0.8: return True, "signal_profit_w_1_5" elif 0.07 > current_profit >= 0.06: if last_candle["r_480"] > -1.0: return True, "signal_profit_w_1_6" elif 0.08 > current_profit >= 0.07: if last_candle["r_480"] > -1.2: return True, "signal_profit_w_1_7" elif 0.09 > current_profit >= 0.08: if last_candle["r_480"] > -1.4: return True, "signal_profit_w_1_8" elif 0.1 > current_profit >= 0.09: if last_candle["r_480"] > -1.6: 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"] > -2.0) 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"] > -2.1) 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"] > -2.2) 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"] > -2.3) 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"] > -2.4) 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"] > -2.5) 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"] > -3.0) 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"] > -3.0) 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"] > -2.8) 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"] > -2.4) 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"] > -2.2) 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"] > -2.0) 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"] > -1.0) 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"] > -1.5) 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"] > -2.0) 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"] > -3.0) 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_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"] > -1.0) and (last_candle["rsi_14"] > 68.0) 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"] > -1.5) and (last_candle["rsi_14"] > 68.0) 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"] > -2.0) and (last_candle["rsi_14"] > 68.0) 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"] > -2.5) and (last_candle["rsi_14"] > 68.0) 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"] > -3.0) and (last_candle["rsi_14"] > 68.0) 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"] > -3.5) 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"] > -4.0) 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"] > -4.5) 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"] > -3.0) 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"] > -2.0) 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"] > -2.0) 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_quick_mode( self, current_profit: float, max_profit: float, last_candle, previous_candle_1, trade: "Trade", current_time: "datetime", ) -> tuple: if (0.06 > current_profit > 0.02) and (last_candle["rsi_14"] > 79.0): return True, "signal_profit_q_1" if (0.06 > current_profit > 0.02) and (last_candle["cti"] > 0.9): return True, "signal_profit_q_2" if (last_candle["close"] < last_candle["atr_high_thresh_q"]) and ( previous_candle_1["close"] > previous_candle_1["atr_high_thresh_q"] ): if current_profit > 0.0: return True, "signal_profit_q_atr" elif current_profit < -0.05: return True, "signal_stoploss_q_atr" if current_profit > 0.0: if (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 (last_candle["pm"] > last_candle["pmax_thresh"]) and ( last_candle["close"] > last_candle["sma_21"] * 1.014 ): return True, "signal_profit_q_pmax_bear" if ( (last_candle["zlema_4"] > last_candle["zlema_1"]) and (previous_candle_1["zlema_4"] < previous_candle_1["zlema_1"]) and (last_candle["cci"] > -100) and (last_candle["hrsi"] > 70) and (current_profit > 0) ): return True, "signal_profit_zlema" # if ((max_profit - current_profit) > 0.01) and (current_profit > 0.00): # return True, 'quick_trailing' 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 ( (-0.03 < current_profit < 0.05) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle["rsi_14"] > 75) ): return True, "signal_ichi_underwater" if (max_loss > 0.07) and (current_profit > 0.02): return True, "signal_ichi_recover_0" if (max_loss > 0.06) and (current_profit > 0.03): return True, "signal_ichi_recover_1" if (max_loss > 0.05) and (current_profit > 0.04): return True, "signal_ichi_recover_2" if (max_loss > 0.04) and (current_profit > 0.05): return True, "signal_ichi_recover_3" if (max_loss > 0.03) and (current_profit > 0.06): return True, "signal_ichi_recover_4" if (0.05 < current_profit < 0.1) and (current_time - timedelta(minutes=720) > trade.open_date_utc): return True, "signal_ichi_slow_trade" if (0.07 < current_profit < 0.1) and (max_profit - current_profit > 0.025) and (max_profit > 0.1): return True, "signal_ichi_trailing" if current_profit < -0.1: return True, "signal_ichi_stoploss" if ( (last_candle["zlema_4"] > last_candle["zlema_1"]) and (previous_candle_1["zlema_4"] < previous_candle_1["zlema_1"]) and (last_candle["cci"] > 100) and (last_candle["hrsi"] > 85) and (current_profit > 0) ): return True, "signal_ichi_zlema" return False, None def custom_stoploss( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, after_fill: bool, **kwargs, ) -> float: """ Tiered custom stoploss to prevent catastrophic losses. Tightens the stop progressively as losses deepen. """ # In profit: let trailing_stop settings handle it (don't conflict) if current_profit > 0.04: return -0.02 # Lock at least 2% once 4% is reached elif current_profit > 0.0: return -1 # Use default stoploss / trailing while building profit elif current_profit > -0.04: # Loss <4%: let default -0.07 handle it, allow recovery return -1 elif current_profit > -0.06: # Loss 4-6%: tighten slightly return -0.06 else: # Loss > 6%: near hard stop, let it trigger return -0.05 def custom_exit( self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, **kwargs ): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] previous_candle_3 = dataframe.iloc[-4] previous_candle_4 = dataframe.iloc[-5] previous_candle_5 = dataframe.iloc[-6] # Time-based exit: only cut stale trades losing > 3% after 12 hours if (current_profit < -0.03) and (current_time - timedelta(minutes=720) > trade.open_date_utc): return "exit_stale_loss_12h" buy_tag = "empty" if hasattr(trade, "buy_tag") and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe["date"] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle["buy_tag"] if buy_signal_candle["buy_tag"] != "" else "empty" 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 # Quick sell mode if all(c in ["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, trade, current_time ) if sell and (signal_name is not None): return 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 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 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 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 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 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 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 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 signal_name + " ( " + buy_tag + ")" # Stoplosses sell, signal_name = self.sell_stoploss(current_profit, last_candle, previous_candle_1) if sell and (signal_name is not None): return 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 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 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 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 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 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 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 signal_name + " ( " + buy_tag + ")" # Sell signal 1 if ( self.sell_condition_1_enable.value and (last_candle["rsi_14"] > self.sell_rsi_bb_1.value) 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 > 0.0: return "sell_signal_1_1_1" + " ( " + buy_tag + ")" else: if current_profit > 0.0: return "sell_signal_1_2_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "sell_signal_1_2_2" + " ( " + buy_tag + ")" # Sell signal 2 elif ( (self.sell_condition_2_enable.value) and (last_candle["rsi_14"] > self.sell_rsi_bb_2.value) 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 > 0.0: return "sell_signal_2_1_1" + " ( " + buy_tag + ")" else: if current_profit > 0.0: return "sell_signal_2_2_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "sell_signal_2_2_2" + " ( " + buy_tag + ")" # Sell signal 4 elif ( self.sell_condition_4_enable.value and (last_candle["rsi_14"] > self.sell_dual_rsi_rsi_4.value) and (last_candle["rsi_14_1h"] > self.sell_dual_rsi_rsi_1h_4.value) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.0: return "sell_signal_4_1_1" + " ( " + buy_tag + ")" else: if current_profit > 0.0: return "sell_signal_4_2_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "sell_signal_4_2_2" + " ( " + buy_tag + ")" # Sell signal 6 elif ( self.sell_condition_6_enable.value 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.value) ): if current_profit > 0.0: return "sell_signal_6_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "sell_signal_6_2" + " ( " + buy_tag + ")" # Sell signal 7 elif ( self.sell_condition_7_enable.value and (last_candle["rsi_14_1h"] > self.sell_rsi_1h_7.value) and (last_candle["crossed_below_ema_12_26"]) ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.0: return "sell_signal_7_1_1" + " ( " + buy_tag + ")" else: if current_profit > 0.0: return "sell_signal_7_2_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "sell_signal_7_2_2" + " ( " + buy_tag + ")" # Sell signal 8 elif self.sell_condition_8_enable.value and ( last_candle["close"] > last_candle["bb20_2_upp_1h"] * self.sell_bb_relative_8.value ): if last_candle["close"] > last_candle["ema_200"]: if current_profit > 0.0: return "sell_signal_8_1_1" + " ( " + buy_tag + ")" else: if current_profit > 0.0: return "sell_signal_8_2_1" + " ( " + buy_tag + ")" elif max_loss > 0.25: return "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) for pair in pairs] informative_pairs.append(("BTC/USDT", self.timeframe)) informative_pairs.append(("BTC/USDT", self.info_timeframe)) return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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) # 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) # 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) # 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.loc[:, "cloud_top"] = informative_1h.loc[:, ["senkou_a", "senkou_b"]].max(axis=1) # EFI - Elders Force Index informative_1h["efi"] = pta.efi(informative_1h["close"], informative_1h["volume"], length=13) # SSL ssl_down, ssl_up = SSLChannels(informative_1h, 10) informative_1h["ssl_down"] = ssl_down informative_1h["ssl_up"] = ssl_up # 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.value, self.buy_pump_pull_threshold_10_24.value ) informative_1h["safe_pump_36_10"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_10_36.value, self.buy_pump_pull_threshold_10_36.value ) informative_1h["safe_pump_48_10"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_10_48.value, self.buy_pump_pull_threshold_10_48.value ) informative_1h["safe_pump_24_20"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_20_24.value, self.buy_pump_pull_threshold_20_24.value ) informative_1h["safe_pump_36_20"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_20_36.value, self.buy_pump_pull_threshold_20_36.value ) informative_1h["safe_pump_48_20"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_20_48.value, self.buy_pump_pull_threshold_20_48.value ) informative_1h["safe_pump_24_30"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_30_24.value, self.buy_pump_pull_threshold_30_24.value ) informative_1h["safe_pump_36_30"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_30_36.value, self.buy_pump_pull_threshold_30_36.value ) informative_1h["safe_pump_48_30"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_30_48.value, self.buy_pump_pull_threshold_30_48.value ) informative_1h["safe_pump_24_40"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_40_24.value, self.buy_pump_pull_threshold_40_24.value ) informative_1h["safe_pump_36_40"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_40_36.value, self.buy_pump_pull_threshold_40_36.value ) informative_1h["safe_pump_48_40"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_40_48.value, self.buy_pump_pull_threshold_40_48.value ) informative_1h["safe_pump_24_50"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_50_24.value, self.buy_pump_pull_threshold_50_24.value ) informative_1h["safe_pump_36_50"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_50_36.value, self.buy_pump_pull_threshold_50_36.value ) informative_1h["safe_pump_48_50"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_50_48.value, self.buy_pump_pull_threshold_50_48.value ) informative_1h["safe_pump_24_60"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_60_24.value, self.buy_pump_pull_threshold_60_24.value ) informative_1h["safe_pump_36_60"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_60_36.value, self.buy_pump_pull_threshold_60_36.value ) informative_1h["safe_pump_48_60"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_60_48.value, self.buy_pump_pull_threshold_60_48.value ) informative_1h["safe_pump_24_70"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_70_24.value, self.buy_pump_pull_threshold_70_24.value ) informative_1h["safe_pump_36_70"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_70_36.value, self.buy_pump_pull_threshold_70_36.value ) informative_1h["safe_pump_48_70"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_70_48.value, self.buy_pump_pull_threshold_70_48.value ) informative_1h["safe_pump_24_80"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_80_24.value, self.buy_pump_pull_threshold_80_24.value ) informative_1h["safe_pump_36_80"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_80_36.value, self.buy_pump_pull_threshold_80_36.value ) informative_1h["safe_pump_48_80"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_80_48.value, self.buy_pump_pull_threshold_80_48.value ) informative_1h["safe_pump_24_90"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_90_24.value, self.buy_pump_pull_threshold_90_24.value ) informative_1h["safe_pump_36_90"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_90_36.value, self.buy_pump_pull_threshold_90_36.value ) informative_1h["safe_pump_48_90"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_90_48.value, self.buy_pump_pull_threshold_90_48.value ) informative_1h["safe_pump_24_100"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_100_24.value, self.buy_pump_pull_threshold_100_24.value ) informative_1h["safe_pump_36_100"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_100_36.value, self.buy_pump_pull_threshold_100_36.value ) informative_1h["safe_pump_48_100"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_100_48.value, self.buy_pump_pull_threshold_100_48.value ) informative_1h["safe_pump_24_110"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_110_24.value, self.buy_pump_pull_threshold_110_24.value ) informative_1h["safe_pump_36_110"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_110_36.value, self.buy_pump_pull_threshold_110_36.value ) informative_1h["safe_pump_48_110"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_110_48.value, self.buy_pump_pull_threshold_110_48.value ) informative_1h["safe_pump_24_120"] = self.safe_pump( informative_1h, 24, self.buy_pump_threshold_120_24.value, self.buy_pump_pull_threshold_120_24.value ) informative_1h["safe_pump_36_120"] = self.safe_pump( informative_1h, 36, self.buy_pump_threshold_120_36.value, self.buy_pump_pull_threshold_120_36.value ) informative_1h["safe_pump_48_120"] = self.safe_pump( informative_1h, 48, self.buy_pump_threshold_120_48.value, self.buy_pump_pull_threshold_120_48.value ) informative_1h["safe_dump_10"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_10_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["safe_dump_20"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_20_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["safe_dump_30"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_30_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["safe_dump_40"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_40_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["safe_dump_50"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_50_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["safe_dump_60"] = ( (informative_1h["hl_pct_change_5"] < self.buy_dump_protection_60_5.value) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) informative_1h["sell_pump_48_1"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_1.value informative_1h["sell_pump_48_2"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_2.value informative_1h["sell_pump_48_3"] = informative_1h["hl_pct_change_48"] > self.sell_pump_threshold_48_3.value informative_1h["sell_pump_36_1"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_1.value informative_1h["sell_pump_36_2"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_2.value informative_1h["sell_pump_36_3"] = informative_1h["hl_pct_change_36"] > self.sell_pump_threshold_36_3.value informative_1h["sell_pump_24_1"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_1.value informative_1h["sell_pump_24_2"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_2.value informative_1h["sell_pump_24_3"] = informative_1h["hl_pct_change_24"] > self.sell_pump_threshold_24_3.value # TD Sequential informative_1h["exceed_high"] = False informative_1h["exceed_low"] = False # count consecutive closes “lower” than the close 4 bars prior. informative_1h["seq_buy"] = informative_1h["close"] < informative_1h["close"].shift(4) informative_1h["seq_buy"] = informative_1h["seq_buy"] * ( informative_1h["seq_buy"] .groupby((informative_1h["seq_buy"] != informative_1h["seq_buy"].shift()).cumsum()) .cumcount() + 1 ) # count consecutive closes “higher” than the close 4 bars prior. informative_1h["seq_sell"] = informative_1h["close"] > informative_1h["close"].shift(4) informative_1h["seq_sell"] = informative_1h["seq_sell"] * ( informative_1h["seq_sell"] .groupby((informative_1h["seq_sell"] != informative_1h["seq_sell"].shift()).cumsum()) .cumcount() + 1 ) for index, row in informative_1h.iterrows(): # check if the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9. seq_b = row["seq_buy"] if seq_b == 8: informative_1h.loc[index, "exceed_low"] = (row["low"] < informative_1h.loc[index - 2, "low"]) | ( row["low"] < informative_1h.loc[index - 1, "low"] ) if seq_b > 8: informative_1h.loc[index, "exceed_low"] = ( row["low"] < informative_1h.loc[index - 3 - (seq_b - 9), "low"] ) | (row["low"] < informative_1h.loc[index - 2 - (seq_b - 9), "low"]) if seq_b == 9: informative_1h.loc[index, "exceed_low"] = ( row["exceed_low"] | informative_1h.loc[index - 1, "exceed_low"] ) # check if the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9. seq_s = row["seq_sell"] if seq_s == 8: informative_1h.loc[index, "exceed_high"] = (row["high"] > informative_1h.loc[index - 2, "high"]) | ( row["high"] > informative_1h.loc[index - 1, "high"] ) if seq_s > 8: informative_1h.loc[index, "exceed_high"] = ( row["high"] > informative_1h.loc[index - 3 - (seq_s - 9), "high"] ) | (row["high"] > informative_1h.loc[index - 2 - (seq_s - 9), "high"]) if seq_s == 9: informative_1h.loc[index, "exceed_high"] = ( row["exceed_high"] | informative_1h.loc[index - 1, "exceed_high"] ) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 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_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"] = ewo(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_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) # hull dataframe["hull_75"] = hull(dataframe, 75) # 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"] # HLC3 dataframe["hlc3"] = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3 # HRSI dataframe["hull"] = 2 * dataframe["hlc3"] - ta.WMA(dataframe["hlc3"], 2) dataframe["hrsi"] = ta.RSI(dataframe["hull"], 2) dataframe["hull_sell"] = 2 * dataframe["low"] - ta.WMA(dataframe["low"], 2) dataframe["hrsi_sell"] = ta.RSI(dataframe["hull_sell"], 2) # Kalman Filter HLC3 dataframe["hlc3KF"] = KalmanFilter(dataframe, source="hlc3") # Kalman Filter LOW dataframe["lowKF"] = KalmanFilter(dataframe, source="low") # ZLEMA BUY dataframe["zlema_1"] = dataframe["hlc3KF"] dataframe["zlema_1_std"] = dataframe["hlc3"] dataframe["ema_data"] = dataframe["hlc3KF"] + (dataframe["hlc3KF"] - dataframe["hlc3KF"].shift(2)) dataframe["ema_data_2"] = dataframe["hlc3KF"] + (dataframe["hlc3KF"] - dataframe["hlc3KF"].shift(1)) dataframe["zlema_4"] = ta.EMA(dataframe["ema_data"], timeperiod=4) dataframe["zlema_2"] = ta.EMA(dataframe["ema_data_2"], timeperiod=2) dataframe["zlema_4_std"] = pta.zlma(dataframe["hlc3"], length=4) # ZLEMA SELL dataframe["zlema_1_sell"] = dataframe["lowKF"] dataframe["zlema_1_std_sell"] = dataframe["low"] dataframe["ema_data_sell"] = dataframe["lowKF"] + (dataframe["lowKF"] - dataframe["lowKF"].shift(2)) dataframe["zlema_4_sell"] = ta.EMA(dataframe["ema_data_sell"], timeperiod=4) dataframe["zlema_4_std_sell"] = pta.zlma(dataframe["low"], length=4) # CCI dataframe["cci"] = ta.CCI(dataframe, source="hlc3", timeperiod=20) # 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) # ATR dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_high_thresh_1"] = dataframe["high"] - (dataframe["atr"] * 5.4) dataframe["atr_high_thresh_2"] = dataframe["high"] - (dataframe["atr"] * 5.2) dataframe["atr_high_thresh_3"] = dataframe["high"] - (dataframe["atr"] * 5.0) dataframe["atr_high_thresh_4"] = dataframe["high"] - (dataframe["atr"] * 2.0) dataframe["atr_high_thresh_q"] = dataframe["high"] - (dataframe["atr"] * 3.0) # 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) dataframe["safe_dips_10"] = self.safe_dips( dataframe, self.buy_dip_threshold_10_1.value, self.buy_dip_threshold_10_2.value, self.buy_dip_threshold_10_3.value, self.buy_dip_threshold_10_4.value, ) dataframe["safe_dips_20"] = self.safe_dips( dataframe, self.buy_dip_threshold_20_1.value, self.buy_dip_threshold_20_2.value, self.buy_dip_threshold_20_3.value, self.buy_dip_threshold_20_4.value, ) dataframe["safe_dips_30"] = self.safe_dips( dataframe, self.buy_dip_threshold_30_1.value, self.buy_dip_threshold_30_2.value, self.buy_dip_threshold_30_3.value, self.buy_dip_threshold_30_4.value, ) dataframe["safe_dips_40"] = self.safe_dips( dataframe, self.buy_dip_threshold_40_1.value, self.buy_dip_threshold_40_2.value, self.buy_dip_threshold_40_3.value, self.buy_dip_threshold_40_4.value, ) dataframe["safe_dips_50"] = self.safe_dips( dataframe, self.buy_dip_threshold_50_1.value, self.buy_dip_threshold_50_2.value, self.buy_dip_threshold_50_3.value, self.buy_dip_threshold_50_4.value, ) dataframe["safe_dips_60"] = self.safe_dips( dataframe, self.buy_dip_threshold_60_1.value, self.buy_dip_threshold_60_2.value, self.buy_dip_threshold_60_3.value, self.buy_dip_threshold_60_4.value, ) dataframe["safe_dips_70"] = self.safe_dips( dataframe, self.buy_dip_threshold_70_1.value, self.buy_dip_threshold_70_2.value, self.buy_dip_threshold_70_3.value, self.buy_dip_threshold_70_4.value, ) dataframe["safe_dips_80"] = self.safe_dips( dataframe, self.buy_dip_threshold_80_1.value, self.buy_dip_threshold_80_2.value, self.buy_dip_threshold_80_3.value, self.buy_dip_threshold_80_4.value, ) dataframe["safe_dips_90"] = self.safe_dips( dataframe, self.buy_dip_threshold_90_1.value, self.buy_dip_threshold_90_2.value, self.buy_dip_threshold_90_3.value, self.buy_dip_threshold_90_4.value, ) dataframe["safe_dips_100"] = self.safe_dips( dataframe, self.buy_dip_threshold_100_1.value, self.buy_dip_threshold_100_2.value, self.buy_dip_threshold_100_3.value, self.buy_dip_threshold_100_4.value, ) dataframe["safe_dips_110"] = self.safe_dips( dataframe, self.buy_dip_threshold_110_1.value, self.buy_dip_threshold_110_2.value, self.buy_dip_threshold_110_3.value, self.buy_dip_threshold_110_4.value, ) dataframe["safe_dips_120"] = self.safe_dips( dataframe, self.buy_dip_threshold_120_1.value, self.buy_dip_threshold_120_2.value, self.buy_dip_threshold_120_3.value, self.buy_dip_threshold_120_4.value, ) dataframe["safe_dips_130"] = self.safe_dips( dataframe, self.buy_dip_threshold_130_1.value, self.buy_dip_threshold_130_2.value, self.buy_dip_threshold_130_3.value, self.buy_dip_threshold_130_4.value, ) # 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 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: # Indicators # ----------------------------------------------------------------------------------------- dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ["date", "open", "high", "low", "close", "volume"] dataframe.rename(columns=lambda s: "btc_" + s if (not s in ignore_columns) else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 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: "btc_" + s if (not s in ignore_columns) else s, inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ --> BTC informative (5m/1h) ___________________________________________________________________________________________ """ if self.has_BTC_base_tf: btc_base_tf = self.dp.get_pair_dataframe("BTC/USDT", 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 = [(s + "_" + self.timeframe) 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/USDT", self.info_timeframe) 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, ffill=True) drop_columns = [ (s + "_" + self.info_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 != "none": informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_1h, self.timeframe, self.info_timeframe, ffill=True ) drop_columns = [(s + "_" + self.info_timeframe) 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: s + "_{}".format(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 = [(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) return dataframe def populate_entry_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["buy_condition_" + str(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"]: item_buy_protection_list.append( dataframe[f"safe_dips_{global_buy_protection_params['safe_dips_type']}"] ) 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 not self.config["runmode"] 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(36).min()) / dataframe["open"].rolling(36).min() ) > self.buy_min_inc_1.value ) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_1.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_1.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_1.value) item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_1.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_1.value) # Condition #2 elif index == 2: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < dataframe["rsi_14_1h"] - self.buy_rsi_1h_diff_2.value) item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_2.value) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_2.value)) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_2.value)) # Condition #3 elif index == 3: # Non-Standard protections item_buy_logic.append(dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_3.value)) # 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_bb40_bbdelta_close_3.value) ) item_buy_logic.append( dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close_3.value) ) item_buy_logic.append( dataframe["tail"].lt(dataframe["bb40_2_delta"] * self.buy_bb40_tail_bbdelta_3.value) ) 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_cti_3.value) # 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_bb20_close_bblowerband_4.value * dataframe["bb20_2_low"] ) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_30"].shift(1) * self.buy_bb20_volume_4.value) ) item_buy_logic.append(dataframe["cti"] < self.buy_cti_4.value) # Condition #5 elif index == 5: # Non-Standard protections item_buy_logic.append(dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_5.value)) # 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_ema_open_mult_5.value) ) 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_bb_offset_5.value)) item_buy_logic.append(dataframe["cti"] < self.buy_cti_5.value) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_5.value)) # 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_ema_open_mult_6.value) ) 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_bb_offset_6.value)) # 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_ema_open_mult_7.value) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["cti"] < self.buy_cti_7.value) # Condition #8 elif index == 8: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_96"]) item_buy_logic.append(dataframe["cti"] < self.buy_cti_8.value) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_8.value)) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_8.value) item_buy_logic.append(dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_8.value)) # 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_ma_offset_9.value) item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_bb_offset_9.value) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_9.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_9.value) item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_9.value) # 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_ma_offset_10.value) item_buy_logic.append(dataframe["close"] < dataframe["bb20_2_low"] * self.buy_bb_offset_10.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_10.value) # Condition #11 elif index == 11: # Non-Standard protections item_buy_logic.append(dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) # Logic item_buy_logic.append( ( (dataframe["close"] - dataframe["open"].rolling(36).min()) / dataframe["open"].rolling(36).min() ) > self.buy_min_inc_11.value ) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_11.value) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_rsi_1h_min_11.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_max_11.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_11.value) item_buy_logic.append(dataframe["mfi"] < self.buy_mfi_11.value) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_12.value) item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_12.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_12.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_12.value) # 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_ma_offset_13.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_13.value) item_buy_logic.append(dataframe["ewo"] < self.buy_ewo_13.value) # 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_ema_open_mult_14.value) ) 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_bb_offset_14.value)) item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_14.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_14.value) # Condition #15 elif index == 15: # Non-Standard protections item_buy_logic.append(dataframe["close"] > dataframe["ema_200_1h"] * self.buy_ema_rel_15.value) # 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_ema_open_mult_15.value) ) item_buy_logic.append( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_15.value) item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_15.value) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_16.value) item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_16.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_16.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_16.value) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_17.value) item_buy_logic.append(dataframe["ewo"] < self.buy_ewo_17.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_17.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_17.value) ) # 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["rsi_14"] < self.buy_rsi_18.value) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_18.value)) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_18.value) ) item_buy_logic.append(dataframe["cti"] < self.buy_cti_18.value) # Condition #19 elif index == 19: # Non-Standard protections # 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["rsi_14_1h"] > self.buy_rsi_1h_min_19.value) item_buy_logic.append(dataframe["chop"] < self.buy_chop_max_19.value) item_buy_logic.append(dataframe["moderi_32"] == True) item_buy_logic.append(dataframe["moderi_64"] == True) item_buy_logic.append(dataframe["moderi_96"] == True) # Condition #20 elif index == 20: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_20.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_20.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_20.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_20.value) ) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_21.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_21.value) item_buy_logic.append(dataframe["cti"] < self.buy_cti_21.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_volume_21.value) ) # 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_volume_22.value) > dataframe["volume"] ) item_buy_logic.append(dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_22.value) item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_22.value)) item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_22.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_22.value) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_buy_logic.append(dataframe["close"] < (dataframe["bb20_2_low"] * self.buy_bb_offset_23.value)) item_buy_logic.append(dataframe["ewo"] > self.buy_ewo_23.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_rsi_23.value) item_buy_logic.append(dataframe["rsi_14_1h"] < self.buy_rsi_1h_23.value) # 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_max.value) item_buy_logic.append(dataframe["rsi_14_1h"] > self.buy_24_rsi_1h_min.value) # 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.value) item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_26_1h"]) item_buy_logic.append(dataframe["close"] < (dataframe["sma_20"] * self.buy_25_ma_offset.value)) item_buy_logic.append(dataframe["open"] > (dataframe["sma_20"] * self.buy_25_ma_offset.value)) item_buy_logic.append( (dataframe["open"] < dataframe["ema_20_1h"]) & (dataframe["low"] < dataframe["ema_20_1h"]) | (dataframe["open"] > dataframe["ema_20_1h"]) & (dataframe["low"] > dataframe["ema_20_1h"]) ) item_buy_logic.append(dataframe["cti"] < self.buy_25_cti.value) # Condition #26 elif index == 26: # Non-Standard protections # Logic item_buy_logic.append( dataframe["close"] < (dataframe["zema_61"] * self.buy_26_zema_low_offset.value) ) item_buy_logic.append(dataframe["cti"] < self.buy_26_cti.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_26_volume.value) ) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_buy_logic.append(dataframe["r_480"] < -self.buy_27_wr_max.value) item_buy_logic.append(dataframe["r_480_1h"] < -self.buy_27_wr_1h_max.value) item_buy_logic.append(dataframe["rsi_14_1h"] + dataframe["rsi_14"] < self.buy_27_rsi_max.value) item_buy_logic.append(dataframe["cti"] < self.buy_27_cti.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_27_volume.value) ) # 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.value) item_buy_logic.append(dataframe["ewo"] > self.buy_28_ewo.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_28_rsi.value) item_buy_logic.append(dataframe["cti"] < self.buy_28_cti.value) # 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.value) item_buy_logic.append(dataframe["ewo"] < self.buy_29_ewo.value) item_buy_logic.append(dataframe["cti"] < self.buy_29_cti.value) # 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.value) item_buy_logic.append(dataframe["ewo"] > self.buy_30_ewo.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_30_rsi.value) item_buy_logic.append(dataframe["cti"] < self.buy_30_cti.value) # 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.value) item_buy_logic.append(dataframe["ewo"] < self.buy_31_ewo.value) item_buy_logic.append(dataframe["r_480"] < self.buy_31_wr.value) # Condition #32 - Quick mode buy elif index == 32: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_32"]) item_buy_logic.append(dataframe["moderi_64"]) item_buy_logic.append(dataframe["moderi_96"]) item_buy_logic.append(dataframe["cti"] < self.buy_32_cti.value) item_buy_logic.append(dataframe["rsi_20"] < dataframe["rsi_20"].shift(1)) item_buy_logic.append(dataframe["rsi_4"] < self.buy_32_rsi.value) item_buy_logic.append(dataframe["ema_20_1h"] > dataframe["ema_25_1h"]) item_buy_logic.append( (dataframe["open"] - dataframe["close"]) / dataframe["close"] < self.buy_32_dip.value ) item_buy_logic.append(dataframe["close"] < (dataframe["sma_15"] * self.buy_32_ma_offset.value)) item_buy_logic.append( ((dataframe["open"] < dataframe["ema_20_1h"]) & (dataframe["low"] < dataframe["ema_20_1h"])) | ((dataframe["open"] > dataframe["ema_20_1h"]) & (dataframe["low"] > dataframe["ema_20_1h"])) ) # Condition #33 - Quick mode buy elif index == 33: # Non-Standard protections # Logic item_buy_logic.append(dataframe["moderi_96"]) item_buy_logic.append(dataframe["cti"] < self.buy_33_cti.value) item_buy_logic.append(dataframe["close"] < (dataframe["ema_13"] * self.buy_33_ma_offset.value)) item_buy_logic.append(dataframe["ewo"] > self.buy_33_ewo.value) item_buy_logic.append(dataframe["rsi_14"] < self.buy_33_rsi.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_33_volume.value) ) # Condition #34 - Quick mode buy elif index == 34: # Non-Standard protections # Logic item_buy_logic.append(dataframe["cti"] < self.buy_34_cti.value) item_buy_logic.append( (dataframe["open"] - dataframe["close"]) / dataframe["close"] < self.buy_34_dip.value ) item_buy_logic.append(dataframe["close"] < dataframe["ema_13"] * self.buy_34_ma_offset.value) item_buy_logic.append(dataframe["ewo"] < self.buy_34_ewo.value) item_buy_logic.append( dataframe["volume"] < (dataframe["volume_mean_4"] * self.buy_34_volume.value) ) # 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"] * 0.984) item_buy_logic.append(dataframe["ewo"] > 9.6) item_buy_logic.append(dataframe["rsi_14"] < 32.0) item_buy_logic.append(dataframe["cti"] < -0.5) # 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"] * 0.98) item_buy_logic.append(dataframe["ewo"] < -8.8) item_buy_logic.append(dataframe["cti"] < -0.8) # Condition #37 - PMAX2 buy elif index == 37: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe["pm"] > dataframe["pmax_thresh"]) item_buy_logic.append(dataframe["close"] < dataframe["sma_75"] * 0.98) item_buy_logic.append(dataframe["ewo"] > 9.8) item_buy_logic.append(dataframe["rsi_14"] < 56.0) item_buy_logic.append(dataframe["cti"] < -0.7) item_buy_logic.append(dataframe["safe_dump_50_1h"]) # 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"] * 0.7) item_buy_logic.append(dataframe["ewo"] < -2.0) item_buy_logic.append(dataframe["cti"] < -0.86) # 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_1h"] > dataframe["senkou_a_1h"]) item_buy_logic.append(dataframe["efi_1h"] > 0) 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["cti"] < -0.73) # Start of trend item_buy_logic.append( ( dataframe["leading_senkou_span_a_1h"].shift(12) < dataframe["leading_senkou_span_b_1h"].shift(12) ) | (dataframe["efi_1h"] < 0) | (dataframe["ssl_up_1h"].shift(12) < dataframe["ssl_down_1h"].shift(12)) ) # Condition #40 - ZLEMA X buy elif index == 40: # Non-Standard protections (add below) # Logic item_buy_logic.append(qtpylib.crossed_above(dataframe["zlema_2"], dataframe["zlema_4"])) item_buy_logic.append(dataframe["hrsi"] < 30) item_buy_logic.append(dataframe["cci"] < -200) item_buy_logic.append(dataframe["rsi_14"] < 30) item_buy_logic.append(dataframe["exceed_low_1h"]) item_buy_logic.append(dataframe["seq_buy_1h"] > 8) item_buy_logic.append(dataframe["volume"] > 0) item_buy = reduce(lambda x, y: x & y, item_buy_logic) dataframe.loc[item_buy, "buy_tag"] += str(index) + " " conditions.append(item_buy) if conditions: dataframe.loc[:, "buy"] = reduce(lambda x, y: x | y, conditions) return dataframe def populate_exit_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 """ # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop if self.config["runmode"].value in ("live", "dry_run"): self.load_hold_trades_config() if not self.hold_trade_ids: # We have no pairs we want to hold until profit, sell return True if trade.id not in self.hold_trade_ids: # This pair is not on the list to hold until profit, sell return True trade_profit_ratio = self.hold_trade_ids[trade.id] current_profit_ratio = trade.calc_profit_ratio(rate) if sell_reason == "force_sell": formatted_profit_ratio = "{}%".format(trade_profit_ratio * 100) formatted_current_profit_ratio = "{}%".format(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 True elif current_profit_ratio >= trade_profit_ratio: # This pair is on the list to hold, and we reached minimum profit, sell return True # This pair is on the list to hold, and we haven't reached minimum profit, hold return False else: return True # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe["close"] * 100 return smadif # Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe["close"] - dataframe["low"]) - (dataframe["high"] - dataframe["close"])) / ( dataframe["high"] - dataframe["low"] ) mfv = mfv.fillna(0.0) # float division by zero mfv *= dataframe["volume"] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe["volume"].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name="cmf") # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name="{0} Williams %R".format(period), ) 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 # 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 = "MA_" + str(MAtype) + "_" + str(length) atr = "ATR_" + str(period) pm = "pm_" + str(period) + "_" + str(multiplier) + "_" + str(length) + "_" + str(MAtype) pmx = "pmX_" + str(period) + "_" + str(multiplier) + "_" + str(length) + "_" + str(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"), None) 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): df = dataframe.copy() 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 # Kalman Filter def KalmanFilter(dtloc, source="close"): dtKF = dtloc.copy().fillna(0) dtKF["TRANGE"] = ta.TRANGE(dtloc).fillna(0) def calc_dtKF(dfr, init=0): global calc_dtKF_value_1 global calc_dtKF_value_2 global calc_dtKF_value_3 global calc_dtKF_source if init == 1: calc_dtKF_value_1 = 0.0 calc_dtKF_value_2 = 0.0 calc_dtKF_value_3 = 0.0 calc_dtKF_source = 0.0 return calc_dtKF_value_1 = 0.2 * (dfr[source] - calc_dtKF_source) + 0.8 * calc_dtKF_value_1 calc_dtKF_value_2 = 0.1 * dfr["TRANGE"] + 0.8 * calc_dtKF_value_2 if calc_dtKF_value_2 != 0: vlambda = abs(calc_dtKF_value_1 / calc_dtKF_value_2) else: vlambda = 0 valpha = (-1 * math.pow(vlambda, 2) + math.sqrt(math.pow(vlambda, 4) + 16 * math.pow(vlambda, 2))) / 8 calc_dtKF_value_3 = valpha * dfr[source] + (1 - valpha) * calc_dtKF_value_3 calc_dtKF_source = dfr[source] return calc_dtKF_value_3 calc_dtKF(None, init=1) dtKF["KF"] = dtKF.apply(calc_dtKF, axis=1) return dtKF["KF"]