# source: https://raw.githubusercontent.com/TheoBrigitte/freqtrade/82c1c1e1f5f3457fa5376f65124042aff88d7493/strategies/nfix/NostalgiaForInfinity772martinsk3.py import copy import logging import pathlib import rapidjson import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.misc import json_load, file_dump_json from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes,DecimalParameter,IntParameter,informative 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 logging import technical.indicators as ftt logger = logging.getLogger(__name__) log = logging.getLogger(__name__) try: import pandas_ta as pta except ImportError: log.error("IMPORTANT - please install the pandas_ta python module which is needed for this strategy. If you're running Docker, add RUN pip install pandas_ta to your Dockerfile, otherwise run: pip install pandas_ta") else: log.info("pandas_ta successfully imported") ########################################################################################################### ## NostalgiaForInfinityV8 by iterativ ## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ########################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ## ## A pairlist with 40 to 80 pairs. Volume pairlist works well. ## ## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ## ## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ## ## Ensure that you don't override any variables in you config.json. Especially ## ## the timeframe (must be 5m). ## ## use_sell_signal must set to true (or not set at all). ## ## sell_profit_only must set to false (or not set at all). ## ## ignore_roi_if_buy_signal must set to true (or not set at all). ## ## ## ########################################################################################################### ## HOLD SUPPORT ## ## 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 ## ## * This feature can be completely disabled with the holdSupportEnabled parameter ## ## ## ########################################################################################################### ## 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_TheoBrigitte_freqtrade__NostalgiaForInfinity772martinsk3__20241203_205711(IStrategy): INTERFACE_VERSION = 2 plot_config = { 'main_plot': { }, 'subplots': { "buy tag": { 'buy_tag': {'color': 'green'} }, } } # ROI table: minimal_roi = { "0": 10, } stoploss = -0.99 # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = '5m' res_timeframe = 'none' info_timeframe = '1h' # 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 # Do you want to use the hold feature? (with hold-trades.json) holdSupportEnabled = 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_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 480 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_17_enable": True, "buy_condition_18_enable": True, "buy_condition_19_enable": True, "buy_condition_20_enable": True, "buy_condition_21_enable": True, "buy_condition_22_enable": True, "buy_condition_23_enable": True, "buy_condition_24_enable": True, "buy_condition_25_enable": True, "buy_condition_26_enable": True, "buy_condition_27_enable": True, "buy_condition_28_enable": True, "buy_condition_29_enable": True, "buy_condition_30_enable": True, "buy_condition_31_enable": True, "buy_condition_32_enable": True, "buy_condition_33_enable": True, "buy_condition_34_enable": True, "buy_condition_35_enable": True, "buy_condition_36_enable": True, "buy_condition_37_enable": True, "buy_condition_38_enable": True, "buy_condition_39_enable": True, "buy_condition_40_enable": True, "buy_condition_41_enable": True, "buy_condition_42_enable": True, "buy_condition_43_enable": True, "buy_condition_44_enable": True, ############# } 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, "base_nb_candles_sell": 22, "high_offset": 1.014, "high_offset_2": 1.01, ## Dead fish "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37 ############# } ############################################################# 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" : True, "ema_slow_len" : "15", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : True, "sma200_rising_val" : "24", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips" : True, "safe_dips_type" : "110", "safe_pump" : True, "safe_pump_type" : "100", "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" : "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" : True, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips" : True, "safe_dips_type" : "70", "safe_pump" : True, "safe_pump_type" : "20", "safe_pump_period" : "36", "btc_1h_not_downtrend" : True }, 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" : True, "safe_dips_type" : "50", "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" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "100", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "50", "safe_dips" : 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" : True, "safe_dips_type" : "130", "safe_pump" : False, "safe_pump_type" : "10", "safe_pump_period" : "36", "btc_1h_not_downtrend" : True }, 39: { "ema_fast" : False, "ema_fast_len" : "100", "ema_slow" : True, "ema_slow_len" : "15", "close_above_ema_fast" : True, "close_above_ema_fast_len" : "100", "close_above_ema_slow" : True, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : False, "safe_dips_type" : "100", "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "48", "btc_1h_not_downtrend" : True }, 40: { "ema_fast" : True, "ema_fast_len" : "12", "ema_slow" : True, "ema_slow_len" : "25", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : True, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : False, "safe_dips_type" : "130", "safe_pump" : False, "safe_pump_type" : "50", "safe_pump_period" : "48", "btc_1h_not_downtrend" : True }, 41: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : True, "safe_dips_type" : "50", "safe_pump" : False, "safe_pump_type" : "120", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True }, 42: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : True, "safe_dips_type" : "110", "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True }, 43: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : True, "safe_dips_type" : "70", "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : True }, 44: { "ema_fast" : False, "ema_fast_len" : "12", "ema_slow" : False, "ema_slow_len" : "12", "close_above_ema_fast" : False, "close_above_ema_fast_len" : "200", "close_above_ema_slow" : False, "close_above_ema_slow_len" : "200", "sma200_rising" : False, "sma200_rising_val" : "30", "sma200_1h_rising" : False, "sma200_1h_rising_val" : "20", "safe_dips" : False, "safe_dips_type" : "100", "safe_pump" : False, "safe_pump_type" : "100", "safe_pump_period" : "24", "btc_1h_not_downtrend" : False } } # SMAOffset #base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) #low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True) high_offset_2 = DecimalParameter(1.010, 1.020, default=sell_params['high_offset_2'], space='sell', optimize=True) # Strict dips - level 10 buy_dip_threshold_10_1 = 0.015 buy_dip_threshold_10_2 = 0.1 buy_dip_threshold_10_3 = 0.24 buy_dip_threshold_10_4 = 0.42 # Strict dips - level 20 buy_dip_threshold_20_1 = 0.016 buy_dip_threshold_20_2 = 0.11 buy_dip_threshold_20_3 = 0.26 buy_dip_threshold_20_4 = 0.44 # Strict dips - level 30 buy_dip_threshold_30_1 = 0.018 buy_dip_threshold_30_2 = 0.12 buy_dip_threshold_30_3 = 0.28 buy_dip_threshold_30_4 = 0.46 # Strict dips - level 40 buy_dip_threshold_40_1 = 0.019 buy_dip_threshold_40_2 = 0.13 buy_dip_threshold_40_3 = 0.3 buy_dip_threshold_40_4 = 0.48 # Normal dips - level 50 buy_dip_threshold_50_1 = 0.02 buy_dip_threshold_50_2 = 0.14 buy_dip_threshold_50_3 = 0.32 buy_dip_threshold_50_4 = 0.5 # Normal dips - level 60 buy_dip_threshold_60_1 = 0.022 buy_dip_threshold_60_2 = 0.18 buy_dip_threshold_60_3 = 0.34 buy_dip_threshold_60_4 = 0.56 # Normal dips - level 70 buy_dip_threshold_70_1 = 0.023 buy_dip_threshold_70_2 = 0.2 buy_dip_threshold_70_3 = 0.36 buy_dip_threshold_70_4 = 0.6 # Normal dips - level 80 buy_dip_threshold_80_1 = 0.024 buy_dip_threshold_80_2 = 0.22 buy_dip_threshold_80_3 = 0.38 buy_dip_threshold_80_4 = 0.66 # Normal dips - level 70 buy_dip_threshold_90_1 = 0.025 buy_dip_threshold_90_2 = 0.23 buy_dip_threshold_90_3 = 0.4 buy_dip_threshold_90_4 = 0.7 # Loose dips - level 100 buy_dip_threshold_100_1 = 0.026 buy_dip_threshold_100_2 = 0.24 buy_dip_threshold_100_3 = 0.42 buy_dip_threshold_100_4 = 0.8 # Loose dips - level 110 buy_dip_threshold_110_1 = 0.027 buy_dip_threshold_110_2 = 0.26 buy_dip_threshold_110_3 = 0.44 buy_dip_threshold_110_4 = 0.84 # Loose dips - level 120 buy_dip_threshold_120_1 = 0.028 buy_dip_threshold_120_2 = 0.28 buy_dip_threshold_120_3 = 0.46 buy_dip_threshold_120_4 = 0.86 # Loose dips - level 130 buy_dip_threshold_130_1 = 0.028 buy_dip_threshold_130_2 = 0.3 buy_dip_threshold_130_3 = 0.48 buy_dip_threshold_130_4 = 0.9 dump= DecimalParameter(-1, 0, default=-0.15, space='buy', optimize=True) dump_buy= DecimalParameter(0, 0.5, default=0.15, space='buy', optimize=True) range_filter_length = IntParameter(10, 200, default=200, space='buy', optimize= False) # 24 hours - level 10 buy_pump_pull_threshold_10_24 = 2.2 buy_pump_threshold_10_24 = 0.42 # 36 hours - level 10 buy_pump_pull_threshold_10_36 = 2.0 buy_pump_threshold_10_36 = 0.58 # 48 hours - level 10 buy_pump_pull_threshold_10_48 = 2.0 buy_pump_threshold_10_48 = 0.8 # 24 hours - level 20 buy_pump_pull_threshold_20_24 = 2.2 buy_pump_threshold_20_24 = 0.46 # 36 hours - level 20 buy_pump_pull_threshold_20_36 = 2.0 buy_pump_threshold_20_36 = 0.6 # 48 hours - level 20 buy_pump_pull_threshold_20_48 = 2.0 buy_pump_threshold_20_48 = 0.81 # 24 hours - level 30 buy_pump_pull_threshold_30_24 = 2.2 buy_pump_threshold_30_24 = 0.5 # 36 hours - level 30 buy_pump_pull_threshold_30_36 = 2.0 buy_pump_threshold_30_36 = 0.62 # 48 hours - level 30 buy_pump_pull_threshold_30_48 = 2.0 buy_pump_threshold_30_48 = 0.82 # 24 hours - level 40 buy_pump_pull_threshold_40_24 = 2.2 buy_pump_threshold_40_24 = 0.54 # 36 hours - level 40 buy_pump_pull_threshold_40_36 = 2.0 buy_pump_threshold_40_36 = 0.63 # 48 hours - level 40 buy_pump_pull_threshold_40_48 = 2.0 buy_pump_threshold_40_48 = 0.84 # 24 hours - level 50 buy_pump_pull_threshold_50_24 = 1.75 buy_pump_threshold_50_24 = 0.6 # 36 hours - level 50 buy_pump_pull_threshold_50_36 = 1.75 buy_pump_threshold_50_36 = 0.64 # 48 hours - level 50 buy_pump_pull_threshold_50_48 = 1.75 buy_pump_threshold_50_48 = 0.85 # 24 hours - level 60 buy_pump_pull_threshold_60_24 = 1.75 buy_pump_threshold_60_24 = 0.62 # 36 hours - level 60 buy_pump_pull_threshold_60_36 = 1.75 buy_pump_threshold_60_36 = 0.66 # 48 hours - level 60 buy_pump_pull_threshold_60_48 = 1.75 buy_pump_threshold_60_48 = 0.9 # 24 hours - level 70 buy_pump_pull_threshold_70_24 = 1.75 buy_pump_threshold_70_24 = 0.63 # 36 hours - level 70 buy_pump_pull_threshold_70_36 = 1.75 buy_pump_threshold_70_36 = 0.67 # 48 hours - level 70 buy_pump_pull_threshold_70_48 = 1.75 buy_pump_threshold_70_48 = 0.95 # 24 hours - level 80 buy_pump_pull_threshold_80_24 = 1.75 buy_pump_threshold_80_24 = 0.64 # 36 hours - level 80 buy_pump_pull_threshold_80_36 = 1.75 buy_pump_threshold_80_36 = 0.68 # 48 hours - level 80 buy_pump_pull_threshold_80_48 = 1.75 buy_pump_threshold_80_48 = 1.0 # 24 hours - level 90 buy_pump_pull_threshold_90_24 = 1.75 buy_pump_threshold_90_24 = 0.65 # 36 hours - level 90 buy_pump_pull_threshold_90_36 = 1.75 buy_pump_threshold_90_36 = 0.69 # 48 hours - level 90 buy_pump_pull_threshold_90_48 = 1.75 buy_pump_threshold_90_48 = 1.1 # 24 hours - level 100 buy_pump_pull_threshold_100_24 = 1.7 buy_pump_threshold_100_24 = 0.66 # 36 hours - level 100 buy_pump_pull_threshold_100_36 = 1.7 buy_pump_threshold_100_36 = 0.7 # 48 hours - level 100 buy_pump_pull_threshold_100_48 = 1.4 buy_pump_threshold_100_48 = 1.6 # 24 hours - level 110 buy_pump_pull_threshold_110_24 = 1.7 buy_pump_threshold_110_24 = 0.7 # 36 hours - level 110 buy_pump_pull_threshold_110_36 = 1.7 buy_pump_threshold_110_36 = 0.74 # 48 hours - level 110 buy_pump_pull_threshold_110_48 = 1.4 buy_pump_threshold_110_48 = 1.8 # 24 hours - level 120 buy_pump_pull_threshold_120_24 = 1.7 buy_pump_threshold_120_24 = 0.78 # 36 hours - level 120 buy_pump_pull_threshold_120_36 = 1.7 buy_pump_threshold_120_36 = 0.78 # 48 hours - level 120 buy_pump_pull_threshold_120_48 = 1.4 buy_pump_threshold_120_48 = 2.0 # 5 hours - level 10 buy_dump_protection_10_5 = 0.4 # 5 hours - level 20 buy_dump_protection_20_5 = 0.44 # 5 hours - level 30 buy_dump_protection_30_5 = 0.50 # 5 hours - level 40 buy_dump_protection_40_5 = 0.58 # 5 hours - level 50 buy_dump_protection_50_5 = 0.66 # 5 hours - level 60 buy_dump_protection_60_5 = 0.74 buy_min_inc_1 = 0.022 buy_rsi_1h_min_1 = 20.0 buy_rsi_1h_max_1 = 84.0 buy_rsi_1 = 36.0 buy_mfi_1 = 50.0 buy_cti_1 = -0.92 buy_rsi_1h_min_2 = 32.0 buy_rsi_1h_max_2 = 84.0 buy_rsi_1h_diff_2 = 38.8 buy_mfi_2 = 49.0 buy_bb_offset_2 = 0.983 buy_volume_2 = 1.6 buy_bb40_bbdelta_close_3 = 0.045 buy_bb40_closedelta_close_3 = 0.023 buy_bb40_tail_bbdelta_3 = 0.418 buy_ema_rel_3 = 0.986 buy_cti_3 = -0.5 buy_bb20_close_bblowerband_4 = 0.979 buy_bb20_volume_4 = 10.0 buy_cti_4 = -0.8 buy_ema_open_mult_5 = 0.018 buy_bb_offset_5 = 0.996 buy_ema_rel_5 = 0.915 buy_cti_5 = -0.84 buy_volume_5 = 1.8 buy_ema_open_mult_6 = 0.021 buy_bb_offset_6 = 0.976 buy_ema_open_mult_7 = 0.030 buy_cti_7 = -0.89 buy_cti_8 = -0.88 buy_rsi_8 = 40.0 buy_bb_offset_8 = 0.99 buy_rsi_1h_8 = 64.0 buy_volume_8 = 1.8 buy_ma_offset_9 = 0.968 buy_bb_offset_9 = 0.942 buy_rsi_1h_min_9 = 20.0 buy_rsi_1h_max_9 = 88.0 buy_mfi_9 = 50.0 buy_ma_offset_10 = 0.98 buy_bb_offset_10 = 0.972 buy_rsi_1h_10 = 50.0 buy_ma_offset_11 = 0.946 buy_min_inc_11 = 0.038 buy_rsi_1h_min_11 = 46.0 buy_rsi_1h_max_11 = 84.0 buy_rsi_11 = 38.0 buy_mfi_11 = 36.0 buy_ma_offset_12 = 0.921 buy_rsi_12 = 28.0 buy_ewo_12 = 1.8 buy_cti_12 = -0.7 buy_ma_offset_13 = 0.99 buy_cti_13 = -0.82 buy_ewo_13 = -9.0 buy_ema_open_mult_14 = 0.014 buy_bb_offset_14 = 0.988 buy_ma_offset_14 = 0.945 buy_cti_14 = -0.86 buy_ema_open_mult_15 = 0.024 buy_ma_offset_15 = 0.958 buy_rsi_15 = 28.0 buy_ema_rel_15 = 0.974 buy_ma_offset_16 = 0.953 buy_rsi_16 = 31.0 buy_ewo_16 = 2.8 buy_cti_16 = -0.84 buy_ma_offset_17 = 0.99 buy_ewo_17 = -9.4 buy_cti_17 = -0.96 buy_volume_17 = 2.0 buy_rsi_18 = 33.0 buy_bb_offset_18 = 0.986 buy_volume_18 = 2.0 buy_cti_18 = -0.86 buy_rsi_1h_min_19 = 30.0 buy_chop_max_19 = 21.3 buy_rsi_20 = 36.0 buy_rsi_1h_20 = 16.0 buy_cti_20 = -0.84 buy_volume_20 = 2.0 buy_rsi_21 = 14.0 buy_rsi_1h_21 = 28.0 buy_cti_21 = -0.902 buy_volume_21 = 2.0 buy_volume_22 = 2.0 buy_bb_offset_22 = 0.984 buy_ma_offset_22 = 0.942 buy_ewo_22 = 5.8 buy_rsi_22 = 36.0 buy_23_bb_offset = 0.984 buy_23_ewo = 7.8 buy_23_rsi = 32.4 buy_23_rsi_1h = 80.0 buy_23_cti = -0.66 buy_23_r = -80.0 buy_23_r_1h = -80.0 buy_24_rsi_max = 50.0 buy_24_rsi_1h_min = 66.9 buy_25_ma_offset = 0.922 buy_25_rsi_4 = 38.0 buy_25_cti = -0.76 buy_26_zema_low_offset = 0.94 buy_26_cti = -0.91 buy_26_r = -35.0 buy_26_r_1h = -60.0 buy_26_volume = 2.0 buy_27_wr_max = 90.0 buy_27_wr_1h_max = 90.0 buy_27_rsi_max = 50 buy_27_cti = -0.93 buy_27_volume = 2.0 buy_28_ma_offset = 0.97 buy_28_ewo = 7.2 buy_28_rsi = 32.5 buy_28_cti = -0.9 buy_29_ma_offset = 0.94 buy_29_ewo = -4.0 buy_29_cti = -0.95 buy_30_ma_offset = 0.97 buy_30_ewo = 7.4 buy_30_rsi = 40.0 buy_30_cti = -0.88 buy_31_ma_offset = 0.962 buy_31_ewo = -10.4 buy_31_wr = -90.0 buy_31_cti = -0.89 buy_32_ma_offset = 0.934 buy_32_dip = 0.005 buy_32_rsi = 46.0 buy_32_cti = -0.8 buy_33_ma_offset = 0.988 buy_33_rsi = 32.0 buy_33_cti = -0.88 buy_33_ewo = 6.4 buy_33_volume = 2.0 buy_34_ma_offset = 0.93 buy_34_dip = 0.005 buy_34_ewo = -6.0 buy_34_cti = -0.88 buy_34_volume = 2.0 buy_35_ma_offset = 0.984 buy_35_ewo = 9.6 buy_35_rsi = 32.0 buy_35_cti = -0.5 buy_36_ma_offset = 0.98 buy_36_ewo = -8.8 buy_36_cti = -0.8 buy_37_ma_offset = 0.98 buy_37_ewo = 9.8 buy_37_rsi = 56.0 buy_37_cti = -0.7 buy_38_ma_offset = 0.98 buy_38_ewo = -5.2 buy_38_cti = -0.96 buy_39_cti = -0.77 buy_39_r = -60.0 buy_39_r_1h = -38.0 buy_40_hrsi = 30.0 buy_40_cci = -240.0 buy_40_rsi = 30.0 buy_40_cti = -0.8 buy_40_r = -90.0 buy_40_r_1h = -90.0 buy_41_cti_1h = -0.84 buy_41_r_1h = -42.0 buy_41_ma_offset = 0.96 buy_41_cti = -0.8 buy_41_r = -75.0 buy_42_cti_1h = 0.5 buy_42_r_1h = -46.0 buy_42_ema_open_mult = 0.018 buy_42_bb_offset = 0.992 buy_43_cti_1h = 0.5 buy_43_r_1h = -80.0 buy_43_bb40_bbdelta_close = 0.046 buy_43_bb40_closedelta_close = 0.02 buy_43_bb40_tail_bbdelta = 0.5 buy_43_cti = -0.6 buy_43_r = -90.0 buy_44_ma_offset = 0.982 buy_44_ewo = -18.143 buy_44_cti = -0.8 buy_44_r_1h = -75.0 # Sell sell_condition_1_enable = True sell_condition_2_enable = True sell_condition_3_enable = True sell_condition_4_enable = True sell_condition_5_enable = True sell_condition_6_enable = True sell_condition_7_enable = True sell_condition_8_enable = True # 48h for pump sell checks sell_pump_threshold_48_1 = 0.9 sell_pump_threshold_48_2 = 0.7 sell_pump_threshold_48_3 = 0.5 # 36h for pump sell checks sell_pump_threshold_36_1 = 0.72 sell_pump_threshold_36_2 = 4.0 sell_pump_threshold_36_3 = 1.0 # 24h for pump sell checks sell_pump_threshold_24_1 = 0.68 sell_pump_threshold_24_2 = 0.62 sell_pump_threshold_24_3 = 0.88 sell_rsi_bb_1 = 79.5 sell_rsi_bb_2 = 81 sell_rsi_main_3 = 82 sell_dual_rsi_rsi_4 = 73.4 sell_dual_rsi_rsi_1h_4 = 79.6 sell_ema_relative_5 = 0.024 sell_rsi_diff_5 = 4.4 sell_rsi_under_6 = 79.0 sell_rsi_1h_7 = 81.7 sell_bb_relative_8 = 1.1 # Profit over EMA200 sell_custom_profit_bull_0 = 0.012 sell_custom_rsi_under_bull_0 = 34.0 sell_custom_profit_bull_1 = 0.02 sell_custom_rsi_under_bull_1 = 35.0 sell_custom_profit_bull_2 = 0.03 sell_custom_rsi_under_bull_2 = 36.0 sell_custom_profit_bull_3 = 0.04 sell_custom_rsi_under_bull_3 = 37.0 sell_custom_profit_bull_4 = 0.05 sell_custom_rsi_under_bull_4 = 42.0 sell_custom_profit_bull_5 = 0.06 sell_custom_rsi_under_bull_5 = 49.0 sell_custom_profit_bull_6 = 0.07 sell_custom_rsi_under_bull_6 = 50.0 sell_custom_profit_bull_7 = 0.08 sell_custom_rsi_under_bull_7 = 54.0 sell_custom_profit_bull_8 = 0.09 sell_custom_rsi_under_bull_8 = 50.0 sell_custom_profit_bull_9 = 0.1 sell_custom_rsi_under_bull_9 = 46.0 sell_custom_profit_bull_10 = 0.12 sell_custom_rsi_under_bull_10 = 42.0 sell_custom_profit_bull_11 = 0.20 sell_custom_rsi_under_bull_11 = 30.0 sell_custom_profit_bear_0 = 0.012 sell_custom_rsi_under_bear_0 = 34.0 sell_custom_profit_bear_1 = 0.02 sell_custom_rsi_under_bear_1 = 35.0 sell_custom_profit_bear_2 = 0.03 sell_custom_rsi_under_bear_2 = 37.0 sell_custom_profit_bear_3 = 0.04 sell_custom_rsi_under_bear_3 = 44.0 sell_custom_profit_bear_4 = 0.05 sell_custom_rsi_under_bear_4 = 48.0 sell_custom_profit_bear_5 = 0.06 sell_custom_rsi_under_bear_5 = 50.0 sell_custom_rsi_over_bear_5 = 78.0 sell_custom_profit_bear_6 = 0.07 sell_custom_rsi_under_bear_6 = 52.0 sell_custom_rsi_over_bear_6 = 78.0 sell_custom_profit_bear_7 = 0.08 sell_custom_rsi_under_bear_7 = 54.0 sell_custom_rsi_over_bear_7 = 80.0 sell_custom_profit_bear_8 = 0.09 sell_custom_rsi_under_bear_8 = 52.0 sell_custom_rsi_over_bear_8 = 82.0 sell_custom_profit_bear_9 = 0.1 sell_custom_rsi_under_bear_9 = 46.0 sell_custom_profit_bear_10 = 0.12 sell_custom_rsi_under_bear_10 = 42.0 sell_custom_profit_bear_11 = 0.20 sell_custom_rsi_under_bear_11 = 30.0 # Profit under EMA200 sell_custom_under_profit_bull_0 = 0.01 sell_custom_under_rsi_under_bull_0 = 38.0 sell_custom_under_profit_bull_1 = 0.02 sell_custom_under_rsi_under_bull_1 = 46.0 sell_custom_under_profit_bull_2 = 0.03 sell_custom_under_rsi_under_bull_2 = 47.0 sell_custom_under_profit_bull_3 = 0.04 sell_custom_under_rsi_under_bull_3 = 48.0 sell_custom_under_profit_bull_4 = 0.05 sell_custom_under_rsi_under_bull_4 = 49.0 sell_custom_under_profit_bull_5 = 0.06 sell_custom_under_rsi_under_bull_5 = 50.0 sell_custom_under_profit_bull_6 = 0.07 sell_custom_under_rsi_under_bull_6 = 52.0 sell_custom_under_profit_bull_7 = 0.08 sell_custom_under_rsi_under_bull_7 = 54.0 sell_custom_under_profit_bull_8 = 0.09 sell_custom_under_rsi_under_bull_8 = 50.0 sell_custom_under_profit_bull_9 = 0.1 sell_custom_under_rsi_under_bull_9 = 46.0 sell_custom_under_profit_bull_10 = 0.12 sell_custom_under_rsi_under_bull_10 = 42.0 sell_custom_under_profit_bull_11 = 0.2 sell_custom_under_rsi_under_bull_11 = 30.0 sell_custom_under_profit_bear_0 = 0.01 sell_custom_under_rsi_under_bear_0 = 38.0 sell_custom_under_profit_bear_1 = 0.02 sell_custom_under_rsi_under_bear_1 = 56.0 sell_custom_under_profit_bear_2 = 0.03 sell_custom_under_rsi_under_bear_2 = 57.0 sell_custom_under_profit_bear_3 = 0.04 sell_custom_under_rsi_under_bear_3 = 58.0 sell_custom_under_profit_bear_4 = 0.05 sell_custom_under_rsi_under_bear_4 = 57.0 sell_custom_under_profit_bear_5 = 0.06 sell_custom_under_rsi_under_bear_5 = 56.0 sell_custom_under_rsi_over_bear_5 = 78.0 sell_custom_under_profit_bear_6 = 0.07 sell_custom_under_rsi_under_bear_6 = 55.0 sell_custom_under_rsi_over_bear_6 = 78.0 sell_custom_under_profit_bear_7 = 0.08 sell_custom_under_rsi_under_bear_7 = 54.0 sell_custom_under_rsi_over_bear_7 = 80.0 sell_custom_under_profit_bear_8 = 0.09 sell_custom_under_rsi_under_bear_8 = 50.0 sell_custom_under_rsi_over_bear_8 = 82.0 sell_custom_under_profit_bear_9 = 0.1 sell_custom_under_rsi_under_bear_9 = 46.0 sell_custom_under_profit_bear_10 = 0.12 sell_custom_under_rsi_under_bear_10 = 42.0 sell_custom_under_profit_bear_11 = 0.2 sell_custom_under_rsi_under_bear_11 = 30.0 # Profit targets for pumped pairs 48h 1 sell_custom_pump_profit_1_1 = 0.01 sell_custom_pump_rsi_1_1 = 34.0 sell_custom_pump_profit_1_2 = 0.02 sell_custom_pump_rsi_1_2 = 40.0 sell_custom_pump_profit_1_3 = 0.04 sell_custom_pump_rsi_1_3 = 42.0 sell_custom_pump_profit_1_4 = 0.1 sell_custom_pump_rsi_1_4 = 34.0 sell_custom_pump_profit_1_5 = 0.2 sell_custom_pump_rsi_1_5 = 30.0 # Profit targets for pumped pairs 36h 1 sell_custom_pump_profit_2_1 = 0.01 sell_custom_pump_rsi_2_1 = 34.0 sell_custom_pump_profit_2_2 = 0.02 sell_custom_pump_rsi_2_2 = 40.0 sell_custom_pump_profit_2_3 = 0.04 sell_custom_pump_rsi_2_3 = 42.0 sell_custom_pump_profit_2_4 = 0.1 sell_custom_pump_rsi_2_4 = 34.0 sell_custom_pump_profit_2_5 = 0.2 sell_custom_pump_rsi_2_5 = 30.0 # Profit targets for pumped pairs 24h 1 sell_custom_pump_profit_3_1 = 0.01 sell_custom_pump_rsi_3_1 = 34.0 sell_custom_pump_profit_3_2 = 0.02 sell_custom_pump_rsi_3_2 = 40.0 sell_custom_pump_profit_3_3 = 0.04 sell_custom_pump_rsi_3_3 = 42.0 sell_custom_pump_profit_3_4 = 0.1 sell_custom_pump_rsi_3_4 = 34.0 sell_custom_pump_profit_3_5 = 0.2 sell_custom_pump_rsi_3_5 = 30.0 # SMA descending sell_custom_dec_profit_min_1 = 0.05 sell_custom_dec_profit_max_1 = 0.12 # Under EMA100 sell_custom_dec_profit_min_2 = 0.07 sell_custom_dec_profit_max_2 = 0.16 # Trail 1 #sell_trail_profit_min_1 = 0.03 #sell_trail_profit_max_1 = 0.05 #sell_trail_down_1 = 0.05 sell_trail_rsi_min_1 = 10.0 sell_trail_rsi_max_1 = 20.0 sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.11, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space='sell', decimals=3, optimize=False, load=True) # Trail 2 #sell_trail_profit_min_2 = 0.1 #sell_trail_profit_max_2 = 0.4 #sell_trail_down_2 = 0.03 sell_trail_rsi_min_2 = 20.0 sell_trail_rsi_max_2 = 50.0 # Trail 3 sell_trail_profit_min_3 = 0.06 sell_trail_profit_max_3 = 0.2 sell_trail_down_3 = 0.05 # Trail 4 sell_trail_profit_min_4 = 0.03 sell_trail_profit_max_4 = 0.06 sell_trail_down_4 = 0.02 # Under & near EMA200, accept profit sell_custom_profit_under_profit_min_1 = 0.001 sell_custom_profit_under_profit_max_1 = 0.008 sell_custom_profit_under_rel_1 = 0.024 sell_custom_profit_under_rsi_diff_1 = 4.4 sell_custom_profit_under_profit_2 = 0.03 sell_custom_profit_under_rel_2 = 0.024 sell_custom_profit_under_rsi_diff_2 = 4.4 # Under & near EMA200, take the loss sell_custom_stoploss_under_rel_1 = 0.002 sell_custom_stoploss_under_rsi_diff_1 = 10.0 # Long duration/recover stoploss 1 sell_custom_stoploss_long_profit_min_1 = -0.08 sell_custom_stoploss_long_profit_max_1 = -0.04 sell_custom_stoploss_long_recover_1 = 0.14 sell_custom_stoploss_long_rsi_diff_1 = 4.0 # Long duration/recover stoploss 2 sell_custom_stoploss_long_recover_2 = 0.06 sell_custom_stoploss_long_rsi_diff_2 = 40.0 # Pumped, descending SMA sell_custom_pump_dec_profit_min_1 = 0.005 sell_custom_pump_dec_profit_max_1 = 0.05 sell_custom_pump_dec_profit_min_2 = 0.04 sell_custom_pump_dec_profit_max_2 = 0.06 sell_custom_pump_dec_profit_min_3 = 0.06 sell_custom_pump_dec_profit_max_3 = 0.09 sell_custom_pump_dec_profit_min_4 = 0.02 sell_custom_pump_dec_profit_max_4 = 0.04 # Pumped 48h 1, under EMA200 sell_custom_pump_under_profit_min_1 = 0.04 sell_custom_pump_under_profit_max_1 = 0.09 #Deadfish is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.08 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0 , space='sell', optimize = is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.5 ,space='sell', optimize = is_optimize_deadfish) # Pumped trail 1 sell_custom_pump_trail_profit_min_1 = 0.05 sell_custom_pump_trail_profit_max_1 = 0.07 sell_custom_pump_trail_down_1 = 0.05 sell_custom_pump_trail_rsi_min_1 = 20.0 sell_custom_pump_trail_rsi_max_1 = 70.0 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_1 = 0.01 sell_custom_stoploss_pump_min_1 = -0.02 sell_custom_stoploss_pump_max_1 = -0.01 sell_custom_stoploss_pump_ma_offset_1 = 0.94 # Stoploss, pumped, 48h 1 sell_custom_stoploss_pump_max_profit_2 = 0.025 sell_custom_stoploss_pump_loss_2 = -0.05 sell_custom_stoploss_pump_ma_offset_2 = 0.92 # Stoploss, pumped, 36h 3 sell_custom_stoploss_pump_max_profit_3 = 0.008 sell_custom_stoploss_pump_loss_3 = -0.12 sell_custom_stoploss_pump_ma_offset_3 = 0.88 # Recover sell_custom_recover_profit_1 = 0.06 sell_custom_recover_min_loss_1 = 0.12 sell_custom_recover_profit_min_2 = 0.01 sell_custom_recover_profit_max_2 = 0.05 sell_custom_recover_min_loss_2 = 0.06 sell_custom_recover_rsi_2 = 46.0 # Profit for long duration trades sell_custom_long_profit_min_1 = 0.03 sell_custom_long_profit_max_1 = 0.04 sell_custom_long_duration_min_1 = 900 ############################################################# hold_trades_cache = None @informative('15m') def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Dynamic RSI # dataframe['mom'] = momentum(dataframe, 5) # dataframe['corr'] = get_rolling_corr(dataframe['mom'], dataframe, 20) # dataframe['dyn_rsi'] = ta.RSI(dataframe['close'], get_rsi_lenght(dataframe['corr'])) # dataframe['dyn_rsi_3r_mean'] = (dataframe['dyn_rsi']).rolling(3).mean() dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) return dataframe @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_trades_cache is None: hold_trades_config_file = NostalgiaForInfinity772martinsk2.get_hold_trades_config_file() if hold_trades_config_file: self.hold_trades_cache = HoldsCache(hold_trades_config_file) if self.hold_trades_cache: self.hold_trades_cache.load() 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.holdSupportEnabled and 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: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_11: return True, 'signal_profit_o_bull_11' elif self.sell_custom_profit_bull_11 > current_profit >= self.sell_custom_profit_bull_10: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_10: return True, 'signal_profit_o_bull_10' elif self.sell_custom_profit_bull_10 > current_profit >= self.sell_custom_profit_bull_9: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_9: return True, 'signal_profit_o_bull_9' elif self.sell_custom_profit_bull_9 > current_profit >= self.sell_custom_profit_bull_8: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_8: return True, 'signal_profit_o_bull_8' elif self.sell_custom_profit_bull_8 > current_profit >= self.sell_custom_profit_bull_7: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_7): return True, 'signal_profit_o_bull_7' elif self.sell_custom_profit_bull_7 > current_profit >= self.sell_custom_profit_bull_6: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_6) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_6' elif self.sell_custom_profit_bull_6 > current_profit >= self.sell_custom_profit_bull_5: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_5) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_5' elif self.sell_custom_profit_bull_5 > current_profit >= self.sell_custom_profit_bull_4: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_4) and (last_candle['cmf'] < 0.0) : return True, 'signal_profit_o_bull_4' elif self.sell_custom_profit_bull_4 > current_profit >= self.sell_custom_profit_bull_3: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_3) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_3' elif self.sell_custom_profit_bull_3 > current_profit >= self.sell_custom_profit_bull_2: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_2) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_2' elif self.sell_custom_profit_bull_2 > current_profit >= self.sell_custom_profit_bull_1: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_1) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_1' elif self.sell_custom_profit_bull_1 > current_profit >= self.sell_custom_profit_bull_0: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bull_0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bull_0' else: if current_profit >= self.sell_custom_profit_bear_11: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_11: return True, 'signal_profit_o_bear_11' elif self.sell_custom_profit_bear_11 > current_profit >= self.sell_custom_profit_bear_10: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_10: return True, 'signal_profit_o_bear_10' elif self.sell_custom_profit_bear_10 > current_profit >= self.sell_custom_profit_bear_9: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_9: return True, 'signal_profit_o_bear_9' elif self.sell_custom_profit_bear_9 > current_profit >= self.sell_custom_profit_bear_8: if last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_8: return True, 'signal_profit_o_bear_8_1' elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_8): return True, 'signal_profit_o_bear_8_2' elif self.sell_custom_profit_bear_8 > current_profit >= self.sell_custom_profit_bear_7: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_7): return True, 'signal_profit_o_bear_7_1' elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_7): return True, 'signal_profit_o_bear_7_2' elif self.sell_custom_profit_bear_7 > current_profit >= self.sell_custom_profit_bear_6: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_6): return True, 'signal_profit_o_bear_6_1' elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_6): return True, 'signal_profit_o_bear_6_2' elif self.sell_custom_profit_bear_6 > current_profit >= self.sell_custom_profit_bear_5: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_5): return True, 'signal_profit_o_bear_5_1' elif (last_candle['rsi_14'] > self.sell_custom_rsi_over_bear_5): return True, 'signal_profit_o_bear_5_2' elif self.sell_custom_profit_bear_5 > current_profit >= self.sell_custom_profit_bear_4: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_4): return True, 'signal_profit_o_bear_4' elif self.sell_custom_profit_bear_4 > current_profit >= self.sell_custom_profit_bear_3: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_3) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_3' elif self.sell_custom_profit_bear_3 > current_profit >= self.sell_custom_profit_bear_2: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_2) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_2' elif self.sell_custom_profit_bear_2 > current_profit >= self.sell_custom_profit_bear_1: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_1) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_o_bear_1' elif self.sell_custom_profit_bear_1 > current_profit >= self.sell_custom_profit_bear_0: if (last_candle['rsi_14'] < self.sell_custom_rsi_under_bear_0) 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: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_11: return True, 'signal_profit_u_bull_11' elif self.sell_custom_under_profit_bull_11 > current_profit >= self.sell_custom_under_profit_bull_10: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_10: return True, 'signal_profit_u_bull_10' elif self.sell_custom_under_profit_bull_10 > current_profit >= self.sell_custom_under_profit_bull_9: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_9: return True, 'signal_profit_u_bull_9' elif self.sell_custom_under_profit_bull_9 > current_profit >= self.sell_custom_under_profit_bull_8: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_8: return True, 'signal_profit_u_bull_8' elif self.sell_custom_under_profit_bull_8 > current_profit >= self.sell_custom_under_profit_bull_7: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_7: return True, 'signal_profit_u_bull_7' elif self.sell_custom_under_profit_bull_7 > current_profit >= self.sell_custom_under_profit_bull_6: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_6: return True, 'signal_profit_u_bull_6' elif self.sell_custom_under_profit_bull_6 > current_profit >= self.sell_custom_under_profit_bull_5: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_5: return True, 'signal_profit_u_bull_5' elif self.sell_custom_under_profit_bull_5 > current_profit >= self.sell_custom_under_profit_bull_4: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_4: return True, 'signal_profit_u_bull_4' elif self.sell_custom_under_profit_bull_4 > current_profit >= self.sell_custom_under_profit_bull_3: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_3: return True, 'signal_profit_u_bull_3' elif self.sell_custom_under_profit_bull_3 > current_profit >= self.sell_custom_under_profit_bull_2: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_2: return True, 'signal_profit_u_bull_2' elif self.sell_custom_under_profit_bull_2 > current_profit >= self.sell_custom_under_profit_bull_1: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_1: return True, 'signal_profit_u_bull_1' elif self.sell_custom_under_profit_bull_1 > current_profit >= self.sell_custom_under_profit_bull_0: if (last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bull_0) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_u_bull_0' else: if current_profit >= self.sell_custom_under_profit_bear_11: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_11: return True, 'signal_profit_u_bear_11' elif self.sell_custom_under_profit_bear_11 > current_profit >= self.sell_custom_under_profit_bear_10: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_10: return True, 'signal_profit_u_bear_10' elif self.sell_custom_under_profit_bear_10 > current_profit >= self.sell_custom_under_profit_bear_9: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_9: return True, 'signal_profit_u_bear_9' elif self.sell_custom_under_profit_bear_9 > current_profit >= self.sell_custom_under_profit_bear_8: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_8: return True, 'signal_profit_u_bear_8_1' elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_8): return True, 'signal_profit_u_bear_8_2' elif self.sell_custom_under_profit_bear_8 > current_profit >= self.sell_custom_under_profit_bear_7: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_7: return True, 'signal_profit_u_bear_7_1' elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_7): return True, 'signal_profit_u_bear_7_2' elif self.sell_custom_under_profit_bear_7 > current_profit >= self.sell_custom_under_profit_bear_6: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_6: return True, 'signal_profit_u_bear_6_1' elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_6): return True, 'signal_profit_u_bear_6_2' elif self.sell_custom_under_profit_bear_6 > current_profit >= self.sell_custom_under_profit_bear_5: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_5: return True, 'signal_profit_u_bear_5_1' elif (last_candle['rsi_14'] > self.sell_custom_under_rsi_over_bear_5): return True, 'signal_profit_u_bear_5_2' elif self.sell_custom_under_profit_bear_5 > current_profit >= self.sell_custom_under_profit_bear_4: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_4: return True, 'signal_profit_u_bear_4' elif self.sell_custom_under_profit_bear_4 > current_profit >= self.sell_custom_under_profit_bear_3: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_3: return True, 'signal_profit_u_bear_3' elif self.sell_custom_under_profit_bear_3 > current_profit >= self.sell_custom_under_profit_bear_2: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_2: return True, 'signal_profit_u_bear_2' elif self.sell_custom_under_profit_bear_2 > current_profit >= self.sell_custom_under_profit_bear_1: if last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_1: return True, 'signal_profit_u_bear_1' elif self.sell_custom_under_profit_bear_1 > current_profit >= self.sell_custom_under_profit_bear_0: if (last_candle['rsi_14'] < self.sell_custom_under_rsi_under_bear_0) 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: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_5: return True, 'signal_profit_p_1_5' elif self.sell_custom_pump_profit_1_5 > current_profit >= self.sell_custom_pump_profit_1_4: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_4: return True, 'signal_profit_p_1_4' elif self.sell_custom_pump_profit_1_4 > current_profit >= self.sell_custom_pump_profit_1_3: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_3: return True, 'signal_profit_p_1_3' elif self.sell_custom_pump_profit_1_3 > current_profit >= self.sell_custom_pump_profit_1_2: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_2: return True, 'signal_profit_p_1_2' elif self.sell_custom_pump_profit_1_2 > current_profit >= self.sell_custom_pump_profit_1_1: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_1_1: 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: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_5: return True, 'signal_profit_p_2_5' elif self.sell_custom_pump_profit_2_5 > current_profit >= self.sell_custom_pump_profit_2_4: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_4: return True, 'signal_profit_p_2_4' elif self.sell_custom_pump_profit_2_4 > current_profit >= self.sell_custom_pump_profit_2_3: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_3: return True, 'signal_profit_p_2_3' elif self.sell_custom_pump_profit_2_3 > current_profit >= self.sell_custom_pump_profit_2_2: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_2: return True, 'signal_profit_p_2_2' elif self.sell_custom_pump_profit_2_2 > current_profit >= self.sell_custom_pump_profit_2_1: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_2_1: 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: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_5: return True, 'signal_profit_p_3_5' elif self.sell_custom_pump_profit_3_5 > current_profit >= self.sell_custom_pump_profit_3_4: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_4: return True, 'signal_profit_p_3_4' elif self.sell_custom_pump_profit_3_4 > current_profit >= self.sell_custom_pump_profit_3_3: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_3: return True, 'signal_profit_p_3_3' elif self.sell_custom_pump_profit_3_3 > current_profit >= self.sell_custom_pump_profit_3_2: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_2: return True, 'signal_profit_p_3_2' elif self.sell_custom_pump_profit_3_2 > current_profit >= self.sell_custom_pump_profit_3_1: if last_candle['rsi_14'] < self.sell_custom_pump_rsi_3_1: 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 > current_profit >= self.sell_custom_dec_profit_min_1) and (last_candle['sma_200_dec_20']): return True, 'signal_profit_d_1' elif (self.sell_custom_dec_profit_max_2 > current_profit >= self.sell_custom_dec_profit_min_2) and (last_candle['close'] < last_candle['ema_100']): return True, 'signal_profit_d_2' return False, None def sell_trail_main(self, current_profit: float, last_candle, max_profit: float) -> tuple: if (self.sell_trail_profit_max_1 > current_profit >= self.sell_trail_profit_min_1) and (self.sell_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_trail_down_1)) and (last_candle['moderi_96'] == False): return True, 'signal_profit_t_1' elif (self.sell_trail_profit_max_2 > current_profit >= self.sell_trail_profit_min_2) and (self.sell_trail_rsi_min_2 < last_candle['rsi_14'] < self.sell_trail_rsi_max_2) and (max_profit > (current_profit + self.sell_trail_down_2)) and (last_candle['ema_25'] < last_candle['ema_50']): return True, 'signal_profit_t_2' elif (self.sell_trail_profit_max_3 > current_profit >= self.sell_trail_profit_min_3) and (max_profit > (current_profit + self.sell_trail_down_3)) and (last_candle['sma_200_dec_20_1h']): return True, 'signal_profit_t_3' elif (self.sell_trail_profit_max_4 > current_profit >= self.sell_trail_profit_min_4) and (max_profit > (current_profit + self.sell_trail_down_4)) and (last_candle['sma_200_dec_24']) and (last_candle['cmf'] < 0.0): return True, 'signal_profit_t_4' return False, None def sell_duration_main(self, current_profit: float, last_candle, trade: 'Trade', current_time: 'datetime') -> tuple: # Pumped pair, short duration if (last_candle['sell_pump_24_1_1h']) and (0.2 > current_profit >= 0.07) and (current_time - timedelta(minutes=30) < trade.open_date_utc): return True, 'signal_profit_p_s_1' elif (self.sell_custom_long_profit_min_1 < current_profit < self.sell_custom_long_profit_max_1) and (current_time - timedelta(minutes=self.sell_custom_long_duration_min_1) > trade.open_date_utc): return True, 'signal_profit_l_1' return False, None def sell_under_min(self, current_profit: float, last_candle) -> tuple: if ((last_candle['moderi_96']) == False): # Downtrend if (self.sell_custom_profit_under_profit_max_1 > current_profit >= self.sell_custom_profit_under_profit_min_1) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_1): return True, 'signal_profit_u_e_1' else: # Uptrend if (current_profit >= self.sell_custom_profit_under_profit_2) and (last_candle['close'] < last_candle['ema_200']) and (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_2) and (last_candle['rsi_14'] > last_candle['rsi_14_1h'] + self.sell_custom_profit_under_rsi_diff_2): return True, 'signal_profit_u_e_2' return False, None def sell_stoploss(self, current_profit: float, last_candle, previous_candle_1) -> tuple: # if (-0.12 <= current_profit < -0.08): # 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 (-0.16 <= current_profit < -0.12): # 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 (-0.2 <= current_profit < -0.16): # 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 < -0.2): # 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 > current_profit >= self.sell_custom_pump_dec_profit_min_1) 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 > current_profit >= self.sell_custom_pump_dec_profit_min_2) 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 > current_profit >= self.sell_custom_pump_dec_profit_min_3) 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 > current_profit >= self.sell_custom_pump_dec_profit_min_4) and (last_candle['sma_200_dec_20']) and (last_candle['sell_pump_24_2_1h']): return True, 'signal_profit_p_d_4' return False, None def sell_pump_extra(self, current_profit: float, last_candle, max_profit: float) -> tuple: # Pumped 48h 1, under EMA200 if (self.sell_custom_pump_under_profit_max_1 > current_profit >= self.sell_custom_pump_under_profit_min_1) and (last_candle['sell_pump_48_1_1h']) and (last_candle['close'] < last_candle['ema_200']): return True, 'signal_profit_p_u_1' # Pumped 36h 2, trail 1 elif (last_candle['sell_pump_36_2_1h']) and (self.sell_custom_pump_trail_profit_max_1 > current_profit >= self.sell_custom_pump_trail_profit_min_1) and (self.sell_custom_pump_trail_rsi_min_1 < last_candle['rsi_14'] < self.sell_custom_pump_trail_rsi_max_1) and (max_profit > (current_profit + self.sell_custom_pump_trail_down_1)): return True, 'signal_profit_p_t_1' return False, None def sell_recover(self, current_profit: float, last_candle, max_loss: float) -> tuple: if (max_loss > self.sell_custom_recover_min_loss_1) and (current_profit >= self.sell_custom_recover_profit_1): return True, 'signal_profit_r_1' elif (max_loss > self.sell_custom_recover_min_loss_2) and (self.sell_custom_recover_profit_max_2 > current_profit >= self.sell_custom_recover_profit_min_2) and (last_candle['rsi_14'] < self.sell_custom_recover_rsi_2) and (last_candle['ema_25'] < last_candle['ema_50']): return True, 'signal_profit_r_2' return False, None def sell_r_1(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if last_candle['r_480'] > -0.5: return True, 'signal_profit_w_1_1' elif 0.03 > current_profit >= 0.02: if last_candle['r_480'] > -0.6: return True, 'signal_profit_w_1_2' elif 0.04 > current_profit >= 0.03: if last_candle['r_480'] > -0.7: return True, 'signal_profit_w_1_3' elif 0.05 > current_profit >= 0.04: if last_candle['r_480'] > -0.8: return True, 'signal_profit_w_1_4' elif 0.06 > current_profit >= 0.05: if last_candle['r_480'] > -0.9: return True, 'signal_profit_w_1_5' elif 0.07 > current_profit >= 0.06: if last_candle['r_480'] > -2.0: return True, 'signal_profit_w_1_6' elif 0.08 > current_profit >= 0.07: if last_candle['r_480'] > -2.2: return True, 'signal_profit_w_1_7' elif 0.09 > current_profit >= 0.08: if last_candle['r_480'] > -2.4: return True, 'signal_profit_w_1_8' elif 0.1 > current_profit >= 0.09: if last_candle['r_480'] > -2.6: return True, 'signal_profit_w_1_9' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 72.0): return True, 'signal_profit_w_1_10' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_w_1_11' elif current_profit >= 0.2: if (last_candle['r_480'] > -1.0) and (last_candle['rsi_14'] > 80.0): return True, 'signal_profit_w_1_12' return False, None def sell_r_2(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_1' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -4.1) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -4.2) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_3' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -4.3) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_4' elif 0.06 > current_profit >= 0.05: if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_5' elif 0.07 > current_profit >= 0.06: if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_6' elif 0.08 > current_profit >= 0.07: if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_7' elif 0.09 > current_profit >= 0.08: if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_8' elif 0.1 > current_profit >= 0.09: if (last_candle['r_480'] > -4.8) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_9' elif 0.12 > current_profit >= 0.1: if (last_candle['r_480'] > -4.4) and (last_candle['rsi_14'] > 80.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_10' elif 0.2 > current_profit >= 0.12: if (last_candle['r_480'] > -3.2) and (last_candle['rsi_14'] > 81.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_11' elif current_profit >= 0.2: if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 81.5) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_2_12' return False, None def sell_r_3(self, current_profit: float, last_candle) -> tuple: if 0.02 > current_profit >= 0.012: if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_1' elif 0.03 > current_profit >= 0.02: if (last_candle['r_480'] > -3.5) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_2' elif 0.04 > current_profit >= 0.03: if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 74.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_3' elif 0.05 > current_profit >= 0.04: if (last_candle['r_480'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['stochrsi_fastk_96'] > 99.0) and (last_candle['stochrsi_fastd_96'] > 99.0): return True, 'signal_profit_w_3_4' return False, None def sell_r_4(self, current_profit: float, last_candle) -> tuple: if (0.02 > current_profit >= 0.012): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 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'] > -2.5) 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'] > -3.0) 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'] > -3.5) 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'] > -4.0) 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'] > -4.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_6' elif (0.08 > current_profit >= 0.07): if (last_candle['r_480'] > -5.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_7' elif (0.09 > current_profit >= 0.08): if (last_candle['r_480'] > -5.5) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_8' elif (0.1 > current_profit >= 0.09): if (last_candle['r_480'] > -4.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_9' elif (0.12 > current_profit >= 0.1): if (last_candle['r_480'] > -3.0) and (last_candle['rsi_14'] > 79.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_10' elif (0.2 > current_profit >= 0.12): if (last_candle['r_480'] > -2.5) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_11' elif (current_profit >= 0.2): if (last_candle['r_480'] > -2.0) and (last_candle['rsi_14'] > 80.0) and (last_candle['cti'] > 0.9): return True, 'signal_profit_w_4_12' return False, None def sell_quick_mode(self, current_profit: float, max_profit:float, last_candle, previous_candle_1) -> tuple: if (0.06 > current_profit > 0.02) and (last_candle['rsi_14'] > 80.0): return True, 'signal_profit_q_1' if (0.06 > current_profit > 0.02) and (last_candle['cti'] > 0.95): return True, 'signal_profit_q_2' if (last_candle['close'] < last_candle['atr_high_thresh_q']) and (previous_candle_1['close'] > previous_candle_1['atr_high_thresh_q']): if (0.05 > current_profit > 0.02): return True, 'signal_profit_q_atr' elif (current_profit < -0.08): return True, 'signal_stoploss_q_atr' if (0.04 > current_profit > 0.02) and (last_candle['pm'] <= last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.1): return True, 'signal_profit_q_pmax_bull' if (0.045 > current_profit > 0.003) and (last_candle['pm'] > last_candle['pmax_thresh']) and (last_candle['close'] > last_candle['sma_21'] * 1.016): return True, 'signal_profit_q_pmax_bear' return False, None def sell_ichi(self, current_profit: float, max_profit:float, max_loss:float, last_candle, previous_candle_1, trade: 'Trade', current_time: 'datetime') -> tuple: if (0.0 < current_profit < 0.05) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle['rsi_14'] > 78.0): return True, 'signal_profit_ichi_u' elif (-0.03 < current_profit < -0.0) and (current_time - timedelta(minutes=1440) > trade.open_date_utc) and (last_candle['rsi_14'] > 75.0): return True, 'signal_stoploss_ichi_u' elif (max_loss > 0.07) and (current_profit > 0.02): return True, 'signal_profit_ichi_r_0' elif (max_loss > 0.06) and (current_profit > 0.03): return True, 'signal_profit_ichi_r_1' elif (max_loss > 0.05) and (current_profit > 0.04): return True, 'signal_profit_ichi_r_2' elif (max_loss > 0.04) and (current_profit > 0.05): return True, 'signal_profit_ichi_r_3' elif (max_loss > 0.03) and (current_profit > 0.06): return True, 'signal_profit_ichi_r_4' elif (0.05 < current_profit < 0.1) and (current_time - timedelta(minutes=720) > trade.open_date_utc): return True, 'signal_profit_ichi_slow' elif (0.07 < current_profit < 0.1) and (max_profit-current_profit > 0.025) and (max_profit > 0.1): return True, 'signal_profit_ichi_t' elif (current_profit < -0.1): return True, 'signal_stoploss_ichi' return False, None def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] previous_candle_3 = dataframe.iloc[-4] previous_candle_4 = dataframe.iloc[-5] previous_candle_5 = dataframe.iloc[-6] buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag 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) filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0) ): return f"sell_stoploss_deadfish( {buy_tag})" if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)): return 'trail_target_1' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)): return 'trail_target_2' elif (current_profit > 3) & (last_candle['rsi'] > 85): return 'RSI-85 target' if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal1' if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) & (last_candle['rsi']>50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']): return 'sell signal1 * 1.01' if (current_profit > 0) & (count_of_buys < 4) & (last_candle['close'] > last_candle['hma_50']) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)): return 'sell signal2' if (current_profit > 0) & (count_of_buys >= 4) & (last_candle['close'] > last_candle['hma_50'] * 1.01) & (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] > last_candle['rsi_slow']) : return 'sell signal2 * 1.01' if (current_profit > 0.05) & (last_candle['rsi'] < 42.0) & (last_candle['cmf'] < -0.4): return f"profit exit1( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.0) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) : return f"profit exit2( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 41.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_1h'] < -0.0): return f"profit exit3( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 44.0) & (last_candle['cmf'] < -0.1) & (last_candle['cmf_15m'] < -0.1) & (last_candle['cmf_1h'] < -0.1) : return f"profit exit4( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 40.0) & (last_candle['cmf'] < -0.2) & (last_candle['cmf_15m'] < -0.2): return f"pprofit exit5( {buy_tag})" elif (current_profit > 0.05) & (last_candle['rsi'] < 43.0) & (last_candle['cmf'] < -0.4) & (last_candle['cmf_15m'] < -0.0) & (last_candle['cmf_1h'] < -0.0) : return f"profit exit6( {buy_tag})" elif (current_profit > 0.02) & (last_candle['rsi'] < 30.0) & (last_candle['cmf'] < -0.4): return f"profit exit bear1( {buy_tag})" elif (current_profit > 0.03) & (last_candle['rsi'] < 35.0) & (last_candle['cmf'] < -0.4): return f"profit exit bear2( {buy_tag})" elif (current_profit > 0.01) & (last_candle['rsi'] < 25.0) & (last_candle['cmf'] < -0.6): return f"profit exit bear3( {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) # CTI informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) # Ichimoku ichi = ichimoku(informative_1h, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) informative_1h['chikou_span'] = ichi['chikou_span'] informative_1h['tenkan_sen'] = ichi['tenkan_sen'] informative_1h['kijun_sen'] = ichi['kijun_sen'] informative_1h['senkou_a'] = ichi['senkou_span_a'] informative_1h['senkou_b'] = ichi['senkou_span_b'] informative_1h['leading_senkou_span_a'] = ichi['leading_senkou_span_a'] informative_1h['leading_senkou_span_b'] = ichi['leading_senkou_span_b'] informative_1h['chikou_span_greater'] = (informative_1h['chikou_span'] > informative_1h['senkou_a']).shift(30).fillna(False) informative_1h.loc[:, 'cloud_top'] = informative_1h.loc[:, ['senkou_a', 'senkou_b']].max(axis=1) # 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, self.buy_pump_pull_threshold_10_24) informative_1h['safe_pump_36_10'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_10_36, self.buy_pump_pull_threshold_10_36) informative_1h['safe_pump_48_10'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_10_48, self.buy_pump_pull_threshold_10_48) informative_1h['safe_pump_24_20'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_20_24, self.buy_pump_pull_threshold_20_24) informative_1h['safe_pump_36_20'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_20_36, self.buy_pump_pull_threshold_20_36) informative_1h['safe_pump_48_20'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_20_48, self.buy_pump_pull_threshold_20_48) informative_1h['safe_pump_24_30'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_30_24, self.buy_pump_pull_threshold_30_24) informative_1h['safe_pump_36_30'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_30_36, self.buy_pump_pull_threshold_30_36) informative_1h['safe_pump_48_30'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_30_48, self.buy_pump_pull_threshold_30_48) informative_1h['safe_pump_24_40'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_40_24, self.buy_pump_pull_threshold_40_24) informative_1h['safe_pump_36_40'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_40_36, self.buy_pump_pull_threshold_40_36) informative_1h['safe_pump_48_40'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_40_48, self.buy_pump_pull_threshold_40_48) informative_1h['safe_pump_24_50'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_50_24, self.buy_pump_pull_threshold_50_24) informative_1h['safe_pump_36_50'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_50_36, self.buy_pump_pull_threshold_50_36) informative_1h['safe_pump_48_50'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_50_48, self.buy_pump_pull_threshold_50_48) informative_1h['safe_pump_24_60'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_60_24, self.buy_pump_pull_threshold_60_24) informative_1h['safe_pump_36_60'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_60_36, self.buy_pump_pull_threshold_60_36) informative_1h['safe_pump_48_60'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_60_48, self.buy_pump_pull_threshold_60_48) informative_1h['safe_pump_24_70'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_70_24, self.buy_pump_pull_threshold_70_24) informative_1h['safe_pump_36_70'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_70_36, self.buy_pump_pull_threshold_70_36) informative_1h['safe_pump_48_70'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_70_48, self.buy_pump_pull_threshold_70_48) informative_1h['safe_pump_24_80'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_80_24, self.buy_pump_pull_threshold_80_24) informative_1h['safe_pump_36_80'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_80_36, self.buy_pump_pull_threshold_80_36) informative_1h['safe_pump_48_80'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_80_48, self.buy_pump_pull_threshold_80_48) informative_1h['safe_pump_24_90'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_90_24, self.buy_pump_pull_threshold_90_24) informative_1h['safe_pump_36_90'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_90_36, self.buy_pump_pull_threshold_90_36) informative_1h['safe_pump_48_90'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_90_48, self.buy_pump_pull_threshold_90_48) informative_1h['safe_pump_24_100'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_100_24, self.buy_pump_pull_threshold_100_24) informative_1h['safe_pump_36_100'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_100_36, self.buy_pump_pull_threshold_100_36) informative_1h['safe_pump_48_100'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_100_48, self.buy_pump_pull_threshold_100_48) informative_1h['safe_pump_24_110'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_110_24, self.buy_pump_pull_threshold_110_24) informative_1h['safe_pump_36_110'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_110_36, self.buy_pump_pull_threshold_110_36) informative_1h['safe_pump_48_110'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_110_48, self.buy_pump_pull_threshold_110_48) informative_1h['safe_pump_24_120'] = self.safe_pump(informative_1h, 24, self.buy_pump_threshold_120_24, self.buy_pump_pull_threshold_120_24) informative_1h['safe_pump_36_120'] = self.safe_pump(informative_1h, 36, self.buy_pump_threshold_120_36, self.buy_pump_pull_threshold_120_36) informative_1h['safe_pump_48_120'] = self.safe_pump(informative_1h, 48, self.buy_pump_threshold_120_48, self.buy_pump_pull_threshold_120_48) informative_1h['safe_dump_10'] = ((informative_1h['hl_pct_change_5'] < self.buy_dump_protection_10_5) | (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) | (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) | (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) | (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) | (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) | (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) informative_1h['sell_pump_48_2'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_2) informative_1h['sell_pump_48_3'] = (informative_1h['hl_pct_change_48'] > self.sell_pump_threshold_48_3) informative_1h['sell_pump_36_1'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_1) informative_1h['sell_pump_36_2'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_2) informative_1h['sell_pump_36_3'] = (informative_1h['hl_pct_change_36'] > self.sell_pump_threshold_36_3) informative_1h['sell_pump_24_1'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_1) informative_1h['sell_pump_24_2'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_2) informative_1h['sell_pump_24_3'] = (informative_1h['hl_pct_change_24'] > self.sell_pump_threshold_24_3) 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'] dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) dataframe['hma_5'] = qtpylib.hull_moving_average(dataframe['close'], window=30) dataframe['hma_4'] = qtpylib.hull_moving_average(dataframe['close'], window=15) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe = dataframe.copy() # EMA 200 dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13) dataframe['ema_15'] = ta.EMA(dataframe, timeperiod=15) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_35'] = ta.EMA(dataframe, timeperiod=35) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) #pump stregth dataframe['zema_30'] = ftt.zema(dataframe, period=30) dataframe['zema_200'] = ftt.zema(dataframe, period=100) dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30'] # 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"] # Profit Maximizer - PMAX dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe['sma_21'] = ta.SMA(dataframe, timeperiod=21) dataframe['sma_68'] = ta.SMA(dataframe, timeperiod=68) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) # HLC3 dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 # HRSI dataframe['hull'] = (2 * dataframe['hlc3'] - ta.WMA(dataframe['hlc3'], 2)) dataframe['hrsi'] = ta.RSI(dataframe['hull'], 2) # ZLEMA dataframe['zlema_2'] = pta.zlma(dataframe['hlc3'], length = 2) dataframe['zlema_4'] = pta.zlma(dataframe['hlc3'], length = 4) # CCI dataframe['cci'] = ta.CCI(dataframe, source='hlc3', timeperiod=20) # 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, self.buy_dip_threshold_10_2, self.buy_dip_threshold_10_3, self.buy_dip_threshold_10_4) dataframe['safe_dips_20'] = self.safe_dips(dataframe, self.buy_dip_threshold_20_1, self.buy_dip_threshold_20_2, self.buy_dip_threshold_20_3, self.buy_dip_threshold_20_4) dataframe['safe_dips_30'] = self.safe_dips(dataframe, self.buy_dip_threshold_30_1, self.buy_dip_threshold_30_2, self.buy_dip_threshold_30_3, self.buy_dip_threshold_30_4) dataframe['safe_dips_40'] = self.safe_dips(dataframe, self.buy_dip_threshold_40_1, self.buy_dip_threshold_40_2, self.buy_dip_threshold_40_3, self.buy_dip_threshold_40_4) dataframe['safe_dips_50'] = self.safe_dips(dataframe, self.buy_dip_threshold_50_1, self.buy_dip_threshold_50_2, self.buy_dip_threshold_50_3, self.buy_dip_threshold_50_4) dataframe['safe_dips_60'] = self.safe_dips(dataframe, self.buy_dip_threshold_60_1, self.buy_dip_threshold_60_2, self.buy_dip_threshold_60_3, self.buy_dip_threshold_60_4) dataframe['safe_dips_70'] = self.safe_dips(dataframe, self.buy_dip_threshold_70_1, self.buy_dip_threshold_70_2, self.buy_dip_threshold_70_3, self.buy_dip_threshold_70_4) dataframe['safe_dips_80'] = self.safe_dips(dataframe, self.buy_dip_threshold_80_1, self.buy_dip_threshold_80_2, self.buy_dip_threshold_80_3, self.buy_dip_threshold_80_4) dataframe['safe_dips_90'] = self.safe_dips(dataframe, self.buy_dip_threshold_90_1, self.buy_dip_threshold_90_2, self.buy_dip_threshold_90_3, self.buy_dip_threshold_90_4) dataframe['safe_dips_100'] = self.safe_dips(dataframe, self.buy_dip_threshold_100_1, self.buy_dip_threshold_100_2, self.buy_dip_threshold_100_3, self.buy_dip_threshold_100_4) dataframe['safe_dips_110'] = self.safe_dips(dataframe, self.buy_dip_threshold_110_1, self.buy_dip_threshold_110_2, self.buy_dip_threshold_110_3, self.buy_dip_threshold_110_4) dataframe['safe_dips_120'] = self.safe_dips(dataframe, self.buy_dip_threshold_120_1, self.buy_dip_threshold_120_2, self.buy_dip_threshold_120_3, self.buy_dip_threshold_120_4) dataframe['safe_dips_130'] = self.safe_dips(dataframe, self.buy_dip_threshold_130_1, self.buy_dip_threshold_130_2, self.buy_dip_threshold_130_3, self.buy_dip_threshold_130_4) # Bollinger bands (hyperopt hard to implement) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] ### Other checks dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2']) dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int') # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe[f'range{self.range_filter_length.value}'] = range_filter(dataframe=dataframe,length=self.range_filter_length.value) dataframe ['test'] = ( (dataframe['close'].rolling(48).max() >= (dataframe['close'] * 1.125 )) & ( (dataframe['close'].rolling(288).max() >= (dataframe['close'] * 1.225 )) ) ) 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' for index in self.buy_protection_params: item_buy_protection_list = [True] global_buy_protection_params = self.buy_protection_params[index] if self.buy_params['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'].value in ('live', 'dry_run'): if self.has_bt_agefilter: item_buy_protection_list.append(dataframe['bt_agefilter_ok']) else: if self.has_downtime_protection: item_buy_protection_list.append(dataframe['live_data_ok']) item_buy_protection_list.append(dataframe['pump_strength'] < 0.15) item_buy_protection_list.append(dataframe['pump_strength'] > self.dump.value) item_buy_protection_list.append(dataframe['test']) item_buy_protection_list.append(dataframe[f'range{self.range_filter_length.value}'] < 1 ) dataframe['pump_strength'] < self.dump.value # 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) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_1) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_1) item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_1) item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_1) item_buy_logic.append(dataframe['cti'] < self.buy_cti_1) # 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) item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_2) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_2)) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_2)) # Condition #3 elif index == 3: # Non-Standard protections item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_3)) # 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)) item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3)) item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_bb40_tail_bbdelta_3)) 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) # 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 * dataframe['bb20_2_low']) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4)) item_buy_logic.append(dataframe['cti'] < self.buy_cti_4) # Condition #5 elif index == 5: # Non-Standard protections item_buy_logic.append(dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_5)) # 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)) 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)) item_buy_logic.append(dataframe['cti'] < self.buy_cti_5) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_5)) # 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)) 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)) # 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)) 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) # 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) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_8)) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_8) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_8)) # 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) item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_9) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_9) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_9) item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_9) # 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) item_buy_logic.append(dataframe['close'] < dataframe['bb20_2_low'] * self.buy_bb_offset_10) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_10) # 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) item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_rsi_1h_min_11) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_max_11) item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_11) item_buy_logic.append(dataframe['mfi'] < self.buy_mfi_11) # Condition #12 elif index == 12: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12) item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_12) item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_12) item_buy_logic.append(dataframe['cti'] < self.buy_cti_12) # 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) item_buy_logic.append(dataframe['cti'] < self.buy_cti_13) item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_13) # 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)) 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)) item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14) item_buy_logic.append(dataframe['cti'] < self.buy_cti_14) # Condition #15 elif index == 15: # Non-Standard protections item_buy_logic.append(dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15) # 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)) 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) item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15) # Condition #16 elif index == 16: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16) item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_16) item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_16) item_buy_logic.append(dataframe['cti'] < self.buy_cti_16) # Condition #17 elif index == 17: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17) item_buy_logic.append(dataframe['ewo'] < self.buy_ewo_17) item_buy_logic.append(dataframe['cti'] < self.buy_cti_17) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_17)) # 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) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_18)) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_18)) item_buy_logic.append(dataframe['cti'] < self.buy_cti_18) # 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) item_buy_logic.append(dataframe['chop'] < self.buy_chop_max_19) 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) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_20) item_buy_logic.append(dataframe['cti'] < self.buy_cti_20) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_20)) # Condition #21 elif index == 21: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_21) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_rsi_1h_21) item_buy_logic.append(dataframe['cti'] < self.buy_cti_21) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_volume_21)) # 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) > dataframe['volume']) item_buy_logic.append(dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_22) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_bb_offset_22)) item_buy_logic.append(dataframe['ewo'] > self.buy_ewo_22) item_buy_logic.append(dataframe['rsi_14'] < self.buy_rsi_22) # Condition #23 elif index == 23: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_23_bb_offset)) item_buy_logic.append(dataframe['ewo'] > self.buy_23_ewo) item_buy_logic.append(dataframe['cti'] < self.buy_23_cti) item_buy_logic.append(dataframe['r_480'] > self.buy_23_r) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_23_r_1h) item_buy_logic.append(dataframe['rsi_14'] < self.buy_23_rsi) item_buy_logic.append(dataframe['rsi_14_1h'] < self.buy_23_rsi_1h) # 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) item_buy_logic.append(dataframe['rsi_14_1h'] > self.buy_24_rsi_1h_min) # Condition #25 elif index == 25: # Non-Standard protections # Logic item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift()) item_buy_logic.append(dataframe['rsi_4'] < self.buy_25_rsi_4) 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)) item_buy_logic.append(dataframe['open'] > (dataframe['sma_20'] * self.buy_25_ma_offset)) 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) # Condition #26 elif index == 26: # Non-Standard protections item_buy_logic.append(dataframe['close'] < dataframe['sma_75']) # Logic item_buy_logic.append(dataframe['close'] < (dataframe['zema_61'] * self.buy_26_zema_low_offset)) item_buy_logic.append(dataframe['cti'] < self.buy_26_cti) item_buy_logic.append(dataframe['r_480'] > self.buy_26_r) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_26_r_1h) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_26_volume)) # Condition #27 elif index == 27: # Non-Standard protections # Logic item_buy_logic.append(dataframe['r_480'] < -self.buy_27_wr_max) item_buy_logic.append(dataframe['r_480_1h'] < -self.buy_27_wr_1h_max) item_buy_logic.append(dataframe['rsi_14_1h'] + dataframe['rsi_14'] < self.buy_27_rsi_max) item_buy_logic.append(dataframe['cti'] < self.buy_27_cti) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_27_volume)) # Condition #28 elif index == 28: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == True) item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_28_ma_offset) item_buy_logic.append(dataframe['ewo'] > self.buy_28_ewo) item_buy_logic.append(dataframe['rsi_14'] < self.buy_28_rsi) item_buy_logic.append(dataframe['cti'] < self.buy_28_cti) # Condition #29 elif index == 29: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == True) item_buy_logic.append(dataframe['close'] < dataframe['hull_75'] * self.buy_29_ma_offset) item_buy_logic.append(dataframe['ewo'] < self.buy_29_ewo) item_buy_logic.append(dataframe['cti'] < self.buy_29_cti) # Condition #30 elif index == 30: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == False) item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_30_ma_offset) item_buy_logic.append(dataframe['ewo'] > self.buy_30_ewo) item_buy_logic.append(dataframe['rsi_14'] < self.buy_30_rsi) item_buy_logic.append(dataframe['cti'] < self.buy_30_cti) # Condition #31 elif index == 31: # Non-Standard protections # Logic item_buy_logic.append(dataframe['moderi_64'] == False) item_buy_logic.append(dataframe['close'] < dataframe['zlema_68'] * self.buy_31_ma_offset ) item_buy_logic.append(dataframe['ewo'] < self.buy_31_ewo) item_buy_logic.append(dataframe['r_480'] < self.buy_31_wr) item_buy_logic.append(dataframe['cti'] < self.buy_31_cti) # 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) item_buy_logic.append(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) item_buy_logic.append(dataframe['rsi_4'] < self.buy_32_rsi) 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) item_buy_logic.append(dataframe['close'] < (dataframe['sma_15'] * self.buy_32_ma_offset)) 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) item_buy_logic.append(dataframe['close'] < (dataframe['ema_13'] * self.buy_33_ma_offset)) item_buy_logic.append(dataframe['ewo'] > self.buy_33_ewo) item_buy_logic.append(dataframe['rsi_14'] < self.buy_33_rsi) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_33_volume)) # Condition #34 - Quick mode buy elif index == 34: # Non-Standard protections # Logic item_buy_logic.append(dataframe['cti'] < self.buy_34_cti) item_buy_logic.append((dataframe['open'] - dataframe['close']) / dataframe['close'] < self.buy_34_dip) item_buy_logic.append(dataframe['close'] < dataframe['ema_13'] * self.buy_34_ma_offset) item_buy_logic.append(dataframe['ewo'] < self.buy_34_ewo) item_buy_logic.append(dataframe['volume'] < (dataframe['volume_mean_4'] * self.buy_34_volume)) # Condition #35 - PMAX0 buy elif index == 35: # Non-Standard protections # Logic item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_35_ma_offset) item_buy_logic.append(dataframe['ewo'] > self.buy_35_ewo) item_buy_logic.append(dataframe['rsi_14'] < self.buy_35_rsi) item_buy_logic.append(dataframe['cti'] < self.buy_35_cti) # Condition #36 - PMAX1 buy elif index == 36: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['pm'] <= dataframe['pmax_thresh']) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_36_ma_offset) item_buy_logic.append(dataframe['ewo'] < self.buy_36_ewo) item_buy_logic.append(dataframe['cti'] < self.buy_36_cti) # 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'] * self.buy_37_ma_offset) item_buy_logic.append(dataframe['ewo'] > self.buy_37_ewo) item_buy_logic.append(dataframe['rsi_14'] < self.buy_37_rsi) item_buy_logic.append(dataframe['cti'] < self.buy_37_cti) 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'] * self.buy_38_ma_offset) item_buy_logic.append(dataframe['ewo'] < self.buy_38_ewo) item_buy_logic.append(dataframe['cti'] < self.buy_38_cti) # Condition #39 - Ichimoku elif index == 39: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['tenkan_sen_1h'] > dataframe['kijun_sen_1h']) item_buy_logic.append(dataframe['close'] > dataframe['cloud_top_1h']) item_buy_logic.append(dataframe['leading_senkou_span_a_1h'] > dataframe['leading_senkou_span_b_1h']) item_buy_logic.append(dataframe['chikou_span_greater_1h']) item_buy_logic.append(dataframe['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'] < self.buy_39_cti) item_buy_logic.append(dataframe['r_480'] > self.buy_39_r) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_39_r_1h) item_buy_logic.append(dataframe['rsi_14_1h'] > dataframe['rsi_14_1h'].shift(12)) # Start of trend item_buy_logic.append( (dataframe['leading_senkou_span_a_1h'].shift(12) < dataframe['leading_senkou_span_b_1h'].shift(12)) | (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'] < self.buy_40_hrsi) item_buy_logic.append(dataframe['cci'] < self.buy_40_cci) item_buy_logic.append(dataframe['rsi_14'] < self.buy_40_rsi) item_buy_logic.append(dataframe['cti'] < self.buy_40_cti) item_buy_logic.append(dataframe['r_480'] > self.buy_40_r) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_40_r_1h) # Condition #41 elif index == 41: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['cti_1h'] < self.buy_41_cti_1h) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_41_r_1h) item_buy_logic.append(dataframe['close'] < dataframe['sma_75'] * self.buy_41_ma_offset) item_buy_logic.append(dataframe['cti'] < self.buy_41_cti) item_buy_logic.append(dataframe['r_480'] < self.buy_41_r) # Condition #42 elif index == 42: # Non-Standard protections (add below) # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['cti_1h'] < self.buy_42_cti_1h) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_42_r_1h) item_buy_logic.append(dataframe['ema_26'] > dataframe['ema_12']) item_buy_logic.append((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_42_ema_open_mult)) item_buy_logic.append((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) item_buy_logic.append(dataframe['close'] < (dataframe['bb20_2_low'] * self.buy_42_bb_offset)) # Condition #43 elif index == 43: # Non-Standard protections # Logic item_buy_logic.append(dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) item_buy_logic.append(dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) item_buy_logic.append(dataframe['cti_1h'] < self.buy_43_cti_1h) item_buy_logic.append(dataframe['r_480_1h'] > self.buy_43_r_1h) item_buy_logic.append(dataframe['bb40_2_low'].shift().gt(0)) item_buy_logic.append(dataframe['bb40_2_delta'].gt(dataframe['close'] * self.buy_43_bb40_bbdelta_close)) item_buy_logic.append(dataframe['closedelta'].gt(dataframe['close'] * self.buy_43_bb40_closedelta_close)) item_buy_logic.append(dataframe['tail'].lt(dataframe['bb40_2_delta'] * self.buy_43_bb40_tail_bbdelta)) item_buy_logic.append(dataframe['close'].lt(dataframe['bb40_2_low'].shift())) item_buy_logic.append(dataframe['close'].le(dataframe['close'].shift())) item_buy_logic.append(dataframe['cti'] < self.buy_43_cti) item_buy_logic.append(dataframe['r_480'] > self.buy_43_r) # Condition #44 elif index == 44: # Non-Standard protections # Logic item_buy_logic.append(dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset)) item_buy_logic.append(dataframe['ewo'] < self.buy_44_ewo) item_buy_logic.append(dataframe['cti'] < self.buy_44_cti) item_buy_logic.append(dataframe['r_480_1h'] < self.buy_44_r_1h) 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_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe def confirm_trade_exit(self, pair: str, trade: "Trade", order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: """ Called right before placing a regular sell order. Timing for this function is critical, so avoid doing heavy computations or network requests in this method. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns True (always confirming). :param pair: Pair that's about to be sold. :param trade: trade object. :param order_type: Order type (as configured in order_types). usually limit or market. :param amount: Amount in quote currency. :param rate: Rate that's going to be used when using limit orders :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled). :param sell_reason: Sell reason. Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss', 'sell_signal', 'force_sell', 'emergency_sell'] :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return bool: When True is returned, then the sell-order is placed on the exchange. False aborts the process """ # Just to be sure our hold data is loaded, should be a no-op call after the first bot loop if self.holdSupportEnabled and self.config['runmode'].value in ('live', 'dry_run'): self.load_hold_trades_config() if not self.hold_trades_cache: # Cache hasn't been setup, likely because the corresponding file does not exist, sell return True if not self.hold_trades_cache.data: # We have no pairs we want to hold until profit, sell return True if trade.id not in self.hold_trades_cache.data: # This pair is not on the list to hold until profit, sell return True trade_profit_ratio = self.hold_trades_cache.data[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 initial_safety_order_trigger = -0.018 max_safety_orders = 8 safety_order_step_scale = 1.2 safety_order_volume_scale = 1.4 def top_percent_change_dca(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 adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None # credits to reinuvader for not blindly executing safety orders # Obtain pair dataframe. dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Only buy when it seems it's climbing back up last_candle = dataframe.iloc[-1].squeeze() #previous_candle = dataframe.iloc[-2].squeeze() #previous2_candle = dataframe.iloc[-3].squeeze() #if last_candle['close'] / previous_candle['close'] < 1.02 : #if last_candle['close'] < previous_candle['close']: filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) if last_candle['pump_strength'] < self.dump.value : logger.info(f"DCA for {trade.pair} waiting for pump_strength ({last_candle['pump_strength']}) to rise above -0.15") return None #elif (count_of_buys > 3): # if (last_candle['cmf_1h'] < 0.00) and (last_candle['rsi_1h'] < 28) and (last_candle['close'] < last_candle['open']): # logger.info(f"DCA for {trade.pair} waiting RSI 1h ({last_candle['rsi_1h']}) to rise above 30 and CMF 1h ({last_candle['cmf_1h']}) to rise above 0") # return None #if (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30): # logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30") #if (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']): # return None #count_of_buys = 0 #for order in trade.orders: # if order.ft_is_open or order.ft_order_side != 'buy': # continue # if order.status == "closed": # count_of_buys += 1 if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) if (self.safety_order_step_scale > 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) elif (self.safety_order_step_scale < 1): safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale)) if current_profit <= (-1 * abs(safety_order_trigger)): try: # This returns first order stake size stake_amount = filled_buys[0].cost # This then calculates current safety order size stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1)) amount = stake_amount / current_rate logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None # 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'), np.NaN) return pm, pmx def calc_streaks(series: Series): # logic tables geq = series >= series.shift(1) # True if rising eq = series == series.shift(1) # True if equal logic_table = concat([geq, eq], axis=1) streaks = [0] # holds the streak duration, starts with 0 for row in logic_table.iloc[1:].itertuples(): # iterate through logic table if row[2]: # same value as before streaks.append(0) continue last_value = streaks[-1] if row[1]: # higher value than before streaks.append(last_value + 1 if last_value >= 0 else 1) # increase or reset to +1 else: # lower value than before streaks.append(last_value - 1 if last_value < 0 else -1) # decrease or reset to -1 return streaks # SSL Channels def SSLChannels(dataframe, length = 7): 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 class Cache: def __init__(self, path): self.path = path self.data = {} self._mtime = None self._previous_data = {} try: self.load() except FileNotFoundError: pass def load(self): if not self._mtime or self.path.stat().st_mtime_ns != self._mtime: self._load() def save(self): if self.data != self._previous_data: self._save() def process_loaded_data(self, data): return data def _load(self): # This method only exists to simplify unit testing with self.path.open("r") as rfh: try: data = json_load(rfh) except rapidjson.JSONDecodeError as exc: log.error("Failed to load JSON from %s: %s", self.path, exc) else: self.data = self.process_loaded_data(data) self._previous_data = copy.deepcopy(self.data) self._mtime = self.path.stat().st_mtime_ns def _save(self): # This method only exists to simplify unit testing file_dump_json(self.path, self.data, is_zip=False, log=True) self._mtime = self.path.stat().st_mtime self._previous_data = copy.deepcopy(self.data) class HoldsCache(Cache): def save(self): raise RuntimeError("The holds cache does not allow programatical save") def process_loaded_data(self, data): trade_ids = data.get("trade_ids") if not trade_ids: return {} rdata = {} 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, self.path ) continue if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) for trade_id %s in %s is not a float", profit_ratio, trade_id, self.path ) if trade_id in open_trades: formatted_profit_ratio = "{}%".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 ) rdata[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) else: # Initial Syntax profit_ratio = data.get("profit_ratio") if profit_ratio: if not isinstance(profit_ratio, float): log.error( "The 'profit_ratio' config value(%s) in %s is not a float", profit_ratio, self.path ) else: profit_ratio = 0.005 formatted_profit_ratio = "{}%".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, self.path ) continue if trade_id in open_trades: log.warning( "The trade %s is configured to HOLD until the profit ratio of %s is met", open_trades[trade_id], formatted_profit_ratio ) rdata[trade_id] = profit_ratio else: log.warning( "The trade_id(%s) is no longer open. Please remove it from 'trade_ids' in %s", trade_id, self.path ) return rdata def range_filter(dataframe, length = 10, min_rate_of_change=0.01): df = dataframe[['date','open','high','low','close','volume']].copy() highest_high = df['high'].rolling(length).max() lowest_low = df['low'].rolling(length).min() df['range'] = ((highest_high - lowest_low) / lowest_low) return df['range']