# source: https://raw.githubusercontent.com/dumpsayamrat/my-freqtrade/8a836fe942bb8e2fac2c2b72b04a76731bfad4ae/user_data/strategies/NFI5MOHO_WIP.py from typing import Optional import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter) from pandas import DataFrame from functools import reduce from freqtrade.persistence import Trade from datetime import datetime ########################################################################################################### ## NostalgiaForInfinityV5 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). ## ## ## ########################################################################################################### ## DONATIONS ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH (ERC20): 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## BEP20/BSC (ETH, BNB, ...): 0x86A0B21a20b39d16424B7c8003E4A7e12d78ABEe ## ## ## ########################################################################################################### # 20210624 # NostalgiaForInfinityV5 + MultiOffsetLamboV0 + Hyper-optimized some parameters. # I hope you do enough testing before proceeding. # Thank you to those who created these strategies. class Github_dumpsayamrat_my_freqtrade__NFI5MOHO_WIP__20250607_065455(IStrategy): def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: return 3.0 INTERFACE_VERSION = 3 order_types = { # required 'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False, # optional, but useful if you ever enable stoploss_on_exchange=True: # 'stoploss_on_exchange_interval': 60, # seconds between re-placing the stop order # 'stoploss_on_exchange_limit_ratio': 0.99, # limit-price = stop_price * 0.99 } ############################################################# buy_params = { ############# # Enable/Disable conditions "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": True, "buy_condition_16_enable": True, "buy_condition_17_enable": True, "buy_condition_18_enable": True, "buy_condition_19_enable": True, "buy_condition_20_enable": True, "buy_condition_21_enable": True, # Hyperopt # Multi Offset """ "base_nb_candles_buy": 42, "buy_chop_min_19": 29.3, "buy_rsi_1h_min_19": 52.4, "ewo_high": 5.262, "ewo_low": -8.164, "low_offset_ema": 0.984, "low_offset_kama": 0.919, "low_offset_sma": 0.97, "low_offset_t3": 0.904, "low_offset_trima": 0.984, """ "base_nb_candles_buy": 72, "buy_chop_min_19": 58.2, "buy_rsi_1h_min_19": 65.3, "ewo_high": 3.319, "ewo_low": -11.101, "low_offset_ema": 0.929, "low_offset_kama": 0.972, "low_offset_sma": 0.955, "low_offset_t3": 0.975, "low_offset_trima": 0.949, } 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, ############# # Hyperopt # Multi Offset "base_nb_candles_sell": 34, "high_offset_ema": 1.047, "high_offset_kama": 1.07, "high_offset_sma": 1.051, "high_offset_t3": 0.999, "high_offset_trima": 1.096, } # ROI table: minimal_roi = { "0": 0.111, "13": 0.048, "50": 0.015, "61": 0.01 } stoploss = -0.99 # Multi Offset base_nb_candles_buy = IntParameter( 5, 80, default=20, load=True, space='buy', optimize=True) base_nb_candles_sell = IntParameter( 5, 80, default=20, load=True, space='sell', optimize=True) low_offset_sma = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) high_offset_sma = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) low_offset_ema = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) high_offset_ema = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) low_offset_trima = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) high_offset_trima = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) low_offset_t3 = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) high_offset_t3 = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) low_offset_kama = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space='buy', optimize=False) high_offset_kama = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space='sell', optimize=False) # Protection ewo_low = DecimalParameter( -20.0, -8.0, default=-20.0, load=True, space='buy', optimize=True) ewo_high = DecimalParameter( 2.0, 12.0, default=6.0, load=True, space='buy', optimize=True) fast_ewo = IntParameter( 10, 50, default=50, load=True, space='buy', optimize=True) slow_ewo = IntParameter( 100, 200, default=200, load=True, space='buy', optimize=True) # MA list ma_types = ['sma', 'ema', 'trima', 't3'] ma_map = { 'sma': { 'low_offset': low_offset_sma.value, 'high_offset': high_offset_sma.value, 'calculate': 'SMA' }, 'ema': { 'low_offset': low_offset_ema.value, 'high_offset': high_offset_ema.value, 'calculate': 'EMA' }, 'trima': { 'low_offset': low_offset_trima.value, 'high_offset': high_offset_trima.value, 'calculate': 'TRIMA' }, 't3': { 'low_offset': low_offset_t3.value, 'high_offset': high_offset_t3.value, 'calculate': 'T3' }, 'kama': { 'low_offset': low_offset_kama.value, 'high_offset': high_offset_kama.value, 'calculate': 'KAMA' } } # 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' inf_1h = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 300 # plot config plot_config = { 'main_plot': { 'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}, }, } ############################################################# buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_18_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_19_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_20_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) buy_condition_21_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=True, load=True) # Spike look_back = IntParameter(9, 20, default=10, space="buy") spike_threshold = DecimalParameter(0.05, 0.15, decimals=3, default=0.08, space="buy") # e.g. 5% spike # Normal dips buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=False, load=True) # Strict dips buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True) # Loose dips buy_dip_threshold_9 = DecimalParameter(0.001, 0.05, default=0.026, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_10 = DecimalParameter(0.01, 0.2, default=0.24, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_11 = DecimalParameter(0.05, 0.4, default=0.42, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_12 = DecimalParameter(0.2, 0.5, default=0.66, space='buy', decimals=3, optimize=False, load=True) # 24 hours buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=False, load=True) # 36 hours buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True) # 48 hours buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=False, load=True) # 24 hours strict buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=False, load=True) # 36 hours strict buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=False, load=True) # 48 hours strict buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=False, load=True) # 24 hours loose buy_pump_pull_threshold_7 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_7 = DecimalParameter(0.4, 1.0, default=0.66, space='buy', decimals=3, optimize=False, load=True) # 36 hours loose buy_pump_pull_threshold_8 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_8 = DecimalParameter(0.4, 1.0, default=0.7, space='buy', decimals=3, optimize=False, load=True) # 48 hours loose buy_pump_pull_threshold_9 = DecimalParameter(1.5, 3.0, default=1.4, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_9 = DecimalParameter(0.4, 1.8, default=1.3, space='buy', decimals=3, optimize=False, load=True) buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.6, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=39.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space='buy', decimals=1, optimize=True, load=True) buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space='buy', decimals=3, optimize=True, load=True) buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', optimize=True, load=True) buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', optimize=True, load=True) buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', optimize=True, load=True) buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=False, load=True) buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space='buy', optimize=True, load=True) buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space='buy', decimals=2, optimize=True, load=True) buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, optimize=True, load=True) buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.982, space='buy', decimals=3, optimize=False, load=True) buy_ema_open_mult_6 = DecimalParameter(0.02, 0.03, default=0.025, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.984, space='buy', decimals=3, optimize=True, load=True) buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, optimize=True, load=True) buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_rel_7 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, optimize=False, load=True) buy_volume_8 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_8 = DecimalParameter(36.0, 40.0, default=20.0, space='buy', decimals=1, optimize=True, load=True) buy_tail_diff_8 = DecimalParameter(3.0, 10.0, default=3.5, space='buy', decimals=1, optimize=True, load=True) buy_volume_9 = DecimalParameter(1.0, 4.0, default=1.0, space='buy', decimals=2, optimize=True, load=True) buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.97, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.985, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_9 = DecimalParameter(36.0, 65.0, default=30.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_10 = DecimalParameter(1.0, 8.0, default=2.4, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.944, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_10 = DecimalParameter(20.0, 40.0, default=37.0, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.939, space='buy', decimals=3, optimize=True, load=True) buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.022, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=56.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_11 = DecimalParameter(30.0, 48.0, default=48.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_12 = DecimalParameter(1.0, 10.0, default=1.7, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, optimize=True, load=True) buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, optimize=True, load=True) buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_13 = DecimalParameter(1.0, 10.0, default=1.6, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.978, space='buy', decimals=3, optimize=True, load=True) buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-10.4, space='buy', decimals=1, optimize=False, load=True) buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.986, space='buy', decimals=3, optimize=True, load=True) buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.97, space='buy', decimals=3, optimize=True, load=True) buy_volume_15 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.018, space='buy', decimals=3, optimize=True, load=True) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.954, space='buy', decimals=3, optimize=True, load=True) buy_rsi_15 = DecimalParameter(30.0, 50.0, default=28.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=False, load=True) buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.952, space='buy', decimals=3, optimize=True, load=True) buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space='buy', decimals=1, optimize=True, load=True) buy_ewo_16 = DecimalParameter(4.0, 8.0, default=2.8, space='buy', decimals=1, optimize=False, load=True) buy_volume_17 = DecimalParameter(0.5, 8.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, optimize=True, load=True) buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, optimize=False, load=True) buy_volume_18 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_18 = DecimalParameter(16.0, 32.0, default=26.0, space='buy', decimals=1, optimize=True, load=True) buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.982, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_min_19 = DecimalParameter(40.0, 70.0, default=50.0, space='buy', decimals=1, optimize=True, load=True) buy_chop_min_19 = DecimalParameter(20.0, 60.0, default=24.1, space='buy', decimals=1, optimize=True, load=True) buy_volume_20 = DecimalParameter(0.5, 6.0, default=1.2, space='buy', decimals=1, optimize=True, load=True) #buy_ema_rel_20 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True) buy_rsi_20 = DecimalParameter(20.0, 36.0, default=26.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_20 = DecimalParameter(14.0, 30.0, default=20.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_21 = DecimalParameter(0.5, 6.0, default=3.0, space='buy', decimals=1, optimize=True, load=True) #buy_ema_rel_21 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True) buy_rsi_21 = DecimalParameter(10.0, 28.0, default=23.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_21 = DecimalParameter(18.0, 40.0, default=24.0, space='buy', decimals=1, optimize=True, load=True) # Sell sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', optimize=True, load=True) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, optimize=True, load=True) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=True, load=True) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=True, load=True) sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=True, load=True) sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=True, load=True) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=True, load=True) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=True, load=True) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=True, load=True) sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=True, load=True) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=True, load=True) sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=True, load=True) sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=True, load=True) sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=True, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=True, load=True) sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=True, load=True) sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=True, load=True) sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.6, space='sell', decimals=3, optimize=True, load=True) sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=True, load=True) sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, optimize=True, load=True) sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=True, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=True, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=True, load=True) sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=True, load=True) sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=True, load=True) sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=True, load=True) sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=True, load=True) sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=True, load=True) sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=True, load=True) ############################################################# def get_ticker_indicator(self): return int(self.timeframe[:-1]) def timeframe_to_minutes(self, timeframe: str) -> int: if timeframe.endswith("m"): return int(timeframe[:-1]) elif timeframe.endswith("h"): return int(timeframe[:-1]) * 60 elif timeframe.endswith("d"): return int(timeframe[:-1]) * 1440 raise ValueError(f"Unsupported timeframe: {timeframe}") def custom_exit( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> Optional[str]: """ Custom exit logic migrated from old custom_sell. Return one of your signal strings to trigger exit, or None to do nothing. """ # Only process near the new candle timestamp candle_interval = self.timeframe_to_minutes(self.timeframe) * 60 # seconds candle_ts = int(current_time.timestamp() // candle_interval * candle_interval) candle_time = datetime.fromtimestamp(candle_ts, tz=current_time.tzinfo) # Allow execution only if current_time is within ±5 seconds of new candle if abs((current_time - candle_time).total_seconds()) > 30: return None # Get the latest analyzed candles dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # How far we've run up from open max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate if last_candle is not None: # Profit + RSI based exits if (current_profit > self.sell_custom_profit_4.value) and (last_candle["rsi"] < self.sell_custom_rsi_4.value): return "signal_profit_4" elif (current_profit > self.sell_custom_profit_3.value) and (last_candle["rsi"] < self.sell_custom_rsi_3.value): return "signal_profit_3" elif (current_profit > self.sell_custom_profit_2.value) and (last_candle["rsi"] < self.sell_custom_rsi_2.value): return "signal_profit_2" elif (current_profit > self.sell_custom_profit_1.value) and (last_candle["rsi"] < self.sell_custom_rsi_1.value): return "signal_profit_1" elif (current_profit > self.sell_custom_profit_0.value) and (last_candle["rsi"] < self.sell_custom_rsi_0.value): return "signal_profit_0" # Under‐EMA200 + RSI exits if (current_profit > self.sell_custom_under_profit_1.value) and (last_candle["rsi"] < self.sell_custom_under_rsi_1.value) and (last_candle["close"] < last_candle["ema_200"]): return "signal_profit_u_1" elif (current_profit > self.sell_custom_under_profit_2.value) and (last_candle["rsi"] < self.sell_custom_under_rsi_2.value) and (last_candle["close"] < last_candle["ema_200"]): return "signal_profit_u_2" elif (current_profit > self.sell_custom_under_profit_3.value) and (last_candle["rsi"] < self.sell_custom_under_rsi_3.value) and (last_candle["close"] < last_candle["ema_200"]): return "signal_profit_u_3" # Decline/SMA‐based exits if (current_profit > self.sell_custom_dec_profit_1.value) and last_candle["sma_200_dec"]: return "signal_profit_d_1" elif (current_profit > self.sell_custom_dec_profit_2.value) and (last_candle["close"] < last_candle["ema_100"]): return "signal_profit_d_2" # Trailing‐stop style exits if (current_profit > self.sell_trail_profit_min_1.value) and (current_profit < self.sell_trail_profit_max_1.value) and (max_profit > current_profit + self.sell_trail_down_1.value): return "signal_profit_t_1" elif (current_profit > self.sell_trail_profit_min_2.value) and (current_profit < self.sell_trail_profit_max_2.value) and (max_profit > current_profit + self.sell_trail_down_2.value): return "signal_profit_t_2" elif (last_candle["close"] < last_candle["ema_200"]) and (current_profit > self.sell_trail_profit_min_3.value) and (current_profit < self.sell_trail_profit_max_3.value) and (max_profit > current_profit + self.sell_trail_down_3.value): return "signal_profit_u_t_1" # Profit-under + RSI-diff exit if (current_profit > 0.0) and (last_candle["close"] < last_candle["ema_200"]) and \ (((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < self.sell_custom_profit_under_rel_1.value) and \ (last_candle["rsi"] > last_candle["rsi_1h"] + self.sell_custom_profit_under_rsi_diff_1.value): return "signal_profit_u_e_1" # Stop-loss under condition if (current_profit < 0.0) and (last_candle["close"] < last_candle["ema_200"]) and \ (((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < self.sell_custom_stoploss_under_rel_1.value) and \ (last_candle["rsi"] > last_candle["rsi_1h"] + self.sell_custom_stoploss_under_rsi_diff_1.value): return "signal_stoploss_u_1" # No custom exit condition met return None 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, '1h') for pair in pairs] 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.inf_1h) # EMA informative_1h['ema_15'] = ta.EMA(informative_1h, timeperiod=15) 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) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) # BB bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) informative_1h['bb_lowerband'] = bollinger['lower'] informative_1h['bb_middleband'] = bollinger['mid'] informative_1h['bb_upperband'] = bollinger['upper'] # Pump protections informative_1h['safe_pump_24'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_1.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_1.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min()))) informative_1h['safe_pump_36'] = ((((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_pump_threshold_2.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_2.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min()))) informative_1h['safe_pump_48'] = ((((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_pump_threshold_3.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_3.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min()))) informative_1h['safe_pump_24_strict'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_4.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_4.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min()))) informative_1h['safe_pump_36_strict'] = ((((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_pump_threshold_5.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_5.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min()))) informative_1h['safe_pump_48_strict'] = ((((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_pump_threshold_6.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_6.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min()))) informative_1h['safe_pump_24_loose'] = ((((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_pump_threshold_7.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_pump_pull_threshold_7.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min()))) informative_1h['safe_pump_36_loose'] = ((((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_pump_threshold_8.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_pump_pull_threshold_8.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min()))) informative_1h['safe_pump_48_loose'] = ((((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_pump_threshold_9.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_pump_pull_threshold_9.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min()))) # Calculate percent change from previous close informative_1h['change_pct'] = (informative_1h['close'] - informative_1h['open']) / informative_1h['open'] # Detect spikes: where change % exceeds threshold informative_1h['is_spike'] = informative_1h['change_pct'] > self.spike_threshold.value # Create a rolling window to check if spike happened in past `look_back` candles informative_1h['spike_recent'] = informative_1h['is_spike'].rolling(window=self.look_back.value, min_periods=1).max().fillna(0).astype(bool) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # BB 40 bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['lower'] = bb_40['lower'] dataframe['mid'] = bb_40['mid'] dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # BB 20 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # EMA 200 dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) # MFI dataframe['mfi'] = ta.MFI(dataframe) # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # Chopiness dataframe['chop']= qtpylib.chopiness(dataframe, 14) # Dip protection dataframe['safe_dips'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_1.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_2.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_3.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_4.value)) dataframe['safe_dips_strict'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_5.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_6.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_7.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_8.value)) dataframe['safe_dips_loose'] = ((((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_9.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_10.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_11.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_dip_threshold_12.value)) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() for i in self.ma_types: ta_func_name = self.ma_map[i]['calculate'] ta_func = getattr(ta, ta_func_name) ma_buy = ta_func(dataframe, timeperiod=self.base_nb_candles_buy.value) ma_sell = ta_func(dataframe, timeperiod=self.base_nb_candles_sell.value) dataframe[f'{i}_offset_buy'] = ma_buy * self.ma_map[i]['low_offset'] dataframe[f'{i}_offset_sell'] = ma_sell * self.ma_map[i]['high_offset'] return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.buy_condition_1_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(50)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_1.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_1.value) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe['mfi'] < self.buy_mfi_1.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_2_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(50)) & (dataframe['safe_pump_24_strict_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) & #(dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) & #(dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) & (dataframe['rsi'] < dataframe['rsi_1h'] - self.buy_rsi_1h_diff_2.value) & (dataframe['mfi'] < self.buy_mfi_2.value) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_2.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_3_enable.value & (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_3.value)) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['spike_recent_1h'] == False) & dataframe['lower'].shift().gt(0) & dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bb40_bbdelta_close_3.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_bb40_closedelta_close_3.value) & dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_bb40_tail_bbdelta_3.value) & dataframe['close'].lt(dataframe['lower'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_4_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband_4.value * dataframe['bb_lowerband']) & (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4.value)) ) ) conditions.append( ( self.buy_condition_5_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_5.value)) & (dataframe['safe_dips']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_5.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_5.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_6_enable.value & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_loose']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_6.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_6.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_7_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['spike_recent_1h'] == False) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_7.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi'] < self.buy_rsi_7.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_8_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_loose']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['rsi'] < self.buy_rsi_8.value) & (dataframe['volume'] > (dataframe['volume'].shift(1) * self.buy_volume_8.value)) & (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['low']) > ((dataframe['close'] - dataframe['open']) * self.buy_tail_diff_8.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_9.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_9.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_9.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_9.value) & (dataframe['mfi'] < self.buy_mfi_9.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_10_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips_loose']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_10.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_10.value) & (dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb_offset_10.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_10.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_11_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_dips_loose']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['safe_pump_36_1h']) & (dataframe['safe_pump_48_loose_1h']) & (dataframe['spike_recent_1h'] == False) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_11.value) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_11.value) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_11.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_max_11.value) & (dataframe['rsi'] < self.buy_rsi_11.value) & (dataframe['mfi'] < self.buy_mfi_11.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_12_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_12.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12.value) & (dataframe['ewo'] > self.buy_ewo_12.value) & (dataframe['rsi'] < self.buy_rsi_12.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_13_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['safe_pump_36_loose_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_13.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13.value) & (dataframe['ewo'] < self.buy_ewo_13.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_14_enable.value & (dataframe['sma_200'] > dataframe['sma_200'].shift(30)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(50)) & (dataframe['safe_dips_loose']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['volume_mean_4'] * self.buy_volume_14.value > dataframe['volume']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_14.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_14.value)) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_15_enable.value & (dataframe['close'] > dataframe['ema_200_1h'] * self.buy_ema_rel_15.value) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_15.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['rsi'] < self.buy_rsi_15.value) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_16_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_strict_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_16.value) > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16.value) & (dataframe['ewo'] > self.buy_ewo_16.value) & (dataframe['rsi'] < self.buy_rsi_16.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_17_enable.value & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_17.value) > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) & (dataframe['ewo'] < self.buy_ewo_17.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_18_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['sma_200'] > dataframe['sma_200'].shift(44)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(36)) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(72)) & (dataframe['safe_dips']) & (dataframe['safe_pump_24_strict_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_18.value) > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_18.value) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_bb_offset_18.value)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_19_enable.value & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(36)) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['safe_pump_24_1h']) & (dataframe['spike_recent_1h'] == False) & (dataframe['close'].shift(1) > dataframe['ema_100_1h']) & (dataframe['low'] < dataframe['ema_100_1h']) & (dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['rsi_1h'] > self.buy_rsi_1h_min_19.value) & (dataframe['chop'] < self.buy_chop_min_19.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_20_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['safe_pump_24_loose_1h']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_20.value) > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_20.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_20.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_21_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['spike_recent_1h'] == False) & ((dataframe['volume_mean_4'] * self.buy_volume_21.value) > dataframe['volume']) & (dataframe['rsi'] < self.buy_rsi_21.value) & (dataframe['rsi_1h'] < self.buy_rsi_1h_21.value) & (dataframe['volume'] > 0) ) ) for i in self.ma_types: conditions.append( ( dataframe['close'] < dataframe[f'{i}_offset_buy']) & ( (dataframe['ewo'] < self.ewo_low.value) | (dataframe['ewo'] > self.ewo_high.value) ) & (dataframe['volume'] > 0) ) for idx, cond in enumerate(conditions, start=1): dataframe.loc[cond, ['enter_long', 'enter_tag']] = ( 1, f'buy_signal_{idx:02d}' ) return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.sell_condition_1_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_1.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['close'].shift(3) > dataframe['bb_upperband'].shift(3)) & (dataframe['close'].shift(4) > dataframe['bb_upperband'].shift(4)) & (dataframe['close'].shift(5) > dataframe['bb_upperband'].shift(5)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_2_enable.value & (dataframe['rsi'] > self.sell_rsi_bb_2.value) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_3_enable.value & (dataframe['rsi'] > self.sell_rsi_main_3.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_4_enable.value & (dataframe['rsi'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi_1h'] > self.sell_dual_rsi_rsi_1h_4.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_6_enable.value & (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > self.sell_rsi_under_6.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_7_enable.value & (dataframe['rsi_1h'] > self.sell_rsi_1h_7.value) & qtpylib.crossed_below(dataframe['ema_12'], dataframe['ema_26']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_8_enable.value & (dataframe['close'] > dataframe['bb_upperband_1h'] * self.sell_bb_relative_8.value) & (dataframe['volume'] > 0) ) ) # for i in self.ma_types: # conditions.append( # ( # (dataframe['close'] > dataframe[f'{i}_offset_sell']) & # (dataframe['volume'] > 0) # ) # ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'exit_long' ] = 1 return dataframe # —————————————————————————————————————————— # 4. Protections – adjusted for 5m strategy # —————————————————————————————————————————— slg_lookback = IntParameter(288, 1728, default=864, # 72h = 3d space="protection") slg_limit = IntParameter(1, 6, default=1, space="protection") slg_pause = IntParameter(72, 576, default=300, # 12h space="protection") lpp_lookback = IntParameter(288, 1152, default=576, space="protection") # 2 days lpp_trades = IntParameter(2, 6, default=3, space="protection") # Last 3 trades lpp_profit = DecimalParameter(-0.45, 0.00, default=-0.40, decimals=3, space="protection") # -20% lpp_pause = IntParameter(144, 576, default=300, space="protection") # 24h = 288 candles (on 5m) @property def protections(self): prot = [ { "method": "StoplossGuard", "lookback_period_candles": self.slg_lookback.value, "trade_limit": self.slg_limit.value, "stop_duration_candles": self.slg_pause.value, "only_per_pair": True }, { "method": "LowProfitPairs", "lookback_period_candles": self.lpp_lookback.value, "trade_limit": self.lpp_trades.value, "required_profit": self.lpp_profit.value, "stop_duration_candles": self.lpp_pause.value, "only_per_pair": True } ] return prot # Elliot Wave Oscillator def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif