# source: https://raw.githubusercontent.com/timuko/FrankieV6_MacheteModVX/ad4d6d871a1832125f55aaf977b972ef8ed2f2ce/user_data/strategies/FrankieV6_MacheteModV3.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter from freqtrade.persistence import Trade from typing import Dict, List, Optional, Tuple from datetime import datetime, timedelta from cachetools import TTLCache from functools import reduce from pandas import DataFrame """ Original strat by @Fiber modded by @Machete Custom stoploss and dynamic roi based on godly work of @werkkrew """ class github_timuko_FrankieV6_MacheteModVX__FrankieV6_MacheteModV3__20220126_175013(IStrategy): INTERFACE_VERSION = 2 startup_candle_count: int = 300 custom_current_price_cache = TTLCache(maxsize=100, ttl=300) process_only_new_candles = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False use_dynamic_roi = True trailing_stop = False trailing_stop_positive = 0.01 trailing_only_offset_is_reached = False trailing_stop_positive_offset = 0.047 use_custom_stoploss = True stoploss = -0.085 timeframe = '5m' inf_1h = '1h' protections_should_optimize_max_drawdown = True buy_should_optimize_guards = True buy_should_optimize_condition_common = True buy_should_optimize_condition_1 = True buy_should_optimize_condition_2 = True buy_should_optimize_condition_3 = True buy_should_optimize_condition_4 = True buy_should_optimize_condition_5 = True buy_should_optimize_condition_6 = True buy_should_optimize_condition_7 = True buy_should_optimize_condition_8 = True buy_should_optimize_condition_9 = True buy_should_optimize_condition_10 = True buy_should_optimize_condition_11 = True buy_should_optimize_condition_12 = True buy_should_optimize_condition_13 = True buy_should_optimize_condition_14 = True buy_should_optimize_condition_15 = True buy_should_optimize_condition_16 = True buy_should_optimize_condition_17 = True buy_should_optimize_condition_18 = True buy_should_optimize_condition_19 = True buy_should_optimize_condition_20 = True buy_should_optimize_condition_21 = True buy_should_optimize_condition_22 = True buy_should_optimize_condition_23 = True buy_should_optimize_condition_24 = True buy_should_optimize_condition_25 = True buy_should_optimize_condition_26 = True buy_should_optimize_condition_27 = True sell_should_optimize_dynamic_roi = False sell_should_optimize_custom_stoploss = True sell_should_optimize_custom_sell = False sell_should_optimize_condition_1 = False sell_should_optimize_condition_2 = False sell_should_optimize_condition_3 = False sell_should_optimize_condition_4 = False sell_should_optimize_condition_5 = False sell_should_optimize_condition_6 = False sell_should_optimize_condition_7 = False sell_should_optimize_condition_8 = False sell_should_optimize_condition_common_9_10_11_12_13 = False sell_should_optimize_condition_9 = False sell_should_optimize_condition_10 = False sell_should_optimize_condition_11 = False sell_should_optimize_condition_12 = False sell_should_optimize_condition_13 = False protections_max_drawdown_max_allowed_drawdown = DecimalParameter(0.01, 0.20, default=0.10, load=True, space='protection', decimals=2, optimize=protections_should_optimize_max_drawdown) buy_guard_ewo_low = DecimalParameter(-20.0, -8.3, default=-20.0, load=True, space='buy', optimize=buy_should_optimize_guards) buy_guard_ewo_high = DecimalParameter(2.0, 12.0, default=6.0, load=True, space='buy', optimize=buy_should_optimize_guards) buy_guard_fast_ewo = IntParameter(10, 50, default=50, load=True, space='buy', optimize=buy_should_optimize_guards) buy_guard_slow_ewo = IntParameter(100, 200, default=200, load=True, space='buy', optimize=buy_should_optimize_guards) buy_guard_pump_24h_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_24h_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_24h_strict_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_24h_strict_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_strict_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_strict_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_strict_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_strict_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_24h_loose_pull_threshold_7 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_24h_loose_threshold_7 = DecimalParameter(0.4, 1.0, default=0.66, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_loose_pull_threshold_8 = DecimalParameter(1.5, 3.0, default=1.7, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_36h_loose_threshold_8 = DecimalParameter(0.4, 1.0, default=0.7, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_loose_pull_threshold_9 = DecimalParameter(1.5, 3.0, default=1.4, space='buy', decimals=2, load=True, optimize=buy_should_optimize_guards) buy_guard_pump_48h_loose_threshold_9 = DecimalParameter(0.4, 1.8, default=0.68, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_normal_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_normal_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_normal_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_normal_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_strict_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_strict_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_strict_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_strict_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_loose_threshold_9 = DecimalParameter(0.001, 0.05, default=0.026, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_loose_threshold_10 = DecimalParameter(0.01, 0.2, default=0.24, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_loose_threshold_11 = DecimalParameter(0.05, 0.4, default=0.42, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_guard_dip_loose_threshold_12 = DecimalParameter(0.2, 0.5, default=0.66, space='buy', decimals=3, load=True, optimize=buy_should_optimize_guards) buy_voting_ensemble = IntParameter(1, 6, default=1, load=True, space='buy', optimize=buy_should_optimize_condition_common) buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_1) buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.032, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_1) buy_rsi_14_1h_min_1 = DecimalParameter(25.0, 40.0, default=38.4, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_1) buy_rsi_14_1h_max_1 = DecimalParameter(70.0, 90.0, default=81.1, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_1) buy_rsi_1 = DecimalParameter(20.0, 40.0, default=39.5, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_1) buy_mfi_1 = DecimalParameter(20.0, 40.0, default=39.2, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_1) buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_2) buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.6, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_2) buy_rsi_14_1h_min_2 = DecimalParameter(30.0, 40.0, default=32.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_2) buy_rsi_14_1h_max_2 = DecimalParameter(70.0, 95.0, default=84.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_2) buy_rsi_14_1h_diff_2 = DecimalParameter(30.0, 50.0, default=39.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_2) buy_mfi_2 = DecimalParameter(30.0, 56.0, default=49.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_2) buy_bb_offset_2 = DecimalParameter(0.97, 0.999, default=0.983, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_2) buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_3) buy_bb40_bbdelta_close_3 = DecimalParameter(0.005, 0.06, default=0.057, space='buy', load=True, optimize=buy_should_optimize_condition_3) buy_bb40_closedelta_close_3 = DecimalParameter(0.01, 0.03, default=0.023, space='buy', load=True, optimize=buy_should_optimize_condition_3) buy_bb40_tail_bbdelta_3 = DecimalParameter(0.15, 0.45, default=0.418, space='buy', load=True, optimize=buy_should_optimize_condition_3) buy_ema_rel_3 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_3) buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_4) buy_bb20_close_bblowerband_4 = DecimalParameter(0.96, 0.99, default=0.979, space='buy', load=True, optimize=buy_should_optimize_condition_4) buy_bb20_volume_4 = DecimalParameter(1.0, 20.0, default=10.0, space='buy', decimals=2, load=True, optimize=buy_should_optimize_condition_4) buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_5) buy_ema_open_mult_5 = DecimalParameter(0.016, 0.03, default=0.019, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_5) buy_bb_offset_5 = DecimalParameter(0.98, 1.0, default=0.999, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_5) buy_ema_rel_5 = DecimalParameter(0.97, 0.999, default=0.982, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_5) buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_6) buy_ema_open_mult_6 = DecimalParameter(0.02, 0.03, default=0.025, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_6) buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.984, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_6) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_7) buy_volume_7 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_7) buy_ema_open_mult_7 = DecimalParameter(0.02, 0.04, default=0.03, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_7) buy_rsi_7 = DecimalParameter(24.0, 50.0, default=36.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_7) buy_ema_rel_7 = DecimalParameter(0.97, 0.999, default=0.986, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_7) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_8) buy_volume_8 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_8) buy_rsi_8 = DecimalParameter(36.0, 40.0, default=20.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_8) buy_tail_diff_8 = DecimalParameter(3.0, 10.0, default=3.5, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_8) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_9) buy_volume_9 = DecimalParameter(1.0, 4.0, default=1.0, space='buy', decimals=2, load=True, optimize=buy_should_optimize_condition_9) buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.97, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_9) buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.985, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_9) buy_rsi_14_1h_min_9 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_9) buy_rsi_14_1h_max_9 = DecimalParameter(70.0, 90.0, default=88.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_9) buy_mfi_9 = DecimalParameter(36.0, 65.0, default=30.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_9) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_10) buy_volume_10 = DecimalParameter(1.0, 8.0, default=2.4, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_10) buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.944, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_10) buy_bb_offset_10 = DecimalParameter(0.97, 0.99, default=0.994, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_10) buy_rsi_14_1h_10 = DecimalParameter(20.0, 40.0, default=37.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_10) buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_11) buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.939, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_11) buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.022, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_11) buy_rsi_14_1h_min_11 = DecimalParameter(40.0, 60.0, default=56.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_11) buy_rsi_14_1h_max_11 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_11) buy_rsi_11 = DecimalParameter(30.0, 48.0, default=48.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_11) buy_mfi_11 = DecimalParameter(36.0, 56.0, default=38.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_11) buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_12) buy_volume_12 = DecimalParameter(1.0, 10.0, default=1.7, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_12) buy_ma_offset_12 = DecimalParameter(0.93, 0.97, default=0.936, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_12) buy_rsi_12 = DecimalParameter(26.0, 40.0, default=30.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_12) buy_ewo_12 = DecimalParameter(2.0, 6.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_12) buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_13) buy_volume_13 = DecimalParameter(1.0, 10.0, default=1.6, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_13) buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.978, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_13) buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-10.4, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_13) buy_condition_14_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_14) buy_volume_14 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_14) buy_ema_open_mult_14 = DecimalParameter(0.01, 0.03, default=0.014, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_14) buy_bb_offset_14 = DecimalParameter(0.98, 1.0, default=0.986, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_14) buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.97, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_14) buy_condition_15_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_15) buy_volume_15 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_15) buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.018, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_15) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.954, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_15) buy_rsi_15 = DecimalParameter(30.0, 50.0, default=28.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_15) buy_ema_rel_15 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_15) buy_condition_16_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_16) buy_volume_16 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_16) buy_ma_offset_16 = DecimalParameter(0.93, 0.97, default=0.952, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_16) buy_rsi_16 = DecimalParameter(26.0, 50.0, default=31.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_16) buy_ewo_16 = DecimalParameter(4.0, 8.0, default=2.8, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_16) buy_condition_17_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_17) buy_volume_17 = DecimalParameter(0.5, 8.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_17) buy_ma_offset_17 = DecimalParameter(0.93, 0.98, default=0.958, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_17) buy_ewo_17 = DecimalParameter(-18.0, -10.0, default=-12.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_17) buy_condition_18_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_18) buy_volume_18 = DecimalParameter(1.0, 6.0, default=2.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_18) buy_rsi_18 = DecimalParameter(16.0, 32.0, default=26.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_18) buy_bb_offset_18 = DecimalParameter(0.98, 1.0, default=0.982, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_18) buy_condition_19_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_19) buy_rsi_14_1h_min_19 = DecimalParameter(40.0, 70.0, default=65.3, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_19) buy_chop_min_19 = DecimalParameter(20.0, 60.0, default=58.2, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_19) buy_condition_20_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_20) buy_volume_20 = DecimalParameter(0.5, 6.0, default=1.2, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_20) buy_rsi_20 = DecimalParameter(20.0, 36.0, default=26.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_20) buy_rsi_14_1h_20 = DecimalParameter(14.0, 30.0, default=20.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_20) buy_condition_21_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_21) buy_volume_21 = DecimalParameter(0.5, 6.0, default=3.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_21) buy_rsi_21 = DecimalParameter(10.0, 28.0, default=23.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_21) buy_rsi_14_1h_21 = DecimalParameter(18.0, 40.0, default=24.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_21) buy_condition_22_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_22) buy_rsi_22 = DecimalParameter(10.0, 50.0, default=45.0, space='buy', decimals=1, load=True, optimize=buy_should_optimize_condition_22) buy_ewo_high_22 = DecimalParameter(2.000, 3.000, default=2.327, space='buy', decimals=3, load=True, optimize=buy_should_optimize_condition_22) buy_condition_23_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_23) buy_multi_offset_low_offset_sma = DecimalParameter(0.9, 0.99, default=0.955, load=True, space='buy', optimize=buy_should_optimize_condition_23) buy_condition_24_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_24) buy_multi_offset_low_offset_ema = DecimalParameter(0.9, 0.99, default=0.929, load=True, space='buy', optimize=buy_should_optimize_condition_24) buy_condition_25_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_25) buy_multi_offset_low_offset_trima = DecimalParameter(0.9, 0.99, default=0.949, load=True, space='buy', optimize=buy_should_optimize_condition_25) buy_condition_26_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_26) buy_multi_offset_low_offset_t3 = DecimalParameter(0.9, 0.99, default=0.975, load=True, space='buy', optimize=buy_should_optimize_condition_26) buy_condition_27_enable = CategoricalParameter([True, False], default=True, space='buy', load=True, optimize=buy_should_optimize_condition_27) buy_multi_offset_low_offset_kama = DecimalParameter(0.9, 0.99, default=0.972, load=True, space='buy', optimize=buy_should_optimize_condition_27) buy_multi_offset_base_nb_candles_buy = IntParameter(5, 80, default=72, load=True, space='buy', optimize=buy_should_optimize_condition_common) sell_dynamic_roi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='sell', optimize=sell_should_optimize_dynamic_roi) sell_dynamic_roi_pullback = CategoricalParameter([True, False], default=True, space='sell', optimize=sell_should_optimize_dynamic_roi) sell_dynamic_roi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='sell', optimize=sell_should_optimize_dynamic_roi) sell_dynamic_roi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='sell', optimize=sell_should_optimize_dynamic_roi) sell_custom_stoploss_atr_multipier = IntParameter(1, 8, default=3, space='sell', optimize=sell_should_optimize_custom_stoploss) sell_custom_sell_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.6, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_dec_profit_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', load=True, optimize=sell_should_optimize_custom_sell) sell_custom_sell_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', load=True, optimize=sell_should_optimize_custom_sell) sell_condition_1_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_1) sell_rsi_bb_1 = DecimalParameter(60.0, 80.0, default=79.5, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_1) sell_condition_2_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_2) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_2) sell_condition_3_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_3) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_3) sell_condition_4_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_4) sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_4) sell_dual_rsi_rsi_14_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_4) sell_condition_5_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_5) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', load=True, optimize=sell_should_optimize_condition_5) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', load=True, optimize=sell_should_optimize_condition_5) sell_condition_6_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_6) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_6) sell_condition_7_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_7) sell_rsi_14_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, load=True, optimize=sell_should_optimize_condition_7) sell_condition_8_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_8) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, load=True, optimize=sell_should_optimize_condition_8) sell_condition_9_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_9) sell_multi_offset_high_offset_sma = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=sell_should_optimize_condition_9) sell_condition_10_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_10) sell_multi_offset_high_offset_ema = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=sell_should_optimize_condition_10) sell_condition_11_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_11) sell_multi_offset_high_offset_trima = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=sell_should_optimize_condition_11) sell_condition_12_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_12) sell_multi_offset_high_offset_t3 = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=sell_should_optimize_condition_12) sell_condition_13_enable = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=sell_should_optimize_condition_13) sell_multi_offset_high_offset_kama = DecimalParameter(0.99, 1.1, default=1.012, load=True, space='sell', optimize=sell_should_optimize_condition_13) sell_multi_offset_base_nb_candles_sell = IntParameter(5, 80, default=20, load=True, space='sell', optimize=sell_should_optimize_condition_common_9_10_11_12_13) order_types = { 'buy': 'market', 'sell': 'market', 'trailing_stop_loss': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } minimal_roi = { "0": 0.213, "39": 0.103, "96": 0.037, "166": 0 } plot_config = { 'main_plot': { 'ma_offset_buy': {'color': 'orange'}, 'ma_offset_sell': {'color': 'orange'}, }, } custom_trade_info = {} @property def protections(self): return [ { "method": "MaxDrawdown", "lookback_period": 1440, # 24h "trade_limit": 1, "stop_duration": 720, # 12h "max_allowed_drawdown": self.protections_max_drawdown_max_allowed_drawdown.value # % of account } ] 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].squeeze() sell_reason = None if (last_candle is not None): if ((current_profit > self.sell_custom_sell_profit_4.value) & (last_candle['rsi_14'] < self.sell_custom_sell_rsi_4.value) ): sell_reason = 'signal_profit_4' elif ((current_profit > self.sell_custom_sell_profit_3.value) & (last_candle['rsi_14'] < self.sell_custom_sell_rsi_3.value) ): sell_reason = 'signal_profit_3' elif ((current_profit > self.sell_custom_sell_profit_2.value) & (last_candle['rsi_14'] < self.sell_custom_sell_rsi_2.value) ): sell_reason = 'signal_profit_2' elif ((current_profit > self.sell_custom_sell_profit_1.value) & (last_candle['rsi_14'] < self.sell_custom_sell_rsi_1.value) ): sell_reason = 'signal_profit_1' elif ((current_profit > self.sell_custom_sell_profit_0.value) & (last_candle['rsi_14'] < self.sell_custom_sell_rsi_0.value) ): sell_reason = 'signal_profit_0' elif ((current_profit > self.sell_custom_sell_under_profit_1.value) & (last_candle['rsi_14'] < self.sell_custom_sell_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']) ): sell_reason = 'signal_profit_u_1' elif ((current_profit > self.sell_custom_sell_under_profit_2.value) & (last_candle['rsi_14'] < self.sell_custom_sell_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']) ): sell_reason = 'signal_profit_u_2' elif ((current_profit > self.sell_custom_sell_under_profit_3.value) & (last_candle['rsi_14'] < self.sell_custom_sell_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']) ): sell_reason = 'signal_profit_u_3' elif ((current_profit > self.sell_custom_sell_dec_profit_1.value) & (last_candle['sma_200_dec']) ): sell_reason = 'signal_profit_d_1' elif ((current_profit > self.sell_custom_sell_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']) ): sell_reason = 'signal_profit_d_2' elif ((current_profit < -0.0) & (last_candle['btc_not_downtrend_1h'] == False) & (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.sell_custom_sell_stoploss_under_rsi_diff_1.value)) ): sell_reason = 'signal_stoploss_btc' elif ((current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_sell_stoploss_under_rel_1.value) & (last_candle['rsi_14'] > (last_candle['rsi_14_1h'] + self.sell_custom_sell_stoploss_under_rsi_diff_1.value)) ): sell_reason = 'signal_stoploss_u_1' return sell_reason def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_1h) for pair in pairs] informative_pairs.append(('BTC/USDT', self.timeframe)) informative_pairs.append(('BTC/USDT', self.inf_1h)) return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) 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) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) informative_1h['rsi_14'] = ta.RSI(informative_1h, timeperiod=14) bb_20 = qtpylib.bollinger_bands(qtpylib.typical_price(informative_1h), window=20, stds=2) #informative_1h['bb_lowerband_20'] = bb_20['lower'] #informative_1h['bb_middleband_20'] = bb_20['mid'] informative_1h['bb_upperband_20'] = bb_20['upper'] informative_1h['protection_pump_24'] = ( (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_guard_pump_24h_threshold_1.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_guard_pump_24h_pull_threshold_1.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min())) ) informative_1h['protection_pump_36'] = ( (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_guard_pump_36h_threshold_2.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_guard_pump_36h_pull_threshold_2.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min())) ) informative_1h['protection_pump_48'] = ( (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_guard_pump_48h_threshold_3.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_guard_pump_48h_pull_threshold_3.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min())) ) informative_1h['protection_pump_24_strict'] = ( (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_guard_pump_24h_strict_threshold_4.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_guard_pump_24h_strict_pull_threshold_4.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min())) ) informative_1h['protection_pump_36_strict'] = ( (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_guard_pump_36h_strict_threshold_5.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_guard_pump_36h_strict_pull_threshold_5.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min())) ) informative_1h['protection_pump_48_strict'] = ( (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_guard_pump_48h_strict_threshold_6.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_guard_pump_48h_strict_pull_threshold_6.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min())) ) informative_1h['protection_pump_24_loose'] = ( (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / informative_1h['close'].rolling(24).min()) < self.buy_guard_pump_24h_loose_threshold_7.value) | (((informative_1h['open'].rolling(24).max() - informative_1h['close'].rolling(24).min()) / self.buy_guard_pump_24h_loose_pull_threshold_7.value) > (informative_1h['close'] - informative_1h['close'].rolling(24).min())) ) informative_1h['protection_pump_36_loose'] = ( (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / informative_1h['close'].rolling(36).min()) < self.buy_guard_pump_36h_loose_threshold_8.value) | (((informative_1h['open'].rolling(36).max() - informative_1h['close'].rolling(36).min()) / self.buy_guard_pump_36h_loose_pull_threshold_8.value) > (informative_1h['close'] - informative_1h['close'].rolling(36).min())) ) informative_1h['protection_pump_48_loose'] = ( (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / informative_1h['close'].rolling(48).min()) < self.buy_guard_pump_48h_loose_threshold_9.value) | (((informative_1h['open'].rolling(48).max() - informative_1h['close'].rolling(48).min()) / self.buy_guard_pump_48h_loose_pull_threshold_9.value) > (informative_1h['close'] - informative_1h['close'].rolling(48).min())) ) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2) dataframe['bb_lowerband_40'] = bb_40['lower'] #dataframe['bb_middleband_40'] = bb_40['mid'] #dataframe['bb_upperband_40'] = bb_40['upper'] dataframe['bbdelta'] = (bb_40['mid'] - bb_40['lower']).abs() dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() bb_20 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband_20'] = bb_20['lower'] #dataframe['bb_middleband_20'] = bb_20['mid'] dataframe['bb_upperband_20'] = bb_20['upper'] dataframe['ema_14'] = ta.EMA(dataframe['close'], timeperiod = 14) 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) dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9) 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) dataframe['mfi_14'] = ta.MFI(dataframe) dataframe['ewo_x'] = EWO(dataframe, self.buy_guard_fast_ewo.value, self.buy_guard_slow_ewo.value) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20) dataframe['chop_14']= qtpylib.chopiness(dataframe, 14) dataframe['protection_dips'] = ( (((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_normal_threshold_1.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_normal_threshold_2.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_normal_threshold_3.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_normal_threshold_4.value) ) dataframe['protection_dips_strict'] = ( (((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_strict_threshold_5.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_strict_threshold_6.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_strict_threshold_7.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_strict_threshold_8.value) ) dataframe['protection_dips_loose'] = ( (((dataframe['open'] - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_loose_threshold_9.value) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_loose_threshold_10.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_loose_threshold_11.value) & (((dataframe['open'].rolling(144).max() - dataframe['close']) / dataframe['close']) < self.buy_guard_dip_loose_threshold_12.value) ) dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() dataframe['sma_offset_buy'] = (ta.SMA(dataframe, self.buy_multi_offset_base_nb_candles_buy.value) * self.buy_multi_offset_low_offset_sma.value) dataframe['sma_offset_sell'] = (ta.SMA(dataframe, self.sell_multi_offset_base_nb_candles_sell.value) * self.sell_multi_offset_high_offset_sma.value) dataframe['ema_offset_buy'] = (ta.EMA(dataframe, self.buy_multi_offset_base_nb_candles_buy.value) * self.buy_multi_offset_low_offset_ema.value) dataframe['ema_offset_sell'] = (ta.EMA(dataframe, self.sell_multi_offset_base_nb_candles_sell.value) * self.sell_multi_offset_high_offset_ema.value) dataframe['trima_offset_buy'] = (ta.TRIMA(dataframe, self.buy_multi_offset_base_nb_candles_buy.value) * self.buy_multi_offset_low_offset_trima.value) dataframe['trima_offset_sell'] = (ta.TRIMA(dataframe, self.sell_multi_offset_base_nb_candles_sell.value) * self.sell_multi_offset_high_offset_trima.value) dataframe['t3_offset_buy'] = (ta.T3(dataframe, self.buy_multi_offset_base_nb_candles_buy.value) * self.buy_multi_offset_low_offset_t3.value) dataframe['t3_offset_sell'] = (ta.T3(dataframe, self.sell_multi_offset_base_nb_candles_sell.value) * self.sell_multi_offset_high_offset_t3.value) dataframe['kama_offset_buy'] = (ta.KAMA(dataframe, self.buy_multi_offset_base_nb_candles_buy.value) * self.buy_multi_offset_low_offset_kama.value) dataframe['kama_offset_sell'] = (ta.KAMA(dataframe, self.sell_multi_offset_base_nb_candles_sell.value) * self.sell_multi_offset_high_offset_kama.value) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi_14'] > 50)) 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 merge_btc_informative_pair(self, dataframe, btc_info_tf, timeframe, inf_1h, ffill): dataframe = merge_informative_pair(dataframe, btc_info_tf, timeframe, inf_1h, ffill) for prefix in ["x", "y", ""]: drop_columns = [(s + "_" + self.inf_1h + "_" + prefix) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) return dataframe def stoploss_tf_indicators(self, dataframe, metadata): dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['rmi'] = RMI(dataframe, length=24, mom=5) ssldown, sslup = SSLChannels_ATR(dataframe, length=21) dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down') dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(),1,0) dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3,1,0) dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0) dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0) if self.dp.runmode.value in ('backtest', 'hyperopt'): self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[['date', 'ssl-dir']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date') self.custom_trade_info[metadata['pair']]['candle-up-trend'] = dataframe[['date', 'candle-up-trend']].copy().set_index('date') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair']) informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) btc_info_tf = self.dp.get_pair_dataframe("BTC/USDT", self.inf_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = self.merge_btc_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) dataframe = self.stoploss_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_signal_count'] = 0 dataframe['buy_condition_1'] = ( ( self.buy_condition_1_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(50)) & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_1h']) & (((dataframe['close'] - dataframe['open'].rolling(36).min()) / dataframe['open'].rolling(36).min()) > self.buy_min_inc_1.value) & (dataframe['rsi_14_1h'] > self.buy_rsi_14_1h_min_1.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_max_1.value) & (dataframe['rsi_14'] < self.buy_rsi_1.value) & (dataframe['mfi_14'] < self.buy_mfi_1.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_1'], 'buy_tag'] += '1 ' dataframe.loc[dataframe['buy_condition_1'], 'buy_signal_count'] += 1 dataframe['buy_condition_2'] = ( ( self.buy_condition_2_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(50)) & (dataframe['protection_pump_24_strict_1h']) & (dataframe['volume_mean_4'] * self.buy_volume_2.value > dataframe['volume']) & (dataframe['rsi_14_1h'] > self.buy_rsi_14_1h_min_2.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_max_2.value) & (dataframe['rsi_14'] < dataframe['rsi_14_1h'] - self.buy_rsi_14_1h_diff_2.value) & (dataframe['mfi_14'] < self.buy_mfi_2.value) & (dataframe['close'] < (dataframe['bb_lowerband_20'] * self.buy_bb_offset_2.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_2'], 'buy_tag'] += '2 ' dataframe.loc[dataframe['buy_condition_2'], 'buy_signal_count'] += 1 dataframe['buy_condition_3'] = ( ( 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['protection_pump_36_strict_1h']) & dataframe['bb_lowerband_40'].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['bb_lowerband_40'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_3'], 'buy_tag'] += '3 ' dataframe.loc[dataframe['buy_condition_3'], 'buy_signal_count'] += 1 dataframe['buy_condition_4'] = ( ( self.buy_condition_4_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_1h']) & (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] < self.buy_bb20_close_bblowerband_4.value * dataframe['bb_lowerband_20']) & (dataframe['volume'] < (dataframe['volume_mean_30'].shift(1) * self.buy_bb20_volume_4.value)) ) ) dataframe.loc[dataframe['buy_condition_4'], 'buy_tag'] += '4 ' dataframe.loc[dataframe['buy_condition_4'], 'buy_signal_count'] += 1 dataframe['buy_condition_5'] = ( ( 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['protection_dips']) & (dataframe['protection_pump_36_strict_1h']) & (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_20'] * self.buy_bb_offset_5.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_5'], 'buy_tag'] += '5 ' dataframe.loc[dataframe['buy_condition_5'], 'buy_signal_count'] += 1 dataframe['buy_condition_6'] = ( ( self.buy_condition_6_enable.value & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_loose']) & (dataframe['protection_pump_36_strict_1h']) & (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_20'] * self.buy_bb_offset_6.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_6'], 'buy_tag'] += '6 ' dataframe.loc[dataframe['buy_condition_6'], 'buy_signal_count'] += 1 dataframe['buy_condition_7'] = ( ( self.buy_condition_7_enable.value & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_strict']) & (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_14'] < self.buy_rsi_7.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_7'], 'buy_tag'] += '7 ' dataframe.loc[dataframe['buy_condition_7'], 'buy_signal_count'] += 1 dataframe['buy_condition_8'] = ( ( self.buy_condition_8_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_loose']) & (dataframe['protection_pump_24_1h']) & (dataframe['rsi_14'] < 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) ) ) dataframe.loc[dataframe['buy_condition_8'], 'buy_tag'] += '8 ' dataframe.loc[dataframe['buy_condition_8'], 'buy_signal_count'] += 1 dataframe['buy_condition_9'] = ( ( self.buy_condition_9_enable.value & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_100'] > dataframe['ema_200']) & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_loose_1h']) & (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_20'] * self.buy_bb_offset_9.value) & (dataframe['rsi_14_1h'] > self.buy_rsi_14_1h_min_9.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_max_9.value) & (dataframe['mfi_14'] < self.buy_mfi_9.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_9'], 'buy_tag'] += '9 ' dataframe.loc[dataframe['buy_condition_9'], 'buy_signal_count'] += 1 dataframe['buy_condition_10'] = ( ( 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['protection_dips_loose']) & (dataframe['protection_pump_24_loose_1h']) & ((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_20'] * self.buy_bb_offset_10.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_10.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_10'], 'buy_tag'] += '10 ' dataframe.loc[dataframe['buy_condition_10'], 'buy_signal_count'] += 1 dataframe['buy_condition_11'] = ( ( self.buy_condition_11_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['protection_dips_loose']) & (dataframe['protection_pump_24_loose_1h']) & (dataframe['protection_pump_36_1h']) & (dataframe['protection_pump_48_loose_1h']) & (((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_14_1h'] > self.buy_rsi_14_1h_min_11.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_max_11.value) & (dataframe['rsi_14'] < self.buy_rsi_11.value) & (dataframe['mfi_14'] < self.buy_mfi_11.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_11'], 'buy_tag'] += '11 ' dataframe.loc[dataframe['buy_condition_11'], 'buy_signal_count'] += 1 dataframe['buy_condition_12'] = ( ( self.buy_condition_12_enable.value & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_12.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_12.value) & (dataframe['ewo_x'] > self.buy_ewo_12.value) & (dataframe['rsi_14'] < self.buy_rsi_12.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_12'], 'buy_tag'] += '12 ' dataframe.loc[dataframe['buy_condition_12'], 'buy_signal_count'] += 1 dataframe['buy_condition_13'] = ( ( 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['protection_dips_strict']) & (dataframe['protection_pump_24_loose_1h']) & (dataframe['protection_pump_36_loose_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_13.value) > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * self.buy_ma_offset_13.value) & (dataframe['ewo_x'] < self.buy_ewo_13.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_13'], 'buy_tag'] += '13 ' dataframe.loc[dataframe['buy_condition_13'], 'buy_signal_count'] += 1 dataframe['buy_condition_14'] = ( ( 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['protection_dips_loose']) & (dataframe['protection_pump_24_1h']) & (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_20'] * self.buy_bb_offset_14.value)) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_14.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_14'], 'buy_tag'] += '14 ' dataframe.loc[dataframe['buy_condition_14'], 'buy_signal_count'] += 1 dataframe['buy_condition_15'] = ( ( 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['protection_dips']) & (dataframe['protection_pump_36_strict_1h']) & (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_14'] < self.buy_rsi_15.value) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_15.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_15'], 'buy_tag'] += '15 ' dataframe.loc[dataframe['buy_condition_15'], 'buy_signal_count'] += 1 dataframe['buy_condition_16'] = ( ( self.buy_condition_16_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_strict_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_16.value) > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_16.value) & (dataframe['ewo_x'] > self.buy_ewo_16.value) & (dataframe['rsi_14'] < self.buy_rsi_16.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_16'], 'buy_tag'] += '16 ' dataframe.loc[dataframe['buy_condition_16'], 'buy_signal_count'] += 1 dataframe['buy_condition_17'] = ( ( self.buy_condition_17_enable.value & (dataframe['protection_dips_strict']) & (dataframe['protection_pump_24_loose_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_17.value) > dataframe['volume']) & (dataframe['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) & (dataframe['ewo_x'] < self.buy_ewo_17.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_17'], 'buy_tag'] += '17 ' dataframe.loc[dataframe['buy_condition_17'], 'buy_signal_count'] += 1 dataframe['buy_condition_18'] = ( ( 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['protection_dips']) & (dataframe['protection_pump_24_strict_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_18.value) > dataframe['volume']) & (dataframe['rsi_14'] < self.buy_rsi_18.value) & (dataframe['close'] < (dataframe['bb_lowerband_20'] * self.buy_bb_offset_18.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_18'], 'buy_tag'] += '18 ' dataframe.loc[dataframe['buy_condition_18'], 'buy_signal_count'] += 1 dataframe['buy_condition_19'] = ( ( 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['protection_dips']) & (dataframe['protection_pump_24_1h']) & (dataframe['close'].shift(1) > dataframe['ema_100_1h']) & (dataframe['low'] < dataframe['ema_100_1h']) & (dataframe['close'] > dataframe['ema_100_1h']) & (dataframe['rsi_14_1h'] > self.buy_rsi_14_1h_min_19.value) & (dataframe['chop_14'] < self.buy_chop_min_19.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_19'], 'buy_tag'] += '19 ' dataframe.loc[dataframe['buy_condition_19'], 'buy_signal_count'] += 1 dataframe['buy_condition_20'] = ( ( self.buy_condition_20_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips']) & (dataframe['protection_pump_24_loose_1h']) & ((dataframe['volume_mean_4'] * self.buy_volume_20.value) > dataframe['volume']) & (dataframe['rsi_14'] < self.buy_rsi_20.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_20.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_20'], 'buy_tag'] += '20 ' dataframe.loc[dataframe['buy_condition_20'], 'buy_signal_count'] += 1 dataframe['buy_condition_21'] = ( ( self.buy_condition_21_enable.value & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['protection_dips_strict']) & ((dataframe['volume_mean_4'] * self.buy_volume_21.value) > dataframe['volume']) & (dataframe['rsi_14'] < self.buy_rsi_21.value) & (dataframe['rsi_14_1h'] < self.buy_rsi_14_1h_21.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_21'], 'buy_tag'] += '21 ' dataframe.loc[dataframe['buy_condition_21'], 'buy_signal_count'] += 1 dataframe['buy_condition_22'] = ( ( self.buy_condition_22_enable.value & (dataframe['sma_9'] < dataframe['ema_14']) & (dataframe['rsi_4'] < dataframe['rsi_20']) & (dataframe['rsi_4'] < 35) & (dataframe['rsi_4'] > 4) & (dataframe['ewo_x'] > self.buy_ewo_high_22.value) & (dataframe['close'] < dataframe['ema_14'] * 0.970) & (dataframe['rsi_14'] < self.buy_rsi_22.value) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_22'], 'buy_tag'] += '22 ' dataframe.loc[dataframe['buy_condition_22'], 'buy_signal_count'] += 1 dataframe['buy_condition_23'] = ( ( self.buy_condition_23_enable.value & (dataframe['close'] < dataframe['sma_offset_buy']) & ((dataframe['ewo_x'] < self.buy_guard_ewo_low.value) | (dataframe['ewo_x'] > self.buy_guard_ewo_high.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_23'], 'buy_tag'] += '23 ' dataframe.loc[dataframe['buy_condition_23'], 'buy_signal_count'] += 1 dataframe['buy_condition_24'] = ( ( self.buy_condition_24_enable.value & (dataframe['close'] < dataframe['ema_offset_buy']) & ((dataframe['ewo_x'] < self.buy_guard_ewo_low.value) | (dataframe['ewo_x'] > self.buy_guard_ewo_high.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_24'], 'buy_tag'] += '24 ' dataframe.loc[dataframe['buy_condition_24'], 'buy_signal_count'] += 1 dataframe['buy_condition_25'] = ( ( self.buy_condition_25_enable.value & (dataframe['close'] < dataframe['trima_offset_buy']) & ((dataframe['ewo_x'] < self.buy_guard_ewo_low.value) | (dataframe['ewo_x'] > self.buy_guard_ewo_high.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_25'], 'buy_tag'] += '25 ' dataframe.loc[dataframe['buy_condition_25'], 'buy_signal_count'] += 1 dataframe['buy_condition_26'] = ( ( self.buy_condition_26_enable.value & (dataframe['close'] < dataframe['t3_offset_buy']) & ((dataframe['ewo_x'] < self.buy_guard_ewo_low.value) | (dataframe['ewo_x'] > self.buy_guard_ewo_high.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_26'], 'buy_tag'] += '26 ' dataframe.loc[dataframe['buy_condition_26'], 'buy_signal_count'] += 1 dataframe['buy_condition_27'] = ( ( self.buy_condition_27_enable.value & (dataframe['close'] < dataframe['kama_offset_buy']) & ((dataframe['ewo_x'] < self.buy_guard_ewo_low.value) | (dataframe['ewo_x'] > self.buy_guard_ewo_high.value)) & (dataframe['volume'] > 0) ) ) dataframe.loc[dataframe['buy_condition_27'], 'buy_tag'] += '27 ' dataframe.loc[dataframe['buy_condition_27'], 'buy_signal_count'] += 1 conditions.append( (dataframe['buy_signal_count'] >= self.buy_voting_ensemble.value) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.sell_condition_1_enable.value & (dataframe['rsi_14'] > self.sell_rsi_bb_1.value) & (dataframe['close'] > dataframe['bb_upperband_20']) & (dataframe['close'].shift(1) > dataframe['bb_upperband_20'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband_20'].shift(2)) & (dataframe['close'].shift(3) > dataframe['bb_upperband_20'].shift(3)) & (dataframe['close'].shift(4) > dataframe['bb_upperband_20'].shift(4)) & (dataframe['close'].shift(5) > dataframe['bb_upperband_20'].shift(5)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_2_enable.value & (dataframe['rsi_14'] > self.sell_rsi_bb_2.value) & (dataframe['close'] > dataframe['bb_upperband_20']) & (dataframe['close'].shift(1) > dataframe['bb_upperband_20'].shift(1)) & (dataframe['close'].shift(2) > dataframe['bb_upperband_20'].shift(2)) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_3_enable.value & (dataframe['rsi_14'] > self.sell_rsi_main_3.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_4_enable.value & (dataframe['rsi_14'] > self.sell_dual_rsi_rsi_4.value) & (dataframe['rsi_14_1h'] > self.sell_dual_rsi_rsi_14_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_14'] > self.sell_rsi_under_6.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_7_enable.value & (dataframe['rsi_14_1h'] > self.sell_rsi_14_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_20_1h'] * self.sell_bb_relative_8.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_9_enable.value & (dataframe['close'] > dataframe['sma_offset_sell']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_10_enable.value & (dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_11_enable.value & (dataframe['close'] > dataframe['trima_offset_sell']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_12_enable.value & (dataframe['close'] > dataframe['t3_offset_sell']) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.sell_condition_13_enable.value & (dataframe['close'] > dataframe['kama_offset_sell']) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe custom_stop_info = {} def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() last_candle = dataframe.iloc[-2].squeeze() new_stoploss_price = last_candle['high'] - (current_candle['atr'] * self.sell_custom_stoploss_atr_multipier.value) if not pair in self.custom_stop_info: self.custom_stop_info[pair] = new_stoploss_price last_stoploss_price = self.custom_stop_info[pair] if new_stoploss_price >= last_stoploss_price: stoploss_price = new_stoploss_price else: stoploss_price = last_stoploss_price if stoploss_price < current_rate: self.custom_stop_info[pair] = stoploss_price return (stoploss_price / current_rate) - 1 return 1 def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]: minimal_roi = self.minimal_roi _, table_roi = self.min_roi_reached_entry(trade_dur) if self.custom_trade_info and trade and trade.pair in self.custom_trade_info: rmi_trend = self.get_custom_trade_info_indicator_by_key('rmi-up-trend', trade.pair, current_time) candle_trend = self.get_custom_trade_info_indicator_by_key('candle-up-trend', trade.pair, current_time) ssl_dir = self.get_custom_trade_info_indicator_by_key('ssl-dir', trade.pair, current_time) min_roi = table_roi max_profit = trade.calc_profit_ratio(trade.max_rate) pullback_value = (max_profit - self.sell_dynamic_roi_pullback_amount.value) in_trend = False if self.sell_dynamic_roi_trend_type.value == 'rmi' or self.sell_dynamic_roi_trend_type.value == 'any': if rmi_trend == 1: in_trend = True if self.sell_dynamic_roi_trend_type.value == 'ssl' or self.sell_dynamic_roi_trend_type.value == 'any': if ssl_dir == 'up': in_trend = True if self.sell_dynamic_roi_trend_type.value == 'candle' or self.sell_dynamic_roi_trend_type.value == 'any': if candle_trend == 1: in_trend = True if (in_trend == True): min_roi = 100 if self.sell_dynamic_roi_pullback.value == True and (current_profit < pullback_value): if self.sell_dynamic_roi_pullback_respect_table.value == True: min_roi = table_roi else: min_roi = current_profit / 2 else: min_roi = table_roi return trade_dur, min_roi def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool: trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) if self.use_dynamic_roi: _, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur) else: _, roi = self.min_roi_reached_entry(trade_dur) if roi is None: return False else: return current_profit > roi def get_current_price(self, pair: str, refresh: bool) -> float: if not refresh: rate = self.custom_current_price_cache.get(pair) if rate: return rate ask_strategy = self.config.get('ask_strategy', {}) if ask_strategy.get('use_order_book', False): ob = self.dp.orderbook(pair, 1) rate = ob[f"{ask_strategy['price_side']}s"][0][0] else: ticker = self.dp.ticker(pair) rate = ticker['last'] self.custom_current_price_cache[pair] = rate return rate def populate_trades(self, pair: str) -> dict: if not pair in self.custom_trade_info: self.custom_trade_info[pair] = {} trade_data = {} trade_data['active_trade'] = False if self.config['runmode'].value in ('live', 'dry_run'): active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all() if active_trade: current_rate = self.get_current_price(pair, True) active_trade[0].adjust_min_max_rates(current_rate, current_rate) return trade_data def get_custom_trade_info_indicator_by_key(self, key, pair, current_time): if self.config['runmode'].value in ('live', 'dry_run'): dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) indicator = dataframe[key].iat[-1] else: indicator = self.custom_trade_info[pair][key].loc[current_time][key] return indicator def RMI(dataframe, *, length=20, mom=5): """ Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912 """ df = dataframe.copy() df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0) df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0) df.fillna(0, inplace=True) df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length) df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length) df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"])) return df["RMI"] def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] # 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