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 ########################################################################################################### ## NostalgiaForInfinityV4 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 ## ## ## ########################################################################################################### class NostalgiaForInfinityV4HO(IStrategy): INTERFACE_VERSION = 2 # # ROI table: minimal_roi = { "0": 10, } stoploss = -1.0 # 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_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 400 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'trailing_stop_loss': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False } ############################################################# # Buy hyperspace params: buy_params = { "buy_bb20_close_bblowerband_4": 0.933, "buy_bb20_volume_4": 32, "buy_bb40_bbdelta_close_3": 0.024, "buy_bb40_closedelta_close_3": 0.022, "buy_bb40_tail_bbdelta_3": 0.329, "buy_bb_offset_10": 0.983, "buy_bb_offset_14": 0.99, "buy_bb_offset_2": 0.977, "buy_bb_offset_5": 0.989, "buy_bb_offset_6": 0.987, "buy_bb_offset_9": 0.978, "buy_condition_10_enable": False, "buy_condition_11_enable": True, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": False, "buy_condition_16_enable": True, "buy_condition_17_enable": False, "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": False, "buy_condition_6_enable": False, "buy_condition_7_enable": True, "buy_condition_8_enable": False, "buy_condition_9_enable": True, "buy_dip_threshold_1": 0.019, "buy_dip_threshold_2": 0.12, "buy_dip_threshold_3": 0.248, "buy_dip_threshold_4": 0.392, "buy_dip_threshold_5": 0.023, "buy_dip_threshold_6": 0.2, "buy_dip_threshold_7": 0.397, "buy_dip_threshold_8": 0.295, "buy_ema_open_mult_14": 0.011, "buy_ema_open_mult_15": 0.021, "buy_ema_open_mult_5": 0.023, "buy_ema_open_mult_6": 0.035, "buy_ema_open_mult_7": 0.038, "buy_ema_rel_15": 0.987, "buy_ema_rel_3": 0.979, "buy_ema_rel_5": 0.994, "buy_ema_rel_8": 0.979, "buy_ewo_12": 2.8, "buy_ewo_13": -8.0, "buy_ewo_16": 5.1, "buy_ewo_17": -17.9, "buy_ma_offset_10": 0.965, "buy_ma_offset_11": 0.948, "buy_ma_offset_12": 0.941, "buy_ma_offset_13": 0.971, "buy_ma_offset_14": 0.957, "buy_ma_offset_15": 0.99, "buy_ma_offset_16": 0.96, "buy_ma_offset_17": 0.97, "buy_ma_offset_9": 0.977, "buy_mfi_1": 21.7, "buy_mfi_11": 53.7, "buy_mfi_2": 36.5, "buy_mfi_9": 40.7, "buy_min_inc_1": 0.032, "buy_min_inc_11": 0.036, "buy_pump_pull_threshold_1": 1.56, "buy_pump_pull_threshold_2": 2.88, "buy_pump_pull_threshold_3": 2.88, "buy_pump_pull_threshold_4": 2.42, "buy_pump_pull_threshold_5": 2.21, "buy_pump_pull_threshold_6": 2.35, "buy_pump_threshold_1": 0.601, "buy_pump_threshold_2": 0.886, "buy_pump_threshold_3": 0.691, "buy_pump_threshold_4": 0.599, "buy_pump_threshold_5": 0.804, "buy_pump_threshold_6": 0.512, "buy_rsi_1": 23.4, "buy_rsi_11": 42.0, "buy_rsi_12": 39.8, "buy_rsi_15": 33.1, "buy_rsi_16": 40.1, "buy_rsi_1h_10": 29.5, "buy_rsi_1h_diff_2": 45.1, "buy_rsi_1h_max_1": 88.9, "buy_rsi_1h_max_11": 80.3, "buy_rsi_1h_max_2": 88.2, "buy_rsi_1h_max_9": 78.4, "buy_rsi_1h_min_1": 25.2, "buy_rsi_1h_min_11": 40.8, "buy_rsi_1h_min_2": 31.8, "buy_rsi_1h_min_9": 38.1, "buy_rsi_7": 39.1, "buy_rsi_8": 41.3, "buy_volume_10": 13.3, "buy_volume_12": 10.0, "buy_volume_14": 7.3, "buy_volume_16": 3.1, "buy_volume_2": 8.3, "buy_volume_5": 4.6, "buy_volume_6": 1.5, "buy_volume_7": 9.1, "buy_volume_9": 3.93, } # Sell hyperspace params: sell_params = { "sell_bb_relative_8": 1.293, "sell_condition_1_enable": False, "sell_condition_2_enable": False, "sell_condition_3_enable": True, "sell_condition_4_enable": False, "sell_condition_5_enable": True, "sell_condition_6_enable": False, "sell_condition_7_enable": False, "sell_condition_8_enable": True, "sell_custom_profit_0": 0.04, "sell_custom_profit_1": 0.012, "sell_custom_profit_2": 0.083, "sell_custom_profit_3": 0.067, "sell_custom_profit_4": 0.587, "sell_custom_rsi_0": 39.492, "sell_custom_rsi_1": 48.33, "sell_custom_rsi_2": 45.91, "sell_custom_rsi_3": 47.92, "sell_custom_rsi_4": 54.5, "sell_custom_under_profit_1": 0.012, "sell_custom_under_profit_2": 0.088, "sell_custom_under_profit_3": 0.121, "sell_dual_rsi_rsi_1h_4": 89.3, "sell_dual_rsi_rsi_4": 78.9, "sell_ema_relative_5": 0.015, "sell_rsi_1h_7": 87.7, "sell_rsi_bb_1": 65.4, "sell_rsi_bb_2": 77.6, "sell_rsi_diff_5": 3.116, "sell_rsi_main_3": 81.1, "sell_rsi_under_6": 81.1, "sell_trail_down_1": 0.154, "sell_trail_down_2": 0.089, "sell_trail_profit_max_1": 0.5, "sell_trail_profit_max_2": 0.13, "sell_trail_profit_min_1": 0.193, "sell_trail_profit_min_2": 0.046, } ############################################################# 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) # Normal dips buy_dip_threshold_1 = DecimalParameter(0.001, 0.05, default=0.02, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_2 = DecimalParameter(0.01, 0.2, default=0.14, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_3 = DecimalParameter(0.05, 0.4, default=0.32, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_4 = DecimalParameter(0.2, 0.5, default=0.5, space='buy', decimals=3, optimize=True, load=True) # Strict dips buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=True, load=True) buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=True, load=True) # 12 hours buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.46, space='buy', decimals=3, optimize=True, load=True) # 36 hours buy_pump_pull_threshold_2 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_2 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=True, load=True) # 48 hours buy_pump_pull_threshold_3 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_3 = DecimalParameter(0.4, 1.0, default=0.85, space='buy', decimals=3, optimize=True, load=True) # 12 hours strict buy_pump_pull_threshold_4 = DecimalParameter(1.5, 3.0, default=2.2, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_4 = DecimalParameter(0.4, 1.0, default=0.4, space='buy', decimals=3, optimize=True, load=True) # 36 hours strict buy_pump_pull_threshold_5 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_5 = DecimalParameter(0.4, 1.0, default=0.56, space='buy', decimals=3, optimize=True, load=True) # 48 hours strict buy_pump_pull_threshold_6 = DecimalParameter(1.5, 3.0, default=2.0, space='buy', decimals=2, optimize=True, load=True) buy_pump_threshold_6 = DecimalParameter(0.4, 1.0, default=0.68, space='buy', decimals=3, optimize=True, 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=80.0, 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, 56.0, default=26.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_2 = DecimalParameter(1.0, 10.0, default=2.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_min_2 = DecimalParameter(30.0, 40.0, default=36.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_2 = DecimalParameter(70.0, 95.0, default=90.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_diff_2 = DecimalParameter(30.0, 50.0, default=34.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_2 = DecimalParameter(30.0, 65.0, default=56.0, space='buy', decimals=1, optimize=True, load=True) buy_bb_offset_2 = DecimalParameter(0.97, 0.99, 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=0.988, space='buy', decimals=3, optimize=True, load=True) buy_bb20_close_bblowerband_4 = DecimalParameter(0.9, 0.99, default=0.979, space='buy', optimize=True, load=True) buy_bb20_volume_4 = IntParameter(16, 35, default=18, space='buy', optimize=True, load=True) buy_volume_5 = DecimalParameter(1.0, 10.0, default=6.0, space='buy', decimals=1, 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=0.988, space='buy', decimals=3, optimize=True, load=True) buy_volume_6 = DecimalParameter(1.0, 10.0, default=1.5, space='buy', decimals=1, optimize=True, load=True) buy_ema_open_mult_6 = DecimalParameter(0.03, 0.04, default=0.025, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_6 = DecimalParameter(0.98, 0.999, default=0.995, 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_rsi_8 = DecimalParameter(30.0, 50.0, default=46.0, space='buy', decimals=1, optimize=True, load=True) buy_ema_rel_8 = DecimalParameter(0.97, 0.999, default=0.988, space='buy', decimals=3, optimize=True, load=True) buy_volume_9 = DecimalParameter(1.0, 4.0, default=2.0, space='buy', decimals=2, optimize=True, load=True) buy_ma_offset_9 = DecimalParameter(0.94, 0.99, default=0.958, space='buy', decimals=3, optimize=True, load=True) buy_bb_offset_9 = DecimalParameter(0.97, 0.99, default=0.984, 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=80.0, space='buy', decimals=1, optimize=True, load=True) buy_mfi_9 = DecimalParameter(36.0, 65.0, default=56.0, space='buy', decimals=1, optimize=True, load=True) buy_volume_10 = DecimalParameter(1.0, 26.0, default=23.0, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_10 = DecimalParameter(0.93, 0.97, default=0.94, 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=39.0, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_11 = DecimalParameter(0.93, 0.99, default=0.938, space='buy', decimals=3, optimize=True, load=True) buy_min_inc_11 = DecimalParameter(0.005, 0.05, default=0.01, space='buy', decimals=3, optimize=True, load=True) buy_rsi_1h_min_11 = DecimalParameter(40.0, 60.0, default=55.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_1h_max_11 = DecimalParameter(70.0, 90.0, default=82.0, space='buy', decimals=1, optimize=True, load=True) buy_rsi_11 = DecimalParameter(30.0, 48.0, default=46.0, 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=2.0, 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=2.8, space='buy', decimals=1, optimize=True, load=True) buy_ma_offset_13 = DecimalParameter(0.93, 0.98, default=0.952, space='buy', decimals=3, optimize=True, load=True) buy_ewo_13 = DecimalParameter(-14.0, -7.0, default=-7.9, space='buy', decimals=1, optimize=True, 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=0.992, space='buy', decimals=3, optimize=True, load=True) buy_ma_offset_14 = DecimalParameter(0.93, 0.99, default=0.998, space='buy', decimals=3, optimize=True, load=True) buy_ema_open_mult_15 = DecimalParameter(0.02, 0.04, default=0.026, space='buy', decimals=3, optimize=True, load=True) buy_ma_offset_15 = DecimalParameter(0.93, 0.99, default=0.985, space='buy', decimals=3, optimize=True, load=True) buy_rsi_15 = DecimalParameter(30.0, 50.0, default=32.0, 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=True, 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=0.95, space='buy', decimals=3, optimize=True, load=True) buy_rsi_16 = DecimalParameter(26.0, 50.0, default=38.0, space='buy', decimals=1, optimize=True, load=True) buy_ewo_16 = DecimalParameter(4.0, 8.0, default=3.6, 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=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=4.382, 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=30.0, 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=36.0, 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_profit_2 = DecimalParameter(0.01, 0.10, default=0.035, space='sell', decimals=3, optimize=True, load=True) sell_custom_under_profit_3 = 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) ############################################################# def get_ticker_indicator(self): return int(self.timeframe[:-1]) 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() max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate) if (last_candle is not None): if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value): return 'signal_profit_4' elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return 'signal_profit_3' elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return 'signal_profit_2' elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return 'signal_profit_1' elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value): return 'signal_profit_0' elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['close'] < last_candle['ema_200']): return 'signal_profit_u_1' elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['sma_200_dec']): return 'signal_profit_u_2' elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['close'] < last_candle['ema_100']): return 'signal_profit_u_3' elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (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) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)): return 'signal_profit_t_2' 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()))) 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, 50, 200) # Alligator dataframe['lips'] = ta.SMA(dataframe, timeperiod=5) dataframe['smma_lips'] = dataframe['lips'].rolling(3).mean() dataframe['teeth'] = ta.SMA(dataframe, timeperiod=8) dataframe['smma_teeth'] = dataframe['teeth'].rolling(5).mean() dataframe['jaw'] = ta.SMA(dataframe, timeperiod=13) dataframe['smma_jaw'] = dataframe['jaw'].rolling(8).mean() # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=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)) # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_30'] = dataframe['volume'].rolling(30).mean() 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_buy_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(20)) & (dataframe['safe_dips']) & (dataframe['safe_pump_48_1h']) & (((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['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_pump_24_strict_1h']) & (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_50_1h'] > dataframe['ema_100_1h']) & (dataframe['ema_100_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_pump_36_1h']) & 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['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['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips']) & (dataframe['safe_pump_36_strict_1h']) & (dataframe['volume_mean_4'] * self.buy_volume_5.value > dataframe['volume']) & (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_50_1h'] > dataframe['ema_200_1h']) & (dataframe['safe_dips_strict']) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.value > dataframe['volume']) & (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']) & (dataframe['volume'].rolling(4).mean() * self.buy_volume_6.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['close'] > (dataframe['ema_200_1h'] * self.buy_ema_rel_8.value)) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['sma_200_1h'] > dataframe['sma_200_1h'].shift(24)) & (dataframe['close'] > dataframe['open']) & (dataframe['close'] > dataframe['smma_lips']) & (dataframe['smma_lips'] > dataframe['smma_teeth']) & (dataframe['smma_teeth'] > dataframe['smma_jaw']) & (dataframe['smma_lips'].shift(1) > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_teeth'].shift(1) > dataframe['smma_jaw'].shift(1)) & (dataframe['smma_lips'] > dataframe['smma_lips'].shift(1)) & (dataframe['smma_teeth'] > dataframe['smma_teeth'].shift(1)) & (dataframe['smma_jaw'] > dataframe['smma_jaw'].shift(1)) & (dataframe['rsi'] < self.buy_rsi_8.value) & (dataframe['volume'] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_100_1h']) & (dataframe['safe_dips_strict']) & (dataframe['volume_mean_4'] * self.buy_volume_9.value > dataframe['volume']) & (dataframe['close'] < dataframe['sma_30'] * 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']) & (dataframe['safe_pump_24_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'] * 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_pump_24_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_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_strict_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'] > 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_strict_1h']) & (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['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['sma_200'] > dataframe['sma_200'].shift(20)) & (dataframe['safe_dips_strict']) & (dataframe['safe_pump_48_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'] * self.buy_bb_offset_14.value)) & (dataframe['close'] < dataframe['sma_30'] * 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_strict']) & (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['sma_30'] * 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['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['close'] < dataframe['ema_20'] * self.buy_ma_offset_17.value) & (dataframe['ewo'] < self.buy_ewo_17.value) & (dataframe['volume'] > 0) ) ) 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'] > 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_5_enable.value & (dataframe['close'] < dataframe['ema_200']) & (((dataframe['ema_200'] - dataframe['close']) / dataframe['close']) < self.sell_ema_relative_5.value) & (dataframe['rsi'] > dataframe['rsi_1h'] + self.sell_rsi_diff_5.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) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe # 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