# source: https://raw.githubusercontent.com/flessner/freqtrade/89d04067a9bdd588029770281eecd7b837fcbad0/user_data/strategies/nfi5mo.py import freqtrade.vendor.qtpylib.indicators as qtpylib 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 # Github_flessner_freqtrade__nfi5mo__20250222_025553 # source: leverage = 1 class Github_flessner_freqtrade__nfi5mo__20250222_025553(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.04 * leverage, "10": 0.025 * leverage, "25": 0.01 * leverage, "50": 0, } # Stoploss: stoploss = -0.01 # Trailing stop: trailing_stop = False # trailing_stop_positive = 0.166 # trailing_stop_positive_offset = 0.263 # trailing_only_offset_is_reached = True # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 32, "buy_bb20_close_bblowerband_4": 0.964, "buy_bb20_volume_4": 17.42, "buy_bb40_bbdelta_close_3": 0.025, "buy_bb40_closedelta_close_3": 0.025, "buy_bb40_tail_bbdelta_3": 0.228, "buy_bb_offset_10": 0.971, "buy_bb_offset_14": 0.987, "buy_bb_offset_18": 0.986, "buy_bb_offset_2": 0.991, "buy_bb_offset_5": 0.999, "buy_bb_offset_6": 0.999, "buy_bb_offset_9": 0.978, "buy_chop_min_19": 43.3, "buy_condition_10_enable": False, "buy_condition_11_enable": False, "buy_condition_12_enable": True, "buy_condition_13_enable": True, "buy_condition_14_enable": True, "buy_condition_15_enable": False, "buy_condition_16_enable": False, "buy_condition_17_enable": True, "buy_condition_18_enable": False, "buy_condition_19_enable": False, "buy_condition_1_enable": True, "buy_condition_20_enable": False, "buy_condition_21_enable": False, "buy_condition_2_enable": True, "buy_condition_3_enable": False, "buy_condition_4_enable": False, "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": 0.048, "buy_dip_threshold_10": 0.13, "buy_dip_threshold_11": 0.105, "buy_dip_threshold_12": 0.412, "buy_dip_threshold_2": 0.112, "buy_dip_threshold_3": 0.378, "buy_dip_threshold_4": 0.48, "buy_dip_threshold_5": 0.044, "buy_dip_threshold_6": 0.037, "buy_dip_threshold_7": 0.325, "buy_dip_threshold_8": 0.476, "buy_dip_threshold_9": 0.045, "buy_ema_open_mult_14": 0.026, "buy_ema_open_mult_15": 0.024, "buy_ema_open_mult_5": 0.02, "buy_ema_open_mult_6": 0.03, "buy_ema_open_mult_7": 0.032, "buy_ema_rel_15": 0.997, "buy_ema_rel_3": 0.98, "buy_ema_rel_5": 0.998, "buy_ema_rel_7": 0.979, "buy_ewo_12": 3.8, "buy_ewo_13": -13.5, "buy_ewo_16": 7.8, "buy_ewo_17": -14.4, "buy_ma_offset_10": 0.945, "buy_ma_offset_11": 0.985, "buy_ma_offset_12": 0.961, "buy_ma_offset_13": 0.971, "buy_ma_offset_14": 0.981, "buy_ma_offset_15": 0.971, "buy_ma_offset_16": 0.935, "buy_ma_offset_17": 0.955, "buy_ma_offset_9": 0.971, "buy_mfi_1": 37.8, "buy_mfi_11": 53.9, "buy_mfi_2": 42.6, "buy_mfi_9": 51.5, "buy_min_inc_1": 0.039, "buy_min_inc_11": 0.013, "buy_pump_pull_threshold_1": 1.81, "buy_pump_pull_threshold_2": 2.39, "buy_pump_pull_threshold_3": 2.6, "buy_pump_pull_threshold_4": 2.7, "buy_pump_pull_threshold_5": 2.77, "buy_pump_pull_threshold_6": 2.21, "buy_pump_pull_threshold_7": 2.17, "buy_pump_pull_threshold_8": 2.88, "buy_pump_pull_threshold_9": 2.71, "buy_pump_threshold_1": 0.968, "buy_pump_threshold_2": 0.405, "buy_pump_threshold_3": 0.82, "buy_pump_threshold_4": 0.978, "buy_pump_threshold_5": 0.683, "buy_pump_threshold_6": 0.754, "buy_pump_threshold_7": 0.42, "buy_pump_threshold_8": 0.469, "buy_pump_threshold_9": 1.592, "buy_rsi_1": 37.8, "buy_rsi_11": 47.7, "buy_rsi_12": 38.2, "buy_rsi_15": 48.4, "buy_rsi_16": 26.3, "buy_rsi_18": 22.0, "buy_rsi_1h_10": 20.0, "buy_rsi_1h_20": 21.7, "buy_rsi_1h_21": 37.8, "buy_rsi_1h_diff_2": 43.1, "buy_rsi_1h_max_1": 84.6, "buy_rsi_1h_max_11": 85.6, "buy_rsi_1h_max_2": 90.3, "buy_rsi_1h_max_9": 79.0, "buy_rsi_1h_min_1": 39.4, "buy_rsi_1h_min_11": 45.7, "buy_rsi_1h_min_19": 57.4, "buy_rsi_1h_min_2": 37.2, "buy_rsi_1h_min_9": 39.5, "buy_rsi_20": 28.5, "buy_rsi_21": 17.8, "buy_rsi_7": 39.5, "buy_rsi_8": 39.6, "buy_tail_diff_8": 5.7, "buy_volume_10": 5.6, "buy_volume_12": 4.5, "buy_volume_13": 2.3, "buy_volume_14": 7.4, "buy_volume_15": 7.1, "buy_volume_16": 7.0, "buy_volume_17": 3.4, "buy_volume_18": 1.4, "buy_volume_2": 4.1, "buy_volume_20": 5.2, "buy_volume_21": 3.9, "buy_volume_7": 1.1, "buy_volume_8": 3.8, "buy_volume_9": 3.95, "ewo_high": 6.899, "ewo_low": -15.271, "fast_ewo": 46, "low_offset_ema": 0.968, "low_offset_kama": 0.934, "low_offset_sma": 0.969, "low_offset_t3": 0.938, "low_offset_trima": 0.973, "slow_ewo": 157, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 20, "high_offset_ema": 1.012, "high_offset_kama": 1.012, "high_offset_sma": 1.012, "high_offset_t3": 1.012, "high_offset_trima": 1.012, } use_custom_stoploss = False # Optimal timeframe for the strategy. timeframe = "5m" inf_1h = "1h" # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count = 300 ############################################################# # Multi Offset base_nb_candles_buy = IntParameter( 5, 80, default=20, load=True, space="buy", optimize=True ) base_nb_candles_sell = IntParameter( 5, 80, default=20, load=True, space="sell", optimize=True ) low_offset_sma = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space="buy", optimize=True ) high_offset_sma = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space="sell", optimize=True ) low_offset_ema = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space="buy", optimize=True ) high_offset_ema = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space="sell", optimize=True ) low_offset_trima = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space="buy", optimize=True ) high_offset_trima = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space="sell", optimize=True ) low_offset_t3 = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space="buy", optimize=True ) high_offset_t3 = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space="sell", optimize=True ) low_offset_kama = DecimalParameter( 0.9, 0.99, default=0.958, load=True, space="buy", optimize=True ) high_offset_kama = DecimalParameter( 0.99, 1.1, default=1.012, load=True, space="sell", optimize=True ) # MA list ma_types = ["sma", "ema", "trima", "t3", "kama"] ma_map = { "sma": { "low_offset": low_offset_sma.value, "high_offset": high_offset_sma.value, "calculate": ta.SMA, }, "ema": { "low_offset": low_offset_ema.value, "high_offset": high_offset_ema.value, "calculate": ta.EMA, }, "trima": { "low_offset": low_offset_trima.value, "high_offset": high_offset_trima.value, "calculate": ta.TRIMA, }, "t3": { "low_offset": low_offset_t3.value, "high_offset": high_offset_t3.value, "calculate": ta.T3, }, "kama": { "low_offset": low_offset_kama.value, "high_offset": high_offset_kama.value, "calculate": ta.KAMA, }, } # Protection ewo_low = DecimalParameter( -20.0, -8.0, default=buy_params["ewo_low"], load=True, space="buy", optimize=True, ) ewo_high = DecimalParameter( 2.0, 12.0, default=buy_params["ewo_high"], load=True, space="buy", optimize=True ) fast_ewo = IntParameter( 10, 50, default=buy_params["fast_ewo"], load=True, space="buy", optimize=True ) slow_ewo = IntParameter( 100, 200, default=buy_params["slow_ewo"], load=True, space="buy", optimize=True ) buy_condition_1_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_1_enable"], space="buy", optimize=True, load=True, ) buy_condition_2_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_2_enable"], space="buy", optimize=True, load=True, ) buy_condition_3_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_3_enable"], space="buy", optimize=True, load=True, ) buy_condition_4_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_4_enable"], space="buy", optimize=True, load=True, ) buy_condition_5_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_5_enable"], space="buy", optimize=True, load=True, ) buy_condition_6_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_6_enable"], space="buy", optimize=True, load=True, ) buy_condition_7_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_7_enable"], space="buy", optimize=True, load=True, ) buy_condition_8_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_8_enable"], space="buy", optimize=True, load=True, ) buy_condition_9_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_9_enable"], space="buy", optimize=True, load=True, ) buy_condition_10_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_10_enable"], space="buy", optimize=True, load=True, ) buy_condition_11_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_11_enable"], space="buy", optimize=True, load=True, ) buy_condition_12_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_12_enable"], space="buy", optimize=True, load=True, ) buy_condition_13_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_13_enable"], space="buy", optimize=True, load=True, ) buy_condition_14_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_14_enable"], space="buy", optimize=True, load=True, ) buy_condition_15_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_15_enable"], space="buy", optimize=True, load=True, ) buy_condition_16_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_16_enable"], space="buy", optimize=True, load=True, ) buy_condition_17_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_17_enable"], space="buy", optimize=True, load=True, ) buy_condition_18_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_18_enable"], space="buy", optimize=True, load=True, ) buy_condition_19_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_19_enable"], space="buy", optimize=True, load=True, ) buy_condition_20_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_20_enable"], space="buy", optimize=True, load=True, ) buy_condition_21_enable = CategoricalParameter( [True, False], default=buy_params["buy_condition_21_enable"], space="buy", optimize=True, load=True, ) # Normal dips buy_dip_threshold_1 = DecimalParameter( 0.001, 0.05, default=buy_params["buy_dip_threshold_1"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_2 = DecimalParameter( 0.01, 0.2, default=buy_params["buy_dip_threshold_2"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_3 = DecimalParameter( 0.05, 0.4, default=buy_params["buy_dip_threshold_3"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_4 = DecimalParameter( 0.2, 0.5, default=buy_params["buy_dip_threshold_4"], space="buy", decimals=3, optimize=True, load=True, ) # Strict dips buy_dip_threshold_5 = DecimalParameter( 0.001, 0.05, default=buy_params["buy_dip_threshold_5"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_6 = DecimalParameter( 0.01, 0.2, default=buy_params["buy_dip_threshold_6"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_7 = DecimalParameter( 0.05, 0.4, default=buy_params["buy_dip_threshold_7"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_8 = DecimalParameter( 0.2, 0.5, default=buy_params["buy_dip_threshold_8"], space="buy", decimals=3, optimize=True, load=True, ) # Loose dips buy_dip_threshold_9 = DecimalParameter( 0.001, 0.05, default=buy_params["buy_dip_threshold_9"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_10 = DecimalParameter( 0.01, 0.2, default=buy_params["buy_dip_threshold_10"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_11 = DecimalParameter( 0.05, 0.4, default=buy_params["buy_dip_threshold_11"], space="buy", decimals=3, optimize=True, load=True, ) buy_dip_threshold_12 = DecimalParameter( 0.2, 0.5, default=buy_params["buy_dip_threshold_12"], space="buy", decimals=3, optimize=True, load=True, ) # 24 hours buy_pump_pull_threshold_1 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_1"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_1 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_1"], space="buy", decimals=3, optimize=True, load=True, ) # 36 hours buy_pump_pull_threshold_2 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_2"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_2 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_2"], space="buy", decimals=3, optimize=True, load=True, ) # 48 hours buy_pump_pull_threshold_3 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_3"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_3 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_3"], space="buy", decimals=3, optimize=True, load=True, ) # 24 hours strict buy_pump_pull_threshold_4 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_4"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_4 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_4"], space="buy", decimals=3, optimize=True, load=True, ) # 36 hours strict buy_pump_pull_threshold_5 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_5"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_5 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_5"], space="buy", decimals=3, optimize=True, load=True, ) # 48 hours strict buy_pump_pull_threshold_6 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_6"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_6 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_6"], space="buy", decimals=3, optimize=True, load=True, ) # 24 hours loose buy_pump_pull_threshold_7 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_7"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_7 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_7"], space="buy", decimals=3, optimize=True, load=True, ) # 36 hours loose buy_pump_pull_threshold_8 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_8"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_8 = DecimalParameter( 0.4, 1.0, default=buy_params["buy_pump_threshold_8"], space="buy", decimals=3, optimize=True, load=True, ) # 48 hours loose buy_pump_pull_threshold_9 = DecimalParameter( 1.5, 3.0, default=buy_params["buy_pump_pull_threshold_9"], space="buy", decimals=2, optimize=True, load=True, ) buy_pump_threshold_9 = DecimalParameter( 0.4, 1.8, default=buy_params["buy_pump_threshold_9"], space="buy", decimals=3, optimize=True, load=True, ) buy_min_inc_1 = DecimalParameter( 0.01, 0.05, default=buy_params["buy_min_inc_1"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_1h_min_1 = DecimalParameter( 25.0, 40.0, default=buy_params["buy_rsi_1h_min_1"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_max_1 = DecimalParameter( 70.0, 90.0, default=buy_params["buy_rsi_1h_max_1"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1 = DecimalParameter( 20.0, 40.0, default=buy_params["buy_rsi_1"], space="buy", decimals=1, optimize=True, load=True, ) buy_mfi_1 = DecimalParameter( 20.0, 40.0, default=buy_params["buy_mfi_1"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_2 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_2"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_min_2 = DecimalParameter( 30.0, 40.0, default=buy_params["buy_rsi_1h_min_2"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_max_2 = DecimalParameter( 70.0, 95.0, default=buy_params["buy_rsi_1h_max_2"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_diff_2 = DecimalParameter( 30.0, 50.0, default=buy_params["buy_rsi_1h_diff_2"], space="buy", decimals=1, optimize=True, load=True, ) buy_mfi_2 = DecimalParameter( 30.0, 56.0, default=buy_params["buy_mfi_2"], space="buy", decimals=1, optimize=True, load=True, ) buy_bb_offset_2 = DecimalParameter( 0.97, 0.999, default=buy_params["buy_bb_offset_2"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb40_bbdelta_close_3 = DecimalParameter( 0.005, 0.06, default=buy_params["buy_bb40_bbdelta_close_3"], space="buy", optimize=True, load=True, ) buy_bb40_closedelta_close_3 = DecimalParameter( 0.01, 0.03, default=buy_params["buy_bb40_closedelta_close_3"], space="buy", optimize=True, load=True, ) buy_bb40_tail_bbdelta_3 = DecimalParameter( 0.15, 0.45, default=buy_params["buy_bb40_tail_bbdelta_3"], space="buy", optimize=True, load=True, ) buy_ema_rel_3 = DecimalParameter( 0.97, 0.999, default=buy_params["buy_ema_rel_3"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb20_close_bblowerband_4 = DecimalParameter( 0.96, 0.99, default=buy_params["buy_bb20_close_bblowerband_4"], space="buy", optimize=True, load=True, ) buy_bb20_volume_4 = DecimalParameter( 1.0, 20.0, default=buy_params["buy_bb20_volume_4"], space="buy", decimals=2, optimize=True, load=True, ) buy_ema_open_mult_5 = DecimalParameter( 0.016, 0.03, default=buy_params["buy_ema_open_mult_5"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb_offset_5 = DecimalParameter( 0.98, 1.0, default=buy_params["buy_bb_offset_5"], space="buy", decimals=3, optimize=True, load=True, ) buy_ema_rel_5 = DecimalParameter( 0.97, 0.999, default=buy_params["buy_ema_rel_5"], space="buy", decimals=3, optimize=True, load=True, ) buy_ema_open_mult_6 = DecimalParameter( 0.02, 0.03, default=buy_params["buy_ema_open_mult_6"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb_offset_6 = DecimalParameter( 0.98, 0.999, default=buy_params["buy_bb_offset_6"], space="buy", decimals=3, optimize=True, load=True, ) buy_volume_7 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_7"], space="buy", decimals=1, optimize=True, load=True, ) buy_ema_open_mult_7 = DecimalParameter( 0.02, 0.04, default=buy_params["buy_ema_open_mult_7"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_7 = DecimalParameter( 24.0, 50.0, default=buy_params["buy_rsi_7"], space="buy", decimals=1, optimize=True, load=True, ) buy_ema_rel_7 = DecimalParameter( 0.97, 0.999, default=buy_params["buy_ema_rel_7"], space="buy", decimals=3, optimize=True, load=True, ) buy_volume_8 = DecimalParameter( 1.0, 6.0, default=buy_params["buy_volume_8"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_8 = DecimalParameter( 36.0, 40.0, default=buy_params["buy_rsi_8"], space="buy", decimals=1, optimize=True, load=True, ) buy_tail_diff_8 = DecimalParameter( 3.0, 10.0, default=buy_params["buy_tail_diff_8"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_9 = DecimalParameter( 1.0, 4.0, default=buy_params["buy_volume_9"], space="buy", decimals=2, optimize=True, load=True, ) buy_ma_offset_9 = DecimalParameter( 0.94, 0.99, default=buy_params["buy_ma_offset_9"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb_offset_9 = DecimalParameter( 0.97, 0.99, default=buy_params["buy_bb_offset_9"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_1h_min_9 = DecimalParameter( 26.0, 40.0, default=buy_params["buy_rsi_1h_min_9"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_max_9 = DecimalParameter( 70.0, 90.0, default=buy_params["buy_rsi_1h_max_9"], space="buy", decimals=1, optimize=True, load=True, ) buy_mfi_9 = DecimalParameter( 36.0, 65.0, default=buy_params["buy_mfi_9"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_10 = DecimalParameter( 1.0, 8.0, default=buy_params["buy_volume_10"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_10 = DecimalParameter( 0.93, 0.97, default=buy_params["buy_ma_offset_10"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb_offset_10 = DecimalParameter( 0.97, 0.99, default=buy_params["buy_bb_offset_10"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_1h_10 = DecimalParameter( 20.0, 40.0, default=buy_params["buy_rsi_1h_10"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_11 = DecimalParameter( 0.93, 0.99, default=buy_params["buy_ma_offset_11"], space="buy", decimals=3, optimize=True, load=True, ) buy_min_inc_11 = DecimalParameter( 0.005, 0.05, default=buy_params["buy_min_inc_11"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_1h_min_11 = DecimalParameter( 40.0, 60.0, default=buy_params["buy_rsi_1h_min_11"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_max_11 = DecimalParameter( 70.0, 90.0, default=buy_params["buy_rsi_1h_max_11"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_11 = DecimalParameter( 30.0, 48.0, default=buy_params["buy_rsi_11"], space="buy", decimals=1, optimize=True, load=True, ) buy_mfi_11 = DecimalParameter( 36.0, 56.0, default=buy_params["buy_mfi_11"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_12 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_12"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_12 = DecimalParameter( 0.93, 0.97, default=buy_params["buy_ma_offset_12"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_12 = DecimalParameter( 26.0, 40.0, default=buy_params["buy_rsi_12"], space="buy", decimals=1, optimize=True, load=True, ) buy_ewo_12 = DecimalParameter( 2.0, 6.0, default=buy_params["buy_ewo_12"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_13 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_13"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_13 = DecimalParameter( 0.93, 0.98, default=buy_params["buy_ma_offset_13"], space="buy", decimals=3, optimize=True, load=True, ) buy_ewo_13 = DecimalParameter( -14.0, -7.0, default=buy_params["buy_ewo_13"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_14 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_14"], space="buy", decimals=1, optimize=True, load=True, ) buy_ema_open_mult_14 = DecimalParameter( 0.01, 0.03, default=buy_params["buy_ema_open_mult_14"], space="buy", decimals=3, optimize=True, load=True, ) buy_bb_offset_14 = DecimalParameter( 0.98, 1.0, default=buy_params["buy_bb_offset_14"], space="buy", decimals=3, optimize=True, load=True, ) buy_ma_offset_14 = DecimalParameter( 0.93, 0.99, default=buy_params["buy_ma_offset_14"], space="buy", decimals=3, optimize=True, load=True, ) buy_volume_15 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_15"], space="buy", decimals=1, optimize=True, load=True, ) buy_ema_open_mult_15 = DecimalParameter( 0.02, 0.04, default=buy_params["buy_ema_open_mult_15"], space="buy", decimals=3, optimize=True, load=True, ) buy_ma_offset_15 = DecimalParameter( 0.93, 0.99, default=buy_params["buy_ma_offset_15"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_15 = DecimalParameter( 30.0, 50.0, default=buy_params["buy_rsi_15"], space="buy", decimals=1, optimize=True, load=True, ) buy_ema_rel_15 = DecimalParameter( 0.97, 0.999, default=buy_params["buy_ema_rel_15"], space="buy", decimals=3, optimize=True, load=True, ) buy_volume_16 = DecimalParameter( 1.0, 10.0, default=buy_params["buy_volume_16"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_16 = DecimalParameter( 0.93, 0.97, default=buy_params["buy_ma_offset_16"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_16 = DecimalParameter( 26.0, 50.0, default=buy_params["buy_rsi_16"], space="buy", decimals=1, optimize=True, load=True, ) buy_ewo_16 = DecimalParameter( 4.0, 8.0, default=buy_params["buy_ewo_16"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_17 = DecimalParameter( 0.5, 8.0, default=buy_params["buy_volume_17"], space="buy", decimals=1, optimize=True, load=True, ) buy_ma_offset_17 = DecimalParameter( 0.93, 0.98, default=buy_params["buy_ma_offset_17"], space="buy", decimals=3, optimize=True, load=True, ) buy_ewo_17 = DecimalParameter( -18.0, -10.0, default=buy_params["buy_ewo_17"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_18 = DecimalParameter( 1.0, 6.0, default=buy_params["buy_volume_18"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_18 = DecimalParameter( 16.0, 32.0, default=buy_params["buy_rsi_18"], space="buy", decimals=1, optimize=True, load=True, ) buy_bb_offset_18 = DecimalParameter( 0.98, 1.0, default=buy_params["buy_bb_offset_18"], space="buy", decimals=3, optimize=True, load=True, ) buy_rsi_1h_min_19 = DecimalParameter( 40.0, 70.0, default=buy_params["buy_rsi_1h_min_19"], space="buy", decimals=1, optimize=True, load=True, ) buy_chop_min_19 = DecimalParameter( 20.0, 60.0, default=buy_params["buy_chop_min_19"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_20 = DecimalParameter( 0.5, 6.0, default=buy_params["buy_volume_20"], space="buy", decimals=1, optimize=True, load=True, ) # buy_ema_rel_20 = DecimalParameter(0.97, 0.999, default=buy_params["buy_ema_rel_20"], space='buy', decimals=3, optimize=True, load=True) buy_rsi_20 = DecimalParameter( 20.0, 36.0, default=buy_params["buy_rsi_20"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_20 = DecimalParameter( 14.0, 30.0, default=buy_params["buy_rsi_1h_20"], space="buy", decimals=1, optimize=True, load=True, ) buy_volume_21 = DecimalParameter( 0.5, 6.0, default=buy_params["buy_volume_21"], space="buy", decimals=1, optimize=True, load=True, ) # buy_ema_rel_21 = DecimalParameter(0.97, 0.999, default=buy_params["buy_ema_rel_21"], space='buy', decimals=3, optimize=True, load=True) buy_rsi_21 = DecimalParameter( 10.0, 28.0, default=buy_params["buy_rsi_21"], space="buy", decimals=1, optimize=True, load=True, ) buy_rsi_1h_21 = DecimalParameter( 18.0, 40.0, default=buy_params["buy_rsi_1h_21"], space="buy", decimals=1, optimize=True, load=True, ) def get_ticker_indicator(self): return int(self.timeframe[:-1]) def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, "1h") for pair in pairs] return informative_pairs def informative_1h_indicators( self, dataframe: DataFrame, metadata: dict ) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe( pair=metadata["pair"], timeframe=self.inf_1h ) # EMA informative_1h["ema_15"] = ta.EMA(informative_1h, timeperiod=15) informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50) informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100) informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200) # SMA informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200) # RSI informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) # BB bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(informative_1h), window=20, stds=2 ) informative_1h["bb_lowerband"] = bollinger["lower"] informative_1h["bb_middleband"] = bollinger["mid"] informative_1h["bb_upperband"] = bollinger["upper"] # Pump protections informative_1h["safe_pump_24"] = ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / informative_1h["close"].rolling(24).min() ) < self.buy_pump_threshold_1.value ) | ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / self.buy_pump_pull_threshold_1.value ) > (informative_1h["close"] - informative_1h["close"].rolling(24).min()) ) informative_1h["safe_pump_36"] = ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / informative_1h["close"].rolling(36).min() ) < self.buy_pump_threshold_2.value ) | ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / self.buy_pump_pull_threshold_2.value ) > (informative_1h["close"] - informative_1h["close"].rolling(36).min()) ) informative_1h["safe_pump_48"] = ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / informative_1h["close"].rolling(48).min() ) < self.buy_pump_threshold_3.value ) | ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / self.buy_pump_pull_threshold_3.value ) > (informative_1h["close"] - informative_1h["close"].rolling(48).min()) ) informative_1h["safe_pump_24_strict"] = ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / informative_1h["close"].rolling(24).min() ) < self.buy_pump_threshold_4.value ) | ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / self.buy_pump_pull_threshold_4.value ) > (informative_1h["close"] - informative_1h["close"].rolling(24).min()) ) informative_1h["safe_pump_36_strict"] = ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / informative_1h["close"].rolling(36).min() ) < self.buy_pump_threshold_5.value ) | ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / self.buy_pump_pull_threshold_5.value ) > (informative_1h["close"] - informative_1h["close"].rolling(36).min()) ) informative_1h["safe_pump_48_strict"] = ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / informative_1h["close"].rolling(48).min() ) < self.buy_pump_threshold_6.value ) | ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / self.buy_pump_pull_threshold_6.value ) > (informative_1h["close"] - informative_1h["close"].rolling(48).min()) ) informative_1h["safe_pump_24_loose"] = ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / informative_1h["close"].rolling(24).min() ) < self.buy_pump_threshold_7.value ) | ( ( ( informative_1h["open"].rolling(24).max() - informative_1h["close"].rolling(24).min() ) / self.buy_pump_pull_threshold_7.value ) > (informative_1h["close"] - informative_1h["close"].rolling(24).min()) ) informative_1h["safe_pump_36_loose"] = ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / informative_1h["close"].rolling(36).min() ) < self.buy_pump_threshold_8.value ) | ( ( ( informative_1h["open"].rolling(36).max() - informative_1h["close"].rolling(36).min() ) / self.buy_pump_pull_threshold_8.value ) > (informative_1h["close"] - informative_1h["close"].rolling(36).min()) ) informative_1h["safe_pump_48_loose"] = ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / informative_1h["close"].rolling(48).min() ) < self.buy_pump_threshold_9.value ) | ( ( ( informative_1h["open"].rolling(48).max() - informative_1h["close"].rolling(48).min() ) / self.buy_pump_pull_threshold_9.value ) > (informative_1h["close"] - informative_1h["close"].rolling(48).min()) ) return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # BB 40 bb_40 = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2) dataframe["lower"] = bb_40["lower"] dataframe["mid"] = bb_40["mid"] dataframe["bbdelta"] = (bb_40["mid"] - dataframe["lower"]).abs() dataframe["closedelta"] = ( dataframe["close"] - dataframe["close"].shift() ).abs() dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs() # BB 20 bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=20, stds=2 ) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] # EMA 200 dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_100"] = ta.EMA(dataframe, timeperiod=100) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe["sma_5"] = ta.SMA(dataframe, timeperiod=5) dataframe["sma_30"] = ta.SMA(dataframe, timeperiod=30) dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20) # MFI dataframe["mfi"] = ta.MFI(dataframe) # EWO dataframe["ewo"] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # Chopiness dataframe["chop"] = qtpylib.chopiness(dataframe, 14) # Dip protection dataframe["safe_dips"] = ( ( ((dataframe["open"] - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_3.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_4.value ) ) dataframe["safe_dips_strict"] = ( ( ((dataframe["open"] - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_5.value ) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_6.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_7.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_8.value ) ) dataframe["safe_dips_loose"] = ( ( ((dataframe["open"] - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_9.value ) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_10.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_11.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_12.value ) ) # Volume dataframe["volume_mean_4"] = dataframe["volume"].rolling(4).mean().shift(1) dataframe["volume_mean_30"] = dataframe["volume"].rolling(30).mean() # Offset for i in self.ma_types: dataframe[f"{i}_offset_buy"] = ( self.ma_map[f"{i}"]["calculate"]( dataframe, self.base_nb_candles_buy.value ) * self.ma_map[f"{i}"]["low_offset"] ) dataframe[f"{i}_offset_sell"] = ( self.ma_map[f"{i}"]["calculate"]( dataframe, self.base_nb_candles_sell.value ) * self.ma_map[f"{i}"]["high_offset"] ) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True ) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( ( self.buy_condition_1_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["sma_200"] > dataframe["sma_200"].shift(50)) & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_1h"]) & ( ( (dataframe["close"] - dataframe["open"].rolling(36).min()) / dataframe["open"].rolling(36).min() ) > self.buy_min_inc_1.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h_min_1.value) & (dataframe["rsi_1h"] < self.buy_rsi_1h_max_1.value) & (dataframe["rsi"] < self.buy_rsi_1.value) & (dataframe["mfi"] < self.buy_mfi_1.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_2_enable.value & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(50)) & (dataframe["safe_pump_24_strict_1h"]) & ( dataframe["volume_mean_4"] * self.buy_volume_2.value > dataframe["volume"] ) & # (dataframe['rsi_1h'] > self.buy_rsi_1h_min_2.value) & # (dataframe['rsi_1h'] < self.buy_rsi_1h_max_2.value) & (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_1h_diff_2.value) & (dataframe["mfi"] < self.buy_mfi_2.value) & ( dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_2.value) ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_3_enable.value & ( dataframe["close"] > (dataframe["ema_200_1h"] * self.buy_ema_rel_3.value) ) & (dataframe["ema_100"] > dataframe["ema_200"]) & (dataframe["ema_100_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_pump_36_strict_1h"]) & dataframe["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["safe_dips"]) & (dataframe["safe_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"] * self.buy_bb_offset_5.value) ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_6_enable.value & (dataframe["ema_100_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips_loose"]) & (dataframe["safe_pump_36_strict_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_6.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & ( dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_6.value) ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_7_enable.value & (dataframe["ema_100"] > dataframe["ema_200"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips_strict"]) & ( dataframe["volume"].rolling(4).mean() * self.buy_volume_7.value > dataframe["volume"] ) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_7.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["rsi"] < self.buy_rsi_7.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_8_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips_loose"]) & (dataframe["safe_pump_24_1h"]) & (dataframe["rsi"] < self.buy_rsi_8.value) & ( dataframe["volume"] > (dataframe["volume"].shift(1) * self.buy_volume_8.value) ) & (dataframe["close"] > dataframe["open"]) & ( (dataframe["close"] - dataframe["low"]) > ( (dataframe["close"] - dataframe["open"]) * self.buy_tail_diff_8.value ) ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_9_enable.value & (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["ema_100"] > dataframe["ema_200"]) & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_loose_1h"]) & ( dataframe["volume_mean_4"] * self.buy_volume_9.value > dataframe["volume"] ) & ( dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_9.value ) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb_offset_9.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h_min_9.value) & (dataframe["rsi_1h"] < self.buy_rsi_1h_max_9.value) & (dataframe["mfi"] < self.buy_mfi_9.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_10_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(24)) & (dataframe["safe_dips_loose"]) & (dataframe["safe_pump_24_loose_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_10.value) > dataframe["volume"] ) & ( dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_10.value ) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb_offset_10.value ) & (dataframe["rsi_1h"] < self.buy_rsi_1h_10.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_11_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["safe_dips_loose"]) & (dataframe["safe_pump_24_loose_1h"]) & (dataframe["safe_pump_36_1h"]) & (dataframe["safe_pump_48_loose_1h"]) & ( ( (dataframe["close"] - dataframe["open"].rolling(36).min()) / dataframe["open"].rolling(36).min() ) > self.buy_min_inc_11.value ) & ( dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_11.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h_min_11.value) & (dataframe["rsi_1h"] < self.buy_rsi_1h_max_11.value) & (dataframe["rsi"] < self.buy_rsi_11.value) & (dataframe["mfi"] < self.buy_mfi_11.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_12_enable.value & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(24)) & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_12.value) > dataframe["volume"] ) & ( dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_12.value ) & (dataframe["ewo"] > self.buy_ewo_12.value) & (dataframe["rsi"] < self.buy_rsi_12.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_13_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(24)) & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_loose_1h"]) & (dataframe["safe_pump_36_loose_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_13.value) > dataframe["volume"] ) & ( dataframe["close"] < dataframe["sma_30"] * self.buy_ma_offset_13.value ) & (dataframe["ewo"] < self.buy_ewo_13.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_14_enable.value & (dataframe["sma_200"] > dataframe["sma_200"].shift(30)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(50)) & (dataframe["safe_dips_loose"]) & (dataframe["safe_pump_24_1h"]) & ( dataframe["volume_mean_4"] * self.buy_volume_14.value > dataframe["volume"] ) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_14.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & ( dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_14.value) ) & ( dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_14.value ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_15_enable.value & ( dataframe["close"] > dataframe["ema_200_1h"] * self.buy_ema_rel_15.value ) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips"]) & (dataframe["safe_pump_36_strict_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_15.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["rsi"] < self.buy_rsi_15.value) & ( dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_15.value ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_16_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_strict_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_16.value) > dataframe["volume"] ) & ( dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_16.value ) & (dataframe["ewo"] > self.buy_ewo_16.value) & (dataframe["rsi"] < self.buy_rsi_16.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_17_enable.value & (dataframe["safe_dips_strict"]) & (dataframe["safe_pump_24_loose_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_17.value) > dataframe["volume"] ) & ( dataframe["close"] < dataframe["ema_20"] * self.buy_ma_offset_17.value ) & (dataframe["ewo"] < self.buy_ewo_17.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_18_enable.value & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_100"] > dataframe["ema_200"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["sma_200"] > dataframe["sma_200"].shift(20)) & (dataframe["sma_200"] > dataframe["sma_200"].shift(44)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(36)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(72)) & (dataframe["safe_dips"]) & (dataframe["safe_pump_24_strict_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_18.value) > dataframe["volume"] ) & (dataframe["rsi"] < self.buy_rsi_18.value) & ( dataframe["close"] < (dataframe["bb_lowerband"] * self.buy_bb_offset_18.value) ) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_19_enable.value & (dataframe["ema_100_1h"] > dataframe["ema_200_1h"]) & (dataframe["sma_200"] > dataframe["sma_200"].shift(36)) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips"]) & (dataframe["safe_pump_24_1h"]) & (dataframe["close"].shift(1) > dataframe["ema_100_1h"]) & (dataframe["low"] < dataframe["ema_100_1h"]) & (dataframe["close"] > dataframe["ema_100_1h"]) & (dataframe["rsi_1h"] > self.buy_rsi_1h_min_19.value) & (dataframe["chop"] < self.buy_chop_min_19.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_20_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips"]) & (dataframe["safe_pump_24_loose_1h"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_20.value) > dataframe["volume"] ) & (dataframe["rsi"] < self.buy_rsi_20.value) & (dataframe["rsi_1h"] < self.buy_rsi_1h_20.value) & (dataframe["volume"] > 0) ) ) conditions.append( ( self.buy_condition_21_enable.value & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["safe_dips_strict"]) & ( (dataframe["volume_mean_4"] * self.buy_volume_21.value) > dataframe["volume"] ) & (dataframe["rsi"] < self.buy_rsi_21.value) & (dataframe["rsi_1h"] < self.buy_rsi_1h_21.value) & (dataframe["volume"] > 0) ) ) for i in self.ma_types: conditions.append( (dataframe["close"] < dataframe[f"{i}_offset_buy"]) & ( (dataframe["ewo"] < self.ewo_low.value) | (dataframe["ewo"] > self.ewo_high.value) ) & (dataframe["volume"] > 0) ) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1 return dataframe # non active exit - only using roi as an exit def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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