import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from datetime import datetime, timedelta from pandas import DataFrame, Series, concat from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open from functools import reduce from technical.indicators import RMI,vwmacd import logging import pandas_ta as pta from numpy import where import time import datetime ##################################################### BeastBot7 Final Rev 5 #################################################################### # don't need hyperopt, thanks free comunity, it have parts other strategies, without freqtrade comunity this strategy is not possible # # if you earn money and consider rewarding the author ETH: 0xda884c4dbe47421ba63f033db3cc2ec49d552365 BTC: 1hxiKHLaPDKTWuXhVgTZvBdPZtjC9msxi # #################################################################################################################################################### # hyperopt for each conditions and finally all conditions true # backtesting: freqtrade backtesting -c config_test.json -s BeastBotXBLR5x --timerange 20210920-20220127 --breakdown day -v --enable-protections # 119 days # for each conditions # Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Max Drawdown # Con1 12 | 11 0 1 | 2.63% | (15.82%) | 0 days 00:15:00 | (8.27%) | # Con2 16 | 15 0 1 | 2.47% | (19.77%) | 0 days 01:19:00 | (5.15%) | # Con3 12 | 12 0 0 | 2.85% | (17.11%) | 0 days 00:31:00 | -- | # Con4 18 | 16 0 2 | 2.29% | (20.61%) | 0 days 03:11:00 | (5.23%) | # Con6 11 | 10 0 1 | 2.73% | (15.05%) | 0 days 03:15:00 | (0.75%) | # Con7 1 | 1 0 0 | 9.37% | (4.69%) | 0 days 00:30:00 | -- | # Con8 13 | 12 0 1 | 2.31% | (15.06%) | 0 days 05:19:00 | (5.09%) | # Con9 7 | 7 0 0 | 2.62% | (9.17%) | 0 days 00:19:00 | -- | # con10 26 | 19 0 7 | 1.03% | (13.41%) | 0 days 02:39:00 | (3.76%) # all conditions true logger = logging.getLogger(__name__) # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif def SROC(dataframe, roclen=21, emalen=13, smooth=21): df = dataframe.copy() roc = ta.ROC(df, timeperiod=roclen) ema = ta.EMA(df, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return sroc def SSLChannels_ATR(dataframe, length=7): """ SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/ Credit to @JimmyNixx for python """ df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class BeastBotXBLR7(IStrategy): INTERFACE_VERSION = 2 timeframe = '5m' inf_1h = '1h' info_timeframe_1d = "1d" has_BTC_info_tf = True # Buy hyperspace params: buy_params = { "buy_bb_delta": 0.025, "buy_bb_factor": 0.996, "buy_bb_width": 0.115, "buy_c10_1": -96.1, "buy_c10_2": -0.95, "buy_c6_1": 0.2, "buy_c6_2": 0.05, "buy_c6_3": 0.007, "buy_c6_4": 0.017, "buy_c6_5": 0.313, "buy_c7_1": 1.05, "buy_c7_2": 0.96, "buy_c7_3": -85, "buy_c7_4": -84, "buy_c7_5": 75.5, "buy_cci": -134, "buy_cci_length": 38, "buy_closedelta": 14.098, "buy_rmi": 49, "buy_rmi_length": 18, "buy_srsi_fk": 45, "buy_c2_1": 0.02, # value loaded from strategy "buy_c2_2": 0.991, # value loaded from strategy "buy_c2_3": -0.7, # value loaded from strategy "buy_c9_1": 40.0, # value loaded from strategy "buy_c9_2": -69.0, # value loaded from strategy "buy_c9_3": -67.9, # value loaded from strategy "buy_c9_4": 42.3, # value loaded from strategy "buy_c9_5": 32.0, # value loaded from strategy "buy_c9_6": 85.7, # value loaded from strategy "buy_c9_7": -81.9, # value loaded from strategy "buy_con1_enable": True, # value loaded from strategy "buy_con2_enable": True, # value loaded from strategy "buy_con3_1": 0.021, # value loaded from strategy "buy_con3_2": 0.981, # value loaded from strategy "buy_con3_3": 0.973, # value loaded from strategy "buy_con3_4": -0.88, # value loaded from strategy "buy_con3_enable": True, # value loaded from strategy "buy_con4_enable": True, # value loaded from strategy "buy_con6_enable": True, # value loaded from strategy "buy_condition_10_enable": True, # value loaded from strategy "buy_condition_7_enable": True, # value loaded from strategy "buy_condition_8_enable": True, # value loaded from strategy "buy_condition_9_enable": True, # value loaded from strategy "buy_dip_threshold_5": 0.05, # value loaded from strategy "buy_dip_threshold_6": 0.2, # value loaded from strategy "buy_dip_threshold_7": 0.4, # value loaded from strategy "buy_dip_threshold_8": 0.5, # value loaded from strategy "buy_macd_41": 0.09, # value loaded from strategy "buy_mfi_1": 29.8, # value loaded from strategy "buy_min_inc_1": 0.025, # value loaded from strategy "buy_pump_pull_threshold_1": 1.75, # value loaded from strategy "buy_pump_threshold_1": 0.5, # value loaded from strategy "buy_rsi_1": 39.8, # value loaded from strategy "buy_rsi_1h_42": 31.1, # value loaded from strategy "buy_rsi_1h_max_1": 73.8, # value loaded from strategy "buy_rsi_1h_min_1": 36.2, # value loaded from strategy "buy_volume_drop_41": 1.7, # value loaded from strategy "buy_volume_pump_41": 0.2, # value loaded from strategy } # Sell hyperspace params: sell_params = { "sell_bb_relative_8": 1.1, # value loaded from strategy "sell_condition_1_enable": True, # value loaded from strategy "sell_condition_2_enable": True, # value loaded from strategy "sell_condition_3_enable": True, # value loaded from strategy "sell_condition_4_enable": True, # value loaded from strategy "sell_condition_5_enable": True, # value loaded from strategy "sell_condition_6_enable": True, # value loaded from strategy "sell_condition_7_enable": True, # value loaded from strategy "sell_condition_8_enable": True, # value loaded from strategy "sell_custom_dec_profit_1": 0.05, # value loaded from strategy "sell_custom_dec_profit_2": 0.07, # value loaded from strategy "sell_custom_profit_0": 0.01, # value loaded from strategy "sell_custom_profit_1": 0.03, # value loaded from strategy "sell_custom_profit_2": 0.05, # value loaded from strategy "sell_custom_profit_3": 0.08, # value loaded from strategy "sell_custom_profit_4": 0.25, # value loaded from strategy "sell_custom_profit_under_rel_1": 0.024, # value loaded from strategy "sell_custom_profit_under_rsi_diff_1": 4.4, # value loaded from strategy "sell_custom_rsi_0": 33.0, # value loaded from strategy "sell_custom_rsi_1": 38.0, # value loaded from strategy "sell_custom_rsi_2": 43.0, # value loaded from strategy "sell_custom_rsi_3": 48.0, # value loaded from strategy "sell_custom_rsi_4": 50.0, # value loaded from strategy "sell_custom_stoploss_under_rel_1": 0.004, # value loaded from strategy "sell_custom_stoploss_under_rsi_diff_1": 8.0, # value loaded from strategy "sell_custom_under_profit_1": 0.02, # value loaded from strategy "sell_custom_under_profit_2": 0.04, # value loaded from strategy "sell_custom_under_profit_3": 0.6, # value loaded from strategy "sell_custom_under_rsi_1": 56.0, # value loaded from strategy "sell_custom_under_rsi_2": 60.0, # value loaded from strategy "sell_custom_under_rsi_3": 62.0, # value loaded from strategy "sell_dual_rsi_rsi_1h_4": 79.6, # value loaded from strategy "sell_dual_rsi_rsi_4": 73.4, # value loaded from strategy "sell_ema_relative_5": 0.024, # value loaded from strategy "sell_profit_trendstop": 0.02, # value loaded from strategy "sell_rsi_1h_7": 81.7, # value loaded from strategy "sell_rsi_bb_1": 79.5, # value loaded from strategy "sell_rsi_bb_2": 81, # value loaded from strategy "sell_rsi_diff_5": 4.4, # value loaded from strategy "sell_rsi_main_3": 82, # value loaded from strategy "sell_rsi_under_6": 79.0, # value loaded from strategy "sell_time_stoploss": 114, # value loaded from strategy "sell_time_trendstop": 113, # value loaded from strategy "sell_trail_down_1": 0.18, # value loaded from strategy "sell_trail_down_2": 0.14, # value loaded from strategy "sell_trail_down_3": 0.01, # value loaded from strategy "sell_trail_profit_max_1": 0.46, # value loaded from strategy "sell_trail_profit_max_2": 0.12, # value loaded from strategy "sell_trail_profit_max_3": 0.1, # value loaded from strategy "sell_trail_profit_min_1": 0.15, # value loaded from strategy "sell_trail_profit_min_2": 0.01, # value loaded from strategy "sell_trail_profit_min_3": 0.05, # value loaded from strategy } minimal_roi = { "0": 100 } # new sell stoploss = -0.99 use_custom_stoploss = False # Recommended use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Required startup_candle_count: int = 300 process_only_new_candles = False # Strategy Specific Variable Storage custom_trade_info = {} custom_fiat = "USD" # Only relevant if stake is BTC or ETH plot_config = { "main_plot": { "ema_50_1h": {"color": "rgba(255,250,200,2.4)"}, "bb_lowerband": {"color": "#792bbb","type": "line"}, "bb_upperband": {"color": "#bc281d","type": "line"} }, "subplots": { "RSI/BTC": { "mfi": {"color": "#e12a7c","type": "line"}, "cci": {"color": "#794491","type": "line"}, "ssl-dir_1h": {"color": "#2773a7","type": "line"}, "ssl-dir": {"color": "#5379a2","type": "line"} } } } custom_trendBTC_info = {} if not 'trend' in custom_trendBTC_info: custom_trendBTC_info['trend'] = {} if not 'not_downtrend' in custom_trendBTC_info['trend']: custom_trendBTC_info['trend']['not_downtrend'] = 0 if not 'st' in custom_trendBTC_info['trend']: custom_trendBTC_info['trend']['st'] = 0 if not 'stx' in custom_trendBTC_info['trend']: custom_trendBTC_info['trend']['stx'] = 0 @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration": 120 }, { "method": "StoplossGuard", "lookback_period": 90, "trade_limit": 2, "stop_duration": 120, "only_per_pair": False }, { "method": "StoplossGuard", "lookback_period": 90, "trade_limit": 1, "stop_duration": 120, "only_per_pair": True }, ] ########################################################################### # Buy Optimize_condition = False buy_con1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_con2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_con3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_con4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_con6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=Optimize_condition, load=True) optc1 = True buy_rmi_length = IntParameter(8, 20, default=8, optimize = optc1, load=True) buy_rmi = IntParameter(30, 50, default=35, optimize= optc1, load=True) buy_cci_length = IntParameter(25, 45, default=25, optimize = optc1, load=True) buy_cci = IntParameter(-135, -90, default=-133, optimize= optc1, load=True) buy_srsi_fk = IntParameter(30, 50, default=25, optimize= optc1, load=True) buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize = optc1, load=True) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize = optc1, load=True) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = optc1, load=True) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = optc1, load=True) optc2 = False buy_c2_1 = DecimalParameter(0.010, 0.025, default=0.018, space='buy', decimals=3, optimize=optc2, load=True) buy_c2_2 = DecimalParameter(0.980, 0.995, default=0.982, space='buy', decimals=3, optimize=optc2, load=True) buy_c2_3 = DecimalParameter(-0.8, -0.3, default=-0.5, space='buy', decimals=1, optimize=optc2, load=True) optc3 = False buy_con3_1 = DecimalParameter(0.010, 0.025, default=0.017, space='buy', decimals=3, optimize=optc3, load=True) buy_con3_2 = DecimalParameter(0.980, 0.995, default=0.984, space='buy', decimals=3, optimize=optc3, load=True) buy_con3_3 = DecimalParameter(0.955, 0.975, default=0.965, space='buy', decimals=3, optimize=optc3, load=True) buy_con3_4 = DecimalParameter(-0.95, -0.70, default=-0.85, space='buy', decimals=2, optimize=optc3, load=True) optc4 = False buy_rsi_1h_42 = DecimalParameter(10.0, 50.0, default=15.0, space='buy', decimals=1, optimize=optc4, load=True) buy_macd_41 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=optc4, load=True) buy_volume_pump_41 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=optc4, load=True) buy_volume_drop_41 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=optc4, load=True) optc6 = True buy_c6_2 = DecimalParameter(0.980, 0.999, default=0.985, space='buy', decimals=3, optimize=optc6, load=True) buy_c6_1 = DecimalParameter(0.08, 0.2, default=0.12, space='buy', decimals=2, optimize=optc6, load=True) buy_c6_2 = DecimalParameter(0.02, 0.4, default=0.28, space='buy', decimals=2, optimize=optc6, load=True) buy_c6_3 = DecimalParameter(0.005, 0.04, default=0.031, space='buy', decimals=3, optimize=optc6, load=True) buy_c6_4 = DecimalParameter(0.01, 0.03, default=0.021, space='buy', decimals=3, optimize=optc6, load=True) buy_c6_5 = DecimalParameter(0.2, 0.4, default=0.264, space='buy', decimals=3, optimize=optc6, load=True) optc7 = True buy_c7_1 = DecimalParameter(0.95, 1.10, default=1.01, space='buy', decimals=2, optimize=optc7, load=True) buy_c7_2 = DecimalParameter(0.95, 1.10, default=0.99, space='buy', decimals=2, optimize=optc7, load=True) buy_c7_3 = IntParameter(-100, -80, default=-94, space='buy', optimize= optc7, load=True) buy_c7_4 = IntParameter(-90, -60, default=-75, space='buy', optimize= optc7, load=True) buy_c7_5 = DecimalParameter(75.1, 90.1, default=80.0, space='buy',decimals=1, optimize= optc7, load=True) optc8 = False buy_min_inc_1 = DecimalParameter(0.01, 0.05, default=0.022, space='buy', decimals=3, optimize=optc8, load=True) buy_rsi_1h_min_1 = DecimalParameter(25.0, 40.0, default=30.0, space='buy', decimals=1, optimize=optc8, load=True) buy_rsi_1h_max_1 = DecimalParameter(70.0, 90.0, default=84.0, space='buy', decimals=1, optimize=optc8, load=True) buy_rsi_1 = DecimalParameter(20.0, 40.0, default=36.0, space='buy', decimals=1, optimize=optc8, load=True) buy_mfi_1 = DecimalParameter(20.0, 40.0, default=26.0, space='buy', decimals=1, optimize=optc8, load=True) optc9 = False buy_c9_1 = DecimalParameter(25.0, 44.0, default=36.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_2 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_3 = DecimalParameter(-80.0, -67.0, default=-75.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_4 = DecimalParameter(35.0, 54.0, default=46.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_5 = DecimalParameter(20.0, 44.0, default=30.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_6 = DecimalParameter(65.0, 94.0, default=84.0, space='buy', decimals=1, optimize=optc9, load=True) buy_c9_7 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc9, load=True) optc10 = True buy_c10_1 = DecimalParameter(-110.0, -80.0, default=-99.0, space='buy', decimals=1, optimize=optc10, load=True) buy_c10_2 = DecimalParameter(-1, -0.5, default=-0.78, space='buy', decimals=2, optimize=optc10, load=True) buy_dip_threshold_5 = DecimalParameter(0.001, 0.05, default=0.015, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_6 = DecimalParameter(0.01, 0.2, default=0.06, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_7 = DecimalParameter(0.05, 0.4, default=0.24, space='buy', decimals=3, optimize=False, load=True) buy_dip_threshold_8 = DecimalParameter(0.2, 0.5, default=0.4, space='buy', decimals=3, optimize=False, load=True) # 24 hours buy_pump_pull_threshold_1 = DecimalParameter(1.5, 3.0, default=1.75, space='buy', decimals=2, optimize=False, load=True) buy_pump_threshold_1 = DecimalParameter(0.4, 1.0, default=0.5, space='buy', decimals=3, optimize=False, load=True) # 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=False, load=True) sell_rsi_bb_2 = DecimalParameter(72.0, 90.0, default=81, space='sell', decimals=1, optimize=False, load=True) sell_rsi_main_3 = DecimalParameter(77.0, 90.0, default=82, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_4 = DecimalParameter(72.0, 84.0, default=73.4, space='sell', decimals=1, optimize=False, load=True) sell_dual_rsi_rsi_1h_4 = DecimalParameter(78.0, 92.0, default=79.6, space='sell', decimals=1, optimize=False, load=True) sell_ema_relative_5 = DecimalParameter(0.005, 0.05, default=0.024, space='sell', optimize=False, load=True) sell_rsi_diff_5 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True) sell_rsi_under_6 = DecimalParameter(72.0, 90.0, default=79.0, space='sell', decimals=1, optimize=False, load=True) sell_rsi_1h_7 = DecimalParameter(80.0, 95.0, default=81.7, space='sell', decimals=1, optimize=False, load=True) sell_bb_relative_8 = DecimalParameter(1.05, 1.3, default=1.1, space='sell', decimals=3, optimize=False, load=True) optimize_sell = False sell_custom_profit_0 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=33.0, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_profit_1 = DecimalParameter(0.01, 0.1, default=0.03, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=38.0, space='sell', decimals=2, optimize=optimize_sell, load=True) sell_custom_profit_2 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=43.0, space='sell', decimals=2, optimize=optimize_sell, load=True) sell_custom_profit_3 = DecimalParameter(0.06, 0.30, default=0.08, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=48.0, space='sell', decimals=2, optimize=optimize_sell, load=True) sell_custom_profit_4 = DecimalParameter(0.3, 0.6, default=0.25, space='sell', decimals=3, optimize=optimize_sell, load=True) sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=50.0, space='sell', decimals=2, optimize=optimize_sell, load=True) optimize_sell_u = False sell_custom_under_profit_1 = DecimalParameter(0.01, 0.10, default=0.02, space='sell', decimals=3, optimize=optimize_sell_u, load=True) sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=56.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True) sell_custom_under_profit_2 = DecimalParameter(0.01, 0.10, default=0.04, space='sell', decimals=3, optimize=optimize_sell_u, load=True) sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=60.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True) sell_custom_under_profit_3 = DecimalParameter(0.01, 0.10, default=0.6, space='sell', decimals=3, optimize=optimize_sell_u, load=True) sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=62.0, space='sell', decimals=1, optimize=optimize_sell_u, load=True) sell_custom_dec_profit_1 = DecimalParameter(0.01, 0.10, default=0.05, space='sell', decimals=3, optimize=False, load=True) sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=0.07, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.15, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.46, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=0.18, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.12, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.14, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_min_3 = DecimalParameter(0.01, 0.1, default=0.05, space='sell', decimals=3, optimize=False, load=True) sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True) sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=0.01, space='sell', decimals=3, optimize=False, load=True) sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=0.024, space='sell', optimize=False, load=True) sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=4.4, space='sell', optimize=False, load=True) sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=0.004, space='sell', optimize=False, load=True) sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=8.0, space='sell', optimize=False, load=True) sell_time_stoploss = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True) sell_time_trendstop = IntParameter(70, 120, default=90, space='sell', optimize=True, load=True) sell_profit_trendstop = DecimalParameter(0.009, 0.02, default=0.015, space='sell', 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() buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag buy_tags = buy_tag.split() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) 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 f'sf_4( {buy_tag})' elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value): return f'sf_3( {buy_tag})' elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value): return f'sf_2( {buy_tag})' elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value): return f'sf_1( {buy_tag})' elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value): return f'sf_0( {buy_tag})' elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']): return f'sf_u_1( {buy_tag})' elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']): return f'sf_u_2( {buy_tag})' elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']): return f'sf_u_3( {buy_tag})' elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']): return f'sf_d_1( {buy_tag})' elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']): return f'sf_d_2( {buy_tag})' 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 f'sf_t_1( {buy_tag})' 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 f'sf_t_2( {buy_tag})' elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)): return f'sf_u_t_1( {buy_tag})' elif (current_profit > 0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_profit_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_profit_under_rsi_diff_1.value): return f'sf_u_e_1( {buy_tag})' elif (current_profit < -0.0) & (last_candle['close'] < last_candle['ema_200']) & (((last_candle['ema_200'] - last_candle['close']) / last_candle['close']) < self.sell_custom_stoploss_under_rel_1.value) & (last_candle['rsi'] > last_candle['rsi_1h'] + self.sell_custom_stoploss_under_rsi_diff_1.value): return f'stoploss ( {buy_tag})' elif (current_profit < -0.05) & (trade_dur > self.sell_time_stoploss.value) & (last_candle['ssl-dir'] == 'down'): return f'stoploss5 ( {buy_tag})' elif ((buy_tag in [' trend ']) & (trade_dur > self.sell_time_trendstop.value) & ((last_candle['ssl-dir'] == 'down') & (current_profit < self.sell_profit_trendstop.value))): return f'trend_stop' elif (current_profit < -0.08): return f'stoploss8 ( {buy_tag})' return None def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: return True def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_1h) for pair in pairs] # informative_pairs.extend([(pair, self.info_timeframe_1d) for pair in pairs]) if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.append((btc_info_pair, self.timeframe)) informative_pairs.append((btc_info_pair, self.inf_1h)) informative_pairs.append((btc_info_pair, self.info_timeframe_1d)) 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) informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) #informative_1h['not_downtrend'] = ((informative_1h['close'] > informative_1h['close'].shift(2)) | (informative_1h['rsi'] > 50)) informative_1h['r_480'] = williams_r(dataframe, period=480) 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['cti'] = pta.cti(informative_1h["close"], length=20) ssldown, sslup = SSLChannels_ATR(informative_1h, 14) informative_1h['ssl-dir'] = np.where(sslup > ssldown,'up','down') # informative_1h['cti'] = pta.cti(informative_1h["close"], length=20) return informative_1h def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # ----------------------------------------------------------------------------------------- if not 'trend' in self.custom_trendBTC_info: self.custom_trendBTC_info['trend'] = {} if not 'not_downtrend' in self.custom_trendBTC_info['trend']: self.custom_trendBTC_info['trend']['not_downtrend'] = 0 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['not_downtrend'] = ((dataframe['close'] > dataframe['close'].shift(2)) | (dataframe['rsi'] > 50)) self.custom_trendBTC_info["trend"]['not_downtrend'] = {} self.custom_trendBTC_info["trend"]['not_downtrend'] = dataframe['not_downtrend'] # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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'] # nuevo # bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] dataframe['bb_width'] = ((dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']) dataframe['bb_delta'] = ((dataframe['bb_lowerband'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband']) dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int') dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs() # CCI hyperopt for val in self.buy_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) dataframe['cci'] = ta.CCI(dataframe, 26) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # RMI hyperopt for val in self.buy_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) #dataframe['rmi'] = RMI(dataframe, length=8, mom=4) # SRSI hyperopt ? stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] # Volume dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean() #cols_to_norm = ['vwmacd','signal','hist'] normalize #dataframe[cols_to_norm] = dataframe[cols_to_norm].apply(lambda x: (x-x.mean())/ x.std(), axis=0) dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_32'] = williams_r(dataframe, period=32) dataframe['r_64'] = williams_r(dataframe, period=64) # dataframe['r_480'] = williams_r(dataframe, period=480) # 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) 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) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) 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)) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: tik = time.perf_counter() """ if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" if metadata['pair'] in btc_info_pair: btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h) btc_info_tfx = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tfx, self.timeframe, self.inf_1h, ffill=True) drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) """ # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) # 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) ssldown, sslup = SSLChannels_ATR(dataframe, 64) dataframe['ssl-up'] = sslup dataframe['ssl-down'] = ssldown dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down') dataframe['rmi'] = RMI(dataframe, length=24, mom=5) tok = time.perf_counter() logger.debug(f"[{metadata['pair']}] informative_1h_indicators took: {tok - tik:0.4f} seconds.") return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] con1 = ( self.buy_con1_enable.value & (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) & ((dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value)) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) ) con2= ( self.buy_con2_enable.value & (dataframe['ema_200_1h'] > dataframe['ema_200_1h'].shift(12)) & (dataframe['ema_200_1h'].shift(12) > dataframe['ema_200_1h'].shift(24)) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_c2_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c2_2.value)) & (dataframe['cti_1h'] > self.buy_c2_3.value) ) con3 = ( self.buy_con3_enable.value & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_con3_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_con3_2.value)) & (dataframe['close'] < dataframe['ema_20'] * self.buy_con3_3.value) & (dataframe['cti'] < self.buy_con3_4.value) ) con4 = ( self.buy_con4_enable.value & (dataframe['rsi_1h'] < self.buy_rsi_1h_42.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_41.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_41.value)) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_41.value) & (dataframe['volume_mean_slow'] * self.buy_volume_pump_41.value < dataframe['volume_mean_slow'].shift(48)) & (dataframe['volume'] > 0) ) con6 = ( self.buy_con6_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (((dataframe['open'].rolling(2).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_1.value) & (((dataframe['open'].rolling(12).max() - dataframe['close']) / dataframe['close']) < self.buy_c6_2.value) & dataframe['bb_lowerband'].shift().gt(0) & dataframe['bb_delta'].gt(dataframe['close'] * self.buy_c6_3.value) & dataframe['closedelta'].gt(dataframe['close'] * self.buy_c6_4.value) & dataframe['tail'].lt(dataframe['bb_delta'] * self.buy_c6_5.value) & dataframe['close'].lt(dataframe['bb_lowerband'].shift()) & dataframe['close'].le(dataframe['close'].shift()) & (dataframe['volume'] > 0) ) con7 = ( self.buy_condition_7_enable.value & (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * self.buy_c7_1.value)) & (dataframe['close'] < (dataframe['bb_lowerband'] * self.buy_c7_2.value)) & (dataframe['r_14'] < self.buy_c7_3.value) & (dataframe['r_64'] < self.buy_c7_4.value) & (dataframe['rsi_1h'] < self.buy_c7_5.value) ) con8 = ( self.buy_condition_8_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) ) con9 = ( self.buy_condition_9_enable.value & (((dataframe['close'] - dataframe['open'].rolling(12).min()) / dataframe['open'].rolling(12).min()) > 0.032) & (dataframe['rsi'] < self.buy_c9_1.value) & (dataframe['r_14'] < self.buy_c9_2.value) & (dataframe['r_32'] < self.buy_c9_3.value) & (dataframe['mfi'] < self.buy_c9_4.value) & (dataframe['rsi_1h'] > self.buy_c9_5.value) & (dataframe['rsi_1h'] < self.buy_c9_6.value) & (dataframe['r_480_1h'] > self.buy_c9_7.value) ) co10 = ( self.buy_condition_10_enable.value & (dataframe['close'].shift(4) < (dataframe['close'].shift(3))) & (dataframe['close'].shift(3) < (dataframe['close'].shift(2))) & (dataframe['close'].shift(2) < (dataframe['close'].shift())) & (dataframe['close'].shift(1) < (dataframe['close'])) & (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['close'] > (dataframe['open'])) & (dataframe['cci'].shift() < dataframe['cci']) & (dataframe['ssl-dir_1h'] == 'up') & (dataframe['cci'] < self.buy_c10_1.value) & (dataframe['cti'] < self.buy_c10_2.value) & (dataframe['volume'] > 0) ) conditions.append(con1) conditions.append(con2) conditions.append(con3) conditions.append(con4) conditions.append(con6) conditions.append(con7) conditions.append(con8) conditions.append(con9) conditions.append(co10) dataframe.loc[con1, 'buy_tag'] = " con1 " dataframe.loc[con2, 'buy_tag'] = " Andalusian " dataframe.loc[con3, 'buy_tag'] = " con3 " dataframe.loc[con4, 'buy_tag'] = " con4 " dataframe.loc[con6, 'buy_tag'] = " con6 " dataframe.loc[con7, 'buy_tag'] = " con7 " dataframe.loc[con8, 'buy_tag'] = " con8 " dataframe.loc[con9, 'buy_tag'] = " con9 " dataframe.loc[co10, 'buy_tag'] = " trend " 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_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) ) ) """ for i in self.ma_types: conditions.append( ( (dataframe['close'] > dataframe[f'{i}_offset_sell']) & (dataframe['volume'] > 0) ) ) """ if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ] = 1 return dataframe