# source: https://raw.githubusercontent.com/thinkong/freqtradestrategies/525204f1ae96eb11d1f83a1c9777ae9b3607b668/user_data/strategies/BeastBoxXBLR6.py 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 ##################################################### BeastBot XBLR6 #################################################################### # don't need hyperopt for binance, 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 github_thinkong_freqtradestrategies__BeastBoxXBLR6__20220316_155321 --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 github_thinkong_freqtradestrategies__BeastBoxXBLR6__20220316_155321(IStrategy): INTERFACE_VERSION = 2 timeframe = '5m' inf_1h = '1h' info_timeframe_1d = "1d" has_BTC_info_tf = True # Buy hyperspace params: buy_params = { "buy_c10_1": -101.4, "buy_c10_2": -0.86, "buy_c9_1": 40.0, "buy_c9_2": -70.4, "buy_c9_3": -67.0, "buy_c9_4": 41.8, "buy_c9_5": 33.6, "buy_c9_6": 82.7, "buy_c9_7": -94.3, "buy_con3_1": 0.016, "buy_con3_2": 0.984, "buy_con3_3": 0.966, "buy_con3_4": -0.88, "buy_macd_41": 0.08, "buy_rsi_1h_42": 19.9, "buy_volume_drop_41": 3.7, "buy_volume_pump_41": 0.1, "buy_bb_delta": 0.023, "buy_bb_factor": 0.99, "buy_bb_width": 0.097, "buy_c2_1": 0.02, "buy_c2_2": 0.991, "buy_c2_3": -0.7, "buy_c6_1": 0.16, "buy_c6_2": 0.03, "buy_c6_3": 0.02, "buy_c6_4": 0.022, "buy_c6_5": 0.387, "buy_c7_1": 1.01, "buy_c7_2": 0.99, "buy_c7_3": -99, "buy_c7_4": -64, "buy_c7_5": 81.4, "buy_cci": -112, "buy_cci_length": 25, "buy_closedelta": 12.232, "buy_con1_enable": True, "buy_con2_enable": True, "buy_con3_enable": True, "buy_con4_enable": True, "buy_con6_enable": True, "buy_condition_10_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_dip_threshold_5": 0.05, "buy_dip_threshold_6": 0.2, "buy_dip_threshold_7": 0.4, "buy_dip_threshold_8": 0.5, "buy_mfi_1": 29.8, "buy_min_inc_1": 0.025, "buy_pump_pull_threshold_1": 1.75, "buy_pump_threshold_1": 0.5, "buy_rmi": 36, "buy_rmi_length": 13, "buy_rsi_1": 39.8, "buy_rsi_1h_max_1": 73.8, "buy_rsi_1h_min_1": 36.2, "buy_srsi_fk": 49, } # Sell hyperspace params: sell_params = { "sell_bb_relative_8": 1.1, "sell_condition_1_enable": True, "sell_condition_2_enable": True, "sell_condition_3_enable": True, "sell_condition_4_enable": True, "sell_condition_5_enable": True, "sell_condition_6_enable": True, "sell_condition_7_enable": True, "sell_condition_8_enable": True, "sell_custom_dec_profit_1": 0.05, "sell_custom_dec_profit_2": 0.07, "sell_custom_profit_0": 0.01, "sell_custom_profit_1": 0.03, "sell_custom_profit_2": 0.05, "sell_custom_profit_3": 0.08, "sell_custom_profit_4": 0.25, "sell_custom_profit_under_rel_1": 0.024, "sell_custom_profit_under_rsi_diff_1": 4.4, "sell_custom_rsi_0": 33.0, "sell_custom_rsi_1": 38.0, "sell_custom_rsi_2": 43.0, "sell_custom_rsi_3": 48.0, "sell_custom_rsi_4": 50.0, "sell_custom_stoploss_under_rel_1": 0.004, "sell_custom_stoploss_under_rsi_diff_1": 8.0, "sell_custom_under_profit_1": 0.02, "sell_custom_under_profit_2": 0.04, "sell_custom_under_profit_3": 0.6, "sell_custom_under_rsi_1": 56.0, "sell_custom_under_rsi_2": 60.0, "sell_custom_under_rsi_3": 62.0, "sell_dual_rsi_rsi_1h_4": 79.6, "sell_dual_rsi_rsi_4": 73.4, "sell_ema_relative_5": 0.024, "sell_profit_trendstop": 0.02, "sell_rsi_1h_7": 81.7, "sell_rsi_bb_1": 79.5, "sell_rsi_bb_2": 81, "sell_rsi_diff_5": 4.4, "sell_rsi_main_3": 82, "sell_rsi_under_6": 79.0, "sell_time_stoploss": 114, "sell_time_trendstop": 113, "sell_trail_down_1": 0.18, "sell_trail_down_2": 0.14, "sell_trail_down_3": 0.01, "sell_trail_profit_max_1": 0.46, "sell_trail_profit_max_2": 0.12, "sell_trail_profit_max_3": 0.1, "sell_trail_profit_min_1": 0.15, "sell_trail_profit_min_2": 0.01, "sell_trail_profit_min_3": 0.05, } 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', 'TRY', 'BRL', 'USDT:USDT', 'BUSD:BUSD', 'USDC:USDC', 'DAI:DAI', 'TUSD:TUSD', 'PAX:PAX', 'USD:USD', 'EUR: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', 'TRY', 'BRL', 'USDT:USDT', 'BUSD:BUSD', 'USDC:USDC', 'DAI:DAI', 'TUSD:TUSD', 'PAX:PAX', 'USD:USD', 'EUR: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