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 pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import ( merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open, ) from functools import reduce import logging # ------------------------------------------------------------------------------------------------- # --- logger for parameter merging output, only remove if you remove it further down too! --------- logger = logging.getLogger(__name__) # ------------------------------------------------------------------------------------------------- class CoreStrategy(IStrategy): INTERFACE_VERSION = 2 # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018, } # minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018, } minimal_roi = {"0": 0.20, "38": 0.074, "78": 0.025, "194": 0} stoploss = -0.228 # effectively disabled. timeframe = "5m" informative_timeframe = "1h" # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 ignore_roi_if_buy_signal = True # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.049 # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, ############# # Enable/Disable conditions "smaoffset_buy_condition_0_enable": True, "smaoffset_buy_condition_1_enable": True, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": False, "v9_buy_condition_2_enable": False, "v9_buy_condition_3_enable": False, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": False, "v9_buy_condition_6_enable": False, "v9_buy_condition_7_enable": False, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, } sell_params = { ############# # Enable/Disable conditions "v9_sell_condition_0_enable": False, "v8_sell_condition_0_enable": True, "v8_sell_condition_1_enable": True, "smaoffset_sell_condition_0_enable": False, } plot_config = { 'main_plot': { }, 'subplots': { "buy tag": { 'buy_tag': {'color': 'green'} }, } } # if you want to see which buy conditions were met # or if there is an trade exit override due to high RSI set to True # logger will output the buy and trade exit conditions cust_log_verbose = False ############################################################################ # Buy SMAOffsetProtectOpt smaoffset_buy_condition_0_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) smaoffset_buy_condition_1_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) smaoffset_sell_condition_0_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) # hyperopt parameters for SMAOffsetProtectOpt base_nb_candles_buy = IntParameter( 5, 80, default=20, space="buy", optimize=False, load=True ) base_nb_candles_sell = IntParameter( 5, 80, default=24, space="sell", optimize=False, load=True ) low_offset = DecimalParameter( 0.9, 0.99, default=0.975, space="buy", optimize=True, load=True ) high_offset = DecimalParameter( 0.99, 1.1, default=1.012, space="sell", optimize=True, load=True ) # Protection fast_ewo = IntParameter(10, 50, default=50, space="buy", optimize=False, load=True) slow_ewo = IntParameter( 100, 200, default=200, space="buy", optimize=False, load=True ) ewo_low = DecimalParameter( -20.0, -8.0, default=-19.881, space="buy", optimize=True, load=True ) ewo_high = DecimalParameter( 2.0, 12.0, default=5.499, space="buy", optimize=True, load=True ) rsi_buy = IntParameter(30, 70, default=50, space="buy", optimize=True, load=True) # Buy CombinedBinHClucAndMADV6 v6_buy_condition_0_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v6_buy_condition_1_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v6_buy_condition_2_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v6_buy_condition_3_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) # Buy CombinedBinHClucV8 v8_buy_condition_0_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v8_buy_condition_1_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v8_buy_condition_2_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v8_buy_condition_3_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v8_buy_condition_4_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) v8_sell_condition_0_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) v8_sell_condition_1_enable = CategoricalParameter( [True, False], default=True, space="sell", optimize=False, load=True ) v8_sell_rsi_main = DecimalParameter( 72.0, 90.0, default=80, space="sell", decimals=2, optimize=False, load=True ) buy_dip_threshold_0 = DecimalParameter( 0.001, 0.1, default=0.015, space="buy", decimals=3, optimize=False, load=True ) buy_dip_threshold_1 = DecimalParameter( 0.08, 0.2, default=0.12, space="buy", decimals=2, optimize=False, load=True ) buy_dip_threshold_2 = DecimalParameter( 0.02, 0.4, default=0.28, space="buy", decimals=2, optimize=False, load=True ) buy_dip_threshold_3 = DecimalParameter( 0.25, 0.44, default=0.36, space="buy", decimals=2, optimize=False, load=True ) buy_bb40_bbdelta_close = DecimalParameter( 0.005, 0.04, default=0.031, space="buy", optimize=False, load=True ) buy_bb40_closedelta_close = DecimalParameter( 0.01, 0.03, default=0.021, space="buy", optimize=False, load=True ) buy_bb40_tail_bbdelta = DecimalParameter( 0.2, 0.4, default=0.264, space="buy", optimize=False, load=True ) buy_bb20_close_bblowerband = DecimalParameter( 0.8, 1.1, default=0.992, space="buy", optimize=False, load=True ) buy_bb20_volume = IntParameter( 18, 36, default=29, space="buy", optimize=False, load=True ) buy_rsi_diff = DecimalParameter( 34.0, 60.0, default=50.48, space="buy", decimals=2, optimize=False, load=True ) buy_min_inc = DecimalParameter( 0.005, 0.05, default=0.01, space="buy", decimals=2, optimize=False, load=True ) buy_rsi_1h = DecimalParameter( 40.0, 70.0, default=67.0, space="buy", decimals=2, optimize=False, load=True ) buy_rsi = DecimalParameter( 30.0, 40.0, default=38.5, space="buy", decimals=2, optimize=False, load=True ) buy_mfi = DecimalParameter( 36.0, 65.0, default=36.0, space="buy", decimals=2, optimize=False, load=True ) buy_volume_1 = DecimalParameter( 1.0, 10.0, default=2.0, space="buy", decimals=2, optimize=False, load=True ) buy_ema_open_mult_1 = DecimalParameter( 0.01, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True ) sell_custom_roi_profit_1 = DecimalParameter( 0.01, 0.03, default=0.01, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_1 = DecimalParameter( 40.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_profit_2 = DecimalParameter( 0.01, 0.20, default=0.04, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_2 = DecimalParameter( 42.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_profit_3 = DecimalParameter( 0.15, 0.30, default=0.08, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_3 = DecimalParameter( 44.0, 58.0, default=56, space="sell", decimals=2, optimize=False, load=True ) sell_custom_roi_profit_4 = DecimalParameter( 0.3, 0.7, default=0.14, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_4 = DecimalParameter( 44.0, 60.0, default=58, space="sell", decimals=2, optimize=False, load=True ) sell_custom_roi_profit_5 = DecimalParameter( 0.01, 0.1, default=0.04, space="sell", decimals=2, optimize=True, load=True ) sell_trail_profit_min_1 = DecimalParameter( 0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=True, load=True ) sell_trail_profit_max_1 = DecimalParameter( 0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=True, load=True ) sell_trail_down_1 = DecimalParameter( 0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=True, load=True ) sell_trail_profit_min_2 = DecimalParameter( 0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=True, load=True ) sell_trail_profit_max_2 = DecimalParameter( 0.08, 0.25, default=0.1, space="sell", decimals=2, optimize=True, load=True ) sell_trail_down_2 = DecimalParameter( 0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=True, load=True ) sell_custom_stoploss_1 = DecimalParameter( -0.15, -0.03, default=-0.05, space="sell", decimals=2, optimize=True, load=True ) # Buy CombinedBinHClucAndMADV9 v9_buy_condition_0_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_1_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_2_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_3_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_4_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_5_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_6_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_7_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_8_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_9_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) v9_buy_condition_10_enable = CategoricalParameter( [True, False], default=False, space="buy", optimize=False, load=True ) # Sell v9_sell_condition_0_enable = CategoricalParameter( [True, False], default=False, space="sell", optimize=False, load=True ) buy_bb20_close_bblowerband_safe_1 = DecimalParameter( 0.7, 1.1, default=0.99, space="buy", optimize=False, load=True ) buy_bb20_close_bblowerband_safe_2 = DecimalParameter( 0.7, 1.1, default=0.982, space="buy", optimize=False, load=True ) buy_volume_pump_1 = DecimalParameter( 0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=False, load=True ) buy_volume_drop_1 = DecimalParameter( 1, 10, default=4, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_1 = DecimalParameter( 10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_2 = DecimalParameter( 10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_3 = DecimalParameter( 10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_4 = DecimalParameter( 10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1 = DecimalParameter( 10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_2 = DecimalParameter( 7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_3 = DecimalParameter( 7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=False, load=True ) buy_macd_1 = DecimalParameter( 0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=False, load=True ) buy_macd_2 = DecimalParameter( 0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=False, load=True ) # minimum conditions to match in buy buy_minimum_conditions = IntParameter( 1, 2, default=1, space="buy", optimize=False, load=True ) def custom_stoploss( self, pair: str, trade: "Trade", current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> float: # Manage losing trades and open room for better ones. if current_profit > 0: return 0.99 else: trade_time_50 = trade.open_date_utc + timedelta(minutes=240) # trade_time_240 = trade.open_date_utc + timedelta(minutes=240) # Trade open more then 60 minutes. For this strategy it's means -> loss # Let's try to minimize the loss if current_time > trade_time_50: try: number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() if (candle["sma_200_dec"]) & (candle["sma_200_dec_1h"]): return 0.01 # We are at bottom. Wait... if candle["rsi_1h"] < 30: return 0.99 # Are we still sinking? if candle["close"] > candle["ema_200"]: if current_rate * 1.025 < candle["open"]: return 0.01 if current_rate * 1.015 < candle["open"]: return 0.01 except IndexError as error: # Whoops, set stoploss at 10% return 0.1 return 0.99 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() if last_candle is not None: if (current_profit > self.sell_custom_roi_profit_4.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_4.value ): return "roi_target_4" elif (current_profit > self.sell_custom_roi_profit_3.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_3.value ): return "roi_target_3" elif (current_profit > self.sell_custom_roi_profit_2.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_2.value ): return "roi_target_2" elif (current_profit > self.sell_custom_roi_profit_1.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_1.value ): return "roi_target_1" elif ( (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & (last_candle["sma_200_dec"]) ): return "roi_target_5" elif ( (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & ( ((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value) ) ): return "trail_target_1" elif ( (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & ( ((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value) ) ): return "trail_target_2" # Sell any positions at a loss if they are held for more than one day. # if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 2: # return 'unclog' return None def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) 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.informative_timeframe ) # EMA 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) informative_1h["sma_200_dec"] = informative_1h["sma_200"] < informative_1h[ "sma_200" ].shift(20) # RSI informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h["ssl_down"] = ssl_down_1h informative_1h["ssl_up"] = ssl_up_1h informative_1h["ssl-dir"] = np.where(ssl_up_1h > ssl_down_1h, "up", "down") return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # strategy BinHV45 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() # strategy ClucMay72018 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"] dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean() # EMA dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # SMA dataframe["sma_5"] = ta.EMA(dataframe, timeperiod=5) dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # MFI dataframe["mfi"] = ta.MFI(dataframe, timeperiod=14) # ------ ATR stuff dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) # ------ SMAOffsetProtectOpt # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f"ma_buy_{val}"] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f"ma_sell_{val}"] = ta.EMA(dataframe, timeperiod=val) # Elliot dataframe["EWO"] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True, ) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # reset additional dataframe rows dataframe.loc[:, "v9_buy_condition_1_enable"] = False dataframe.loc[:, "v9_buy_condition_2_enable"] = False dataframe.loc[:, "v9_buy_condition_3_enable"] = False dataframe.loc[:, "v9_buy_condition_4_enable"] = False dataframe.loc[:, "v9_buy_condition_5_enable"] = False dataframe.loc[:, "v9_buy_condition_6_enable"] = False dataframe.loc[:, "v9_buy_condition_7_enable"] = False dataframe.loc[:, "v9_buy_condition_8_enable"] = False dataframe.loc[:, "v9_buy_condition_9_enable"] = False dataframe.loc[:, "v9_buy_condition_10_enable"] = False dataframe.loc[:, "v6_buy_condition_0_enable"] = False dataframe.loc[:, "v6_buy_condition_1_enable"] = False dataframe.loc[:, "v6_buy_condition_2_enable"] = False dataframe.loc[:, "v6_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_0_enable"] = False dataframe.loc[:, "v8_buy_condition_1_enable"] = False dataframe.loc[:, "v8_buy_condition_2_enable"] = False dataframe.loc[:, "v8_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_4_enable"] = False dataframe.loc[:, "smaoffset_buy_condition_0_enable"] = False dataframe.loc[:, "smaoffset_buy_condition_1_enable"] = False dataframe.loc[:, "conditions_count"] = 0 dataframe.loc[:, 'buy_tag'] = '' dataframe["ma_buy"] = ( dataframe[f"ma_buy_{self.base_nb_candles_buy.value}"] * self.low_offset.value ) dataframe.loc[ ( (dataframe["close"] < dataframe["ma_buy"]) & (dataframe["EWO"] > self.ewo_high.value) & (dataframe["rsi"] < self.rsi_buy.value) & (self.smaoffset_buy_condition_0_enable.value == True) ), ['smaoffset_buy_condition_0_enable', 'buy_tag']] = (1, 'buy_signal_smaoffset_0') dataframe.loc[ ( (dataframe["close"] < dataframe["ma_buy"]) & (dataframe["EWO"] < self.ewo_low.value) & (self.smaoffset_buy_condition_1_enable.value == True) ), ['smaoffset_buy_condition_1_enable', 'buy_tag']] = (1, 'buy_signal_smaoffset_1') dataframe.loc[ ( (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_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & dataframe["lower"].shift().gt(0) & dataframe["bbdelta"].gt( dataframe["close"] * self.buy_bb40_bbdelta_close.value ) & dataframe["closedelta"].gt( dataframe["close"] * self.buy_bb40_closedelta_close.value ) & dataframe["tail"].lt( dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta.value ) & dataframe["close"].lt(dataframe["lower"].shift()) & dataframe["close"].le(dataframe["close"].shift()) & (self.v8_buy_condition_0_enable.value == True) ), ['v8_buy_condition_0_enable', 'buy_tag']] = (1, 'buy_signal_v8_0') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & (dataframe["close"] < dataframe["ema_50"]) & ( dataframe["close"] < self.buy_bb20_close_bblowerband.value * dataframe["bb_lowerband"] ) & ( dataframe["volume"] < ( dataframe["volume_mean_slow"].shift(1) * self.buy_bb20_volume.value ) ) & (self.v8_buy_condition_1_enable.value == True) ), ['v8_buy_condition_1_enable', 'buy_tag']] = (1, 'buy_signal_v8_1') dataframe.loc[ ( (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_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_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_3.value ) & (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_diff.value) & (self.v8_buy_condition_2_enable.value == True) ), ['v8_buy_condition_2_enable', 'buy_tag']] = (1, 'buy_signal_v8_2') dataframe.loc[ ( (dataframe["sma_200"] > dataframe["sma_200"].shift(20)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(16)) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_3.value ) & ( ( (dataframe["open"].rolling(24).min() - dataframe["close"]) / dataframe["close"] ) > self.buy_min_inc.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h.value) & (dataframe["rsi"] < self.buy_rsi.value) & (dataframe["mfi"] < self.buy_mfi.value) & (self.v8_buy_condition_3_enable.value == True) ), ['v8_buy_condition_3_enable', 'buy_tag']] = (1, 'buy_signal_v8_3') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_100_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & ( ( (dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_1.value ) & ( ( (dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_2.value ) & ( ( (dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"] ) < self.buy_dip_threshold_3.value ) & ( dataframe["volume"].rolling(4).mean() * self.buy_volume_1.value > dataframe["volume"] ) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_1.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v8_buy_condition_4_enable.value == True) ), ['v8_buy_condition_4_enable', 'buy_tag']] = (1, 'buy_signal_v8_4') # start from here dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_1.value ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (self.v9_buy_condition_1_enable.value == True) ), ['v9_buy_condition_1_enable', 'buy_tag']] = (1, 'buy_signal_v9_1') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_2.value ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (self.v9_buy_condition_2_enable.value == True) ), ['v9_buy_condition_2_enable', 'buy_tag']] = (1, 'buy_signal_v9_2') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["rsi"] < self.buy_rsi_3.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (self.v9_buy_condition_3_enable.value == True) ), ['v9_buy_condition_3_enable', 'buy_tag']] = (1, 'buy_signal_v9_3') dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_1.value) & (dataframe["close"] < dataframe["bb_lowerband"]) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (self.v9_buy_condition_4_enable.value == True) ), ['v9_buy_condition_4_enable', 'buy_tag']] = (1, 'buy_signal_v9_4') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (self.v9_buy_condition_5_enable.value == True) ), ['v9_buy_condition_5_enable', 'buy_tag']] = (1, 'buy_signal_v9_5') dataframe.loc[ ( (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_2.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (self.v9_buy_condition_6_enable.value == True) ), ['v9_buy_condition_6_enable', 'buy_tag']] = (1, 'buy_signal_v9_6') dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_2.value) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (self.v9_buy_condition_7_enable.value == True) ), ['v9_buy_condition_7_enable', 'buy_tag']] = (1, 'buy_signal_v9_7') dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_3.value) & (dataframe["rsi"] < self.buy_rsi_1.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (self.v9_buy_condition_8_enable.value == True) ), ['v9_buy_condition_8_enable', 'buy_tag']] = (1, 'buy_signal_v9_8') dataframe.loc[ ( (dataframe["rsi_1h"] < self.buy_rsi_1h_4.value) & (dataframe["rsi"] < self.buy_rsi_2.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (self.v9_buy_condition_9_enable.value == True) ), ['v9_buy_condition_9_enable', 'buy_tag']] = (1, 'buy_signal_v9_9') dataframe.loc[ ( (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["rsi"] < dataframe["rsi_1h"] - 43.276) & (self.v9_buy_condition_10_enable.value == True) ), ['v9_buy_condition_10_enable', 'buy_tag']] = (1, 'buy_signal_v9_10') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.99 * dataframe["bb_lowerband"]) & ( ( dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 21) ) | ( dataframe["volume_mean_slow"] > (dataframe["volume_mean_slow"].shift(30) * 0.4) ) ) & (self.v6_buy_condition_0_enable.value == True) ), ['v6_buy_condition_0_enable', 'buy_tag']] = (1, 'buy_signal_v6_0') dataframe.loc[ ( (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.975 * dataframe["bb_lowerband"]) & ( ( dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 20) ) | ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4 ) ) & (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market. & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (self.v6_buy_condition_1_enable.value == True) ), ['v6_buy_condition_1_enable', 'buy_tag']] = (1, 'buy_signal_v6_1') dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.02) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & ( (dataframe["volume"] < (dataframe["volume"].shift() * 4)) | ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4 ) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v6_buy_condition_2_enable.value == True) ), ['v6_buy_condition_2_enable', 'buy_tag']] = (1, 'buy_signal_v6_2') dataframe.loc[ ( (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v6_buy_condition_3_enable.value == True) ), ['v6_buy_condition_3_enable', 'buy_tag']] = (1, 'buy_signal_v6_3') # count the amount of conditions met dataframe.loc[:, "conditions_count"] = ( dataframe["v9_buy_condition_1_enable"].astype(int) + dataframe["v9_buy_condition_2_enable"].astype(int) + dataframe["v9_buy_condition_3_enable"].astype(int) + dataframe["v9_buy_condition_4_enable"].astype(int) + dataframe["v9_buy_condition_5_enable"].astype(int) + dataframe["v9_buy_condition_6_enable"].astype(int) + dataframe["v9_buy_condition_7_enable"].astype(int) + dataframe["v9_buy_condition_8_enable"].astype(int) + dataframe["v9_buy_condition_9_enable"].astype(int) + dataframe["v9_buy_condition_10_enable"].astype(int) + dataframe["v6_buy_condition_0_enable"].astype(int) + dataframe["v6_buy_condition_1_enable"].astype(int) + dataframe["v6_buy_condition_2_enable"].astype(int) + dataframe["v6_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_0_enable"].astype(int) + dataframe["v8_buy_condition_1_enable"].astype(int) + dataframe["v8_buy_condition_2_enable"].astype(int) + dataframe["v8_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_4_enable"].astype(int) + dataframe["smaoffset_buy_condition_0_enable"].astype(int) + dataframe["smaoffset_buy_condition_1_enable"].astype(int) ) # append the minimum amount of conditions to be met conditions.append( dataframe["conditions_count"] >= self.buy_minimum_conditions.value ) conditions.append(dataframe["volume"].gt(0)) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1 # verbose logging enable only for verbose information or troubleshooting if self.cust_log_verbose == True: for index, row in dataframe.iterrows(): if row["buy"] == 1: buy_cond_details = f"count={int(row['conditions_count'])}/v9_1={int(row['v9_buy_condition_1_enable'])}/v9_2={int(row['v9_buy_condition_2_enable'])}/v9_3={int(row['v9_buy_condition_3_enable'])}/v9_4={int(row['v9_buy_condition_4_enable'])}/v9_5={int(row['v9_buy_condition_5_enable'])}/v9_6={int(row['v9_buy_condition_6_enable'])}/v9_7={int(row['v9_buy_condition_7_enable'])}/v9_8={int(row['v9_buy_condition_8_enable'])}/v9_9={int(row['v9_buy_condition_9_enable'])}/v9_10={int(row['v9_buy_condition_10_enable'])}/v6_0={int(row['v6_buy_condition_0_enable'])}/v6_1={int(row['v6_buy_condition_1_enable'])}/v6_2={int(row['v6_buy_condition_2_enable'])}/v6_3={int(row['v6_buy_condition_3_enable'])}/v8_0={int(row['v8_buy_condition_0_enable'])}/v8_1={int(row['v8_buy_condition_1_enable'])}/v8_2={int(row['v8_buy_condition_2_enable'])}/v8_3={int(row['v8_buy_condition_3_enable'])}/v8_4={int(row['v8_buy_condition_4_enable'])}/sma_0={int(row['smaoffset_buy_condition_0_enable'])}/sma_1={int(row['smaoffset_buy_condition_1_enable'])}" logger.info( f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}" ) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe["ma_sell"] = ( dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"] * self.high_offset.value ) if self.smaoffset_sell_condition_0_enable.value: conditions.append( ( (qtpylib.crossed_below(dataframe["close"], dataframe["ma_sell"])) & (dataframe["volume"] > 0) ) ) if self.v9_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_middleband"] * 1.01) & ( dataframe["volume"] > 0 ) # Don't be gready, sell fast # Make sure Volume is not 0 ) ) if self.v8_sell_condition_0_enable.value: conditions.append( ( (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) ) ) if self.v8_sell_condition_1_enable.value: conditions.append( ( (dataframe["rsi_1h"] > self.v8_sell_rsi_main.value) & (dataframe["volume"] > 0) ) ) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1 return dataframe # --- custom indicators --------------------------------------------------------------------------- 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"] # SSL Channels def SSLChannels(dataframe, length=7): 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"] 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 class BinClucMadv1(CoreStrategy): INTERFACE_VERSION = 2 stoploss = -0.99 # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, "smaoffset_buy_condition_0_enable": False, "smaoffset_buy_condition_1_enable": False, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": True, "v9_buy_condition_2_enable": True, "v9_buy_condition_3_enable": True, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": True, "v9_buy_condition_6_enable": True, "v9_buy_condition_7_enable": True, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, } class BinClucMadv2(CoreStrategy): INTERFACE_VERSION = 2 stoploss = -0.99 # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, "smaoffset_buy_condition_0_enable": False, "smaoffset_buy_condition_1_enable": False, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": False, "v9_buy_condition_2_enable": False, "v9_buy_condition_3_enable": False, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": False, "v9_buy_condition_6_enable": False, "v9_buy_condition_7_enable": False, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, } class BinClucMadSMAv1(CoreStrategy): INTERFACE_VERSION = 2 stoploss = -0.228 # effectively disabled. # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, "smaoffset_buy_condition_0_enable": True, "smaoffset_buy_condition_1_enable": True, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": False, "v9_buy_condition_2_enable": False, "v9_buy_condition_3_enable": False, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": False, "v9_buy_condition_6_enable": False, "v9_buy_condition_7_enable": False, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, } class BinClucMadSMAv2(CoreStrategy): INTERFACE_VERSION = 2 stoploss = -0.228 # effectively disabled. # Custom stoploss use_custom_stoploss = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 buy_params = { "buy_minimum_conditions": 1, "smaoffset_buy_condition_0_enable": True, "smaoffset_buy_condition_1_enable": True, "v6_buy_condition_0_enable": False, # avg 0.47 dd 27% "v6_buy_condition_1_enable": True, # no trade "v6_buy_condition_2_enable": True, # avg 2.32 "v6_buy_condition_3_enable": True, # avg 1.12 dd 6% "v8_buy_condition_0_enable": True, # avg 0.74 "v8_buy_condition_1_enable": False, # avg 0.41 dd 37% "v8_buy_condition_2_enable": True, # avg 1.37 "v8_buy_condition_3_enable": False, # avg 0.41 "v8_buy_condition_4_enable": True, # avg 1.29 "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": True, "v9_buy_condition_2_enable": True, "v9_buy_condition_3_enable": True, "v9_buy_condition_4_enable": False, "v9_buy_condition_5_enable": True, "v9_buy_condition_6_enable": True, "v9_buy_condition_7_enable": True, "v9_buy_condition_8_enable": False, "v9_buy_condition_9_enable": False, "v9_buy_condition_10_enable": False, }