# source: https://raw.githubusercontent.com/Danson77/Freqtrade_AIO_UI/f0bf29851d0577f244c17c0aa7d593ba3a7f3551/user_data/backtest_reports/Registerd_with_100USDT/DS_Gusta_5m/DS_Gusta_5m.py # directory_url: https://github.com/Danson77/Freqtrade_AIO_UI/blob/main/user_data/backtest_reports/Registerd_with_100USDT/DS_Gusta_5m/ # User: Danson77 # Repository: Freqtrade_AIO_UI # --------------------# --- Do not remove these libs --- import warnings from functools import reduce from datetime import datetime import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import pandas_ta as pta import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import ( CategoricalParameter, DecimalParameter, IntParameter, merge_informative_pair, stoploss_from_open, ) from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series from technical.indicators import RMI, zema, ichimoku warnings.filterwarnings("ignore", category=FutureWarning) ############################################################################ # Custom indicators and helper functions ############################################################################ def ha_typical_price(bars: DataFrame) -> Series: res = (bars["ha_high"] + bars["ha_low"] + bars["ha_close"]) / 3.0 return Series(index=bars.index, data=res) def vwma(dataframe: DataFrame, length: int = 10) -> Series: """Volume Weighted Moving Average""" pv = dataframe["close"] * dataframe["volume"] vol_sma = ta.SMA(dataframe["volume"], timeperiod=length) pv_sma = ta.SMA(pv, timeperiod=length) return Series(pv_sma / vol_sma, index=dataframe.index) def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series: slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma), index=dataframe.index) return slow_ma >= slow_ma.shift(1) def EWO(dataframe: DataFrame, ema_length: int = 5, ema2_length: int = 35) -> Series: ema1 = ta.EMA(dataframe, timeperiod=ema_length) ema2 = ta.EMA(dataframe, timeperiod=ema2_length) emadif = (ema1 - ema2) / dataframe["low"] * 100 return Series(emadif, index=dataframe.index) def SROC(dataframe: DataFrame, roclen: int = 21, emalen: int = 13, smooth: int = 21) -> Series: roc = ta.ROC(dataframe, timeperiod=roclen) ema = ta.EMA(dataframe, timeperiod=emalen) sroc = ta.ROC(ema, timeperiod=smooth) return Series(sroc, index=dataframe.index) ######################################################################################################################################################## # Percent Changes ######################################################################################################################################################## def range_percent_change(dataframe: DataFrame, method: str, length: int) -> Series: """ Rolling Percentage Change Maximum across interval. :param dataframe: OHLC dataframe :param method: 'HL' High to Low / 'OC' Open to Close :param length: lookback length """ if method == "HL": return ( (dataframe["high"].rolling(length).max() - dataframe["low"].rolling(length).min()) / dataframe["low"].rolling(length).min() ) elif method == "OC": return ( (dataframe["open"].rolling(length).max() - dataframe["close"].rolling(length).min()) / dataframe["close"].rolling(length).min() ) else: raise ValueError(f"Method {method} not defined!") ######################################################################################################################################################## # Williams %R ######################################################################################################################################################## def williams_r(dataframe: DataFrame, period: int = 14) -> Series: 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", index=dataframe.index, ) return wr * -100 ######################################################################################################################################################## # Chaikin Money Flow ######################################################################################################################################################## def chaikin_money_flow(dataframe: DataFrame, n: int = 20, fillna: bool = False) -> Series: mfv = ( ((dataframe["close"] - dataframe["low"]) - (dataframe["high"] - dataframe["close"])) / (dataframe["high"] - dataframe["low"]) ) mfv = mfv.replace([np.inf, -np.inf], np.nan).fillna(0.0) mfv *= dataframe["volume"] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe["volume"].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name="cmf", index=dataframe.index) ######################################################################################################################################################## # PMAX ######################################################################################################################################################## def pmax(df: DataFrame, period: int, multiplier: int, length: int, MAtype: int, src: int): """ Safe NumPy 2.x compatible PMAX implementation. Returns: pm : Series[float] pmx : Series[object] with 'up' / 'down' / None """ period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 else: raise ValueError(f"Unsupported src: {src}") # MAtype: # 1 EMA # 2 DEMA # 3 T3 # 4 SMA # 5 VIDYA # 6 TEMA # 7 WMA # 8 VWMA # 9 zema if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: raise NotImplementedError("VIDYA branch is not implemented in this strategy.") elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(masrc, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) else: raise ValueError(f"Unsupported MAtype: {MAtype}") atr = ta.ATR(df, timeperiod=period) basic_ub = mavalue + ((multiplier / 10.0) * atr) basic_lb = mavalue - ((multiplier / 10.0) * atr) basic_ub_np = Series(basic_ub, index=df.index).to_numpy(dtype=float) basic_lb_np = Series(basic_lb, index=df.index).to_numpy(dtype=float) mavalue_np = Series(mavalue, index=df.index).to_numpy(dtype=float) final_ub = np.zeros(len(df), dtype=float) final_lb = np.zeros(len(df), dtype=float) for i in range(period, len(df)): final_ub[i] = ( basic_ub_np[i] if (basic_ub_np[i] < final_ub[i - 1] or mavalue_np[i - 1] > final_ub[i - 1]) else final_ub[i - 1] ) final_lb[i] = ( basic_lb_np[i] if (basic_lb_np[i] > final_lb[i - 1] or mavalue_np[i - 1] < final_lb[i - 1]) else final_lb[i - 1] ) pm_arr = np.zeros(len(df), dtype=float) for i in range(period, len(df)): if pm_arr[i - 1] == final_ub[i - 1]: pm_arr[i] = final_ub[i] if mavalue_np[i] <= final_ub[i] else final_lb[i] elif pm_arr[i - 1] == final_lb[i - 1]: pm_arr[i] = final_lb[i] if mavalue_np[i] >= final_lb[i] else final_ub[i] else: pm_arr[i] = 0.0 pm = Series(pm_arr, index=df.index) pmx_arr = np.full(len(df), None, dtype=object) up_mask = (pm_arr > 0.0) & (mavalue_np >= pm_arr) down_mask = (pm_arr > 0.0) & (mavalue_np < pm_arr) pmx_arr[up_mask] = "up" pmx_arr[down_mask] = "down" pmx = Series(pmx_arr, index=df.index) return pm, pmx ######################################################################################################################################################## # Mom DIV ######################################################################################################################################################## def momdiv( dataframe: DataFrame, mom_length: int = 10, bb_length: int = 20, bb_dev: float = 2.0, lookback: int = 30, ) -> DataFrame: mom: Series = ta.MOM(dataframe, timeperiod=mom_length) upperband, middleband, lowerband = ta.BBANDS( mom, timeperiod=bb_length, nbdevup=bb_dev, nbdevdn=bb_dev, matype=0 ) buy = qtpylib.crossed_below(mom, lowerband) sell = qtpylib.crossed_above(mom, upperband) hh = dataframe["high"].rolling(lookback).max() ll = dataframe["low"].rolling(lookback).min() coh = dataframe["high"] >= hh col = dataframe["low"] <= ll return DataFrame( { "momdiv_mom": mom, "momdiv_upperb": upperband, "momdiv_lowerb": lowerband, "momdiv_buy": buy, "momdiv_sell": sell, "momdiv_coh": coh, "momdiv_col": col, }, index=dataframe.index, ) ######################################################################################################################################################## # T3 ######################################################################################################################################################## def T3(dataframe: DataFrame, length: int = 5) -> Series: """ T3 Average by HPotter on Tradingview """ df = dataframe.copy() df["xe1"] = ta.EMA(df["close"], timeperiod=length) df["xe2"] = ta.EMA(df["xe1"], timeperiod=length) df["xe3"] = ta.EMA(df["xe2"], timeperiod=length) df["xe4"] = ta.EMA(df["xe3"], timeperiod=length) df["xe5"] = ta.EMA(df["xe4"], timeperiod=length) df["xe6"] = ta.EMA(df["xe5"], timeperiod=length) b = 0.7 c1 = -(b**3) c2 = 3 * b * b + 3 * (b**3) c3 = -6 * b * b - 3 * b - 3 * (b**3) c4 = 1 + 3 * b + (b**3) + 3 * b * b df["T3Average"] = c1 * df["xe6"] + c2 * df["xe5"] + c3 * df["xe4"] + c4 * df["xe3"] return df["T3Average"] ######################################################################################################################################################## ''' BB_RPB_TSL @author jilv220 Simple bollinger brand strategy inspired by this blog RPB, Real Pull Back The trailing custom stoploss taken from BigZ04_TSL from Perkmeister (modded by ilya) ''' ######################################################################################################################################################## class Github_Danson77_Freqtrade_AIO_UI__DS_Gusta_5m__20260520_191933(IStrategy): INTERFACE_VERSION = 3 ######################################################################################################################################################## # Hyperopt ######################################################################################################################################################## buy_params = { "max_slip": 0.983, "buy_bb_width_1h": 0.954, "buy_roc_1h": 86, "buy_threshold": 0.003, "buy_bb_factor": 0.999, "buy_bb_delta": 0.025, "buy_bb_width": 0.095, "buy_cci": -116, "buy_cci_length": 25, "buy_rmi": 49, "buy_rmi_length": 17, "buy_srsi_fk": 32, "buy_closedelta": 17.922, "buy_ema_diff": 0.026, "buy_ema_high": 0.968, "buy_ema_low": 0.935, "buy_ewo": -5.001, "buy_rsi": 23, "buy_rsi_fast": 44, "buy_ema_high_2": 1.087, "buy_ema_low_2": 0.970, "buy_ewo_high_2": 4.179, "buy_rsi_ewo_2": 35, "buy_rsi_fast_ewo_2": 45, "buy_r_deadfish_bb_factor": 1.014, "buy_r_deadfish_bb_width": 0.299, "buy_r_deadfish_ema": 1.054, "buy_r_deadfish_volume_factor": 1.59, "buy_r_deadfish_cti": -0.115, "buy_r_deadfish_r14": -44.34, "buy_clucha_bbdelta_close": 0.049, "buy_clucha_bbdelta_tail": 1.146, "buy_clucha_close_bblower": 0.018, "buy_clucha_closedelta_close": 0.017, "buy_clucha_rocr_1h": 0.526, "buy_adx": 13, "buy_ewo_high": 8.594, "buy_fastd": 28, "buy_fastk": 39, "buy_gumbo_ema": 1.121, "buy_gumbo_ewo_low": -9.442, "buy_gumbo_cti": -0.374, "buy_gumbo_r14": -51.971, "buy_sqzmom_ema": 0.981, "buy_sqzmom_ewo": -3.966, "buy_sqzmom_r14": -45.068, "buy_nfix_49_cti": -0.105, "buy_nfix_49_r14": -81.827, } sell_params = { "sell_cmf": -0.046, "sell_ema": 0.988, "sell_ema_close_delta": 0.022, "sell_deadfish_profit": -0.063, "sell_deadfish_bb_factor": 0.954, "sell_deadfish_bb_width": 0.043, "sell_deadfish_volume_factor": 2.37, "sell_cti_r_cti": 0.844, "sell_cti_r_r": -19.99, } minimal_roi = { "0": 0.205, "81": 0.038, "292": 0.005, } stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.0135 trailing_only_offset_is_reached = False use_custom_stoploss = True ######################################################################################################################################################## # Main ######################################################################################################################################################## timeframe = "5m" inf_1h = "1h" process_only_new_candles = True startup_candle_count = 800 use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False order_types = { "entry": "market", "exit": "market", "emergency_exit": "market", "force_entry": "market", "force_exit": "market", "stoploss": "market", "stoploss_on_exchange": False, "stoploss_on_exchange_interval": 60, "stoploss_on_exchange_limit_ratio": 0.99, } order_time_in_force = { "entry": "gtc", "exit": "gtc", } plot_config = { "main_plot": { "ma_buy": {"color": "orange"}, "ma_sell": {"color": "orange"}, }, } ######################################################################################################################################################## # Parameters ######################################################################################################################################################## is_optimize_dip = False buy_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip) buy_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip) buy_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip) buy_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip) buy_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip) is_optimize_break = False buy_bb_width = DecimalParameter(0.065, 0.135, default=0.095, optimize=is_optimize_break) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.025, optimize=is_optimize_break) is_optimize_local_uptrend = False buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize=is_optimize_local_uptrend) buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize=False) buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize=is_optimize_local_uptrend) is_optimize_ewo = False buy_rsi_fast = IntParameter(35, 50, default=45, optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=35, optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942, optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084, optimize=is_optimize_ewo) is_optimize_ewo_2 = False buy_rsi_fast_ewo_2 = IntParameter(15, 50, default=45, optimize=is_optimize_ewo_2) buy_rsi_ewo_2 = IntParameter(15, 50, default=35, optimize=is_optimize_ewo_2) buy_ema_low_2 = DecimalParameter(0.90, 1.2, default=0.970, optimize=is_optimize_ewo_2) buy_ema_high_2 = DecimalParameter(0.90, 1.2, default=1.087, optimize=is_optimize_ewo_2) buy_ewo_high_2 = DecimalParameter(2, 12, default=4.179, optimize=is_optimize_ewo_2) is_optimize_r_deadfish = False buy_r_deadfish_ema = DecimalParameter(0.90, 1.2, default=1.087, optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_r_deadfish) buy_r_deadfish_bb_factor = DecimalParameter(0.90, 1.2, default=1.0, optimize=is_optimize_r_deadfish) buy_r_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_r_deadfish) is_optimize_r_deadfish_protection = False buy_r_deadfish_cti = DecimalParameter(-0.6, -0.0, default=-0.5, optimize=is_optimize_r_deadfish_protection) buy_r_deadfish_r14 = DecimalParameter(-60, -44, default=-60, optimize=is_optimize_r_deadfish_protection) is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.01, 0.05, default=0.02206, optimize=is_optimize_clucha) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=1.02515, optimize=is_optimize_clucha) buy_clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=0.04401, optimize=is_optimize_clucha) buy_clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=0.47782, optimize=is_optimize_clucha) is_optimize_gumbo = False buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_gumbo) buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize=is_optimize_gumbo) is_optimize_gumbo_protection = False buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_gumbo_protection) buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_gumbo_protection) is_optimize_sqzmom_protection = False buy_sqzmom_ema = DecimalParameter(0.9, 1.2, default=0.97, optimize=is_optimize_sqzmom_protection) buy_sqzmom_ewo = DecimalParameter(-12, 12, default=0, optimize=is_optimize_sqzmom_protection) buy_sqzmom_r14 = DecimalParameter(-100, -22, default=-50, optimize=is_optimize_sqzmom_protection) is_optimize_nfix_49_protection = False buy_nfix_49_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_nfix_49_protection) buy_nfix_49_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_nfix_49_protection) is_optimize_btc_safe = False buy_btc_safe = IntParameter(-300, 50, default=-200, optimize=is_optimize_btc_safe) buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize=is_optimize_btc_safe) buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize=is_optimize_btc_safe) is_optimize_check = False buy_roc_1h = IntParameter(-25, 200, default=10, optimize=is_optimize_check) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=0.3, optimize=is_optimize_check) is_optimize_slip = False max_slip = DecimalParameter(0.33, 1.00, default=0.33, decimals=3, optimize=is_optimize_slip, space="buy", load=True) sell_btc_safe = IntParameter(-400, -300, default=-365, optimize=False) is_optimize_sell_stoploss = False sell_cmf = DecimalParameter(-0.4, 0.0, default=0.0, optimize=is_optimize_sell_stoploss) sell_ema_close_delta = DecimalParameter(0.022, 0.027, default=0.024, optimize=is_optimize_sell_stoploss) sell_ema = DecimalParameter(0.97, 0.99, default=0.987, optimize=is_optimize_sell_stoploss) is_optimize_deadfish = False sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, optimize=is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, optimize=is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, optimize=is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_deadfish) is_optimize_bleeding = False sell_bleeding_cti = DecimalParameter(-0.9, -0.0, default=-0.5, optimize=is_optimize_bleeding) sell_bleeding_r14 = DecimalParameter(-100, -44, default=-60, optimize=is_optimize_bleeding) sell_bleeding_volume_factor = DecimalParameter(1, 2.5, default=1.0, optimize=is_optimize_bleeding) is_optimize_cti_r = False sell_cti_r_cti = DecimalParameter(0.55, 1, default=0.5, optimize=is_optimize_cti_r) sell_cti_r_r = DecimalParameter(-15, 0, default=-20, optimize=is_optimize_cti_r) ######################################################################################################################################################## ######################################################################################################################################################## # Informative 1h ######################################################################################################################################################## def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.inf_1h) # hard guard for missing 1h data if informative_1h is None or informative_1h.empty: return DataFrame() informative_1h = informative_1h.copy() informative_1h["ema_8"] = ta.EMA(informative_1h, timeperiod=8) 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["cti"] = pta.cti(informative_1h["close"], length=20) informative_1h["cti_40"] = pta.cti(informative_1h["close"], length=40) crsi_closechange = informative_1h["close"] / informative_1h["close"].shift(1) crsi_updown = np.where( crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0) ) informative_1h["crsi"] = ( ta.RSI(informative_1h["close"], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(informative_1h["close"], 100) ) / 3 informative_1h["r_96"] = williams_r(informative_1h, period=96) informative_1h["r_480"] = williams_r(informative_1h, period=480) bollinger2 = qtpylib.bollinger_bands( qtpylib.typical_price(informative_1h), window=20, stds=2 ) informative_1h["bb_lowerband2"] = bollinger2["lower"] informative_1h["bb_middleband2"] = bollinger2["mid"] informative_1h["bb_upperband2"] = bollinger2["upper"] informative_1h["bb_width"] = ( (informative_1h["bb_upperband2"] - informative_1h["bb_lowerband2"]) / informative_1h["bb_middleband2"] ) informative_1h["roc"] = ta.ROC(informative_1h, timeperiod=9) mom = momdiv(informative_1h) informative_1h["momdiv_buy"] = mom["momdiv_buy"] informative_1h["momdiv_sell"] = mom["momdiv_sell"] informative_1h["momdiv_coh"] = mom["momdiv_coh"] informative_1h["momdiv_col"] = mom["momdiv_col"] informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) informative_1h["cmf"] = chaikin_money_flow(informative_1h, 20) # only compute HA when informative dataframe is non-empty inf_heikinashi = qtpylib.heikinashi(informative_1h) informative_1h["ha_close"] = inf_heikinashi["close"] informative_1h["rocr"] = ta.ROCR(informative_1h["ha_close"], timeperiod=168) informative_1h["T3"] = T3(informative_1h) informative_1h["EWO"] = EWO(informative_1h, 50, 200) informative_1h["hl_pct_change_5"] = range_percent_change(informative_1h, "HL", 5) informative_1h["low_5"] = informative_1h["low"].shift().rolling(5).min() informative_1h["safe_dump_50"] = ( (informative_1h["hl_pct_change_5"] < 0.66) | (informative_1h["close"] < informative_1h["low_5"]) | (informative_1h["close"] > informative_1h["open"]) ) return informative_1h ######################################################################################################################################################## # Indicators ######################################################################################################################################################## def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) if informative_1h is not None and not informative_1h.empty: dataframe = merge_informative_pair( dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True ) else: # fallback columns when 1h data is missing nan_cols = [ "ema_8_1h", "ema_50_1h", "ema_100_1h", "ema_200_1h", "cti_1h", "cti_40_1h", "crsi_1h", "r_96_1h", "r_480_1h", "bb_lowerband2_1h", "bb_middleband2_1h", "bb_upperband2_1h", "bb_width_1h", "roc_1h", "rsi_1h", "cmf_1h", "ha_close_1h", "rocr_1h", "T3_1h", "EWO_1h", "hl_pct_change_5_1h", "low_5_1h", ] false_cols = [ "momdiv_buy_1h", "momdiv_sell_1h", "momdiv_coh_1h", "momdiv_col_1h", "safe_dump_50_1h", ] for col in nan_cols: if col not in dataframe.columns: dataframe[col] = np.nan for col in false_cols: if col not in dataframe.columns: dataframe[col] = False dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe ######################################################################################################################################################## # Informative pairs ######################################################################################################################################################## def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, self.inf_1h) for pair in pairs] ######################################################################################################################################################## # TF Indicators ######################################################################################################################################################## def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband2"] = bollinger2["lower"] dataframe["bb_middleband2"] = bollinger2["mid"] dataframe["bb_upperband2"] = bollinger2["upper"] 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_upperband2"] - dataframe["bb_lowerband2"]) / dataframe["bb_middleband2"] ) dataframe["bb_delta"] = ( (dataframe["bb_lowerband2"] - dataframe["bb_lowerband3"]) / dataframe["bb_lowerband2"] ) for val in self.buy_cci_length.range: dataframe[f"cci_length_{val}"] = ta.CCI(dataframe, val) dataframe["cci"] = ta.CCI(dataframe, 26) dataframe["cci_long"] = ta.CCI(dataframe, 170) for val in self.buy_rmi_length.range: dataframe[f"rmi_length_{val}"] = RMI(dataframe, length=val, mom=4) stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe["srsi_fk"] = stoch["fastk"] dataframe["srsi_fd"] = stoch["fastd"] dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs() dataframe["sma_9"] = ta.SMA(dataframe, timeperiod=9) dataframe["sma_15"] = ta.SMA(dataframe, timeperiod=15) dataframe["sma_20"] = ta.SMA(dataframe, timeperiod=20) dataframe["sma_21"] = ta.SMA(dataframe, timeperiod=21) dataframe["sma_28"] = ta.SMA(dataframe, timeperiod=28) dataframe["sma_30"] = ta.SMA(dataframe, timeperiod=30) dataframe["sma_75"] = ta.SMA(dataframe, timeperiod=75) dataframe["cti"] = pta.cti(dataframe["close"], length=20) dataframe["cmf"] = chaikin_money_flow(dataframe, 20) crsi_closechange = dataframe["close"] / dataframe["close"].shift(1) crsi_updown = np.where( crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0) ) dataframe["crsi"] = ( ta.RSI(dataframe["close"], timeperiod=3) + ta.RSI(crsi_updown, timeperiod=2) + ta.ROC(dataframe["close"], 100) ) / 3 dataframe["ema_4"] = ta.EMA(dataframe, timeperiod=4) dataframe["ema_8"] = ta.EMA(dataframe, timeperiod=8) dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) dataframe["ema_13"] = ta.EMA(dataframe, timeperiod=13) dataframe["ema_16"] = ta.EMA(dataframe, timeperiod=16) 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["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4) dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20) dataframe["EWO"] = EWO(dataframe, 50, 200) 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_96"] = williams_r(dataframe, period=96) dataframe["r_480"] = williams_r(dataframe, period=480) 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["mfi"] = ta.MFI(dataframe) heikinashi = qtpylib.heikinashi(dataframe) dataframe["ha_open"] = heikinashi["open"] dataframe["ha_close"] = heikinashi["close"] dataframe["ha_high"] = heikinashi["high"] dataframe["ha_low"] = heikinashi["low"] bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2) dataframe["bb_lowerband2_40"] = bollinger2_40["lower"] dataframe["bb_middleband2_40"] = bollinger2_40["mid"] dataframe["bb_upperband2_40"] = bollinger2_40["upper"] dataframe["bb_delta_cluc"] = (dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]).abs() dataframe["ha_closedelta"] = (dataframe["ha_close"] - dataframe["ha_close"].shift()).abs() dataframe["tail"] = (dataframe["ha_close"] - dataframe["ha_low"]).abs() dataframe["ema_slow"] = ta.EMA(dataframe["ha_close"], timeperiod=50) dataframe["rocr"] = ta.ROCR(dataframe["ha_close"], timeperiod=28) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] dataframe["adx"] = ta.ADX(dataframe) dataframe["pm"], dataframe["pmx"] = pmax( heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3 ) dataframe["source"] = ( dataframe["high"] + dataframe["low"] + dataframe["open"] + dataframe["close"] ) / 4 dataframe["pmax_thresh"] = ta.EMA(dataframe["source"], timeperiod=9) mom = momdiv(dataframe) dataframe["momdiv_buy"] = mom["momdiv_buy"] dataframe["momdiv_sell"] = mom["momdiv_sell"] dataframe["momdiv_coh"] = mom["momdiv_coh"] dataframe["momdiv_col"] = mom["momdiv_col"] dataframe["T3"] = T3(dataframe) dataframe["trange"] = ta.TRANGE(dataframe) dataframe["range_ma_28"] = ta.SMA(dataframe["trange"], 28) dataframe["kc_upperband_28_1"] = dataframe["sma_28"] + dataframe["range_ma_28"] dataframe["kc_lowerband_28_1"] = dataframe["sma_28"] - dataframe["range_ma_28"] dataframe["range_ma_20"] = ta.SMA(dataframe["trange"], 20) dataframe["kc_upperband_20_2"] = dataframe["sma_20"] + dataframe["range_ma_20"] * 2 dataframe["kc_lowerband_20_2"] = dataframe["sma_20"] - dataframe["range_ma_20"] * 2 dataframe["kc_bb_delta"] = ( (dataframe["kc_lowerband_20_2"] - dataframe["bb_lowerband2"]) / dataframe["bb_lowerband2"] * 100 ) dataframe["hh_20"] = ta.MAX(dataframe["high"], 20) dataframe["ll_20"] = ta.MIN(dataframe["low"], 20) dataframe["avg_hh_ll_20"] = (dataframe["hh_20"] + dataframe["ll_20"]) / 2 dataframe["avg_close_20"] = ta.SMA(dataframe["close"], 20) dataframe["avg_val_20"] = (dataframe["avg_hh_ll_20"] + dataframe["avg_close_20"]) / 2 dataframe["linreg_val_20"] = ta.LINEARREG( dataframe["close"] - dataframe["avg_val_20"], timeperiod=20 ) rsi = 0.1 * (dataframe["rsi"] - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe["moderi_96"] = moderi(dataframe, 96) return dataframe ######################################################################################################################################################## # Buy Signal ######################################################################################################################################################## def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, "enter_tag"] = "" dataframe.loc[:, "enter_long"] = 0 is_dip = ( (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) ) is_sqzOff = ( (dataframe["bb_lowerband2"] < dataframe["kc_lowerband_28_1"]) & (dataframe["bb_upperband2"] > dataframe["kc_upperband_28_1"]) ) is_break = ( (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) ) is_local_uptrend = ( (dataframe["ema_26"] > dataframe["ema_12"]) & (dataframe["ema_26"] - dataframe["ema_12"] > dataframe["open"] * self.buy_ema_diff.value) & (dataframe["ema_26"].shift() - dataframe["ema_12"].shift() > dataframe["open"] / 100) & (dataframe["close"] < dataframe["bb_lowerband2"] * self.buy_bb_factor.value) & (dataframe["closedelta"] > dataframe["close"] * self.buy_closedelta.value / 1000) ) is_ewo = ( (dataframe["rsi_fast"] < self.buy_rsi_fast.value) & (dataframe["close"] < dataframe["ema_8"] * self.buy_ema_low.value) & (dataframe["EWO"] > self.buy_ewo.value) & (dataframe["close"] < dataframe["ema_16"] * self.buy_ema_high.value) & (dataframe["rsi"] < self.buy_rsi.value) ) is_ewo_2 = ( (dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) & (dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) & (dataframe["rsi_fast"] < self.buy_rsi_fast_ewo_2.value) & (dataframe["close"] < dataframe["ema_8"] * self.buy_ema_low_2.value) & (dataframe["EWO"] > self.buy_ewo_high_2.value) & (dataframe["close"] < dataframe["ema_16"] * self.buy_ema_high_2.value) & (dataframe["rsi"] < self.buy_rsi_ewo_2.value) ) is_r_deadfish = ( (dataframe["ema_100"] < dataframe["ema_200"] * self.buy_r_deadfish_ema.value) & (dataframe["bb_width"] > self.buy_r_deadfish_bb_width.value) & (dataframe["close"] < dataframe["bb_middleband2"] * self.buy_r_deadfish_bb_factor.value) & ( dataframe["volume_mean_12"] > dataframe["volume_mean_24"] * self.buy_r_deadfish_volume_factor.value ) & (dataframe["cti"] < self.buy_r_deadfish_cti.value) & (dataframe["r_14"] < self.buy_r_deadfish_r14.value) ) is_clucHA = ( (dataframe["rocr_1h"] > self.buy_clucha_rocr_1h.value) & ( (dataframe["bb_lowerband2_40"].shift() > 0) & (dataframe["bb_delta_cluc"] > dataframe["ha_close"] * self.buy_clucha_bbdelta_close.value) & ( dataframe["ha_closedelta"] > dataframe["ha_close"] * self.buy_clucha_closedelta_close.value ) & (dataframe["tail"] < dataframe["bb_delta_cluc"] * self.buy_clucha_bbdelta_tail.value) & (dataframe["ha_close"] < dataframe["bb_lowerband2_40"].shift()) & (dataframe["ha_close"] < dataframe["ha_close"].shift()) ) ) is_gumbo = ( (dataframe["EWO"] < self.buy_gumbo_ewo_low.value) & (dataframe["bb_middleband2_1h"] >= dataframe["T3_1h"]) & (dataframe["T3"] <= dataframe["ema_8"] * self.buy_gumbo_ema.value) & (dataframe["cti"] < self.buy_gumbo_cti.value) & (dataframe["r_14"] < self.buy_gumbo_r14.value) ) is_sqzmom = ( is_sqzOff & (dataframe["linreg_val_20"].shift(2) > dataframe["linreg_val_20"].shift(1)) & (dataframe["linreg_val_20"].shift(1) < dataframe["linreg_val_20"]) & (dataframe["linreg_val_20"] < 0) & (dataframe["close"] < dataframe["ema_13"] * self.buy_sqzmom_ema.value) & (dataframe["EWO"] < self.buy_sqzmom_ewo.value) & (dataframe["r_14"] < self.buy_sqzmom_r14.value) ) is_nfi_13 = ( (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["close"] < dataframe["sma_30"] * 0.99) & (dataframe["cti"] < -0.92) & (dataframe["EWO"] < -5.585) & (dataframe["cti_1h"] < -0.88) & (dataframe["crsi_1h"] > 10.0) ) is_nfi_32 = ( (dataframe["rsi_slow"] < dataframe["rsi_slow"].shift(1)) & (dataframe["rsi_fast"] < 46) & (dataframe["rsi"] > 25.0) & (dataframe["close"] < dataframe["sma_15"] * 0.93) & (dataframe["cti"] < -0.9) ) is_nfi_33 = ( (dataframe["close"] < (dataframe["ema_13"] * 0.978)) & (dataframe["EWO"] > 8) & (dataframe["cti"] < -0.88) & (dataframe["rsi"] < 32) & (dataframe["r_14"] < -98.0) & (dataframe["volume"] < (dataframe["volume_mean_4"] * 2.5)) ) is_nfix_5 = ( (dataframe["ema_200_1h"] > dataframe["ema_200_1h"].shift(12)) & (dataframe["ema_200_1h"].shift(12) > dataframe["ema_200_1h"].shift(24)) & (dataframe["close"] < dataframe["sma_75"] * 0.932) & (dataframe["EWO"] > 3.6) & (dataframe["cti"] < -0.9) & (dataframe["r_14"] < -97.0) ) is_nfix_49 = ( (dataframe["ema_26"].shift(3) > dataframe["ema_12"].shift(3)) & ( dataframe["ema_26"].shift(3) - dataframe["ema_12"].shift(3) > dataframe["open"].shift(3) * 0.032 ) & ( dataframe["ema_26"].shift(9) - dataframe["ema_12"].shift(9) > dataframe["open"].shift(3) / 100 ) & (dataframe["close"].shift(3) < dataframe["ema_20"].shift(3) * 0.916) & (dataframe["rsi"].shift(3) < 32.5) & (dataframe["crsi"].shift(3) > 18.0) & (dataframe["cti"] < self.buy_nfix_49_cti.value) & (dataframe["r_14"] < self.buy_nfix_49_r14.value) ) is_nfi7_33 = ( (dataframe["moderi_96"]) & (dataframe["cti"] < -0.88) & (dataframe["close"] < (dataframe["ema_13"] * 0.988)) & (dataframe["EWO"] > 6.4) & (dataframe["rsi"] < 32.0) & (dataframe["volume"] < (dataframe["volume_mean_4"] * 2.0)) ) is_nfi7_37 = ( (dataframe["pm"] > dataframe["pmax_thresh"]) & (dataframe["close"] < dataframe["sma_75"] * 0.98) & (dataframe["EWO"] > 9.8) & (dataframe["rsi"] < 56.0) & (dataframe["cti"] < -0.7) & (dataframe["safe_dump_50_1h"]) ) is_additional_check = ( (dataframe["roc_1h"] < self.buy_roc_1h.value) & (dataframe["bb_width_1h"] < self.buy_bb_width_1h.value) ) is_BB_checked = is_dip & is_break conditions.append(is_BB_checked) dataframe.loc[is_BB_checked, "enter_tag"] = "bb " conditions.append(is_local_uptrend) dataframe.loc[is_local_uptrend, "enter_tag"] = "local_uptrend " conditions.append(is_ewo) dataframe.loc[is_ewo, "enter_tag"] = "ewo " conditions.append(is_r_deadfish) dataframe.loc[is_r_deadfish, "enter_tag"] = "r_deadfish " # conditions.append(is_clucHA) # dataframe.loc[is_clucHA, 'enter_tag'] += 'clucHA ' # conditions.append(is_gumbo) # dataframe.loc[is_gumbo, 'enter_tag'] += 'gumbo ' conditions.append(is_sqzmom) dataframe.loc[is_sqzmom, "enter_tag"] = "sqzmom " conditions.append(is_nfi_13) dataframe.loc[is_nfi_13, "enter_tag"] = "nfi_13 " conditions.append(is_nfi_32) dataframe.loc[is_nfi_32, "enter_tag"] = "nfi_32 " conditions.append(is_nfi_33) dataframe.loc[is_nfi_33, "enter_tag"] = "nfi_33 " conditions.append(is_nfix_49) dataframe.loc[is_nfix_49, "enter_tag"] = "nfix_49 " conditions.append(is_nfi7_33) dataframe.loc[is_nfi7_33, "enter_tag"] = "nfi7_33 " conditions.append(is_nfi7_37) dataframe.loc[is_nfi7_37, "enter_tag"] = "nfi7_37 " if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "enter_long"] = 1 return dataframe ######################################################################################################################################################## # Confirm Entry ######################################################################################################################################################## def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs, ) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) max_slip = self.max_slip.value if len(dataframe) < 1: return False last = dataframe.iloc[-1].squeeze() if rate > last["close"]: slippage = ((rate / last["close"]) - 1) * 100 return slippage < max_slip return True ######################################################################################################################################################## # Custom Stoploss ######################################################################################################################################################## def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: sl_new = 1 if current_profit > 0.2: sl_new = 0.05 elif current_profit > 0.1: sl_new = 0.03 elif current_profit > 0.06: sl_new = 0.02 elif current_profit > 0.03: sl_new = 0.015 return sl_new ######################################################################################################################################################## # Custom Sell From NFIX ######################################################################################################################################################## def custom_exit( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 3: return None last_candle = dataframe.iloc[-1] previous_candle_1 = dataframe.iloc[-2] previous_candle_2 = dataframe.iloc[-3] max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate max_loss = (trade.open_rate - trade.min_rate) / trade.min_rate enter_tag = "empty" if hasattr(trade, "enter_tag") and trade.enter_tag is not None: enter_tag = trade.enter_tag # sell trail if 0.012 > current_profit >= 0.0: if (max_profit > (current_profit + 0.045)) and (last_candle["rsi"] < 46.0): return f"sell_profit_t_0_1({enter_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle["rsi"] < 32.0): return f"sell_profit_t_0_2({enter_tag})" elif (max_profit > (current_profit + 0.05)) and (last_candle["rsi"] < 48.0): return f"sell_profit_t_0_3({enter_tag})" elif 0.02 > current_profit >= 0.012: if (max_profit > (current_profit + 0.01)) and (last_candle["rsi"] < 39.0): return f"sell_profit_t_1_1({enter_tag})" elif ( (max_profit > (current_profit + 0.035)) and (last_candle["rsi"] < 45.0) and (last_candle["cmf"] < -0.0) and (last_candle["cmf_1h"] < -0.0) ): return f"sell_profit_t_1_2({enter_tag})" elif ( (max_profit > (current_profit + 0.02)) and (last_candle["rsi"] < 40.0) and (last_candle["cmf"] < -0.0) and (last_candle["cti_1h"] > 0.8) ): return f"sell_profit_t_1_4({enter_tag})" elif ( (max_profit > (current_profit + 0.04)) and (last_candle["rsi"] < 49.0) and (last_candle["cmf_1h"] < -0.0) ): return f"sell_profit_t_1_5({enter_tag})" elif ( (max_profit > (current_profit + 0.06)) and (last_candle["rsi"] < 43.0) and (last_candle["cmf"] < -0.0) ): return f"sell_profit_t_1_7({enter_tag})" elif ( (max_profit > (current_profit + 0.025)) and (last_candle["rsi"] < 40.0) and (last_candle["cmf"] < -0.1) and (last_candle["rsi_1h"] < 50.0) ): return f"sell_profit_t_1_9({enter_tag})" elif ( (max_profit > (current_profit + 0.025)) and (last_candle["rsi"] < 46.0) and (last_candle["cmf"] < -0.0) and (last_candle["r_480_1h"] > -20.0) ): return f"sell_profit_t_1_10({enter_tag})" elif (max_profit > (current_profit + 0.025)) and (last_candle["rsi"] < 42.0): return f"sell_profit_t_1_11({enter_tag})" elif ( (max_profit > (current_profit + 0.01)) and (last_candle["rsi"] < 44.0) and (last_candle["cmf"] < -0.25) ): return f"sell_profit_t_1_12({enter_tag})" if 0.012 > current_profit >= 0.0: if (last_candle["cti"] > self.sell_cti_r_cti.value) and ( last_candle["r_14"] > self.sell_cti_r_r.value ): return f"sell_profit_t_cti_r_0_1({enter_tag})" if current_profit > 0.02: if last_candle["momdiv_sell_1h"] is True: return f"signal_profit_q_momdiv_1h({enter_tag})" if last_candle["momdiv_sell"] is True: return f"signal_profit_q_momdiv({enter_tag})" if last_candle["momdiv_coh"] is True: return f"signal_profit_q_momdiv_coh({enter_tag})" if last_candle["close"] < last_candle["ema_200"]: if 0.02 > current_profit >= 0.01: if (last_candle["rsi"] < 34.0) and (last_candle["cmf"] < 0.0): return f"sell_profit_u_bear_1_1({enter_tag})" elif (last_candle["rsi"] < 44.0) and (last_candle["cmf"] < -0.4): return f"sell_profit_u_bear_1_2({enter_tag})" if (0.06 > current_profit > 0.02) and (last_candle["rsi"] > 80.0): return f"signal_profit_q_1({enter_tag})" if (0.06 > current_profit > 0.02) and (last_candle["cti"] > 0.95): return f"signal_profit_q_2({enter_tag})" if ( (0.06 > current_profit > 0.02) and (last_candle["pm"] <= last_candle["pmax_thresh"]) and (last_candle["close"] > last_candle["sma_21"] * 1.1) ): return f"signal_profit_q_pmax_bull({enter_tag})" if ( (0.06 > current_profit > 0.02) and (last_candle["pm"] > last_candle["pmax_thresh"]) and (last_candle["close"] > last_candle["sma_21"] * 1.016) ): return f"signal_profit_q_pmax_bear({enter_tag})" if ( (current_profit < -0.05) and (last_candle["close"] < last_candle["ema_200"] * 0.988) and (last_candle["cmf"] < -0.046) and (((last_candle["ema_200"] - last_candle["close"]) / last_candle["close"]) < 0.022) and (last_candle["rsi"] > previous_candle_1["rsi"]) and (last_candle["rsi"] > (last_candle["rsi_1h"] + 10.0)) ): return f"sell_stoploss_u_e_1({enter_tag})" if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle["close"] < last_candle["ema_200"]) and (last_candle["bb_width"] < self.sell_deadfish_bb_width.value) and (last_candle["close"] > last_candle["bb_middleband2"] * self.sell_deadfish_bb_factor.value) and ( last_candle["volume_mean_12"] < last_candle["volume_mean_24"] * self.sell_deadfish_volume_factor.value ) ): return f"sell_stoploss_deadfish({enter_tag})" return None ######################################################################################################################################################## # Sell Signal ######################################################################################################################################################## def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe["volume"] > 0), "exit_long"] = 0 return dataframe