# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/91c1f22df4c5b0ac540cf09a447fe0dd78d9d5cd/user_data/strategies/DevDsl2Approx/DevDsl2Approx.py import logging from datetime import datetime import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import pandas as pd import talib.abstract as ta import technical.indicators as ftt from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, stoploss_from_open from pandas import DataFrame, Series logger = logging.getLogger(__name__) def vwap_bands(dataframe: DataFrame, window: int = 20, num_of_std: float = 1.0): df = dataframe.copy() df["vwap"] = qtpylib.rolling_vwap(df, window=window) rolling_std = df["vwap"].rolling(window=window).std() df["vwap_low"] = df["vwap"] - (rolling_std * num_of_std) df["vwap_high"] = df["vwap"] + (rolling_std * num_of_std) return df["vwap_low"], df["vwap"], df["vwap_high"] def vwma(series: Series, volume: Series, length: int) -> Series: vol_sum = volume.rolling(length).sum() return (series * volume).rolling(length).sum() / vol_sum.replace(0, np.nan) def smma(series: Series, period: int) -> Series: result = pd.Series(np.nan, index=series.index, dtype=float) if len(series) < period: return result result.iloc[period - 1] = series.iloc[:period].mean() for idx in range(period, len(series)): result.iloc[idx] = ((result.iloc[idx - 1] * (period - 1)) + series.iloc[idx]) / period return result def dsl_rsi_level(rsi: Series, length: int) -> Series: upper = pd.Series(np.nan, index=rsi.index, dtype=float) lower = pd.Series(np.nan, index=rsi.index, dtype=float) alpha = 2.0 / (length + 1.0) upper_prev = 50.0 lower_prev = 50.0 for idx, value in enumerate(rsi.fillna(50.0)): if value > 50.0: upper_prev = upper_prev + alpha * (value - upper_prev) if value < 50.0: lower_prev = lower_prev + alpha * (value - lower_prev) upper.iloc[idx] = upper_prev lower.iloc[idx] = lower_prev return upper - lower class Github_vaskosmihaylov_nfi_custom_strategies__DevDsl2Approx__20260513_073522(IStrategy): """ A public-fingerprint reconstruction of `Dev_dsl2` tuned for local Bybit futures backtesting. The strategy keeps the original indicator family but now runs as a balanced long/short 5m futures strategy so it can be evaluated against the available 2026 Bybit USDT dataset. """ INTERFACE_VERSION = 3 timeframe = "5m" can_short = True startup_candle_count = 240 process_only_new_candles = True minimal_roi = {"0": 100.0} stoploss = -0.99 trailing_stop = False use_custom_stoploss = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False order_types = { "entry": "market", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False, } BUY_DEFAULTS = { "bb_factor": 0.992, "vwap_factor": 0.998, "rsi_14_max": 34, "rsi_slow_max": 42, "rsi_84_max": 58, "dc_percent_max": 0.32, "bbdelta_close_min": 0.011, "close_delta_min": 0.006, "dsl_delta_min": 0.2, "goodloss_max": 0.06, "donchian_room_min": 0.010, "volume_factor": 0.80, "smma_length": 14, "dc_length": 20, "trend_relax_factor": 0.975, "donchian_smma_factor": 0.97, } SELL_DEFAULTS = { "rsi_exit_min": 62, "fastk_exit_min": 78, "high_offset": 1.012, "vwap_factor": 1.002, "profit_fade_min": 0.022, "trend_fail_min": 0.012, "band_exhaustion_rsi_mfi_rmi_min": 60, } SHORT_DEFAULTS = { "bb_factor": 1.000, "vwap_factor": 1.000, "rsi_14_min": 70, "rsi_slow_min": 56, "rsi_84_min": 50, "dc_percent_min": 0.85, "dsl_delta_max": 0.0, "goodrise_max": 0.12, "donchian_room_min": 0.010, "volume_factor": 0.80, "trend_relax_factor": 0.995, "donchian_smma_factor": 1.01, "cover_rsi_max": 38, "cover_fastk_max": 22, "low_offset": 0.988, "cover_vwap_factor": 0.998, "band_compression_rsi_mfi_rmi_max": 40, } RISK_DEFAULTS = { "hard_stoploss": -0.08, "profit_floor_1": 0.016, "profit_floor_2": 0.06, "stop_1": 0.012, "stop_2": 0.04, "time_stop_minutes": 16 * 60, "time_stop_profit": 0.002, } buy_bb_factor = DecimalParameter( 0.985, 0.999, default=BUY_DEFAULTS["bb_factor"], decimals=3, space="buy", optimize=True, ) buy_vwap_factor = DecimalParameter( 0.990, 1.000, default=BUY_DEFAULTS["vwap_factor"], decimals=3, space="buy", optimize=True, ) buy_rsi_14_max = IntParameter(24, 40, default=BUY_DEFAULTS["rsi_14_max"], space="buy", optimize=True) buy_rsi_slow_max = IntParameter(30, 50, default=BUY_DEFAULTS["rsi_slow_max"], space="buy", optimize=True) buy_rsi_84_max = IntParameter(45, 70, default=BUY_DEFAULTS["rsi_84_max"], space="buy", optimize=True) buy_dc_percent_max = DecimalParameter( 0.12, 0.45, default=BUY_DEFAULTS["dc_percent_max"], decimals=2, space="buy", optimize=True, ) buy_bbdelta_close_min = DecimalParameter( 0.004, 0.03, default=BUY_DEFAULTS["bbdelta_close_min"], decimals=3, space="buy", optimize=True, ) buy_close_delta_min = DecimalParameter( 0.001, 0.02, default=BUY_DEFAULTS["close_delta_min"], decimals=3, space="buy", optimize=True, ) buy_dsl_delta_min = DecimalParameter( -2.0, 5.0, default=BUY_DEFAULTS["dsl_delta_min"], decimals=1, space="buy", optimize=True, ) buy_goodloss_max = DecimalParameter( 0.02, 0.12, default=BUY_DEFAULTS["goodloss_max"], decimals=2, space="buy", optimize=True, ) buy_donchian_room_min = DecimalParameter( 0.004, 0.04, default=BUY_DEFAULTS["donchian_room_min"], decimals=3, space="buy", optimize=True, ) short_bb_factor = DecimalParameter( 1.001, 1.03, default=SHORT_DEFAULTS["bb_factor"], decimals=3, space="sell", optimize=True, ) short_vwap_factor = DecimalParameter( 1.000, 1.02, default=SHORT_DEFAULTS["vwap_factor"], decimals=3, space="sell", optimize=True, ) short_rsi_14_min = IntParameter(55, 86, default=SHORT_DEFAULTS["rsi_14_min"], space="sell", optimize=True) short_rsi_slow_min = IntParameter(45, 80, default=SHORT_DEFAULTS["rsi_slow_min"], space="sell", optimize=True) short_rsi_84_min = IntParameter(40, 75, default=SHORT_DEFAULTS["rsi_84_min"], space="sell", optimize=True) short_dc_percent_min = DecimalParameter( 0.55, 0.92, default=SHORT_DEFAULTS["dc_percent_min"], decimals=2, space="sell", optimize=True, ) short_dsl_delta_max = DecimalParameter( -5.0, 2.0, default=SHORT_DEFAULTS["dsl_delta_max"], decimals=1, space="sell", optimize=True, ) short_goodrise_max = DecimalParameter( 0.03, 0.14, default=SHORT_DEFAULTS["goodrise_max"], decimals=2, space="sell", optimize=True, ) short_donchian_room_min = DecimalParameter( 0.004, 0.04, default=SHORT_DEFAULTS["donchian_room_min"], decimals=3, space="sell", optimize=True, ) sell_rsi_exit_min = IntParameter(55, 78, default=SELL_DEFAULTS["rsi_exit_min"], space="sell", optimize=True) sell_fastk_exit_min = IntParameter(68, 95, default=SELL_DEFAULTS["fastk_exit_min"], space="sell", optimize=True) sell_high_offset = DecimalParameter( 1.004, 1.03, default=SELL_DEFAULTS["high_offset"], decimals=3, space="sell", optimize=True, ) sell_vwap_factor = DecimalParameter( 1.000, 1.02, default=SELL_DEFAULTS["vwap_factor"], decimals=3, space="sell", optimize=True, ) plot_config = { "main_plot": { "vwap_low": {"color": "#7a8ca5"}, "vwap_upperband": {"color": "#d17b5f"}, "dc_upper": {"color": "#47a447"}, "dc_lower": {"color": "#c95f5f"}, "tenkan_sen": {"color": "#f2b134"}, "high_offset_sma": {"color": "#b06ab3"}, "low_offset_sma": {"color": "#3a7f55"}, }, "subplots": { "Momentum": { "rsi_14": {"color": "#4b8bbe"}, "rsi_slow": {"color": "#306998"}, "dsl_lvl": {"color": "#ff7f0e"}, }, "StochRSI": { "fastk_rsi": {"color": "#2ca02c"}, "fastd_rsi": {"color": "#d62728"}, }, }, } def version(self) -> str: return "2026.04.23-devdsl2-bybit-v2" def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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"] dataframe["volume_mean_4"] = dataframe["volume"].rolling(4).mean().shift(1) dataframe["sma_15"] = ta.SMA(dataframe, timeperiod=15) dataframe["high_offset_sma"] = dataframe["sma_15"] * self.sell_high_offset.value dataframe["low_offset_sma"] = dataframe["sma_15"] * self.SHORT_DEFAULTS["low_offset"] dataframe["vwma"] = vwma(dataframe["close"], dataframe["volume"], 20) dataframe["smma_smma_length_value"] = smma(dataframe["close"], self.BUY_DEFAULTS["smma_length"]) dataframe["trend_close_5m"] = dataframe["close"] dataframe["trend_close_15m"] = ta.EMA(dataframe["close"], timeperiod=3) dataframe["trend_close_30m"] = ta.EMA(dataframe["close"], timeperiod=6) dataframe["trend_close_1h"] = ta.EMA(dataframe["close"], timeperiod=12) dataframe["trend_close_2h"] = ta.EMA(dataframe["close"], timeperiod=24) dataframe["trend_close_4h"] = ta.EMA(dataframe["close"], timeperiod=48) dataframe["trend_close_6h"] = ta.EMA(dataframe["close"], timeperiod=72) dataframe["trend_open_6h"] = ta.EMA(dataframe["open"], timeperiod=72) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband2"] = bollinger["lower"] dataframe["bb_middleband2"] = bollinger["mid"] dataframe["bb_upperband2"] = bollinger["upper"] dataframe["bbdelta"] = (dataframe["bb_middleband2"] - dataframe["bb_lowerband2"]).abs() dataframe["close_delta2"] = (dataframe["close"] - dataframe["close"].shift()).abs() dataframe["lower_far"] = (dataframe["bb_lowerband2"] - dataframe["close"]) / dataframe["close"] dataframe["upper_near"] = (dataframe["bb_upperband2"] - dataframe["close"]) / dataframe["close"] dataframe["upper_far"] = (dataframe["close"] - dataframe["bb_upperband2"]) / dataframe["close"] dataframe["lower_near"] = (dataframe["close"] - dataframe["bb_lowerband2"]) / dataframe["close"] vwap_low, vwap_mid, vwap_high = vwap_bands(dataframe, 20, 1) dataframe["vwap_low"] = vwap_low dataframe["vwap_high"] = vwap_high dataframe["vwap_upperband"] = vwap_high dataframe["vwap_middleband"] = vwap_mid dc_length = self.BUY_DEFAULTS["dc_length"] dataframe["dc_upper"] = dataframe["high"].shift(1).rolling(dc_length).max() dataframe["dc_lower"] = dataframe["low"].shift(1).rolling(dc_length).min() dataframe["dc_middle"] = (dataframe["dc_upper"] + dataframe["dc_lower"]) / 2.0 dc_range = (dataframe["dc_upper"] - dataframe["dc_lower"]).replace(0, np.nan) dataframe["dc_percent"] = (dataframe["close"] - dataframe["dc_lower"]) / dc_range dataframe["dc_chf"] = (dataframe["close"] - dataframe["dc_middle"]) / dataframe["dc_middle"].replace(0, np.nan) dataframe["rsi_14"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20) dataframe["rsi_84"] = ta.RSI(dataframe, timeperiod=84) dataframe["mfi"] = ta.MFI(dataframe, timeperiod=14) rsi_min = dataframe["rsi_14"].rolling(14).min() rsi_max = dataframe["rsi_14"].rolling(14).max() stoch_rsi = 100 * (dataframe["rsi_14"] - rsi_min) / (rsi_max - rsi_min).replace(0, np.nan) dataframe["fastk_rsi"] = stoch_rsi.rolling(3).mean() dataframe["fastd_rsi"] = dataframe["fastk_rsi"].rolling(3).mean() rmi_source = dataframe["close"] - dataframe["close"].shift(4) dataframe["rmi"] = ta.RSI(rmi_source.fillna(0), timeperiod=14) dataframe["rsi_mfi_rmi_length"] = (dataframe["rsi_14"] + dataframe["mfi"] + dataframe["rmi"]) / 3.0 momentum = dataframe["close"] - dataframe["close"].shift(5) positive_mom = momentum.clip(lower=0) negative_mom = (-momentum).clip(lower=0) pmom = positive_mom.rolling(3).mean() nmom = negative_mom.rolling(3).mean() dataframe["positive_mom_pmom_nmom"] = ((positive_mom > negative_mom) & (pmom > nmom)).astype(int) dataframe["positive_pmom_nmom_prev"] = ( (pmom.shift(1) > nmom.shift(1)) & (pmom.shift(2) > nmom.shift(2)) ).astype(int) dataframe["negative_mom_pmom_nmom"] = ((negative_mom > positive_mom) & (nmom > pmom)).astype(int) dataframe["negative_pmom_nmom_prev"] = ( (nmom.shift(1) > pmom.shift(1)) & (nmom.shift(2) > pmom.shift(2)) ).astype(int) ichimoku = ftt.ichimoku( dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30, ) dataframe["tenkan_sen"] = ichimoku["tenkan_sen"] dataframe["cloud_red"] = ichimoku["cloud_red"].astype(int) dataframe["dsl_lvl"] = dsl_rsi_level(dataframe["rsi_14"], 7) dataframe["strike_strike"] = np.sign(dataframe["close"].diff()).rolling(3).sum() dataframe["averimpet_mma_length_value"] = ( (dataframe["close"] - dataframe["smma_smma_length_value"]).abs() / dataframe["smma_smma_length_value"].replace(0, np.nan) ) rsi_mean = dataframe["rsi_14"].rolling(20).mean() rsi_std = dataframe["rsi_14"].rolling(20).std() dataframe["dev_ma_period_rsi_period_stdev_multiplier"] = rsi_mean + (rsi_std * 1.6) dataframe["disp_up_ma_period_rsi_period_stdev_multiplier_dispersion"] = ( rsi_std / rsi_mean.abs().replace(0, np.nan) ) dataframe["goodloss"] = ((dataframe["trend_open_6h"] - dataframe["close"]) / dataframe["close"]).clip(lower=0) dataframe["goodrise"] = ((dataframe["close"] - dataframe["trend_open_6h"]) / dataframe["close"]).clip(lower=0) dataframe["dsl_delta"] = dataframe["dsl_lvl"] - dataframe["dsl_lvl"].shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 dataframe["enter_tag"] = "" general_guard = ( (dataframe["volume"] > 0) & (dataframe["volume"] > dataframe["volume_mean_4"] * self.BUY_DEFAULTS["volume_factor"]) & (dataframe["goodloss"] < self.buy_goodloss_max.value) & (dataframe["upper_near"] > self.buy_donchian_room_min.value) ) trend_guard = ( (dataframe["trend_close_6h"] > dataframe["trend_open_6h"] * self.BUY_DEFAULTS["trend_relax_factor"]) | ((dataframe["close"] > dataframe["tenkan_sen"]) & (dataframe["cloud_red"] == 0)) ) bear_guard = ( ( (dataframe["trend_close_6h"] < dataframe["trend_open_6h"] * self.SHORT_DEFAULTS["trend_relax_factor"]) & (dataframe["close"] < dataframe["trend_open_6h"]) ) | ((dataframe["close"] < dataframe["tenkan_sen"]) & (dataframe["cloud_red"] == 1)) ) deep_reversion = ( (dataframe["close"] < dataframe["vwap_low"] * self.buy_vwap_factor.value) & (dataframe["close"] < dataframe["bb_lowerband2"] * self.buy_bb_factor.value) & ((dataframe["bbdelta"] / dataframe["close"]) > self.buy_bbdelta_close_min.value) & ((dataframe["close_delta2"] / dataframe["close"]) > self.buy_close_delta_min.value) & (dataframe["rsi_14"] < self.buy_rsi_14_max.value) & (dataframe["rsi_slow"] < self.buy_rsi_slow_max.value) & (dataframe["rsi_84"] < self.buy_rsi_84_max.value) & (dataframe["dc_percent"] < self.buy_dc_percent_max.value) & (dataframe["fastk_rsi"] > dataframe["fastd_rsi"]) & (dataframe["dsl_delta"] > self.buy_dsl_delta_min.value) & (dataframe["positive_mom_pmom_nmom"] == 1) ) donchian_reclaim = ( (dataframe["close"] <= dataframe["dc_lower"] * 1.01) & (dataframe["dc_chf"] < -0.01) & (dataframe["lower_far"] < -0.005) & (dataframe["fastk_rsi"] > dataframe["fastd_rsi"]) & (dataframe["positive_pmom_nmom_prev"] == 1) & (dataframe["strike_strike"] <= 0) & (dataframe["dsl_delta"] > 0) & ( dataframe["close"] > dataframe["smma_smma_length_value"] * self.BUY_DEFAULTS["donchian_smma_factor"] ) ) short_general_guard = ( (dataframe["volume"] > 0) & (dataframe["volume"] > dataframe["volume_mean_4"] * self.SHORT_DEFAULTS["volume_factor"]) & (dataframe["goodrise"] < self.short_goodrise_max.value) ) short_reversion = ( (dataframe["close"] > dataframe["vwap_high"] * self.short_vwap_factor.value) & (dataframe["close"] > dataframe["bb_upperband2"] * self.short_bb_factor.value) & (dataframe["upper_far"] > 0) & (dataframe["rsi_14"] > self.short_rsi_14_min.value) & (dataframe["rsi_slow"] > self.short_rsi_slow_min.value) & (dataframe["rsi_84"] > self.short_rsi_84_min.value) & (dataframe["dc_percent"] > self.short_dc_percent_min.value) & (dataframe["fastk_rsi"] < dataframe["fastd_rsi"]) & (dataframe["dsl_delta"] < self.short_dsl_delta_max.value) & (dataframe["close"] > dataframe["trend_close_30m"]) ) donchian_short = ( (dataframe["close"] >= dataframe["dc_upper"] * 0.99) & (dataframe["dc_chf"] > 0.01) & (dataframe["upper_far"] > 0.005) & (dataframe["fastk_rsi"] < dataframe["fastd_rsi"]) & (dataframe["strike_strike"] >= 0) & (dataframe["dsl_delta"] < 0) & ( dataframe["close"] > dataframe["smma_smma_length_value"] * self.SHORT_DEFAULTS["donchian_smma_factor"] ) ) entry_dsl_vwap = general_guard & trend_guard & deep_reversion entry_donchian = general_guard & donchian_reclaim entry_short_reversion = short_general_guard & bear_guard & short_reversion entry_short_donchian = short_general_guard & bear_guard & donchian_short dataframe.loc[entry_dsl_vwap, ["enter_long", "enter_tag"]] = (1, "dsl_vwap_reclaim") dataframe.loc[entry_donchian, ["enter_long", "enter_tag"]] = (1, "donchian_dsl_reclaim") dataframe.loc[entry_short_reversion, ["enter_short", "enter_tag"]] = (1, "dsl_vwap_short") dataframe.loc[entry_short_donchian, ["enter_short", "enter_tag"]] = (1, "donchian_dsl_short") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 dataframe["exit_tag"] = "" vwap_extension = ( (dataframe["close"] > dataframe["vwap_upperband"] * self.sell_vwap_factor.value) & (dataframe["rsi_14"] > self.sell_rsi_exit_min.value) & (dataframe["fastk_rsi"] > self.sell_fastk_exit_min.value) ) band_exhaustion = ( (dataframe["close"] > dataframe["high_offset_sma"]) & (dataframe["close"] > dataframe["bb_upperband2"]) & (dataframe["fastk_rsi"] < dataframe["fastd_rsi"]) & (dataframe["rsi_mfi_rmi_length"] > self.SELL_DEFAULTS["band_exhaustion_rsi_mfi_rmi_min"]) ) donchian_take = ( (dataframe["close"] > dataframe["dc_upper"]) & (dataframe["rsi_14"] > self.sell_rsi_exit_min.value) & (dataframe["dsl_lvl"] < dataframe["dsl_lvl"].shift(1)) ) vwap_cover = ( (dataframe["close"] < dataframe["vwap_low"] * self.SHORT_DEFAULTS["cover_vwap_factor"]) & (dataframe["rsi_14"] < self.SHORT_DEFAULTS["cover_rsi_max"]) & (dataframe["fastk_rsi"] < self.SHORT_DEFAULTS["cover_fastk_max"]) & (dataframe["dsl_lvl"] > dataframe["dsl_lvl"].shift(1)) ) band_compression = ( (dataframe["close"] < dataframe["low_offset_sma"]) & (dataframe["close"] < dataframe["bb_lowerband2"]) & (dataframe["fastk_rsi"] > dataframe["fastd_rsi"]) & (dataframe["dc_percent"] < 0.2) & (dataframe["rsi_mfi_rmi_length"] < self.SHORT_DEFAULTS["band_compression_rsi_mfi_rmi_max"]) ) donchian_cover = ( (dataframe["close"] < dataframe["dc_lower"]) & (dataframe["rsi_14"] < self.SHORT_DEFAULTS["cover_rsi_max"]) & (dataframe["dsl_lvl"] > dataframe["dsl_lvl"].shift(1)) ) dataframe.loc[vwap_extension, ["exit_long", "exit_tag"]] = (1, "vwap_extension") dataframe.loc[band_exhaustion, ["exit_long", "exit_tag"]] = (1, "band_exhaustion") dataframe.loc[donchian_take, ["exit_long", "exit_tag"]] = (1, "donchian_take") dataframe.loc[vwap_cover, ["exit_short", "exit_tag"]] = (1, "vwap_cover") dataframe.loc[band_compression, ["exit_short", "exit_tag"]] = (1, "band_compression") dataframe.loc[donchian_cover, ["exit_short", "exit_tag"]] = (1, "donchian_cover") return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: hard_stoploss = self.RISK_DEFAULTS["hard_stoploss"] profit_floor_1 = self.RISK_DEFAULTS["profit_floor_1"] profit_floor_2 = self.RISK_DEFAULTS["profit_floor_2"] stop_1 = self.RISK_DEFAULTS["stop_1"] stop_2 = self.RISK_DEFAULTS["stop_2"] if current_profit > profit_floor_2: stop_profit = stop_2 + (current_profit - profit_floor_2) elif current_profit > profit_floor_1: stop_profit = stop_1 + ( (current_profit - profit_floor_1) * (stop_2 - stop_1) / (profit_floor_2 - profit_floor_1) ) else: stop_profit = hard_stoploss if stop_profit >= current_profit: return -0.99 return stoploss_from_open( stop_profit, current_profit, is_short=trade.is_short, leverage=trade.leverage, ) def custom_exit( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ): if self.dp is None: return None dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty: return None last_candle = dataframe.iloc[-1] previous_candle = dataframe.iloc[-2] if len(dataframe) > 1 else last_candle held_minutes = (current_time - trade.open_date_utc).total_seconds() / 60.0 if trade.is_short: if ( current_profit > self.SELL_DEFAULTS["trend_fail_min"] and last_candle["cloud_red"] == 0 and last_candle["close"] > last_candle["trend_open_6h"] ): return "trend_fail_short" if ( current_profit > self.SELL_DEFAULTS["profit_fade_min"] and last_candle["dsl_lvl"] > previous_candle["dsl_lvl"] ): return "profit_fade_short" if ( held_minutes > self.RISK_DEFAULTS["time_stop_minutes"] and current_profit > self.RISK_DEFAULTS["time_stop_profit"] and last_candle["close"] > last_candle["trend_close_6h"] ): return "time_stop_short" else: if ( current_profit > self.SELL_DEFAULTS["trend_fail_min"] and last_candle["cloud_red"] == 1 and last_candle["close"] < last_candle["trend_open_6h"] ): return "trend_fail" if ( current_profit > self.SELL_DEFAULTS["profit_fade_min"] and last_candle["dsl_lvl"] < previous_candle["dsl_lvl"] ): return "profit_fade" if ( held_minutes > self.RISK_DEFAULTS["time_stop_minutes"] and current_profit > self.RISK_DEFAULTS["time_stop_profit"] and last_candle["close"] < last_candle["trend_close_6h"] ): return "time_stop" return None