# source: https://raw.githubusercontent.com/LoLoSenPai/freqtrade-hl-bot/11a5d3238922a0376470e2096cb27b52c83df4e6/user_data/strategies/BtcBearRallyFadeV1.py from datetime import datetime from pandas import DataFrame import talib.abstract as ta from freqtrade.strategy import DecimalParameter, IStrategy, IntParameter, informative class Github_LoLoSenPai_freqtrade_hl_bot__BtcBearRallyFadeV1__20260510_172513(IStrategy): """Research strategy: short BTC bear-market rallies into EMA resistance.""" INTERFACE_VERSION = 3 can_short = True timeframe = "15m" startup_candle_count = 420 process_only_new_candles = True position_adjustment_enable = False max_entry_position_adjustment = 0 minimal_roi = { "0": 0.045, "150": 0.024, "420": 0.0, } stoploss = -0.052 trailing_stop = True trailing_stop_positive = 0.012 trailing_stop_positive_offset = 0.030 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False rsi_min = IntParameter(34, 46, default=39, space="buy", optimize=False) rsi_max = IntParameter(50, 66, default=61, space="buy", optimize=False) daily_rsi_min = IntParameter(26, 40, default=31, space="buy", optimize=False) daily_rsi_max = IntParameter(44, 62, default=56, space="buy", optimize=False) adx_min = IntParameter(14, 30, default=18, space="buy", optimize=False) volume_factor = DecimalParameter(0.3, 1.5, default=0.55, decimals=2, space="buy", optimize=False) rally_buffer = DecimalParameter(0.000, 0.012, default=0.004, decimals=3, space="buy", optimize=False) max_extension_atr = DecimalParameter(0.8, 3.0, default=1.6, decimals=1, space="buy", optimize=False) ema_exit_buffer = DecimalParameter(0.000, 0.012, default=0.004, decimals=3, space="sell", optimize=False) profit_take_rsi = IntParameter(18, 34, default=27, space="sell", optimize=False) @property def protections(self) -> list[dict]: return [ {"method": "CooldownPeriod", "stop_duration_candles": 3}, { "method": "StoplossGuard", "lookback_period_candles": 72, "trade_limit": 2, "stop_duration_candles": 16, "required_profit": 0.0, "only_per_pair": False, "only_per_side": True, }, { "method": "MaxDrawdown", "calculation_mode": "equity", "lookback_period_candles": 144, "trade_limit": 8, "stop_duration_candles": 24, "max_allowed_drawdown": 0.08, }, ] @staticmethod def _is_btc_pair(pair: str) -> bool: return pair.upper().startswith("BTC/") @informative("1h") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(6) return dataframe @informative("4h") def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(4) return dataframe @informative("1d") def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(5) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_8"] = ta.EMA(dataframe, timeperiod=8) dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) dataframe["volume_mean_20"] = dataframe["volume"].rolling(20, min_periods=20).mean() dataframe["range_atr"] = (dataframe["high"] - dataframe["low"]) / dataframe["atr"] dataframe["ema50_extension_atr"] = (dataframe["ema_50"] - dataframe["close"]) / dataframe["atr"] dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(6) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 if not self._is_btc_pair(metadata["pair"]): return dataframe volume_ok = ( (dataframe["volume"] > 0) & (dataframe["volume_mean_20"] > 0) & (dataframe["volume"] >= dataframe["volume_mean_20"] * self.volume_factor.value) ) daily_risk_off = ( (dataframe["close_1d"] < dataframe["ema_50_1d"]) & (dataframe["ema_50_slope_1d"] < 0) & (dataframe["rsi_1d"] > self.daily_rsi_min.value) & (dataframe["rsi_1d"] < self.daily_rsi_max.value) ) higher_tf_bear = ( daily_risk_off & (dataframe["close_4h"] < dataframe["ema_200_4h"]) & (dataframe["close_4h"] < dataframe["ema_50_4h"]) & (dataframe["ema_50_slope_4h"] < 0) & (dataframe["minus_di_4h"] > dataframe["plus_di_4h"]) & (dataframe["close_1h"] < dataframe["ema_200_1h"]) ) rally_into_resistance = ( (dataframe["high"] >= dataframe["ema_20_1h"] * (1 - self.rally_buffer.value)) | (dataframe["high"] >= dataframe["ema_50_1h"] * (1 - self.rally_buffer.value)) ) rejection = ( (dataframe["close"] < dataframe["open"]) & (dataframe["close"] < dataframe["ema_8"]) & (dataframe["close"] < dataframe["ema_20"]) & (dataframe["close"] < dataframe["close"].shift(1)) ) not_late = ( (dataframe["ema50_extension_atr"] < self.max_extension_atr.value) & (dataframe["range_atr"] < 2.8) & (dataframe["rsi"] > self.rsi_min.value) & (dataframe["rsi"] < self.rsi_max.value) ) short_conditions = ( volume_ok & higher_tf_bear & rally_into_resistance & rejection & not_late & (dataframe["close"] < dataframe["ema_200"]) & (dataframe["ema_50"] < dataframe["ema_200"]) & (dataframe["adx"] > self.adx_min.value) & (dataframe["minus_di"] > dataframe["plus_di"]) ) dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = (1, "btc_bear_rally_fade") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 return dataframe def custom_exit( self, pair: str, trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> str | bool | None: if not self.dp: return None dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe.empty or not trade.is_short: return None candle = dataframe.iloc[-1] if current_profit > 0.02 and candle["rsi"] < self.profit_take_rsi.value: return "fade_rsi_profit_take" if current_profit > 0.012 and current_rate > candle["ema_20"]: return "fade_profit_ema20_reclaim" if current_rate > candle["ema_50"] * (1 + self.ema_exit_buffer.value): return "fade_ema50_reclaim" if candle["close_1h"] > candle["ema_50_1h"] and current_profit < 0.01: return "fade_1h_reclaim" return None def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs, ) -> float: return 1.0