from datetime import datetime import pandas as pd import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, stoploss_from_open from pandas import DataFrame class StochasticOscillatorReversalStrategy(IStrategy): INTERFACE_VERSION = 3 timeframe = "1d" can_short: bool = False minimal_roi = { "0": 100, } stoploss = -0.15 trailing_stop = False use_custom_stoploss = False process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 240 k_period = IntParameter(5, 30, default=14, space="buy") d_period = IntParameter(2, 20, default=3, space="buy") smooth_k = IntParameter(1, 10, default=3, space="buy") oversold = IntParameter(5, 40, default=20, space="buy") overbought = IntParameter(60, 98, default=80, space="buy") exit_level = IntParameter(40, 60, default=50, space="sell") atr_multiplier = DecimalParameter(1.5, 3.0, default=2.0, space="sell") trail_atr_multiplier = DecimalParameter(1.0, 2.0, default=1.5, space="sell") order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } order_time_in_force = { "entry": "GTC", "exit": "GTC", } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: stoch = ta.STOCH( dataframe, fastk_period=int(self.k_period.value), slowk_period=int(self.smooth_k.value), slowk_matype=0, slowd_period=int(self.d_period.value), slowd_matype=0, ) dataframe["stoch_k"] = stoch["slowk"] dataframe["stoch_d"] = stoch["slowd"] dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 dataframe["enter_tag"] = "" prev_k = dataframe["stoch_k"].shift(1) prev_d = dataframe["stoch_d"].shift(1) cross_up = (dataframe["stoch_k"] > dataframe["stoch_d"]) & (prev_k <= prev_d) cross_dn = (dataframe["stoch_k"] < dataframe["stoch_d"]) & (prev_k >= prev_d) vol_ok = dataframe["volume"] > 0 dataframe.loc[ cross_up & (dataframe["stoch_k"] < float(self.oversold.value)) & vol_ok, ["enter_long", "enter_tag"] ] = [1, "stoch_oversold_cross"] dataframe.loc[ cross_dn & (dataframe["stoch_k"] > float(self.overbought.value)) & vol_ok, ["enter_short", "enter_tag"] ] = [1, "stoch_overbought_cross"] conflict = (dataframe["enter_long"] == 1) & (dataframe["enter_short"] == 1) dataframe.loc[conflict, ["enter_long", "enter_short", "enter_tag"]] = [0, 0, ""] return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 vol_ok = dataframe["volume"] > 0 lvl = float(self.exit_level.value) dataframe.loc[(dataframe["stoch_k"] >= lvl) & vol_ok, "exit_long"] = 1 dataframe.loc[(dataframe["stoch_k"] <= lvl) & vol_ok, "exit_short"] = 1 return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> float: try: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe is None or len(dataframe) < 20: return 1.0 if "date" in dataframe.columns: df = dataframe if not pd.api.types.is_datetime64_any_dtype(df["date"]): df = df.copy() df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce") df = df.dropna(subset=["date"]).sort_values("date") else: df = dataframe.copy() df["date"] = pd.to_datetime(df.index, utc=True, errors="coerce") df = df.dropna(subset=["date"]).sort_values("date") entry_date = pd.Timestamp(trade.open_date_utc) if entry_date.tzinfo is None: entry_date = entry_date.tz_localize("UTC") else: entry_date = entry_date.tz_convert("UTC") hist = df.loc[df["date"] <= entry_date] if hist.empty: return 1.0 entry_row = hist.iloc[-1] atr_at_entry = float(entry_row.get("atr", 0.0) or 0.0) if atr_at_entry <= 0.0 or pd.isna(atr_at_entry): return 1.0 current_atr = float(df["atr"].iloc[-1] or 0.0) if current_atr <= 0.0 or pd.isna(current_atr): return 1.0 initial_mult = float(self.atr_multiplier.value) trail_mult = float(self.trail_atr_multiplier.value) atr_pct_entry = atr_at_entry / float(trade.open_rate) open_relative_stop = -atr_pct_entry * initial_mult if current_profit > 1.5 * atr_pct_entry: trail_distance = current_atr * trail_mult if bool(getattr(trade, "is_short", False)): stop_price = float(current_rate) + float(trail_distance) if stop_price > float(trade.open_rate): stop_price = float(trade.open_rate) open_relative_stop = max( open_relative_stop, (float(trade.open_rate) - stop_price) / float(trade.open_rate) ) else: stop_price = float(current_rate) - float(trail_distance) if stop_price < float(trade.open_rate): stop_price = float(trade.open_rate) open_relative_stop = max(open_relative_stop, (stop_price / float(trade.open_rate)) - 1.0) sl = float(stoploss_from_open(open_relative_stop, current_profit)) if sl <= 0.0: return 1.0 return sl except Exception as e: if hasattr(self, "log"): self.log.error(f"Custom stoploss error for {pair}: {e!s}") return 1.0 def custom_exit( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs ) -> str | None: if current_profit <= 0: return None max_profit = trade.max_profit if hasattr(trade, "max_profit") else current_profit # 只在高盈利位置回撤时才出，给趋势足够空间 if max_profit > 0.40 and (max_profit - current_profit) > 0.15: return "trailing_profit_15pct" if max_profit > 0.20 and (max_profit - current_profit) > 0.10: return "trailing_profit_10pct" return None @property def plot_config(self): return { "main_plot": {"close": {"color": "black"}}, "subplots": { "Stoch": {"stoch_k": {"color": "blue"}, "stoch_d": {"color": "orange"}}, "ATR": {"atr": {"color": "white"}}, "Volume": {"volume": {"color": "gray", "type": "bar"}, "volume_mean": {"color": "blue"}}, }, }