diff --git a/gzl/Selector.py b/gzl/Selector.py
new file mode 100644
index 0000000..8f6790d
--- /dev/null
+++ b/gzl/Selector.py
@@ -0,0 +1,633 @@
+from typing import Dict, List, Optional, Any
+
+from scipy.signal import find_peaks
+import numpy as np
+import pandas as pd
+
+
+# --------------------------- 通用指标 --------------------------- #
+
+def compute_kdj(df: pd.DataFrame, n: int = 9) -> pd.DataFrame:
+    if df.empty:
+        return df.assign(K=np.nan, D=np.nan, J=np.nan)
+
+    low_n = df["low"].rolling(window=n, min_periods=1).min()
+    high_n = df["high"].rolling(window=n, min_periods=1).max()
+    rsv = (df["close"] - low_n) / (high_n - low_n + 1e-9) * 100
+
+    K = np.zeros_like(rsv, dtype=float)
+    D = np.zeros_like(rsv, dtype=float)
+    for i in range(len(df)):
+        if i == 0:
+            K[i] = D[i] = 50.0
+        else:
+            K[i] = 2 / 3 * K[i - 1] + 1 / 3 * rsv.iloc[i]
+            D[i] = 2 / 3 * D[i - 1] + 1 / 3 * K[i]
+    J = 3 * K - 2 * D
+    return df.assign(K=K, D=D, J=J)
+
+
+def compute_bbi(df: pd.DataFrame) -> pd.Series:
+    ma3 = df["close"].rolling(3).mean()
+    ma6 = df["close"].rolling(6).mean()
+    ma12 = df["close"].rolling(12).mean()
+    ma24 = df["close"].rolling(24).mean()
+    return (ma3 + ma6 + ma12 + ma24) / 4
+
+
+def compute_rsv(
+    df: pd.DataFrame,
+    n: int,
+) -> pd.Series:
+    """
+    按公式：RSV(N) = 100 × (C - LLV(L,N)) ÷ (HHV(C,N) - LLV(L,N))
+    - C 用收盘价最高值 (HHV of close)
+    - L 用最低价最低值 (LLV of low)
+    """
+    low_n = df["low"].rolling(window=n, min_periods=1).min()
+    high_close_n = df["close"].rolling(window=n, min_periods=1).max()
+    rsv = (df["close"] - low_n) / (high_close_n - low_n + 1e-9) * 100.0
+    return rsv
+
+
+def compute_dif(df: pd.DataFrame, fast: int = 12, slow: int = 26) -> pd.Series:
+    """计算 MACD 指标中的 DIF (EMA fast - EMA slow)。"""
+    ema_fast = df["close"].ewm(span=fast, adjust=False).mean()
+    ema_slow = df["close"].ewm(span=slow, adjust=False).mean()
+    return ema_fast - ema_slow
+
+
+def bbi_deriv_uptrend(
+    bbi: pd.Series,
+    *,
+    min_window: int,
+    max_window: int | None = None,
+    q_threshold: float = 0.0,
+) -> bool:
+    """
+    判断 BBI 是否“整体上升”。
+
+    令最新交易日为 T，在区间 [T-w+1, T]（w 自适应，w ≥ min_window 且 ≤ max_window）
+    内，先将 BBI 归一化：BBI_norm(t) = BBI(t) / BBI(T-w+1)。
+
+    再计算一阶差分 Δ(t) = BBI_norm(t) - BBI_norm(t-1)。  
+    若 Δ(t) 的前 q_threshold 分位数 ≥ 0，则认为该窗口通过；只要存在
+    **最长** 满足条件的窗口即可返回 True。q_threshold=0 时退化为
+    “全程单调不降”（旧版行为）。
+
+    Parameters
+    ----------
+    bbi : pd.Series
+        BBI 序列（最新值在最后一位）。
+    min_window : int
+        检测窗口的最小长度。
+    max_window : int | None
+        检测窗口的最大长度；None 表示不设上限。
+    q_threshold : float, default 0.0
+        允许一阶差分为负的比例（0 ≤ q_threshold ≤ 1）。
+    """
+    if not 0.0 <= q_threshold <= 1.0:
+        raise ValueError("q_threshold 必须位于 [0, 1] 区间内")
+
+    bbi = bbi.dropna()
+    if len(bbi) < min_window:
+        return False
+
+    longest = min(len(bbi), max_window or len(bbi))
+
+    # 自最长窗口向下搜索，找到任一满足条件的区间即通过
+    for w in range(longest, min_window - 1, -1):
+        seg = bbi.iloc[-w:]                # 区间 [T-w+1, T]
+        norm = seg / seg.iloc[0]           # 归一化
+        diffs = np.diff(norm.values)       # 一阶差分
+        if np.quantile(diffs, q_threshold) >= 0:
+            return True
+    return False
+
+
+def _find_peaks(
+    df: pd.DataFrame,
+    *,
+    column: str = "high",
+    distance: Optional[int] = None,
+    prominence: Optional[float] = None,
+    height: Optional[float] = None,
+    width: Optional[float] = None,
+    rel_height: float = 0.5,
+    **kwargs: Any,
+) -> pd.DataFrame:
+    
+    if column not in df.columns:
+        raise KeyError(f"'{column}' not found in DataFrame columns: {list(df.columns)}")
+
+    y = df[column].to_numpy()
+
+    indices, props = find_peaks(
+        y,
+        distance=distance,
+        prominence=prominence,
+        height=height,
+        width=width,
+        rel_height=rel_height,
+        **kwargs,
+    )
+
+    peaks_df = df.iloc[indices].copy()
+    peaks_df["is_peak"] = True
+
+    # Flatten SciPy arrays into columns (only those with same length as indices)
+    for key, arr in props.items():
+        if isinstance(arr, (list, np.ndarray)) and len(arr) == len(indices):
+            peaks_df[f"peak_{key}"] = arr
+
+    return peaks_df
+
+
+# --------------------------- Selector 类 --------------------------- #
+class BBIKDJSelector:
+    """
+    自适应 *BBI(导数)* + *KDJ* 选股器
+        • BBI: 允许 bbi_q_threshold 比例的回撤
+        • KDJ: J < threshold ；或位于历史 J 的 j_q_threshold 分位及以下
+        • MACD: DIF > 0
+        • 收盘价波动幅度 ≤ price_range_pct
+    """
+
+    def __init__(
+        self,
+        j_threshold: float = -5,
+        bbi_min_window: int = 90,
+        max_window: int = 90,
+        price_range_pct: float = 100.0,
+        bbi_q_threshold: float = 0.05,
+        j_q_threshold: float = 0.10,
+    ) -> None:
+        self.j_threshold = j_threshold
+        self.bbi_min_window = bbi_min_window
+        self.max_window = max_window
+        self.price_range_pct = price_range_pct
+        self.bbi_q_threshold = bbi_q_threshold  # ← 原 q_threshold
+        self.j_q_threshold = j_q_threshold      # ← 新增
+
+    # ---------- 单支股票过滤 ---------- #
+    def _passes_filters(self, hist: pd.DataFrame) -> bool:
+        hist = hist.copy()
+        hist["BBI"] = compute_bbi(hist)
+
+        # 0. 收盘价波动幅度约束（最近 max_window 根 K 线）
+        win = hist.tail(self.max_window)
+        high, low = win["close"].max(), win["close"].min()
+        if low <= 0 or (high / low - 1) > self.price_range_pct:           
+            return False
+
+        # 1. BBI 上升（允许部分回撤）
+        if not bbi_deriv_uptrend(
+            hist["BBI"],
+            min_window=self.bbi_min_window,
+            max_window=self.max_window,
+            q_threshold=self.bbi_q_threshold,
+        ):            
+            return False
+
+        # 2. KDJ 过滤 —— 双重条件
+        kdj = compute_kdj(hist)
+        j_today = float(kdj.iloc[-1]["J"])
+
+        # 最近 max_window 根 K 线的 J 分位
+        j_window = kdj["J"].tail(self.max_window).dropna()
+        if j_window.empty:
+            return False
+        j_quantile = float(j_window.quantile(self.j_q_threshold))
+
+        if not (j_today < self.j_threshold or j_today <= j_quantile):
+            
+            return False
+
+        # 3. MACD：DIF > 0
+        hist["DIF"] = compute_dif(hist)
+        return hist["DIF"].iloc[-1] > 0
+
+    # ---------- 多股票批量 ---------- #
+    def select(
+        self, date: pd.Timestamp, data: Dict[str, pd.DataFrame]
+    ) -> List[str]:
+        picks: List[str] = []
+        for code, df in data.items():
+            hist = df[df["date"] <= date]
+            if hist.empty:
+                continue
+            # 额外预留 20 根 K 线缓冲
+            hist = hist.tail(self.max_window + 20)
+            if self._passes_filters(hist):
+                picks.append(code)
+        return picks
+    
+    
+class SuperB1Selector:
+    """SuperB1 选股器
+
+    过滤逻辑概览
+    ----------------
+    1. **历史匹配 (t_m)** — 在 *lookback_n* 个交易日窗口内，至少存在一日
+       满足 :class:`BBIKDJSelector`。
+
+    2. **盘整区间** — 区间 ``[t_m, date-1]`` 收盘价波动率不超过 ``close_vol_pct``。
+
+    3. **当日下跌** — ``(close_{date-1} - close_date) / close_{date-1}``
+       ≥ ``price_drop_pct``。
+
+    4. **J 值极低** — ``J < j_threshold`` *或* 位于历史 ``j_q_threshold`` 分位。
+    """
+
+    # ---------------------------------------------------------------------
+    # 构造函数
+    # ---------------------------------------------------------------------
+    def __init__(
+        self,
+        *,
+        lookback_n: int = 60,
+        close_vol_pct: float = 0.05,
+        price_drop_pct: float = 0.03,
+        j_threshold: float = -5,
+        j_q_threshold: float = 0.10,
+        # ↓↓↓ 新增：嵌套 BBIKDJSelector 配置
+        B1_params: Optional[Dict[str, Any]] = None        
+    ) -> None:        
+        # ---------- 参数合法性检查 ----------
+        if lookback_n < 2:
+            raise ValueError("lookback_n 应 ≥ 2")
+        if not (0 < close_vol_pct < 1):
+            raise ValueError("close_vol_pct 应位于 (0, 1) 区间")
+        if not (0 < price_drop_pct < 1):
+            raise ValueError("price_drop_pct 应位于 (0, 1) 区间")
+        if not (0 <= j_q_threshold <= 1):
+            raise ValueError("j_q_threshold 应位于 [0, 1] 区间")
+        if B1_params is None:
+            raise ValueError("bbi_params没有给出")
+
+        # ---------- 基本参数 ----------
+        self.lookback_n = lookback_n
+        self.close_vol_pct = close_vol_pct
+        self.price_drop_pct = price_drop_pct
+        self.j_threshold = j_threshold
+        self.j_q_threshold = j_q_threshold
+
+        # ---------- 内部 BBIKDJSelector ----------
+        self.bbi_selector = BBIKDJSelector(**(B1_params or {}))
+
+        # 为保证给 BBIKDJSelector 提供足够历史，预留额外缓冲
+        self._extra_for_bbi = self.bbi_selector.max_window + 20
+
+    # 单支股票过滤核心
+    def _passes_filters(self, hist: pd.DataFrame) -> bool:
+        """*hist* 必须按日期升序，且最后一行为目标 *date*。"""
+        if len(hist) < 2:
+            return False
+
+        # ---------- Step-0: 数据量判断 ----------
+        if len(hist) < self.lookback_n + self._extra_for_bbi:
+            return False
+
+        # ---------- Step-1: 搜索满足 BBIKDJ 的 t_m ----------
+        lb_hist = hist.tail(self.lookback_n + 1)  # +1 以排除自身
+        tm_idx: int | None = None
+        # 遍历回溯窗口
+        for idx in lb_hist.index[:-1]:            
+            if self.bbi_selector._passes_filters(hist.loc[:idx]):
+                tm_idx = idx
+                stable_seg = hist.loc[tm_idx : hist.index[-2], "close"]
+                if len(stable_seg) < 3:
+                    tm_idx = None
+                    break
+                high, low = stable_seg.max(), stable_seg.min()
+                if low <= 0 or (high / low - 1) > self.close_vol_pct:                                      
+                    tm_idx = None
+                    continue
+                else:
+                    break
+        if tm_idx is None:            
+            return False        
+        
+
+        # ---------- Step-3: 当日相对前一日跌幅 ----------
+        close_today, close_prev = hist["close"].iloc[-1], hist["close"].iloc[-2]
+        if close_prev <= 0 or (close_prev - close_today) / close_prev < self.price_drop_pct:            
+            return False
+
+        # ---------- Step-4: J 值极低 ----------
+        kdj = compute_kdj(hist)
+        j_today = float(kdj["J"].iloc[-1])
+        j_window = kdj["J"].iloc[-self.lookback_n:].dropna()
+        j_q_val = float(j_window.quantile(self.j_q_threshold)) if not j_window.empty else np.nan
+        if not (j_today < self.j_threshold or j_today <= j_q_val):            
+            return False
+
+        return True
+
+    # 批量选股接口
+    def select(self, date: pd.Timestamp, data: Dict[str, pd.DataFrame]) -> List[str]:        
+        picks: List[str] = []
+        min_len = self.lookback_n + self._extra_for_bbi
+
+        for code, df in data.items():
+            hist = df[df["date"] <= date].tail(min_len)
+            if len(hist) < min_len:
+                continue
+            if self._passes_filters(hist):
+                picks.append(code)
+
+        return picks
+
+
+class PeakKDJSelector:
+    """
+    Peaks + KDJ 选股器    
+    """
+
+    def __init__(
+        self,
+        j_threshold: float = -5,
+        max_window: int = 90,
+        fluc_threshold: float = 0.03,
+        gap_threshold: float = 0.02,
+        j_q_threshold: float = 0.10,
+    ) -> None:
+        self.j_threshold = j_threshold
+        self.max_window = max_window
+        self.fluc_threshold = fluc_threshold  # 当日↔peak_(t-n) 波动率上限
+        self.gap_threshold = gap_threshold    # oc_prev 必须高于区间最低收盘价的比例
+        self.j_q_threshold = j_q_threshold
+
+    # ---------- 单支股票过滤 ---------- #
+        # ---------- 单支股票过滤 ---------- #
+    def _passes_filters(self, hist: pd.DataFrame) -> bool:
+        if hist.empty:
+            return False
+
+        hist = hist.copy().sort_values("date")
+        hist["oc_max"] = hist[["open", "close"]].max(axis=1)
+
+        # 1. 提取 peaks
+        peaks_df = _find_peaks(
+            hist,
+            column="oc_max",
+            distance=6,
+            prominence=0.5,
+        )
+        
+        # 至少两个峰      
+        date_today = hist.iloc[-1]["date"]
+        peaks_df = peaks_df[peaks_df["date"] < date_today]
+        if len(peaks_df) < 2:               
+            return False
+
+        peak_t = peaks_df.iloc[-1]          # 最新一个峰
+        peaks_list = peaks_df.reset_index(drop=True)
+        oc_t = peak_t.oc_max
+        total_peaks = len(peaks_list)
+
+        # 2. 回溯寻找 peak_(t-n)
+        target_peak = None        
+        for idx in range(total_peaks - 2, -1, -1):
+            peak_prev = peaks_list.loc[idx]
+            oc_prev = peak_prev.oc_max
+            if oc_t <= oc_prev:             # 要求 peak_t > peak_(t-n)
+                continue
+
+            # 只有当“总峰数 ≥ 3”时才检查区间内其他峰 oc_max
+            if total_peaks >= 3 and idx < total_peaks - 2:
+                inter_oc = peaks_list.loc[idx + 1 : total_peaks - 2, "oc_max"]
+                if not (inter_oc < oc_prev).all():
+                    continue
+
+            # 新增： oc_prev 高于区间最低收盘价 gap_threshold
+            date_prev = peak_prev.date
+            mask = (hist["date"] > date_prev) & (hist["date"] < peak_t.date)
+            min_close = hist.loc[mask, "close"].min()
+            if pd.isna(min_close):
+                continue                    # 区间无数据
+            if oc_prev <= min_close * (1 + self.gap_threshold):
+                continue
+
+            target_peak = peak_prev
+            
+            break
+
+        if target_peak is None:
+            return False
+
+        # 3. 当日收盘价波动率
+        close_today = hist.iloc[-1]["close"]
+        fluc_pct = abs(close_today - target_peak.close) / target_peak.close
+        if fluc_pct > self.fluc_threshold:
+            return False
+
+        # 4. KDJ 过滤
+        kdj = compute_kdj(hist)
+        j_today = float(kdj.iloc[-1]["J"])
+        j_window = kdj["J"].tail(self.max_window).dropna()
+        if j_window.empty:
+            return False
+        j_quantile = float(j_window.quantile(self.j_q_threshold))
+        if not (j_today < self.j_threshold or j_today <= j_quantile):
+            return False
+
+        return True
+
+    # ---------- 多股票批量 ---------- #
+    def select(
+        self,
+        date: pd.Timestamp,
+        data: Dict[str, pd.DataFrame],
+    ) -> List[str]:
+        picks: List[str] = []
+        for code, df in data.items():
+            hist = df[df["date"] <= date]
+            if hist.empty:
+                continue
+            hist = hist.tail(self.max_window + 20)  # 额外缓冲
+            if self._passes_filters(hist):
+                picks.append(code)
+        return picks
+    
+
+class BBIShortLongSelector:
+    """
+    BBI 上升 + 短/长期 RSV 条件 + DIF > 0 选股器
+    """
+    def __init__(
+        self,
+        n_short: int = 3,
+        n_long: int = 21,
+        m: int = 3,
+        bbi_min_window: int = 90,
+        max_window: int = 150,
+        bbi_q_threshold: float = 0.05,
+    ) -> None:
+        if m < 2:
+            raise ValueError("m 必须 ≥ 2")
+        self.n_short = n_short
+        self.n_long = n_long
+        self.m = m
+        self.bbi_min_window = bbi_min_window
+        self.max_window = max_window
+        self.bbi_q_threshold = bbi_q_threshold   # 新增参数
+
+    # ---------- 单支股票过滤 ---------- #
+    def _passes_filters(self, hist: pd.DataFrame) -> bool:
+        hist = hist.copy()
+        hist["BBI"] = compute_bbi(hist)
+
+        # 1. BBI 上升（允许部分回撤）
+        if not bbi_deriv_uptrend(
+            hist["BBI"],
+            min_window=self.bbi_min_window,
+            max_window=self.max_window,
+            q_threshold=self.bbi_q_threshold,
+        ):
+            return False
+
+        # 2. 计算短/长期 RSV -----------------
+        hist["RSV_short"] = compute_rsv(hist, self.n_short)
+        hist["RSV_long"] = compute_rsv(hist, self.n_long)
+
+        if len(hist) < self.m:
+            return False                        # 数据不足
+
+        win = hist.iloc[-self.m :]              # 最近 m 天
+        long_ok = (win["RSV_long"] >= 80).all() # 长期 RSV 全 ≥ 80
+
+        short_series = win["RSV_short"]
+        short_start_end_ok = (
+            short_series.iloc[0] >= 80 and short_series.iloc[-1] >= 80
+        )
+        short_has_below_20 = (short_series < 20).any()
+
+        if not (long_ok and short_start_end_ok and short_has_below_20):
+            return False
+
+        # 3. MACD：DIF > 0 -------------------
+        hist["DIF"] = compute_dif(hist)
+        return hist["DIF"].iloc[-1] > 0
+
+    # ---------- 多股票批量 ---------- #
+    def select(
+        self,
+        date: pd.Timestamp,
+        data: Dict[str, pd.DataFrame],
+    ) -> List[str]:
+        picks: List[str] = []
+        for code, df in data.items():
+            hist = df[df["date"] <= date]
+            if hist.empty:
+                continue
+            # 预留足够长度：RSV 计算窗口 + BBI 检测窗口 + m
+            need_len = (
+                max(self.n_short, self.n_long)
+                + self.bbi_min_window
+                + self.m
+            )
+            hist = hist.tail(max(need_len, self.max_window))
+            if self._passes_filters(hist):
+                picks.append(code)
+        return picks
+
+
+class BreakoutVolumeKDJSelector:
+    """
+    放量突破 + KDJ + DIF>0 + 收盘价波动幅度 选股器   
+    """
+
+    def __init__(
+        self,
+        j_threshold: float = 0.0,
+        up_threshold: float = 3.0,
+        volume_threshold: float = 2.0 / 3,
+        offset: int = 15,
+        max_window: int = 120,
+        price_range_pct: float = 10.0,
+        j_q_threshold: float = 0.10,        # ← 新增
+    ) -> None:
+        self.j_threshold = j_threshold
+        self.up_threshold = up_threshold
+        self.volume_threshold = volume_threshold
+        self.offset = offset
+        self.max_window = max_window
+        self.price_range_pct = price_range_pct
+        self.j_q_threshold = j_q_threshold  # ← 新增
+
+    # ---------- 单支股票过滤 ---------- #
+    def _passes_filters(self, hist: pd.DataFrame) -> bool:
+        if len(hist) < self.offset + 2:
+            return False
+
+        hist = hist.tail(self.max_window).copy()
+
+        # ---- 收盘价波动幅度约束 ----
+        high, low = hist["close"].max(), hist["close"].min()
+        if low <= 0 or (high / low - 1) > self.price_range_pct:
+            return False
+
+        # ---- 技术指标 ----
+        hist = compute_kdj(hist)
+        hist["pct_chg"] = hist["close"].pct_change() * 100
+        hist["DIF"] = compute_dif(hist)
+
+        # 0) 指定日约束：J < j_threshold 或位于历史分位；且 DIF > 0
+        j_today = float(hist["J"].iloc[-1])
+
+        j_window = hist["J"].tail(self.max_window).dropna()
+        if j_window.empty:
+            return False
+        j_quantile = float(j_window.quantile(self.j_q_threshold))
+
+        # 若不满足任一 J 条件，则淘汰
+        if not (j_today < self.j_threshold or j_today <= j_quantile):
+            return False
+        if hist["DIF"].iloc[-1] <= 0:
+            return False
+
+        # ---- 放量突破条件 ----
+        n = len(hist)
+        wnd_start = max(0, n - self.offset - 1)
+        last_idx = n - 1
+
+        for t_idx in range(wnd_start, last_idx):  # 探索突破日 T
+            row = hist.iloc[t_idx]
+
+            # 1) 单日涨幅
+            if row["pct_chg"] < self.up_threshold:
+                continue
+
+            # 2) 相对放量
+            vol_T = row["volume"]
+            if vol_T <= 0:
+                continue
+            vols_except_T = hist["volume"].drop(index=hist.index[t_idx])
+            if not (vols_except_T <= self.volume_threshold * vol_T).all():
+                continue
+
+            # 3) 创新高
+            if row["close"] <= hist["close"].iloc[:t_idx].max():
+                continue
+
+            # 4) T 之后 J 值维持高位
+            if not (hist["J"].iloc[t_idx:last_idx] > hist["J"].iloc[-1] - 10).all():
+                continue
+
+            return True  # 满足所有条件
+
+        return False
+
+    # ---------- 多股票批量 ---------- #
+    def select(
+        self, date: pd.Timestamp, data: Dict[str, pd.DataFrame]
+    ) -> List[str]:
+        picks: List[str] = []
+        for code, df in data.items():
+            hist = df[df["date"] <= date]
+            if hist.empty:
+                continue
+            if self._passes_filters(hist):
+                picks.append(code)
+        return picks
diff --git a/gzl/select_stock.py b/gzl/select_stock.py
new file mode 100644
index 0000000..172cb79
--- /dev/null
+++ b/gzl/select_stock.py
@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import argparse
+import importlib
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Dict, Iterable, List
+
+import pandas as pd
+
+# ---------- 日志 ----------
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        # 将日志写入文件
+        logging.FileHandler("select_results.log", encoding="utf-8"),
+    ],
+)
+logger = logging.getLogger("select")
+
+
+# ---------- 工具 ----------
+
+def load_data(data_dir: Path, codes: Iterable[str]) -> Dict[str, pd.DataFrame]:
+    frames: Dict[str, pd.DataFrame] = {}
+    for code in codes:
+        fp = data_dir / f"{code}.csv"
+        if not fp.exists():
+            logger.warning("%s 不存在，跳过", fp.name)
+            continue
+        df = pd.read_csv(fp, parse_dates=["date"]).sort_values("date")
+        frames[code] = df
+    return frames
+
+
+def load_config(cfg_path: Path) -> List[Dict[str, Any]]:
+    if not cfg_path.exists():
+        logger.error("配置文件 %s 不存在", cfg_path)
+        sys.exit(1)
+    with cfg_path.open(encoding="utf-8") as f:
+        cfg_raw = json.load(f)
+
+    # 兼容三种结构：单对象、对象数组、或带 selectors 键
+    if isinstance(cfg_raw, list):
+        cfgs = cfg_raw
+    elif isinstance(cfg_raw, dict) and "selectors" in cfg_raw:
+        cfgs = cfg_raw["selectors"]
+    else:
+        cfgs = [cfg_raw]
+
+    if not cfgs:
+        logger.error("configs.json 未定义任何 Selector")
+        sys.exit(1)
+
+    return cfgs
+
+
+def instantiate_selector(cfg: Dict[str, Any]):
+    """动态加载 Selector 类并实例化"""
+    cls_name: str = cfg.get("class")
+    if not cls_name:
+        raise ValueError("缺少 class 字段")
+
+    try:
+        module = importlib.import_module("Selector")
+        cls = getattr(module, cls_name)
+    except (ModuleNotFoundError, AttributeError) as e:
+        raise ImportError(f"无法加载 Selector.{cls_name}: {e}") from e
+
+    params = cfg.get("params", {})
+    return cfg.get("alias", cls_name), cls(**params)
+
+
+# ---------- 主函数 ----------
+
+def main():
+    p = argparse.ArgumentParser(description="Run selectors defined in configs.json")
+    p.add_argument("--data-dir", default="./data", help="CSV 行情目录")
+    p.add_argument("--config", default="./configs.json", help="Selector 配置文件")
+    p.add_argument("--date", help="交易日 YYYY-MM-DD；缺省=数据最新日期")
+    p.add_argument("--tickers", default="all", help="'all' 或逗号分隔股票代码列表")
+    args = p.parse_args()
+
+    # --- 加载行情 ---
+    data_dir = Path(args.data_dir)
+    if not data_dir.exists():
+        logger.error("数据目录 %s 不存在", data_dir)
+        sys.exit(1)
+
+    codes = (
+        [f.stem for f in data_dir.glob("*.csv")]
+        if args.tickers.lower() == "all"
+        else [c.strip() for c in args.tickers.split(",") if c.strip()]
+    )
+    if not codes:
+        logger.error("股票池为空！")
+        sys.exit(1)
+
+    data = load_data(data_dir, codes)
+    if not data:
+        logger.error("未能加载任何行情数据")
+        sys.exit(1)
+
+    trade_date = (
+        pd.to_datetime(args.date)
+        if args.date
+        else max(pd.to_datetime(df["date"].max()) for df in data.values())
+    )
+    if not args.date:
+        logger.info("未指定 --date，使用最近日期 %s", trade_date.date())
+
+    # --- 加载 Selector 配置 ---
+    selector_cfgs = load_config(Path(args.config))
+
+    # --- 逐个 Selector 运行 ---
+    for cfg in selector_cfgs:
+        if cfg.get("activate", True) is False:
+            continue
+        try:
+            alias, selector = instantiate_selector(cfg)
+        except Exception as e:
+            logger.error("跳过配置 %s：%s", cfg, e)
+            continue
+
+        picks = selector.select(trade_date, data)
+
+        # 将结果写入日志，同时输出到控制台
+        logger.info("")
+        logger.info("============== 选股结果 [%s] ==============", alias)
+        logger.info("交易日: %s", trade_date.date())
+        logger.info("符合条件股票数: %d", len(picks))
+        logger.info("%s", ", ".join(picks) if picks else "无符合条件股票")
+
+
+if __name__ == "__main__":
+    main()